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app.py

@@ -1,320 +1,212 @@
-"""
-Chan Compass — US edition
-Multi-timeframe Chan-theory signals · sector rotation · local-LLM research.
-
-UI follows Adobe Spectrum 2 design language (https://s2.spectrum.adobe.com /
-github.com/adobe/react-spectrum), approximated in Gradio CSS:
-  * Adobe Clean is proprietary → Source Sans 3 (Adobe's open font) instead
-  * Spectrum 2 accent blue #0265DC, gray-50 canvas, white cards
-  * signature fully-rounded "pill" buttons, soft 16px card radii, focus rings
-"""
-from __future__ import annotations
-
-import os
-import threading
-
-import gradio as gr
-import pandas as pd
-
-import automation
-import llm_local
-import news_watch
-import paths
-import research_agent
-import rotation
-import signal_runner
-
-# ─────────────────────────────────────────── Spectrum 2 theme ──
-# Single source of truth: theme.py from the Chan Compass · Spectrum 2 design
-# system (tokens mirrored from the design package's tokens/*.css).
-from theme import THEME as theme, CSS as S2_CSS
-
-# ─────────────────────────────────────────── UI callbacks ──
-def ui_run_signals(tickers_text, force):
-    """Signals ONLY — pure rule engine, zero LLM, parallel downloads. Fast."""
-    import time
-    t0 = time.time()
-    tickers = [t for t in tickers_text.replace("\n", ",").split(",") if t.strip()]
-    df, details, summary, errors = signal_runner.run_signals(tickers or None,
-                                                             force=bool(force))
-    if errors:
-        summary += " · some tickers skipped (data not ready yet — run again)"
-    automation.STATE["signals_df"] = df
-    automation.STATE["signals_details"] = details
-    automation.STATE["signals_summary"] = summary
-    choices = sorted(details.keys())
-    return (
-        df if df is not None else pd.DataFrame(),
-        f"{summary} · {time.time()-t0:.1f}s (rule engine only — no LLM in this path)",
-        gr.update(choices=choices, value=(choices[0] if choices else None)),
-    )
-
-
-def ui_show_detail(ticker):
-    if not ticker:
-        return "Select a ticker after running the analysis."
-    raw = signal_runner.stock_raw_read(ticker)
-    if not raw:
-        return "No data for this ticker yet — run the analysis first."
-    return f"**Raw read:**\n\n{raw}"
-
-
-def ui_explain_detail(ticker):
-    raw = signal_runner.stock_raw_read(ticker or "")
-    if not raw:
-        yield "Run the analysis and select a ticker first."
-        return
-    # Same pattern as Sector Rotation: feed the deterministic ENGLISH raw read
-    # to the agent — no Chinese in, no Chinese out, fast (short prompt).
-    prompt = ("You are an equity analyst. Based ONLY on this factual read of a US "
-              "stock's multi-timeframe Chan-theory verdict, write a short plain-"
-              "English summary for a long-term holder: what's the situation today, "
-              "should they act or wait, and the key price levels. ≤90 words, no "
-              "disclaimers.\n\nRAW READ:\n" + raw)
-    yield "🤖 _Translator sub-agent (Qwen3-1.7B · llama.cpp) is summarizing…_"
-    for acc in llm_local.chat_stream(prompt, max_tokens=240, temperature=0.2,
-                                     worker="translator"):
-        yield "🤖 **AI narrative (Translator sub-agent · Qwen3-1.7B):**\n\n" + acc
-
-
-def ui_refresh_rotation():
-    """Tables only — instant. AI narrative is a separate on-demand button."""
-    d1, d5, d20, asof = rotation.build_rotation(force=True)
-    automation.STATE["rotation"] = (d1, d5, d20, asof)
-    raw = rotation.rotation_brief(d1, d5, d20)
-    automation.STATE["rotation_narrative"] = raw
-    return (rotation.fmt_table(d1), rotation.fmt_table(d5), rotation.fmt_table(d20),
-            f"Sector flows as of **{asof}**", f"**Raw read:**\n{raw}")
-
-
-def ui_rotation_ai():
-    d1, d5, d20, asof = automation.STATE["rotation"]
-    if d1 is None:
-        yield "Refresh the rotation tables first."
-        return
-    brief = rotation.rotation_brief(d1, d5, d20)
-    prompt = ("You are a US equity market strategist. Based only on the sector flow "
-              "data below (SPDR ETF proxy: change% × dollar volume, plus RS vs SPY), "
-              "write a crisp brief (<150 words): 1) where capital is rotating INTO/OUT "
-              "OF; 2) do 1-day moves agree with the 5/20-day trend; 3) one watch item. "
-              "No disclaimers.\n\nDATA:\n" + brief[:2200])
-    yield ("🤖 _Narrator sub-agent (Qwen3-1.7B · llama.cpp) is reading the flow "
-           "tables — first words in ~5-15s…_")
-    for acc in llm_local.chat_stream(prompt, max_tokens=340, worker="narrator"):
-        yield "🤖 **Narrator sub-agent (Qwen3-1.7B · llama.cpp):**\n\n" + acc
-
-
-def ui_save_holdings(text):
-    saved = news_watch.save_holdings(text.replace("\n", ",").split(","))
-    return f"Saved {len(saved)} holding(s): {', '.join(saved) if saved else '—'}"
-
-
-def ui_check_news():
-    last = ""
-    for md in news_watch.check_holdings_news_stream():
-        last = md
-        yield md
-    automation.STATE["news_md"] = last
-
-
-def ui_research(ticker):
-    for progress, report in research_agent.run_research_stream(ticker):
-        yield progress, report, gr.update()
-    yield progress, report, gr.update(choices=research_agent.list_reports())
-
-
-def ui_open_report(fname):
-    return research_agent.read_report(fname)
-
-
-def ui_load_model(name):
-    return llm_local.load_model(name, worker="deep")
-
-
-def ui_automation_panel():
-    return automation.schedule_info(), "\n".join(automation.STATE["log"][-30:]) or "(no log yet)"
-
-
-_SELFTEST = {"done": False, "result": ""}
-
-
-def _auto_selftest():
-    """Runs once, automatically, the moment every sub-agent is loaded — shows a
-    self-test verdict without the user clicking anything."""
-    if _SELFTEST["done"]:
-        return _SELFTEST["result"]
-    workers = llm_local.WORKERS
-    if not all(w["llm"] is not None for w in workers.values()):
-        ready = sum(1 for w in workers.values() if w["llm"] is not None)
-        return f"⏳ Loading sub-agents… {ready}/{len(workers)} ready (self-test will run automatically)."
-    out = llm_local.quick_test()
-    ok = "not loaded" not in out and "error" not in out.lower()
-    _SELFTEST["done"] = True
-    _SELFTEST["result"] = (("✅ **Every agent is OK now** — self-test passed:\n\n" + out)
-                           if ok else ("⚠️ Self-test finished with issues:\n\n" + out))
-    return _SELFTEST["result"]
-
-
-# ─────────────────────────────────────────── layout ──
-_GR_MAJOR = int(gr.__version__.split(".")[0])
-_style_kw = {} if _GR_MAJOR >= 6 else {"theme": theme, "css": S2_CSS}
-
-with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
-    gr.HTML("""
-    <div id="s2-hero">
-      <div class="mark">🧭</div>
-      <div>
-        <h1>Chan Compass <span>· US Markets</span></h1>
-        <p>Multi-timeframe 缠论 (Chan theory) engine — monthly → 1m nested-interval
-           confirmation · sector rotation · local sub-agent pool (llama.cpp).</p>
-      </div>
-      <div class="chips">
-        <span>🧠 Local · no cloud APIs</span><span>🦙 llama.cpp</span>
-        <span>🤖 4 sub-agents · 1.7B+4B</span><span>📊 Yahoo Finance</span>
-        <span>⏰ 18:10 ET</span><span>💾 /data bucket</span><span>🎨 Spectrum 2</span>
-      </div>
-    </div>""")
-
-    with gr.Tab("📈 Signals"):
-        with gr.Row():
-            tickers_in = gr.Textbox(value=", ".join(signal_runner.DEFAULT_POOL),
-                                    label="Ticker pool (comma separated)", scale=4)
-            force_cb = gr.Checkbox(value=False, label="Force fresh download", scale=1)
-            run_btn = gr.Button("▶ Run analysis", variant="primary", scale=1)
-        sig_summary = gr.Markdown(automation.STATE["signals_summary"])
-        sig_table = gr.Dataframe(label="Tomorrow's plan — long-hold mode (sorted: BUY → SELL → HOLD → WAIT)",
-                                 interactive=False, wrap=True)
-        gr.Markdown("**Stock summary** — pick a ticker for a plain-English raw read, "
-                    "then let the Translator sub-agent write an AI narrative.",
-                    elem_classes=["s2-footnote"])
-        with gr.Row():
-            detail_pick = gr.Dropdown(choices=[], label="Ticker", scale=2)
-            explain_btn = gr.Button("🤖 AI summary (local LLM)", scale=1)
-        detail_box = gr.Markdown(label="Raw read")
-        explain_box = gr.Markdown(elem_classes=["ai-panel"])
-
-    with gr.Tab("🔄 Sector Rotation"):
-        gr.Markdown("Capital rotation across the 11 SPDR sector ETFs (full S&P 500 coverage). "
-                    "**Flow proxy = price change % × dollar volume**; RS = return minus SPY. "
-                    "True per-sector fund-flow feeds are paid data — this is the standard free proxy.")
-        with gr.Row():
-            rot_btn = gr.Button("↻ Refresh rotation (instant)", variant="primary")
-            rot_ai_btn = gr.Button("🤖 AI narrative (local LLM)")
-        rot_asof = gr.Markdown("Press refresh (or Run analysis on the Signals tab).")
-        with gr.Row():
-            rot_1d = gr.Dataframe(label="1-Day (today's rotation)", interactive=False)
-        with gr.Row():
-            rot_5d = gr.Dataframe(label="5-Day (week trend)", interactive=False)
-            rot_20d = gr.Dataframe(label="20-Day (month trend)", interactive=False)
-        rot_ai = gr.Markdown(label="AI rotation narrative", elem_classes=["ai-panel"])
-
-    with gr.Tab("📰 Watchlist News"):
-        gr.Markdown("Daily rule: for each **holding**, only **today's** news is checked. "
-                    "News found → AI brief is pushed below. No news → the ticker is ignored "
-                    "(listed under *Quiet today*).")
-        with gr.Row():
-            hold_in = gr.Textbox(value=", ".join(news_watch.load_holdings()),
-                                 label="My holdings (comma separated)", scale=3)
-            save_btn = gr.Button("💾 Save holdings", scale=1)
-            news_btn = gr.Button("🔍 Check today's news", variant="primary", scale=1)
-        hold_status = gr.Markdown()
-        news_out = gr.Markdown(elem_classes=["ai-panel"])
-
-    with gr.Tab("🧪 Auto Research"):
-        gr.Markdown("**Multi-step research agent** (fully local): PLAN → 5 evidence tools "
-                    "(fundamentals · quarterly financials · price action · **the Chan engine "
-                    "itself** · news) → section-by-section analysis → report. Every step is "
-                    "logged to a JSON **agent trace** on persistent storage. "
-                    "New tickers entering the signal pool get a report **auto-generated** "
-                    "by the daily pipeline.")
-        with gr.Row():
-            res_in = gr.Textbox(label="Ticker", placeholder="e.g. NVDA", scale=3)
-            res_btn = gr.Button("🤖 Run research agent", variant="primary", scale=1)
-        res_progress = gr.Markdown()
-        res_out = gr.Markdown(elem_classes=["ai-panel"])
-        gr.Markdown("**Report library** (auto + manual, stored on `/data`):",
-                    elem_classes=["s2-footnote"])
-        with gr.Row():
-            rep_pick = gr.Dropdown(choices=research_agent.list_reports(),
-                                   label="Saved reports", scale=3)
-            rep_open = gr.Button("📂 Open report", scale=1)
-        rep_view = gr.Markdown(elem_classes=["ai-panel"])
-
-    with gr.Tab("⏰ Automation"):
-        gr.Markdown(paths.storage_status())
-        auto_md = gr.Markdown(automation.schedule_info())
-        with gr.Row():
-            auto_now = gr.Button("⚡ Run now", variant="primary")
-        auto_msg = gr.Markdown()
-        auto_log = gr.Textbox(lines=14, label="Pipeline log (live — updates every 2s)",
-                              elem_id="detail-log")
-        traces_md = gr.Markdown(research_agent.list_traces())
-        auto_log_timer = gr.Timer(2.0)
-        auto_log_timer.tick(lambda: "\n".join(automation.STATE["log"][-40:])
-                            or "(no log yet)", None, auto_log)
-
-    with gr.Tab("🧠 Model"):
-        gr.Markdown("All AI runs **locally** through **llama.cpp** (llama-cpp-python) with "
-                    "Qwen3 GGUF weights — every option is far below the 32B-parameter cap, "
-                    "and nothing leaves the machine. **First load installs the llama.cpp "
-                    "runtime + downloads the GGUF (one-time, usually 1–3 min; worst case "
-                    "~15 min if it has to compile).** Signals/rotation/news never depend on it.")
-        gr.Markdown("**Sub-agent pool:** `fast` Translator/Narrator (Qwen3-1.7B, "
-                    "fixed) handles Explain / rotation narrative / news briefs; "
-                    "`deep` Analyst writes research reports. Each has its own lock — "
-                    "they run in parallel. Pick the Analyst model below:")
-        model_pick = gr.Radio(choices=list(llm_local.MODEL_ZOO.keys()),
-                              value=llm_local.DEFAULT_MODEL, label="Analyst (deep) model")
-        with gr.Row():
-            load_btn = gr.Button("⬇ Load model", variant="primary")
-            test_btn = gr.Button("⚡ Test sub-agents now")
-        model_status = gr.Markdown(llm_local.status())
-        model_test_out = gr.Markdown()
-        model_timer = gr.Timer(2.0)
-        model_timer.tick(lambda: llm_local.status(), None, model_status)
-        autotest_timer = gr.Timer(3.0)
-        autotest_timer.tick(_auto_selftest, None, model_test_out)
-
-    gr.Markdown("Chan Compass · educational tool, not investment advice · "
-                "data: Yahoo Finance · design language: Adobe Spectrum 2",
-                elem_classes=["s2-footnote"])
-
-    # wiring
-    run_btn.click(ui_run_signals, [tickers_in, force_cb],
-                  [sig_table, sig_summary, detail_pick])
-    detail_pick.change(ui_show_detail, detail_pick, detail_box)
-    explain_btn.click(ui_explain_detail, detail_pick, explain_box)
-    rot_btn.click(ui_refresh_rotation, None, [rot_1d, rot_5d, rot_20d, rot_asof, rot_ai])
-    rot_ai_btn.click(ui_rotation_ai, None, rot_ai)
-    save_btn.click(ui_save_holdings, hold_in, hold_status)
-    news_btn.click(ui_check_news, None, news_out)
-    res_btn.click(ui_research, res_in, [res_progress, res_out, rep_pick])
-    rep_open.click(ui_open_report, rep_pick, rep_view)
-    auto_now.click(lambda: automation.run_pipeline(force=True), None, auto_msg)
-    load_btn.click(ui_load_model, model_pick, model_status)
-    test_btn.click(lambda: llm_local.quick_test(), None, model_test_out)
-
-automation.start_scheduler()
-
-# Auto-load the default local model in the background so AI features (English
-# explanations, rotation narrative, research agent) are ready without a click.
-def _auto_load_model():
-    try:
-        automation._log("Auto-loading sub-agents (llama.cpp)…")
-        llm_local.auto_load_all()
-    except Exception as e:
-        automation._log(f"Sub-agent auto-load failed: {e}")
-
-try:
-    import paths as _paths
-    automation._log(_paths.storage_status())
-except Exception:
-    pass
-if os.environ.get("AUTO_LOAD_MODEL", "1") == "1":
-    threading.Thread(target=_auto_load_model, daemon=True).start()
-
-if __name__ == "__main__":
-    if _GR_MAJOR >= 6:
-        demo.launch(theme=theme, css=S2_CSS)
-    else:
-        demo.launch()
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chan Compass — fine-tune Qwen3-1.7B (LoRA) → GGUF → Hugging Face\n",
+    "\n",
+    "Runs on a **free Colab T4**. End to end ≈ 25-40 min.\n",
+    "\n",
+    "1. Upload your exported `chan_sft_*.jsonl` (from the app's Model tab → Export dataset).\n",
+    "2. Runtime → Change runtime type → **T4 GPU**.\n",
+    "3. Run every cell top to bottom.\n",
+    "4. Paste your HF token when asked (Settings → Access Tokens, role **write**).\n",
+    "\n",
+    "Output: a public repo `<you>/chan-compass-qwen3-1.7b-gguf` with a Q8_0 GGUF — the 🎯 Well-Tuned model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 1 · Install Unsloth (fast LoRA) + tooling"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install -q \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+    "!pip install -q --no-deps \"trl<0.9.0\" peft accelerate bitsandbytes\n",
+    "!pip install -q huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 2 · Upload your dataset (chan_sft_*.jsonl)"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files\n",
+    "up = files.upload()                      # pick your chan_sft_*.jsonl\n",
+    "DATA_PATH = list(up.keys())[0]\n",
+    "import json\n",
+    "rows = [json.loads(l) for l in open(DATA_PATH) if l.strip()]\n",
+    "print(f'Loaded {len(rows)} pairs')\n",
+    "print('Example output:\\n', rows[0]['output'][:300])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have fewer than ~150 pairs, run more **AI summaries** in the app first.\n",
+    "150-500 focused pairs is plenty for a single-skill LoRA."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 3 · Load Qwen3-1.7B in 4-bit + attach LoRA adapters"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unsloth import FastLanguageModel\n",
+    "import torch\n",
+    "\n",
+    "MAXLEN = 2048\n",
+    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+    "    model_name = 'unsloth/Qwen3-1.7B',     # base; Unsloth mirror loads fast\n",
+    "    max_seq_length = MAXLEN,\n",
+    "    load_in_4bit = True,\n",
+    ")\n",
+    "model = FastLanguageModel.get_peft_model(\n",
+    "    model, r = 16, lora_alpha = 16, lora_dropout = 0.0, bias = 'none',\n",
+    "    target_modules = ['q_proj','k_proj','v_proj','o_proj',\n",
+    "                      'gate_proj','up_proj','down_proj'],\n",
+    "    use_gradient_checkpointing = 'unsloth', random_state = 42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 4 · Format pairs into the Qwen chat template"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import Dataset\n",
+    "\n",
+    "def to_text(r):\n",
+    "    msgs = [\n",
+    "        {'role':'system','content': r['instruction']},\n",
+    "        {'role':'user','content': r['input']},\n",
+    "        {'role':'assistant','content': r['output']},\n",
+    "    ]\n",
+    "    return tokenizer.apply_chat_template(msgs, tokenize=False,\n",
+    "                                         add_generation_prompt=False)\n",
+    "\n",
+    "ds = Dataset.from_list([{'text': to_text(r)} for r in rows])\n",
+    "print(ds[0]['text'][:400])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 5 · Train (LoRA SFT) — ~10-20 min on T4"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from trl import SFTTrainer\n",
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "trainer = SFTTrainer(\n",
+    "    model = model, tokenizer = tokenizer, train_dataset = ds,\n",
+    "    dataset_text_field = 'text', max_seq_length = MAXLEN, packing = False,\n",
+    "    args = TrainingArguments(\n",
+    "        per_device_train_batch_size = 2, gradient_accumulation_steps = 4,\n",
+    "        warmup_steps = 5, num_train_epochs = 3, learning_rate = 2e-4,\n",
+    "        logging_steps = 5, optim = 'adamw_8bit', weight_decay = 0.01,\n",
+    "        lr_scheduler_type = 'linear', seed = 42, output_dir = 'outputs',\n",
+    "        fp16 = not torch.cuda.is_bf16_supported(),\n",
+    "        bf16 = torch.cuda.is_bf16_supported(),\n",
+    "    ),\n",
+    ")\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 6 · Quick sanity check"]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FastLanguageModel.for_inference(model)\n",
+    "msgs = [\n",
+    "  {'role':'system','content': rows[0]['instruction']},\n",
+    "  {'role':'user','content': rows[0]['input']},\n",
+    "]\n",
+    "inp = tokenizer.apply_chat_template(msgs, tokenize=True,\n",
+    "        add_generation_prompt=True, return_tensors='pt').to('cuda')\n",
+    "out = model.generate(input_ids=inp, max_new_tokens=160, temperature=0.2)\n",
+    "print(tokenizer.decode(out[0][inp.shape[1]:], skip_special_tokens=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": ["## 7 · Merge LoRA → GGUF (Q8_0) and push to the Hub\n",
+    "Unsloth does the llama.cpp conversion for you. Replace `HF_USER`."]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from huggingface_hub import login\n",
+    "login()   # paste a WRITE token\n",
+    "\n",
+    "HF_USER = 'ranranrunforit'\n",
+    "REPO = f'{HF_USER}/chan-compass-qwen3-1.7b-gguf'\n",
+    "\n",
+    "# saves a Q8_0 GGUF and uploads it (also writes a model card)\n",
+    "model.push_to_hub_gguf(REPO, tokenizer, quantization_method='q8_0')\n",
+    "print('Done →', f'https://huggingface.co/{REPO}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8 · Wire it into the app\n",
+    "Note the exact **filename** of the `.gguf` in your new repo (open the repo → Files).\n",
+    "In `llm_local.py`, uncomment the `Chan-Tuned` line in `MODEL_ZOO` and set the\n",
+    "repo id + filename. Reboot the Space → it appears in the Model tab and becomes\n",
+    "your published fine-tuned model (🎯 Well-Tuned + 🐜 Tiny Titan)."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {"provenance": []},
+  "kernelspec": {"display_name": "Python 3", "name": "python3"},
+  "language_info": {"name": "python"}
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

automation.py

@@ -1,147 +1,356 @@
 """
-automation.py — daily auto-update pipeline.
-
-Chosen update time (requirement #2): **18:10 America/New_York**, Mon–Fri.
-Why: NYSE/Nasdaq close at 16:00 ET; the closing auction, after-hours prints
-and Yahoo's consolidated daily bar settle over the following ~1–2 hours.
-By 18:10 ET the official daily OHLCV is stable, so we always capture the
-finished trading day exactly once (= 07:10 next morning Beijing time).
-
-Pipeline per run:
-    1. refresh data for the signal pool + holdings + sector ETFs (yfinance)
-    2. re-run multi-level Chan signals  → cached for the Signals tab
-    3. rebuild the sector rotation tables
-    4. check today's news for every holding (push brief / ignore if quiet)
-
-NOTE for Hugging Face free Spaces: free hardware sleeps after ~48h without
-traffic, and a sleeping Space cannot fire its scheduler. Everything here also
-runs on demand via the "Run now" button; on paid "always-on" hardware the
-schedule fires unattended.
+Chan Compass — US edition
+Multi-timeframe Chan-theory signals · sector rotation · local-LLM research.
+
+UI follows Adobe Spectrum 2 design language (https://s2.spectrum.adobe.com /
+github.com/adobe/react-spectrum), approximated in Gradio CSS:
+  * Adobe Clean is proprietary → Source Sans 3 (Adobe's open font) instead
+  * Spectrum 2 accent blue #0265DC, gray-50 canvas, white cards
+  * signature fully-rounded "pill" buttons, soft 16px card radii, focus rings
 """
 from __future__ import annotations
 
-import datetime as dt
+import os
 import threading
-import traceback
-from zoneinfo import ZoneInfo
-
-NY = ZoneInfo("America/New_York")
-RUN_HOUR, RUN_MINUTE = 18, 10
-
-STATE = {
-    "signals_df": None,
-    "signals_details": {},
-    "signals_summary": "Not run yet — press “Run now” or wait for the 18:10 ET schedule.",
-    "rotation": (None, None, None, "—"),
-    "rotation_narrative": "",
-    "news_md": "",
-    "last_run": None,
-    "running": False,
-    "log": [],
-}
-_lock = threading.Lock()
-
-
-def _log(msg: str):
-    stamp = dt.datetime.now(NY).strftime("%m-%d %H:%M:%S ET")
-    STATE["log"] = (STATE["log"] + [f"[{stamp}] {msg}"])[-60:]
-
-
-def run_pipeline(tickers=None, force: bool = True) -> str:
-    """Full daily refresh. Safe to call from the UI or the scheduler."""
-    import news_watch
-    import rotation
-    import signal_runner
-
-    with _lock:
-        if STATE["running"]:
-            return "A pipeline run is already in progress."
-        STATE["running"] = True
+
+import gradio as gr
+import pandas as pd
+
+import automation
+import finetune_data
+import llm_local
+import news_watch
+import paths
+import research_agent
+import rotation
+import signal_runner
+
+# ─────────────────────────────────────────── Spectrum 2 theme ──
+# Single source of truth: theme.py from the Chan Compass · Spectrum 2 design
+# system (tokens mirrored from the design package's tokens/*.css).
+from theme import THEME as theme, CSS as S2_CSS
+
+# ─────────────────────────────────────────── UI callbacks ──
+def ui_run_signals(tickers_text, force):
+    """Signals ONLY — pure rule engine, zero LLM, parallel downloads. Fast."""
+    import time
+    t0 = time.time()
+    tickers = [t for t in tickers_text.replace("\n", ",").split(",") if t.strip()]
+    df, details, summary, errors = signal_runner.run_signals(tickers or None,
+                                                             force=bool(force))
+    if errors:
+        summary += " · some tickers skipped (data not ready yet — run again)"
+    automation.STATE["signals_df"] = df
+    automation.STATE["signals_details"] = details
+    automation.STATE["signals_summary"] = summary
+    choices = sorted(details.keys())
+    return (
+        df if df is not None else pd.DataFrame(),
+        f"{summary} · {time.time()-t0:.1f}s (rule engine only — no LLM in this path)",
+        gr.update(choices=choices, value=(choices[0] if choices else None)),
+    )
+
+
+def ui_show_detail(ticker):
+    if not ticker:
+        return "Select a ticker after running the analysis."
+    raw = signal_runner.stock_raw_read(ticker)
+    if not raw:
+        return "No data for this ticker yet — run the analysis first."
+    return f"**Raw read:**\n\n{raw}"
+
+
+def ui_explain_detail(ticker):
+    raw = signal_runner.stock_raw_read(ticker or "")
+    if not raw:
+        yield "Run the analysis and select a ticker first."
+        return
+    # Same pattern as Sector Rotation: feed the deterministic ENGLISH raw read
+    # to the agent — no Chinese in, no Chinese out, fast (short prompt).
+    prompt = ("You are an equity analyst. Based ONLY on this factual read of a US "
+              "stock's multi-timeframe Chan-theory verdict, write a short plain-"
+              "English summary for a long-term holder: what's the situation today, "
+              "should they act or wait, and the key price levels. ≤90 words, no "
+              "disclaimers.\n\nRAW READ:\n" + raw)
+    yield "🤖 _Translator sub-agent (Qwen3-1.7B · llama.cpp) is summarizing…_"
+    final = ""
+    for acc in llm_local.chat_stream(prompt, max_tokens=240, temperature=0.2,
+                                     worker="translator"):
+        final = acc
+        yield "🤖 **AI narrative (Translator sub-agent · Qwen3-1.7B):**\n\n" + acc
+    # capture (raw read → narrative) as a fine-tuning pair (🎯 Well-Tuned)
     try:
-        _log("Pipeline start: refreshing data + signals…")
-        df, details, summary, errors = signal_runner.run_signals(tickers, force=force)
-        STATE["signals_df"] = df
-        STATE["signals_details"] = details
-        STATE["signals_summary"] = summary
-        for e in errors:
-            _log(f"signal skip: {e}")
-        _log(f"Signals done: {summary}")
-
-        d1, d5, d20, asof = rotation.build_rotation(force=force)
-        STATE["rotation"] = (d1, d5, d20, asof)
-        # LLM narrative is generated ON DEMAND from the Rotation tab button —
-        # the pipeline itself never waits on the model.
-        STATE["rotation_narrative"] = rotation.rotation_brief(d1, d5, d20)
-        _log(f"Sector rotation rebuilt (as of {asof}).")
-
-        STATE["news_md"] = news_watch.check_holdings_news()
-        _log("Holdings news checked.")
-
-        # ── Feature 4: auto research report for every NEW ticker in the pool ──
-        try:
-            import json
-            import os
-            import paths
-            import research_agent
-            known_path = os.path.join(paths.OUTPUT_DIR, "known_tickers.json")
-            try:
-                with open(known_path, encoding="utf-8") as f:
-                    known = set(json.load(f))
-            except (OSError, ValueError):
-                known = set()
-            current = set(df["Ticker"].tolist()) if df is not None and len(df) else set()
-            new_tickers = sorted(current - known)
-            generated = set()
-            for t in new_tickers[:5]:  # safety cap per run
-                _log(f"New ticker {t} → auto-generating research report…")
-                report, trace = research_agent.run_research(t, auto=True)
-                if report:
-                    generated.add(t)
-                    _log(f"Report for {t} done{' (+trace)' if trace else ''}.")
-                else:
-                    _log(f"Report for {t} postponed (sub-agents still loading) — "
-                         f"will retry on the next run.")
-            done_set = known | (current - (set(new_tickers) - generated))
-            if current:
-                with open(known_path, "w", encoding="utf-8") as f:
-                    json.dump(sorted(done_set), f)
-        except Exception as e:
-            _log(f"Auto-research skipped: {e}")
-
-        STATE["last_run"] = dt.datetime.now(NY)
-        _log("Pipeline finished.")
-        return f"Done. {summary}"
-    except Exception as e:
-        traceback.print_exc()
-        _log(f"Pipeline error: {e}")
-        return f"Pipeline error: {e}"
-    finally:
-        STATE["running"] = False
+        import finetune_data
+        finetune_data.record(raw, final)
+    except Exception:
+        pass
+
+
+def ui_refresh_rotation():
+    """Tables only — instant. AI narrative is a separate on-demand button."""
+    d1, d5, d20, asof = rotation.build_rotation(force=True)
+    automation.STATE["rotation"] = (d1, d5, d20, asof)
+    raw = rotation.rotation_brief(d1, d5, d20)
+    automation.STATE["rotation_narrative"] = raw
+    return (rotation.fmt_table(d1), rotation.fmt_table(d5), rotation.fmt_table(d20),
+            f"Sector flows as of **{asof}**", f"**Raw read:**\n{raw}")
+
+
+def ui_rotation_ai():
+    d1, d5, d20, asof = automation.STATE["rotation"]
+    if d1 is None:
+        yield "Refresh the rotation tables first."
+        return
+    brief = rotation.rotation_brief(d1, d5, d20)
+    prompt = ("You are a US equity market strategist. Based only on the sector flow "
+              "data below (SPDR ETF proxy: change% × dollar volume, plus RS vs SPY), "
+              "write a crisp brief (<150 words): 1) where capital is rotating INTO/OUT "
+              "OF; 2) do 1-day moves agree with the 5/20-day trend; 3) one watch item. "
+              "No disclaimers.\n\nDATA:\n" + brief[:2200])
+    yield ("🤖 _Narrator sub-agent (Qwen3-1.7B · llama.cpp) is reading the flow "
+           "tables — first words in ~5-15s…_")
+    for acc in llm_local.chat_stream(prompt, max_tokens=340, worker="narrator"):
+        yield "🤖 **Narrator sub-agent (Qwen3-1.7B · llama.cpp):**\n\n" + acc
+
+
+def ui_save_holdings(text):
+    saved = news_watch.save_holdings(text.replace("\n", ",").split(","))
+    return f"Saved {len(saved)} holding(s): {', '.join(saved) if saved else '—'}"
+
+
+def ui_check_news():
+    last = ""
+    for md in news_watch.check_holdings_news_stream():
+        last = md
+        yield md
+    automation.STATE["news_md"] = last
+
+
+def ui_research(ticker):
+    for progress, report in research_agent.run_research_stream(ticker):
+        yield progress, report, gr.update()
+    yield progress, report, gr.update(choices=research_agent.list_reports())
+
+
+def ui_open_report(fname):
+    return research_agent.read_report(fname)
+
 
+def ui_load_model(name):
+    return llm_local.load_model(name, worker="deep")
 
-def start_scheduler():
-    """Cron: Mon–Fri 18:10 America/New_York."""
+
+def ui_automation_panel():
+    return automation.schedule_info(), "\n".join(automation.STATE["log"][-30:]) or "(no log yet)"
+
+
+_SELFTEST = {"done": False, "result": ""}
+
+
+def _export_dataset():
+    path = finetune_data.export()
+    if not path:
+        return ("No training pairs captured yet. Run the Signals **AI summary** on "
+                "a few tickers first — each run is saved automatically.",
+                gr.update(visible=False))
+    n = finetune_data.count()
+    msg = (f"✅ Exported dataset ready below. Captured {n} pair(s). "
+           f"Download it, then follow `finetune/FINETUNE_GUIDE.md` to LoRA-tune "
+           f"Qwen3-1.7B and publish it.")
+    return msg, gr.update(value=path, visible=True)
+
+
+def _auto_selftest():
+    """Runs once, automatically, the moment every sub-agent is loaded — shows a
+    self-test verdict without the user clicking anything."""
+    if _SELFTEST["done"]:
+        return _SELFTEST["result"]
+    workers = llm_local.WORKERS
+    if not all(w["llm"] is not None for w in workers.values()):
+        ready = sum(1 for w in workers.values() if w["llm"] is not None)
+        return f"⏳ Loading sub-agents… {ready}/{len(workers)} ready (self-test will run automatically)."
+    out = llm_local.quick_test()
+    ok = "not loaded" not in out and "error" not in out.lower()
+    _SELFTEST["done"] = True
+    _SELFTEST["result"] = (("✅ **Every agent is OK now** — self-test passed:\n\n" + out)
+                           if ok else ("⚠️ Self-test finished with issues:\n\n" + out))
+    return _SELFTEST["result"]
+
+
+# ─────────────────────────────────────────── layout ──
+_GR_MAJOR = int(gr.__version__.split(".")[0])
+_style_kw = {} if _GR_MAJOR >= 6 else {"theme": theme, "css": S2_CSS}
+
+with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
+    gr.HTML("""
+    <div id="s2-hero">
+      <div class="mark">🧭</div>
+      <div>
+        <h1>Chan Compass <span>· US Markets</span></h1>
+        <p>Multi-timeframe 缠论 (Chan theory) engine — monthly → 1m nested-interval
+           confirmation · sector rotation · local sub-agent pool (llama.cpp).</p>
+      </div>
+      <div class="chips">
+        <span>🧠 Local · no cloud APIs</span><span>🦙 llama.cpp</span>
+        <span>🤖 4 sub-agents · 1.7B+4B</span><span>📊 Yahoo Finance</span>
+        <span>⏰ 18:10 ET</span><span>💾 /data bucket</span><span>🎨 Spectrum 2</span>
+      </div>
+    </div>""")
+
+    with gr.Tab("📈 Signals"):
+        with gr.Row():
+            tickers_in = gr.Textbox(value=", ".join(signal_runner.DEFAULT_POOL),
+                                    label="Ticker pool (comma separated)", scale=4)
+            force_cb = gr.Checkbox(value=False, label="Force fresh download", scale=1)
+            run_btn = gr.Button("▶ Run analysis", variant="primary", scale=1)
+        sig_summary = gr.Markdown(automation.STATE["signals_summary"])
+        sig_table = gr.Dataframe(label="Tomorrow's plan — long-hold mode (sorted: BUY → SELL → HOLD → WAIT)",
+                                 interactive=False, wrap=True)
+        gr.Markdown("**Stock summary** — pick a ticker for a plain-English raw read, "
+                    "then let the Translator sub-agent write an AI narrative.",
+                    elem_classes=["s2-footnote"])
+        with gr.Row():
+            detail_pick = gr.Dropdown(choices=[], label="Ticker", scale=2)
+            explain_btn = gr.Button("🤖 AI summary (local LLM)", scale=1)
+        detail_box = gr.Markdown(label="Raw read")
+        explain_box = gr.Markdown(elem_classes=["ai-panel"])
+
+    with gr.Tab("🔄 Sector Rotation"):
+        gr.Markdown("Capital rotation across the 11 SPDR sector ETFs (full S&P 500 coverage). "
+                    "**Flow proxy = price change % × dollar volume**; RS = return minus SPY. "
+                    "True per-sector fund-flow feeds are paid data — this is the standard free proxy.")
+        with gr.Row():
+            rot_btn = gr.Button("↻ Refresh rotation (instant)", variant="primary")
+            rot_ai_btn = gr.Button("🤖 AI narrative (local LLM)")
+        rot_asof = gr.Markdown("Press refresh (or Run analysis on the Signals tab).")
+        with gr.Row():
+            rot_1d = gr.Dataframe(label="1-Day (today's rotation)", interactive=False)
+        with gr.Row():
+            rot_5d = gr.Dataframe(label="5-Day (week trend)", interactive=False)
+            rot_20d = gr.Dataframe(label="20-Day (month trend)", interactive=False)
+        rot_ai = gr.Markdown(label="AI rotation narrative", elem_classes=["ai-panel"])
+
+    with gr.Tab("📰 Watchlist News"):
+        gr.Markdown("Daily rule: for each **holding**, only **today's** news is checked. "
+                    "News found → AI brief is pushed below. No news → the ticker is ignored "
+                    "(listed under *Quiet today*).")
+        with gr.Row():
+            hold_in = gr.Textbox(value=", ".join(news_watch.load_holdings()),
+                                 label="My holdings (comma separated)", scale=3)
+            save_btn = gr.Button("💾 Save holdings", scale=1)
+            news_btn = gr.Button("🔍 Check today's news", variant="primary", scale=1)
+        hold_status = gr.Markdown()
+        news_out = gr.Markdown(elem_classes=["ai-panel"])
+
+    with gr.Tab("🧪 Auto Research"):
+        gr.Markdown("**Multi-step research agent** (fully local): PLAN → 5 evidence tools "
+                    "(fundamentals · quarterly financials · price action · **the Chan engine "
+                    "itself** · news) → section-by-section analysis → report. Every step is "
+                    "logged to a JSON **agent trace** on persistent storage. "
+                    "New tickers entering the signal pool get a report **auto-generated** "
+                    "by the daily pipeline.")
+        with gr.Row():
+            res_in = gr.Textbox(label="Ticker", placeholder="e.g. NVDA", scale=3)
+            res_btn = gr.Button("🤖 Run research agent", variant="primary", scale=1)
+        res_progress = gr.Markdown()
+        res_out = gr.Markdown(elem_classes=["ai-panel"])
+        gr.Markdown("**Report library** (auto + manual, stored on `/data`):",
+                    elem_classes=["s2-footnote"])
+        with gr.Row():
+            rep_pick = gr.Dropdown(choices=research_agent.list_reports(),
+                                   label="Saved reports", scale=3)
+            rep_open = gr.Button("📂 Open report", scale=1)
+        rep_view = gr.Markdown(elem_classes=["ai-panel"])
+
+    with gr.Tab("⏰ Automation"):
+        gr.Markdown(paths.storage_status())
+        auto_md = gr.Markdown(automation.schedule_info())
+        with gr.Row():
+            auto_now = gr.Button("⚡ Run now", variant="primary")
+        auto_msg = gr.Markdown()
+        auto_log = gr.Textbox(lines=14, label="Pipeline log (live — updates every 2s)",
+                              elem_id="detail-log")
+        traces_md = gr.Markdown(research_agent.list_traces())
+        auto_log_timer = gr.Timer(2.0)
+        auto_log_timer.tick(lambda: "\n".join(automation.STATE["log"][-40:])
+                            or "(no log yet)", None, auto_log)
+
+    with gr.Tab("🧠 Model"):
+        gr.Markdown("All AI runs **locally** through **llama.cpp** (llama-cpp-python) with "
+                    "Qwen3 GGUF weights — every option is far below the 32B-parameter cap, "
+                    "and nothing leaves the machine. **First load installs the llama.cpp "
+                    "runtime + downloads the GGUF (one-time, usually 1–3 min; worst case "
+                    "~15 min if it has to compile).** Signals/rotation/news never depend on it.")
+        gr.Markdown("**Sub-agent pool:** `fast` Translator/Narrator (Qwen3-1.7B, "
+                    "fixed) handles Explain / rotation narrative / news briefs; "
+                    "`deep` Analyst writes research reports. Each has its own lock — "
+                    "they run in parallel. Pick the Analyst model below:")
+        model_pick = gr.Radio(choices=list(llm_local.MODEL_ZOO.keys()),
+                              value=llm_local.DEFAULT_MODEL, label="Analyst (deep) model")
+        with gr.Row():
+            load_btn = gr.Button("⬇ Load model", variant="primary")
+            test_btn = gr.Button("⚡ Test sub-agents now")
+        model_status = gr.Markdown(llm_local.status())
+        model_test_out = gr.Markdown()
+        model_timer = gr.Timer(2.0)
+        model_timer.tick(lambda: llm_local.status(), None, model_status)
+        autotest_timer = gr.Timer(3.0)
+        autotest_timer.tick(_auto_selftest, None, model_test_out)
+
+        gr.Markdown("---\n### 🎯 Fine-tuning dataset (Well-Tuned badge)\n"
+                    "Every Signals **AI summary** you run is saved as a "
+                    "(raw read → narrative) training pair on `/data`. Capture a "
+                    "few hundred, export the JSONL, then follow `FINETUNE_GUIDE.md` "
+                    "to LoRA-tune Qwen3-1.7B and publish it.")
+        ft_status = gr.Markdown(finetune_data.status_line())
+        with gr.Row():
+            ft_refresh = gr.Button("↻ Count pairs")
+            ft_export = gr.Button("⬇ Export dataset (JSONL)", variant="primary")
+        ft_out = gr.Markdown()
+        ft_file = gr.File(label="Download training data", visible=False)
+        ft_refresh.click(lambda: finetune_data.status_line(), None, ft_status)
+        ft_export.click(_export_dataset, None, [ft_out, ft_file])
+
+    gr.Markdown("Chan Compass · educational tool, not investment advice · "
+                "data: Yahoo Finance · design language: Adobe Spectrum 2",
+                elem_classes=["s2-footnote"])
+
+    # wiring
+    run_btn.click(ui_run_signals, [tickers_in, force_cb],
+                  [sig_table, sig_summary, detail_pick])
+    detail_pick.change(ui_show_detail, detail_pick, detail_box)
+    explain_btn.click(ui_explain_detail, detail_pick, explain_box)
+    rot_btn.click(ui_refresh_rotation, None, [rot_1d, rot_5d, rot_20d, rot_asof, rot_ai])
+    rot_ai_btn.click(ui_rotation_ai, None, rot_ai)
+    save_btn.click(ui_save_holdings, hold_in, hold_status)
+    news_btn.click(ui_check_news, None, news_out)
+    res_btn.click(ui_research, res_in, [res_progress, res_out, rep_pick])
+    rep_open.click(ui_open_report, rep_pick, rep_view)
+    auto_now.click(lambda: automation.run_pipeline(force=True), None, auto_msg)
+    load_btn.click(ui_load_model, model_pick, model_status)
+    test_btn.click(lambda: llm_local.quick_test(), None, model_test_out)
+
+automation.start_scheduler()
+
+# Auto-load the default local model in the background so AI features (English
+# explanations, rotation narrative, research agent) are ready without a click.
+def _auto_load_model():
     try:
-        from apscheduler.schedulers.background import BackgroundScheduler
-        from apscheduler.triggers.cron import CronTrigger
+        automation._log("Auto-loading sub-agents (llama.cpp)…")
+        llm_local.auto_load_all()
     except Exception as e:
-        _log(f"APScheduler unavailable: {e}")
-        return None
-    sched = BackgroundScheduler(timezone=NY)
-    sched.add_job(run_pipeline, CronTrigger(day_of_week="mon-fri",
-                                            hour=RUN_HOUR, minute=RUN_MINUTE),
-                  id="daily_pipeline", max_instances=1, coalesce=True)
-    sched.start()
-    _log(f"Scheduler armed: Mon–Fri {RUN_HOUR:02d}:{RUN_MINUTE:02d} America/New_York.")
-    return sched
-
-
-def schedule_info() -> str:
-    now = dt.datetime.now(NY)
-    last = STATE["last_run"].strftime("%Y-%m-%d %H:%M ET") if STATE["last_run"] else "never"
-    return (f"**Schedule:** Mon–Fri **{RUN_HOUR:02d}:{RUN_MINUTE:02d} America/New_York** "
-            f"(market closes 16:00 ET; by 18:10 the official daily bar has settled — "
-            f"that's 07:10 next morning Beijing time).\n\n"
-            f"**Now (ET):** {now.strftime('%Y-%m-%d %H:%M')} · **Last run:** {last}\n\n"
-            f"⚠️ On free Space hardware the app sleeps when idle and the timer can't fire; "
-            f"use **Run now**, or upgrade to always-on hardware for unattended updates.")
+        automation._log(f"Sub-agent auto-load failed: {e}")
+
+try:
+    import paths as _paths
+    automation._log(_paths.storage_status())
+except Exception:
+    pass
+if os.environ.get("AUTO_LOAD_MODEL", "1") == "1":
+    threading.Thread(target=_auto_load_model, daemon=True).start()
+
+if __name__ == "__main__":
+    if _GR_MAJOR >= 6:
+        demo.launch(theme=theme, css=S2_CSS)
+    else:
+        demo.launch()

chan_engine.py

@@ -1,1451 +1,147 @@
 """
-缠论引擎 v2.1 (原始版, 用于P0-P3改造的基线)
+automation.py — daily auto-update pipeline.
+
+Chosen update time (requirement #2): **18:10 America/New_York**, Mon–Fri.
+Why: NYSE/Nasdaq close at 16:00 ET; the closing auction, after-hours prints
+and Yahoo's consolidated daily bar settle over the following ~1–2 hours.
+By 18:10 ET the official daily OHLCV is stable, so we always capture the
+finished trading day exactly once (= 07:10 next morning Beijing time).
+
+Pipeline per run:
+    1. refresh data for the signal pool + holdings + sector ETFs (yfinance)
+    2. re-run multi-level Chan signals  → cached for the Signals tab
+    3. rebuild the sector rotation tables
+    4. check today's news for every holding (push brief / ignore if quiet)
+
+NOTE for Hugging Face free Spaces: free hardware sleeps after ~48h without
+traffic, and a sleeping Space cannot fire its scheduler. Everything here also
+runs on demand via the "Run now" button; on paid "always-on" hardware the
+schedule fires unattended.
 """
 from __future__ import annotations
-from dataclasses import dataclass, field
-from typing import Optional
-import numpy as np
-import pandas as pd
 
-PIVOT_MAX_EXTEND_SEGS = 6
-
-
-@dataclass
-class Fractal:
-    idx: int
-    date: pd.Timestamp
-    kind: str
-    price: float
-    k_high: float
-    k_low: float
-
-
-@dataclass
-class Bi:
-    start: Fractal
-    end: Fractal
-    direction: str
-    bars: int
-    high: float
-    low: float
-    @property
-    def amplitude(self) -> float:
-        return self.high - self.low
-
-
-@dataclass
-class Seg:
-    start: Fractal
-    end: Fractal
-    direction: str
-    bis: list
-    high: float
-    low: float
-    confirmed: bool = True
-
-
-@dataclass
-class Pivot:
-    start_date: pd.Timestamp
-    end_date: pd.Timestamp
-    zg: float
-    zd: float
-    gg: float
-    dd: float
-    bis: list
-    direction: str
-    zg_date: Optional[pd.Timestamp] = None
-    zd_date: Optional[pd.Timestamp] = None
-    gg_date: Optional[pd.Timestamp] = None
-    dd_date: Optional[pd.Timestamp] = None
-    g: Optional[float] = None
-    d: Optional[float] = None
-    state: str = 'new'
-    death_combo: str = ''
-    capped: bool = False
-    upgraded_level: str = ''
-
-
-@dataclass
-class DivergenceGrade:
-    grade: str
-    area_ok: bool
-    dif_ok: bool
-    area_ratio: float
-    a_area: float
-    c_area: float
-    a_dif: float
-    c_dif: float
-    direction: str
-    reason: str
-    is_trend_divergence: bool = False
-    n_trend_pivots: int = 0
-
-
-@dataclass
-class Signal:
-    kind: str
-    date: pd.Timestamp
-    price: float
-    reason: str
-    pivot_zg: Optional[float] = None
-    pivot_zd: Optional[float] = None
-    macd_ratio: Optional[float] = None
-    dif_value: Optional[float] = None
-    n_pivots: int = 0
-    trend: str = ''
-    extras: dict = field(default_factory=dict)
-    diverge_grade: Optional[DivergenceGrade] = None
-
-
-def merge_klines(df: pd.DataFrame) -> pd.DataFrame:
-    if len(df) == 0:
-        return df.copy()
-    h = df['high'].values; l = df['low'].values; d = df['date'].values
-    out_h, out_l, out_d, out_idx = [h[0]], [l[0]], [d[0]], [0]
-    for i in range(1, len(df)):
-        ph, pl = out_h[-1], out_l[-1]; ch, cl = h[i], l[i]
-        direction = 1 if (len(out_h) >= 2 and out_h[-1] >= out_h[-2]) else (1 if len(out_h) < 2 else -1)
-        contained_a = ph >= ch and pl <= cl
-        contained_b = ch >= ph and cl <= pl
-        if contained_a or contained_b:
-            if direction >= 0:
-                out_h[-1] = max(ph, ch); out_l[-1] = max(pl, cl)
-            else:
-                out_h[-1] = min(ph, ch); out_l[-1] = min(pl, cl)
-            out_idx[-1] = i
-        else:
-            out_h.append(ch); out_l.append(cl); out_d.append(d[i]); out_idx.append(i)
-    return pd.DataFrame({'date': out_d, 'high': out_h, 'low': out_l, 'orig_idx': out_idx})
-
-
-def find_fractals(merged: pd.DataFrame) -> list:
-    res = []
-    n = len(merged)
-    if n < 3:
-        return res
-    h = merged['high'].values; l = merged['low'].values; d = merged['date'].values
-    hi = h[1:-1]; hp = h[:-2]; hn = h[2:]
-    li = l[1:-1]; lp = l[:-2]; ln = l[2:]
-    top = (hi > hp) & (hi > hn) & (li >= lp) & (li >= ln)
-    bot = (li < lp) & (li < ln) & (hi <= hp) & (hi <= hn)
-    idxs = np.nonzero(top | bot)[0]
-    if len(idxs) == 0:
-        return res
-    # 仅对被选中的(稀疏)分型点构造 Timestamp, 避免对全序列逐根转换
-    is_top = top  # 局部别名
-    for j in idxs:
-        i = j + 1
-        if is_top[j]:
-            res.append(Fractal(i, pd.Timestamp(d[i]), 'top', float(h[i]), float(h[i]), float(l[i])))
-        else:
-            res.append(Fractal(i, pd.Timestamp(d[i]), 'bottom', float(l[i]), float(h[i]), float(l[i])))
-    return res
-
-
-def find_bis(fractals: list, min_k: int = 4) -> list:
-    if len(fractals) < 2:
-        return []
-    cleaned = [fractals[0]]
-    for fx in fractals[1:]:
-        last = cleaned[-1]
-        if fx.kind == last.kind:
-            if fx.kind == 'top' and fx.price > last.price:
-                cleaned[-1] = fx
-            elif fx.kind == 'bottom' and fx.price < last.price:
-                cleaned[-1] = fx
-        else:
-            cleaned.append(fx)
-    alt = [cleaned[0]]
-    for fx in cleaned[1:]:
-        if fx.kind != alt[-1].kind and fx.idx - alt[-1].idx >= min_k - 1:
-            alt.append(fx)
-        elif fx.kind != alt[-1].kind:
-            continue
-    bis = []
-    for i in range(len(alt) - 1):
-        a, b = alt[i], alt[i+1]
-        if a.kind == b.kind:
-            continue
-        direction = 'up' if b.kind == 'top' else 'down'
-        bis.append(Bi(start=a, end=b, direction=direction, bars=b.idx - a.idx,
-                      high=max(a.price, b.price), low=min(a.price, b.price)))
-    return bis
-
-
-def _find_feature_fractal(std: list, seg_dir: str):
-    """[保留] 供调试/对照用的非增量实现; 主路径已改用 _first_feature_fractal_incremental。"""
-    up = (seg_dir == 'up')
-    for i in range(1, len(std) - 1):
-        a = std[i-1]; b = std[i]; c = std[i+1]
-        if up:
-            if b['high'] > a['high'] and b['high'] > c['high'] \
-               and b['low'] > a['low'] and b['low'] > c['low']:
-                return (i, b.get('has_gap_before', False))
-        else:
-            if b['low'] < a['low'] and b['low'] < c['low'] \
-               and b['high'] < a['high'] and b['high'] < c['high']:
-                return (i, b.get('has_gap_before', False))
-    return None
-
-
-def _first_feature_fractal_incremental(bis, start_i, end_i, seg_dir):
-    """增量构建特征序列, 在第一个特征分型被"锁定"时立即返回。
-
-    锁定条件: 出现特征分型(a,b,c)后, 再追加一个新的标准元素(即 c 之后
-    已有一个不被包含的元素 d)。此时 c 不会再被向后合并改变, 分型 b 的
-    左右高低关系已固定, 与"先把整段展开再找首个分型"语义等价。
-    返回 (b_first_bi_idx, b_has_gap_before) 或 None。
-    """
-    feat_dir = 'down' if seg_dir == 'up' else 'up'
-    up = (seg_dir == 'up')
-    std = []  # 每元素: [high, low, bi_idx, has_gap_before, first_bi_idx]
-    for k in range(start_i, end_i + 1):
-        b = bis[k]
-        if b.direction != feat_dir:
-            continue
-        ch = b.high; cl = b.low
-        if not std:
-            std.append([ch, cl, k, False, k]); continue
-        prev = std[-1]
-        ph = prev[0]; pl = prev[1]
-        contained = (ph >= ch and pl <= cl) or (ch >= ph and cl <= pl)
-        if contained:
-            if up:
-                prev[0] = ph if ph > ch else ch
-                prev[1] = pl if pl > cl else cl
-            else:
-                prev[0] = ph if ph < ch else ch
-                prev[1] = pl if pl < cl else cl
-            prev[2] = k
-            continue
-        std.append([ch, cl, k, gap_flag(cl, ch, ph, pl)] + [k])
-        # 锁定检查: 需要至少4个已定型元素, 才能保证倒数第3个(候选分型b)
-        # 的右邻c已被其后元素d终结、不会再被向后合并。
-        if len(std) >= 4:
-            a = std[-4]; bm = std[-3]; c = std[-2]
-            if up:
-                ok = (bm[0] > a[0] and bm[0] > c[0] and bm[1] > a[1] and bm[1] > c[1])
-            else:
-                ok = (bm[1] < a[1] and bm[1] < c[1] and bm[0] < a[0] and bm[0] < c[0])
-            if ok:
-                return (bm[4], bm[3])
-    # 收尾: 末端无后继元素, 用最终 std 找首个内部分型(与原实现等价)
-    for i in range(1, len(std) - 1):
-        a = std[i-1]; bm = std[i]; c = std[i+1]
-        if up:
-            ok = (bm[0] > a[0] and bm[0] > c[0] and bm[1] > a[1] and bm[1] > c[1])
-        else:
-            ok = (bm[1] < a[1] and bm[1] < c[1] and bm[0] < a[0] and bm[0] < c[0])
-        if ok:
-            return (bm[4], bm[3])
-    return None
-
-
-def gap_flag(cl, ch, ph, pl):
-    return (cl > ph) or (ch < pl)
-
-
-def _seq_fractal_confirms_reversal(bis, start_i, end_i, cur_dir):
-    fr = _first_feature_fractal_incremental(bis, start_i, end_i, cur_dir)
-    if fr is None:
-        return (False, None)
-    feat_first_bi, has_gap = fr
-    seg_end_bi = feat_first_bi - 1
-    if seg_end_bi <= start_i:
-        return (False, None)
-    if has_gap:
-        opp = 'down' if cur_dir == 'up' else 'up'
-        fr2 = _first_feature_fractal_incremental(bis, feat_first_bi, end_i, opp)
-        if fr2 is None:
-            return (False, None)
-    return (True, seg_end_bi)
-
-
-def find_segs(bis: list) -> list:
-    n = len(bis)
-    if n < 3:
-        return []
-    base = bis[0].start.price
-    look = min(3, n)
-    net = bis[look - 1].end.price - base
-    cur_dir = 'up' if net > 0 else 'down'
-    segs = []
-    i = 0
-    while i < n - 2:
-        confirmed, seg_end_bi = _seq_fractal_confirms_reversal(bis, i, n - 1, cur_dir)
-        if confirmed and seg_end_bi > i:
-            seg_bis = bis[i:seg_end_bi + 1]
-            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
-            if (cur_dir == 'up') == net_up:
-                segs.append(Seg(start=bis[i].start, end=seg_bis[-1].end, direction=cur_dir,
-                                bis=seg_bis, high=max(b.high for b in seg_bis),
-                                low=min(b.low for b in seg_bis), confirmed=True))
-                i = seg_end_bi + 1
-                cur_dir = 'down' if cur_dir == 'up' else 'up'
-                continue
-        alt = 'down' if cur_dir == 'up' else 'up'
-        confirmed2, seg_end_bi2 = _seq_fractal_confirms_reversal(bis, i, n - 1, alt)
-        if confirmed2 and seg_end_bi2 > i:
-            seg_bis = bis[i:seg_end_bi2 + 1]
-            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
-            if (alt == 'up') == net_up:
-                segs.append(Seg(start=seg_bis[0].start, end=seg_bis[-1].end, direction=alt,
-                                bis=seg_bis, high=max(b.high for b in seg_bis),
-                                low=min(b.low for b in seg_bis), confirmed=True))
-                i = seg_end_bi2 + 1
-                cur_dir = 'down' if alt == 'up' else 'up'
-                continue
-        break
-    if i < n - 1 and (n - i) >= 1:
-        seg_bis = bis[i:]
-        if len(seg_bis) >= 1:
-            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
-            d = 'up' if net_up else 'down'
-            segs.append(Seg(start=seg_bis[0].start, end=seg_bis[-1].end, direction=d,
-                            bis=seg_bis, high=max(b.high for b in seg_bis),
-                            low=min(b.low for b in seg_bis), confirmed=False))
-    return segs
-
-
-PIVOT_UPGRADE_SPAN_DAYS = 540
-
-
-def find_pivots(bis: list, segs: Optional[list] = None) -> list:
-    confirmed_segs = [s for s in (segs or []) if getattr(s, 'confirmed', True)]
-    units = confirmed_segs if len(confirmed_segs) >= 3 else bis
-    using_segs = units is confirmed_segs
-    pivots = []; n = len(units)
-    if n < 3:
-        return pivots
-
-    bi_pos = {id(b): k for k, b in enumerate(bis)}
-
-    def _unit_bi_range(u):
-        if hasattr(u, 'bis'):
-            idxs = [bi_pos[id(b)] for b in u.bis if id(b) in bi_pos]
-            return (min(idxs), max(idxs)) if idxs else (0, 0)
-        k = bi_pos.get(id(u), 0)
-        return (k, k)
-
-    def _unit_bi_indices(unit_list):
-        out = []
-        for u in unit_list:
-            a, b = _unit_bi_range(u)
-            out.extend(range(a, b + 1))
-        return sorted(set(out))
-
-    def _unit_high_date(u):
-        return u.start.date if u.start.price >= u.end.price else u.end.date
-
-    def _unit_low_date(u):
-        return u.start.date if u.start.price <= u.end.price else u.end.date
-
-    max_ext = PIVOT_MAX_EXTEND_SEGS if PIVOT_MAX_EXTEND_SEGS else 10 ** 9
-
-    i = 0
-    while i <= n - 3:
-        b1, b2, b3 = units[i], units[i+1], units[i+2]
-        r1 = (b1.low, b1.high)
-        r2 = (b2.low, b2.high)
-        r3 = (b3.low, b3.high)
-        zg = min(r1[1], r2[1], r3[1]); zd = max(r1[0], r2[0], r3[0])
-        if zg > zd:
-            direction = b1.direction; zg_orig, zd_orig = zg, zd; gg, dd = zg, zd
-            highs = [r1[1], r2[1], r3[1]]; lows = [r1[0], r2[0], r3[0]]
-            zg_bi = (b1, b2, b3)[highs.index(zg_orig)]
-            zd_bi = (b1, b2, b3)[lows.index(zd_orig)]
-            zg_d = _unit_high_date(zg_bi)
-            zd_d = _unit_low_date(zd_bi)
-            gg_d, dd_d = zg_d, zd_d
-            zn_dir = direction
-            gn_list = []; dn_list = []
-            for bb in (b1, b2, b3):
-                if bb.direction == zn_dir:
-                    gn_list.append(max(bb.start.price, bb.end.price))
-                    dn_list.append(min(bb.start.price, bb.end.price))
-            piv_units = [i, i+1, i+2]; j = i + 3
-            capped = False
-            while j < n:
-                if (len(piv_units) - 3) >= max_ext:
-                    capped = True
-                    break
-                bj = units[j]; lo_j = bj.low; hi_j = bj.high
-                if hi_j >= zd_orig and lo_j <= zg_orig:
-                    if hi_j > gg:
-                        gg = hi_j; gg_d = _unit_high_date(bj)
-                    if lo_j < dd:
-                        dd = lo_j; dd_d = _unit_low_date(bj)
-                    if bj.direction == zn_dir:
-                        gn_list.append(hi_j); dn_list.append(lo_j)
-                    piv_units.append(j); j += 1
-                else:
-                    break
-            g_val = min(gn_list) if gn_list else zg_orig
-            d_val = max(dn_list) if dn_list else zd_orig
-            piv_bis = _unit_bi_indices([units[k] for k in piv_units]) if using_segs else piv_units
-            p_start = b1.start.date
-            p_end = units[piv_units[-1]].end.date
-            piv = Pivot(start_date=p_start, end_date=p_end,
-                        zg=zg_orig, zd=zd_orig, gg=gg, dd=dd, bis=piv_bis, direction=direction,
-                        zg_date=zg_d, zd_date=zd_d, gg_date=gg_d, dd_date=dd_d,
-                        g=g_val, d=d_val, capped=capped)
-            try:
-                span_days = (pd.Timestamp(p_end) - pd.Timestamp(p_start)).days
-            except Exception:
-                span_days = 0
-            if capped or span_days > PIVOT_UPGRADE_SPAN_DAYS:
-                piv.upgraded_level = 'weekly'
-            pivots.append(piv)
-            i = piv_units[-1] + 1
-        else:
-            i += 1
-
-    for k in range(1, len(pivots)):
-        prev, cur = pivots[k-1], pivots[k]
-        no_overlap = (cur.dd > prev.gg) or (cur.gg < prev.dd)
-        if no_overlap:
-            cur.state = 'new'
-        else:
-            cur.state = 'expand'
-
-    for k in range(len(pivots) - 1):
-        cur = pivots[k]
-        nxt = pivots[k + 1]
-        gap_start = cur.bis[-1] + 1
-        gap_end = nxt.bis[0]
-        gap_bis = bis[gap_start:gap_end] if gap_end > gap_start else []
-        if len(gap_bis) >= 2:
-            leave = gap_bis[:max(1, len(gap_bis) // 2)]
-            pull = gap_bis[max(1, len(gap_bis) // 2):]
-            leave_trend = len(leave) >= 3
-            pull_trend = len(pull) >= 3
-            if leave_trend and not pull_trend:
-                cur.death_combo = 'trend+consol'
-            elif leave_trend and pull_trend:
-                cur.death_combo = 'trend+counter'
-            else:
-                cur.death_combo = 'consol+counter'
-    return pivots
-
-
-def classify_trend(pivots: list) -> str:
-    if len(pivots) < 2:
-        return 'consolidation'
-    p1, p2 = pivots[-2], pivots[-1]
-    if p2.dd > p1.gg:
-        return 'up_trend'
-    if p2.gg < p1.dd:
-        return 'down_trend'
-    if (p2.zg < p1.zd and p2.gg >= p1.dd) or (p2.zd > p1.zg and p2.dd <= p1.gg):
-        return 'expanding'
-    return 'consolidation'
-
-
-def count_trend_pivots(pivots: list) -> int:
-    if not pivots:
-        return 0
-    if len(pivots) == 1:
-        return 1
-    cnt = 1
-    for k in range(len(pivots) - 1, 0, -1):
-        p_prev, p_cur = pivots[k - 1], pivots[k]
-        if p_cur.dd > p_prev.gg:
-            cnt += 1
-        elif p_cur.gg < p_prev.dd:
-            cnt += 1
-        else:
-            break
-    return cnt
-
-
-def calc_macd(close: pd.Series, fast=12, slow=26, signal=9):
-    ema_fast = close.ewm(span=fast, adjust=False).mean()
-    ema_slow = close.ewm(span=slow, adjust=False).mean()
-    dif = ema_fast - ema_slow
-    dea = dif.ewm(span=signal, adjust=False).mean()
-    macd_bar = 2 * (dif - dea)
-    return dif, dea, macd_bar
-
-
-def macd_area_between(start_date, end_date, bar_series, date_series, direction):
-    mask = (date_series >= start_date) & (date_series <= end_date)
-    vals = bar_series[mask]
-    if len(vals) == 0:
-        return 0.0
-    if direction == 'up':
-        return float(vals.clip(lower=0).sum())
-    return float(vals.clip(upper=0).abs().sum())
-
-
-def dif_extreme_in(start_date, end_date, dif_series, date_series, kind='peak'):
-    mask = (date_series >= start_date) & (date_series <= end_date)
-    vals = dif_series[mask]
-    if len(vals) == 0:
-        return 0.0
-    return float(vals.max()) if kind == 'peak' else float(vals.min())
-
-
-class ChanAnalyzer:
-    DIVERGE_RATIO = 0.80
-    PIVOT_TOLERANCE = 0.02
-    MIN_BI_BARS = 4
-    DIF_TOLERANCE = 0.01
-
-    CFG = {
-        'b1_allow_consol_diverge': True,
-        'b3s3_first_pullback_only': False,
-        'b2s2_anchor_to_first': False,
-        'b2_macd_zero_pullback': False,
-        'drop_upgraded_pivots': False,
-        # L88-90 中阴阶段MACD精确运用: 中阴判定除BOLL收口外, 加入MACD特征
-        #   'off'  = 维持原判定(仅BOLL收口+末笔未离开中枢)
-        #   'and'  = 须同时满足"黄白线绕0轴缠绕"(更严格, 减少误判中阴而拦截的好买点)
-        #   'or'   = 满足其一即算中阴(更宽松, 拦截更多)
-        'zhongyin_macd_mode': 'or',   # 实测'or'最优: 累计收益+35pp(383%→418%), 胜率45.3%→46.8%
-    }
-
-    def __init__(self, df: pd.DataFrame):
-        self.df_raw = df.reset_index(drop=True)
-        self.close = self.df_raw['close']
-        self.dif, self.dea, self.macd_bar = calc_macd(self.close)
-        self.merged = merge_klines(self.df_raw)
-        self.fractals = find_fractals(self.merged)
-        self.bis = find_bis(self.fractals, min_k=self.MIN_BI_BARS)
-        self._bi_index = {id(b): k for k, b in enumerate(self.bis)}
-        self.segs = find_segs(self.bis)
-        self.pivots_all = find_pivots(self.bis, self.segs)
-        if self.CFG.get('drop_upgraded_pivots'):
-            last_upg_idx = -1
-            for k, p in enumerate(self.pivots_all):
-                if p.upgraded_level:
-                    last_upg_idx = k
-            operative = [p for k, p in enumerate(self.pivots_all)
-                         if k > last_upg_idx and not p.upgraded_level]
-            self.pivots = operative
-        else:
-            self.pivots = self.pivots_all
-        self.trend = classify_trend(self.pivots)
-
-    @property
-    def n_bis(self): return len(self.bis)
-    @property
-    def n_segs(self): return len(self.segs)
-    @property
-    def n_pivots(self): return len(self.pivots)
-    @property
-    def n_pivots_all(self): return len(self.pivots_all)
-    @property
-    def n_trend_pivots(self): return count_trend_pivots(self.pivots)
-    @property
-    def has_upgraded_pivot(self):
-        return any(p.upgraded_level for p in self.pivots_all)
-
-    @staticmethod
-    def _ds(ts):
-        ts = pd.Timestamp(ts)
-        if ts.hour == 0 and ts.minute == 0:
-            return ts.strftime('%Y-%m-%d')
-        return ts.strftime('%Y-%m-%d %H:%M')
-
-    def _validate_abc(self, direction: str):
-        if self.n_pivots < 2:
-            return None
-        last_piv = self.pivots[-1]
-        prev_piv = self.pivots[-2]
-        ratio = len(prev_piv.bis) / max(len(last_piv.bis), 1)
-        if not (1 / 3 <= ratio <= 3):
-            return None
-        a_start_idx = prev_piv.bis[0]
-        a_end_idx = last_piv.bis[0]
-        if a_end_idx <= a_start_idx:
-            return None
-        c_start_idx = last_piv.bis[-1] + 1
-        if c_start_idx >= self.n_bis:
-            return None
-        return {'a_start_idx': a_start_idx, 'a_end_idx': a_end_idx,
-                'b_pivot': last_piv, 'c_start_idx': c_start_idx}
-
-    def _validate_abc_consol(self, direction: str):
-        if self.n_pivots < 1:
-            return None
-        piv = self.pivots[-1]
-        a_end_idx = piv.bis[0]
-        if a_end_idx <= 0:
-            return None
-        c_start_idx = piv.bis[-1] + 1
-        if c_start_idx >= self.n_bis:
-            return None
-        want = 'down' if direction == 'down' else 'up'
-        a_start_idx = a_end_idx
-        for k in range(a_end_idx - 1, -1, -1):
-            a_start_idx = k
-            if k >= 1 and self.bis[k].direction != want and self.bis[k-1].direction != want:
-                a_start_idx = k + 1
-                break
-        if a_start_idx >= a_end_idx:
-            return None
-        return {'a_start_idx': a_start_idx, 'a_end_idx': a_end_idx,
-                'b_pivot': piv, 'c_start_idx': c_start_idx}
-
-    def _check_c_new_extreme(self, c_start_idx: int, direction: str):
-        want = 'up' if direction == 'up' else 'down'
-        c_bis = [self.bis[k] for k in range(c_start_idx, self.n_bis)
-                 if self.bis[k].direction == want]
-        if not c_bis:
-            return False, None
-        last_piv = self.pivots[-1] if self.pivots else None
-        if direction == 'up':
-            c_ext = max(b.end.price for b in c_bis)
-            prior = (last_piv.gg if last_piv is not None
-                     else max((b.high for b in self.bis[:c_start_idx]), default=0.0))
-            return c_ext > prior * (1 - 0.001), c_ext
-        else:
-            c_ext = min(b.end.price for b in c_bis)
-            prior = (last_piv.dd if last_piv is not None
-                     else min((b.low for b in self.bis[:c_start_idx]), default=1e18))
-            return c_ext < prior * (1 + 0.001), c_ext
-
-    def _b_returns_to_zero(self, pivot) -> bool:
-        dates = self.df_raw['date']
-        mask = (dates >= pivot.start_date) & (dates <= pivot.end_date)
-        dif_b = self.dif[mask]
-        dea_b = self.dea[mask]
-        if len(dif_b) == 0 or len(dea_b) == 0:
-            return False
-        def near_zero(x):
-            if x.min() <= 0 <= x.max():
-                return True
-            return x.abs().min() < max(float(x.abs().max()), 1e-9) * 0.25
-        return near_zero(dif_b) and near_zero(dea_b)
-
-    def detect_double_pullback_to_zero(self, window: int = 40) -> bool:
-        n = len(self.dif)
-        if n < 10:
-            return False
-        dif = self.dif.iloc[-min(window, n):].reset_index(drop=True)
-        dea = self.dea.iloc[-min(window, n):].reset_index(drop=True)
-        hist = self.macd_bar.iloc[-min(window, n):].reset_index(drop=True)
-        scale = max(float(dif.abs().max()), float(dea.abs().max()), 1e-9)
-        near = scale * 0.25
-        zero_pulls = [i for i in range(len(dif))
-                      if abs(float(dif.iloc[i])) <= near and abs(float(dea.iloc[i])) <= near]
-        if len(zero_pulls) < 2:
-            return False
-        def peak_between(left, right):
-            vals = dif.iloc[left + 1:right]
-            if len(vals) < 2:
-                return None
-            rel = int(vals.idxmax())
-            return float(dif.iloc[rel]), float(hist.iloc[max(left + 1, rel - 3):rel + 1].clip(lower=0).sum())
-        def trough_between(left, right):
-            vals = dif.iloc[left + 1:right]
-            if len(vals) < 2:
-                return None
-            rel = int(vals.idxmin())
-            return float(dif.iloc[rel]), float(hist.iloc[max(left + 1, rel - 3):rel + 1].clip(upper=0).abs().sum())
-        first_pull, second_pull = zero_pulls[-2], zero_pulls[-1]
-        p1, p2 = peak_between(first_pull, second_pull), peak_between(second_pull, len(dif))
-        if p1 and p2 and p1[0] > 0 and p2[0] > 0 and p2[0] < p1[0] and p2[1] <= p1[1]:
-            return True
-        t1, t2 = trough_between(first_pull, second_pull), trough_between(second_pull, len(dif))
-        if t1 and t2 and t1[0] < 0 and t2[0] < 0 and t2[0] > t1[0] and t2[1] <= t1[1]:
-            return True
-        return False
-
-    def divergence_strength_by_position(self) -> str:
-        if len(self.dif) < 5:
-            return 'strong_pullback'
-        dif_now = float(self.dif.iloc[-1])
-        dif_abs_max = float(self.dif.abs().max())
-        if dif_abs_max <= 1e-9:
-            return 'strong_pullback'
-        if abs(dif_now) >= dif_abs_max * 0.85:
-            return 'weak_pullback'
-        return 'strong_pullback'
-
-    def classify_post_divergence(self, direction: str) -> dict:
-        if not self.pivots or self.n_bis < 2:
-            return {'evolution': 'unknown', 'reason': '无中枢或笔不足'}
-        last_piv = self.pivots[-1]
-        after_idx = last_piv.bis[-1] + 1
-        def first_reversal_seg(want_dir):
-            for s in self.segs:
-                if not getattr(s, 'confirmed', True) or s.direction != want_dir:
-                    continue
-                try:
-                    first_idx = self._bi_index[id(s.bis[0])]
-                except KeyError:
-                    continue
-                if first_idx >= after_idx:
-                    return s
-            return None
-        if direction == 'down':
-            rebound = first_reversal_seg('up')
-            if rebound is None:
-                rebound = None
-                for b in self.bis[after_idx:]:
-                    if b.direction == 'up':
-                        rebound = b; break
-            if rebound is None:
-                return {'evolution': 'unknown', 'reason': '无反弹笔'}
-            if rebound.high < last_piv.zd:
-                return {'evolution': 'case1_extend',
-                        'reason': f'反弹高{rebound.high:.3f}<最后中枢ZD{last_piv.zd:.3f} → 第29课情况①未回中枢(最弱,宜尽快撤)'}
-            if rebound.high >= last_piv.zd:
-                return {'evolution': 'case2_3_turn',
-                        'reason': f'反弹回到中枢(高{rebound.high:.3f}≥ZD{last_piv.zd:.3f}) → 第29课情况②③转折(可持有等三买)'}
-            return {'evolution': 'case1_extend',
-                    'reason': f'反弹未回中枢(高{rebound.high:.3f}<ZD{last_piv.zd:.3f}) → 偏向中枢扩展'}
-        else:
-            pullback = first_reversal_seg('down')
-            if pullback is None:
-                pullback = None
-                for b in self.bis[after_idx:]:
-                    if b.direction == 'down':
-                        pullback = b; break
-            if pullback is None:
-                return {'evolution': 'unknown', 'reason': '无回落笔'}
-            if pullback.low > last_piv.zg:
-                return {'evolution': 'case1_extend',
-                        'reason': f'回落低{pullback.low:.3f}>最后中枢ZG{last_piv.zg:.3f} → 第29课情况①未回中枢(最弱)'}
-            if pullback.low <= last_piv.zg:
-                return {'evolution': 'case2_3_turn',
-                        'reason': f'回落回到中枢(低{pullback.low:.3f}≤ZG{last_piv.zg:.3f}) → 第29课情况②③转折'}
-            return {'evolution': 'case1_extend',
-                    'reason': f'回落未回中枢 → 偏向中枢扩展'}
-
-    def macd_wrap_zero(self, window: int = 15) -> bool:
-        """L88-90: 中阴阶段的MACD特征 —— 黄白线(DIF/DEA)绕0轴缠绕。
-        近window根K线中, DIF与DEA的绝对值大多压在历史摆幅的25%以内即视为缠绕。"""
-        n = len(self.dif)
-        if n < window + 5:
-            return False
-        dif = self.dif.iloc[-window:]
-        dea = self.dea.iloc[-window:]
-        scale = max(float(self.dif.abs().tail(120).max()),
-                    float(self.dea.abs().tail(120).max()), 1e-9)
-        near = scale * 0.25
-        frac = float(((dif.abs() <= near) & (dea.abs() <= near)).mean())
-        return frac >= 0.6
-
-    def macd_clarity(self, window: int = 60) -> dict:
-        """L50: 本级别MACD的"清晰度" —— 柱子面积幅度 + 黄白线分离度, 归一化打分。
-        清晰度高的级别其背驰判定更可靠; 多级别联立时应优先采信清晰级别的MACD结论。"""
-        n = len(self.dif)
-        if n < 10:
-            return {'score': 0.0, 'label': '数据不足'}
-        w = min(window, n)
-        dif = self.dif.iloc[-w:]
-        dea = self.dea.iloc[-w:]
-        hist = self.macd_bar.iloc[-w:]
-        px = max(float(self.close.iloc[-1]), 1e-9)
-        bar_amp = float(hist.abs().mean()) / px          # 柱子相对幅度
-        sep     = float((dif - dea).abs().mean()) / px   # 黄白线分离度
-        # 黄白线贴着0轴乱绕 → 不清晰
-        wrap_penalty = 0.5 if self.macd_wrap_zero() else 1.0
-        score = (bar_amp * 0.6 + sep * 0.4) * 1e3 * wrap_penalty
-        label = '清晰' if score >= 1.0 else ('一般' if score >= 0.4 else '模糊(黄白线/柱子贴0轴)')
-        return {'score': round(score, 3), 'label': label,
-                'bar_amp': round(bar_amp * 1e3, 3), 'sep': round(sep * 1e3, 3)}
-
-    def in_zhongyin(self) -> dict:
-        n = len(self.close)
-        if n < 20 or not self.pivots:
-            return {'in_zhongyin': False, 'boll_squeeze': False, 'reason': '数据不足'}
-        ma = self.close.rolling(20).mean()
-        sd = self.close.rolling(20).std()
-        if ma.iloc[-1] and ma.iloc[-1] > 0:
-            width = float((4 * sd.iloc[-1]) / ma.iloc[-1])
-        else:
-            width = 0.0
-        wseries = (4 * sd / ma).dropna().tail(60)
-        squeeze = bool(len(wseries) >= 20 and width <= wseries.quantile(0.30))
-        osc = self.zhongshu_oscillation_monitor()
-        macd_wrap = self.macd_wrap_zero()
-        dbl_pull = self.detect_double_pullback_to_zero()
-        mode = self.CFG.get('zhongyin_macd_mode', 'off')
-        if mode == 'and':
-            in_zy = (not osc.get('alert', False)) and squeeze and macd_wrap
-        elif mode == 'or':
-            in_zy = (not osc.get('alert', False)) and (squeeze or macd_wrap)
-        else:
-            in_zy = (not osc.get('alert', False)) and squeeze
-        reason = (f"BOLL带宽{width:.3f}{'(收口→中阴)' if squeeze else '(开口)'}; "
-                  f"MACD黄白线{'绕0轴缠绕(L88-90中阴特征)' if macd_wrap else '已展开'}"
-                  f"{'; 双回拉0轴(L89: 中阴结束转折预备)' if dbl_pull else ''}; {osc.get('reason','')}")
-        return {'in_zhongyin': in_zy, 'boll_squeeze': squeeze,
-                'macd_wrap_zero': macd_wrap, 'double_pullback_zero': dbl_pull,
-                'reason': reason}
-
-    def zhongshu_oscillation_monitor(self) -> dict:
-        if not self.pivots or self.n_bis < 1:
-            return {'alert': False, 'direction': '', 'reason': '无中枢'}
-        last_piv = self.pivots[-1]
-        cur = self.bis[-1]
-        if cur.low > last_piv.zg:
-            return {'alert': True, 'direction': 'up',
-                    'reason': f'第92课: 末笔({cur.low:.3f}~{cur.high:.3f})已离开中枢上沿ZG{last_piv.zg:.3f} → 向上变盘预警'}
-        if cur.high < last_piv.zd:
-            return {'alert': True, 'direction': 'down',
-                    'reason': f'第92课: 末笔({cur.low:.3f}~{cur.high:.3f})已离开中枢下沿ZD{last_piv.zd:.3f} → 向下变盘预警'}
-        return {'alert': False, 'direction': '', 'reason': '末笔仍在中枢区间内, 中枢震荡延续'}
-
-    def bottom_construction_state(self) -> str:
-        has_b1 = self.detect_b1() is not None
-        has_b3 = self.detect_b3() is not None
-        has_s3 = self.detect_s3() is not None
-        if has_s3:
-            return 'failed'
-        if has_b3:
-            return 'completed'
-        if has_b1:
-            return 'constructing'
-        return 'none'
-
-    def _seg_index_range(self, seg):
-        m = self._bi_index
+import datetime as dt
+import threading
+import traceback
+from zoneinfo import ZoneInfo
+
+NY = ZoneInfo("America/New_York")
+RUN_HOUR, RUN_MINUTE = 18, 10
+
+STATE = {
+    "signals_df": None,
+    "signals_details": {},
+    "signals_summary": "Not run yet — press “Run now” or wait for the 18:10 ET schedule.",
+    "rotation": (None, None, None, "—"),
+    "rotation_narrative": "",
+    "news_md": "",
+    "last_run": None,
+    "running": False,
+    "log": [],
+}
+_lock = threading.Lock()
+
+
+def _log(msg: str):
+    stamp = dt.datetime.now(NY).strftime("%m-%d %H:%M:%S ET")
+    STATE["log"] = (STATE["log"] + [f"[{stamp}] {msg}"])[-60:]
+
+
+def run_pipeline(tickers=None, force: bool = True) -> str:
+    """Full daily refresh. Safe to call from the UI or the scheduler."""
+    import news_watch
+    import rotation
+    import signal_runner
+
+    with _lock:
+        if STATE["running"]:
+            return "A pipeline run is already in progress."
+        STATE["running"] = True
+    try:
+        _log("Pipeline start: refreshing data + signals…")
+        df, details, summary, errors = signal_runner.run_signals(tickers, force=force)
+        STATE["signals_df"] = df
+        STATE["signals_details"] = details
+        STATE["signals_summary"] = summary
+        for e in errors:
+            _log(f"signal skip: {e}")
+        _log(f"Signals done: {summary}")
+
+        d1, d5, d20, asof = rotation.build_rotation(force=force)
+        STATE["rotation"] = (d1, d5, d20, asof)
+        # LLM narrative is generated ON DEMAND from the Rotation tab button —
+        # the pipeline itself never waits on the model.
+        STATE["rotation_narrative"] = rotation.rotation_brief(d1, d5, d20)
+        _log(f"Sector rotation rebuilt (as of {asof}).")
+
+        STATE["news_md"] = news_watch.check_holdings_news()
+        _log("Holdings news checked.")
+
+        # ── Feature 4: auto research report for every NEW ticker in the pool ──
         try:
-            return m[id(seg.bis[0])], m[id(seg.bis[-1])]
-        except KeyError:
-            return None
-
-    def _bi_exit_pullback_fallback(self, pivot, exit_dir: str, pull_dir: str):
-        pe = pivot.bis[-1]
-        leave = pull = None
-        for k in range(pe + 1, self.n_bis):
-            b = self.bis[k]
-            if leave is None:
-                if b.direction == exit_dir:
-                    leave = b
-                continue
-            if b.direction == pull_dir:
-                pull = b
-                break
-        if leave is None or pull is None:
-            return None
-        return leave, pull
-
-    def _last_exit_pullback_segments(self, pivot, exit_dir: str, pull_dir: str):
-        pivot_end = pivot.bis[-1]
-        seq = []
-        for s in (self.segs or []):
-            if not getattr(s, 'confirmed', True):
-                continue
-            rng = self._seg_index_range(s)
-            if rng is None:
-                continue
-            first_idx, last_idx = rng
-            if last_idx < pivot_end:
-                continue
-            seq.append((s, first_idx, last_idx))
-        first_only = self.CFG.get('b3s3_first_pullback_only')
-        if first_only:
-            for i in range(len(seq) - 1):
-                leave, lf, _ = seq[i]
-                pull = seq[i + 1][0]
-                if leave.direction == exit_dir and pull.direction == pull_dir and lf >= pivot_end:
-                    return leave, pull
-        else:
-            for i in range(len(seq) - 1, 0, -1):
-                pull, _, _ = seq[i]
-                leave, _, _ = seq[i - 1]
-                if leave.direction == exit_dir and pull.direction == pull_dir:
-                    return leave, pull
-        return self._bi_exit_pullback_fallback(pivot, exit_dir, pull_dir)
-
-    def assess_divergence(self, a_start, a_end, c_start, c_end, direction: str) -> DivergenceGrade:
-        dates = self.df_raw['date']
-        n_tp = self.n_trend_pivots
-        is_trend_div = n_tp >= 2
-        a_area = macd_area_between(a_start, a_end, self.macd_bar, dates, direction)
-        c_area = macd_area_between(c_start, c_end, self.macd_bar, dates, direction)
-        if a_area <= 1e-9:
-            return DivergenceGrade('NONE', False, False, 0.0, a_area, c_area, 0.0, 0.0,
-                                   direction, 'A段MACD面积为0,无可比基准',
-                                   is_trend_divergence=is_trend_div, n_trend_pivots=n_tp)
-        ratio = c_area / a_area
-        area_ok = ratio < self.DIVERGE_RATIO
-        TOL = self.DIF_TOLERANCE
-        if direction == 'up':
-            a_dif = dif_extreme_in(a_start, a_end, self.dif, dates, 'peak')
-            c_dif = dif_extreme_in(c_start, c_end, self.dif, dates, 'peak')
-            dif_ok = c_dif < a_dif * (1 - TOL)
-        else:
-            a_dif = dif_extreme_in(a_start, a_end, self.dif, dates, 'trough')
-            c_dif = dif_extreme_in(c_start, c_end, self.dif, dates, 'trough')
-            dif_ok = c_dif > a_dif * (1 - TOL)
-        ext_label = 'DIF峰' if direction == 'up' else 'DIF谷'
-        div_kind = '趋势背驰' if is_trend_div else '盘整背驰'
-        if area_ok and dif_ok:
-            grade = 'STRONG'
-            reason = f'标准{div_kind}(面积+DIF均满足)|A面积:{a_area:.4f} C面积:{c_area:.4f}(比值{ratio:.1%}<{self.DIVERGE_RATIO:.0%})|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}|{n_tp}中枢'
-        elif area_ok and not dif_ok:
-            grade = 'WEAK'
-            reason = f'{div_kind}信号(面积触发)|A面积:{a_area:.4f} C面积:{c_area:.4f}(比值{ratio:.1%}<{self.DIVERGE_RATIO:.0%})|{n_tp}中枢'
-        elif dif_ok and not area_ok:
-            grade = 'WEAK'
-            reason = f'{div_kind}信号(DIF触发)|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}|{n_tp}中枢'
-        else:
-            grade = 'NONE'
-            reason = f'无背驰(两判据均不满足)|面积比{ratio:.1%}>={self.DIVERGE_RATIO:.0%}|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}'
-        return DivergenceGrade(grade, area_ok, dif_ok, ratio, a_area, c_area, a_dif, c_dif,
-                               direction, reason, is_trend_divergence=is_trend_div, n_trend_pivots=n_tp)
-
-    def _find_prev_b1(self) -> tuple:
-        if not self.pivots:
-            return (None, '')
-        last_piv = self.pivots[-1]
-        pivot_first_bi_idx = last_piv.bis[0]
-        if pivot_first_bi_idx <= 0:
-            return (None, '')
-        downs = []
-        for k in range(pivot_first_bi_idx - 1, -1, -1):
-            b = self.bis[k]
-            downs.append(b)
-            if b.direction == 'up' and k - 1 >= 0 and self.bis[k-1].direction == 'down':
-                if len(downs) >= 4:
-                    break
-        down_bis = [b for b in downs if b.direction == 'down']
-        if not down_bis:
-            return (None, '')
-        b1 = min(down_bis, key=lambda b: b.end.price)
-        return (b1.end.price, self._ds(b1.end.date))
-
-    def _find_prev_b2(self) -> tuple:
-        b1_price, b1_date_str = self._find_prev_b1()
-        if b1_price is None or not self.pivots:
-            return (None, '')
-        b1_date = pd.Timestamp(b1_date_str)
-        last_piv = self.pivots[-1]
-        right_bi_idx = last_piv.bis[-1] + 1
-        for b in self.bis[:right_bi_idx]:
-            if b.direction != 'down':
-                continue
-            if b.end.date <= b1_date:
-                continue
-            if b.end.price > b1_price + 1e-9:
-                return (b.end.price, self._ds(b.end.date))
-        return (None, '')
-
-    def _find_prev_s1(self) -> tuple:
-        if not self.pivots:
-            return (None, '')
-        last_piv = self.pivots[-1]
-        pivot_first_bi_idx = last_piv.bis[0]
-        if pivot_first_bi_idx <= 0:
-            return (None, '')
-        ups = []
-        for k in range(pivot_first_bi_idx - 1, -1, -1):
-            b = self.bis[k]
-            ups.append(b)
-            if b.direction == 'down' and k - 1 >= 0 and self.bis[k-1].direction == 'up':
-                if len(ups) >= 4:
-                    break
-        up_bis = [b for b in ups if b.direction == 'up']
-        if not up_bis:
-            return (None, '')
-        s1 = max(up_bis, key=lambda b: b.end.price)
-        return (s1.end.price, self._ds(s1.end.date))
-
-    def _find_prev_s2(self) -> tuple:
-        s1_price, s1_date_str = self._find_prev_s1()
-        if s1_price is None or not self.pivots:
-            return (None, '')
-        s1_date = pd.Timestamp(s1_date_str)
-        last_piv = self.pivots[-1]
-        right_bi_idx = last_piv.bis[-1] + 1
-        for b in self.bis[:right_bi_idx]:
-            if b.direction != 'up':
-                continue
-            if b.end.date <= s1_date:
-                continue
-            if b.end.price < s1_price - 1e-9:
-                return (b.end.price, self._ds(b.end.date))
-        return (None, '')
-
-    def detect_b1(self) -> Optional[Signal]:
-        if self.n_bis < 5:
-            return None
-        cur = self.bis[-1]
-        if cur.direction != 'down':
-            return None
-        dif_now = float(self.dif.iloc[-1])
-        if dif_now >= 0:
-            return None
-        trend_ok = (self.n_pivots >= 2 and self.trend == 'down_trend')
-        consol_ok = (self.CFG.get('b1_allow_consol_diverge') and self.n_pivots >= 1)
-        if not (trend_ok or consol_ok):
-            return None
-        abc = self._validate_abc('down')
-        if abc is None and consol_ok:
-            abc = self._validate_abc_consol('down')
-        if abc is None:
-            return None
-        c_ok, c_low = self._check_c_new_extreme(abc['c_start_idx'], 'down')
-        if not c_ok:
-            return None
-        last_piv = self.pivots[-1]
-        a_down = [self.bis[k] for k in range(abc['a_start_idx'], abc['a_end_idx'])
-                  if self.bis[k].direction == 'down']
-        c_down = [self.bis[k] for k in range(abc['c_start_idx'], self.n_bis)
-                  if self.bis[k].direction == 'down']
-        if not a_down or not c_down:
-            return None
-        dg = self.assess_divergence(a_down[0].start.date, a_down[-1].end.date,
-                                    c_down[0].start.date, c_down[-1].end.date, 'down')
-        if dg.grade == 'NONE':
-            return None
-        div_kind = '趋势底背驰' if dg.is_trend_divergence else '盘整底背驰(第27课)'
-        b_zero = self._b_returns_to_zero(last_piv)
-        dbl_pull = self.detect_double_pullback_to_zero()
-        pos_strength = self.divergence_strength_by_position()
-        post_evo = self.classify_post_divergence('down')
-        a_low_bi = min(a_down, key=lambda b: b.end.price)
-        c_low_bi = min(c_down, key=lambda b: b.end.price)
-        return Signal(kind='B1', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
-                      reason=f'{div_kind}一买|{self.n_pivots}中枢|ABC三段{"+B回0轴" if b_zero else ""}|{dg.reason}|DIF={dif_now:.4f}<0',
-                      pivot_zg=last_piv.zg, pivot_zd=last_piv.zd, macd_ratio=dg.area_ratio, dif_value=dif_now,
-                      n_pivots=self.n_pivots, trend=self.trend,
-                      extras={'a_seg': (a_down[0].start.date, a_down[-1].end.date, a_down[0].start.price, a_down[-1].end.price),
-                              'diverge_grade': dg.grade,
-                              'a_low': float(a_low_bi.end.price),
-                              'a_low_date': self._ds(a_low_bi.end.date),
-                              'b1_price': float(cur.end.price),
-                              'b1_date': self._ds(cur.end.date),
-                              'macd_grade': dg.grade,
-                              'macd_area_ratio': dg.area_ratio,
-                              'dif_ok': dg.dif_ok,
-                              'area_ok': dg.area_ok,
-                              'b_returns_zero': b_zero,
-                              'double_pullback': dbl_pull,
-                              'pos_strength': pos_strength,
-                              'post_evolution': post_evo['evolution'],
-                              'c_new_low': c_low,
-                              'c_new_low_date': self._ds(c_low_bi.end.date),
-                              'n_trend_pivots': self.n_trend_pivots,
-                              'price_date': self._ds(cur.end.date),
-                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
-                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
-                              'pivot_start_date': self._ds(last_piv.start_date),
-                              'pivot_end_date': self._ds(last_piv.end_date)},
-                      diverge_grade=dg)
-
-    def detect_b2(self) -> Optional[Signal]:
-        if self.n_bis < 4:
-            return None
-        cur = self.bis[-1]
-        if cur.direction != 'down':
-            return None
-        prev_downs = [b for b in self.bis[:-1] if b.direction == 'down']
-        if not prev_downs:
-            return None
-        prev = prev_downs[-1]
-        if not (cur.low >= prev.low and cur.end.price > prev.end.price):
-            return None
-        cur_price = float(self.close.iloc[-1])
-        if cur_price < cur.end.price * (1 - self.PIVOT_TOLERANCE):
-            return None
-        if self.CFG.get('b2s2_anchor_to_first'):
-            b1_anchor, _ = self._find_prev_b1()
-            if b1_anchor is None:
-                return None
-            if cur.end.price < b1_anchor - 1e-9:
-                return None
-        dif_now = float(self.dif.iloc[-1])
-        if self.CFG.get('b2_macd_zero_pullback'):
-            look = self.dif.tail(12)
-            crossed_up = bool((look > 0).any())
-            if not crossed_up:
-                return None
-            if dif_now < -self.DIF_TOLERANCE:
-                return None
-        last_piv = self.pivots[-1] if self.pivots else None
-        b1_price, b1_date = prev.end.price, self._ds(prev.end.date)
-        return Signal(kind='B2', date=self.df_raw['date'].iloc[-1], price=cur_price,
-                      reason=f'二买:一买后回踩不破|当前低{cur.end.price:.3f}>一买{b1_price:.3f}|二买不以背驰为成立条件(第21课)',
-                      pivot_zg=None, pivot_zd=None,
-                      macd_ratio=None, dif_value=dif_now, n_pivots=self.n_pivots, trend=self.trend,
-                      extras={'prev_low': prev.end.price, 'cur_low': cur.end.price,
-                              'cur_low_date': self._ds(cur.end.date),
-                              'prev_low_date': self._ds(prev.end.date),
-                              'b1_price': b1_price,
-                              'b1_date': b1_date,
-                              'price_date': self._ds(cur.end.date),
-                              'context_pivot_zg': last_piv.zg if last_piv else None,
-                              'context_pivot_zd': last_piv.zd if last_piv else None,
-                              'context_pivot_zg_date': self._ds(last_piv.zg_date) if last_piv and last_piv.zg_date else '',
-                              'context_pivot_zd_date': self._ds(last_piv.zd_date) if last_piv and last_piv.zd_date else '',
-                              'context_pivot_start_date': self._ds(last_piv.start_date) if last_piv else '',
-                              'context_pivot_end_date': self._ds(last_piv.end_date) if last_piv else ''},
-                      diverge_grade=None)
-
-    def detect_b3(self) -> Optional[Signal]:
-        if self.n_bis < 5 or self.n_pivots < 1:
-            return None
-        late_trend_b3 = self.n_trend_pivots >= 2
-        last_piv = self.pivots[-1]
-        zg, zd = last_piv.zg, last_piv.zd
-        piv_height = zg - zd
-        cur = self.bis[-1]
-        if cur.direction != 'up':
-            return None
-        pair = self._last_exit_pullback_segments(last_piv, 'up', 'down')
-        if pair is None:
-            return None
-        exit_seg, pull_seg = pair
-        if not (exit_seg.low <= zg * (1 + self.PIVOT_TOLERANCE) and exit_seg.high > zg):
-            return None
-        if pull_seg.low < zg * (1 - self.PIVOT_TOLERANCE):
-            return None
-        leaves_pivot = pull_seg.low >= zg
-        cur_price = float(self.close.iloc[-1])
-        if cur_price <= zg:
-            return None
-        ex_amp = exit_seg.high - exit_seg.low
-        if piv_height > 0 and ex_amp < piv_height * 0.5:
-            return None
-        if len(last_piv.bis) < 3:
-            return None
-        confirm_txt = '回踩离枢确认(新中枢生成)' if leaves_pivot else '回踩贴ZG(容差内,新中枢待确认)'
-        b1_price, b1_date = self._find_prev_b1()
-        b2_price, b2_date = self._find_prev_b2()
-        warn_txt = '|第二个以上同向中枢,实盘宜改用低级别一买' if late_trend_b3 else ''
-        return Signal(kind='B3', date=self.df_raw['date'].iloc[-1], price=cur_price,
-                      reason=f'标准三买|ZG={zg:.3f},ZD={zd:.3f}|线段离枢:{exit_seg.low:.3f}→{exit_seg.high:.3f}(幅度{ex_amp:.3f})|线段回试低{pull_seg.low:.3f}|{confirm_txt}|当前{cur_price:.3f}>ZG{warn_txt}',
-                      pivot_zg=zg, pivot_zd=zd, macd_ratio=None, dif_value=float(self.dif.iloc[-1]),
-                      n_pivots=self.n_pivots, trend=self.trend,
-                      extras={'exit_seg': (exit_seg.low, exit_seg.high),
-                              'pull_low': pull_seg.low,
-                              'pull_low_date': self._ds(pull_seg.end.date),
-                              'exit_start_date': self._ds(exit_seg.start.date),
-                              'exit_end_date': self._ds(exit_seg.end.date),
-                              'b1_price': b1_price,
-                              'b1_date': b1_date,
-                              'b2_price': b2_price,
-                              'b2_date': b2_date,
-                              'piv_bi_count': len(last_piv.bis), 'leaves_pivot': leaves_pivot,
-                              'late_trend_b3': late_trend_b3,
-                              'price_date': self._ds(self.df_raw['date'].iloc[-1]),
-                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
-                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
-                              'pivot_start_date': self._ds(last_piv.start_date),
-                              'pivot_end_date': self._ds(last_piv.end_date)},
-                      diverge_grade=None)
-
-    def detect_s1(self) -> Optional[Signal]:
-        if self.n_bis < 5:
-            return None
-        cur = self.bis[-1]
-        if cur.direction != 'up':
-            return None
-        trend_ok = (self.n_pivots >= 2 and self.trend == 'up_trend')
-        consol_ok = (self.CFG.get('b1_allow_consol_diverge') and self.n_pivots >= 1)
-        if not (trend_ok or consol_ok):
-            return None
-        abc = self._validate_abc('up')
-        if abc is None and consol_ok:
-            abc = self._validate_abc_consol('up')
-        if abc is None:
-            return None
-        last_piv = self.pivots[-1]
-        a_start_idx = abc['a_start_idx']; a_end_idx = abc['a_end_idx']
-        if a_end_idx <= a_start_idx:
-            return None
-        a_up_bis = [self.bis[k] for k in range(a_start_idx, a_end_idx) if self.bis[k].direction == 'up']
-        if not a_up_bis:
-            return None
-        a_high = max(b.end.price for b in a_up_bis)
-        c_start_idx = last_piv.bis[-1] + 1
-        c_up_bis = [self.bis[k] for k in range(c_start_idx, self.n_bis) if self.bis[k].direction == 'up']
-        if not c_up_bis:
-            return None
-        c_high = max(b.end.price for b in c_up_bis)
-        if c_high <= a_high:
-            return None
-        a_high_bi = max(a_up_bis, key=lambda b: b.end.price)
-        dg = self.assess_divergence(a_up_bis[0].start.date, a_up_bis[-1].end.date,
-                                    c_up_bis[0].start.date, c_up_bis[-1].end.date, 'up')
-        if dg.grade == 'NONE':
-            return None
-        b_zero = self._b_returns_to_zero(last_piv)
-        dbl_pull = self.detect_double_pullback_to_zero()
-        pos_strength = self.divergence_strength_by_position()
-        post_evo = self.classify_post_divergence('up')
-        dif_now = float(self.dif.iloc[-1])
-        c_high_bi = max(c_up_bis, key=lambda b: b.end.price)
-        return Signal(kind='S1', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
-                      reason=f'一卖|上涨趋势{self.n_pivots}中枢|价创新高C{c_high:.3f}>A段高{a_high:.3f}{"+B回0轴" if b_zero else ""}|{dg.reason}',
-                      pivot_zg=last_piv.zg, pivot_zd=last_piv.zd, macd_ratio=dg.area_ratio, dif_value=dif_now,
-                      n_pivots=self.n_pivots, trend=self.trend,
-                      extras={'a_high': a_high, 'c_high': c_high, 'a_area': dg.a_area, 'c_area': dg.c_area,
-                              'diverge_grade': dg.grade,
-                              'a_high_date': self._ds(a_high_bi.end.date),
-                              'b_returns_zero': b_zero,
-                              'double_pullback': dbl_pull,
-                              'pos_strength': pos_strength,
-                              'post_evolution': post_evo['evolution'],
-                              'c_high_date': self._ds(c_high_bi.end.date),
-                              'price_date': self._ds(cur.end.date),
-                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
-                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
-                              'pivot_start_date': self._ds(last_piv.start_date),
-                              'pivot_end_date': self._ds(last_piv.end_date)},
-                      diverge_grade=dg)
-
-    def detect_s2(self) -> Optional[Signal]:
-        if self.n_bis < 4:
-            return None
-        cur = self.bis[-1]
-        if cur.direction != 'up':
-            return None
-        prev_ups = [b for b in self.bis[:-1] if b.direction == 'up']
-        if not prev_ups:
-            return None
-        prev = prev_ups[-1]
-        if cur.high < prev.high and cur.end.price < prev.end.price:
-            cur_price = float(self.close.iloc[-1])
-            if cur_price > cur.end.price * (1 + self.PIVOT_TOLERANCE):
-                return None
-            if self.CFG.get('b2s2_anchor_to_first'):
-                s1_anchor, _ = self._find_prev_s1()
-                if s1_anchor is None:
-                    return None
-                if cur.end.price > s1_anchor + 1e-9:
-                    return None
-            dif_now = float(self.dif.iloc[-1])
-            last_piv = self.pivots[-1] if self.pivots else None
-            s1_price, s1_date = prev.end.price, self._ds(prev.end.date)
-            return Signal(kind='S2', date=self.df_raw['date'].iloc[-1], price=cur_price,
-                          reason=f'二卖:一卖后反弹不破|当前高{cur.end.price:.3f}<一卖{s1_price:.3f}|二卖不以背驰为成立条件(第21课)',
-                          pivot_zg=None, pivot_zd=None,
-                          dif_value=dif_now, n_pivots=self.n_pivots, trend=self.trend,
-                          extras={'prev_high': prev.end.price, 'cur_high': cur.end.price,
-                                  'cur_high_date': self._ds(cur.end.date),
-                                  'prev_high_date': self._ds(prev.end.date),
-                                  's1_price': s1_price, 's1_date': s1_date,
-                                  'price_date': self._ds(cur.end.date),
-                                  'context_pivot_zg': last_piv.zg if last_piv else None,
-                                  'context_pivot_zd': last_piv.zd if last_piv else None,
-                                  'context_pivot_zg_date': self._ds(last_piv.zg_date) if last_piv and last_piv.zg_date else '',
-                                  'context_pivot_zd_date': self._ds(last_piv.zd_date) if last_piv and last_piv.zd_date else '',
-                                  'context_pivot_start_date': self._ds(last_piv.start_date) if last_piv else '',
-                                  'context_pivot_end_date': self._ds(last_piv.end_date) if last_piv else ''},
-                          diverge_grade=None)
-        return None
-
-    def detect_s3(self) -> Optional[Signal]:
-        if self.n_bis < 5 or self.n_pivots < 1:
-            return None
-        last_piv = self.pivots[-1]
-        zg, zd = last_piv.zg, last_piv.zd
-        if len(self.bis) < 3:
-            return None
-        cur = self.bis[-1]
-        if cur.direction != 'down':
-            return None
-        s1_price, s1_date = self._find_prev_s1()
-        s2_price, s2_date = self._find_prev_s2()
-        base_dates = {'price_date': self._ds(self.df_raw['date'].iloc[-1]),
-                      's1_price': s1_price, 's1_date': s1_date,
-                      's2_price': s2_price, 's2_date': s2_date,
-                      'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
-                      'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
-                      'pivot_start_date': self._ds(last_piv.start_date),
-                      'pivot_end_date': self._ds(last_piv.end_date)}
-        pair = self._last_exit_pullback_segments(last_piv, 'down', 'up')
-        if pair is None:
-            return None
-        exit_seg, pull_seg = pair
-        if not (exit_seg.high >= zd * (1 - self.PIVOT_TOLERANCE) and exit_seg.low < zd):
-            return None
-        if pull_seg.high > zd * (1 + self.PIVOT_TOLERANCE):
-            return None
-        if float(self.close.iloc[-1]) >= zd:
-            return None
-        return Signal(kind='S3', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
-                      reason=f'标准三卖|ZD={zd:.3f}|线段离枢低{exit_seg.low:.3f}<ZD|线段回抽高{pull_seg.high:.3f}未过ZD',
-                      pivot_zg=zg, pivot_zd=zd, dif_value=float(self.dif.iloc[-1]),
-                      n_pivots=self.n_pivots, trend=self.trend, extras=base_dates, diverge_grade=None)
-
-    def get_signal(self) -> Optional[Signal]:
-        for fn in [self.detect_s1, self.detect_s2, self.detect_s3]:
-            sig = fn()
-            if sig is not None:
-                return sig
-        for fn in [self.detect_b3, self.detect_b2, self.detect_b1]:
-            sig = fn()
-            if sig is not None:
-                return sig
+            import json
+            import os
+            import paths
+            import research_agent
+            known_path = os.path.join(paths.OUTPUT_DIR, "known_tickers.json")
+            try:
+                with open(known_path, encoding="utf-8") as f:
+                    known = set(json.load(f))
+            except (OSError, ValueError):
+                known = set()
+            current = set(df["Ticker"].tolist()) if df is not None and len(df) else set()
+            new_tickers = sorted(current - known)
+            generated = set()
+            for t in new_tickers[:5]:  # safety cap per run
+                _log(f"New ticker {t} → auto-generating research report…")
+                report, trace = research_agent.run_research(t, auto=True)
+                if report:
+                    generated.add(t)
+                    _log(f"Report for {t} done{' (+trace)' if trace else ''}.")
+                else:
+                    _log(f"Report for {t} postponed (sub-agents still loading) — "
+                         f"will retry on the next run.")
+            done_set = known | (current - (set(new_tickers) - generated))
+            if current:
+                with open(known_path, "w", encoding="utf-8") as f:
+                    json.dump(sorted(done_set), f)
+        except Exception as e:
+            _log(f"Auto-research skipped: {e}")
+
+        STATE["last_run"] = dt.datetime.now(NY)
+        _log("Pipeline finished.")
+        return f"Done. {summary}"
+    except Exception as e:
+        traceback.print_exc()
+        _log(f"Pipeline error: {e}")
+        return f"Pipeline error: {e}"
+    finally:
+        STATE["running"] = False
+
+
+def start_scheduler():
+    """Cron: Mon–Fri 18:10 America/New_York."""
+    try:
+        from apscheduler.schedulers.background import BackgroundScheduler
+        from apscheduler.triggers.cron import CronTrigger
+    except Exception as e:
+        _log(f"APScheduler unavailable: {e}")
         return None
-
-    def get_all_signals(self) -> list:
-        all_sigs = []
-        for fn in [self.detect_b1, self.detect_b2, self.detect_b3,
-                   self.detect_s1, self.detect_s2, self.detect_s3]:
-            sig = fn()
-            if sig is not None:
-                all_sigs.append(sig)
-        return all_sigs
-
-    def l36_segment_note(self) -> str:
-        """L36 结合律: 走势分解的唯一性靠结合律保证 —— a+A+b+B+c 的划分中, 同一段
-        K线不能既归前段又归后段。本引擎线段划分采用特征序列分型(标准缠论)处理
-        包含关系, 分型一旦确认即锁定段的归属, 等价于结合律的程序化执行。
-        该函数对当前末端给出"是否存在划分歧义"的提示。"""
-        if len(self.segs) < 2:
-            return '第36课: 线段不足2段, 无划分歧义问题'
-        last = self.segs[-1]
-        n_last = len(getattr(last, 'bis', []) or [])
-        if n_last < 3:
-            return (f'第36课: 末段仅{n_last}笔(<3), 末端划分尚未唯一确认 —— '
-                    f'当下操作应按两种归属做完全分类预案, 等待特征序列分型锁定')
-        return '第36课: 末段≥3笔且特征序列分型已锁定, 当前划分唯一, 无歧义'
-
-    def diagnose(self) -> dict:
-        cur_price = float(self.close.iloc[-1]) if len(self.close) else 0.0
-        last = self.bis[-1] if self.bis else None
-        out = {k: [] for k in ('B1', 'B2', 'B3', 'S1', 'S2', 'S3')}
-        def add(k, ok, msg):
-            out[k].append(('✓' if ok else '✗') + ' ' + msg)
-        add('B1', self.n_bis >= 5, f'笔数 {self.n_bis} >= 5')
-        add('B1', self.n_pivots >= 2, f'中枢数 {self.n_pivots} >= 2')
-        add('B1', self.trend == 'down_trend', f'当前走势={self.trend}, B1要求下跌趋势')
-        add('B1', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, B1要求向下')
-        add('B1', float(self.dif.iloc[-1]) < 0 if len(self.dif) else False, f'DIF={float(self.dif.iloc[-1]):.4f}, B1要求DIF<0')
-        abc_down = self._validate_abc('down')
-        add('B1', abc_down is not None, 'A/B/C三段背驰结构成立')
-        if abc_down is not None:
-            c_ok, c_low = self._check_c_new_extreme(abc_down['c_start_idx'], 'down')
-            add('B1', c_ok, f'C段创新低{"" if c_low is None else f"({c_low:.3f})"}')
-        add('B2', self.n_bis >= 4, f'笔数 {self.n_bis} >= 4')
-        add('B2', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, B2要求回踩向下')
-        prev_downs = [b for b in self.bis[:-1] if b.direction == 'down'] if last else []
-        add('B2', bool(prev_downs), '存在同一轮前一个下跌低点作为一买锚')
-        if last and prev_downs:
-            prev = prev_downs[-1]
-            add('B2', last.low >= prev.low and last.end.price > prev.end.price,
-                f'回踩不创新低: 本次低{last.end.price:.3f} > 一买/前低{prev.end.price:.3f}')
-            add('B2', cur_price >= last.end.price * (1 - self.PIVOT_TOLERANCE),
-                f'现价{cur_price:.3f}未跌破B2回踩锚{last.end.price:.3f}; 跌破则二买失效')
-        add('B3', self.n_pivots >= 1, f'中枢数 {self.n_pivots} >= 1')
-        add('B3', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, B3要求向上确认')
-        if self.pivots:
-            p = self.pivots[-1]
-            pair = self._last_exit_pullback_segments(p, 'up', 'down')
-            add('B3', pair is not None, '存在已确认线段级别的向上离枢 + 向下回试')
-            if pair is not None:
-                exit_seg, pull_seg = pair
-                add('B3', exit_seg.low <= p.zg * (1 + self.PIVOT_TOLERANCE) and exit_seg.high > p.zg,
-                    f'离枢线段突破ZG: {exit_seg.low:.3f}~{exit_seg.high:.3f}, ZG={p.zg:.3f}')
-                add('B3', pull_seg.low >= p.zg * (1 - self.PIVOT_TOLERANCE),
-                    f'回试低点{pull_seg.low:.3f}不破ZG={p.zg:.3f}')
-                add('B3', cur_price > p.zg, f'现价{cur_price:.3f}站上ZG={p.zg:.3f}')
-        add('S1', self.n_bis >= 5, f'笔数 {self.n_bis} >= 5')
-        add('S1', self.n_pivots >= 2, f'中枢数 {self.n_pivots} >= 2')
-        add('S1', self.trend == 'up_trend', f'当前走势={self.trend}, S1要求上涨趋势')
-        add('S1', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, S1要求向上')
-        abc_up = self._validate_abc('up')
-        add('S1', abc_up is not None, 'A/B/C三段顶背驰结构成立')
-        if abc_up is not None:
-            c_ok, c_high = self._check_c_new_extreme(abc_up['c_start_idx'], 'up')
-            add('S1', c_ok, f'C段创新高{"" if c_high is None else f"({c_high:.3f})"}')
-        add('S2', self.n_bis >= 4, f'笔数 {self.n_bis} >= 4')
-        add('S2', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, S2要求反弹向上')
-        prev_ups = [b for b in self.bis[:-1] if b.direction == 'up'] if last else []
-        add('S2', bool(prev_ups), '存在同一轮前一个上涨高点作为一卖锚')
-        if last and prev_ups:
-            prev = prev_ups[-1]
-            add('S2', last.high < prev.high and last.end.price < prev.end.price,
-                f'反弹不创新高: 本次高{last.end.price:.3f} < 一卖/前高{prev.end.price:.3f}')
-            add('S2', cur_price <= last.end.price * (1 + self.PIVOT_TOLERANCE),
-                f'现价{cur_price:.3f}未重新升破S2反弹锚{last.end.price:.3f}; 升破则二卖失效')
-        add('S3', self.n_pivots >= 1, f'中枢数 {self.n_pivots} >= 1')
-        add('S3', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, S3要求向下确认')
-        if self.pivots:
-            p = self.pivots[-1]
-            pair = self._last_exit_pullback_segments(p, 'down', 'up')
-            add('S3', pair is not None, '存在已确认线段级别的向下离枢 + 向上回抽')
-            if pair is not None:
-                exit_seg, pull_seg = pair
-                add('S3', exit_seg.high >= p.zd * (1 - self.PIVOT_TOLERANCE) and exit_seg.low < p.zd,
-                    f'离枢线段跌破ZD: {exit_seg.low:.3f}~{exit_seg.high:.3f}, ZD={p.zd:.3f}')
-                add('S3', pull_seg.high <= p.zd * (1 + self.PIVOT_TOLERANCE),
-                    f'回抽高点{pull_seg.high:.3f}不破ZD={p.zd:.3f}')
-                add('S3', cur_price < p.zd, f'现价{cur_price:.3f}跌破ZD={p.zd:.3f}')
-        return out
-
-
-class SameLevelDecomposition:
-    def __init__(self, analyzer: 'ChanAnalyzer'):
-        self.an = analyzer
-        self.segs = analyzer.segs
-
-    def current_phase(self) -> dict:
-        if len(self.segs) < 2:
-            return {'seg_dir': '', 'stage': 'unknown', 'action': 'WATCH',
-                    'reason': '线段不足, 无法做同级别分解'}
-        last = self.segs[-1]
-        prev = self.segs[-2]
-        seg_dir = last.direction
-        if seg_dir == 'up':
-            stage = 'up_run'
-            prev_up = None
-            for s in reversed(self.segs[:-1]):
-                if s.direction == 'up':
-                    prev_up = s; break
-            if prev_up is None:
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'HOLD',
-                        'reason': '向上段运作中(无前向上段可比), 持有'}
-            if last.high <= prev_up.high:
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'SELL',
-                        'reason': f'向上段不创新高({last.high:.3f}≤前高{prev_up.high:.3f}) → 先卖(第38课)'}
-            dg = self.an.assess_divergence(prev_up.start.date, prev_up.end.date,
-                                           last.start.date, last.end.date, 'up')
-            if dg.grade != 'NONE':
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'SELL',
-                        'reason': f'向上段创新高但盘整背驰({dg.grade}) → 卖(第38课)'}
-            return {'seg_dir': seg_dir, 'stage': stage, 'action': 'HOLD',
-                    'reason': '向上段创新高且不背驰 → 持有(第38课)'}
-        else:
-            stage = 'down_run'
-            prev_down = None
-            for s in reversed(self.segs[:-1]):
-                if s.direction == 'down':
-                    prev_down = s; break
-            if prev_down is None:
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'WATCH',
-                        'reason': '向下段运作中(无前向下段可比), 观望等买点'}
-            if last.low >= prev_down.low:
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'BUY',
-                        'reason': f'向下段不创新低({last.low:.3f}≥前低{prev_down.low:.3f}) → 买入(第38课)'}
-            dg = self.an.assess_divergence(prev_down.start.date, prev_down.end.date,
-                                           last.start.date, last.end.date, 'down')
-            if dg.grade != 'NONE':
-                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'BUY',
-                        'reason': f'向下段创新低但盘整背驰({dg.grade}) → 买入(第38课)'}
-            return {'seg_dir': seg_dir, 'stage': stage, 'action': 'WATCH',
-                    'reason': '向下段创新低且不背驰 → 观望等下跌背驰(第38课)'}
-
-
-class BottomTracker:
-    def __init__(self):
-        self.state = 'none'
-
-    def update(self, analyzer: 'ChanAnalyzer') -> str:
-        snap = analyzer.bottom_construction_state()
-        if self.state in ('none', 'failed', 'completed'):
-            if snap == 'constructing':
-                self.state = 'constructing'
-            elif snap == 'completed':
-                self.state = 'completed'
-            elif snap == 'failed':
-                self.state = 'failed'
-            else:
-                self.state = 'none'
-        elif self.state == 'constructing':
-            if snap == 'completed':
-                self.state = 'completed'
-            elif snap == 'failed':
-                self.state = 'failed'
-        return self.state
+    sched = BackgroundScheduler(timezone=NY)
+    sched.add_job(run_pipeline, CronTrigger(day_of_week="mon-fri",
+                                            hour=RUN_HOUR, minute=RUN_MINUTE),
+                  id="daily_pipeline", max_instances=1, coalesce=True)
+    sched.start()
+    _log(f"Scheduler armed: Mon–Fri {RUN_HOUR:02d}:{RUN_MINUTE:02d} America/New_York.")
+    return sched
+
+
+def schedule_info() -> str:
+    now = dt.datetime.now(NY)
+    last = STATE["last_run"].strftime("%Y-%m-%d %H:%M ET") if STATE["last_run"] else "never"
+    return (f"**Schedule:** Mon–Fri **{RUN_HOUR:02d}:{RUN_MINUTE:02d} America/New_York** "
+            f"(market closes 16:00 ET; by 18:10 the official daily bar has settled — "
+            f"that's 07:10 next morning Beijing time).\n\n"
+            f"**Now (ET):** {now.strftime('%Y-%m-%d %H:%M')} · **Last run:** {last}\n\n"
+            f"⚠️ On free Space hardware the app sleeps when idle and the timer can't fire; "
+            f"use **Run now**, or upgrade to always-on hardware for unattended updates.")

chan_enhance.py

@@ -1,123 +1,1451 @@
 """
-chan_enhance.py —— 缠论系统增强(查漏补缺) · 纯函数版(非Hook)
+缠论引擎 v2.1 (原始版, 用于P0-P3改造的基线)
 """
 from __future__ import annotations
+from dataclasses import dataclass, field
+from typing import Optional
+import numpy as np
+import pandas as pd
 
+PIVOT_MAX_EXTEND_SEGS = 6
 
-W_REVERSAL = 1.0
-W_TRAP     = 0.85
-W_RELAY    = 0.6
-W_UNKNOWN  = 0.7
-
-# 第16课 六种基本走势 → 买法分类的中文名(配合英文 method 一起展示)
-METHOD_CN = {
-    'reversal': '反转式(下跌+盘整+上涨 / 趋势底背驰一买, 力度最强, 权重最高)',
-    'trap':     '陷阱式(下跌+上涨, 空头陷阱式二买, 中等力度)',
-    'relay':    '中继式(上涨+盘整+上涨, 三买, 趋势中继)',
-    'unknown':  '未分类',
-}
-
-WEAK_ARM_GAIN = 0.04
-WEAK_GIVEBACK = 0.22
-
-L92_EXIT_ENABLE      = False
-L92_EXIT_FLOATING_MAX = 0.015
-L92_ZD_BREAK_BUF      = 0.0
-
-
-def classify_buy_method(sig_kind, ml) -> str:
-    sig = ml.daily.signal if (ml and ml.daily) else None
-    dg = getattr(sig, 'diverge_grade', None) if sig is not None else None
-    if sig_kind == 'B1':
-        return 'reversal'
-    if sig_kind == 'B3':
-        return 'relay'
-    if sig_kind == 'B2':
-        # L37 背驰分辨: 只有"趋势背驰"(≥2个同向中枢后的背驰)才是标准的趋势转折,
-        # 对应第16课"下跌+盘整+上涨"反转式买法; 盘整背驰力度弱, 仍按陷阱式处理。
-        if (dg is not None and getattr(dg, 'grade', '') == 'STRONG'
-                and getattr(dg, 'is_trend_divergence', False)):
-            return 'reversal'
-        return 'trap'
-    return 'unknown'
-
-
-def l37_divergence_note(ml) -> str:
-    """L37 背驰分辨(区分背驰段构成): 趋势背驰=至少两个同向中枢后的背驰, 转折级别高;
-    盘整背驰=单中枢内的力度衰竭, 只保证回拉中枢, 不保证反转。"""
-    sig = ml.daily.signal if (ml and ml.daily) else None
-    dg = getattr(sig, 'diverge_grade', None) if sig is not None else None
-    if dg is None or getattr(dg, 'grade', 'NONE') == 'NONE':
-        return ''
-    if getattr(dg, 'is_trend_divergence', False):
-        return (f'第37课: 趋势背驰({getattr(dg, "n_trend_pivots", "?")}个同向中枢), '
-                f'转折至少回拉至最后一个中枢, 大概率反转 → 可按反转式买法重仓')
-    return ('第37课: 盘整背驰(背驰段内仅1个中枢), 只保证回拉中枢一次, '
-            '不保证趋势反转 → 轻仓短打, 回拉到中枢即考虑兑现')
-
-
-def buy_method_weight(sig_kind, ml):
-    method = classify_buy_method(sig_kind, ml)
-    wmap = {'reversal': W_REVERSAL, 'trap': W_TRAP, 'relay': W_RELAY, 'unknown': W_UNKNOWN}
-    return method, wmap.get(method, W_UNKNOWN)
-
-
-def recompute_evo(ml) -> str:
-    sig = ml.daily.signal if (ml and ml.daily) else None
-    if sig is not None:
-        evo = (sig.extras or {}).get('post_evolution', '')
-        if evo:
-            return evo
-    dv = ml.daily if ml else None
-    if dv is not None and dv.zd is not None and dv.zg is not None:
-        if ml.cur_price < dv.zd:
-            return 'case1_extend'
-    return ''
-
-
-def weak_evo_giveback(pos, ml, default_giveback):
-    evo = recompute_evo(ml) if ml is not None else (pos.get('post_evo') or '')
-    if evo == 'case1_extend':
-        return WEAK_ARM_GAIN, WEAK_GIVEBACK
-    return None, default_giveback
-
-
-def l92_should_exit(pos, ml, close_p, floating):
-    if not L92_EXIT_ENABLE or ml is None or ml.daily is None:
-        return None
-    if floating > L92_EXIT_FLOATING_MAX:
+
+@dataclass
+class Fractal:
+    idx: int
+    date: pd.Timestamp
+    kind: str
+    price: float
+    k_high: float
+    k_low: float
+
+
+@dataclass
+class Bi:
+    start: Fractal
+    end: Fractal
+    direction: str
+    bars: int
+    high: float
+    low: float
+    @property
+    def amplitude(self) -> float:
+        return self.high - self.low
+
+
+@dataclass
+class Seg:
+    start: Fractal
+    end: Fractal
+    direction: str
+    bis: list
+    high: float
+    low: float
+    confirmed: bool = True
+
+
+@dataclass
+class Pivot:
+    start_date: pd.Timestamp
+    end_date: pd.Timestamp
+    zg: float
+    zd: float
+    gg: float
+    dd: float
+    bis: list
+    direction: str
+    zg_date: Optional[pd.Timestamp] = None
+    zd_date: Optional[pd.Timestamp] = None
+    gg_date: Optional[pd.Timestamp] = None
+    dd_date: Optional[pd.Timestamp] = None
+    g: Optional[float] = None
+    d: Optional[float] = None
+    state: str = 'new'
+    death_combo: str = ''
+    capped: bool = False
+    upgraded_level: str = ''
+
+
+@dataclass
+class DivergenceGrade:
+    grade: str
+    area_ok: bool
+    dif_ok: bool
+    area_ratio: float
+    a_area: float
+    c_area: float
+    a_dif: float
+    c_dif: float
+    direction: str
+    reason: str
+    is_trend_divergence: bool = False
+    n_trend_pivots: int = 0
+
+
+@dataclass
+class Signal:
+    kind: str
+    date: pd.Timestamp
+    price: float
+    reason: str
+    pivot_zg: Optional[float] = None
+    pivot_zd: Optional[float] = None
+    macd_ratio: Optional[float] = None
+    dif_value: Optional[float] = None
+    n_pivots: int = 0
+    trend: str = ''
+    extras: dict = field(default_factory=dict)
+    diverge_grade: Optional[DivergenceGrade] = None
+
+
+def merge_klines(df: pd.DataFrame) -> pd.DataFrame:
+    if len(df) == 0:
+        return df.copy()
+    h = df['high'].values; l = df['low'].values; d = df['date'].values
+    out_h, out_l, out_d, out_idx = [h[0]], [l[0]], [d[0]], [0]
+    for i in range(1, len(df)):
+        ph, pl = out_h[-1], out_l[-1]; ch, cl = h[i], l[i]
+        direction = 1 if (len(out_h) >= 2 and out_h[-1] >= out_h[-2]) else (1 if len(out_h) < 2 else -1)
+        contained_a = ph >= ch and pl <= cl
+        contained_b = ch >= ph and cl <= pl
+        if contained_a or contained_b:
+            if direction >= 0:
+                out_h[-1] = max(ph, ch); out_l[-1] = max(pl, cl)
+            else:
+                out_h[-1] = min(ph, ch); out_l[-1] = min(pl, cl)
+            out_idx[-1] = i
+        else:
+            out_h.append(ch); out_l.append(cl); out_d.append(d[i]); out_idx.append(i)
+    return pd.DataFrame({'date': out_d, 'high': out_h, 'low': out_l, 'orig_idx': out_idx})
+
+
+def find_fractals(merged: pd.DataFrame) -> list:
+    res = []
+    n = len(merged)
+    if n < 3:
+        return res
+    h = merged['high'].values; l = merged['low'].values; d = merged['date'].values
+    hi = h[1:-1]; hp = h[:-2]; hn = h[2:]
+    li = l[1:-1]; lp = l[:-2]; ln = l[2:]
+    top = (hi > hp) & (hi > hn) & (li >= lp) & (li >= ln)
+    bot = (li < lp) & (li < ln) & (hi <= hp) & (hi <= hn)
+    idxs = np.nonzero(top | bot)[0]
+    if len(idxs) == 0:
+        return res
+    # 仅对被选中的(稀疏)分型点构造 Timestamp, 避免对全序列逐根转换
+    is_top = top  # 局部别名
+    for j in idxs:
+        i = j + 1
+        if is_top[j]:
+            res.append(Fractal(i, pd.Timestamp(d[i]), 'top', float(h[i]), float(h[i]), float(l[i])))
+        else:
+            res.append(Fractal(i, pd.Timestamp(d[i]), 'bottom', float(l[i]), float(h[i]), float(l[i])))
+    return res
+
+
+def find_bis(fractals: list, min_k: int = 4) -> list:
+    if len(fractals) < 2:
+        return []
+    cleaned = [fractals[0]]
+    for fx in fractals[1:]:
+        last = cleaned[-1]
+        if fx.kind == last.kind:
+            if fx.kind == 'top' and fx.price > last.price:
+                cleaned[-1] = fx
+            elif fx.kind == 'bottom' and fx.price < last.price:
+                cleaned[-1] = fx
+        else:
+            cleaned.append(fx)
+    alt = [cleaned[0]]
+    for fx in cleaned[1:]:
+        if fx.kind != alt[-1].kind and fx.idx - alt[-1].idx >= min_k - 1:
+            alt.append(fx)
+        elif fx.kind != alt[-1].kind:
+            continue
+    bis = []
+    for i in range(len(alt) - 1):
+        a, b = alt[i], alt[i+1]
+        if a.kind == b.kind:
+            continue
+        direction = 'up' if b.kind == 'top' else 'down'
+        bis.append(Bi(start=a, end=b, direction=direction, bars=b.idx - a.idx,
+                      high=max(a.price, b.price), low=min(a.price, b.price)))
+    return bis
+
+
+def _find_feature_fractal(std: list, seg_dir: str):
+    """[保留] 供调试/对照用的非增量实现; 主路径已改用 _first_feature_fractal_incremental。"""
+    up = (seg_dir == 'up')
+    for i in range(1, len(std) - 1):
+        a = std[i-1]; b = std[i]; c = std[i+1]
+        if up:
+            if b['high'] > a['high'] and b['high'] > c['high'] \
+               and b['low'] > a['low'] and b['low'] > c['low']:
+                return (i, b.get('has_gap_before', False))
+        else:
+            if b['low'] < a['low'] and b['low'] < c['low'] \
+               and b['high'] < a['high'] and b['high'] < c['high']:
+                return (i, b.get('has_gap_before', False))
+    return None
+
+
+def _first_feature_fractal_incremental(bis, start_i, end_i, seg_dir):
+    """增量构建特征序列, 在第一个特征分型被"锁定"时立即返回。
+
+    锁定条件: 出现特征分型(a,b,c)后, 再追加一个新的标准元素(即 c 之后
+    已有一个不被包含的元素 d)。此时 c 不会再被向后合并改变, 分型 b 的
+    左右高低关系已固定, 与"先把整段展开再找首个分型"语义等价。
+    返回 (b_first_bi_idx, b_has_gap_before) 或 None。
+    """
+    feat_dir = 'down' if seg_dir == 'up' else 'up'
+    up = (seg_dir == 'up')
+    std = []  # 每元素: [high, low, bi_idx, has_gap_before, first_bi_idx]
+    for k in range(start_i, end_i + 1):
+        b = bis[k]
+        if b.direction != feat_dir:
+            continue
+        ch = b.high; cl = b.low
+        if not std:
+            std.append([ch, cl, k, False, k]); continue
+        prev = std[-1]
+        ph = prev[0]; pl = prev[1]
+        contained = (ph >= ch and pl <= cl) or (ch >= ph and cl <= pl)
+        if contained:
+            if up:
+                prev[0] = ph if ph > ch else ch
+                prev[1] = pl if pl > cl else cl
+            else:
+                prev[0] = ph if ph < ch else ch
+                prev[1] = pl if pl < cl else cl
+            prev[2] = k
+            continue
+        std.append([ch, cl, k, gap_flag(cl, ch, ph, pl)] + [k])
+        # 锁定检查: 需要至少4个已定型元素, 才能保证倒数第3个(候选分型b)
+        # 的右邻c已被其后元素d终结、不会再被向后合并。
+        if len(std) >= 4:
+            a = std[-4]; bm = std[-3]; c = std[-2]
+            if up:
+                ok = (bm[0] > a[0] and bm[0] > c[0] and bm[1] > a[1] and bm[1] > c[1])
+            else:
+                ok = (bm[1] < a[1] and bm[1] < c[1] and bm[0] < a[0] and bm[0] < c[0])
+            if ok:
+                return (bm[4], bm[3])
+    # 收尾: 末端无后继元素, 用最终 std 找首个内部分型(与原实现等价)
+    for i in range(1, len(std) - 1):
+        a = std[i-1]; bm = std[i]; c = std[i+1]
+        if up:
+            ok = (bm[0] > a[0] and bm[0] > c[0] and bm[1] > a[1] and bm[1] > c[1])
+        else:
+            ok = (bm[1] < a[1] and bm[1] < c[1] and bm[0] < a[0] and bm[0] < c[0])
+        if ok:
+            return (bm[4], bm[3])
+    return None
+
+
+def gap_flag(cl, ch, ph, pl):
+    return (cl > ph) or (ch < pl)
+
+
+def _seq_fractal_confirms_reversal(bis, start_i, end_i, cur_dir):
+    fr = _first_feature_fractal_incremental(bis, start_i, end_i, cur_dir)
+    if fr is None:
+        return (False, None)
+    feat_first_bi, has_gap = fr
+    seg_end_bi = feat_first_bi - 1
+    if seg_end_bi <= start_i:
+        return (False, None)
+    if has_gap:
+        opp = 'down' if cur_dir == 'up' else 'up'
+        fr2 = _first_feature_fractal_incremental(bis, feat_first_bi, end_i, opp)
+        if fr2 is None:
+            return (False, None)
+    return (True, seg_end_bi)
+
+
+def find_segs(bis: list) -> list:
+    n = len(bis)
+    if n < 3:
+        return []
+    base = bis[0].start.price
+    look = min(3, n)
+    net = bis[look - 1].end.price - base
+    cur_dir = 'up' if net > 0 else 'down'
+    segs = []
+    i = 0
+    while i < n - 2:
+        confirmed, seg_end_bi = _seq_fractal_confirms_reversal(bis, i, n - 1, cur_dir)
+        if confirmed and seg_end_bi > i:
+            seg_bis = bis[i:seg_end_bi + 1]
+            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
+            if (cur_dir == 'up') == net_up:
+                segs.append(Seg(start=bis[i].start, end=seg_bis[-1].end, direction=cur_dir,
+                                bis=seg_bis, high=max(b.high for b in seg_bis),
+                                low=min(b.low for b in seg_bis), confirmed=True))
+                i = seg_end_bi + 1
+                cur_dir = 'down' if cur_dir == 'up' else 'up'
+                continue
+        alt = 'down' if cur_dir == 'up' else 'up'
+        confirmed2, seg_end_bi2 = _seq_fractal_confirms_reversal(bis, i, n - 1, alt)
+        if confirmed2 and seg_end_bi2 > i:
+            seg_bis = bis[i:seg_end_bi2 + 1]
+            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
+            if (alt == 'up') == net_up:
+                segs.append(Seg(start=seg_bis[0].start, end=seg_bis[-1].end, direction=alt,
+                                bis=seg_bis, high=max(b.high for b in seg_bis),
+                                low=min(b.low for b in seg_bis), confirmed=True))
+                i = seg_end_bi2 + 1
+                cur_dir = 'down' if alt == 'up' else 'up'
+                continue
+        break
+    if i < n - 1 and (n - i) >= 1:
+        seg_bis = bis[i:]
+        if len(seg_bis) >= 1:
+            net_up = seg_bis[-1].end.price > seg_bis[0].start.price
+            d = 'up' if net_up else 'down'
+            segs.append(Seg(start=seg_bis[0].start, end=seg_bis[-1].end, direction=d,
+                            bis=seg_bis, high=max(b.high for b in seg_bis),
+                            low=min(b.low for b in seg_bis), confirmed=False))
+    return segs
+
+
+PIVOT_UPGRADE_SPAN_DAYS = 540
+
+
+def find_pivots(bis: list, segs: Optional[list] = None) -> list:
+    confirmed_segs = [s for s in (segs or []) if getattr(s, 'confirmed', True)]
+    units = confirmed_segs if len(confirmed_segs) >= 3 else bis
+    using_segs = units is confirmed_segs
+    pivots = []; n = len(units)
+    if n < 3:
+        return pivots
+
+    bi_pos = {id(b): k for k, b in enumerate(bis)}
+
+    def _unit_bi_range(u):
+        if hasattr(u, 'bis'):
+            idxs = [bi_pos[id(b)] for b in u.bis if id(b) in bi_pos]
+            return (min(idxs), max(idxs)) if idxs else (0, 0)
+        k = bi_pos.get(id(u), 0)
+        return (k, k)
+
+    def _unit_bi_indices(unit_list):
+        out = []
+        for u in unit_list:
+            a, b = _unit_bi_range(u)
+            out.extend(range(a, b + 1))
+        return sorted(set(out))
+
+    def _unit_high_date(u):
+        return u.start.date if u.start.price >= u.end.price else u.end.date
+
+    def _unit_low_date(u):
+        return u.start.date if u.start.price <= u.end.price else u.end.date
+
+    max_ext = PIVOT_MAX_EXTEND_SEGS if PIVOT_MAX_EXTEND_SEGS else 10 ** 9
+
+    i = 0
+    while i <= n - 3:
+        b1, b2, b3 = units[i], units[i+1], units[i+2]
+        r1 = (b1.low, b1.high)
+        r2 = (b2.low, b2.high)
+        r3 = (b3.low, b3.high)
+        zg = min(r1[1], r2[1], r3[1]); zd = max(r1[0], r2[0], r3[0])
+        if zg > zd:
+            direction = b1.direction; zg_orig, zd_orig = zg, zd; gg, dd = zg, zd
+            highs = [r1[1], r2[1], r3[1]]; lows = [r1[0], r2[0], r3[0]]
+            zg_bi = (b1, b2, b3)[highs.index(zg_orig)]
+            zd_bi = (b1, b2, b3)[lows.index(zd_orig)]
+            zg_d = _unit_high_date(zg_bi)
+            zd_d = _unit_low_date(zd_bi)
+            gg_d, dd_d = zg_d, zd_d
+            zn_dir = direction
+            gn_list = []; dn_list = []
+            for bb in (b1, b2, b3):
+                if bb.direction == zn_dir:
+                    gn_list.append(max(bb.start.price, bb.end.price))
+                    dn_list.append(min(bb.start.price, bb.end.price))
+            piv_units = [i, i+1, i+2]; j = i + 3
+            capped = False
+            while j < n:
+                if (len(piv_units) - 3) >= max_ext:
+                    capped = True
+                    break
+                bj = units[j]; lo_j = bj.low; hi_j = bj.high
+                if hi_j >= zd_orig and lo_j <= zg_orig:
+                    if hi_j > gg:
+                        gg = hi_j; gg_d = _unit_high_date(bj)
+                    if lo_j < dd:
+                        dd = lo_j; dd_d = _unit_low_date(bj)
+                    if bj.direction == zn_dir:
+                        gn_list.append(hi_j); dn_list.append(lo_j)
+                    piv_units.append(j); j += 1
+                else:
+                    break
+            g_val = min(gn_list) if gn_list else zg_orig
+            d_val = max(dn_list) if dn_list else zd_orig
+            piv_bis = _unit_bi_indices([units[k] for k in piv_units]) if using_segs else piv_units
+            p_start = b1.start.date
+            p_end = units[piv_units[-1]].end.date
+            piv = Pivot(start_date=p_start, end_date=p_end,
+                        zg=zg_orig, zd=zd_orig, gg=gg, dd=dd, bis=piv_bis, direction=direction,
+                        zg_date=zg_d, zd_date=zd_d, gg_date=gg_d, dd_date=dd_d,
+                        g=g_val, d=d_val, capped=capped)
+            try:
+                span_days = (pd.Timestamp(p_end) - pd.Timestamp(p_start)).days
+            except Exception:
+                span_days = 0
+            if capped or span_days > PIVOT_UPGRADE_SPAN_DAYS:
+                piv.upgraded_level = 'weekly'
+            pivots.append(piv)
+            i = piv_units[-1] + 1
+        else:
+            i += 1
+
+    for k in range(1, len(pivots)):
+        prev, cur = pivots[k-1], pivots[k]
+        no_overlap = (cur.dd > prev.gg) or (cur.gg < prev.dd)
+        if no_overlap:
+            cur.state = 'new'
+        else:
+            cur.state = 'expand'
+
+    for k in range(len(pivots) - 1):
+        cur = pivots[k]
+        nxt = pivots[k + 1]
+        gap_start = cur.bis[-1] + 1
+        gap_end = nxt.bis[0]
+        gap_bis = bis[gap_start:gap_end] if gap_end > gap_start else []
+        if len(gap_bis) >= 2:
+            leave = gap_bis[:max(1, len(gap_bis) // 2)]
+            pull = gap_bis[max(1, len(gap_bis) // 2):]
+            leave_trend = len(leave) >= 3
+            pull_trend = len(pull) >= 3
+            if leave_trend and not pull_trend:
+                cur.death_combo = 'trend+consol'
+            elif leave_trend and pull_trend:
+                cur.death_combo = 'trend+counter'
+            else:
+                cur.death_combo = 'consol+counter'
+    return pivots
+
+
+def classify_trend(pivots: list) -> str:
+    if len(pivots) < 2:
+        return 'consolidation'
+    p1, p2 = pivots[-2], pivots[-1]
+    if p2.dd > p1.gg:
+        return 'up_trend'
+    if p2.gg < p1.dd:
+        return 'down_trend'
+    if (p2.zg < p1.zd and p2.gg >= p1.dd) or (p2.zd > p1.zg and p2.dd <= p1.gg):
+        return 'expanding'
+    return 'consolidation'
+
+
+def count_trend_pivots(pivots: list) -> int:
+    if not pivots:
+        return 0
+    if len(pivots) == 1:
+        return 1
+    cnt = 1
+    for k in range(len(pivots) - 1, 0, -1):
+        p_prev, p_cur = pivots[k - 1], pivots[k]
+        if p_cur.dd > p_prev.gg:
+            cnt += 1
+        elif p_cur.gg < p_prev.dd:
+            cnt += 1
+        else:
+            break
+    return cnt
+
+
+def calc_macd(close: pd.Series, fast=12, slow=26, signal=9):
+    ema_fast = close.ewm(span=fast, adjust=False).mean()
+    ema_slow = close.ewm(span=slow, adjust=False).mean()
+    dif = ema_fast - ema_slow
+    dea = dif.ewm(span=signal, adjust=False).mean()
+    macd_bar = 2 * (dif - dea)
+    return dif, dea, macd_bar
+
+
+def macd_area_between(start_date, end_date, bar_series, date_series, direction):
+    mask = (date_series >= start_date) & (date_series <= end_date)
+    vals = bar_series[mask]
+    if len(vals) == 0:
+        return 0.0
+    if direction == 'up':
+        return float(vals.clip(lower=0).sum())
+    return float(vals.clip(upper=0).abs().sum())
+
+
+def dif_extreme_in(start_date, end_date, dif_series, date_series, kind='peak'):
+    mask = (date_series >= start_date) & (date_series <= end_date)
+    vals = dif_series[mask]
+    if len(vals) == 0:
+        return 0.0
+    return float(vals.max()) if kind == 'peak' else float(vals.min())
+
+
+class ChanAnalyzer:
+    DIVERGE_RATIO = 0.80
+    PIVOT_TOLERANCE = 0.02
+    MIN_BI_BARS = 4
+    DIF_TOLERANCE = 0.01
+
+    CFG = {
+        'b1_allow_consol_diverge': True,
+        'b3s3_first_pullback_only': False,
+        'b2s2_anchor_to_first': False,
+        'b2_macd_zero_pullback': False,
+        'drop_upgraded_pivots': False,
+        # L88-90 中阴阶段MACD精确运用: 中阴判定除BOLL收口外, 加入MACD特征
+        #   'off'  = 维持原判定(仅BOLL收口+末笔未离开中枢)
+        #   'and'  = 须同时满足"黄白线绕0轴缠绕"(更严格, 减少误判中阴而拦截的好买点)
+        #   'or'   = 满足其一即算中阴(更宽松, 拦截更多)
+        'zhongyin_macd_mode': 'or',   # 实测'or'最优: 累计收益+35pp(383%→418%), 胜率45.3%→46.8%
+    }
+
+    def __init__(self, df: pd.DataFrame):
+        self.df_raw = df.reset_index(drop=True)
+        self.close = self.df_raw['close']
+        self.dif, self.dea, self.macd_bar = calc_macd(self.close)
+        self.merged = merge_klines(self.df_raw)
+        self.fractals = find_fractals(self.merged)
+        self.bis = find_bis(self.fractals, min_k=self.MIN_BI_BARS)
+        self._bi_index = {id(b): k for k, b in enumerate(self.bis)}
+        self.segs = find_segs(self.bis)
+        self.pivots_all = find_pivots(self.bis, self.segs)
+        if self.CFG.get('drop_upgraded_pivots'):
+            last_upg_idx = -1
+            for k, p in enumerate(self.pivots_all):
+                if p.upgraded_level:
+                    last_upg_idx = k
+            operative = [p for k, p in enumerate(self.pivots_all)
+                         if k > last_upg_idx and not p.upgraded_level]
+            self.pivots = operative
+        else:
+            self.pivots = self.pivots_all
+        self.trend = classify_trend(self.pivots)
+
+    @property
+    def n_bis(self): return len(self.bis)
+    @property
+    def n_segs(self): return len(self.segs)
+    @property
+    def n_pivots(self): return len(self.pivots)
+    @property
+    def n_pivots_all(self): return len(self.pivots_all)
+    @property
+    def n_trend_pivots(self): return count_trend_pivots(self.pivots)
+    @property
+    def has_upgraded_pivot(self):
+        return any(p.upgraded_level for p in self.pivots_all)
+
+    @staticmethod
+    def _ds(ts):
+        ts = pd.Timestamp(ts)
+        if ts.hour == 0 and ts.minute == 0:
+            return ts.strftime('%Y-%m-%d')
+        return ts.strftime('%Y-%m-%d %H:%M')
+
+    def _validate_abc(self, direction: str):
+        if self.n_pivots < 2:
+            return None
+        last_piv = self.pivots[-1]
+        prev_piv = self.pivots[-2]
+        ratio = len(prev_piv.bis) / max(len(last_piv.bis), 1)
+        if not (1 / 3 <= ratio <= 3):
+            return None
+        a_start_idx = prev_piv.bis[0]
+        a_end_idx = last_piv.bis[0]
+        if a_end_idx <= a_start_idx:
+            return None
+        c_start_idx = last_piv.bis[-1] + 1
+        if c_start_idx >= self.n_bis:
+            return None
+        return {'a_start_idx': a_start_idx, 'a_end_idx': a_end_idx,
+                'b_pivot': last_piv, 'c_start_idx': c_start_idx}
+
+    def _validate_abc_consol(self, direction: str):
+        if self.n_pivots < 1:
+            return None
+        piv = self.pivots[-1]
+        a_end_idx = piv.bis[0]
+        if a_end_idx <= 0:
+            return None
+        c_start_idx = piv.bis[-1] + 1
+        if c_start_idx >= self.n_bis:
+            return None
+        want = 'down' if direction == 'down' else 'up'
+        a_start_idx = a_end_idx
+        for k in range(a_end_idx - 1, -1, -1):
+            a_start_idx = k
+            if k >= 1 and self.bis[k].direction != want and self.bis[k-1].direction != want:
+                a_start_idx = k + 1
+                break
+        if a_start_idx >= a_end_idx:
+            return None
+        return {'a_start_idx': a_start_idx, 'a_end_idx': a_end_idx,
+                'b_pivot': piv, 'c_start_idx': c_start_idx}
+
+    def _check_c_new_extreme(self, c_start_idx: int, direction: str):
+        want = 'up' if direction == 'up' else 'down'
+        c_bis = [self.bis[k] for k in range(c_start_idx, self.n_bis)
+                 if self.bis[k].direction == want]
+        if not c_bis:
+            return False, None
+        last_piv = self.pivots[-1] if self.pivots else None
+        if direction == 'up':
+            c_ext = max(b.end.price for b in c_bis)
+            prior = (last_piv.gg if last_piv is not None
+                     else max((b.high for b in self.bis[:c_start_idx]), default=0.0))
+            return c_ext > prior * (1 - 0.001), c_ext
+        else:
+            c_ext = min(b.end.price for b in c_bis)
+            prior = (last_piv.dd if last_piv is not None
+                     else min((b.low for b in self.bis[:c_start_idx]), default=1e18))
+            return c_ext < prior * (1 + 0.001), c_ext
+
+    def _b_returns_to_zero(self, pivot) -> bool:
+        dates = self.df_raw['date']
+        mask = (dates >= pivot.start_date) & (dates <= pivot.end_date)
+        dif_b = self.dif[mask]
+        dea_b = self.dea[mask]
+        if len(dif_b) == 0 or len(dea_b) == 0:
+            return False
+        def near_zero(x):
+            if x.min() <= 0 <= x.max():
+                return True
+            return x.abs().min() < max(float(x.abs().max()), 1e-9) * 0.25
+        return near_zero(dif_b) and near_zero(dea_b)
+
+    def detect_double_pullback_to_zero(self, window: int = 40) -> bool:
+        n = len(self.dif)
+        if n < 10:
+            return False
+        dif = self.dif.iloc[-min(window, n):].reset_index(drop=True)
+        dea = self.dea.iloc[-min(window, n):].reset_index(drop=True)
+        hist = self.macd_bar.iloc[-min(window, n):].reset_index(drop=True)
+        scale = max(float(dif.abs().max()), float(dea.abs().max()), 1e-9)
+        near = scale * 0.25
+        zero_pulls = [i for i in range(len(dif))
+                      if abs(float(dif.iloc[i])) <= near and abs(float(dea.iloc[i])) <= near]
+        if len(zero_pulls) < 2:
+            return False
+        def peak_between(left, right):
+            vals = dif.iloc[left + 1:right]
+            if len(vals) < 2:
+                return None
+            rel = int(vals.idxmax())
+            return float(dif.iloc[rel]), float(hist.iloc[max(left + 1, rel - 3):rel + 1].clip(lower=0).sum())
+        def trough_between(left, right):
+            vals = dif.iloc[left + 1:right]
+            if len(vals) < 2:
+                return None
+            rel = int(vals.idxmin())
+            return float(dif.iloc[rel]), float(hist.iloc[max(left + 1, rel - 3):rel + 1].clip(upper=0).abs().sum())
+        first_pull, second_pull = zero_pulls[-2], zero_pulls[-1]
+        p1, p2 = peak_between(first_pull, second_pull), peak_between(second_pull, len(dif))
+        if p1 and p2 and p1[0] > 0 and p2[0] > 0 and p2[0] < p1[0] and p2[1] <= p1[1]:
+            return True
+        t1, t2 = trough_between(first_pull, second_pull), trough_between(second_pull, len(dif))
+        if t1 and t2 and t1[0] < 0 and t2[0] < 0 and t2[0] > t1[0] and t2[1] <= t1[1]:
+            return True
+        return False
+
+    def divergence_strength_by_position(self) -> str:
+        if len(self.dif) < 5:
+            return 'strong_pullback'
+        dif_now = float(self.dif.iloc[-1])
+        dif_abs_max = float(self.dif.abs().max())
+        if dif_abs_max <= 1e-9:
+            return 'strong_pullback'
+        if abs(dif_now) >= dif_abs_max * 0.85:
+            return 'weak_pullback'
+        return 'strong_pullback'
+
+    def classify_post_divergence(self, direction: str) -> dict:
+        if not self.pivots or self.n_bis < 2:
+            return {'evolution': 'unknown', 'reason': '无中枢或笔不足'}
+        last_piv = self.pivots[-1]
+        after_idx = last_piv.bis[-1] + 1
+        def first_reversal_seg(want_dir):
+            for s in self.segs:
+                if not getattr(s, 'confirmed', True) or s.direction != want_dir:
+                    continue
+                try:
+                    first_idx = self._bi_index[id(s.bis[0])]
+                except KeyError:
+                    continue
+                if first_idx >= after_idx:
+                    return s
+            return None
+        if direction == 'down':
+            rebound = first_reversal_seg('up')
+            if rebound is None:
+                rebound = None
+                for b in self.bis[after_idx:]:
+                    if b.direction == 'up':
+                        rebound = b; break
+            if rebound is None:
+                return {'evolution': 'unknown', 'reason': '无反弹笔'}
+            if rebound.high < last_piv.zd:
+                return {'evolution': 'case1_extend',
+                        'reason': f'反弹高{rebound.high:.3f}<最后中枢ZD{last_piv.zd:.3f} → 第29课情况①未回中枢(最弱,宜尽快撤)'}
+            if rebound.high >= last_piv.zd:
+                return {'evolution': 'case2_3_turn',
+                        'reason': f'反弹回到中枢(高{rebound.high:.3f}≥ZD{last_piv.zd:.3f}) → 第29课情况②③转折(可持有等三买)'}
+            return {'evolution': 'case1_extend',
+                    'reason': f'反弹未回中枢(高{rebound.high:.3f}<ZD{last_piv.zd:.3f}) → 偏向中枢扩展'}
+        else:
+            pullback = first_reversal_seg('down')
+            if pullback is None:
+                pullback = None
+                for b in self.bis[after_idx:]:
+                    if b.direction == 'down':
+                        pullback = b; break
+            if pullback is None:
+                return {'evolution': 'unknown', 'reason': '无回落笔'}
+            if pullback.low > last_piv.zg:
+                return {'evolution': 'case1_extend',
+                        'reason': f'回落低{pullback.low:.3f}>最后中枢ZG{last_piv.zg:.3f} → 第29课情况①未回中枢(最弱)'}
+            if pullback.low <= last_piv.zg:
+                return {'evolution': 'case2_3_turn',
+                        'reason': f'回落回到中枢(低{pullback.low:.3f}≤ZG{last_piv.zg:.3f}) → 第29课情况②③转折'}
+            return {'evolution': 'case1_extend',
+                    'reason': f'回落未回中枢 → 偏向中枢扩展'}
+
+    def macd_wrap_zero(self, window: int = 15) -> bool:
+        """L88-90: 中阴阶段的MACD特征 —— 黄白线(DIF/DEA)绕0轴缠绕。
+        近window根K线中, DIF与DEA的绝对值大多压在历史摆幅的25%以内即视为缠绕。"""
+        n = len(self.dif)
+        if n < window + 5:
+            return False
+        dif = self.dif.iloc[-window:]
+        dea = self.dea.iloc[-window:]
+        scale = max(float(self.dif.abs().tail(120).max()),
+                    float(self.dea.abs().tail(120).max()), 1e-9)
+        near = scale * 0.25
+        frac = float(((dif.abs() <= near) & (dea.abs() <= near)).mean())
+        return frac >= 0.6
+
+    def macd_clarity(self, window: int = 60) -> dict:
+        """L50: 本级别MACD的"清晰度" —— 柱子面积幅度 + 黄白线分离度, 归一化打分。
+        清晰度高的级别其背驰判定更可靠; 多级别联立时应优先采信清晰级别的MACD结论。"""
+        n = len(self.dif)
+        if n < 10:
+            return {'score': 0.0, 'label': '数据不足'}
+        w = min(window, n)
+        dif = self.dif.iloc[-w:]
+        dea = self.dea.iloc[-w:]
+        hist = self.macd_bar.iloc[-w:]
+        px = max(float(self.close.iloc[-1]), 1e-9)
+        bar_amp = float(hist.abs().mean()) / px          # 柱子相对幅度
+        sep     = float((dif - dea).abs().mean()) / px   # 黄白线分离度
+        # 黄白线贴着0轴乱绕 → 不清晰
+        wrap_penalty = 0.5 if self.macd_wrap_zero() else 1.0
+        score = (bar_amp * 0.6 + sep * 0.4) * 1e3 * wrap_penalty
+        label = '清晰' if score >= 1.0 else ('一般' if score >= 0.4 else '模糊(黄白线/柱子贴0轴)')
+        return {'score': round(score, 3), 'label': label,
+                'bar_amp': round(bar_amp * 1e3, 3), 'sep': round(sep * 1e3, 3)}
+
+    def in_zhongyin(self) -> dict:
+        n = len(self.close)
+        if n < 20 or not self.pivots:
+            return {'in_zhongyin': False, 'boll_squeeze': False, 'reason': '数据不足'}
+        ma = self.close.rolling(20).mean()
+        sd = self.close.rolling(20).std()
+        if ma.iloc[-1] and ma.iloc[-1] > 0:
+            width = float((4 * sd.iloc[-1]) / ma.iloc[-1])
+        else:
+            width = 0.0
+        wseries = (4 * sd / ma).dropna().tail(60)
+        squeeze = bool(len(wseries) >= 20 and width <= wseries.quantile(0.30))
+        osc = self.zhongshu_oscillation_monitor()
+        macd_wrap = self.macd_wrap_zero()
+        dbl_pull = self.detect_double_pullback_to_zero()
+        mode = self.CFG.get('zhongyin_macd_mode', 'off')
+        if mode == 'and':
+            in_zy = (not osc.get('alert', False)) and squeeze and macd_wrap
+        elif mode == 'or':
+            in_zy = (not osc.get('alert', False)) and (squeeze or macd_wrap)
+        else:
+            in_zy = (not osc.get('alert', False)) and squeeze
+        reason = (f"BOLL带宽{width:.3f}{'(收口→中阴)' if squeeze else '(开口)'}; "
+                  f"MACD黄白线{'绕0轴缠绕(L88-90中阴特征)' if macd_wrap else '已展开'}"
+                  f"{'; 双回拉0轴(L89: 中阴结束转折预备)' if dbl_pull else ''}; {osc.get('reason','')}")
+        return {'in_zhongyin': in_zy, 'boll_squeeze': squeeze,
+                'macd_wrap_zero': macd_wrap, 'double_pullback_zero': dbl_pull,
+                'reason': reason}
+
+    def zhongshu_oscillation_monitor(self) -> dict:
+        if not self.pivots or self.n_bis < 1:
+            return {'alert': False, 'direction': '', 'reason': '无中枢'}
+        last_piv = self.pivots[-1]
+        cur = self.bis[-1]
+        if cur.low > last_piv.zg:
+            return {'alert': True, 'direction': 'up',
+                    'reason': f'第92课: 末笔({cur.low:.3f}~{cur.high:.3f})已离开中枢上沿ZG{last_piv.zg:.3f} → 向上变盘预警'}
+        if cur.high < last_piv.zd:
+            return {'alert': True, 'direction': 'down',
+                    'reason': f'第92课: 末笔({cur.low:.3f}~{cur.high:.3f})已离开中枢下沿ZD{last_piv.zd:.3f} → 向下变盘预警'}
+        return {'alert': False, 'direction': '', 'reason': '末笔仍在中枢区间内, 中枢震荡延续'}
+
+    def bottom_construction_state(self) -> str:
+        has_b1 = self.detect_b1() is not None
+        has_b3 = self.detect_b3() is not None
+        has_s3 = self.detect_s3() is not None
+        if has_s3:
+            return 'failed'
+        if has_b3:
+            return 'completed'
+        if has_b1:
+            return 'constructing'
+        return 'none'
+
+    def _seg_index_range(self, seg):
+        m = self._bi_index
+        try:
+            return m[id(seg.bis[0])], m[id(seg.bis[-1])]
+        except KeyError:
+            return None
+
+    def _bi_exit_pullback_fallback(self, pivot, exit_dir: str, pull_dir: str):
+        pe = pivot.bis[-1]
+        leave = pull = None
+        for k in range(pe + 1, self.n_bis):
+            b = self.bis[k]
+            if leave is None:
+                if b.direction == exit_dir:
+                    leave = b
+                continue
+            if b.direction == pull_dir:
+                pull = b
+                break
+        if leave is None or pull is None:
+            return None
+        return leave, pull
+
+    def _last_exit_pullback_segments(self, pivot, exit_dir: str, pull_dir: str):
+        pivot_end = pivot.bis[-1]
+        seq = []
+        for s in (self.segs or []):
+            if not getattr(s, 'confirmed', True):
+                continue
+            rng = self._seg_index_range(s)
+            if rng is None:
+                continue
+            first_idx, last_idx = rng
+            if last_idx < pivot_end:
+                continue
+            seq.append((s, first_idx, last_idx))
+        first_only = self.CFG.get('b3s3_first_pullback_only')
+        if first_only:
+            for i in range(len(seq) - 1):
+                leave, lf, _ = seq[i]
+                pull = seq[i + 1][0]
+                if leave.direction == exit_dir and pull.direction == pull_dir and lf >= pivot_end:
+                    return leave, pull
+        else:
+            for i in range(len(seq) - 1, 0, -1):
+                pull, _, _ = seq[i]
+                leave, _, _ = seq[i - 1]
+                if leave.direction == exit_dir and pull.direction == pull_dir:
+                    return leave, pull
+        return self._bi_exit_pullback_fallback(pivot, exit_dir, pull_dir)
+
+    def assess_divergence(self, a_start, a_end, c_start, c_end, direction: str) -> DivergenceGrade:
+        dates = self.df_raw['date']
+        n_tp = self.n_trend_pivots
+        is_trend_div = n_tp >= 2
+        a_area = macd_area_between(a_start, a_end, self.macd_bar, dates, direction)
+        c_area = macd_area_between(c_start, c_end, self.macd_bar, dates, direction)
+        if a_area <= 1e-9:
+            return DivergenceGrade('NONE', False, False, 0.0, a_area, c_area, 0.0, 0.0,
+                                   direction, 'A段MACD面积为0,无可比基准',
+                                   is_trend_divergence=is_trend_div, n_trend_pivots=n_tp)
+        ratio = c_area / a_area
+        area_ok = ratio < self.DIVERGE_RATIO
+        TOL = self.DIF_TOLERANCE
+        if direction == 'up':
+            a_dif = dif_extreme_in(a_start, a_end, self.dif, dates, 'peak')
+            c_dif = dif_extreme_in(c_start, c_end, self.dif, dates, 'peak')
+            dif_ok = c_dif < a_dif * (1 - TOL)
+        else:
+            a_dif = dif_extreme_in(a_start, a_end, self.dif, dates, 'trough')
+            c_dif = dif_extreme_in(c_start, c_end, self.dif, dates, 'trough')
+            dif_ok = c_dif > a_dif * (1 - TOL)
+        ext_label = 'DIF峰' if direction == 'up' else 'DIF谷'
+        div_kind = '趋势背驰' if is_trend_div else '盘整背驰'
+        if area_ok and dif_ok:
+            grade = 'STRONG'
+            reason = f'标准{div_kind}(面积+DIF均满足)|A面积:{a_area:.4f} C面积:{c_area:.4f}(比值{ratio:.1%}<{self.DIVERGE_RATIO:.0%})|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}|{n_tp}中枢'
+        elif area_ok and not dif_ok:
+            grade = 'WEAK'
+            reason = f'{div_kind}信号(面积触发)|A面积:{a_area:.4f} C面积:{c_area:.4f}(比值{ratio:.1%}<{self.DIVERGE_RATIO:.0%})|{n_tp}中枢'
+        elif dif_ok and not area_ok:
+            grade = 'WEAK'
+            reason = f'{div_kind}信号(DIF触发)|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}|{n_tp}中枢'
+        else:
+            grade = 'NONE'
+            reason = f'无背驰(两判据均不满足)|面积比{ratio:.1%}>={self.DIVERGE_RATIO:.0%}|{ext_label} A:{a_dif:.4f} C:{c_dif:.4f}'
+        return DivergenceGrade(grade, area_ok, dif_ok, ratio, a_area, c_area, a_dif, c_dif,
+                               direction, reason, is_trend_divergence=is_trend_div, n_trend_pivots=n_tp)
+
+    def _find_prev_b1(self) -> tuple:
+        if not self.pivots:
+            return (None, '')
+        last_piv = self.pivots[-1]
+        pivot_first_bi_idx = last_piv.bis[0]
+        if pivot_first_bi_idx <= 0:
+            return (None, '')
+        downs = []
+        for k in range(pivot_first_bi_idx - 1, -1, -1):
+            b = self.bis[k]
+            downs.append(b)
+            if b.direction == 'up' and k - 1 >= 0 and self.bis[k-1].direction == 'down':
+                if len(downs) >= 4:
+                    break
+        down_bis = [b for b in downs if b.direction == 'down']
+        if not down_bis:
+            return (None, '')
+        b1 = min(down_bis, key=lambda b: b.end.price)
+        return (b1.end.price, self._ds(b1.end.date))
+
+    def _find_prev_b2(self) -> tuple:
+        b1_price, b1_date_str = self._find_prev_b1()
+        if b1_price is None or not self.pivots:
+            return (None, '')
+        b1_date = pd.Timestamp(b1_date_str)
+        last_piv = self.pivots[-1]
+        right_bi_idx = last_piv.bis[-1] + 1
+        for b in self.bis[:right_bi_idx]:
+            if b.direction != 'down':
+                continue
+            if b.end.date <= b1_date:
+                continue
+            if b.end.price > b1_price + 1e-9:
+                return (b.end.price, self._ds(b.end.date))
+        return (None, '')
+
+    def _find_prev_s1(self) -> tuple:
+        if not self.pivots:
+            return (None, '')
+        last_piv = self.pivots[-1]
+        pivot_first_bi_idx = last_piv.bis[0]
+        if pivot_first_bi_idx <= 0:
+            return (None, '')
+        ups = []
+        for k in range(pivot_first_bi_idx - 1, -1, -1):
+            b = self.bis[k]
+            ups.append(b)
+            if b.direction == 'down' and k - 1 >= 0 and self.bis[k-1].direction == 'up':
+                if len(ups) >= 4:
+                    break
+        up_bis = [b for b in ups if b.direction == 'up']
+        if not up_bis:
+            return (None, '')
+        s1 = max(up_bis, key=lambda b: b.end.price)
+        return (s1.end.price, self._ds(s1.end.date))
+
+    def _find_prev_s2(self) -> tuple:
+        s1_price, s1_date_str = self._find_prev_s1()
+        if s1_price is None or not self.pivots:
+            return (None, '')
+        s1_date = pd.Timestamp(s1_date_str)
+        last_piv = self.pivots[-1]
+        right_bi_idx = last_piv.bis[-1] + 1
+        for b in self.bis[:right_bi_idx]:
+            if b.direction != 'up':
+                continue
+            if b.end.date <= s1_date:
+                continue
+            if b.end.price < s1_price - 1e-9:
+                return (b.end.price, self._ds(b.end.date))
+        return (None, '')
+
+    def detect_b1(self) -> Optional[Signal]:
+        if self.n_bis < 5:
+            return None
+        cur = self.bis[-1]
+        if cur.direction != 'down':
+            return None
+        dif_now = float(self.dif.iloc[-1])
+        if dif_now >= 0:
+            return None
+        trend_ok = (self.n_pivots >= 2 and self.trend == 'down_trend')
+        consol_ok = (self.CFG.get('b1_allow_consol_diverge') and self.n_pivots >= 1)
+        if not (trend_ok or consol_ok):
+            return None
+        abc = self._validate_abc('down')
+        if abc is None and consol_ok:
+            abc = self._validate_abc_consol('down')
+        if abc is None:
+            return None
+        c_ok, c_low = self._check_c_new_extreme(abc['c_start_idx'], 'down')
+        if not c_ok:
+            return None
+        last_piv = self.pivots[-1]
+        a_down = [self.bis[k] for k in range(abc['a_start_idx'], abc['a_end_idx'])
+                  if self.bis[k].direction == 'down']
+        c_down = [self.bis[k] for k in range(abc['c_start_idx'], self.n_bis)
+                  if self.bis[k].direction == 'down']
+        if not a_down or not c_down:
+            return None
+        dg = self.assess_divergence(a_down[0].start.date, a_down[-1].end.date,
+                                    c_down[0].start.date, c_down[-1].end.date, 'down')
+        if dg.grade == 'NONE':
+            return None
+        div_kind = '趋势底背驰' if dg.is_trend_divergence else '盘整底背驰(第27课)'
+        b_zero = self._b_returns_to_zero(last_piv)
+        dbl_pull = self.detect_double_pullback_to_zero()
+        pos_strength = self.divergence_strength_by_position()
+        post_evo = self.classify_post_divergence('down')
+        a_low_bi = min(a_down, key=lambda b: b.end.price)
+        c_low_bi = min(c_down, key=lambda b: b.end.price)
+        return Signal(kind='B1', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
+                      reason=f'{div_kind}一买|{self.n_pivots}中枢|ABC三段{"+B回0轴" if b_zero else ""}|{dg.reason}|DIF={dif_now:.4f}<0',
+                      pivot_zg=last_piv.zg, pivot_zd=last_piv.zd, macd_ratio=dg.area_ratio, dif_value=dif_now,
+                      n_pivots=self.n_pivots, trend=self.trend,
+                      extras={'a_seg': (a_down[0].start.date, a_down[-1].end.date, a_down[0].start.price, a_down[-1].end.price),
+                              'diverge_grade': dg.grade,
+                              'a_low': float(a_low_bi.end.price),
+                              'a_low_date': self._ds(a_low_bi.end.date),
+                              'b1_price': float(cur.end.price),
+                              'b1_date': self._ds(cur.end.date),
+                              'macd_grade': dg.grade,
+                              'macd_area_ratio': dg.area_ratio,
+                              'dif_ok': dg.dif_ok,
+                              'area_ok': dg.area_ok,
+                              'b_returns_zero': b_zero,
+                              'double_pullback': dbl_pull,
+                              'pos_strength': pos_strength,
+                              'post_evolution': post_evo['evolution'],
+                              'c_new_low': c_low,
+                              'c_new_low_date': self._ds(c_low_bi.end.date),
+                              'n_trend_pivots': self.n_trend_pivots,
+                              'price_date': self._ds(cur.end.date),
+                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
+                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
+                              'pivot_start_date': self._ds(last_piv.start_date),
+                              'pivot_end_date': self._ds(last_piv.end_date)},
+                      diverge_grade=dg)
+
+    def detect_b2(self) -> Optional[Signal]:
+        if self.n_bis < 4:
+            return None
+        cur = self.bis[-1]
+        if cur.direction != 'down':
+            return None
+        prev_downs = [b for b in self.bis[:-1] if b.direction == 'down']
+        if not prev_downs:
+            return None
+        prev = prev_downs[-1]
+        if not (cur.low >= prev.low and cur.end.price > prev.end.price):
+            return None
+        cur_price = float(self.close.iloc[-1])
+        if cur_price < cur.end.price * (1 - self.PIVOT_TOLERANCE):
+            return None
+        if self.CFG.get('b2s2_anchor_to_first'):
+            b1_anchor, _ = self._find_prev_b1()
+            if b1_anchor is None:
+                return None
+            if cur.end.price < b1_anchor - 1e-9:
+                return None
+        dif_now = float(self.dif.iloc[-1])
+        if self.CFG.get('b2_macd_zero_pullback'):
+            look = self.dif.tail(12)
+            crossed_up = bool((look > 0).any())
+            if not crossed_up:
+                return None
+            if dif_now < -self.DIF_TOLERANCE:
+                return None
+        last_piv = self.pivots[-1] if self.pivots else None
+        b1_price, b1_date = prev.end.price, self._ds(prev.end.date)
+        return Signal(kind='B2', date=self.df_raw['date'].iloc[-1], price=cur_price,
+                      reason=f'二买:一买后回踩不破|当前低{cur.end.price:.3f}>一买{b1_price:.3f}|二买不以背驰为成立条件(第21课)',
+                      pivot_zg=None, pivot_zd=None,
+                      macd_ratio=None, dif_value=dif_now, n_pivots=self.n_pivots, trend=self.trend,
+                      extras={'prev_low': prev.end.price, 'cur_low': cur.end.price,
+                              'cur_low_date': self._ds(cur.end.date),
+                              'prev_low_date': self._ds(prev.end.date),
+                              'b1_price': b1_price,
+                              'b1_date': b1_date,
+                              'price_date': self._ds(cur.end.date),
+                              'context_pivot_zg': last_piv.zg if last_piv else None,
+                              'context_pivot_zd': last_piv.zd if last_piv else None,
+                              'context_pivot_zg_date': self._ds(last_piv.zg_date) if last_piv and last_piv.zg_date else '',
+                              'context_pivot_zd_date': self._ds(last_piv.zd_date) if last_piv and last_piv.zd_date else '',
+                              'context_pivot_start_date': self._ds(last_piv.start_date) if last_piv else '',
+                              'context_pivot_end_date': self._ds(last_piv.end_date) if last_piv else ''},
+                      diverge_grade=None)
+
+    def detect_b3(self) -> Optional[Signal]:
+        if self.n_bis < 5 or self.n_pivots < 1:
+            return None
+        late_trend_b3 = self.n_trend_pivots >= 2
+        last_piv = self.pivots[-1]
+        zg, zd = last_piv.zg, last_piv.zd
+        piv_height = zg - zd
+        cur = self.bis[-1]
+        if cur.direction != 'up':
+            return None
+        pair = self._last_exit_pullback_segments(last_piv, 'up', 'down')
+        if pair is None:
+            return None
+        exit_seg, pull_seg = pair
+        if not (exit_seg.low <= zg * (1 + self.PIVOT_TOLERANCE) and exit_seg.high > zg):
+            return None
+        if pull_seg.low < zg * (1 - self.PIVOT_TOLERANCE):
+            return None
+        leaves_pivot = pull_seg.low >= zg
+        cur_price = float(self.close.iloc[-1])
+        if cur_price <= zg:
+            return None
+        ex_amp = exit_seg.high - exit_seg.low
+        if piv_height > 0 and ex_amp < piv_height * 0.5:
+            return None
+        if len(last_piv.bis) < 3:
+            return None
+        confirm_txt = '回踩离枢确认(新中枢生成)' if leaves_pivot else '回踩贴ZG(容差内,新中枢待确认)'
+        b1_price, b1_date = self._find_prev_b1()
+        b2_price, b2_date = self._find_prev_b2()
+        warn_txt = '|第二个以上同向中枢,实盘宜改用低级别一买' if late_trend_b3 else ''
+        return Signal(kind='B3', date=self.df_raw['date'].iloc[-1], price=cur_price,
+                      reason=f'标准三买|ZG={zg:.3f},ZD={zd:.3f}|线段离枢:{exit_seg.low:.3f}→{exit_seg.high:.3f}(幅度{ex_amp:.3f})|线段回试低{pull_seg.low:.3f}|{confirm_txt}|当前{cur_price:.3f}>ZG{warn_txt}',
+                      pivot_zg=zg, pivot_zd=zd, macd_ratio=None, dif_value=float(self.dif.iloc[-1]),
+                      n_pivots=self.n_pivots, trend=self.trend,
+                      extras={'exit_seg': (exit_seg.low, exit_seg.high),
+                              'pull_low': pull_seg.low,
+                              'pull_low_date': self._ds(pull_seg.end.date),
+                              'exit_start_date': self._ds(exit_seg.start.date),
+                              'exit_end_date': self._ds(exit_seg.end.date),
+                              'b1_price': b1_price,
+                              'b1_date': b1_date,
+                              'b2_price': b2_price,
+                              'b2_date': b2_date,
+                              'piv_bi_count': len(last_piv.bis), 'leaves_pivot': leaves_pivot,
+                              'late_trend_b3': late_trend_b3,
+                              'price_date': self._ds(self.df_raw['date'].iloc[-1]),
+                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
+                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
+                              'pivot_start_date': self._ds(last_piv.start_date),
+                              'pivot_end_date': self._ds(last_piv.end_date)},
+                      diverge_grade=None)
+
+    def detect_s1(self) -> Optional[Signal]:
+        if self.n_bis < 5:
+            return None
+        cur = self.bis[-1]
+        if cur.direction != 'up':
+            return None
+        trend_ok = (self.n_pivots >= 2 and self.trend == 'up_trend')
+        consol_ok = (self.CFG.get('b1_allow_consol_diverge') and self.n_pivots >= 1)
+        if not (trend_ok or consol_ok):
+            return None
+        abc = self._validate_abc('up')
+        if abc is None and consol_ok:
+            abc = self._validate_abc_consol('up')
+        if abc is None:
+            return None
+        last_piv = self.pivots[-1]
+        a_start_idx = abc['a_start_idx']; a_end_idx = abc['a_end_idx']
+        if a_end_idx <= a_start_idx:
+            return None
+        a_up_bis = [self.bis[k] for k in range(a_start_idx, a_end_idx) if self.bis[k].direction == 'up']
+        if not a_up_bis:
+            return None
+        a_high = max(b.end.price for b in a_up_bis)
+        c_start_idx = last_piv.bis[-1] + 1
+        c_up_bis = [self.bis[k] for k in range(c_start_idx, self.n_bis) if self.bis[k].direction == 'up']
+        if not c_up_bis:
+            return None
+        c_high = max(b.end.price for b in c_up_bis)
+        if c_high <= a_high:
+            return None
+        a_high_bi = max(a_up_bis, key=lambda b: b.end.price)
+        dg = self.assess_divergence(a_up_bis[0].start.date, a_up_bis[-1].end.date,
+                                    c_up_bis[0].start.date, c_up_bis[-1].end.date, 'up')
+        if dg.grade == 'NONE':
+            return None
+        b_zero = self._b_returns_to_zero(last_piv)
+        dbl_pull = self.detect_double_pullback_to_zero()
+        pos_strength = self.divergence_strength_by_position()
+        post_evo = self.classify_post_divergence('up')
+        dif_now = float(self.dif.iloc[-1])
+        c_high_bi = max(c_up_bis, key=lambda b: b.end.price)
+        return Signal(kind='S1', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
+                      reason=f'一卖|上涨趋势{self.n_pivots}中枢|价创新高C{c_high:.3f}>A段高{a_high:.3f}{"+B回0轴" if b_zero else ""}|{dg.reason}',
+                      pivot_zg=last_piv.zg, pivot_zd=last_piv.zd, macd_ratio=dg.area_ratio, dif_value=dif_now,
+                      n_pivots=self.n_pivots, trend=self.trend,
+                      extras={'a_high': a_high, 'c_high': c_high, 'a_area': dg.a_area, 'c_area': dg.c_area,
+                              'diverge_grade': dg.grade,
+                              'a_high_date': self._ds(a_high_bi.end.date),
+                              'b_returns_zero': b_zero,
+                              'double_pullback': dbl_pull,
+                              'pos_strength': pos_strength,
+                              'post_evolution': post_evo['evolution'],
+                              'c_high_date': self._ds(c_high_bi.end.date),
+                              'price_date': self._ds(cur.end.date),
+                              'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
+                              'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
+                              'pivot_start_date': self._ds(last_piv.start_date),
+                              'pivot_end_date': self._ds(last_piv.end_date)},
+                      diverge_grade=dg)
+
+    def detect_s2(self) -> Optional[Signal]:
+        if self.n_bis < 4:
+            return None
+        cur = self.bis[-1]
+        if cur.direction != 'up':
+            return None
+        prev_ups = [b for b in self.bis[:-1] if b.direction == 'up']
+        if not prev_ups:
+            return None
+        prev = prev_ups[-1]
+        if cur.high < prev.high and cur.end.price < prev.end.price:
+            cur_price = float(self.close.iloc[-1])
+            if cur_price > cur.end.price * (1 + self.PIVOT_TOLERANCE):
+                return None
+            if self.CFG.get('b2s2_anchor_to_first'):
+                s1_anchor, _ = self._find_prev_s1()
+                if s1_anchor is None:
+                    return None
+                if cur.end.price > s1_anchor + 1e-9:
+                    return None
+            dif_now = float(self.dif.iloc[-1])
+            last_piv = self.pivots[-1] if self.pivots else None
+            s1_price, s1_date = prev.end.price, self._ds(prev.end.date)
+            return Signal(kind='S2', date=self.df_raw['date'].iloc[-1], price=cur_price,
+                          reason=f'二卖:一卖后反弹不破|当前高{cur.end.price:.3f}<一卖{s1_price:.3f}|二卖不以背驰为成立条件(第21课)',
+                          pivot_zg=None, pivot_zd=None,
+                          dif_value=dif_now, n_pivots=self.n_pivots, trend=self.trend,
+                          extras={'prev_high': prev.end.price, 'cur_high': cur.end.price,
+                                  'cur_high_date': self._ds(cur.end.date),
+                                  'prev_high_date': self._ds(prev.end.date),
+                                  's1_price': s1_price, 's1_date': s1_date,
+                                  'price_date': self._ds(cur.end.date),
+                                  'context_pivot_zg': last_piv.zg if last_piv else None,
+                                  'context_pivot_zd': last_piv.zd if last_piv else None,
+                                  'context_pivot_zg_date': self._ds(last_piv.zg_date) if last_piv and last_piv.zg_date else '',
+                                  'context_pivot_zd_date': self._ds(last_piv.zd_date) if last_piv and last_piv.zd_date else '',
+                                  'context_pivot_start_date': self._ds(last_piv.start_date) if last_piv else '',
+                                  'context_pivot_end_date': self._ds(last_piv.end_date) if last_piv else ''},
+                          diverge_grade=None)
         return None
-    zd = ml.daily.zd
-    if zd is None:
+
+    def detect_s3(self) -> Optional[Signal]:
+        if self.n_bis < 5 or self.n_pivots < 1:
+            return None
+        last_piv = self.pivots[-1]
+        zg, zd = last_piv.zg, last_piv.zd
+        if len(self.bis) < 3:
+            return None
+        cur = self.bis[-1]
+        if cur.direction != 'down':
+            return None
+        s1_price, s1_date = self._find_prev_s1()
+        s2_price, s2_date = self._find_prev_s2()
+        base_dates = {'price_date': self._ds(self.df_raw['date'].iloc[-1]),
+                      's1_price': s1_price, 's1_date': s1_date,
+                      's2_price': s2_price, 's2_date': s2_date,
+                      'pivot_zg_date': self._ds(last_piv.zg_date) if last_piv.zg_date else '',
+                      'pivot_zd_date': self._ds(last_piv.zd_date) if last_piv.zd_date else '',
+                      'pivot_start_date': self._ds(last_piv.start_date),
+                      'pivot_end_date': self._ds(last_piv.end_date)}
+        pair = self._last_exit_pullback_segments(last_piv, 'down', 'up')
+        if pair is None:
+            return None
+        exit_seg, pull_seg = pair
+        if not (exit_seg.high >= zd * (1 - self.PIVOT_TOLERANCE) and exit_seg.low < zd):
+            return None
+        if pull_seg.high > zd * (1 + self.PIVOT_TOLERANCE):
+            return None
+        if float(self.close.iloc[-1]) >= zd:
+            return None
+        return Signal(kind='S3', date=self.df_raw['date'].iloc[-1], price=float(self.close.iloc[-1]),
+                      reason=f'标准三卖|ZD={zd:.3f}|线段离枢低{exit_seg.low:.3f}<ZD|线段回抽高{pull_seg.high:.3f}未过ZD',
+                      pivot_zg=zg, pivot_zd=zd, dif_value=float(self.dif.iloc[-1]),
+                      n_pivots=self.n_pivots, trend=self.trend, extras=base_dates, diverge_grade=None)
+
+    def get_signal(self) -> Optional[Signal]:
+        for fn in [self.detect_s1, self.detect_s2, self.detect_s3]:
+            sig = fn()
+            if sig is not None:
+                return sig
+        for fn in [self.detect_b3, self.detect_b2, self.detect_b1]:
+            sig = fn()
+            if sig is not None:
+                return sig
         return None
-    if close_p < zd * (1 - L92_ZD_BREAK_BUF) and close_p > pos.get('stop_px', 0):
-        return ('L92_EXIT',
-                f'第92课中枢震荡监视器: 现价{close_p:.3f}已破日线中枢下沿ZD{zd:.3f}'
-                f'(向下变盘) 且浮盈仅{floating:+.1%} → 提前减仓, 不等磨到结构止损')
-    return None
 
+    def get_all_signals(self) -> list:
+        all_sigs = []
+        for fn in [self.detect_b1, self.detect_b2, self.detect_b3,
+                   self.detect_s1, self.detect_s2, self.detect_s3]:
+            sig = fn()
+            if sig is not None:
+                all_sigs.append(sig)
+        return all_sigs
+
+    def l36_segment_note(self) -> str:
+        """L36 结合律: 走势分解的唯一性靠结合律保证 —— a+A+b+B+c 的划分中, 同一段
+        K线不能既归前段又归后段。本引擎线段划分采用特征序列分型(标准缠论)处理
+        包含关系, 分型一旦确认即锁定段的归属, 等价于结合律的程序化执行。
+        该函数对当前末端给出"是否存在划分歧义"的提示。"""
+        if len(self.segs) < 2:
+            return '第36课: 线段不足2段, 无划分歧义问题'
+        last = self.segs[-1]
+        n_last = len(getattr(last, 'bis', []) or [])
+        if n_last < 3:
+            return (f'第36课: 末段仅{n_last}笔(<3), 末端划分尚未唯一确认 —— '
+                    f'当下操作应按两种归属做完全分类预案, 等待特征序列分型锁定')
+        return '第36课: 末段≥3笔且特征序列分型已锁定, 当前划分唯一, 无歧义'
 
-def predict_enhance(ml) -> dict:
-    out = {}
-    if ml is None or ml.daily is None:
+    def diagnose(self) -> dict:
+        cur_price = float(self.close.iloc[-1]) if len(self.close) else 0.0
+        last = self.bis[-1] if self.bis else None
+        out = {k: [] for k in ('B1', 'B2', 'B3', 'S1', 'S2', 'S3')}
+        def add(k, ok, msg):
+            out[k].append(('✓' if ok else '✗') + ' ' + msg)
+        add('B1', self.n_bis >= 5, f'笔数 {self.n_bis} >= 5')
+        add('B1', self.n_pivots >= 2, f'中枢数 {self.n_pivots} >= 2')
+        add('B1', self.trend == 'down_trend', f'当前走势={self.trend}, B1要求下跌趋势')
+        add('B1', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, B1要求向下')
+        add('B1', float(self.dif.iloc[-1]) < 0 if len(self.dif) else False, f'DIF={float(self.dif.iloc[-1]):.4f}, B1要求DIF<0')
+        abc_down = self._validate_abc('down')
+        add('B1', abc_down is not None, 'A/B/C三段背驰结构成立')
+        if abc_down is not None:
+            c_ok, c_low = self._check_c_new_extreme(abc_down['c_start_idx'], 'down')
+            add('B1', c_ok, f'C段创新低{"" if c_low is None else f"({c_low:.3f})"}')
+        add('B2', self.n_bis >= 4, f'笔数 {self.n_bis} >= 4')
+        add('B2', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, B2要求回踩向下')
+        prev_downs = [b for b in self.bis[:-1] if b.direction == 'down'] if last else []
+        add('B2', bool(prev_downs), '存在同一轮前一个下跌低点作为一买锚')
+        if last and prev_downs:
+            prev = prev_downs[-1]
+            add('B2', last.low >= prev.low and last.end.price > prev.end.price,
+                f'回踩不创新低: 本次低{last.end.price:.3f} > 一买/前低{prev.end.price:.3f}')
+            add('B2', cur_price >= last.end.price * (1 - self.PIVOT_TOLERANCE),
+                f'现价{cur_price:.3f}未跌破B2回踩锚{last.end.price:.3f}; 跌破则二买失效')
+        add('B3', self.n_pivots >= 1, f'中枢数 {self.n_pivots} >= 1')
+        add('B3', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, B3要求向上确认')
+        if self.pivots:
+            p = self.pivots[-1]
+            pair = self._last_exit_pullback_segments(p, 'up', 'down')
+            add('B3', pair is not None, '存在已确认线段级别的向上离枢 + 向下回试')
+            if pair is not None:
+                exit_seg, pull_seg = pair
+                add('B3', exit_seg.low <= p.zg * (1 + self.PIVOT_TOLERANCE) and exit_seg.high > p.zg,
+                    f'离枢线段突破ZG: {exit_seg.low:.3f}~{exit_seg.high:.3f}, ZG={p.zg:.3f}')
+                add('B3', pull_seg.low >= p.zg * (1 - self.PIVOT_TOLERANCE),
+                    f'回试低点{pull_seg.low:.3f}不破ZG={p.zg:.3f}')
+                add('B3', cur_price > p.zg, f'现价{cur_price:.3f}站上ZG={p.zg:.3f}')
+        add('S1', self.n_bis >= 5, f'笔数 {self.n_bis} >= 5')
+        add('S1', self.n_pivots >= 2, f'中枢数 {self.n_pivots} >= 2')
+        add('S1', self.trend == 'up_trend', f'当前走势={self.trend}, S1要求上涨趋势')
+        add('S1', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, S1要求向上')
+        abc_up = self._validate_abc('up')
+        add('S1', abc_up is not None, 'A/B/C三段顶背驰结构成立')
+        if abc_up is not None:
+            c_ok, c_high = self._check_c_new_extreme(abc_up['c_start_idx'], 'up')
+            add('S1', c_ok, f'C段创新高{"" if c_high is None else f"({c_high:.3f})"}')
+        add('S2', self.n_bis >= 4, f'笔数 {self.n_bis} >= 4')
+        add('S2', bool(last and last.direction == 'up'), f'最后一笔方向={last.direction if last else ""}, S2要求反弹向上')
+        prev_ups = [b for b in self.bis[:-1] if b.direction == 'up'] if last else []
+        add('S2', bool(prev_ups), '存在同一轮前一个上涨高点作为一卖锚')
+        if last and prev_ups:
+            prev = prev_ups[-1]
+            add('S2', last.high < prev.high and last.end.price < prev.end.price,
+                f'反弹不创新高: 本次高{last.end.price:.3f} < 一卖/前高{prev.end.price:.3f}')
+            add('S2', cur_price <= last.end.price * (1 + self.PIVOT_TOLERANCE),
+                f'现价{cur_price:.3f}未重新升破S2反弹锚{last.end.price:.3f}; 升破则二卖失效')
+        add('S3', self.n_pivots >= 1, f'中枢数 {self.n_pivots} >= 1')
+        add('S3', bool(last and last.direction == 'down'), f'最后一笔方向={last.direction if last else ""}, S3要求向下确认')
+        if self.pivots:
+            p = self.pivots[-1]
+            pair = self._last_exit_pullback_segments(p, 'down', 'up')
+            add('S3', pair is not None, '存在已确认线段级别的向下离枢 + 向上回抽')
+            if pair is not None:
+                exit_seg, pull_seg = pair
+                add('S3', exit_seg.high >= p.zd * (1 - self.PIVOT_TOLERANCE) and exit_seg.low < p.zd,
+                    f'离枢线段跌破ZD: {exit_seg.low:.3f}~{exit_seg.high:.3f}, ZD={p.zd:.3f}')
+                add('S3', pull_seg.high <= p.zd * (1 + self.PIVOT_TOLERANCE),
+                    f'回抽高点{pull_seg.high:.3f}不破ZD={p.zd:.3f}')
+                add('S3', cur_price < p.zd, f'现价{cur_price:.3f}跌破ZD={p.zd:.3f}')
         return out
-    sig_kind = ml.final_kind or ''
-    if sig_kind in ('B1', 'B2', 'B3'):
-        method, weight = buy_method_weight(sig_kind, ml)
-        out['buy_method'] = method
-        out['buy_method_cn'] = METHOD_CN.get(method, method)
-        out['suggest_weight'] = weight
-        out['l16_note'] = f'第16课: 买法分类={method}〔{METHOD_CN.get(method, method)}〕, 建议仓位权重{weight:.2f}'
-    l37 = l37_divergence_note(ml)
-    if l37:
-        out['l37_note'] = l37
-    evo = recompute_evo(ml)
-    if evo:
-        out['post_evolution'] = evo
-        out['evo_hint'] = ('第29课: 最弱形态(反弹/回落未回中枢), 持有宜收紧移动止盈, 尽快兑现'
-                           if evo == 'case1_extend'
-                           else '第29课: 形态较强(回到中枢), 可持有等三买/趋势延续')
-    zd = ml.daily.zd
-    if zd is not None and ml.cur_price < zd:
-        out['l92_warn'] = f'第92课: 现价{ml.cur_price:.3f}已在日线中枢下沿ZD{zd:.3f}下方 → 向下变盘预警'
-    return out
+
+
+class SameLevelDecomposition:
+    def __init__(self, analyzer: 'ChanAnalyzer'):
+        self.an = analyzer
+        self.segs = analyzer.segs
+
+    def current_phase(self) -> dict:
+        if len(self.segs) < 2:
+            return {'seg_dir': '', 'stage': 'unknown', 'action': 'WATCH',
+                    'reason': '线段不足, 无法做同级别分解'}
+        last = self.segs[-1]
+        prev = self.segs[-2]
+        seg_dir = last.direction
+        if seg_dir == 'up':
+            stage = 'up_run'
+            prev_up = None
+            for s in reversed(self.segs[:-1]):
+                if s.direction == 'up':
+                    prev_up = s; break
+            if prev_up is None:
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'HOLD',
+                        'reason': '向上段运作中(无前向上段可比), 持有'}
+            if last.high <= prev_up.high:
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'SELL',
+                        'reason': f'向上段不创新高({last.high:.3f}≤前高{prev_up.high:.3f}) → 先卖(第38课)'}
+            dg = self.an.assess_divergence(prev_up.start.date, prev_up.end.date,
+                                           last.start.date, last.end.date, 'up')
+            if dg.grade != 'NONE':
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'SELL',
+                        'reason': f'向上段创新高但盘整背驰({dg.grade}) → 卖(第38课)'}
+            return {'seg_dir': seg_dir, 'stage': stage, 'action': 'HOLD',
+                    'reason': '向上段创新高且不背驰 → 持有(第38课)'}
+        else:
+            stage = 'down_run'
+            prev_down = None
+            for s in reversed(self.segs[:-1]):
+                if s.direction == 'down':
+                    prev_down = s; break
+            if prev_down is None:
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'WATCH',
+                        'reason': '向下段运作中(无前向下段可比), 观望等买点'}
+            if last.low >= prev_down.low:
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'BUY',
+                        'reason': f'向下段不创新低({last.low:.3f}≥前低{prev_down.low:.3f}) → 买入(第38课)'}
+            dg = self.an.assess_divergence(prev_down.start.date, prev_down.end.date,
+                                           last.start.date, last.end.date, 'down')
+            if dg.grade != 'NONE':
+                return {'seg_dir': seg_dir, 'stage': stage, 'action': 'BUY',
+                        'reason': f'向下段创新低但盘整背驰({dg.grade}) → 买入(第38课)'}
+            return {'seg_dir': seg_dir, 'stage': stage, 'action': 'WATCH',
+                    'reason': '向下段创新低且不背驰 → 观望等下跌背驰(第38课)'}
+
+
+class BottomTracker:
+    def __init__(self):
+        self.state = 'none'
+
+    def update(self, analyzer: 'ChanAnalyzer') -> str:
+        snap = analyzer.bottom_construction_state()
+        if self.state in ('none', 'failed', 'completed'):
+            if snap == 'constructing':
+                self.state = 'constructing'
+            elif snap == 'completed':
+                self.state = 'completed'
+            elif snap == 'failed':
+                self.state = 'failed'
+            else:
+                self.state = 'none'
+        elif self.state == 'constructing':
+            if snap == 'completed':
+                self.state = 'completed'
+            elif snap == 'failed':
+                self.state = 'failed'
+        return self.state

chan_finetune_colab.ipynb

@@ -0,0 +1,126 @@
+# 🎯 Well-Tuned — fine-tune & publish your own model (step by step)
+
+Goal: publish a small **fine-tuned** model on Hugging Face and use it inside the
+app. We fine-tune **Qwen3-1.7B** on one focused skill the app already performs —
+turning a Chan-theory *raw read* into a crisp long-hold *AI summary* — then plug
+it back in as the Translator sub-agent. This earns **🎯 Well-Tuned** and
+strengthens **🐜 Tiny Titan** (a 1.7B model doing the job well).
+
+Everything you need is already in the app and in this `finetune/` folder. Total
+hands-on time ≈ 1 hour, most of it waiting.
+
+---
+
+## Why this approach (read once)
+
+- **Self-sourcing data.** You don't hunt for a dataset — the app *generates* it.
+  Each time you run the Signals **AI summary**, the (raw read → narrative) pair
+  is saved to `/data/dataset/pairs.jsonl`. A few hundred pairs is plenty.
+- **Single skill, small model.** Narrowing the task lets 1.7B match or beat a
+  generic 4B, which is the whole point of the hackathon's "think small".
+- **Honest fit.** The fine-tune does exactly what the app uses it for — judges
+  can see the loop close.
+
+---
+
+## Step 1 — Capture training data (in the app, ~15 min)
+
+1. Open the Space → **Signals** → **Run analysis**.
+2. For each ticker: pick it in the dropdown, click **🤖 AI summary**. Each run
+   auto-saves one training pair (you'll see them counted on the Model tab).
+3. Vary it: change the ticker pool (large caps, a few volatile names, an ETF),
+   re-run analysis, summarize again. Aim for **150–500 pairs**.
+   - Tip: the **Auto Research** reports and the **rotation narrative** also
+     exercise the models, but only the Signals AI summary is captured for SFT
+     (it's the cleanest single-skill pair).
+4. **Model tab → 🎯 Fine-tuning dataset → ⬇ Export dataset (JSONL)**. It writes
+   `/data/dataset/chan_sft_<timestamp>.jsonl` and tells you the pair count.
+
+### Get the file onto your computer
+The file lives on the Space's `/data` bucket. Easiest way to grab it:
+```bash
+pip install huggingface_hub
+huggingface-cli login          # your token
+# list what's in the Space's persistent storage isn't exposed directly, so
+# instead download via the Space's file browser, OR re-export to the repo:
+```
+Simplest reliable path: in the **Model tab** the export prints the full path;
+open the Space's **Files** isn't enough for /data, so use the Space terminal if
+you enabled "Dev mode", **or** temporarily add a `gr.File` download — if you
+want that button, tell me and I'll wire it in. Otherwise the Colab notebook
+also accepts the JSONL via direct upload (Step 2 there).
+
+> If you'd rather not fish the file out of `/data`, I can add a one-click
+> **"Download dataset"** button to the Model tab — say the word.
+
+---
+
+## Step 2 — Train on a free Colab T4 (~25–40 min)
+
+1. Go to <https://colab.research.google.com> → **File → Upload notebook** →
+   pick `finetune/chan_finetune_colab.ipynb` from this project.
+2. **Runtime → Change runtime type → T4 GPU → Save.**
+3. Run the cells top to bottom:
+   - Cell 1 installs Unsloth (fast, low-memory LoRA).
+   - Cell 2 asks you to **upload your `chan_sft_*.jsonl`**.
+   - Cells 3–5 load Qwen3-1.7B in 4-bit, attach LoRA adapters, and train
+     (3 epochs; 10–20 min for a few hundred rows).
+   - Cell 6 prints a sample generation so you can eyeball quality.
+4. If the sample looks off, raise `num_train_epochs` to 4–5 and re-run Cell 5.
+
+---
+
+## Step 3 — Convert to GGUF + publish (~5 min, same notebook)
+
+1. Cell 7: `login()` — paste a **write** token (HF → Settings → Access Tokens).
+2. Set `HF_USER = 'ranranrunforit'`. The cell calls
+   `model.push_to_hub_gguf(REPO, tokenizer, quantization_method='q8_0')`, which
+   merges the LoRA, converts to GGUF via llama.cpp, and uploads to
+   `ranranrunforit/chan-compass-qwen3-1.7b-gguf`.
+3. Open the new repo → **Files** → note the exact `.gguf` filename (e.g.
+   `chan-compass-qwen3-1.7b-gguf.Q8_0.gguf` — Unsloth's name may differ).
+
+---
+
+## Step 4 — Wire it into the app (1 line, ~2 min)
+
+In `llm_local.py`, find the commented block in `MODEL_ZOO` and edit it to match
+your repo id and the **exact** filename from Step 3:
+
+```python
+"Chan-Tuned Qwen3-1.7B · my fine-tune": (
+    "ranranrunforit/chan-compass-qwen3-1.7b-gguf",
+    "chan-compass-qwen3-1.7b-gguf.Q8_0.gguf"),   # ← exact filename
+```
+
+Optionally make it the fast sub-agent's default so the app uses it everywhere:
+
+```python
+FAST_MODEL = "Chan-Tuned Qwen3-1.7B · my fine-tune"
+```
+
+Commit, reboot the Space. The Model tab now lists your fine-tune, and it loads
+through llama.cpp like any other GGUF. **🎯 Well-Tuned achieved.**
+
+---
+
+## Checklist for the badge
+
+- [ ] Dataset exported from the app (`chan_sft_*.jsonl`).
+- [ ] LoRA trained on Qwen3-1.7B (Colab T4).
+- [ ] Merged + converted to GGUF, **published to a public HF repo**.
+- [ ] App's `MODEL_ZOO` points at that repo; it loads and answers.
+- [ ] (Nice) Mention the fine-tune in your demo + blog (`BLOG_DRAFT.md`).
+
+## Troubleshooting
+
+- **Colab OOM on T4:** keep `load_in_4bit=True`, batch size 2, max_seq_length
+  2048 (defaults here). Don't bump LoRA `r` above 32.
+- **`push_to_hub_gguf` slow/fails:** re-run the cell; conversion downloads
+  llama.cpp once. If it still fails, save the merged model with
+  `model.save_pretrained_merged(...)` and convert locally with
+  `llama.cpp/convert_hf_to_gguf.py` (also documented in Unsloth's README).
+- **Model loads but talks oddly:** too few pairs or too many epochs. 200+ pairs,
+  3 epochs is the sweet spot. Keep temperature low (the app uses 0.2 here).
+- **Space can't find the file:** the filename in `MODEL_ZOO` must match the repo
+  exactly (case-sensitive), and the repo must be public (or set HF_TOKEN).

chan_glue.py

@@ -1,70 +1,123 @@
 """
-chan_glue.py — wires the user's notebook-style modules together at runtime.
-
-The original chan_engine.py / chan_multilevel.py were written as notebook cells:
-chan_multilevel references `ChanAnalyzer` / `Signal` via a commented-out import.
-Because both files use `from __future__ import annotations` and resolve names at
-call time, we can simply inject the symbols into the module namespace — the Chan
-analysis logic itself is left 100% untouched.
-
-Also provides a small LRU-cached analyzer factory (the original chan_common.py
-was A-share specific and is not used in the US version).
+chan_enhance.py —— 缠论系统增强(查漏补缺) · 纯函数版(非Hook)
 """
 from __future__ import annotations
 
-import hashlib
-from collections import OrderedDict
-
-import pandas as pd
-
-import chan_engine
-import chan_multilevel
-
-# ── inject cross-module symbols (replaces the commented `from chan_engine import …`) ──
-chan_multilevel.ChanAnalyzer = chan_engine.ChanAnalyzer
-chan_multilevel.Signal = chan_engine.Signal
-chan_engine.PIVOT_MAX_EXTEND_SEGS = chan_engine.PIVOT_MAX_EXTEND_SEGS  # no-op, keeps linters calm
-
-# Re-exports for app code
-ChanAnalyzer = chan_engine.ChanAnalyzer
-Signal = chan_engine.Signal
-MultiLevelChan = chan_multilevel.MultiLevelChan
-MultiLevelSignal = chan_multilevel.MultiLevelSignal
-resample_weekly = chan_multilevel.resample_weekly
-resample_monthly = chan_multilevel.resample_monthly
-set_analyzer_factory = chan_multilevel.set_analyzer_factory
-
-# ── cached analyzer factory ──────────────────────────────────────────────
-_CACHE: "OrderedDict[str, chan_engine.ChanAnalyzer]" = OrderedDict()
-_CACHE_MAX = 64
-
-
-def _df_key(level: str, df: pd.DataFrame) -> str:
-    n = len(df)
-    if n == 0:
-        return f"{level}-empty"
-    last = str(df['date'].iloc[-1])
-    first = str(df['date'].iloc[0])
-    tail_close = float(df['close'].iloc[-1])
-    raw = f"{level}|{n}|{first}|{last}|{tail_close:.6f}"
-    return hashlib.md5(raw.encode()).hexdigest()
-
-
-def cached_analyzer(level: str, df: pd.DataFrame):
-    key = _df_key(level, df)
-    if key in _CACHE:
-        _CACHE.move_to_end(key)
-        return _CACHE[key]
-    an = chan_engine.ChanAnalyzer(df.reset_index(drop=True))
-    _CACHE[key] = an
-    while len(_CACHE) > _CACHE_MAX:
-        _CACHE.popitem(last=False)
-    return an
-
-
-def install():
-    """Install the cached analyzer factory into MultiLevelChan."""
-    set_analyzer_factory(cached_analyzer)
-
 
-install()
+W_REVERSAL = 1.0
+W_TRAP     = 0.85
+W_RELAY    = 0.6
+W_UNKNOWN  = 0.7
+
+# 第16课 六种基本走势 → 买法分类的中文名(配合英文 method 一起展示)
+METHOD_CN = {
+    'reversal': '反转式(下跌+盘整+上涨 / 趋势底背驰一买, 力度最强, 权重最高)',
+    'trap':     '陷阱式(下跌+上涨, 空头陷阱式二买, 中等力度)',
+    'relay':    '中继式(上涨+盘整+上涨, 三买, 趋势中继)',
+    'unknown':  '未分类',
+}
+
+WEAK_ARM_GAIN = 0.04
+WEAK_GIVEBACK = 0.22
+
+L92_EXIT_ENABLE      = False
+L92_EXIT_FLOATING_MAX = 0.015
+L92_ZD_BREAK_BUF      = 0.0
+
+
+def classify_buy_method(sig_kind, ml) -> str:
+    sig = ml.daily.signal if (ml and ml.daily) else None
+    dg = getattr(sig, 'diverge_grade', None) if sig is not None else None
+    if sig_kind == 'B1':
+        return 'reversal'
+    if sig_kind == 'B3':
+        return 'relay'
+    if sig_kind == 'B2':
+        # L37 背驰分辨: 只有"趋势背驰"(≥2个同向中枢后的背驰)才是标准的趋势转折,
+        # 对应第16课"下跌+盘整+上涨"反转式买法; 盘整背驰力度弱, 仍按陷阱式处理。
+        if (dg is not None and getattr(dg, 'grade', '') == 'STRONG'
+                and getattr(dg, 'is_trend_divergence', False)):
+            return 'reversal'
+        return 'trap'
+    return 'unknown'
+
+
+def l37_divergence_note(ml) -> str:
+    """L37 背驰分辨(区分背驰段构成): 趋势背驰=至少两个同向中枢后的背驰, 转折级别高;
+    盘整背驰=单中枢内的力度衰竭, 只保证回拉中枢, 不保证反转。"""
+    sig = ml.daily.signal if (ml and ml.daily) else None
+    dg = getattr(sig, 'diverge_grade', None) if sig is not None else None
+    if dg is None or getattr(dg, 'grade', 'NONE') == 'NONE':
+        return ''
+    if getattr(dg, 'is_trend_divergence', False):
+        return (f'第37课: 趋势背驰({getattr(dg, "n_trend_pivots", "?")}个同向中枢), '
+                f'转折至少回拉至最后一个中枢, 大概率反转 → 可按反转式买法重仓')
+    return ('第37课: 盘整背驰(背驰段内仅1个中枢), 只保证回拉中枢一次, '
+            '不保证趋势反转 → 轻仓短打, 回拉到中枢即考虑兑现')
+
+
+def buy_method_weight(sig_kind, ml):
+    method = classify_buy_method(sig_kind, ml)
+    wmap = {'reversal': W_REVERSAL, 'trap': W_TRAP, 'relay': W_RELAY, 'unknown': W_UNKNOWN}
+    return method, wmap.get(method, W_UNKNOWN)
+
+
+def recompute_evo(ml) -> str:
+    sig = ml.daily.signal if (ml and ml.daily) else None
+    if sig is not None:
+        evo = (sig.extras or {}).get('post_evolution', '')
+        if evo:
+            return evo
+    dv = ml.daily if ml else None
+    if dv is not None and dv.zd is not None and dv.zg is not None:
+        if ml.cur_price < dv.zd:
+            return 'case1_extend'
+    return ''
+
+
+def weak_evo_giveback(pos, ml, default_giveback):
+    evo = recompute_evo(ml) if ml is not None else (pos.get('post_evo') or '')
+    if evo == 'case1_extend':
+        return WEAK_ARM_GAIN, WEAK_GIVEBACK
+    return None, default_giveback
+
+
+def l92_should_exit(pos, ml, close_p, floating):
+    if not L92_EXIT_ENABLE or ml is None or ml.daily is None:
+        return None
+    if floating > L92_EXIT_FLOATING_MAX:
+        return None
+    zd = ml.daily.zd
+    if zd is None:
+        return None
+    if close_p < zd * (1 - L92_ZD_BREAK_BUF) and close_p > pos.get('stop_px', 0):
+        return ('L92_EXIT',
+                f'第92课中枢震荡监视器: 现价{close_p:.3f}已破日线中枢下沿ZD{zd:.3f}'
+                f'(向下变盘) 且浮盈仅{floating:+.1%} → 提前减仓, 不等磨到结构止损')
+    return None
+
+
+def predict_enhance(ml) -> dict:
+    out = {}
+    if ml is None or ml.daily is None:
+        return out
+    sig_kind = ml.final_kind or ''
+    if sig_kind in ('B1', 'B2', 'B3'):
+        method, weight = buy_method_weight(sig_kind, ml)
+        out['buy_method'] = method
+        out['buy_method_cn'] = METHOD_CN.get(method, method)
+        out['suggest_weight'] = weight
+        out['l16_note'] = f'第16课: 买法分类={method}〔{METHOD_CN.get(method, method)}〕, 建议仓位权重{weight:.2f}'
+    l37 = l37_divergence_note(ml)
+    if l37:
+        out['l37_note'] = l37
+    evo = recompute_evo(ml)
+    if evo:
+        out['post_evolution'] = evo
+        out['evo_hint'] = ('第29课: 最弱形态(反弹/回落未回中枢), 持有宜收紧移动止盈, 尽快兑现'
+                           if evo == 'case1_extend'
+                           else '第29课: 形态较强(回到中枢), 可持有等三买/趋势延续')
+    zd = ml.daily.zd
+    if zd is not None and ml.cur_price < zd:
+        out['l92_warn'] = f'第92课: 现价{ml.cur_price:.3f}已在日线中枢下沿ZD{zd:.3f}下方 → 向下变盘预警'
+    return out

chan_multilevel.py

@@ -1,884 +1,70 @@
-"""缠论多级别联立分析
-
-【本版改动 · 卖点区间套修复 (第24/44课)】
+"""
+chan_glue.py — wires the user's notebook-style modules together at runtime.
 
-旧逻辑的两个病根(对应回测中"卖在低位"与"提前卖飞"):
-  ① 卖在低位: 严格模式要求"30分钟出现三类卖点S3"才印证日线卖点。但S3是
-     次级别已经跌破其中枢、反抽不回的【转折确认点】—— 它出现时价格早已
-     离开顶部一大截。拿S3当卖出闸门, 等于规定"必须先跌下来才准卖", 结构上
-     注定卖不到一卖的高位, 最后落在二卖/三卖的低位。
-  ② 提前卖飞: 宽松模式下"30m最后一笔向下"即印证。第24课明示: 小级别背驰
-     (更别说仅仅一笔向下)未必引发大级别转折。任何一次30m级别的正常回调都
-     可能把日线WEAK级别的卖点"确认"掉 → 还没到顶就出局。
-     而且旧代码对次级别卖点【不做时间嵌套检查】: 30m在日线C段开始之前的
-     旧背驰也会被当作印证 —— "区间套"三个字里的"区间"丢了。
+The original chan_engine.py / chan_multilevel.py were written as notebook cells:
+chan_multilevel references `ChanAnalyzer` / `Signal` via a commented-out import.
+Because both files use `from __future__ import annotations` and resolve names at
+call time, we can simply inject the symbols into the module namespace — the Chan
+analysis logic itself is left 100% untouched.
 
-新逻辑 (CFG['sell_nested_interval']=True):
-  第44课区间套的本义: 大级别进入背驰段后, 在该背驰段【时间区间内】找次级别
-  的背驰段, 再在其中找次次级别的背驰段, 逐级收敛到精确转折点。
-  对日线S1/S2, 次级别印证按优先级:
-    ① 嵌套顶背驰: 30m出现S1, 且其C段顶部落在日线背驰段(最后中枢之后→当下)
-       的时间窗口内、顶部价位贴近日线C段高点 → 精确卖点, 卖在高位区。
-    ② 次级别转折确认: 30m出现S3, 或30m末笔已跌破其最近中枢下沿ZD(第92课
-       向下变盘) → 转折已确认, 偏晚但必须卖。
-    ③ 两者皆无 → 次级别动能未竭, 第24课: 顶未到 → 【不卖】(防卖飞),
-       返回 action=HOLD + sell_armed=True 进入"区间套布防"状态:
-       回测端持仓转入布防, 之后任一条件触发(嵌套背驰出现/破30m中枢ZD/
-       较布防峰值回落超阈值)即离场 —— 既不提前卖飞, 也不一路坐滑梯到三卖。
-  S3(日线三卖)不走嵌套背驰: 三卖本身就是转折确认型卖点, 维持原确认逻辑。
+Also provides a small LRU-cached analyzer factory (the original chan_common.py
+was A-share specific and is not used in the US version).
 """
 from __future__ import annotations
-from dataclasses import dataclass, field
-from typing import Optional
-import numpy as np
-import pandas as pd
-
-# from chan_engine import ChanAnalyzer, Signal
-
-
-# ── 卖点区间套参数 (第24/44课) ──
-NESTED_TOP_PRICE_TOL   = 0.03   # 嵌套顶背驰的顶部须贴近父级C段高点(3%以内), 否则属上一波动能
-NESTED_WINDOW_PAD_DAYS = 3      # 时间嵌套窗口的左侧容差(日)
-SELL_ARM_PEAK_DROP     = 0.04   # 布防后较峰值价回落≥4% → 顶部确认离场(回测端使用)
-SELL_ARM_DISARM_BREAK  = 0.03   # 布防后强势创新高超3%且卖点消失 → 背驰被消化, 撤防(第26课)
-
-
-def _default_make_analyzer(level, df):
-    return ChanAnalyzer(df.reset_index(drop=True))
-
-_MAKE_ANALYZER = _default_make_analyzer
-
-def set_analyzer_factory(fn):
-    global _MAKE_ANALYZER
-    _MAKE_ANALYZER = fn or _default_make_analyzer
-
-
-def resample_weekly(df_daily: pd.DataFrame) -> pd.DataFrame:
-    if df_daily.empty:
-        return df_daily.copy()
-    d = df_daily.copy()
-    d['date'] = pd.to_datetime(d['date'])
-    d = d.sort_values('date').reset_index(drop=True)
-    wk_period = d['date'].dt.to_period('W-FRI')
-    agg = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'}
-    if 'volume' in d.columns: agg['volume'] = 'sum'
-    if 'amount' in d.columns: agg['amount'] = 'sum'
-    g = d.groupby(wk_period, sort=True)
-    wk = g.agg(agg)
-    last_real = g['date'].max()
-    wk = wk.dropna(subset=['open', 'high', 'low', 'close']).reset_index(drop=True)
-    wk['date'] = list(last_real.values)
-    return wk
-
-
-def resample_monthly(df_daily: pd.DataFrame) -> pd.DataFrame:
-    if df_daily.empty:
-        return df_daily.copy()
-    d = df_daily.copy()
-    d['date'] = pd.to_datetime(d['date'])
-    d = d.sort_values('date').reset_index(drop=True)
-    mp = d['date'].dt.to_period('M')
-    agg = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'}
-    if 'volume' in d.columns: agg['volume'] = 'sum'
-    if 'amount' in d.columns: agg['amount'] = 'sum'
-    g = d.groupby(mp, sort=True)
-    mo = g.agg(agg)
-    last_real = g['date'].max()
-    mo = mo.dropna(subset=['open', 'high', 'low', 'close']).reset_index(drop=True)
-    mo['date'] = list(last_real.values)
-    return mo
-
-
-@dataclass
-class LevelView:
-    level: str
-    trend: str
-    n_pivots: int
-    n_bis: int
-    last_bi_dir: str
-    signal: Optional[Signal]
-    zg: Optional[float]
-    zd: Optional[float]
-    dif: Optional[float]
-    last_date: Optional[pd.Timestamp]
-    diagnostics: dict = field(default_factory=dict)
-
-
-@dataclass
-class MultiLevelSignal:
-    code: str
-    analysis_date: pd.Timestamp
-    weekly: LevelView
-    daily: LevelView
-    m30: Optional[LevelView]
-    action: str
-    confidence: str
-    final_kind: str
-    cur_price: float
-    chain: list = field(default_factory=list)
-    blocked_reason: str = ''
-    note: str = ''
-    confidence_reasons: list = field(default_factory=list)
-    diagnostics: dict = field(default_factory=dict)
-    monthly: Optional[LevelView] = None
-    # ── 卖点区间套布防 (第44课) ──
-    sell_armed: bool = False              # 日线S1/S2背驰已现、次级别动能未竭 → 持仓布防
-    arm_zd: Optional[float] = None        # 布防线①: 30m最近中枢下沿ZD(跌破即次级别转折确认)
-    arm_high: Optional[float] = None      # 父级背驰段C段高点(用于"新高消化背驰"撤防判定)
-
-    def explain(self) -> str:
-        lines = [f'  [{self.code}] {self.analysis_date.strftime("%Y-%m-%d")}  ¥{self.cur_price:.3f}']
-        lines.append(f'  最终: {self.action}  置信度={self.confidence}'
-                     + (f'  信号={self.final_kind}' if self.final_kind else ''))
-        lines.append('  ── 逐级裁决链 ──')
-        for lvl, concl, src in self.chain:
-            lines.append(f'    [{lvl:<7s}] {concl}')
-            if src:
-                lines.append(f'              └ 依据: {src}')
-        if self.blocked_reason:
-            lines.append(f'  ⚠ 拦截: {self.blocked_reason}')
-        if self.note:
-            lines.append(f'  说明: {self.note}')
-        if self.confidence_reasons:
-            lines.append('  置信度降档明细:')
-            for i, r in enumerate(self.confidence_reasons, 1):
-                lines.append(f'    {i}. {r}')
-        if self.diagnostics:
-            lines.append('  日线买卖点逐项诊断:')
-            for k in ('B1', 'B2', 'B3', 'S1', 'S2', 'S3'):
-                rows = self.diagnostics.get(k) or []
-                if not rows:
-                    continue
-                ok = all(str(x).startswith('✓') for x in rows)
-                lines.append(f'    {k} {"满足" if ok else "不满足"}')
-                for row in rows:
-                    lines.append(f'      {row}')
-        else:
-            lines.append('  日线买卖点逐项诊断: 未生成')
-        return '\n'.join(lines)
-
-
-class MultiLevelChan:
-    CFG = {
-        'use_monthly_gate': True,
-        'monthly_ma_filter': False,
-        'require_sublevel_sell_confirm': False,
-        'mode': 'short',
-        'zhongyin_block_buy': False,
-        'expose_m30_nosignal': False,
-        'require_60m_buy_confirm': False,  # L44区间套: 60m为日线直接次级别, 买点须经其印证
-        # L50 MACD级别选择: 看MACD要选"黄白线和柱子清晰"的级别。30m判据模糊而60m
-        # 清晰且60m已印证时, 采信60m的印证结论(避免被模糊级别的噪声误杀好买点)。
-        'l50_macd_level_select': False,
-        # ── 卖点区间套(第24/44课): 嵌套顶背驰精确定位 + 布防防卖飞 ──
-        #   True  = S1/S2用"时间嵌套的次级别顶背驰"定位高点; 未嵌套确认时不卖而布防
-        #   False = 维持旧逻辑(30m要S3 / 宽松末笔向下即卖)
-        'sell_nested_interval': True,
-        # L24: 周线上涨+日线盘整背驰的S1/S2 → 不全清, 转布防短差(撤防可骑回趋势)
-        'l24_weekly_uptrend_arm': False,  # 实测净亏(好卖点也被转布防), 默认关
-    }
-
-    # ── 速度优化: 次级别K线只取最近N根做缠论分解 ──
-    # 第17/44课: 次级别(60m/30m/15m/5m/1m)的职责是"印证当下转折", 其买卖点
-    # 只取决于最近的笔/线段/中枢结构; 几年前的分钟级历史对当下印证毫无贡献,
-    # 却让每个回测日都对上万根分钟K线重做合并/分型/笔/段, 是最大的耗时点。
-    # 截断只作用于次级别印证, 日/周/月线仍用全history(年线、月线大方向不受影响)。
-    SUB_TAIL = {'15m': 4000, '5m': 4800, '1m': 2000}
-
-    def __init__(self, df_daily, df_weekly=None, df_monthly=None, df_30m=None, df_60m=None,
-                 df_15m=None, df_5m=None, df_1m=None, code='', strict=True):
-        self.code = code
-        self.strict = strict
-        self.df_daily = df_daily.reset_index(drop=True) if not df_daily.empty else df_daily
-        self.df_30m = df_30m.reset_index(drop=True) if df_30m is not None and not df_30m.empty else None
-        self.df_60m = df_60m.reset_index(drop=True) if df_60m is not None and not df_60m.empty else None
-        self.df_15m = df_15m.reset_index(drop=True) if df_15m is not None and not df_15m.empty else None
-        self.df_5m = df_5m.reset_index(drop=True) if df_5m is not None and not df_5m.empty else None
-        self.df_1m = df_1m.reset_index(drop=True) if df_1m is not None and not df_1m.empty else None
-        self.df_weekly = df_weekly.reset_index(drop=True) if df_weekly is not None and not df_weekly.empty else None
-        self.df_monthly = df_monthly.reset_index(drop=True) if df_monthly is not None and not df_monthly.empty else None
-
-    @staticmethod
-    def _make_view(level, df, an=None, diagnose=True):
-        if an is None:
-            if df is None or len(df) < 30:
-                return None
-            try:
-                an = _MAKE_ANALYZER(level, df)
-            except Exception:
-                return None
-            if an is None:
-                return None
-        sig = an.get_signal()
-        zg = an.pivots[-1].zg if an.n_pivots > 0 else None
-        zd = an.pivots[-1].zd if an.n_pivots > 0 else None
-        return LevelView(level=level, trend=an.trend, n_pivots=an.n_pivots, n_bis=an.n_bis,
-                         last_bi_dir=an.bis[-1].direction if an.n_bis else '', signal=sig,
-                         zg=zg, zd=zd, dif=float(an.dif.iloc[-1]) if len(an.dif) else None,
-                         last_date=pd.Timestamp(df['date'].iloc[-1]),
-                         diagnostics=(an.diagnose() if diagnose else {}))
-
-    # ──────────────────────────────────────────────────────────────────
-    #  卖点区间套核心: 时间嵌套的次级别顶背驰 (第44课)
-    # ──────────────────────────────────────────────────────────────────
-    @staticmethod
-    def _parent_sell_window(parent_sig):
-        """父级(日线)背驰段的时间窗口与顶部价。
-        S1: 背驰段C段 = 最后中枢结束 → 当下; 顶 = C段高点 c_high。
-        S2: 窗口 = 一卖出现 → 当下(反抽段); 顶 = 一卖高点 prev_high。"""
-        ex = (parent_sig.extras or {}) if parent_sig is not None else {}
-        if parent_sig is None:
-            return '', '', None
-        if parent_sig.kind == 'S1':
-            w_start = ex.get('pivot_end_date') or ex.get('a_high_date') or ''
-            top = ex.get('c_high')
-        else:  # S2
-            w_start = ex.get('s1_date') or ex.get('prev_high_date') or ''
-            top = ex.get('prev_high')
-        w_end = ex.get('price_date') or ''
-        return w_start, w_end, top
-
-    def _nested_sell_check(self, sub_an, parent_sig, lvl_name, parent_kind):
-        """第44课区间套(卖点版): 在父级背驰段时间窗口内找次级别的嵌套顶背驰。
-        优先级: ①嵌套S1(精确高点, 卖在高位)
-               ②S3 / 末笔破次级别中枢ZD(转折已确认, 偏晚但必卖)
-               ③都没有 → 第24课: 次级别动能未竭, 顶未到 → 不卖(防卖飞), 转布防。
-        返回 (confirmed: bool, note: str)。"""
-        w_start, w_end, parent_top = self._parent_sell_window(parent_sig)
-
-        def in_window(dstr):
-            if not w_start or not dstr:
-                return True   # 信息不足时不因窗口否决(保守放行, 由价位贴近度把关)
-            try:
-                d = pd.Timestamp(dstr)
-                lo = pd.Timestamp(w_start) - pd.Timedelta(days=NESTED_WINDOW_PAD_DAYS)
-                hi = (pd.Timestamp(w_end) if w_end else d) + pd.Timedelta(days=1)
-                return lo <= d <= hi
-            except Exception:
-                return True
-
-        rejected = ''
-        # ① 嵌套顶背驰 S1 —— 区间套的本体: 背驰段中套背驰段
-        s1 = sub_an.detect_s1()
-        if s1 is not None:
-            ex2 = s1.extras or {}
-            td = ex2.get('c_high_date', '')
-            tp = ex2.get('c_high')
-            near_top = (parent_top is None or tp is None
-                        or tp >= parent_top * (1 - NESTED_TOP_PRICE_TOL))
-            if in_window(td) and near_top:
-                ptxt = f'(顶¥{tp:.3f}@{td})' if tp else ''
-                return True, (f'{lvl_name}嵌套顶背驰S1{ptxt}落在日线{parent_kind}背驰段窗口内 '
-                              f'—— 第44课区间套: 背驰段中套背驰段, 精确定位顶部, 卖在高位区')
-            rejected = (f'{lvl_name}虽有S1但【不嵌套】(顶@{td or "?"}不在父级背驰段'
-                        f'[{w_start}~{w_end}]窗口内, 或低于父级顶{NESTED_TOP_PRICE_TOL:.0%}以上)'
-                        f' → 属上一波的动能衰竭, 不作本次印证(防卖飞); ')
-        # ①b 父级为S2时, 次级别S2(一卖后反抽确认)也可嵌套印证
-        if parent_kind == 'S2':
-            s2 = sub_an.detect_s2()
-            if s2 is not None:
-                ex2 = s2.extras or {}
-                td = ex2.get('cur_high_date', '')
-                if in_window(td):
-                    return True, (f'{lvl_name}嵌套二卖S2(反抽顶@{td})落在日线S2窗口内 '
-                                  f'—— 第14课: 大级别二卖由次级别相应卖点定位')
-        # ② 转折已确认型: S3 / 末笔已破次级别中枢下沿
-        s3 = sub_an.detect_s3()
-        if s3 is not None:
-            return True, (f'{lvl_name}出现三类卖点S3 —— 第44课: 最后一个次级别中枢出现三卖, '
-                          f'转折已确认(偏晚, 但必须卖)')
-        try:
-            osc = sub_an.zhongshu_oscillation_monitor()
-            if osc.get('alert') and osc.get('direction') == 'down':
-                return True, (f'{lvl_name}末笔已离开其最近中枢下沿(第92课向下变盘) '
-                              f'—— 次级别转折确认, 高位区兑现')
-        except Exception:
-            pass
-        # ③ 次级别上冲动能已竭的当下确认: 末笔向下 + MACD柱已翻负(黄白线收敛下行)。
-        #    第24/44课: 区间套要的是"次级别走势的当下转折"; 末笔转下且红柱消失,
-        #    说明次级别这一冲已经结束 —— 此时日线背驰卖点立即执行, 不再等更深的破位
-        #    (这正是旧版"等30m三卖"卖在低位的病根)。只有次级别仍在上冲(末笔向上/
-        #    红柱未消)时才转入布防, 防的才是真正的"卖飞"。
-        try:
-            last_bi = sub_an.bis[-1] if sub_an.n_bis else None
-            bar_now = float(sub_an.macd_bar.iloc[-1]) if len(sub_an.macd_bar) else 0.0
-            if last_bi is not None and last_bi.direction == 'down' and bar_now < 0:
-                return True, (f'{lvl_name}末笔向下且MACD柱已翻负 —— 第24课: 次级别上冲动能已竭, '
-                              f'当下转折确认, 日线背驰卖点立即执行(不等{lvl_name}跌出三卖)')
-        except Exception:
-            pass
-        return False, (rejected + f'{lvl_name}动能未竭(末笔仍向上/MACD红柱未消): 无嵌套顶背驰/无S3 '
-                       f'→ 第24课: 次级别未转折, 大级别顶未到 → 暂不卖(防卖飞), 转入区间套布防')
-
-    def _confirm_30m_for_buy(self, m30, parent_kind=''):
-        if m30 is None or m30.n_bis < 5:
-            return False, '30分钟笔数不足(<5), 无法做次级别精确印证'
-        if parent_kind == 'B2':
-            if m30.detect_b1() is not None:
-                return True, '30分钟出现一类买点B1 —— 第14课: 大级别第二类买点由次一级别相应走势的一类买点构成'
-            if m30.detect_b2() is not None:
-                return True, '30分钟出现二类买点B2 —— 次级别一买后的回试确认, 属延后确认, 精度低于B1'
-            return False, '30分钟未出现B1/B2 —— 大级别二买缺少次级别买点确认'
-        for fn, name in ((m30.detect_b1,'B1'),(m30.detect_b3,'B3'),(m30.detect_b2,'B2')):
-            if fn() is not None:
-                return True, f'30分钟出现标准买点{name} —— 第44课: 次级别走势确认转折, 日线买点成立'
-        if self.strict:
-            return False, '30分钟未出现任何标准买点(B1/B2/B3) —— 严格模式: 第44课次级别印证未满足, 日线买点暂不成立'
-        if m30.bis[-1].direction == 'up':
-            return True, '30分钟最后一笔向上, 次级别已启动(宽松确认)'
-        return False, '30分钟最后一笔仍向下且无买点, 次级别未确认转折'
-
-    def _confirm_30m_for_sell(self, m30, parent_kind='', parent_sig=None):
-        if m30 is None or m30.n_bis < 5:
-            return False, '30分钟笔数不足(<5), 无法做次级别精确印证'
-        # ── 新: 卖点区间套(第24/44课) —— S1/S2用嵌套顶背驰定位高点 ──
-        if (self.CFG.get('sell_nested_interval') and parent_sig is not None
-                and parent_kind in ('S1', 'S2')):
-            return self._nested_sell_check(m30, parent_sig, '30分钟', parent_kind)
-        # ── 旧逻辑(S3父级 / 开关关闭时) ──
-        if parent_kind == 'S2':
-            if m30.detect_s1() is not None:
-                return True, '30分钟出现一类卖点S1 —— 大级别第二类卖点由次一级别相应走势的一类卖点精确定位'
-            if m30.detect_s2() is not None:
-                return True, '30分钟出现二类卖点S2 —— 次级别一卖后的反抽确认, 属延后确认, 精度低于S1'
-            return False, '30分钟未出现S1/S2 —— 大级别二卖缺少次级别卖点确认'
-        if m30.detect_s3() is not None:
-            return True, '30分钟出现三类卖点S3 —— 严格满足第44课"小背驰-大转折定理"的必要条件(最后一个次级别中枢出现三卖)'
-        if self.strict:
-            return False, '30分钟未出现三类卖点S3 —— 严格模式: 第44课明确要求"最后一个次级别中枢出现三卖", 必要条件未满足, 日线卖点不���成大级别转折'
-        if m30.detect_s1() is not None:
-            return True, '30分钟出现一类卖点(顶背驰), 次级别转折确认(宽松)'
-        if m30.bis[-1].direction == 'down':
-            return True, '30分钟最后一笔向下, 次级别已转弱(宽松确认)'
-        return False, '30分钟未出现卖点且最后一笔仍向上, 次级别未确认转折'
 
-    def _confirm_finer(self, an, is_buy, lvl_name, parent_name, parent_kind='', parent_sig=None):
-        if an is None or an.n_bis < 5:
-            return False, f'{lvl_name}笔数不足(<5), 无法做次级别精确印证'
-        if is_buy:
-            if parent_kind == 'B2':
-                if an.detect_b1() is not None:
-                    return True, f'{lvl_name}出现B1 —— 第14课: {parent_name}二买由次一级别一买构成'
-                if an.detect_b2() is not None:
-                    return True, f'{lvl_name}出现B2 —— 次级别一买后的回试确认, 属延后确认, 精度低于B1'
-                return False, f'{lvl_name}未出现B1/B2 —— {parent_name}二买缺少次级别买点确认'
-            for fn, name in ((an.detect_b1,'B1'),(an.detect_b3,'B3'),(an.detect_b2,'B2')):
-                if fn() is not None:
-                    return True, f'{lvl_name}出现标准买点{name} —— 第44课逐级处理: {lvl_name}走势确认{parent_name}的转折'
-            if self.strict:
-                return False, f'{lvl_name}未出现标准买点(B1/B2/B3) —— 严格模式: 末级印证未满足, 信号降级'
-            if an.bis[-1].direction == 'up':
-                return True, f'{lvl_name}最后一笔向上, 次级别已启动(宽松确认)'
-            return False, f'{lvl_name}最后一笔仍向下且无买点, 末级未确认, 信号降级'
-        else:
-            # ── 新: 卖点区间套向更细级别逐级传递(同一父级背驰段窗口) ──
-            if (self.CFG.get('sell_nested_interval') and parent_sig is not None
-                    and parent_kind in ('S1', 'S2')):
-                ok, note = self._nested_sell_check(an, parent_sig, lvl_name, parent_kind)
-                if ok:
-                    return True, note + f' —— 第44课逐级处理: {lvl_name}确认{parent_name}的转折'
-                return False, note
-            if parent_kind == 'S2':
-                if an.detect_s1() is not None:
-                    return True, f'{lvl_name}出现S1 —— {parent_name}二卖由次一级别一卖精确定位'
-                if an.detect_s2() is not None:
-                    return True, f'{lvl_name}出现S2 —— 次级别一卖后的反抽确认, 属延后确认, 精度低于S1'
-                return False, f'{lvl_name}未出现S1/S2 —— {parent_name}二卖缺少次级别卖点确认'
-            if an.detect_s3() is not None:
-                return True, f'{lvl_name}出现三类卖点S3 —— 第44课逐级处理: {lvl_name}走势确认{parent_name}的转折'
-            if self.strict:
-                return False, f'{lvl_name}未出现三类卖点S3 —— 严格模式: 末级印证未满足, 信号降级'
-            if an.detect_s1() is not None:
-                return True, f'{lvl_name}出现一类卖点(顶背驰), 次级别转折确认(宽松)'
-            if an.bis[-1].direction == 'down':
-                return True, f'{lvl_name}最后一笔向下, 次级别已转弱(宽松确认)'
-            return False, f'{lvl_name}未出现卖点且最后一笔仍向上, 末级未确认, 信号降级'
+import hashlib
+from collections import OrderedDict
 
-    def _confirm_5m(self, m5, is_buy, parent_kind='', parent_sig=None):
-        return self._confirm_finer(m5, is_buy, '5分钟', '30分钟', parent_kind, parent_sig)
-
-    def _confirm_1m(self, m1, is_buy, parent_kind='', parent_sig=None):
-        return self._confirm_finer(m1, is_buy, '1分钟', '5分钟', parent_kind, parent_sig)
-
-    def analyze(self, analysis_date=None, positions=None):
-        if self.df_daily.empty or len(self.df_daily) < 30:
-            return None
-        df_d = self.df_daily
-        if analysis_date is not None:
-            analysis_date = pd.Timestamp(analysis_date)
-            df_d = df_d[df_d['date'] <= analysis_date].reset_index(drop=True)
-        if len(df_d) < 30:
-            return None
-        last_date = pd.Timestamp(df_d['date'].iloc[-1])
-        cur_price = float(df_d['close'].iloc[-1])
-
-        if self.df_weekly is not None:
-            df_w = self.df_weekly[self.df_weekly['date'] <= last_date].reset_index(drop=True)
-        else:
-            df_w = resample_weekly(df_d)
-        if self.df_monthly is not None:
-            df_mo = self.df_monthly[self.df_monthly['date'] <= last_date].reset_index(drop=True)
-        else:
-            df_mo = resample_monthly(df_d)
-        _tail = self.SUB_TAIL or {}
-        def _cut_sub(df, lvl):
-            if df is None:
-                return None
-            sub = df[df['date'] <= last_date + pd.Timedelta(days=1)]
-            n = _tail.get(lvl, 0)
-            if n and len(sub) > n:
-                sub = sub.tail(n)
-            return sub.reset_index(drop=True)
-        df_6  = _cut_sub(self.df_60m, '60m')
-        df_m  = _cut_sub(self.df_30m, '30m')
-        df_15 = _cut_sub(self.df_15m, '15m')
-        df_5  = _cut_sub(self.df_5m, '5m')
-        df_1  = _cut_sub(self.df_1m, '1m')
-
-        wv = self._make_view('weekly', df_w, diagnose=False)
-        mov = self._make_view('monthly', df_mo, diagnose=False) if self.CFG.get('use_monthly_gate') else None
-        daily_an = None
-        try:
-            if len(df_d) >= 30:
-                daily_an = _MAKE_ANALYZER('daily', df_d)
-        except Exception:
-            daily_an = None
-        dv = self._make_view('daily', df_d, an=daily_an, diagnose=False) if daily_an is not None \
-            else self._make_view('daily', df_d, diagnose=False)
-
-        m60_an = None
-        m60_built = False
-        def _get_m60():
-            nonlocal m60_an, m60_built
-            if not m60_built:
-                m60_built = True
-                if df_6 is not None and len(df_6) >= 30:
-                    try: m60_an = _MAKE_ANALYZER('60m', df_6)
-                    except Exception: m60_an = None
-            return m60_an
-
-        m30_an = None
-        m30_built = False
-        def _get_m30():
-            nonlocal m30_an, m30_built
-            if not m30_built:
-                m30_built = True
-                if df_m is not None and len(df_m) >= 30:
-                    try: m30_an = _MAKE_ANALYZER('30m', df_m)
-                    except Exception: m30_an = None
-            return m30_an
-
-        def _mv():
-            an = _get_m30()
-            if an is None:
-                return None
-            return self._make_view('30m', df_m, an=an, diagnose=False)
-
-        diagnostics = daily_an.diagnose() if daily_an is not None else {}
-
-        chain = []
-        yearline_dir = 'unknown'; yearline_val = None
-        if len(df_d) >= 60:
-            _n = min(250, len(df_d))
-            yearline_val = float(df_d['close'].tail(_n).mean()) if _n >= 20 else None
-            _ma = df_d['close'].rolling(min(250, len(df_d)), min_periods=20).mean()
-            if len(_ma) and not pd.isna(_ma.iloc[-1]):
-                yearline_val = float(_ma.iloc[-1])
-        if yearline_val is not None:
-            above = cur_price >= yearline_val
-            yearline_dir = 'above' if above else 'below'
-            ytxt = (f'现价¥{cur_price:.3f} {"≥" if above else "<"} 年线MA250 ¥{yearline_val:.3f} '
-                    f'→ {"站上年线, 长线可做多" if above else "年线下方, 第106课不做多(只看反弹/卖点)"}')
-            chain.append(('年线', ytxt,
-                          '第7/106课: 年线(MA250)是长线生命线; 站上才考虑做多, 跌破年线长线转空'))
-        else:
-            chain.append(('年线', '日线历史不足, 年线MA250 暂不可用', '第7/106课'))
-
-        monthly_dir = 'unknown'
-        if mov is not None:
-            monthly_dir = mov.trend
-            chain.append(('monthly', f'{monthly_dir}  (月线定大方向: L69 月线看最实质大方向)',
-                          '第69/108课: 月线分型/笔/线段确定中期大方向; 底部=第一类买点到中枢首次走出三买前'))
-            if self.CFG.get('monthly_ma_filter') and len(df_mo) >= 5:
-                ma5_m = float(df_mo['close'].tail(5).mean())
-                if cur_price < ma5_m and monthly_dir != 'down_trend':
-                    monthly_dir = 'down_trend'
-                    chain.append(('monthly', f'价({cur_price:.2f})在5月线({ma5_m:.2f})下 → 大方向按偏空处理',
-                                  '第106课: 5月线是长线的关键, 牛市第一轮调整不跌破5月线'))
-
-        if wv is None:
-            chain.append(('weekly', '周线数据不足(<30根), 方向未知', ''))
-            weekly_dir = 'unknown'
-        else:
-            weekly_dir = wv.trend
-            dir_txt = {'up_trend':'上涨趋势 → 日线只接受【买点】','down_trend':'下跌趋势 → 日线只接受【卖点】/观望',
-                       'consolidation':'盘整 → 日线买卖点都看,但降级'}.get(weekly_dir, weekly_dir)
-            chain.append(('weekly', f'{weekly_dir}  {dir_txt}',
-                          '第43课: 大级别走势类型限定小级别的操作方向; 不允许"上涨+上涨""下跌+下跌"'))
-
-        if dv is None:
-            return None
-        daily_sig = dv.signal
-        if daily_sig is None:
-            chain.append(('daily', f'{dv.trend}, 无买卖点信号', ''))
-        else:
-            chain.append(('daily', f'出现 {daily_sig.kind}: {daily_sig.reason[:400]}', '第21课: 买卖点完备性定理'))
-
-        if daily_sig is None:
-            action, conf, note = self._no_signal_decision(wv, dv, cur_price)
-            m30_ns = _mv() if self.CFG.get('expose_m30_nosignal') else None
-            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=m30_ns,
-                                    action=action, confidence=conf, final_kind='', cur_price=cur_price, chain=chain, note=note,
-                                    diagnostics=diagnostics, monthly=mov)
-
-        is_buy = daily_sig.kind in ('B1','B2','B3')
-        is_sell = daily_sig.kind in ('S1','S2','S3')
-
-        if self.CFG.get('zhongyin_block_buy') and is_buy and daily_sig.kind != 'B3' and daily_an is not None:
-            zy = daily_an.in_zhongyin()
-            if zy.get('in_zhongyin'):
-                chain.append(('中阴', f'第88-90课: 当前处中阴(方向未定+BOLL收口) → 暂不开新仓: {zy["reason"][:120]}', '第89课: 中阴阶段方向未定, 不宜开新仓'))
-                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=None,
-                                        action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
-                                        chain=chain, blocked_reason='中阴方向未定', note='中阴阶段暂不开仓(L88-90)',
-                                        diagnostics=diagnostics, monthly=mov)
-
-        if positions:
-            fail_kinds = []
-            for bk, pos in positions.items():
-                stop = pos.get('stop_px')
-                if stop is not None and cur_price < stop:
-                    fail_kinds.append(bk)
-            if fail_kinds:
-                chain.append(('证伪', f'持仓{"/".join(fail_kinds)}跌破建仓日锁定止损线 → 清仓', '第13/20课: 买点结构被破坏即证伪'))
-                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                        action='SELL', confidence='HIGH', final_kind='STOP', cur_price=cur_price,
-                                        chain=chain, note='结构证伪止损, 次日清仓',
-                                        diagnostics=diagnostics, monthly=mov)
-
-        blocked = ''
-        downgrade = False
-        if weekly_dir == 'up_trend' and is_sell:
-            downgrade = True
-            chain.append(('联立','周线上涨 + 日线卖点 → 第44课: 小级别(日线)顶背驰未必引发大级别(周线)转折, 卖点降级为"减仓/短差"',
-                          '第24课: 日线背驰除非周线同时背驰,否则不制造周线大顶'))
-        elif weekly_dir == 'down_trend' and is_buy:
-            if daily_sig.kind in ('B1','B2'):
-                chain.append(('联立','周线下跌 + 日线一/二买 → 下跌趋势的转折尝试, 必须由30分钟严格印证','第29课: 下跌的转折=上涨或盘整, 由背驰导致'))
-            else:
-                blocked = '周线下跌趋势中出现日线三买 —— 第43课: 三买属上涨结构, 与周线下跌矛盾, 大概率是下跌中继的假三买'
-        elif monthly_dir == 'down_trend' and is_buy and daily_sig.kind == 'B3' and not blocked:
-            blocked = '月线下跌大方向中出现日线三买 —— 第43/69课: 三买属上涨结构, 与月线大方向矛盾, 大概率假三买'
-        elif weekly_dir == 'up_trend' and is_buy:
-            chain.append(('联立','周线上涨 + 日线买点 → 方向一致, 进入30分钟精确定位','第43课: 大小级别同向, 操作最顺'))
-        elif weekly_dir == 'down_trend' and is_sell:
-            chain.append(('联立','周线下跌 + 日线卖点 → 方向一致, 趋势性卖出','第43课: 大小级别同向'))
-        elif weekly_dir == 'consolidation':
-            downgrade = True
-            chain.append(('联立','周线盘整 → 日线信号有效但降级(盘整中买卖点力度弱)','第29课: 盘整中的转折力度弱于趋势'))
-
-        # ── 周线二买 → 日线一买精确定位锚 (第14/17课) ──
-        if is_buy and wv is not None and wv.signal is not None \
-                and getattr(wv.signal, 'kind', '') == 'B2':
-            _dex = (daily_sig.extras or {}) if daily_sig is not None else {}
-            _anchor = _dex.get('b1_price') or _dex.get('cur_low')
-            if _anchor:
-                daily_sig.extras = dict(_dex)
-                daily_sig.extras['weekly_b2_daily_b1_anchor'] = float(_anchor)
-                chain.append(('联立',
-                              f'✓ 周线二买 + 日线买点共振: 周线B2由日线一买构成(第14课), '
-                              f'定位锚=日线一买位¥{float(_anchor):.3f}, 跌破该锚则周线二买证伪',
-                              '第17课: 大级别买点的精确定位要落到次级别的具体买点价位'))
-            else:
-                chain.append(('联立',
-                              '周线二买成立但日线尚无可定位的一买锚 → 等日线给出一买价位再重仓',
-                              '第14课'))
-
-        if blocked:
-            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                    action='WATCH', confidence='NONE', final_kind=daily_sig.kind, cur_price=cur_price,
-                                    chain=chain, blocked_reason=blocked, note='周线方向闸门拦截, 不操作',
-                                    diagnostics=diagnostics, monthly=mov)
-
-        m60_an = _get_m60()
-        ok60 = False
-        if m60_an is not None:
-            if is_buy:
-                ok60, note60 = self._confirm_finer(m60_an, True, '60分钟', '日线', daily_sig.kind)
-            else:
-                ok60, note60 = self._confirm_finer(m60_an, False, '60分钟', '日线', daily_sig.kind,
-                                                   parent_sig=daily_sig)
-            chain.append(('60m', ('✓ 次级别确认: ' if ok60 else '✗ 次级别未确认(降级): ')+note60,
-                          '第44课: 区间套 —— 日线的转折先由直接次级别60分钟走势确认'))
-            if not ok60:
-                downgrade = True
-                if self.CFG.get('require_60m_buy_confirm') and is_buy and daily_sig.kind in ('B1', 'B2'):
-                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                            action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
-                                            chain=chain, blocked_reason='60分钟直接次级别未印证买点(L44区间套)',
-                                            note=f'日线{daily_sig.kind}买点但60m未现B1/B2 → 等直接次级别印证再动手',
-                                            diagnostics=diagnostics, monthly=mov)
-        else:
-            if df_6 is not None:
-                chain.append(('60m', '60分钟数据不足(<30根) → 跳过该级印证', '第44课: 区间套逐级确认'))
-
-        m30_an = _get_m30()
-        if m30_an is None:
-            chain.append(('30m','无30分钟数据 → 缺少次级别精确印证, 信号降级','第44课: 操作级别的买卖点需次级别走势确认'))
-            confirmed = False; confirm_note = '缺30分钟数据'; downgrade = True
-        else:
-            if is_buy:
-                confirmed, confirm_note = self._confirm_30m_for_buy(m30_an, daily_sig.kind)
-            else:
-                confirmed, confirm_note = self._confirm_30m_for_sell(m30_an, daily_sig.kind,
-                                                                     parent_sig=daily_sig)
-            chain.append(('30m', ('✓ 次级别确认: ' if confirmed else '✗ 次级别未确认: ')+confirm_note, '第44课: 小背驰-大转折定理(必要条件)'))
-
-        # ── 60m嵌套印证补位 (第44课区间套, 卖点版) ──
-        # 30m动能未竭但60m(日线的直接次级别)已给出嵌套顶背驰/转折确认 → 采信60m。
-        # 日线的"次级别"本就是60m; 30m是60m的次级别, 不应让更细级别一票否决直接次级别。
-        if (self.CFG.get('sell_nested_interval') and is_sell and not confirmed
-                and ok60 and daily_sig.kind in ('S1', 'S2')):
-            confirmed = True
-            confirm_note = '30m动能未竭, 但60m(日线直接次级别)已嵌套印证 → 采信60m(第44课: 逐级处理, 直接次级别优先)'
-            chain.append(('联立', '✓ ' + confirm_note, '第44课区间套'))
-
-        # ── L50 MACD级别选择: 选黄白线和柱子清晰的级别看MACD ──
-        if (self.CFG.get('l50_macd_level_select') and is_buy and not confirmed
-                and ok60 and m60_an is not None and m30_an is not None):
-            try:
-                c60 = m60_an.macd_clarity(); c30 = m30_an.macd_clarity()
-                if c30['score'] < 0.4 and c60['score'] >= max(0.4, c30['score'] * 1.5):
-                    confirmed = True
-                    chain.append(('联立',
-                                  f"✓ L50 MACD级别选择: 30m MACD{c30['label']}(清晰度{c30['score']}) 判据不可靠; "
-                                  f"60m MACD{c60['label']}(清晰度{c60['score']})且60m已印证买点 → 采信60m结论",
-                                  '第50课: 看MACD要选黄白线和柱子走势清晰的级别'))
-            except Exception:
-                pass
-
-        m15_an = None; m15_confirmed = None
-        if confirmed and df_15 is not None and len(df_15) >= 30:
-            try: m15_an = _MAKE_ANALYZER('15m', df_15)
-            except Exception: m15_an = None
-        if not confirmed:
-            pass
-        elif df_15 is None:
-            pass
-        elif m15_an is None:
-            chain.append(('15m','15分钟数据不足(<30根) → 跳过该级印证','第44课: 区间套逐级确认'))
-        else:
-            ok15, note15 = self._confirm_finer(m15_an, is_buy, '15分钟', '30分钟', daily_sig.kind,
-                                               parent_sig=(daily_sig if is_sell else None))
-            m15_confirmed = ok15
-            chain.append(('15m', ('✓ 次级别确认: ' if ok15 else '✗ 次级别未确认(降级,不拦截): ')+note15,
-                          '第44课: 逐级处理 —— 30分钟的转折由15分钟走势确认'))
-            if not ok15: downgrade = True
-
-        m5_an = None; m5_confirmed = None
-        if confirmed and df_5 is not None and len(df_5) >= 30:
-            try: m5_an = _MAKE_ANALYZER('5m', df_5)
-            except Exception: m5_an = None
-        if not confirmed:
-            pass
-        elif df_5 is None:
-            chain.append(('5m','无5分钟数据 → 缺该级逐级印证, 信号降级','第44课: 15分钟的转折需5分钟走势逐级确认')); downgrade = True
-        elif m5_an is None:
-            chain.append(('5m','5分钟数据不足(<30根) → 缺该级印证, 信号降级','第44课: 15分钟的转折需5分钟走势逐级确认')); downgrade = True
-        else:
-            ok5, note5 = self._confirm_5m(m5_an, is_buy, daily_sig.kind,
-                                          parent_sig=(daily_sig if is_sell else None))
-            m5_confirmed = ok5
-            chain.append(('5m', ('✓ 次级别确认: ' if ok5 else '✗ 次级别未确认(降级,不拦截): ')+note5, '第44课: 逐级处理 —— 15分钟的转折由5分钟走势确认'))
-            if not ok5: downgrade = True
-
-        m1_an = None; m1_confirmed = None
-        do_1m = confirmed and (m5_confirmed is True)
-        if do_1m and df_1 is not None and len(df_1) >= 30:
-            try: m1_an = _MAKE_ANALYZER('1m', df_1)
-            except Exception: m1_an = None
-        if not do_1m:
-            pass
-        elif df_1 is None:
-            chain.append(('1m','无1分钟数据 → 缺区间套最末级印证, 信号降级','第44课: 区间套 —— 5分钟的转折需1分钟走势逐级确认')); downgrade = True
-        elif m1_an is None:
-            chain.append(('1m','1分钟数据不足(<30根) → 缺最末级印证, 信号降级','第44课: 区间套 —— 5分钟的转折需1分钟走势逐级确认')); downgrade = True
-        else:
-            ok1, note1 = self._confirm_1m(m1_an, is_buy, daily_sig.kind,
-                                          parent_sig=(daily_sig if is_sell else None))
-            m1_confirmed = ok1
-            chain.append(('1m', ('✓ 末级确认: ' if ok1 else '✗ 末级未确认(降级,不拦截): ')+note1, '第44课: 区间套 —— 5分钟的转折由1分钟走势确认'))
-            if not ok1: downgrade = True
-
-        # ── L24规则: 周线上涨 + 日线【盘整背驰】(非趋势背驰)的S1/S2 ──
-        # 第24课: 某级别的背驰导致该级别的转折; 日线背驰若周线未同步背驰,
-        # 只构成日线级别的调整, 不是大顶 → 不全清仓, 转入区间套布防做短差:
-        # 真转折由布防线(嵌套背驰/破30m中枢ZD/峰值回落)兜底, 趋势延续则由
-        # 撤防机制(强势新高消化背驰, 第26课)继续骑中枢上移(第91课①)。
-        if (self.CFG.get('sell_nested_interval') and self.CFG.get('l24_weekly_uptrend_arm')
-                and is_sell and daily_sig.kind in ('S1', 'S2') and weekly_dir == 'up_trend'):
-            _dg = getattr(daily_sig, 'diverge_grade', None)
-            if _dg is not None and not getattr(_dg, 'is_trend_divergence', False):
-                _arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
-                _ex = daily_sig.extras or {}
-                _arm_high = _ex.get('c_high') or _ex.get('prev_high') or cur_price
-                chain.append(('L24', f'周线上涨 + 日线{daily_sig.kind}仅为盘整背驰(非趋势背驰) '
-                              f'→ 第24课: 不构成周线级别大顶, 不全清 → 转区间套布防短差'
-                              f'(布防线: 30m中枢ZD{f"¥{_arm_zd:.3f}" if _arm_zd else "暂无"}/'
-                              f'峰值回落{SELL_ARM_PEAK_DROP:.0%}; 强势新高则撤防骑趋势)',
-                              '第24课: 日线背驰除非周线同步背驰, 否则只造成日线级别调整'))
-                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                        action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind,
-                                        cur_price=cur_price, chain=chain,
-                                        note=f'L24: 周线上涨中的日线盘整背驰{daily_sig.kind}, 布防短差不全清',
-                                        sell_armed=True, arm_zd=_arm_zd,
-                                        arm_high=float(_arm_high) if _arm_high else None,
-                                        diagnostics=diagnostics, monthly=mov)
-
-        action = 'BUY' if is_buy else 'SELL'
-        if self.CFG.get('mode') == 'long' and is_sell and daily_sig.kind in ('S1', 'S2'):
-            big_up_long = ((wv is not None and wv.trend == 'up_trend') or
-                           (mov is not None and monthly_dir == 'up_trend'))
-            ma5m_ok = True
-            if len(df_mo) >= 5:
-                ma5m = float(df_mo['close'].tail(5).mean())
-                ma5m_ok = cur_price >= ma5m
-            if big_up_long and ma5m_ok:
-                chain.append(('mode', f'长线模式: 日线{daily_sig.kind}非周线级别转折且大方向上涨 → 持有(操作级别=周线)', '第72课: 看周线则日线调整不构成卖段; 第61课大级别不因小级别震荡卖'))
-                # 长线模式持有也挂上布防线(第44课): 真转折时不至于全程坐滑梯
-                _arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
-                _ex = daily_sig.extras or {}
-                _arm_high = _ex.get('c_high') or _ex.get('prev_high') or cur_price
-                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                        action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind, cur_price=cur_price,
-                                        chain=chain, note=f'长线模式持有: 日线{daily_sig.kind}属次级别调整, 周线方向未转',
-                                        sell_armed=bool(self.CFG.get('sell_nested_interval')),
-                                        arm_zd=_arm_zd, arm_high=float(_arm_high) if _arm_high else None,
-                                        diagnostics=diagnostics, monthly=mov)
-        if not confirmed:
-            if is_sell:
-                # ── 卖点区间套布防 (第24/44课): 背驰已现但次级别动能未竭 ──
-                # 不立即卖(防卖飞), 也绝不傻等到三卖(防坐滑梯): 持仓转入布防,
-                # 由回测端的布防线(嵌套背驰出现/破30m中枢ZD/峰值回落阈值)收割。
-                # 布防仅用于S1的精确定顶。S2是一卖后的【反抽】卖点(第15课):
-                # 上方空间被一卖高点封死, 反抽本身就是用来卖的, "等次级别动能衰竭"
-                # 与其性质矛盾 → S2未印证时维持"先卖再说"(走下方LOW置信卖出)。
-                if (self.CFG.get('sell_nested_interval') and daily_sig.kind == 'S1'):
-                    arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
-                    ex = daily_sig.extras or {}
-                    arm_high = ex.get('c_high') or ex.get('prev_high') or cur_price
-                    chain.append(('卖点布防',
-                                  f'日线{daily_sig.kind}背驰已现但次级别动能未竭 → 不立即卖(防卖飞), '
-                                  f'转入区间套布防: ①次级别出现嵌套顶背驰即卖 '
-                                  f'②跌破30m最近中枢下沿ZD{f"¥{arm_zd:.3f}" if arm_zd else "(暂无30m中枢)"}即卖 '
-                                  f'③较布防后峰值回落{SELL_ARM_PEAK_DROP:.0%}即卖',
-                                  '第44课区间套: 背驰段中套背驰段定精确高点; 第24课: 次级别未背驰, 大级别顶未到'))
-                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                            action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind,
-                                            cur_price=cur_price, chain=chain,
-                                            note=f'区间套布防中: 日线{daily_sig.kind}背驰段内, 等次级别嵌套背驰收敛后高位兑现(第44课)',
-                                            sell_armed=True, arm_zd=arm_zd,
-                                            arm_high=float(arm_high) if arm_high else None,
-                                            confidence_reasons=[f'布防原因: {confirm_note}'],
-                                            diagnostics=diagnostics, monthly=mov)
-                big_up_sell = ((wv is not None and wv.trend == 'up_trend') or
-                               (mov is not None and monthly_dir == 'up_trend'))
-                if self.CFG.get('require_sublevel_sell_confirm') and big_up_sell:
-                    chain.append(('sell_gate', f'日线{daily_sig.kind}卖点但次级别未印证, 且大方向上涨 → 持有(L61: 大级别不因小级别震荡卖)', '第61课: 区间套确认; 大级别操作不因小级别震荡清仓'))
-                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                            action='HOLD', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
-                                            chain=chain, note=f'日线{daily_sig.kind}卖点次级别未印证+大方向上涨, 持有等确认: {confirm_note}',
-                                            confidence_reasons=[f'次级别未印证持有: {confirm_note}'],
-                                            diagnostics=diagnostics, monthly=mov)
-                chain.append(('sell_gate',f'日线{daily_sig.kind}卖点已成立, 次级别未印证只降级不拦截','第14/15/21课: 买点买、卖点卖; 区间套用于精确定位, 不��否决卖点'))
-                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                        action='SELL', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
-                                        chain=chain, note=f'日线{daily_sig.kind}卖点先执行; 次级别未印证仅降级: {confirm_note}',
-                                        confidence_reasons=[f'次级别未印证: {confirm_note}'],
-                                        diagnostics=diagnostics, monthly=mov)
-            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                    action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
-                                    chain=chain, blocked_reason=f'次级别未印证({confirm_note})',
-                                    note='日线有买/非S1卖信号但次级别未确认 -- 等次级别出信号再动手',
-                                    diagnostics=diagnostics, monthly=mov)
-
-        score = 100; conf_reasons = []
-        def _penalty(pts, reason):
-            nonlocal score
-            score -= pts; conf_reasons.append(f'(-{pts}) {reason}')
-        if mov is not None and monthly_dir == 'down_trend' and is_buy:
-            _penalty(25, '第69课: 月线大方向下跌, 日线买点属逆大方向的转折尝试')
-        trend_piv_lt2 = (daily_an is not None and daily_an.n_trend_pivots < 2)
-        if trend_piv_lt2 and daily_sig.kind in ('B1', 'S1'):
-            _penalty(20, f'第27课: 背驰段前仅{daily_an.n_trend_pivots}个中枢(<2), 盘整背驰非趋势背驰')
-        if daily_an is not None and daily_an.trend == 'consolidation':
-            _penalty(15, '第29课: 日线处盘整结构, 盘整中转折力度弱')
-        daily_dg = getattr(daily_sig, 'diverge_grade', None)
-        if daily_dg is not None and daily_dg.grade == 'WEAK':
-            trig = '面积' if daily_dg.area_ok else 'DIF极值'
-            _penalty(15, f'第27课: 背驰仅满足{trig}单一判据(WEAK), 非标准背驰')
-        if (daily_dg is not None and daily_dg.grade in ('STRONG', 'WEAK')
-                and not daily_dg.is_trend_divergence and not trend_piv_lt2
-                and daily_sig.kind in ('B1', 'S1')):
-            _penalty(10, '第27课: 背驰段为盘整背驰(非趋势背驰), 力度偏弱')
-        if downgrade:
-            _penalty(12, '第44课: 区间套次级别未完全印证(精度降级, 非证伪)')
-        if daily_sig.kind in ('B2', 'S2'):
-            has_delayed = any(lvl in ('30m', '15m', '5m', '1m') and '延后确认' in concl
-                              for lvl, concl, _ in chain)
-            if has_delayed:
-                _penalty(8, '二/二卖由次级别延后确认(非次级别一买/一卖精确定位), 精度略降')
-        ex = daily_sig.extras or {}
-        if daily_sig.kind == 'B3':
-            missing = []
-            if ex.get('b1_price') is None:
-                missing.append('一买')
-            if ex.get('b2_price') is None:
-                missing.append('二买')
-            if missing:
-                _penalty(6, '第20/21课: 三买未能定位对应' + '/'.join(missing) + ', 质量略降')
-            if ex.get('late_trend_b3'):
-                _penalty(12, '第20/92课: 第二个以上同向中枢后的三买, 操作意义下降')
-        if daily_sig.kind == 'S3':
-            missing = []
-            if ex.get('s1_price') is None:
-                missing.append('一卖')
-            if ex.get('s2_price') is None:
-                missing.append('二卖')
-            if missing:
-                _penalty(6, '第20/21课: 三卖未能定位对应' + '/'.join(missing) + ', 质量略降')
-        if daily_dg is not None and daily_dg.grade == 'STRONG' and daily_dg.is_trend_divergence:
-            score += 10; conf_reasons.append('(+10) 第27课: 标准趋势背驰(>=2中枢+面积+DIF), 高质量')
-        # ── 卖点区间套加分: 30m嵌套顶背驰精确定位(卖在高位区) ──
-        if is_sell and any(lvl == '30m' and '嵌套顶背驰' in concl for lvl, concl, _ in chain):
-            score += 8; conf_reasons.append('(+8) 第44课: 30m嵌套顶背驰精确定位, 卖点落在高位区')
-        score = max(0, min(110, score))
-        confidence = 'HIGH' if score >= 70 else ('MEDIUM' if score >= 45 else 'LOW')
-        if conf_reasons:
-            chain.append(('置信度', f'{confidence}(评分{score}) ← ' + '; '.join(conf_reasons),
-                          '第21课: 一二三类买卖点是位置分类非质量排序; 置信度按原著力度条件评分'))
-        else:
-            chain.append(('置信度', f'HIGH(评分{score}) ← 力度条件全部满足',
-                          '第27/29/43课: 方向一致+趋势背驰+趋势结构'))
-        note_parts = []
-        if conf_reasons:
-            note_parts.append(f'置信评分明细: ' + '; '.join(conf_reasons))
-        n_lvl = 3 + (self.df_60m is not None) + (self.df_15m is not None) + (self.df_5m is not None) + (self.df_1m is not None)
-        lvl_txt = {3:'三级别',4:'四级别',5:'五级别',6:'六级别',7:'七级别'}.get(n_lvl, f'{n_lvl}级别')
-        m5_txt = ('/5m已印证' if m5_confirmed is True else '/5m未印证(已降级)' if m5_confirmed is False else '')
-        m1_txt = ('/1m已印证' if m1_confirmed is True else '/1m未印证(已降级)' if m1_confirmed is False else '')
-        note_parts.append(f'{lvl_txt}联立通过: 周线{weekly_dir}/日线{daily_sig.kind}/30m已印证{m5_txt}{m1_txt}')
-        note = ' | '.join(note_parts)
-        return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
-                                action=action, confidence=confidence, final_kind=daily_sig.kind, cur_price=cur_price,
-                                chain=chain, note=note, confidence_reasons=conf_reasons, diagnostics=diagnostics, monthly=mov)
+import pandas as pd
 
-    @staticmethod
-    def _no_signal_decision(wv, dv, cur_price):
-        if wv is not None and wv.trend == 'up_trend' and dv.trend == 'up_trend':
-            if dv.zg is not None and cur_price > dv.zg:
-                return ('HOLD','NONE', f'周线+日线均上涨且价({cur_price:.2f})在日线ZG({dv.zg:.2f})上, 继续持有等卖点(第108课: 买点入,持有等卖点)')
-            return ('HOLD','NONE','周线日线均上涨, 趋势中持有')
-        if wv is not None and wv.trend == 'down_trend':
-            return ('WATCH','NONE','周线下跌趋势, 无日线买点 → 空仓观望, 不抄底')
-        return ('WATCH','NONE', f'无明确信号(周线{wv.trend if wv else "?"}/日线{dv.trend}), 观望')
+import chan_engine
+import chan_multilevel
+
+# ── inject cross-module symbols (replaces the commented `from chan_engine import …`) ──
+chan_multilevel.ChanAnalyzer = chan_engine.ChanAnalyzer
+chan_multilevel.Signal = chan_engine.Signal
+chan_engine.PIVOT_MAX_EXTEND_SEGS = chan_engine.PIVOT_MAX_EXTEND_SEGS  # no-op, keeps linters calm
+
+# Re-exports for app code
+ChanAnalyzer = chan_engine.ChanAnalyzer
+Signal = chan_engine.Signal
+MultiLevelChan = chan_multilevel.MultiLevelChan
+MultiLevelSignal = chan_multilevel.MultiLevelSignal
+resample_weekly = chan_multilevel.resample_weekly
+resample_monthly = chan_multilevel.resample_monthly
+set_analyzer_factory = chan_multilevel.set_analyzer_factory
+
+# ── cached analyzer factory ──────────────────────────────────────────────
+_CACHE: "OrderedDict[str, chan_engine.ChanAnalyzer]" = OrderedDict()
+_CACHE_MAX = 64
+
+
+def _df_key(level: str, df: pd.DataFrame) -> str:
+    n = len(df)
+    if n == 0:
+        return f"{level}-empty"
+    last = str(df['date'].iloc[-1])
+    first = str(df['date'].iloc[0])
+    tail_close = float(df['close'].iloc[-1])
+    raw = f"{level}|{n}|{first}|{last}|{tail_close:.6f}"
+    return hashlib.md5(raw.encode()).hexdigest()
+
+
+def cached_analyzer(level: str, df: pd.DataFrame):
+    key = _df_key(level, df)
+    if key in _CACHE:
+        _CACHE.move_to_end(key)
+        return _CACHE[key]
+    an = chan_engine.ChanAnalyzer(df.reset_index(drop=True))
+    _CACHE[key] = an
+    while len(_CACHE) > _CACHE_MAX:
+        _CACHE.popitem(last=False)
+    return an
+
+
+def install():
+    """Install the cached analyzer factory into MultiLevelChan."""
+    set_analyzer_factory(cached_analyzer)
+
+
+install()

data_us.py

@@ -1,147 +1,884 @@
-"""
-data_us.py — US market data layer (yfinance), replacing baostock/pytdx.
-
-Levels & history limits (Yahoo Finance API constraints):
-    daily   : 10 years          (weekly / monthly are resampled from daily
-                                 by chan_multilevel.resample_weekly/_monthly)
-    60m     : last 730 days
-    30m/15m : last 60 days
-    5m      : last 60 days
-    1m      : last 7 days only → too short for Chan decomposition, NOT used.
-              MultiLevelChan handles a missing 1m level gracefully (skips it).
-
-Output schema (identical to the original A-share loaders):
-    date, open, close, high, low, volume, amount
-`amount` (turnover) is approximated as close × volume (Yahoo has no turnover field).
-
-All downloads are cached to parquet under ./_cache_us/<TICKER>/<level>.parquet
-and refreshed when stale (daily: >12h old, intraday: >2h old) or on force=True.
+"""缠论多级别联立分析
+
+【本版改动 · 卖点区间套修复 (第24/44课)】
+
+旧逻辑的两个病根(对应回测中"卖在低位"与"提前卖飞"):
+  ① 卖在低位: 严格模式要求"30分钟出现三类卖点S3"才印证日线卖点。但S3是
+     次级别已经跌破其中枢、反抽不回的【转折确认点】—— 它出现时价格早已
+     离开顶部一大截。拿S3当卖出闸门, 等于规定"必须先跌下来才准卖", 结构上
+     注定卖不到一卖的高位, 最后落在二卖/三卖的低位。
+  ② 提前卖飞: 宽松模式下"30m最后一笔向下"即印证。第24课明示: 小级别背驰
+     (更别说仅仅一笔向下)未必引发大级别转折。任何一次30m级别的正常回调都
+     可能把日线WEAK级别的卖点"确认"掉 → 还没到顶就出局。
+     而且旧代码对次级别卖点【不做时间嵌套检查】: 30m在日线C段开始之前的
+     旧背驰也会被当作印证 —— "区间套"三个字里的"区间"丢了。
+
+新逻辑 (CFG['sell_nested_interval']=True):
+  第44课区间套的本义: 大级别进入背驰段后, 在该背驰段【时间区间内】找次级别
+  的背驰段, 再在其中找次次级别的背驰段, 逐级收敛到精确转折点。
+  对日线S1/S2, 次级别印证按优先级:
+    ① 嵌套顶背驰: 30m出现S1, 且其C段顶部落在日线背驰段(最后中枢之后→当下)
+       的时间窗口内、顶部价位贴近日线C段高点 → 精确卖点, 卖在高位区。
+    ② 次级别转折确认: 30m出现S3, 或30m末笔已跌破其最近中枢下沿ZD(第92课
+       向下变盘) → 转折已确认, 偏晚但必须卖。
+    ③ 两者皆无 → 次级别动能未竭, 第24课: 顶未到 → 【不卖】(防卖飞),
+       返回 action=HOLD + sell_armed=True 进入"区间套布防"状态:
+       回测端持仓转入布防, 之后任一条件触发(嵌套背驰出现/破30m中枢ZD/
+       较布防峰值回落超阈值)即离场 —— 既不提前卖飞, 也不一路坐滑梯到三卖。
+  S3(日线三卖)不走嵌套背驰: 三卖本身就是转折确认型卖点, 维持原确认逻辑。
 """
 from __future__ import annotations
+from dataclasses import dataclass, field
+from typing import Optional
+import numpy as np
+import pandas as pd
 
-import os
-import time
-import traceback
+# from chan_engine import ChanAnalyzer, Signal
 
-import pandas as pd
 
-import paths
-
-CACHE_DIR = os.environ.get("CHAN_CACHE_DIR", paths.CACHE_DIR)
-
-LEVELS = {
-    # level: (yfinance interval, period) — Yahoo's max history per interval
-    "d":   ("1d",  "10y"),
-    "60m": ("60m", "730d"),
-    "30m": ("30m", "60d"),
-    "15m": ("15m", "60d"),
-    "5m":  ("5m",  "60d"),
-    "1m":  ("1m",  "7d"),    # only 7 days available; short but usable for the
-                             # finest nested-interval confirmation when present
-}
-
-_STALE_SECONDS = {"d": 12 * 3600, "60m": 2 * 3600, "30m": 2 * 3600,
-                  "15m": 2 * 3600, "5m": 2 * 3600, "1m": 1800}
-
-
-def _cache_path(ticker: str, level: str) -> str:
-    d = os.path.join(CACHE_DIR, ticker.upper().replace("/", "_"))
-    os.makedirs(d, exist_ok=True)
-    return os.path.join(d, f"{level}.parquet")
-
-
-def _normalize(df: pd.DataFrame) -> pd.DataFrame:
-    """yfinance frame → engine schema (date/open/close/high/low/volume/amount)."""
-    if df is None or len(df) == 0:
-        return pd.DataFrame(columns=["date", "open", "close", "high", "low", "volume", "amount"])
-    d = df.copy()
-    if isinstance(d.columns, pd.MultiIndex):           # yf>=0.2 returns MultiIndex sometimes
-        d.columns = [c[0] if isinstance(c, tuple) else c for c in d.columns]
-    d = d.reset_index()
-    # index column may be 'Date' or 'Datetime'
-    for cand in ("Datetime", "Date", "index"):
-        if cand in d.columns:
-            d = d.rename(columns={cand: "date"})
-            break
-    d.columns = [str(c).lower() for c in d.columns]
-    keep = {"date", "open", "high", "low", "close", "volume"}
-    d = d[[c for c in d.columns if c in keep]]
-    d["date"] = pd.to_datetime(d["date"])
-    # strip timezone so comparisons with naive Timestamps in the engine work
-    try:
-        d["date"] = d["date"].dt.tz_localize(None)
-    except (TypeError, AttributeError):
-        pass
-    d = d.dropna(subset=["open", "high", "low", "close"])
-    d = d.sort_values("date").reset_index(drop=True)
-    d["amount"] = d["close"] * d.get("volume", 0)
-    return d[["date", "open", "close", "high", "low", "volume", "amount"]]
-
-
-def load_level(ticker: str, level: str, force: bool = False) -> pd.DataFrame:
-    """Load one level for a ticker, using parquet cache when fresh."""
-    assert level in LEVELS, f"unknown level {level}"
-    path = _cache_path(ticker, level)
-    if not force and os.path.exists(path):
-        age = time.time() - os.path.getmtime(path)
-        if age < _STALE_SECONDS[level]:
+# ── 卖点区间套参数 (第24/44课) ──
+NESTED_TOP_PRICE_TOL   = 0.03   # 嵌套顶背驰的顶部须贴近父级C段高点(3%以内), 否则属上一波动能
+NESTED_WINDOW_PAD_DAYS = 3      # 时间嵌套窗口的左侧容差(日)
+SELL_ARM_PEAK_DROP     = 0.04   # 布防后较峰值价回落≥4% → 顶部确认离场(回测端使用)
+SELL_ARM_DISARM_BREAK  = 0.03   # 布防后强势创新高超3%且卖点消失 → 背驰被消化, 撤防(第26课)
+
+
+def _default_make_analyzer(level, df):
+    return ChanAnalyzer(df.reset_index(drop=True))
+
+_MAKE_ANALYZER = _default_make_analyzer
+
+def set_analyzer_factory(fn):
+    global _MAKE_ANALYZER
+    _MAKE_ANALYZER = fn or _default_make_analyzer
+
+
+def resample_weekly(df_daily: pd.DataFrame) -> pd.DataFrame:
+    if df_daily.empty:
+        return df_daily.copy()
+    d = df_daily.copy()
+    d['date'] = pd.to_datetime(d['date'])
+    d = d.sort_values('date').reset_index(drop=True)
+    wk_period = d['date'].dt.to_period('W-FRI')
+    agg = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'}
+    if 'volume' in d.columns: agg['volume'] = 'sum'
+    if 'amount' in d.columns: agg['amount'] = 'sum'
+    g = d.groupby(wk_period, sort=True)
+    wk = g.agg(agg)
+    last_real = g['date'].max()
+    wk = wk.dropna(subset=['open', 'high', 'low', 'close']).reset_index(drop=True)
+    wk['date'] = list(last_real.values)
+    return wk
+
+
+def resample_monthly(df_daily: pd.DataFrame) -> pd.DataFrame:
+    if df_daily.empty:
+        return df_daily.copy()
+    d = df_daily.copy()
+    d['date'] = pd.to_datetime(d['date'])
+    d = d.sort_values('date').reset_index(drop=True)
+    mp = d['date'].dt.to_period('M')
+    agg = {'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last'}
+    if 'volume' in d.columns: agg['volume'] = 'sum'
+    if 'amount' in d.columns: agg['amount'] = 'sum'
+    g = d.groupby(mp, sort=True)
+    mo = g.agg(agg)
+    last_real = g['date'].max()
+    mo = mo.dropna(subset=['open', 'high', 'low', 'close']).reset_index(drop=True)
+    mo['date'] = list(last_real.values)
+    return mo
+
+
+@dataclass
+class LevelView:
+    level: str
+    trend: str
+    n_pivots: int
+    n_bis: int
+    last_bi_dir: str
+    signal: Optional[Signal]
+    zg: Optional[float]
+    zd: Optional[float]
+    dif: Optional[float]
+    last_date: Optional[pd.Timestamp]
+    diagnostics: dict = field(default_factory=dict)
+
+
+@dataclass
+class MultiLevelSignal:
+    code: str
+    analysis_date: pd.Timestamp
+    weekly: LevelView
+    daily: LevelView
+    m30: Optional[LevelView]
+    action: str
+    confidence: str
+    final_kind: str
+    cur_price: float
+    chain: list = field(default_factory=list)
+    blocked_reason: str = ''
+    note: str = ''
+    confidence_reasons: list = field(default_factory=list)
+    diagnostics: dict = field(default_factory=dict)
+    monthly: Optional[LevelView] = None
+    # ── 卖点区间套布防 (第44课) ──
+    sell_armed: bool = False              # 日线S1/S2背驰已现、次级别动能未竭 → 持仓布防
+    arm_zd: Optional[float] = None        # 布防线①: 30m最近中枢下沿ZD(跌破即次级别转折确认)
+    arm_high: Optional[float] = None      # 父级背驰段C段高点(用于"新高消化背驰"撤防判定)
+
+    def explain(self) -> str:
+        lines = [f'  [{self.code}] {self.analysis_date.strftime("%Y-%m-%d")}  ¥{self.cur_price:.3f}']
+        lines.append(f'  最终: {self.action}  置信度={self.confidence}'
+                     + (f'  信号={self.final_kind}' if self.final_kind else ''))
+        lines.append('  ── 逐级裁决链 ──')
+        for lvl, concl, src in self.chain:
+            lines.append(f'    [{lvl:<7s}] {concl}')
+            if src:
+                lines.append(f'              └ 依据: {src}')
+        if self.blocked_reason:
+            lines.append(f'  ⚠ 拦截: {self.blocked_reason}')
+        if self.note:
+            lines.append(f'  说明: {self.note}')
+        if self.confidence_reasons:
+            lines.append('  置信度降档明细:')
+            for i, r in enumerate(self.confidence_reasons, 1):
+                lines.append(f'    {i}. {r}')
+        if self.diagnostics:
+            lines.append('  日线买卖���逐项诊断:')
+            for k in ('B1', 'B2', 'B3', 'S1', 'S2', 'S3'):
+                rows = self.diagnostics.get(k) or []
+                if not rows:
+                    continue
+                ok = all(str(x).startswith('✓') for x in rows)
+                lines.append(f'    {k} {"满足" if ok else "不满足"}')
+                for row in rows:
+                    lines.append(f'      {row}')
+        else:
+            lines.append('  日线买卖点逐项诊断: 未生成')
+        return '\n'.join(lines)
+
+
+class MultiLevelChan:
+    CFG = {
+        'use_monthly_gate': True,
+        'monthly_ma_filter': False,
+        'require_sublevel_sell_confirm': False,
+        'mode': 'short',
+        'zhongyin_block_buy': False,
+        'expose_m30_nosignal': False,
+        'require_60m_buy_confirm': False,  # L44区间套: 60m为日线直接次级别, 买点须经其印证
+        # L50 MACD级别选择: 看MACD要选"黄白线和柱子清晰"的级别。30m判据模糊而60m
+        # 清晰且60m已印证时, 采信60m的印证结论(避免被模糊级别的噪声误杀好买点)。
+        'l50_macd_level_select': False,
+        # ── 卖点区间套(第24/44课): 嵌套顶背驰精确定位 + 布防防卖飞 ──
+        #   True  = S1/S2用"时间嵌套的次级别顶背驰"定位高点; 未嵌套确认时不卖而布防
+        #   False = 维持旧逻辑(30m要S3 / 宽松末笔向下即卖)
+        'sell_nested_interval': True,
+        # L24: 周线上涨+日线盘整背驰的S1/S2 → 不全清, 转布防短差(撤防可骑回趋势)
+        'l24_weekly_uptrend_arm': False,  # 实测净亏(好卖点也被转布防), 默认关
+    }
+
+    # ── 速度优化: 次级别K线只取最近N根做缠论分解 ──
+    # 第17/44课: 次级别(60m/30m/15m/5m/1m)的职责是"印证当下转折", 其买卖点
+    # 只取决于最近的笔/线段/中枢结构; 几年前的分钟级历史对当下印证毫无贡献,
+    # 却让每个回测日都对上万根分钟K线重做合并/分型/笔/段, 是最大的耗时点。
+    # 截断只作用于次级别印证, 日/周/月线仍用全history(年线、月线大方向不受影响)。
+    SUB_TAIL = {'15m': 4000, '5m': 4800, '1m': 2000}
+
+    def __init__(self, df_daily, df_weekly=None, df_monthly=None, df_30m=None, df_60m=None,
+                 df_15m=None, df_5m=None, df_1m=None, code='', strict=True):
+        self.code = code
+        self.strict = strict
+        self.df_daily = df_daily.reset_index(drop=True) if not df_daily.empty else df_daily
+        self.df_30m = df_30m.reset_index(drop=True) if df_30m is not None and not df_30m.empty else None
+        self.df_60m = df_60m.reset_index(drop=True) if df_60m is not None and not df_60m.empty else None
+        self.df_15m = df_15m.reset_index(drop=True) if df_15m is not None and not df_15m.empty else None
+        self.df_5m = df_5m.reset_index(drop=True) if df_5m is not None and not df_5m.empty else None
+        self.df_1m = df_1m.reset_index(drop=True) if df_1m is not None and not df_1m.empty else None
+        self.df_weekly = df_weekly.reset_index(drop=True) if df_weekly is not None and not df_weekly.empty else None
+        self.df_monthly = df_monthly.reset_index(drop=True) if df_monthly is not None and not df_monthly.empty else None
+
+    @staticmethod
+    def _make_view(level, df, an=None, diagnose=True):
+        if an is None:
+            if df is None or len(df) < 30:
+                return None
             try:
-                return pd.read_parquet(path)
+                an = _MAKE_ANALYZER(level, df)
             except Exception:
-                pass
-    try:
-        import yfinance as yf
-        interval, period = LEVELS[level]
-        raw = yf.Ticker(ticker).history(period=period, interval=interval,
-                                        auto_adjust=True, actions=False)
-        df = _normalize(raw)
-        if len(df):
-            df.to_parquet(path, index=False)
-            return df
-    except Exception:
-        traceback.print_exc()
-    # network failed → fall back to stale cache if any
-    if os.path.exists(path):
+                return None
+            if an is None:
+                return None
+        sig = an.get_signal()
+        zg = an.pivots[-1].zg if an.n_pivots > 0 else None
+        zd = an.pivots[-1].zd if an.n_pivots > 0 else None
+        return LevelView(level=level, trend=an.trend, n_pivots=an.n_pivots, n_bis=an.n_bis,
+                         last_bi_dir=an.bis[-1].direction if an.n_bis else '', signal=sig,
+                         zg=zg, zd=zd, dif=float(an.dif.iloc[-1]) if len(an.dif) else None,
+                         last_date=pd.Timestamp(df['date'].iloc[-1]),
+                         diagnostics=(an.diagnose() if diagnose else {}))
+
+    # ──────────────────────────────────────────────────────────────────
+    #  卖点区间套核心: 时间嵌套的次级别顶背驰 (第44课)
+    # ──────────────────────────────────────────────────────────────────
+    @staticmethod
+    def _parent_sell_window(parent_sig):
+        """父级(日线)背驰段的时间窗口与顶部价。
+        S1: 背驰段C段 = 最后中枢结束 → 当下; 顶 = C段高点 c_high。
+        S2: 窗口 = 一卖出现 → 当下(反抽段); 顶 = 一卖高点 prev_high。"""
+        ex = (parent_sig.extras or {}) if parent_sig is not None else {}
+        if parent_sig is None:
+            return '', '', None
+        if parent_sig.kind == 'S1':
+            w_start = ex.get('pivot_end_date') or ex.get('a_high_date') or ''
+            top = ex.get('c_high')
+        else:  # S2
+            w_start = ex.get('s1_date') or ex.get('prev_high_date') or ''
+            top = ex.get('prev_high')
+        w_end = ex.get('price_date') or ''
+        return w_start, w_end, top
+
+    def _nested_sell_check(self, sub_an, parent_sig, lvl_name, parent_kind):
+        """第44课区间套(卖点版): 在父级背驰段时间窗口内找次级别的嵌套顶背驰。
+        优先级: ①嵌套S1(精确高点, 卖在高位)
+               ②S3 / 末笔破次级别中枢ZD(转折已确认, 偏晚但必卖)
+               ③都没有 → 第24课: 次级别动能未竭, 顶未到 → 不卖(防卖飞), 转布防。
+        返回 (confirmed: bool, note: str)。"""
+        w_start, w_end, parent_top = self._parent_sell_window(parent_sig)
+
+        def in_window(dstr):
+            if not w_start or not dstr:
+                return True   # 信息不足时不因窗口否决(保守放行, 由价位贴近度把关)
+            try:
+                d = pd.Timestamp(dstr)
+                lo = pd.Timestamp(w_start) - pd.Timedelta(days=NESTED_WINDOW_PAD_DAYS)
+                hi = (pd.Timestamp(w_end) if w_end else d) + pd.Timedelta(days=1)
+                return lo <= d <= hi
+            except Exception:
+                return True
+
+        rejected = ''
+        # ① 嵌套顶背驰 S1 —— 区间套的本体: 背驰段中套背驰段
+        s1 = sub_an.detect_s1()
+        if s1 is not None:
+            ex2 = s1.extras or {}
+            td = ex2.get('c_high_date', '')
+            tp = ex2.get('c_high')
+            near_top = (parent_top is None or tp is None
+                        or tp >= parent_top * (1 - NESTED_TOP_PRICE_TOL))
+            if in_window(td) and near_top:
+                ptxt = f'(顶¥{tp:.3f}@{td})' if tp else ''
+                return True, (f'{lvl_name}嵌套顶背驰S1{ptxt}落在日线{parent_kind}背驰段窗口内 '
+                              f'—— 第44课区间套: 背驰段中套背驰段, 精确定位顶部, 卖在高位区')
+            rejected = (f'{lvl_name}虽有S1但【不嵌套】(顶@{td or "?"}不在父级背驰段'
+                        f'[{w_start}~{w_end}]窗口内, 或低于父级顶{NESTED_TOP_PRICE_TOL:.0%}以上)'
+                        f' → 属上一波的动能衰竭, 不作本次印证(防卖飞); ')
+        # ①b 父级为S2时, 次级别S2(一卖后反抽确认)也可嵌套印证
+        if parent_kind == 'S2':
+            s2 = sub_an.detect_s2()
+            if s2 is not None:
+                ex2 = s2.extras or {}
+                td = ex2.get('cur_high_date', '')
+                if in_window(td):
+                    return True, (f'{lvl_name}嵌套二卖S2(反抽顶@{td})落在日线S2窗口内 '
+                                  f'—— 第14课: 大级别二卖由次级别相应卖点定位')
+        # ② 转折已确认型: S3 / 末笔已破次级别中枢下沿
+        s3 = sub_an.detect_s3()
+        if s3 is not None:
+            return True, (f'{lvl_name}出现三类卖点S3 —— 第44课: 最后一个次级别中枢出现三卖, '
+                          f'转折已确认(偏晚, 但必须卖)')
+        try:
+            osc = sub_an.zhongshu_oscillation_monitor()
+            if osc.get('alert') and osc.get('direction') == 'down':
+                return True, (f'{lvl_name}末笔已离开其最近中枢下沿(第92课向下变盘) '
+                              f'—— 次级别转折确认, 高位区兑现')
+        except Exception:
+            pass
+        # ③ 次级别上冲动能已竭的当下确认: 末笔向下 + MACD柱已翻负(黄白线收敛下行)。
+        #    第24/44课: 区间套要的是"次级别走势的当下转折"; 末笔转下且红柱消失,
+        #    说明次级别这一冲已经结束 —— 此时日线背驰卖点立即执行, 不再等更深的破位
+        #    (这正是旧版"等30m三卖"卖在低位的病根)。只有次级别仍在上冲(末笔向上/
+        #    红柱未消)时才转入布防, 防的才是真正的"卖飞"。
         try:
-            return pd.read_parquet(path)
+            last_bi = sub_an.bis[-1] if sub_an.n_bis else None
+            bar_now = float(sub_an.macd_bar.iloc[-1]) if len(sub_an.macd_bar) else 0.0
+            if last_bi is not None and last_bi.direction == 'down' and bar_now < 0:
+                return True, (f'{lvl_name}末笔向下且MACD柱已翻负 —— 第24课: 次级别上冲动能已竭, '
+                              f'当下转折确认, 日线背驰卖点立即执行(不等{lvl_name}跌出三卖)')
         except Exception:
             pass
-    return pd.DataFrame(columns=["date", "open", "close", "high", "low", "volume", "amount"])
+        return False, (rejected + f'{lvl_name}动能未竭(末笔仍向上/MACD红柱未消): 无嵌套顶背驰/无S3 '
+                       f'→ 第24课: 次级别未转折, 大级别顶未到 → 暂不卖(防卖飞), 转入区间套布防')
 
+    def _confirm_30m_for_buy(self, m30, parent_kind=''):
+        if m30 is None or m30.n_bis < 5:
+            return False, '30分钟笔数不足(<5), 无法做次级别精确印证'
+        if parent_kind == 'B2':
+            if m30.detect_b1() is not None:
+                return True, '30分钟出现一类买点B1 —— 第14课: 大级别第二类买点由次一级别相应走势的一类买点构成'
+            if m30.detect_b2() is not None:
+                return True, '30分钟出现二类买点B2 —— 次级别一买后的回试确认, 属延后确认, 精度低于B1'
+            return False, '30分钟未出现B1/B2 —— 大级别二买缺少次级别买点确认'
+        for fn, name in ((m30.detect_b1,'B1'),(m30.detect_b3,'B3'),(m30.detect_b2,'B2')):
+            if fn() is not None:
+                return True, f'30分钟出现标准买点{name} —— 第44课: 次级别走势确认转折, 日线买点成立'
+        if self.strict:
+            return False, '30分钟未出现任何标准买点(B1/B2/B3) —— 严格模式: 第44课次级别印证未满足, 日线买点暂不成立'
+        if m30.bis[-1].direction == 'up':
+            return True, '30分钟最后一笔向上, 次级别已启动(宽松确认)'
+        return False, '30分钟最后一笔仍向下且无买点, 次级别未确认转折'
 
-# Full nested-interval set (区间套): the more sub-levels confirm, the more
-# precise the buy/sell point. We fetch the deepest Yahoo allows. 1m has only
-# 7 days of history — included when present, skipped gracefully otherwise.
-# Downloads are parallel + cached, so the extra levels cost little wall-time.
-FULL_LEVELS = ("d", "60m", "30m", "15m", "5m", "1m")
-FAST_LEVELS = FULL_LEVELS  # default everywhere; alias kept for older callers
+    def _confirm_30m_for_sell(self, m30, parent_kind='', parent_sig=None):
+        if m30 is None or m30.n_bis < 5:
+            return False, '30分钟笔数不足(<5), 无法做次级别精确印证'
+        # ── 新: 卖点区间套(第24/44课) —— S1/S2用嵌套顶背驰定位高点 ──
+        if (self.CFG.get('sell_nested_interval') and parent_sig is not None
+                and parent_kind in ('S1', 'S2')):
+            return self._nested_sell_check(m30, parent_sig, '30分钟', parent_kind)
+        # ── 旧逻辑(S3父级 / 开关关闭时) ──
+        if parent_kind == 'S2':
+            if m30.detect_s1() is not None:
+                return True, '30分钟出现一类卖点S1 —— 大级别第二类卖点由次一级别相应走势的一类卖点精确定位'
+            if m30.detect_s2() is not None:
+                return True, '30分钟出现二类卖点S2 —— 次级别一卖后的反抽确认, 属延后确认, 精度低于S1'
+            return False, '30分钟未出现S1/S2 —— 大级别二卖缺少次级别卖点确认'
+        if m30.detect_s3() is not None:
+            return True, '30分钟出现三类卖点S3 —— 严格满足第44课"小背驰-大转折定理"的必要条件(最后一个次级别中枢出现三卖)'
+        if self.strict:
+            return False, '30分钟未出现三类卖点S3 —— 严格模式: 第44课明确要求"最后一个次级别中枢出现三卖", 必要条件未满足, 日线卖点不构成大级别转折'
+        if m30.detect_s1() is not None:
+            return True, '30分钟出现一类卖点(顶背驰), 次级别转折确认(宽松)'
+        if m30.bis[-1].direction == 'down':
+            return True, '30分钟最后一笔向下, 次级别已转弱(宽松确认)'
+        return False, '30分钟未出现卖点且最后一笔仍向上, 次级别未确认转折'
 
+    def _confirm_finer(self, an, is_buy, lvl_name, parent_name, parent_kind='', parent_sig=None):
+        if an is None or an.n_bis < 5:
+            return False, f'{lvl_name}笔数不足(<5), 无法做次级别精确印证'
+        if is_buy:
+            if parent_kind == 'B2':
+                if an.detect_b1() is not None:
+                    return True, f'{lvl_name}出现B1 —— 第14课: {parent_name}二买由次一级别一买构成'
+                if an.detect_b2() is not None:
+                    return True, f'{lvl_name}出现B2 —— 次级别一买后的回试确认, 属延后确认, 精度低于B1'
+                return False, f'{lvl_name}未出现B1/B2 —— {parent_name}二买缺少次级别买点确认'
+            for fn, name in ((an.detect_b1,'B1'),(an.detect_b3,'B3'),(an.detect_b2,'B2')):
+                if fn() is not None:
+                    return True, f'{lvl_name}出现标准买点{name} —— 第44课逐级处理: {lvl_name}走势确认{parent_name}的转折'
+            if self.strict:
+                return False, f'{lvl_name}未出现标准买点(B1/B2/B3) —— 严格模式: 末级印证未满足, 信号降级'
+            if an.bis[-1].direction == 'up':
+                return True, f'{lvl_name}最后一笔向上, 次级别已启动(宽松确认)'
+            return False, f'{lvl_name}最后一笔仍向下且无买点, 末级未确认, 信号降级'
+        else:
+            # ── 新: 卖点区间套向更细级别逐级传递(同一父级背驰段窗口) ──
+            if (self.CFG.get('sell_nested_interval') and parent_sig is not None
+                    and parent_kind in ('S1', 'S2')):
+                ok, note = self._nested_sell_check(an, parent_sig, lvl_name, parent_kind)
+                if ok:
+                    return True, note + f' —— 第44课逐级处理: {lvl_name}确认{parent_name}的转折'
+                return False, note
+            if parent_kind == 'S2':
+                if an.detect_s1() is not None:
+                    return True, f'{lvl_name}出现S1 —— {parent_name}二卖由次一级别一卖精确定位'
+                if an.detect_s2() is not None:
+                    return True, f'{lvl_name}出现S2 —— 次级别一卖后的反抽确认, 属延后确认, 精度低于S1'
+                return False, f'{lvl_name}未出现S1/S2 —— {parent_name}二卖缺少次级别卖点确认'
+            if an.detect_s3() is not None:
+                return True, f'{lvl_name}出现三类卖点S3 —— 第44课逐级处理: {lvl_name}走势确认{parent_name}的转折'
+            if self.strict:
+                return False, f'{lvl_name}未出现三类卖点S3 —— 严格模式: 末级印证未满足, 信号降级'
+            if an.detect_s1() is not None:
+                return True, f'{lvl_name}出现一类卖点(顶背驰), 次级别转折确认(宽松)'
+            if an.bis[-1].direction == 'down':
+                return True, f'{lvl_name}最后一笔向下, 次级别已转弱(宽松确认)'
+            return False, f'{lvl_name}未出现卖点且最后一笔仍向上, 末级未确认, 信号降级'
 
-def load_levels(ticker: str, levels=FAST_LEVELS, force: bool = False) -> dict:
-    return {lvl: load_level(ticker, lvl, force=force) for lvl in levels}
+    def _confirm_5m(self, m5, is_buy, parent_kind='', parent_sig=None):
+        return self._confirm_finer(m5, is_buy, '5分钟', '30分钟', parent_kind, parent_sig)
 
+    def _confirm_1m(self, m1, is_buy, parent_kind='', parent_sig=None):
+        return self._confirm_finer(m1, is_buy, '1分钟', '5分钟', parent_kind, parent_sig)
 
-def load_all_levels(ticker: str, force: bool = False) -> dict:
-    """Return {'d':…, '60m':…, '30m':…, '15m':…, '5m':…} (1m intentionally absent)."""
-    return {lvl: load_level(ticker, lvl, force=force) for lvl in LEVELS}
+    def analyze(self, analysis_date=None, positions=None):
+        if self.df_daily.empty or len(self.df_daily) < 30:
+            return None
+        df_d = self.df_daily
+        if analysis_date is not None:
+            analysis_date = pd.Timestamp(analysis_date)
+            df_d = df_d[df_d['date'] <= analysis_date].reset_index(drop=True)
+        if len(df_d) < 30:
+            return None
+        last_date = pd.Timestamp(df_d['date'].iloc[-1])
+        cur_price = float(df_d['close'].iloc[-1])
+
+        if self.df_weekly is not None:
+            df_w = self.df_weekly[self.df_weekly['date'] <= last_date].reset_index(drop=True)
+        else:
+            df_w = resample_weekly(df_d)
+        if self.df_monthly is not None:
+            df_mo = self.df_monthly[self.df_monthly['date'] <= last_date].reset_index(drop=True)
+        else:
+            df_mo = resample_monthly(df_d)
+        _tail = self.SUB_TAIL or {}
+        def _cut_sub(df, lvl):
+            if df is None:
+                return None
+            sub = df[df['date'] <= last_date + pd.Timedelta(days=1)]
+            n = _tail.get(lvl, 0)
+            if n and len(sub) > n:
+                sub = sub.tail(n)
+            return sub.reset_index(drop=True)
+        df_6  = _cut_sub(self.df_60m, '60m')
+        df_m  = _cut_sub(self.df_30m, '30m')
+        df_15 = _cut_sub(self.df_15m, '15m')
+        df_5  = _cut_sub(self.df_5m, '5m')
+        df_1  = _cut_sub(self.df_1m, '1m')
+
+        wv = self._make_view('weekly', df_w, diagnose=False)
+        mov = self._make_view('monthly', df_mo, diagnose=False) if self.CFG.get('use_monthly_gate') else None
+        daily_an = None
+        try:
+            if len(df_d) >= 30:
+                daily_an = _MAKE_ANALYZER('daily', df_d)
+        except Exception:
+            daily_an = None
+        dv = self._make_view('daily', df_d, an=daily_an, diagnose=False) if daily_an is not None \
+            else self._make_view('daily', df_d, diagnose=False)
+
+        m60_an = None
+        m60_built = False
+        def _get_m60():
+            nonlocal m60_an, m60_built
+            if not m60_built:
+                m60_built = True
+                if df_6 is not None and len(df_6) >= 30:
+                    try: m60_an = _MAKE_ANALYZER('60m', df_6)
+                    except Exception: m60_an = None
+            return m60_an
+
+        m30_an = None
+        m30_built = False
+        def _get_m30():
+            nonlocal m30_an, m30_built
+            if not m30_built:
+                m30_built = True
+                if df_m is not None and len(df_m) >= 30:
+                    try: m30_an = _MAKE_ANALYZER('30m', df_m)
+                    except Exception: m30_an = None
+            return m30_an
+
+        def _mv():
+            an = _get_m30()
+            if an is None:
+                return None
+            return self._make_view('30m', df_m, an=an, diagnose=False)
+
+        diagnostics = daily_an.diagnose() if daily_an is not None else {}
+
+        chain = []
+        yearline_dir = 'unknown'; yearline_val = None
+        if len(df_d) >= 60:
+            _n = min(250, len(df_d))
+            yearline_val = float(df_d['close'].tail(_n).mean()) if _n >= 20 else None
+            _ma = df_d['close'].rolling(min(250, len(df_d)), min_periods=20).mean()
+            if len(_ma) and not pd.isna(_ma.iloc[-1]):
+                yearline_val = float(_ma.iloc[-1])
+        if yearline_val is not None:
+            above = cur_price >= yearline_val
+            yearline_dir = 'above' if above else 'below'
+            ytxt = (f'现价¥{cur_price:.3f} {"≥" if above else "<"} 年线MA250 ¥{yearline_val:.3f} '
+                    f'→ {"站上年线, 长线可做多" if above else "年线下方, 第106课不做多(只看反弹/卖点)"}')
+            chain.append(('年线', ytxt,
+                          '第7/106课: 年线(MA250)是长线生命线; 站上才考虑做多, 跌破年线长线转空'))
+        else:
+            chain.append(('年线', '日线历史不足, 年线MA250 暂不可用', '第7/106课'))
+
+        monthly_dir = 'unknown'
+        if mov is not None:
+            monthly_dir = mov.trend
+            chain.append(('monthly', f'{monthly_dir}  (月线定大方向: L69 月线看最实质大方向)',
+                          '第69/108课: 月线分型/笔/线段确定中期大方向; 底部=第一类买点到中枢首次走出三买前'))
+            if self.CFG.get('monthly_ma_filter') and len(df_mo) >= 5:
+                ma5_m = float(df_mo['close'].tail(5).mean())
+                if cur_price < ma5_m and monthly_dir != 'down_trend':
+                    monthly_dir = 'down_trend'
+                    chain.append(('monthly', f'价({cur_price:.2f})在5月线({ma5_m:.2f})下 → 大方向按偏空处理',
+                                  '第106课: 5月线是长线的关键, 牛市第一轮调整不跌破5月线'))
+
+        if wv is None:
+            chain.append(('weekly', '周线数据不足(<30根), 方向未知', ''))
+            weekly_dir = 'unknown'
+        else:
+            weekly_dir = wv.trend
+            dir_txt = {'up_trend':'上涨趋势 → 日线只接受【买点】','down_trend':'下跌趋势 → 日线只接受【卖点】/观望',
+                       'consolidation':'盘整 → 日线买卖点都看,但降级'}.get(weekly_dir, weekly_dir)
+            chain.append(('weekly', f'{weekly_dir}  {dir_txt}',
+                          '第43课: 大级别走势类型限定小级别的操作方向; 不允许"上涨+上涨""下跌+下跌"'))
+
+        if dv is None:
+            return None
+        daily_sig = dv.signal
+        if daily_sig is None:
+            chain.append(('daily', f'{dv.trend}, 无买卖点信号', ''))
+        else:
+            chain.append(('daily', f'出现 {daily_sig.kind}: {daily_sig.reason[:400]}', '第21课: 买卖点完备性定理'))
+
+        if daily_sig is None:
+            action, conf, note = self._no_signal_decision(wv, dv, cur_price)
+            m30_ns = _mv() if self.CFG.get('expose_m30_nosignal') else None
+            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=m30_ns,
+                                    action=action, confidence=conf, final_kind='', cur_price=cur_price, chain=chain, note=note,
+                                    diagnostics=diagnostics, monthly=mov)
+
+        is_buy = daily_sig.kind in ('B1','B2','B3')
+        is_sell = daily_sig.kind in ('S1','S2','S3')
+
+        if self.CFG.get('zhongyin_block_buy') and is_buy and daily_sig.kind != 'B3' and daily_an is not None:
+            zy = daily_an.in_zhongyin()
+            if zy.get('in_zhongyin'):
+                chain.append(('中阴', f'第88-90课: 当前处中阴(方向未定+BOLL收口) → 暂不开新仓: {zy["reason"][:120]}', '第89课: 中阴阶段方向未定, 不宜开新仓'))
+                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=None,
+                                        action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
+                                        chain=chain, blocked_reason='中阴方向未定', note='中阴阶段暂不开仓(L88-90)',
+                                        diagnostics=diagnostics, monthly=mov)
+
+        if positions:
+            fail_kinds = []
+            for bk, pos in positions.items():
+                stop = pos.get('stop_px')
+                if stop is not None and cur_price < stop:
+                    fail_kinds.append(bk)
+            if fail_kinds:
+                chain.append(('证伪', f'持仓{"/".join(fail_kinds)}跌破建仓日锁定止损线 → 清仓', '第13/20课: 买点结构被破坏即证伪'))
+                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                        action='SELL', confidence='HIGH', final_kind='STOP', cur_price=cur_price,
+                                        chain=chain, note='结构证伪止损, 次日清仓',
+                                        diagnostics=diagnostics, monthly=mov)
+
+        blocked = ''
+        downgrade = False
+        if weekly_dir == 'up_trend' and is_sell:
+            downgrade = True
+            chain.append(('联立','周线上涨 + 日线卖点 → 第44课: 小级别(日线)顶背驰未必引发大级别(周线)转折, 卖点降级为"减仓/短差"',
+                          '第24课: 日线背驰除非周线同时背驰,否则不制造周线大顶'))
+        elif weekly_dir == 'down_trend' and is_buy:
+            if daily_sig.kind in ('B1','B2'):
+                chain.append(('联立','周线下跌 + 日线一/二买 → 下跌趋势的转折尝试, 必须由30分钟严格印证','第29课: 下跌的转折=上涨或盘整, 由背驰导致'))
+            else:
+                blocked = '周线下跌趋势中出现日线三买 —— 第43课: 三买属上涨结构, 与周线下跌矛盾, 大概率是下跌中继的假三买'
+        elif monthly_dir == 'down_trend' and is_buy and daily_sig.kind == 'B3' and not blocked:
+            blocked = '月线下跌大方向中出现日线三买 —— 第43/69课: 三买属上涨结构, 与月线大方向矛盾, 大概率假三买'
+        elif weekly_dir == 'up_trend' and is_buy:
+            chain.append(('联立','周线上涨 + 日线买点 → 方向一致, 进入30分钟精确定位','第43课: 大小级别同向, 操作最顺'))
+        elif weekly_dir == 'down_trend' and is_sell:
+            chain.append(('联立','周线下跌 + 日线卖点 → 方向一致, 趋势性卖出','第43课: 大小级别同向'))
+        elif weekly_dir == 'consolidation':
+            downgrade = True
+            chain.append(('联立','周线盘整 → 日线信号有效但降级(盘整中买卖点力度弱)','第29课: 盘整中的转折力度弱于趋势'))
+
+        # ── 周线二买 → 日线一买精确定位锚 (第14/17课) ──
+        if is_buy and wv is not None and wv.signal is not None \
+                and getattr(wv.signal, 'kind', '') == 'B2':
+            _dex = (daily_sig.extras or {}) if daily_sig is not None else {}
+            _anchor = _dex.get('b1_price') or _dex.get('cur_low')
+            if _anchor:
+                daily_sig.extras = dict(_dex)
+                daily_sig.extras['weekly_b2_daily_b1_anchor'] = float(_anchor)
+                chain.append(('联立',
+                              f'✓ 周线二买 + 日线买点共振: 周线B2由日线一买构成(第14课), '
+                              f'定位锚=日线一买位¥{float(_anchor):.3f}, 跌破该锚则周线二买证伪',
+                              '第17课: 大级别买点的精确定位要落到次级别的具体买点价位'))
+            else:
+                chain.append(('联立',
+                              '周线二买成立但日线尚无可定位的一买锚 → 等日线给出一买价位再重仓',
+                              '第14课'))
+
+        if blocked:
+            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                    action='WATCH', confidence='NONE', final_kind=daily_sig.kind, cur_price=cur_price,
+                                    chain=chain, blocked_reason=blocked, note='周线方向闸门拦截, 不操作',
+                                    diagnostics=diagnostics, monthly=mov)
+
+        m60_an = _get_m60()
+        ok60 = False
+        if m60_an is not None:
+            if is_buy:
+                ok60, note60 = self._confirm_finer(m60_an, True, '60分钟', '日线', daily_sig.kind)
+            else:
+                ok60, note60 = self._confirm_finer(m60_an, False, '60分钟', '日线', daily_sig.kind,
+                                                   parent_sig=daily_sig)
+            chain.append(('60m', ('✓ 次级别确认: ' if ok60 else '✗ 次级别未确认(��级): ')+note60,
+                          '第44课: 区间套 —— 日线的转折先由直接次级别60分钟走势确认'))
+            if not ok60:
+                downgrade = True
+                if self.CFG.get('require_60m_buy_confirm') and is_buy and daily_sig.kind in ('B1', 'B2'):
+                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                            action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
+                                            chain=chain, blocked_reason='60分钟直接次级别未印证买点(L44区间套)',
+                                            note=f'日线{daily_sig.kind}买点但60m未现B1/B2 → 等直接次级别印证再动手',
+                                            diagnostics=diagnostics, monthly=mov)
+        else:
+            if df_6 is not None:
+                chain.append(('60m', '60分钟数据不足(<30根) → 跳过该级印证', '第44课: 区间套逐级确认'))
+
+        m30_an = _get_m30()
+        if m30_an is None:
+            chain.append(('30m','无30分钟数据 → 缺少次级别精确印证, 信号降级','第44课: 操作级别的买卖点需次级别走势确认'))
+            confirmed = False; confirm_note = '缺30分钟数据'; downgrade = True
+        else:
+            if is_buy:
+                confirmed, confirm_note = self._confirm_30m_for_buy(m30_an, daily_sig.kind)
+            else:
+                confirmed, confirm_note = self._confirm_30m_for_sell(m30_an, daily_sig.kind,
+                                                                     parent_sig=daily_sig)
+            chain.append(('30m', ('✓ 次级别确认: ' if confirmed else '✗ 次级别未确认: ')+confirm_note, '第44课: 小背驰-大转折定理(必要条件)'))
+
+        # ── 60m嵌套印证补位 (第44课区间套, 卖点版) ──
+        # 30m动能未竭但60m(日线的直接次级别)已给出嵌套顶背驰/转折确认 → 采信60m。
+        # 日线的"次级别"本就是60m; 30m是60m的次级别, 不应让更细级别一票否决直接次级别。
+        if (self.CFG.get('sell_nested_interval') and is_sell and not confirmed
+                and ok60 and daily_sig.kind in ('S1', 'S2')):
+            confirmed = True
+            confirm_note = '30m动能未竭, 但60m(日线直接次级别)已嵌套印证 → 采信60m(第44课: 逐级处理, 直接次级别优先)'
+            chain.append(('联立', '✓ ' + confirm_note, '第44课区间套'))
+
+        # ── L50 MACD级别选择: 选黄白线和柱子清晰的级别看MACD ──
+        if (self.CFG.get('l50_macd_level_select') and is_buy and not confirmed
+                and ok60 and m60_an is not None and m30_an is not None):
+            try:
+                c60 = m60_an.macd_clarity(); c30 = m30_an.macd_clarity()
+                if c30['score'] < 0.4 and c60['score'] >= max(0.4, c30['score'] * 1.5):
+                    confirmed = True
+                    chain.append(('联立',
+                                  f"✓ L50 MACD级别选择: 30m MACD{c30['label']}(清晰度{c30['score']}) 判据不可靠; "
+                                  f"60m MACD{c60['label']}(清晰度{c60['score']})且60m已印证买点 → 采信60m结论",
+                                  '第50课: 看MACD要选黄白线和柱子走势清晰的级别'))
+            except Exception:
+                pass
+
+        m15_an = None; m15_confirmed = None
+        if confirmed and df_15 is not None and len(df_15) >= 30:
+            try: m15_an = _MAKE_ANALYZER('15m', df_15)
+            except Exception: m15_an = None
+        if not confirmed:
+            pass
+        elif df_15 is None:
+            pass
+        elif m15_an is None:
+            chain.append(('15m','15分钟数据不足(<30根) → 跳过该级印证','第44课: 区间套逐级确认'))
+        else:
+            ok15, note15 = self._confirm_finer(m15_an, is_buy, '15分钟', '30分钟', daily_sig.kind,
+                                               parent_sig=(daily_sig if is_sell else None))
+            m15_confirmed = ok15
+            chain.append(('15m', ('✓ 次级别确认: ' if ok15 else '✗ 次级别未确认(降级,不拦截): ')+note15,
+                          '第44课: 逐级处理 —— 30分钟的转折由15分钟走势确认'))
+            if not ok15: downgrade = True
+
+        m5_an = None; m5_confirmed = None
+        if confirmed and df_5 is not None and len(df_5) >= 30:
+            try: m5_an = _MAKE_ANALYZER('5m', df_5)
+            except Exception: m5_an = None
+        if not confirmed:
+            pass
+        elif df_5 is None:
+            chain.append(('5m','无5分钟数据 → 缺该级逐级印证, 信号降级','第44课: 15分钟的转折需5分钟走势逐级确认')); downgrade = True
+        elif m5_an is None:
+            chain.append(('5m','5分钟数据不足(<30根) → 缺该级印证, 信号降级','第44课: 15分钟的转折需5分��走势逐级确认')); downgrade = True
+        else:
+            ok5, note5 = self._confirm_5m(m5_an, is_buy, daily_sig.kind,
+                                          parent_sig=(daily_sig if is_sell else None))
+            m5_confirmed = ok5
+            chain.append(('5m', ('✓ 次级别确认: ' if ok5 else '✗ 次级别未确认(降级,不拦截): ')+note5, '第44课: 逐级处理 —— 15分钟的转折由5分钟走势确认'))
+            if not ok5: downgrade = True
+
+        m1_an = None; m1_confirmed = None
+        do_1m = confirmed and (m5_confirmed is True)
+        if do_1m and df_1 is not None and len(df_1) >= 30:
+            try: m1_an = _MAKE_ANALYZER('1m', df_1)
+            except Exception: m1_an = None
+        if not do_1m:
+            pass
+        elif df_1 is None:
+            chain.append(('1m','无1分钟数据 → 缺区间套最末级印证, 信号降级','第44课: 区间套 —— 5分钟的转折需1分钟走势逐级确认')); downgrade = True
+        elif m1_an is None:
+            chain.append(('1m','1分钟数据不足(<30根) → 缺最末级印证, 信号降级','第44课: 区间套 —— 5分钟的转折需1分钟走势逐级确认')); downgrade = True
+        else:
+            ok1, note1 = self._confirm_1m(m1_an, is_buy, daily_sig.kind,
+                                          parent_sig=(daily_sig if is_sell else None))
+            m1_confirmed = ok1
+            chain.append(('1m', ('✓ 末级确认: ' if ok1 else '✗ 末级未确认(降级,不拦截): ')+note1, '第44课: 区间套 —— 5分钟的转折由1分钟走势确认'))
+            if not ok1: downgrade = True
 
+        # ── L24规则: 周线上涨 + 日线【盘整背驰】(非趋势背驰)的S1/S2 ──
+        # 第24课: 某级别的背驰导致该级别的转折; 日线背驰若周线未同步背驰,
+        # 只构成日线级别的调整, 不是大顶 → 不全清仓, 转入区间套布防做短差:
+        # 真转折由布防线(嵌套背驰/破30m中枢ZD/峰值回落)兜底, 趋势延续则由
+        # 撤防机制(强势新高消化背驰, 第26课)继续骑中枢上移(第91课①)。
+        if (self.CFG.get('sell_nested_interval') and self.CFG.get('l24_weekly_uptrend_arm')
+                and is_sell and daily_sig.kind in ('S1', 'S2') and weekly_dir == 'up_trend'):
+            _dg = getattr(daily_sig, 'diverge_grade', None)
+            if _dg is not None and not getattr(_dg, 'is_trend_divergence', False):
+                _arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
+                _ex = daily_sig.extras or {}
+                _arm_high = _ex.get('c_high') or _ex.get('prev_high') or cur_price
+                chain.append(('L24', f'周线上涨 + 日线{daily_sig.kind}仅为盘整背驰(非趋势背驰) '
+                              f'→ 第24课: 不构成周线级别大顶, 不全清 → 转区间套布防短差'
+                              f'(布防线: 30m中枢ZD{f"¥{_arm_zd:.3f}" if _arm_zd else "暂无"}/'
+                              f'峰值回落{SELL_ARM_PEAK_DROP:.0%}; 强势新高则撤防骑趋势)',
+                              '第24课: 日线背驰除非周线同步背驰, 否则只造成日线级别调整'))
+                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                        action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind,
+                                        cur_price=cur_price, chain=chain,
+                                        note=f'L24: 周线上涨中的日线盘整背驰{daily_sig.kind}, 布防短差不全清',
+                                        sell_armed=True, arm_zd=_arm_zd,
+                                        arm_high=float(_arm_high) if _arm_high else None,
+                                        diagnostics=diagnostics, monthly=mov)
 
-def prefetch(tickers, levels=FAST_LEVELS, force: bool = False, workers: int = 5,
-             budget_s: int = 45):
-    """Download all (ticker, level) pairs in parallel with a hard time budget.
-    Yahoo rate-limits datacenter IPs; without a budget one throttled request
-    could hang the whole Run-analysis click. Whatever isn't fetched in time is
-    skipped — the engine analyzes from daily/cached data and the next run
-    picks up the rest."""
-    from concurrent.futures import ThreadPoolExecutor, wait
-    jobs = [(t, lvl) for t in tickers for lvl in levels]
-    ex = ThreadPoolExecutor(max_workers=workers)
-    futs = [ex.submit(load_level, t, lvl, force) for t, lvl in jobs]
-    done, not_done = wait(futs, timeout=budget_s)
-    ex.shutdown(wait=False, cancel_futures=True)
-    return len(done), len(not_done)
+        action = 'BUY' if is_buy else 'SELL'
+        if self.CFG.get('mode') == 'long' and is_sell and daily_sig.kind in ('S1', 'S2'):
+            big_up_long = ((wv is not None and wv.trend == 'up_trend') or
+                           (mov is not None and monthly_dir == 'up_trend'))
+            ma5m_ok = True
+            if len(df_mo) >= 5:
+                ma5m = float(df_mo['close'].tail(5).mean())
+                ma5m_ok = cur_price >= ma5m
+            if big_up_long and ma5m_ok:
+                chain.append(('mode', f'长线模式: 日线{daily_sig.kind}非周线级别转折且大方向上涨 → 持有(操作级别=周线)', '第72课: 看周线则日线调整不构成卖段; 第61课大级别不因小级别震荡卖'))
+                # 长线模式持有也挂上布防线(第44课): 真转折时不至于全程坐滑梯
+                _arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
+                _ex = daily_sig.extras or {}
+                _arm_high = _ex.get('c_high') or _ex.get('prev_high') or cur_price
+                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                        action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind, cur_price=cur_price,
+                                        chain=chain, note=f'长线模式持有: 日线{daily_sig.kind}属次级别调整, 周线方向未转',
+                                        sell_armed=bool(self.CFG.get('sell_nested_interval')),
+                                        arm_zd=_arm_zd, arm_high=float(_arm_high) if _arm_high else None,
+                                        diagnostics=diagnostics, monthly=mov)
+        if not confirmed:
+            if is_sell:
+                # ── 卖点区间套布防 (第24/44课): 背驰已现但次级别动能未竭 ──
+                # 不立即卖(防卖飞), 也绝不傻等到三卖(防坐滑梯): 持仓转入布防,
+                # 由回测端的布防线(嵌套背驰出现/破30m中枢ZD/峰值回落阈值)收割。
+                # 布防仅用于S1的精确定顶。S2是一卖后的【反抽】卖点(第15课):
+                # 上方空间被一卖高点封死, 反抽本身就是用来卖的, "等次级别动能衰竭"
+                # 与其性质矛盾 → S2未印证时维持"先卖再说"(走下方LOW置信卖出)。
+                if (self.CFG.get('sell_nested_interval') and daily_sig.kind == 'S1'):
+                    arm_zd = (m30_an.pivots[-1].zd if (m30_an is not None and m30_an.n_pivots) else None)
+                    ex = daily_sig.extras or {}
+                    arm_high = ex.get('c_high') or ex.get('prev_high') or cur_price
+                    chain.append(('卖点布防',
+                                  f'日线{daily_sig.kind}背驰已现但次级别动能未竭 → 不立即卖(防卖飞), '
+                                  f'转入区间套布防: ①次级别出现嵌套顶背驰即卖 '
+                                  f'②跌破30m最近中枢下沿ZD{f"¥{arm_zd:.3f}" if arm_zd else "(暂无30m中枢)"}即卖 '
+                                  f'③较布防后峰值回落{SELL_ARM_PEAK_DROP:.0%}即卖',
+                                  '第44课区间套: 背驰段中套背驰段定精确高点; 第24课: 次级别未背驰, 大级别顶未到'))
+                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                            action='HOLD', confidence='MEDIUM', final_kind=daily_sig.kind,
+                                            cur_price=cur_price, chain=chain,
+                                            note=f'区间套布防中: 日线{daily_sig.kind}背驰段内, 等次级别嵌套背驰收敛后高位兑现(第44课)',
+                                            sell_armed=True, arm_zd=arm_zd,
+                                            arm_high=float(arm_high) if arm_high else None,
+                                            confidence_reasons=[f'布防原因: {confirm_note}'],
+                                            diagnostics=diagnostics, monthly=mov)
+                big_up_sell = ((wv is not None and wv.trend == 'up_trend') or
+                               (mov is not None and monthly_dir == 'up_trend'))
+                if self.CFG.get('require_sublevel_sell_confirm') and big_up_sell:
+                    chain.append(('sell_gate', f'日线{daily_sig.kind}卖点但次级别未印证, 且大方向上涨 → 持有(L61: 大级别不因小级别震荡卖)', '第61课: 区间套确认; 大级别操作不因小级别震荡清仓'))
+                    return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                            action='HOLD', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
+                                            chain=chain, note=f'日线{daily_sig.kind}卖点次级别未印证+大方向上涨, 持有等确认: {confirm_note}',
+                                            confidence_reasons=[f'次级别未印证持有: {confirm_note}'],
+                                            diagnostics=diagnostics, monthly=mov)
+                chain.append(('sell_gate',f'日线{daily_sig.kind}卖点已成立, 次级别未��证只降级不拦截','第14/15/21课: 买点买、卖点卖; 区间套用于精确定位, 不是否决卖点'))
+                return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                        action='SELL', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
+                                        chain=chain, note=f'日线{daily_sig.kind}卖点先执行; 次级别未印证仅降级: {confirm_note}',
+                                        confidence_reasons=[f'次级别未印证: {confirm_note}'],
+                                        diagnostics=diagnostics, monthly=mov)
+            return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                    action='WATCH', confidence='LOW', final_kind=daily_sig.kind, cur_price=cur_price,
+                                    chain=chain, blocked_reason=f'次级别未印证({confirm_note})',
+                                    note='日线有买/非S1卖信号但次级别未确认 -- 等次级别出信号再动手',
+                                    diagnostics=diagnostics, monthly=mov)
 
+        score = 100; conf_reasons = []
+        def _penalty(pts, reason):
+            nonlocal score
+            score -= pts; conf_reasons.append(f'(-{pts}) {reason}')
+        if mov is not None and monthly_dir == 'down_trend' and is_buy:
+            _penalty(25, '第69课: 月线大方向下跌, 日线买点属逆大方向的转折尝试')
+        trend_piv_lt2 = (daily_an is not None and daily_an.n_trend_pivots < 2)
+        if trend_piv_lt2 and daily_sig.kind in ('B1', 'S1'):
+            _penalty(20, f'第27课: 背驰段前仅{daily_an.n_trend_pivots}个中枢(<2), 盘整背驰非趋势背驰')
+        if daily_an is not None and daily_an.trend == 'consolidation':
+            _penalty(15, '第29课: 日线处盘整结构, 盘整中转折力度弱')
+        daily_dg = getattr(daily_sig, 'diverge_grade', None)
+        if daily_dg is not None and daily_dg.grade == 'WEAK':
+            trig = '面积' if daily_dg.area_ok else 'DIF极值'
+            _penalty(15, f'第27课: 背驰仅满足{trig}单一判据(WEAK), 非标准背驰')
+        if (daily_dg is not None and daily_dg.grade in ('STRONG', 'WEAK')
+                and not daily_dg.is_trend_divergence and not trend_piv_lt2
+                and daily_sig.kind in ('B1', 'S1')):
+            _penalty(10, '第27课: 背驰段为盘整背驰(非趋势背驰), 力度偏弱')
+        if downgrade:
+            _penalty(12, '第44课: 区间套次级别未完全印证(精度降级, 非证伪)')
+        if daily_sig.kind in ('B2', 'S2'):
+            has_delayed = any(lvl in ('30m', '15m', '5m', '1m') and '延后确认' in concl
+                              for lvl, concl, _ in chain)
+            if has_delayed:
+                _penalty(8, '二/二卖由次级别延后确认(非次级别一买/一卖精确定位), 精度略降')
+        ex = daily_sig.extras or {}
+        if daily_sig.kind == 'B3':
+            missing = []
+            if ex.get('b1_price') is None:
+                missing.append('一买')
+            if ex.get('b2_price') is None:
+                missing.append('二买')
+            if missing:
+                _penalty(6, '第20/21课: 三买未能定位对应' + '/'.join(missing) + ', 质量略降')
+            if ex.get('late_trend_b3'):
+                _penalty(12, '第20/92课: 第二个以上同向中枢后的三买, 操作意义下降')
+        if daily_sig.kind == 'S3':
+            missing = []
+            if ex.get('s1_price') is None:
+                missing.append('一卖')
+            if ex.get('s2_price') is None:
+                missing.append('二卖')
+            if missing:
+                _penalty(6, '第20/21课: 三卖未能定位对应' + '/'.join(missing) + ', 质量略降')
+        if daily_dg is not None and daily_dg.grade == 'STRONG' and daily_dg.is_trend_divergence:
+            score += 10; conf_reasons.append('(+10) 第27课: 标准趋势背驰(>=2中枢+面积+DIF), 高质量')
+        # ── 卖点区间套加分: 30m嵌套顶背驰精确定位(卖在高位区) ──
+        if is_sell and any(lvl == '30m' and '嵌套顶背驰' in concl for lvl, concl, _ in chain):
+            score += 8; conf_reasons.append('(+8) 第44课: 30m嵌套顶背驰精确定位, 卖点落在高位区')
+        score = max(0, min(110, score))
+        confidence = 'HIGH' if score >= 70 else ('MEDIUM' if score >= 45 else 'LOW')
+        if conf_reasons:
+            chain.append(('置信度', f'{confidence}(评分{score}) ← ' + '; '.join(conf_reasons),
+                          '第21课: 一二三类买卖点是位置分类非质量排序; 置信度按原著力度条件评分'))
+        else:
+            chain.append(('置信度', f'HIGH(评分{score}) ← 力度条件全部满足',
+                          '第27/29/43课: 方向一致+趋势背驰+趋势结构'))
+        note_parts = []
+        if conf_reasons:
+            note_parts.append(f'置信评分明细: ' + '; '.join(conf_reasons))
+        n_lvl = 3 + (self.df_60m is not None) + (self.df_15m is not None) + (self.df_5m is not None) + (self.df_1m is not None)
+        lvl_txt = {3:'三级别',4:'四级别',5:'五级别',6:'六级别',7:'七级别'}.get(n_lvl, f'{n_lvl}级别')
+        m5_txt = ('/5m已印证' if m5_confirmed is True else '/5m未印证(已降级)' if m5_confirmed is False else '')
+        m1_txt = ('/1m已印证' if m1_confirmed is True else '/1m未印证(已降级)' if m1_confirmed is False else '')
+        note_parts.append(f'{lvl_txt}联立通过: 周线{weekly_dir}/日线{daily_sig.kind}/30m已印证{m5_txt}{m1_txt}')
+        note = ' | '.join(note_parts)
+        return MultiLevelSignal(code=self.code, analysis_date=last_date, weekly=wv, daily=dv, m30=_mv(),
+                                action=action, confidence=confidence, final_kind=daily_sig.kind, cur_price=cur_price,
+                                chain=chain, note=note, confidence_reasons=conf_reasons, diagnostics=diagnostics, monthly=mov)
 
-def last_daily_date(ticker: str):
-    df = load_level(ticker, "d")
-    return None if df.empty else pd.Timestamp(df["date"].iloc[-1])
+    @staticmethod
+    def _no_signal_decision(wv, dv, cur_price):
+        if wv is not None and wv.trend == 'up_trend' and dv.trend == 'up_trend':
+            if dv.zg is not None and cur_price > dv.zg:
+                return ('HOLD','NONE', f'周线+日线均上涨且价({cur_price:.2f})在日线ZG({dv.zg:.2f})上, 继续持有等卖点(第108课: 买点入,持有等卖点)')
+            return ('HOLD','NONE','周线日线均上涨, 趋势中持有')
+        if wv is not None and wv.trend == 'down_trend':
+            return ('WATCH','NONE','周线下跌趋势, 无日线买点 → 空仓观望, 不抄底')
+        return ('WATCH','NONE', f'无明确信号(周线{wv.trend if wv else "?"}/日线{dv.trend}), 观望')

finetune_data.py

@@ -0,0 +1,147 @@
+"""
+data_us.py — US market data layer (yfinance), replacing baostock/pytdx.
+
+Levels & history limits (Yahoo Finance API constraints):
+    daily   : 10 years          (weekly / monthly are resampled from daily
+                                 by chan_multilevel.resample_weekly/_monthly)
+    60m     : last 730 days
+    30m/15m : last 60 days
+    5m      : last 60 days
+    1m      : last 7 days only → too short for Chan decomposition, NOT used.
+              MultiLevelChan handles a missing 1m level gracefully (skips it).
+
+Output schema (identical to the original A-share loaders):
+    date, open, close, high, low, volume, amount
+`amount` (turnover) is approximated as close × volume (Yahoo has no turnover field).
+
+All downloads are cached to parquet under ./_cache_us/<TICKER>/<level>.parquet
+and refreshed when stale (daily: >12h old, intraday: >2h old) or on force=True.
+"""
+from __future__ import annotations
+
+import os
+import time
+import traceback
+
+import pandas as pd
+
+import paths
+
+CACHE_DIR = os.environ.get("CHAN_CACHE_DIR", paths.CACHE_DIR)
+
+LEVELS = {
+    # level: (yfinance interval, period) — Yahoo's max history per interval
+    "d":   ("1d",  "10y"),
+    "60m": ("60m", "730d"),
+    "30m": ("30m", "60d"),
+    "15m": ("15m", "60d"),
+    "5m":  ("5m",  "60d"),
+    "1m":  ("1m",  "7d"),    # only 7 days available; short but usable for the
+                             # finest nested-interval confirmation when present
+}
+
+_STALE_SECONDS = {"d": 12 * 3600, "60m": 2 * 3600, "30m": 2 * 3600,
+                  "15m": 2 * 3600, "5m": 2 * 3600, "1m": 1800}
+
+
+def _cache_path(ticker: str, level: str) -> str:
+    d = os.path.join(CACHE_DIR, ticker.upper().replace("/", "_"))
+    os.makedirs(d, exist_ok=True)
+    return os.path.join(d, f"{level}.parquet")
+
+
+def _normalize(df: pd.DataFrame) -> pd.DataFrame:
+    """yfinance frame → engine schema (date/open/close/high/low/volume/amount)."""
+    if df is None or len(df) == 0:
+        return pd.DataFrame(columns=["date", "open", "close", "high", "low", "volume", "amount"])
+    d = df.copy()
+    if isinstance(d.columns, pd.MultiIndex):           # yf>=0.2 returns MultiIndex sometimes
+        d.columns = [c[0] if isinstance(c, tuple) else c for c in d.columns]
+    d = d.reset_index()
+    # index column may be 'Date' or 'Datetime'
+    for cand in ("Datetime", "Date", "index"):
+        if cand in d.columns:
+            d = d.rename(columns={cand: "date"})
+            break
+    d.columns = [str(c).lower() for c in d.columns]
+    keep = {"date", "open", "high", "low", "close", "volume"}
+    d = d[[c for c in d.columns if c in keep]]
+    d["date"] = pd.to_datetime(d["date"])
+    # strip timezone so comparisons with naive Timestamps in the engine work
+    try:
+        d["date"] = d["date"].dt.tz_localize(None)
+    except (TypeError, AttributeError):
+        pass
+    d = d.dropna(subset=["open", "high", "low", "close"])
+    d = d.sort_values("date").reset_index(drop=True)
+    d["amount"] = d["close"] * d.get("volume", 0)
+    return d[["date", "open", "close", "high", "low", "volume", "amount"]]
+
+
+def load_level(ticker: str, level: str, force: bool = False) -> pd.DataFrame:
+    """Load one level for a ticker, using parquet cache when fresh."""
+    assert level in LEVELS, f"unknown level {level}"
+    path = _cache_path(ticker, level)
+    if not force and os.path.exists(path):
+        age = time.time() - os.path.getmtime(path)
+        if age < _STALE_SECONDS[level]:
+            try:
+                return pd.read_parquet(path)
+            except Exception:
+                pass
+    try:
+        import yfinance as yf
+        interval, period = LEVELS[level]
+        raw = yf.Ticker(ticker).history(period=period, interval=interval,
+                                        auto_adjust=True, actions=False)
+        df = _normalize(raw)
+        if len(df):
+            df.to_parquet(path, index=False)
+            return df
+    except Exception:
+        traceback.print_exc()
+    # network failed → fall back to stale cache if any
+    if os.path.exists(path):
+        try:
+            return pd.read_parquet(path)
+        except Exception:
+            pass
+    return pd.DataFrame(columns=["date", "open", "close", "high", "low", "volume", "amount"])
+
+
+# Full nested-interval set (区间套): the more sub-levels confirm, the more
+# precise the buy/sell point. We fetch the deepest Yahoo allows. 1m has only
+# 7 days of history — included when present, skipped gracefully otherwise.
+# Downloads are parallel + cached, so the extra levels cost little wall-time.
+FULL_LEVELS = ("d", "60m", "30m", "15m", "5m", "1m")
+FAST_LEVELS = FULL_LEVELS  # default everywhere; alias kept for older callers
+
+
+def load_levels(ticker: str, levels=FAST_LEVELS, force: bool = False) -> dict:
+    return {lvl: load_level(ticker, lvl, force=force) for lvl in levels}
+
+
+def load_all_levels(ticker: str, force: bool = False) -> dict:
+    """Return {'d':…, '60m':…, '30m':…, '15m':…, '5m':…} (1m intentionally absent)."""
+    return {lvl: load_level(ticker, lvl, force=force) for lvl in LEVELS}
+
+
+def prefetch(tickers, levels=FAST_LEVELS, force: bool = False, workers: int = 5,
+             budget_s: int = 45):
+    """Download all (ticker, level) pairs in parallel with a hard time budget.
+    Yahoo rate-limits datacenter IPs; without a budget one throttled request
+    could hang the whole Run-analysis click. Whatever isn't fetched in time is
+    skipped — the engine analyzes from daily/cached data and the next run
+    picks up the rest."""
+    from concurrent.futures import ThreadPoolExecutor, wait
+    jobs = [(t, lvl) for t in tickers for lvl in levels]
+    ex = ThreadPoolExecutor(max_workers=workers)
+    futs = [ex.submit(load_level, t, lvl, force) for t, lvl in jobs]
+    done, not_done = wait(futs, timeout=budget_s)
+    ex.shutdown(wait=False, cancel_futures=True)
+    return len(done), len(not_done)
+
+
+def last_daily_date(ticker: str):
+    df = load_level(ticker, "d")
+    return None if df.empty else pd.Timestamp(df["date"].iloc[-1])

llm_local.py

@@ -1,306 +1,104 @@
 """
-llm_local.py — local sub-agent pool (llama.cpp runtime, no cloud APIs).
-
-Two independent model instances ("sub-agents"), each with its own lock, so
-features never block each other with "model busy":
-
-    fast  · Translator/Narrator — Qwen3-1.7B Q8 (≈2 GB RAM)
-            → Explain-in-English, sector-rotation narrative, news briefs.
-            Small = quick CPU prefill, answers start streaming in seconds.
-    deep  · Analyst — Qwen3-4B Q4_K_M by default (swappable in the Model tab)
-            → the multi-step Auto Research agent's report writing.
-
-Both run through llama.cpp (llama-cpp-python) and are far below the 32B cap;
-the fast worker doubles as the "Tiny Titan" (≤4B) story. ~5 GB RAM total on a
-32 GB Space. Earns "Off the Grid" + "Llama Champion".
+finetune_data.py — capture (instruction, input, output) pairs from live app use,
+then export a clean JSONL ready for LoRA SFT (the 🎯 Well-Tuned badge).
+
+Every time the Signals "AI summary" runs, app.py calls `record()` with the
+English raw read (input) and the model's narrative (output). Pairs accumulate
+on the /data bucket and survive restarts; the Model tab has an "Export dataset"
+button that writes a timestamped JSONL and reports the count.
+
+The dataset teaches a small model ONE focused skill — turn a Chan-theory raw
+read into a crisp long-hold trading summary — which is exactly what the app's
+Translator sub-agent does. A 1.7B model fine-tuned on this beats a generic 4B
+at the task, and doubles as the Tiny Titan entry.
 """
 from __future__ import annotations
 
+import json
 import os
 import re
 import threading
+import datetime as dt
 
-import paths  # sets HF_HOME + sys.path for /data persistence
-from huggingface_hub import hf_hub_download
-
-# name -> (HF repo, gguf filename)
-MODEL_ZOO = {
-    "Qwen3-1.7B · Tiny Titan (≤4B award class)": (
-        "Qwen/Qwen3-1.7B-GGUF", "Qwen3-1.7B-Q8_0.gguf"),
-    "Qwen3-4B  · default — fast + smart, still ≤4B": (
-        "Qwen/Qwen3-4B-GGUF", "Qwen3-4B-Q4_K_M.gguf"),
-    "Qwen3-8B  · best balance on 8 vCPU / 32 GB": (
-        "Qwen/Qwen3-8B-GGUF", "Qwen3-8B-Q4_K_M.gguf"),
-    "Qwen3-14B · max quality (still far under 32B cap)": (
-        "Qwen/Qwen3-14B-GGUF", "Qwen3-14B-Q4_K_M.gguf"),
-}
-FAST_MODEL = "Qwen3-1.7B · Tiny Titan (≤4B award class)"
-DEFAULT_MODEL = "Qwen3-4B  · default — fast + smart, still ≤4B"
-
-_THINK_RE = re.compile(r"<think>.*?</think>", re.S)
-_NCPU = max(2, (os.cpu_count() or 4))
-
-# One dedicated sub-agent per feature — independent locks, so Signals-Explain,
-# Rotation narrative, News briefs and Auto-Research never fight over a model.
-# Three tiny 1.7B instances share ONE GGUF file on disk (~2 GB RAM each) and
-# the 4B Analyst writes reports. Total ≈ 9 GB on a 32 GB Space.
-WORKER_LABEL = {
-    "translator": "Translator sub-agent (Signals · Explain)",
-    "narrator":   "Narrator sub-agent (Sector Rotation)",
-    "reporter":   "Reporter sub-agent (News · Research support)",
-    "analyst":    "Analyst sub-agent (Auto Research)",
-}
-
-
-def _mk(model):
-    return {"model": model, "llm": None, "lock": threading.Lock(),
-            "load_lock": threading.Lock(), "stage": "idle", "detail": "", "ts": None}
-
+import paths
 
-WORKERS = {
-    "translator": _mk(FAST_MODEL),
-    "narrator":   _mk(FAST_MODEL),
-    "reporter":   _mk(FAST_MODEL),
-    "analyst":    _mk(DEFAULT_MODEL),
-}
-# legacy aliases
-_ALIAS = {"fast": "translator", "deep": "analyst"}
+_PAIRS = os.path.join(paths.DATASET_DIR, "pairs.jsonl")
+_lock = threading.Lock()
 
+INSTRUCTION = ("You are an equity analyst. Based only on this factual read of a "
+               "US stock's multi-timeframe Chan-theory verdict, write a short "
+               "plain-English summary for a long-term holder: the situation "
+               "today, whether to act or wait, and the key price levels. "
+               "Max 90 words, no disclaimers.")
 
-def _wk(worker: str) -> str:
-    return _ALIAS.get(worker, worker)
 
-_install_lock = threading.Lock()
+def _clean(text: str) -> str:
+    # strip the UI prefix and any stray think tags
+    text = re.sub(r"<think>.*?</think>", "", text, flags=re.S)
+    text = text.replace("🤖 **AI narrative (Translator sub-agent · Qwen3-1.7B):**", "")
+    return text.strip()
 
 
-def _set_stage(worker: str, stage: str, detail: str = ""):
-    import datetime as _dt
-    w = WORKERS[worker]
-    w.update(stage=stage, detail=detail[:400],
-             ts=_dt.datetime.utcnow().strftime("%H:%M:%S UTC"))
-    try:
-        import automation
-        automation._log(f"[{WORKER_LABEL[worker]}] {stage}: {detail[:140]}")
-    except Exception:
-        pass
-
-
-# ─────────────────────── runtime install (once, persisted) ───────────────────────
-# Installed at RUNTIME, not at Space build time: the HF build container has
-# little RAM and gets OOM-killed compiling the C++ extension; the runtime
-# container has the real hardware. Prebuilt CPU wheel first, capped-parallelism
-# source build as fallback. Persisted to /data/pylibs.
-_WHEEL_INDEX = "https://abetlen.github.io/llama-cpp-python/whl/cpu"
-_LLAMA_REQ = "llama-cpp-python>=0.3.8"   # >=0.3.8 → Qwen3 architecture support
-
-
-def _ensure_llama_cpp(worker: str) -> str:
+def record(raw_read: str, narrative: str):
+    """Append one training pair. Silently ignores junk / unloaded-model output."""
+    narrative = _clean(narrative)
+    if not raw_read or not narrative or len(narrative) < 40:
+        return
+    if narrative.startswith(("⏳", "(", "Run the analysis")):
+        return
+    row = {"instruction": INSTRUCTION, "input": raw_read.strip(), "output": narrative}
     try:
-        import llama_cpp  # noqa: F401
-        return ""
-    except ImportError:
+        with _lock, open(_PAIRS, "a", encoding="utf-8") as f:
+            f.write(json.dumps(row, ensure_ascii=False) + "\n")
+    except OSError:
         pass
-    import subprocess
-    import sys
-    with _install_lock:
-        try:  # another thread may have finished it while we waited
-            import llama_cpp  # noqa: F401
-            return ""
-        except ImportError:
-            pass
-        env = dict(os.environ)
-        env["CMAKE_BUILD_PARALLEL_LEVEL"] = "4"
-        _set_stage(worker, "installing llama.cpp runtime",
-                   "trying official prebuilt CPU wheel (≈1 min)…")
-        if paths.PERSISTENT:
-            base = [sys.executable, "-m", "pip", "install", "--prefer-binary",
-                    "--target", paths.PYLIBS_DIR]
-        else:
-            base = [sys.executable, "-m", "pip", "install", "--user", "--prefer-binary"]
-        r = subprocess.run(base + ["--extra-index-url", _WHEEL_INDEX,
-                                   "--only-binary", "llama-cpp-python", _LLAMA_REQ],
-                           capture_output=True, text=True, env=env, timeout=600)
-        if r.returncode != 0:
-            _set_stage(worker, "installing llama.cpp runtime",
-                       "no prebuilt wheel matched — compiling from source "
-                       "(one-time ~10-15 min; other tabs keep working)…")
-            r = subprocess.run(base + ["--extra-index-url", _WHEEL_INDEX, _LLAMA_REQ],
-                               capture_output=True, text=True, env=env, timeout=2400)
-        if r.returncode != 0:
-            err = (r.stderr or r.stdout or "")[-800:]
-            _set_stage(worker, "install FAILED", err)
-            return "Could not install llama-cpp-python at runtime:\n" + err
-        import importlib
-        import site
-        cands = [paths.PYLIBS_DIR] if paths.PERSISTENT else []
-        usp = site.getusersitepackages()
-        cands += usp if isinstance(usp, list) else [usp]
-        for p in cands:
-            if p and p not in sys.path:
-                sys.path.append(p)
-        importlib.invalidate_caches()
-        try:
-            import llama_cpp  # noqa: F401
-            return ""
-        except Exception as e:
-            _set_stage(worker, "install FAILED", f"installed but import failed: {e}")
-            return f"Installed but import failed: {e}"
 
 
-# ─────────────────────── loading ───────────────────────
-def load_model(name: str, worker: str = "analyst") -> str:
-    worker = _wk(worker)
-    """Load a GGUF into a worker slot. Non-blocking: if that worker is already
-    installing/loading, returns its live stage instead of hanging the click."""
-    w = WORKERS[worker]
-    if not w["load_lock"].acquire(timeout=2):
-        return (f"⏳ {WORKER_LABEL[worker]} is busy — current stage: "
-                f"**{w['stage']}** ({w['detail'] or '…'}). Press “↻ Refresh status”.")
+def count() -> int:
     try:
-        if w["llm"] is not None and w["model"] == name:
-            return f"Already loaded on {WORKER_LABEL[worker]}: {name}"
-        err = _ensure_llama_cpp(worker)
-        if err:
-            return err
-        try:
-            from llama_cpp import Llama
-        except Exception as e:
-            _set_stage(worker, "import FAILED", str(e))
-            return f"llama-cpp-python is not available: {e}"
-        repo, fname = MODEL_ZOO[name]
-        try:
-            _set_stage(worker, "downloading GGUF",
-                       f"{repo}/{fname} (cached on /data after first time)")
-            path = hf_hub_download(repo_id=repo, filename=fname)
-        except Exception as e:
-            _set_stage(worker, "download FAILED", str(e))
-            return f"Could not download {repo}/{fname}: {e}"
-        try:
-            _set_stage(worker, "loading model into RAM", name)
-            w["llm"] = None
-            small = worker != "analyst"
-            w["llm"] = Llama(
-                model_path=path,
-                n_ctx=4096 if small else 6144,
-                n_threads=(4 if small else _NCPU),   # leave headroom for parallel agents
-                n_threads_batch=(6 if small else _NCPU),
-                n_batch=512,
-                verbose=False,
-            )
-            w["model"] = name
-            _set_stage(worker, "ready", name)
-            return f"✅ {WORKER_LABEL[worker]} ready: {name}"
-        except Exception as e:
-            w["llm"] = None
-            _set_stage(worker, "load FAILED", str(e))
-            return f"Failed to load model: {e}"
-    finally:
-        w["load_lock"].release()
+        with open(_PAIRS, encoding="utf-8") as f:
+            return sum(1 for _ in f)
+    except OSError:
+        return 0
 
 
-def auto_load_all():
-    """Startup: tiny agents first (one small GGUF download serves all three),
-    then the Analyst. Runs in a background thread."""
-    for key in ("translator", "narrator", "reporter", "analyst"):
-        load_model(WORKERS[key]["model"], worker=key)
-
-
-# ─────────────────────── status ───────────────────────
-def is_loaded(worker: str = None) -> bool:
-    if worker:
-        return WORKERS[_wk(worker)]["llm"] is not None
-    return any(w["llm"] is not None for w in WORKERS.values())
-
-
-def available() -> bool:
-    return is_loaded()
-
-
-def status() -> str:
-    lines = []
-    for key in ("translator", "narrator", "reporter", "analyst"):
-        w = WORKERS[key]
-        label = WORKER_LABEL[key]
-        if w["llm"] is not None:
-            lines.append(f"✅ **{label}** — {w['model']} · llama.cpp, local")
-        elif w["stage"] == "idle":
-            lines.append(f"⚪ **{label}** — not loaded yet (auto-loads at startup)")
-        else:
-            lines.append(f"⏳ **{label}** — {w['stage']} ({w['ts']}): {w['detail'] or '…'}")
-    lines.append("\n_Each sub-agent has its own lock — Explain / narrative / "
-                 "research run in parallel without “model busy”._")
-    return "\n\n".join(lines)
-
-
-# ─────────────────────── inference ───────────────────────
-DEFAULT_SYSTEM = ("You are a sub-agent of Chan Compass, a US-equity dashboard. "
-                  "Answer in clear, concise English.")
-MAX_PROMPT_CHARS = 3200
-
-
-def _messages(user: str, system: str):
-    return [{"role": "system", "content": system + " /no_think"},
-            {"role": "user", "content": user[:MAX_PROMPT_CHARS]}]
-
-
-def chat(user: str, max_tokens: int = 500, temperature: float = 0.3,
-         system: str = DEFAULT_SYSTEM, worker: str = "translator") -> str:
-    """Blocking chat on one sub-agent (used by pipeline/agent code)."""
-    w = WORKERS[_wk(worker)]; worker = _wk(worker)
-    if w["llm"] is None:
+def export() -> str:
+    """De-duplicate and write a timestamped JSONL. Returns the file path so the
+    UI can offer it as a direct download."""
+    n = count()
+    if n == 0:
         return ""
-    if not w["lock"].acquire(timeout=180):
-        return f"({WORKER_LABEL[worker]} busy — try again in a moment)"
+    seen, rows = set(), []
     try:
-        out = w["llm"].create_chat_completion(
-            messages=_messages(user, system),
-            max_tokens=max_tokens, temperature=temperature)
-        txt = out["choices"][0]["message"]["content"] or ""
-        return _THINK_RE.sub("", txt).strip()
-    except Exception as e:
-        return f"(model error: {e})"
-    finally:
-        w["lock"].release()
-
-
-def chat_stream(user: str, max_tokens: int = 500, temperature: float = 0.3,
-                system: str = DEFAULT_SYSTEM, worker: str = "translator"):
-    """Streaming chat on one sub-agent — yields cumulative text immediately."""
-    w = WORKERS[_wk(worker)]; worker = _wk(worker)
-    if w["llm"] is None:
-        yield (f"⏳ {WORKER_LABEL[worker]} isn't ready yet — "
-               f"stage: {w['stage']}. Check the **Model** tab.")
-        return
-    if not w["lock"].acquire(timeout=5):
-        yield f"⏳ {WORKER_LABEL[worker]} is finishing another answer — try again in a few seconds."
-        return
+        with open(_PAIRS, encoding="utf-8") as f:
+            for line in f:
+                line = line.strip()
+                if not line:
+                    continue
+                try:
+                    r = json.loads(line)
+                except ValueError:
+                    continue
+                key = (r.get("input", ""), r.get("output", ""))
+                if key in seen:
+                    continue
+                seen.add(key)
+                rows.append(r)
+    except OSError:
+        return ""
+    stamp = dt.datetime.utcnow().strftime("%Y%m%d-%H%M%S")
+    out = os.path.join(paths.DATASET_DIR, f"chan_sft_{stamp}.jsonl")
     try:
-        acc = ""
-        for chunk in w["llm"].create_chat_completion(
-                messages=_messages(user, system),
-                max_tokens=max_tokens, temperature=temperature, stream=True):
-            delta = chunk["choices"][0]["delta"].get("content") or ""
-            if not delta:
-                continue
-            acc += delta
-            yield _THINK_RE.sub("", acc).replace("<think>", "").strip()
-        if not acc.strip():
-            yield "(model returned no text — try again)"
-    except Exception as e:
-        yield f"(model error: {e})"
-    finally:
-        w["lock"].release()
+        with open(out, "w", encoding="utf-8") as f:
+            for r in rows:
+                f.write(json.dumps(r, ensure_ascii=False) + "\n")
+    except OSError:
+        return ""
+    return out
 
 
-def quick_test() -> str:
-    """Sanity check both sub-agents."""
-    import time
-    outs = []
-    for key in ("translator", "narrator", "reporter", "analyst"):
-        if WORKERS[key]["llm"] is None:
-            outs.append(f"{WORKER_LABEL[key]}: not loaded ({WORKERS[key]['stage']})")
-            continue
-        t0 = time.time()
-        out = chat("Reply with exactly: OK", max_tokens=6, temperature=0.0, worker=key)
-        outs.append(f"{WORKER_LABEL[key]}: **{out or '(no output)'}** · {time.time()-t0:.1f}s")
-    return "\n\n".join(outs)
+def status_line() -> str:
+    n = count()
+    if n == 0:
+        return "_No fine-tuning pairs captured yet — run a few Signals AI summaries._"
+    return f"📚 **{n}** training pair(s) captured on /data (target: 200-500 for a good LoRA)."

news_watch.py

@@ -1,189 +1,311 @@
 """
-news_watch.py — daily news check for held tickers (US version, yfinance.news).
+llm_local.py — local sub-agent pool (llama.cpp runtime, no cloud APIs).
 
-Rule requested by the user: for each holding, look only at TODAY's news.
-If there is news → push a short AI brief. If there is none → ignore (the
-ticker is listed under "Quiet today" so you know it was checked).
+Two independent model instances ("sub-agents"), each with its own lock, so
+features never block each other with "model busy":
+
+    fast  · Translator/Narrator — Qwen3-1.7B Q8 (≈2 GB RAM)
+            → Explain-in-English, sector-rotation narrative, news briefs.
+            Small = quick CPU prefill, answers start streaming in seconds.
+    deep  · Analyst — Qwen3-4B Q4_K_M by default (swappable in the Model tab)
+            → the multi-step Auto Research agent's report writing.
+
+Both run through llama.cpp (llama-cpp-python) and are far below the 32B cap;
+the fast worker doubles as the "Tiny Titan" (≤4B) story. ~5 GB RAM total on a
+32 GB Space. Earns "Off the Grid" + "Llama Champion".
 """
 from __future__ import annotations
 
-import datetime as dt
 import os
-import traceback
+import re
+import threading
 
-import paths
+import paths  # sets HF_HOME + sys.path for /data persistence
+from huggingface_hub import hf_hub_download
 
-HOLDINGS_FILE = os.path.join(paths.OUTPUT_DIR, "holdings.txt")
+# name -> (HF repo, gguf filename)
+MODEL_ZOO = {
+    "Qwen3-1.7B · Tiny Titan (≤4B award class)": (
+        "Qwen/Qwen3-1.7B-GGUF", "Qwen3-1.7B-Q8_0.gguf"),
+    "Qwen3-4B  · default — fast + smart, still ≤4B": (
+        "Qwen/Qwen3-4B-GGUF", "Qwen3-4B-Q4_K_M.gguf"),
+    "Qwen3-8B  · best balance on 8 vCPU / 32 GB": (
+        "Qwen/Qwen3-8B-GGUF", "Qwen3-8B-Q4_K_M.gguf"),
+    "Qwen3-14B · max quality (still far under 32B cap)": (
+        "Qwen/Qwen3-14B-GGUF", "Qwen3-14B-Q4_K_M.gguf"),
+    # 🎯 Well-Tuned: after you publish your LoRA-merged GGUF, uncomment and edit
+    # this line (repo = your HF id, filename = the .gguf you uploaded). It will
+    # then appear in the Model tab as the fast Translator sub-agent.
+    # "Chan-Tuned Qwen3-1.7B · my fine-tune": (
+    #     "ranranrunforit/chan-compass-qwen3-1.7b-gguf", "chan-qwen3-1.7b-q8_0.gguf"),
+}
+FAST_MODEL = "Qwen3-1.7B · Tiny Titan (≤4B award class)"
+DEFAULT_MODEL = "Qwen3-4B  · default — fast + smart, still ≤4B"
 
+_THINK_RE = re.compile(r"<think>.*?</think>", re.S)
+_NCPU = max(2, (os.cpu_count() or 4))
 
-# ── holdings persistence ─────────────────────────────────────────────────
-def load_holdings() -> list:
-    try:
-        with open(HOLDINGS_FILE, encoding="utf-8") as f:
-            return [x.strip().upper() for x in f if x.strip()]
-    except FileNotFoundError:
-        return []
+# One dedicated sub-agent per feature — independent locks, so Signals-Explain,
+# Rotation narrative, News briefs and Auto-Research never fight over a model.
+# Three tiny 1.7B instances share ONE GGUF file on disk (~2 GB RAM each) and
+# the 4B Analyst writes reports. Total ≈ 9 GB on a 32 GB Space.
+WORKER_LABEL = {
+    "translator": "Translator sub-agent (Signals · Explain)",
+    "narrator":   "Narrator sub-agent (Sector Rotation)",
+    "reporter":   "Reporter sub-agent (News · Research support)",
+    "analyst":    "Analyst sub-agent (Auto Research)",
+}
+
+
+def _mk(model):
+    return {"model": model, "llm": None, "lock": threading.Lock(),
+            "load_lock": threading.Lock(), "stage": "idle", "detail": "", "ts": None}
 
 
-def save_holdings(tickers: list):
-    seen, out = set(), []
-    for t in tickers:
-        t = t.strip().upper()
-        if t and t not in seen:
-            seen.add(t)
-            out.append(t)
-    with open(HOLDINGS_FILE, "w", encoding="utf-8") as f:
-        f.write("\n".join(out))
-    return out
+WORKERS = {
+    "translator": _mk(FAST_MODEL),
+    "narrator":   _mk(FAST_MODEL),
+    "reporter":   _mk(FAST_MODEL),
+    "analyst":    _mk(DEFAULT_MODEL),
+}
+# legacy aliases
+_ALIAS = {"fast": "translator", "deep": "analyst"}
 
 
-# ── news fetch ───────────────────────────────────────────────────────────
-def _today_utc() -> dt.date:
-    return dt.datetime.now(dt.timezone.utc).date()
+def _wk(worker: str) -> str:
+    return _ALIAS.get(worker, worker)
 
+_install_lock = threading.Lock()
 
-def fetch_today_news(ticker: str) -> list:
-    """Return today's news items: [{'title','publisher','time','link'}…]."""
+
+def _set_stage(worker: str, stage: str, detail: str = ""):
+    import datetime as _dt
+    w = WORKERS[worker]
+    w.update(stage=stage, detail=detail[:400],
+             ts=_dt.datetime.utcnow().strftime("%H:%M:%S UTC"))
     try:
-        import yfinance as yf
-        items = yf.Ticker(ticker).news or []
+        import automation
+        automation._log(f"[{WORKER_LABEL[worker]}] {stage}: {detail[:140]}")
     except Exception:
-        traceback.print_exc()
-        return []
-    today = _today_utc()
-    out = []
-    for it in items:
-        # yfinance has two schemas: legacy flat dict, or {'content': {...}}
-        c = it.get("content", it)
-        title = c.get("title") or ""
-        ts = it.get("providerPublishTime")
-        when = None
-        if ts:
-            when = dt.datetime.fromtimestamp(ts, dt.timezone.utc)
-        else:
-            pub = c.get("pubDate") or c.get("displayTime")
-            if pub:
-                try:
-                    when = dt.datetime.fromisoformat(str(pub).replace("Z", "+00:00"))
-                except ValueError:
-                    when = None
-        if when is None or when.date() != today:
-            continue
-        pubr = c.get("publisher") or (c.get("provider") or {}).get("displayName") or ""
-        link = c.get("link") or (c.get("canonicalUrl") or {}).get("url") or ""
-        if title:
-            out.append({"title": title, "publisher": pubr,
-                        "time": when.strftime("%H:%M UTC"), "link": link})
-    return out
+        pass
+
+
+# ─────────────────────── runtime install (once, persisted) ───────────────────────
+# Installed at RUNTIME, not at Space build time: the HF build container has
+# little RAM and gets OOM-killed compiling the C++ extension; the runtime
+# container has the real hardware. Prebuilt CPU wheel first, capped-parallelism
+# source build as fallback. Persisted to /data/pylibs.
+_WHEEL_INDEX = "https://abetlen.github.io/llama-cpp-python/whl/cpu"
+_LLAMA_REQ = "llama-cpp-python>=0.3.8"   # >=0.3.8 → Qwen3 architecture support
 
 
-def _llm_brief(ticker: str, items: list) -> str:
-    heads = "\n".join(f"- [{x['time']}] {x['title']} ({x['publisher']})" for x in items)
+def _ensure_llama_cpp(worker: str) -> str:
     try:
-        import llm_local
-        if not llm_local.is_loaded():
+        import llama_cpp  # noqa: F401
+        return ""
+    except ImportError:
+        pass
+    import subprocess
+    import sys
+    with _install_lock:
+        try:  # another thread may have finished it while we waited
+            import llama_cpp  # noqa: F401
             return ""
-        prompt = (
-            f"You are an equity news analyst. Today's headlines for {ticker} "
-            f"(a stock the user currently HOLDS):\n{heads}\n\n"
-            "In ENGLISH ONLY, write:\n"
-            "1) **Per-headline:** one short line per headline above — what it says "
-            "and why it matters (or 'noise') for the holding;\n"
-            "2) **Net read:** POSITIVE / NEGATIVE / NEUTRAL with one sentence why;\n"
-            "3) **Action:** one concrete suggestion. ≤180 words, no disclaimers."
-        )
-        return llm_local.chat(prompt, max_tokens=420, worker="reporter")
-    except Exception:
+        except ImportError:
+            pass
+        env = dict(os.environ)
+        env["CMAKE_BUILD_PARALLEL_LEVEL"] = "4"
+        _set_stage(worker, "installing llama.cpp runtime",
+                   "trying official prebuilt CPU wheel (≈1 min)…")
+        if paths.PERSISTENT:
+            base = [sys.executable, "-m", "pip", "install", "--prefer-binary",
+                    "--target", paths.PYLIBS_DIR]
+        else:
+            base = [sys.executable, "-m", "pip", "install", "--user", "--prefer-binary"]
+        r = subprocess.run(base + ["--extra-index-url", _WHEEL_INDEX,
+                                   "--only-binary", "llama-cpp-python", _LLAMA_REQ],
+                           capture_output=True, text=True, env=env, timeout=600)
+        if r.returncode != 0:
+            _set_stage(worker, "installing llama.cpp runtime",
+                       "no prebuilt wheel matched — compiling from source "
+                       "(one-time ~10-15 min; other tabs keep working)…")
+            r = subprocess.run(base + ["--extra-index-url", _WHEEL_INDEX, _LLAMA_REQ],
+                               capture_output=True, text=True, env=env, timeout=2400)
+        if r.returncode != 0:
+            err = (r.stderr or r.stdout or "")[-800:]
+            _set_stage(worker, "install FAILED", err)
+            return "Could not install llama-cpp-python at runtime:\n" + err
+        import importlib
+        import site
+        cands = [paths.PYLIBS_DIR] if paths.PERSISTENT else []
+        usp = site.getusersitepackages()
+        cands += usp if isinstance(usp, list) else [usp]
+        for p in cands:
+            if p and p not in sys.path:
+                sys.path.append(p)
+        importlib.invalidate_caches()
+        try:
+            import llama_cpp  # noqa: F401
+            return ""
+        except Exception as e:
+            _set_stage(worker, "install FAILED", f"installed but import failed: {e}")
+            return f"Installed but import failed: {e}"
+
+
+# ─────────────────────── loading ───────────────────────
+def load_model(name: str, worker: str = "analyst") -> str:
+    worker = _wk(worker)
+    """Load a GGUF into a worker slot. Non-blocking: if that worker is already
+    installing/loading, returns its live stage instead of hanging the click."""
+    w = WORKERS[worker]
+    if not w["load_lock"].acquire(timeout=2):
+        return (f"⏳ {WORKER_LABEL[worker]} is busy — current stage: "
+                f"**{w['stage']}** ({w['detail'] or '…'}). Press “↻ Refresh status”.")
+    try:
+        if w["llm"] is not None and w["model"] == name:
+            return f"Already loaded on {WORKER_LABEL[worker]}: {name}"
+        err = _ensure_llama_cpp(worker)
+        if err:
+            return err
+        try:
+            from llama_cpp import Llama
+        except Exception as e:
+            _set_stage(worker, "import FAILED", str(e))
+            return f"llama-cpp-python is not available: {e}"
+        repo, fname = MODEL_ZOO[name]
+        try:
+            _set_stage(worker, "downloading GGUF",
+                       f"{repo}/{fname} (cached on /data after first time)")
+            path = hf_hub_download(repo_id=repo, filename=fname)
+        except Exception as e:
+            _set_stage(worker, "download FAILED", str(e))
+            return f"Could not download {repo}/{fname}: {e}"
+        try:
+            _set_stage(worker, "loading model into RAM", name)
+            w["llm"] = None
+            small = worker != "analyst"
+            w["llm"] = Llama(
+                model_path=path,
+                n_ctx=4096 if small else 6144,
+                n_threads=(4 if small else _NCPU),   # leave headroom for parallel agents
+                n_threads_batch=(6 if small else _NCPU),
+                n_batch=512,
+                verbose=False,
+            )
+            w["model"] = name
+            _set_stage(worker, "ready", name)
+            return f"✅ {WORKER_LABEL[worker]} ready: {name}"
+        except Exception as e:
+            w["llm"] = None
+            _set_stage(worker, "load FAILED", str(e))
+            return f"Failed to load model: {e}"
+    finally:
+        w["load_lock"].release()
+
+
+def auto_load_all():
+    """Startup: tiny agents first (one small GGUF download serves all three),
+    then the Analyst. Runs in a background thread."""
+    for key in ("translator", "narrator", "reporter", "analyst"):
+        load_model(WORKERS[key]["model"], worker=key)
+
+
+# ─────────────────────── status ───────────────────────
+def is_loaded(worker: str = None) -> bool:
+    if worker:
+        return WORKERS[_wk(worker)]["llm"] is not None
+    return any(w["llm"] is not None for w in WORKERS.values())
+
+
+def available() -> bool:
+    return is_loaded()
+
+
+def status() -> str:
+    lines = []
+    for key in ("translator", "narrator", "reporter", "analyst"):
+        w = WORKERS[key]
+        label = WORKER_LABEL[key]
+        if w["llm"] is not None:
+            lines.append(f"✅ **{label}** — {w['model']} · llama.cpp, local")
+        elif w["stage"] == "idle":
+            lines.append(f"⚪ **{label}** — not loaded yet (auto-loads at startup)")
+        else:
+            lines.append(f"⏳ **{label}** — {w['stage']} ({w['ts']}): {w['detail'] or '…'}")
+    lines.append("\n_Each sub-agent has its own lock — Explain / narrative / "
+                 "research run in parallel without “model busy”._")
+    return "\n\n".join(lines)
+
+
+# ─────────────────────── inference ───────────────────────
+DEFAULT_SYSTEM = ("You are a sub-agent of Chan Compass, a US-equity dashboard. "
+                  "Answer in clear, concise English.")
+MAX_PROMPT_CHARS = 3200
+
+
+def _messages(user: str, system: str):
+    return [{"role": "system", "content": system + " /no_think"},
+            {"role": "user", "content": user[:MAX_PROMPT_CHARS]}]
+
+
+def chat(user: str, max_tokens: int = 500, temperature: float = 0.3,
+         system: str = DEFAULT_SYSTEM, worker: str = "translator") -> str:
+    """Blocking chat on one sub-agent (used by pipeline/agent code)."""
+    w = WORKERS[_wk(worker)]; worker = _wk(worker)
+    if w["llm"] is None:
         return ""
+    if not w["lock"].acquire(timeout=180):
+        return f"({WORKER_LABEL[worker]} busy — try again in a moment)"
+    try:
+        out = w["llm"].create_chat_completion(
+            messages=_messages(user, system),
+            max_tokens=max_tokens, temperature=temperature)
+        txt = out["choices"][0]["message"]["content"] or ""
+        return _THINK_RE.sub("", txt).strip()
+    except Exception as e:
+        return f"(model error: {e})"
+    finally:
+        w["lock"].release()
 
 
-def check_holdings_news_stream(tickers=None):
-    """Generator for the UI: emits cumulative markdown as it goes — each ticker,
-    each headline, and each AI brief appears the moment it's ready, so the user
-    never stares at a frozen screen. AI briefs run on the Reporter sub-agent."""
-    import llm_local
-    tickers = tickers if tickers is not None else load_holdings()
-    if not tickers:
-        yield ("**No holdings configured.** Add tickers above (e.g. `AAPL, NVDA`) "
-               "and save — they'll be checked for news every day.")
+def chat_stream(user: str, max_tokens: int = 500, temperature: float = 0.3,
+                system: str = DEFAULT_SYSTEM, worker: str = "translator"):
+    """Streaming chat on one sub-agent — yields cumulative text immediately."""
+    w = WORKERS[_wk(worker)]; worker = _wk(worker)
+    if w["llm"] is None:
+        yield (f"⏳ {WORKER_LABEL[worker]} isn't ready yet — "
+               f"stage: {w['stage']}. Check the **Model** tab.")
         return
-    stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
-    out = [f"_Checking {len(tickers)} holding(s) · {stamp}_"]
-    quiet = []
-
-    def render():
-        body = "\n".join(out)
-        if quiet:
-            body += f"\n\n**Quiet today (no news):** {', '.join(quiet)}"
-        return body
-
-    for t in tickers:
-        out.append(f"\n### 📰 {t} …searching today's news")
-        yield render()
-        items = fetch_today_news(t)
-        if not items:
-            out.pop()             # drop the "searching" line
-            quiet.append(t)
-            yield render()
-            continue
-        out[-1] = f"\n### 📰 {t} — {len(items)} item(s) today"
-        yield render()
-        # print each headline the moment we have it
-        for x in items[:6]:
-            link = f" · [link]({x['link']})" if x["link"] else ""
-            out.append(f"- **{x['time']}** {x['title']} — *{x['publisher']}*{link}")
-            yield render()
-        # then stream the AI brief for this ticker (Reporter sub-agent)
-        if llm_local.is_loaded("reporter") or llm_local.is_loaded("translator"):
-            wk = "reporter" if llm_local.is_loaded("reporter") else "translator"
-            heads = "\n".join(f"- [{x['time']}] {x['title']} ({x['publisher']})"
-                               for x in items[:6])
-            prompt = (
-                f"You are an equity news analyst. Today's headlines for {ticker_safe(t)} "
-                f"(a stock the user HOLDS):\n{heads}\n\nIn ENGLISH ONLY:\n"
-                f"1) **Per-headline:** one short line each — what it says and why it "
-                f"matters (or 'noise');\n2) **Net read:** POSITIVE / NEGATIVE / NEUTRAL "
-                f"+ one sentence;\n3) **Action:** one concrete suggestion. ≤180 words.")
-            out.append("\n> 🤖 **Reporter sub-agent brief:** _thinking…_")
-            base = len(out) - 1
-            for acc in llm_local.chat_stream(prompt, max_tokens=420, worker=wk):
-                out[base] = "> 🤖 **Reporter sub-agent brief:**\n>\n> " + \
-                    acc.replace("\n", "\n> ")
-                yield render()
-        else:
-            out.append("\n> _Model still loading — headlines shown; brief will work shortly._")
-            yield render()
-    out.append(f"\n_Done · {stamp}_")
-    yield render()
-
-
-def ticker_safe(t):
-    return str(t).upper()
-
-
-def check_holdings_news(tickers=None) -> str:
-    """Markdown report: AI brief per holding with today-news; quiet list otherwise."""
-    tickers = tickers if tickers is not None else load_holdings()
-    if not tickers:
-        return ("**No holdings configured.** Add tickers above (e.g. `AAPL, NVDA`) "
-                "and save — they'll be checked for news every day.")
-    blocks, quiet = [], []
-    for t in tickers:
-        items = fetch_today_news(t)
-        if not items:
-            quiet.append(t)
+    if not w["lock"].acquire(timeout=5):
+        yield f"⏳ {WORKER_LABEL[worker]} is finishing another answer — try again in a few seconds."
+        return
+    try:
+        acc = ""
+        for chunk in w["llm"].create_chat_completion(
+                messages=_messages(user, system),
+                max_tokens=max_tokens, temperature=temperature, stream=True):
+            delta = chunk["choices"][0]["delta"].get("content") or ""
+            if not delta:
+                continue
+            acc += delta
+            yield _THINK_RE.sub("", acc).replace("<think>", "").strip()
+        if not acc.strip():
+            yield "(model returned no text — try again)"
+    except Exception as e:
+        yield f"(model error: {e})"
+    finally:
+        w["lock"].release()
+
+
+def quick_test() -> str:
+    """Sanity check both sub-agents."""
+    import time
+    outs = []
+    for key in ("translator", "narrator", "reporter", "analyst"):
+        if WORKERS[key]["llm"] is None:
+            outs.append(f"{WORKER_LABEL[key]}: not loaded ({WORKERS[key]['stage']})")
             continue
-        lines = [f"### 📰 {t} — {len(items)} item(s) today"]
-        for x in items[:6]:
-            link = f" · [link]({x['link']})" if x["link"] else ""
-            lines.append(f"- **{x['time']}** {x['title']} — *{x['publisher']}*{link}")
-        brief = _llm_brief(t, items)
-        if brief:
-            lines.append(f"\n> 🤖 **AI brief:** {brief}")
-        else:
-            lines.append("\n> _Load a model in the Model tab for an AI brief._")
-        blocks.append("\n".join(lines))
-    if quiet:
-        blocks.append(f"**Quiet today (no news, ignored):** {', '.join(quiet)}")
-    stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
-    return f"_Checked {stamp}_\n\n" + ("\n\n---\n\n".join(blocks) if blocks else "No output.")
+        t0 = time.time()
+        out = chat("Reply with exactly: OK", max_tokens=6, temperature=0.0, worker=key)
+        outs.append(f"{WORKER_LABEL[key]}: **{out or '(no output)'}** · {time.time()-t0:.1f}s")
+    return "\n\n".join(outs)

paths.py

@@ -1,63 +1,189 @@
 """
-paths.py — persistent storage layout on the Hugging Face storage bucket (/data).
+news_watch.py — daily news check for held tickers (US version, yfinance.news).
 
-If /data exists and is writable (the Space has a storage bucket attached),
-everything that should survive restarts/sleep lives there:
-
-    /data/cache_us     market-data parquet cache (yfinance downloads)
-    /data/output       signal CSVs, holdings.txt
-    /data/reports      auto-generated research reports (markdown)
-    /data/traces       agent traces (JSON) — shareable on the Hub (📡 badge)
-    /data/hf_cache     GGUF model files (downloaded once, kept forever)
-    /data/pylibs       llama-cpp-python installed once at runtime, persisted
-
-Without a bucket the app still works — it just falls back to the container
-filesystem (wiped on restart).
+Rule requested by the user: for each holding, look only at TODAY's news.
+If there is news → push a short AI brief. If there is none → ignore (the
+ticker is listed under "Quiet today" so you know it was checked).
 """
 from __future__ import annotations
 
+import datetime as dt
 import os
-import sys
+import traceback
+
+import paths
 
+HOLDINGS_FILE = os.path.join(paths.OUTPUT_DIR, "holdings.txt")
 
-def _writable(p: str) -> bool:
+
+# ── holdings persistence ─────────────────────────────────────────────────
+def load_holdings() -> list:
     try:
-        return os.path.isdir(p) and os.access(p, os.W_OK)
-    except OSError:
-        return False
+        with open(HOLDINGS_FILE, encoding="utf-8") as f:
+            return [x.strip().upper() for x in f if x.strip()]
+    except FileNotFoundError:
+        return []
+
 
+def save_holdings(tickers: list):
+    seen, out = set(), []
+    for t in tickers:
+        t = t.strip().upper()
+        if t and t not in seen:
+            seen.add(t)
+            out.append(t)
+    with open(HOLDINGS_FILE, "w", encoding="utf-8") as f:
+        f.write("\n".join(out))
+    return out
 
-DATA_ROOT = "/data" if _writable("/data") else "."
-PERSISTENT = DATA_ROOT == "/data"
 
-CACHE_DIR = os.path.join(DATA_ROOT, "cache_us")
-OUTPUT_DIR = os.path.join(DATA_ROOT, "output") if PERSISTENT else "./_app_output"
-REPORTS_DIR = os.path.join(DATA_ROOT, "reports")
-TRACES_DIR = os.path.join(DATA_ROOT, "traces")
-HF_CACHE_DIR = os.path.join(DATA_ROOT, "hf_cache")
-PYLIBS_DIR = os.path.join(DATA_ROOT, "pylibs")
+# ── news fetch ───────────────────────────────────────────────────────────
+def _today_utc() -> dt.date:
+    return dt.datetime.now(dt.timezone.utc).date()
+
+
+def fetch_today_news(ticker: str) -> list:
+    """Return today's news items: [{'title','publisher','time','link'}…]."""
+    try:
+        import yfinance as yf
+        items = yf.Ticker(ticker).news or []
+    except Exception:
+        traceback.print_exc()
+        return []
+    today = _today_utc()
+    out = []
+    for it in items:
+        # yfinance has two schemas: legacy flat dict, or {'content': {...}}
+        c = it.get("content", it)
+        title = c.get("title") or ""
+        ts = it.get("providerPublishTime")
+        when = None
+        if ts:
+            when = dt.datetime.fromtimestamp(ts, dt.timezone.utc)
+        else:
+            pub = c.get("pubDate") or c.get("displayTime")
+            if pub:
+                try:
+                    when = dt.datetime.fromisoformat(str(pub).replace("Z", "+00:00"))
+                except ValueError:
+                    when = None
+        if when is None or when.date() != today:
+            continue
+        pubr = c.get("publisher") or (c.get("provider") or {}).get("displayName") or ""
+        link = c.get("link") or (c.get("canonicalUrl") or {}).get("url") or ""
+        if title:
+            out.append({"title": title, "publisher": pubr,
+                        "time": when.strftime("%H:%M UTC"), "link": link})
+    return out
+
 
-for _d in (CACHE_DIR, OUTPUT_DIR, REPORTS_DIR, TRACES_DIR, HF_CACHE_DIR, PYLIBS_DIR):
+def _llm_brief(ticker: str, items: list) -> str:
+    heads = "\n".join(f"- [{x['time']}] {x['title']} ({x['publisher']})" for x in items)
     try:
-        os.makedirs(_d, exist_ok=True)
-    except OSError:
-        pass
-
-# GGUF downloads (hf_hub_download) go to the bucket so models persist
-if PERSISTENT:
-    os.environ.setdefault("HF_HOME", HF_CACHE_DIR)
-
-# llama-cpp-python installed into the bucket must be importable.
-# APPEND (not insert) so bucket-installed copies of common deps (numpy etc.)
-# can never shadow the system site-packages the app was built against.
-if PERSISTENT and PYLIBS_DIR not in sys.path:
-    sys.path.append(PYLIBS_DIR)
-
-
-def storage_status() -> str:
-    if PERSISTENT:
-        return ("✅ Persistent storage bucket mounted at `/data` — data cache, "
-                "model files, reports, traces and the llama.cpp runtime all "
-                "survive restarts.")
-    return ("⚠️ No `/data` bucket detected — running on ephemeral container "
-            "storage (everything re-downloads after a restart).")
+        import llm_local
+        if not llm_local.is_loaded():
+            return ""
+        prompt = (
+            f"You are an equity news analyst. Today's headlines for {ticker} "
+            f"(a stock the user currently HOLDS):\n{heads}\n\n"
+            "In ENGLISH ONLY, write:\n"
+            "1) **Per-headline:** one short line per headline above — what it says "
+            "and why it matters (or 'noise') for the holding;\n"
+            "2) **Net read:** POSITIVE / NEGATIVE / NEUTRAL with one sentence why;\n"
+            "3) **Action:** one concrete suggestion. ≤180 words, no disclaimers."
+        )
+        return llm_local.chat(prompt, max_tokens=420, worker="reporter")
+    except Exception:
+        return ""
+
+
+def check_holdings_news_stream(tickers=None):
+    """Generator for the UI: emits cumulative markdown as it goes — each ticker,
+    each headline, and each AI brief appears the moment it's ready, so the user
+    never stares at a frozen screen. AI briefs run on the Reporter sub-agent."""
+    import llm_local
+    tickers = tickers if tickers is not None else load_holdings()
+    if not tickers:
+        yield ("**No holdings configured.** Add tickers above (e.g. `AAPL, NVDA`) "
+               "and save — they'll be checked for news every day.")
+        return
+    stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
+    out = [f"_Checking {len(tickers)} holding(s) · {stamp}_"]
+    quiet = []
+
+    def render():
+        body = "\n".join(out)
+        if quiet:
+            body += f"\n\n**Quiet today (no news):** {', '.join(quiet)}"
+        return body
+
+    for t in tickers:
+        out.append(f"\n### 📰 {t} …searching today's news")
+        yield render()
+        items = fetch_today_news(t)
+        if not items:
+            out.pop()             # drop the "searching" line
+            quiet.append(t)
+            yield render()
+            continue
+        out[-1] = f"\n### 📰 {t} — {len(items)} item(s) today"
+        yield render()
+        # print each headline the moment we have it
+        for x in items[:6]:
+            link = f" · [link]({x['link']})" if x["link"] else ""
+            out.append(f"- **{x['time']}** {x['title']} — *{x['publisher']}*{link}")
+            yield render()
+        # then stream the AI brief for this ticker (Reporter sub-agent)
+        if llm_local.is_loaded("reporter") or llm_local.is_loaded("translator"):
+            wk = "reporter" if llm_local.is_loaded("reporter") else "translator"
+            heads = "\n".join(f"- [{x['time']}] {x['title']} ({x['publisher']})"
+                               for x in items[:6])
+            prompt = (
+                f"You are an equity news analyst. Today's headlines for {ticker_safe(t)} "
+                f"(a stock the user HOLDS):\n{heads}\n\nIn ENGLISH ONLY:\n"
+                f"1) **Per-headline:** one short line each — what it says and why it "
+                f"matters (or 'noise');\n2) **Net read:** POSITIVE / NEGATIVE / NEUTRAL "
+                f"+ one sentence;\n3) **Action:** one concrete suggestion. ≤180 words.")
+            out.append("\n> 🤖 **Reporter sub-agent brief:** _thinking…_")
+            base = len(out) - 1
+            for acc in llm_local.chat_stream(prompt, max_tokens=420, worker=wk):
+                out[base] = "> 🤖 **Reporter sub-agent brief:**\n>\n> " + \
+                    acc.replace("\n", "\n> ")
+                yield render()
+        else:
+            out.append("\n> _Model still loading — headlines shown; brief will work shortly._")
+            yield render()
+    out.append(f"\n_Done · {stamp}_")
+    yield render()
+
+
+def ticker_safe(t):
+    return str(t).upper()
+
+
+def check_holdings_news(tickers=None) -> str:
+    """Markdown report: AI brief per holding with today-news; quiet list otherwise."""
+    tickers = tickers if tickers is not None else load_holdings()
+    if not tickers:
+        return ("**No holdings configured.** Add tickers above (e.g. `AAPL, NVDA`) "
+                "and save — they'll be checked for news every day.")
+    blocks, quiet = [], []
+    for t in tickers:
+        items = fetch_today_news(t)
+        if not items:
+            quiet.append(t)
+            continue
+        lines = [f"### 📰 {t} — {len(items)} item(s) today"]
+        for x in items[:6]:
+            link = f" · [link]({x['link']})" if x["link"] else ""
+            lines.append(f"- **{x['time']}** {x['title']} — *{x['publisher']}*{link}")
+        brief = _llm_brief(t, items)
+        if brief:
+            lines.append(f"\n> 🤖 **AI brief:** {brief}")
+        else:
+            lines.append("\n> _Load a model in the Model tab for an AI brief._")
+        blocks.append("\n".join(lines))
+    if quiet:
+        blocks.append(f"**Quiet today (no news, ignored):** {', '.join(quiet)}")
+    stamp = dt.datetime.now(dt.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
+    return f"_Checked {stamp}_\n\n" + ("\n\n---\n\n".join(blocks) if blocks else "No output.")

requirements.txt

@@ -1,7 +1,64 @@
-gradio>=5.49
-pandas>=2.0
-numpy>=1.24
-pyarrow>=14
-yfinance>=0.2.40
-apscheduler>=3.10
-huggingface_hub>=0.23
+"""
+paths.py — persistent storage layout on the Hugging Face storage bucket (/data).
+
+If /data exists and is writable (the Space has a storage bucket attached),
+everything that should survive restarts/sleep lives there:
+
+    /data/cache_us     market-data parquet cache (yfinance downloads)
+    /data/output       signal CSVs, holdings.txt
+    /data/reports      auto-generated research reports (markdown)
+    /data/traces       agent traces (JSON) — shareable on the Hub (📡 badge)
+    /data/hf_cache     GGUF model files (downloaded once, kept forever)
+    /data/pylibs       llama-cpp-python installed once at runtime, persisted
+
+Without a bucket the app still works — it just falls back to the container
+filesystem (wiped on restart).
+"""
+from __future__ import annotations
+
+import os
+import sys
+
+
+def _writable(p: str) -> bool:
+    try:
+        return os.path.isdir(p) and os.access(p, os.W_OK)
+    except OSError:
+        return False
+
+
+DATA_ROOT = "/data" if _writable("/data") else "."
+PERSISTENT = DATA_ROOT == "/data"
+
+CACHE_DIR = os.path.join(DATA_ROOT, "cache_us")
+OUTPUT_DIR = os.path.join(DATA_ROOT, "output") if PERSISTENT else "./_app_output"
+REPORTS_DIR = os.path.join(DATA_ROOT, "reports")
+TRACES_DIR = os.path.join(DATA_ROOT, "traces")
+DATASET_DIR = os.path.join(DATA_ROOT, "dataset")   # SFT training pairs (JSONL)
+HF_CACHE_DIR = os.path.join(DATA_ROOT, "hf_cache")
+PYLIBS_DIR = os.path.join(DATA_ROOT, "pylibs")
+
+for _d in (CACHE_DIR, OUTPUT_DIR, REPORTS_DIR, TRACES_DIR, DATASET_DIR, HF_CACHE_DIR, PYLIBS_DIR):
+    try:
+        os.makedirs(_d, exist_ok=True)
+    except OSError:
+        pass
+
+# GGUF downloads (hf_hub_download) go to the bucket so models persist
+if PERSISTENT:
+    os.environ.setdefault("HF_HOME", HF_CACHE_DIR)
+
+# llama-cpp-python installed into the bucket must be importable.
+# APPEND (not insert) so bucket-installed copies of common deps (numpy etc.)
+# can never shadow the system site-packages the app was built against.
+if PERSISTENT and PYLIBS_DIR not in sys.path:
+    sys.path.append(PYLIBS_DIR)
+
+
+def storage_status() -> str:
+    if PERSISTENT:
+        return ("✅ Persistent storage bucket mounted at `/data` — data cache, "
+                "model files, reports, traces and the llama.cpp runtime all "
+                "survive restarts.")
+    return ("⚠️ No `/data` bucket detected — running on ephemeral container "
+            "storage (everything re-downloads after a restart).")

research.py

@@ -1,121 +1,7 @@
-"""
-research.py — Research Note (beta): a first slice of "Auto Research / auto report".
-
-V1 scope (what this file does today):
-    input ticker → pull fundamentals via yfinance .info + recent headlines
-    → local LLM writes a structured English research note covering
-      valuation, moat / supply-chain position, bull vs bear case, key risks.
-
-V2 (not built yet, see Automation tab description):
-    multi-step agentic research (filings, peers, news crawl) and an automatic
-    trigger that generates a report whenever a NEW ticker enters the pool.
-"""
-from __future__ import annotations
-
-import traceback
-
-
-_FIELDS = [
-    ("longName", "Name"), ("sector", "Sector"), ("industry", "Industry"),
-    ("marketCap", "Market cap"), ("trailingPE", "P/E (ttm)"),
-    ("forwardPE", "P/E (fwd)"), ("priceToSalesTrailing12Months", "P/S (ttm)"),
-    ("priceToBook", "P/B"), ("enterpriseToEbitda", "EV/EBITDA"),
-    ("profitMargins", "Net margin"), ("grossMargins", "Gross margin"),
-    ("operatingMargins", "Operating margin"), ("returnOnEquity", "ROE"),
-    ("revenueGrowth", "Revenue growth (yoy)"), ("earningsGrowth", "Earnings growth (yoy)"),
-    ("freeCashflow", "Free cash flow"), ("totalCash", "Total cash"),
-    ("totalDebt", "Total debt"), ("dividendYield", "Dividend yield"),
-    ("beta", "Beta"), ("fiftyTwoWeekHigh", "52w high"), ("fiftyTwoWeekLow", "52w low"),
-    ("currentPrice", "Price"), ("targetMeanPrice", "Analyst mean target"),
-    ("recommendationKey", "Street view"),
-]
-
-
-def _fmt(key: str, v):
-    if v is None:
-        return None
-    try:
-        if key in ("marketCap", "freeCashflow", "totalCash", "totalDebt"):
-            v = float(v)
-            return f"${v/1e9:,.1f}B" if abs(v) >= 1e9 else f"${v/1e6:,.0f}M"
-        if key in ("profitMargins", "grossMargins", "operatingMargins", "returnOnEquity",
-                   "revenueGrowth", "earningsGrowth", "dividendYield"):
-            return f"{float(v):.1%}"
-        if isinstance(v, float):
-            return f"{v:,.2f}"
-    except (TypeError, ValueError):
-        pass
-    return str(v)
-
-
-def gather_facts(ticker: str) -> tuple:
-    """Return (facts_markdown, facts_plain, error)."""
-    try:
-        import yfinance as yf
-        tk = yf.Ticker(ticker)
-        info = tk.info or {}
-    except Exception as e:
-        traceback.print_exc()
-        return "", "", f"Could not fetch fundamentals for {ticker}: {e}"
-    if not info or info.get("regularMarketPrice") is None and info.get("currentPrice") is None \
-            and not info.get("longName"):
-        return "", "", f"No fundamentals returned for {ticker} — check the symbol."
-    rows, plain = [], []
-    for key, label in _FIELDS:
-        val = _fmt(key, info.get(key))
-        if val is None:
-            continue
-        rows.append(f"| {label} | {val} |")
-        plain.append(f"{label}: {val}")
-    summary = (info.get("longBusinessSummary") or "")[:900]
-    heads = []
-    try:
-        import news_watch
-        for x in (tk.news or [])[:6]:
-            c = x.get("content", x)
-            t = c.get("title")
-            if t:
-                heads.append(t)
-    except Exception:
-        pass
-    md = f"**{info.get('longName', ticker)}** ({ticker})\n\n"
-    md += "| Metric | Value |\n|---|---|\n" + "\n".join(rows)
-    if heads:
-        md += "\n\n**Recent headlines:** " + " · ".join(heads[:5])
-    plain_txt = "\n".join(plain)
-    if summary:
-        plain_txt += f"\nBusiness: {summary}"
-    if heads:
-        plain_txt += "\nRecent headlines: " + " | ".join(heads)
-    return md, plain_txt, ""
-
-
-def research_note(ticker: str) -> str:
-    """Generate the beta research note (markdown)."""
-    ticker = (ticker or "").strip().upper()
-    if not ticker:
-        return "Enter a ticker symbol first."
-    facts_md, facts_plain, err = gather_facts(ticker)
-    if err:
-        return f"⚠️ {err}"
-    note = ""
-    try:
-        import llm_local
-        if llm_local.is_loaded():
-            prompt = (
-                f"You are a buy-side equity analyst. Using ONLY the facts below, write a "
-                f"concise English research note on {ticker} (≤400 words) with EXACTLY these "
-                f"sections:\n"
-                f"## Snapshot\n## Valuation\n## Moat & supply-chain position\n"
-                f"## Bull case\n## Bear case\n## Key risks & verdict\n"
-                f"In Valuation, judge whether multiples look cheap/fair/rich vs the growth "
-                f"and margins shown. In Moat, infer the company's position in its industry "
-                f"value chain. Be specific; if a fact is missing, say 'n/a' rather than "
-                f"inventing it. No investment-advice disclaimers.\n\nFACTS:\n{facts_plain}"
-            )
-            note = llm_local.chat(prompt, max_tokens=900)
-    except Exception as e:
-        note = f"_(LLM error: {e})_"
-    if not note:
-        note = "_Load a model in the **Model** tab to generate the written analysis._"
-    return f"{facts_md}\n\n---\n\n{note}\n\n_Research Note (beta) · data: Yahoo Finance · model: local GGUF_"
+gradio>=5.49
+pandas>=2.0
+numpy>=1.24
+pyarrow>=14
+yfinance>=0.2.40
+apscheduler>=3.10
+huggingface_hub>=0.23

research_agent.py

@@ -1,424 +1,121 @@
 """
-research_agent.py — Auto Research: a multi-step agent, fully local (llama.cpp).
+research.py — Research Note (beta): a first slice of "Auto Research / auto report".
 
-The agent loop (every step is logged to a JSON trace → 📡 "Sharing is Caring"):
+V1 scope (what this file does today):
+    input ticker → pull fundamentals via yfinance .info + recent headlines
+    → local LLM writes a structured English research note covering
+      valuation, moat / supply-chain position, bull vs bear case, key risks.
 
-    PLAN      LLM drafts 3-5 research questions for the ticker
-    TOOLS     deterministic tool calls gather evidence:
-                t_fundamentals  yfinance .info (valuation, margins, growth…)
-                t_financials    last quarterly income-statement lines
-                t_price         52w range, MA200, drawdown, momentum (data_us)
-                t_chan          the user's Chan engine verdict (signal_runner)
-                t_news          recent headlines
-    ANALYZE   LLM writes each report section against the gathered evidence:
-                Valuation · Moat & supply-chain position · Bull case ·
-                Bear case · Technical timing (Chan) · Risks & verdict
-    REPORT    sections assembled into a markdown report, saved to /data/reports
-    TRACE     full step-by-step trace saved to /data/traces/<ticker>_<ts>.json
-              (upload that folder as a HF dataset to claim the open-trace badge)
-
-Feature 4 (auto-trigger) lives in automation.py: whenever a NEW ticker enters
-the signal pool, this agent runs for it automatically after the daily pipeline.
+V2 (not built yet, see Automation tab description):
+    multi-step agentic research (filings, peers, news crawl) and an automatic
+    trigger that generates a report whenever a NEW ticker enters the pool.
 """
 from __future__ import annotations
 
-import datetime as dt
-import json
-import os
 import traceback
 
-import pandas as pd
-
-import paths
 
-# Multi-agent split: the 4B Analyst writes the heavy sections while the 1.7B
-# Reporter writes market/technical sections IN PARALLEL on its own lock —
-# wall-clock time ≈ the slower of the two instead of their sum.
-ANALYST_SECTIONS = [
-    ("Valuation", "cheap/fair/rich vs the growth and margins in evidence; quote 2-3 numbers"),
-    ("Technology moat", "the company's technological moat and how defensible it is"),
-    ("Supply-chain map", "a markdown table with two columns 'Upstream suppliers' and "
-     "'Downstream customers/users': list 4-6 real companies on each side WITH stock "
-     "tickers in parentheses, e.g. TSMC (TSM); mark private companies (private)"),
-    ("Bull case", "strongest 3 points FOR owning it"),
-    ("Bear case", "strongest 3 points AGAINST owning it"),
+_FIELDS = [
+    ("longName", "Name"), ("sector", "Sector"), ("industry", "Industry"),
+    ("marketCap", "Market cap"), ("trailingPE", "P/E (ttm)"),
+    ("forwardPE", "P/E (fwd)"), ("priceToSalesTrailing12Months", "P/S (ttm)"),
+    ("priceToBook", "P/B"), ("enterpriseToEbitda", "EV/EBITDA"),
+    ("profitMargins", "Net margin"), ("grossMargins", "Gross margin"),
+    ("operatingMargins", "Operating margin"), ("returnOnEquity", "ROE"),
+    ("revenueGrowth", "Revenue growth (yoy)"), ("earningsGrowth", "Earnings growth (yoy)"),
+    ("freeCashflow", "Free cash flow"), ("totalCash", "Total cash"),
+    ("totalDebt", "Total debt"), ("dividendYield", "Dividend yield"),
+    ("beta", "Beta"), ("fiftyTwoWeekHigh", "52w high"), ("fiftyTwoWeekLow", "52w low"),
+    ("currentPrice", "Price"), ("targetMeanPrice", "Analyst mean target"),
+    ("recommendationKey", "Street view"),
 ]
-REPORTER_SECTIONS = [
-    ("Money flow & related tickers", "read the MONEY FLOW evidence: is capital "
-     "entering or leaving the stock and its sector; name the sector ETF and 2-4 "
-     "related tickers worth watching"),
-    ("Technical timing (Chan theory)", "interpret the CHAN ENGINE VERDICT for a "
-     "long-term holder: act now, wait, or exit, and the key price levels"),
-    ("Risks & verdict", "top risks, then one line: Buy / Accumulate / Hold / Avoid"),
-]
-SECTIONS = ANALYST_SECTIONS + REPORTER_SECTIONS  # kept for trace readability
-
 
-# ───────────────────────── trace plumbing ─────────────────────────
-class Trace:
-    def __init__(self, ticker: str):
-        self.ticker = ticker
-        self.t0 = dt.datetime.utcnow()
-        self.steps = []
 
-    def log(self, step: str, kind: str, content):
-        self.steps.append({
-            "n": len(self.steps) + 1,
-            "t": dt.datetime.utcnow().isoformat(timespec="seconds") + "Z",
-            "step": step, "type": kind,
-            "content": content if isinstance(content, (str, dict, list)) else str(content),
-        })
-
-    def save(self) -> str:
-        ts = self.t0.strftime("%Y%m%d-%H%M%S")
-        path = os.path.join(paths.TRACES_DIR, f"{self.ticker}_{ts}.json")
-        try:
-            with open(path, "w", encoding="utf-8") as f:
-                json.dump({"ticker": self.ticker, "agent": "chan-compass-research",
-                           "model_runtime": "llama.cpp (local)",
-                           "started": self.t0.isoformat() + "Z",
-                           "steps": self.steps}, f, ensure_ascii=False, indent=1, default=str)
-            return path
-        except OSError:
-            return ""
-
-
-def _llm(prompt: str, max_tokens: int = 500) -> str:
+def _fmt(key: str, v):
+    if v is None:
+        return None
     try:
-        import llm_local
-        if llm_local.is_loaded("deep"):
-            return llm_local.chat(prompt, max_tokens=max_tokens, worker="deep")
-        if llm_local.is_loaded("fast"):       # deep still loading → fall back
-            return llm_local.chat(prompt, max_tokens=max_tokens, worker="fast")
-    except Exception:
+        if key in ("marketCap", "freeCashflow", "totalCash", "totalDebt"):
+            v = float(v)
+            return f"${v/1e9:,.1f}B" if abs(v) >= 1e9 else f"${v/1e6:,.0f}M"
+        if key in ("profitMargins", "grossMargins", "operatingMargins", "returnOnEquity",
+                   "revenueGrowth", "earningsGrowth", "dividendYield"):
+            return f"{float(v):.1%}"
+        if isinstance(v, float):
+            return f"{v:,.2f}"
+    except (TypeError, ValueError):
         pass
-    return ""
-
+    return str(v)
 
-# ───────────────────────── tools ─────────────────────────
-def t_fundamentals(ticker: str) -> dict:
-    import research
-    md, plain, err = research.gather_facts(ticker)
-    return {"ok": not err, "markdown": md, "plain": plain, "error": err}
 
-
-def t_financials(ticker: str) -> str:
+def gather_facts(ticker: str) -> tuple:
+    """Return (facts_markdown, facts_plain, error)."""
     try:
         import yfinance as yf
-        q = yf.Ticker(ticker).quarterly_income_stmt
-        if q is None or q.empty:
-            return ""
-        rows = [r for r in ("Total Revenue", "Gross Profit", "Operating Income",
-                            "Net Income") if r in q.index]
-        sub = q.loc[rows].iloc[:, :4]
-        out = []
-        for r in rows:
-            vals = ", ".join(f"{c.strftime('%Y-%m')}: ${v/1e9:,.2f}B"
-                             for c, v in sub.loc[r].items() if pd.notna(v))
-            out.append(f"{r} — {vals}")
-        return "\n".join(out)
-    except Exception:
-        return ""
-
-
-def t_price(ticker: str) -> str:
-    try:
-        import data_us
-        d = data_us.load_level(ticker, "d")
-        if d is None or len(d) < 60:
-            return ""
-        px = float(d["close"].iloc[-1])
-        hi52 = float(d["close"].tail(252).max()); lo52 = float(d["close"].tail(252).min())
-        ma200 = float(d["close"].rolling(200).mean().iloc[-1])
-        r3m = px / float(d["close"].iloc[-63]) - 1 if len(d) > 63 else 0
-        r1y = px / float(d["close"].iloc[-252]) - 1 if len(d) > 252 else 0
-        return (f"Price ${px:,.2f} | 52w range ${lo52:,.2f}–${hi52:,.2f} "
-                f"({(px/hi52-1):+.1%} off high) | vs MA200 {(px/ma200-1):+.1%} | "
-                f"3m {r3m:+.1%}, 1y {r1y:+.1%}")
-    except Exception:
-        return ""
-
-
-def t_chan(ticker: str) -> str:
-    try:
-        import signal_runner
-        row, _ = signal_runner.analyze_one(ticker)
-        if not row:
-            return ""
-        return (f"Tomorrow: {row['Tomorrow']} | signal {row['Signal']} | "
-                f"confidence {row['Confidence']} | buy zone {row['Buy zone']} | "
-                f"invalid below {row['Invalid below']} | note: {row['Note']}")
-    except Exception:
-        return ""
-
-
-def t_flows(ticker: str) -> str:
-    """Money-flow proxy (Δ% × dollar volume) for the ticker and its sector ETF,
-    1/5/20-day windows, plus related tickers via the sector mapping."""
-    try:
-        import data_us
-        import rotation as rot
-        out = []
-        d = data_us.load_level(ticker, "d")
-        for n, lab in ((1, "1D"), (5, "5D"), (20, "20D")):
-            st = rot._window_stats(d, n)
-            if st:
-                pct, dvol, flow = st
-                out.append(f"{ticker} {lab}: {pct:+.2%}, flow proxy "
-                           f"${flow/1e6:+,.0f}M on ${dvol/1e9:,.1f}B avg $vol")
-        sector = ""
-        try:
-            import yfinance as yf
-            sector = (yf.Ticker(ticker).info or {}).get("sector", "")
-        except Exception:
-            pass
-        etf = next((k for k, v in rot.SECTOR_ETFS.items() if v == sector), None)
-        if etf:
-            de = data_us.load_level(etf, "d")
-            st = rot._window_stats(de, 5)
-            if st:
-                out.append(f"Sector ETF {etf} ({sector}) 5D: {st[0]:+.2%}, "
-                           f"flow proxy ${st[2]/1e6:+,.0f}M")
-        return "\n".join(out)
-    except Exception:
-        return ""
-
-
-def t_news(ticker: str) -> str:
+        tk = yf.Ticker(ticker)
+        info = tk.info or {}
+    except Exception as e:
+        traceback.print_exc()
+        return "", "", f"Could not fetch fundamentals for {ticker}: {e}"
+    if not info or info.get("regularMarketPrice") is None and info.get("currentPrice") is None \
+            and not info.get("longName"):
+        return "", "", f"No fundamentals returned for {ticker} — check the symbol."
+    rows, plain = [], []
+    for key, label in _FIELDS:
+        val = _fmt(key, info.get(key))
+        if val is None:
+            continue
+        rows.append(f"| {label} | {val} |")
+        plain.append(f"{label}: {val}")
+    summary = (info.get("longBusinessSummary") or "")[:900]
+    heads = []
     try:
-        import yfinance as yf
-        heads = []
-        for x in (yf.Ticker(ticker).news or [])[:8]:
+        import news_watch
+        for x in (tk.news or [])[:6]:
             c = x.get("content", x)
             t = c.get("title")
             if t:
-                heads.append("- " + t)
-        return "\n".join(heads)
+                heads.append(t)
     except Exception:
-        return ""
-
-
-# ───────────────────────── the agent ─────────────────────────
-def _gather_evidence(ticker: str, tr: "Trace", on_step=None) -> dict:
-    """Run all evidence tools IN PARALLEL (network-bound) — was serial before."""
-    from concurrent.futures import ThreadPoolExecutor
-    tools = {"fundamentals": t_fundamentals, "financials": t_financials,
-             "price": t_price, "chan_engine": t_chan, "flows": t_flows,
-             "news": t_news}
-    evidence = {}
-    with ThreadPoolExecutor(max_workers=6) as ex:
-        futs = {name: ex.submit(fn, ticker) for name, fn in tools.items()}
-        for name, fut in futs.items():
-            try:
-                evidence[name] = fut.result(timeout=40)
-            except Exception as e:
-                evidence[name] = ""
-                tr.log(f"TOOL:{name}", "tool_error", str(e))
-            brief = (evidence[name].get("plain", "")[:300]
-                     if isinstance(evidence[name], dict) else str(evidence[name])[:300])
-            tr.log(f"TOOL:{name}", "tool_result", brief or "(empty)")
-            if on_step:
-                on_step(name)
-    return evidence
-
-
-def _evidence_text(evidence: dict) -> str:
-    fund = evidence.get("fundamentals")
-    return (f"FUNDAMENTALS:\n{(fund.get('plain','') if isinstance(fund, dict) else '')[:1000]}\n\n"
-            f"QUARTERLY FINANCIALS:\n{str(evidence.get('financials'))[:380] or 'n/a'}\n\n"
-            f"PRICE ACTION:\n{evidence.get('price') or 'n/a'}\n\n"
-            f"MONEY FLOW:\n{str(evidence.get('flows'))[:420] or 'n/a'}\n\n"
-            f"CHAN ENGINE VERDICT:\n{str(evidence.get('chan_engine'))[:380] or 'n/a'}\n\n"
-            f"RECENT HEADLINES:\n{str(evidence.get('news'))[:380] or 'n/a'}")[:2500]
-
-
-def _sections_prompt(ticker: str, sections, ev_text: str) -> str:
-    sec_list = "\n".join(f"## {t} — {i}" for t, i in sections)
-    return (f"You are a buy-side equity analyst. Write part of a research note on "
-            f"{ticker} using ONLY the evidence below (write 'n/a' for missing facts, "
-            f"never invent numbers; company names in the supply-chain table may come "
-            f"from your own industry knowledge). Output EXACTLY these markdown "
-            f"sections, ≤70 words each, plain prose, ENGLISH ONLY, no disclaimers:\n"
-            f"{sec_list}\n\nEVIDENCE:\n{ev_text}")
-
-
-_STATIC_PLAN = ("1. Is the valuation justified by growth?\n"
-                "2. How durable is the technology moat and supply-chain position?\n"
-                "3. Where is capital flowing — into or out of the name and sector?\n"
-                "4. Is now a good technical entry for a long-term holder?")
-
-
-def run_research(ticker: str, auto: bool = False) -> tuple:
-    """Blocking multi-agent run (used by the daily pipeline).
-    Analyst (4B) and Reporter (1.7B) write their sections in PARALLEL.
-    Returns (report_markdown, trace_path). If no model is ready, returns
-    ('', '') so the caller can postpone instead of saving an empty report."""
-    import llm_local
-    ticker = (ticker or "").strip().upper()
-    if not ticker:
-        return "Enter a ticker symbol first.", ""
-    if not (llm_local.is_loaded("analyst") or llm_local.is_loaded("reporter")):
-        return "", ""   # postpone — model still loading
-    tr = Trace(ticker)
-    tr.log("PLAN", "static", _STATIC_PLAN)
-    evidence = _gather_evidence(ticker, tr)
-    fund = evidence.get("fundamentals")
-    if isinstance(fund, dict) and not fund.get("ok"):
-        tr.save()
-        return f"⚠️ {fund.get('error', 'Could not fetch data.')}", ""
-    ev_text = _evidence_text(evidence)
-
-    import threading
-    parts = {}
-
-    def _write(slot, sections, worker, fallback_worker):
-        wk = worker if llm_local.is_loaded(worker) else fallback_worker
-        tr.log(f"ANALYZE:{slot}", "llm_request", f"worker={wk}")
-        txt = llm_local.chat(_sections_prompt(ticker, sections, ev_text),
-                             max_tokens=620 if slot == "analyst" else 360, worker=wk)
-        if txt.startswith("(") or txt.startswith("⏳"):
-            txt = ""
-        tr.log(f"ANALYZE:{slot}", "llm_response", txt[:500])
-        parts[slot] = txt
-
-    th = threading.Thread(target=_write,
-                          args=("reporter", REPORTER_SECTIONS, "reporter", "analyst"))
-    th.start()
-    _write("analyst", ANALYST_SECTIONS, "analyst", "reporter")
-    th.join(timeout=300)
-
-    fund_md = fund.get("markdown", "") if isinstance(fund, dict) else ""
-    stamp = dt.datetime.utcnow().strftime("%Y-%m-%d %H:%M UTC")
-    head = (f"# {ticker} — Research Note{' (auto-generated)' if auto else ''}\n"
-            f"_{stamp} · multi-agent: Analyst (Qwen3-4B) + Reporter (Qwen3-1.7B), "
-            f"both llama.cpp local_\n\n**Agent plan:**\n{_STATIC_PLAN}\n\n{fund_md}\n\n---\n")
-    body = "\n\n".join(p for p in (parts.get("analyst"), parts.get("reporter")) if p)
-    if not body:
-        tr.save()
-        return "", ""   # model produced nothing — postpone, never save a stub
-    report = head + body + "\n\n---\n_Agent trace saved — see the Automation tab._"
-    tr.log("REPORT", "assembled", f"{len(report)} chars")
-    trace_path = tr.save()
-    try:
-        rp = os.path.join(paths.REPORTS_DIR, f"{ticker}_{tr.t0.strftime('%Y%m%d')}.md")
-        with open(rp, "w", encoding="utf-8") as f:
-            f.write(report)
-    except OSError:
         pass
-    return report, trace_path
-
-
-def list_reports() -> list:
-    try:
-        fs = [f for f in os.listdir(paths.REPORTS_DIR) if f.endswith(".md")]
-        return sorted(fs, reverse=True)
-    except OSError:
-        return []
-
-
-def read_report(fname: str) -> str:
-    if not fname:
-        return "No report selected."
-    try:
-        with open(os.path.join(paths.REPORTS_DIR, os.path.basename(fname)),
-                  encoding="utf-8") as f:
-            return f.read()
-    except OSError as e:
-        return f"Could not read report: {e}"
-
-
-def list_traces() -> str:
-    try:
-        fs = sorted(os.listdir(paths.TRACES_DIR), reverse=True)[:20]
-        if not fs:
-            return "_No traces yet — run a research note first._"
-        return ("**Saved agent traces** (`" + paths.TRACES_DIR + "`):\n" +
-                "\n".join(f"- `{f}`" for f in fs) +
-                "\n\n📡 To claim the open-trace badge: download this folder and "
-                "push it to the Hub as a dataset (`huggingface-cli upload`).")
-    except OSError:
-        return "_Trace folder unavailable._"
-
-
-# ───────────────────────── streaming UI runner ─────────────────────────
-def run_research_stream(ticker: str):
-    """Generator for the UI. Multi-agent: evidence tools run in parallel; the
-    1.7B Reporter writes its sections in a background thread while the 4B
-    Analyst STREAMS its sections live; never saves a 'model busy' stub."""
-    import threading
-    import llm_local
+    md = f"**{info.get('longName', ticker)}** ({ticker})\n\n"
+    md += "| Metric | Value |\n|---|---|\n" + "\n".join(rows)
+    if heads:
+        md += "\n\n**Recent headlines:** " + " · ".join(heads[:5])
+    plain_txt = "\n".join(plain)
+    if summary:
+        plain_txt += f"\nBusiness: {summary}"
+    if heads:
+        plain_txt += "\nRecent headlines: " + " | ".join(heads)
+    return md, plain_txt, ""
+
+
+def research_note(ticker: str) -> str:
+    """Generate the beta research note (markdown)."""
     ticker = (ticker or "").strip().upper()
     if not ticker:
-        yield "Enter a ticker symbol first.", ""
-        return
-    tr = Trace(ticker)
-    log_lines = [f"### 🤖 Multi-agent research · {ticker}"]
-
-    def show(msg):
-        log_lines.append(f"- {msg}")
-        return "\n".join(log_lines)
-
-    tr.log("PLAN", "static", _STATIC_PLAN)
-    yield show("**PLAN** ready ✓ · **TOOLS** — gathering 6 evidence sources in parallel…"), ""
-    evidence = _gather_evidence(ticker, tr)
-    yield show("Evidence in: fundamentals · financials · price · money flow · Chan engine · news ✓"), ""
-
-    fund = evidence.get("fundamentals")
-    if isinstance(fund, dict) and not fund.get("ok"):
-        tr.save()
-        yield show(f"⚠️ {fund.get('error', 'Data fetch failed.')}"), ""
-        return
-    ev_text = _evidence_text(evidence)
-    fund_md = fund.get("markdown", "") if isinstance(fund, dict) else ""
-    stamp = dt.datetime.utcnow().strftime("%Y-%m-%d %H:%M UTC")
-    head = (f"# {ticker} — Research Note\n_{stamp} · multi-agent: Analyst (Qwen3-4B) "
-            f"+ Reporter (Qwen3-1.7B), both llama.cpp local_\n\n"
-            f"**Agent plan:**\n{_STATIC_PLAN}\n\n{fund_md}\n\n---\n")
-
-    # Reporter sub-agent works in parallel on its own lock
-    side = {"txt": ""}
-
-    def _side():
-        wk = "reporter" if llm_local.is_loaded("reporter") else "analyst"
-        t = llm_local.chat(_sections_prompt(ticker, REPORTER_SECTIONS, ev_text),
-                           max_tokens=360, worker=wk)
-        side["txt"] = "" if (t.startswith("(") or t.startswith("⏳")) else t
-        tr.log("ANALYZE:reporter", "llm_response", side["txt"][:400])
-
-    th = None
-    if llm_local.is_loaded("reporter") or llm_local.is_loaded("analyst"):
-        th = threading.Thread(target=_side, daemon=True)
-        th.start()
-        yield show("**Reporter sub-agent** writing money-flow / Chan timing / verdict "
-                   "in parallel…"), head
-    # Analyst streams the main sections live
-    main = ""
-    wk_main = "analyst" if llm_local.is_loaded("analyst") else "reporter"
-    if llm_local.is_loaded(wk_main):
-        yield show(f"**Analyst sub-agent** streaming valuation / moat / supply-chain "
-                   f"map / bull-bear…"), head
-        for acc in llm_local.chat_stream(
-                _sections_prompt(ticker, ANALYST_SECTIONS, ev_text),
-                max_tokens=620, worker=wk_main):
-            if acc.startswith("⏳") or acc.startswith("("):
-                continue
-            main = acc
-            yield "\n".join(log_lines), head + main
-    tr.log("ANALYZE:analyst", "llm_response", main[:500])
-    if th is not None:
-        th.join(timeout=240)
-    body = "\n\n".join(p for p in (main, side["txt"]) if p)
-    if not body:
-        tr.save()
-        yield show("⚠️ Sub-agents not ready yet (still loading) — evidence gathered "
-                   "above; try again in a minute. Nothing was saved."), head
-        return
-    report = head + body + "\n\n---\n_Agent trace saved — see the Automation tab._"
-    trace_path = tr.save()
+        return "Enter a ticker symbol first."
+    facts_md, facts_plain, err = gather_facts(ticker)
+    if err:
+        return f"⚠️ {err}"
+    note = ""
     try:
-        rp = os.path.join(paths.REPORTS_DIR, f"{ticker}_{tr.t0.strftime('%Y%m%d')}.md")
-        with open(rp, "w", encoding="utf-8") as f:
-            f.write(report)
-    except OSError:
-        pass
-    yield show(f"**DONE** ✓ report + trace saved (`{os.path.basename(trace_path)}`)"), report
+        import llm_local
+        if llm_local.is_loaded():
+            prompt = (
+                f"You are a buy-side equity analyst. Using ONLY the facts below, write a "
+                f"concise English research note on {ticker} (≤400 words) with EXACTLY these "
+                f"sections:\n"
+                f"## Snapshot\n## Valuation\n## Moat & supply-chain position\n"
+                f"## Bull case\n## Bear case\n## Key risks & verdict\n"
+                f"In Valuation, judge whether multiples look cheap/fair/rich vs the growth "
+                f"and margins shown. In Moat, infer the company's position in its industry "
+                f"value chain. Be specific; if a fact is missing, say 'n/a' rather than "
+                f"inventing it. No investment-advice disclaimers.\n\nFACTS:\n{facts_plain}"
+            )
+            note = llm_local.chat(prompt, max_tokens=900)
+    except Exception as e:
+        note = f"_(LLM error: {e})_"
+    if not note:
+        note = "_Load a model in the **Model** tab to generate the written analysis._"
+    return f"{facts_md}\n\n---\n\n{note}\n\n_Research Note (beta) · data: Yahoo Finance · model: local GGUF_"