Upload 16 files

app.py

@@ -52,30 +52,29 @@ def ui_run_signals(tickers_text, force):
 
 def ui_show_detail(ticker):
     if not ticker:
-        return "Select a ticker after running the pipeline."
-    return automation.STATE["signals_details"].get(ticker, "No detail for this ticker yet.")
+        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):
-    txt = automation.STATE["signals_details"].get(ticker or "", "")
-    if not txt:
+    raw = signal_runner.stock_raw_read(ticker or "")
+    if not raw:
         yield "Run the analysis and select a ticker first."
         return
-    # keep only the ruling chain — the per-signal diagnostics block is huge and
-    # would cost a minute of silent CPU prompt-processing for no benefit
-    core = txt.split("日线买卖点逐项诊断")[0][:1500]
-    prompt = ("Below is a Chan-theory multi-timeframe decision log in Chinese for a "
-              "US stock. Respond in ENGLISH ONLY — translate every Chinese term; no "
-              "Chinese characters may appear in your answer. Do NOT quote the log. "
-              "Give a SHORT summary in exactly this format:\n"
-              "**Action:** <BUY/SELL/HOLD/WAIT + one clause>\n"
-              "**Why:** <2-3 short bullets: which timeframe gates passed/failed>\n"
-              "**Invalidation:** <one line: what price/event flips the call>\n"
-              "Max 80 words total.\n\n" + core)
-    yield ("🤖 _Translator sub-agent (Qwen3-1.7B · llama.cpp) is reading the "
-           "decision log — first words in ~5-15s…_")
-    for acc in llm_local.chat_stream(prompt, max_tokens=220, temperature=0.1, worker="translator"):
-        yield "🤖 **Translator sub-agent (Qwen3-1.7B · llama.cpp):**\n\n" + acc
+    # 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():
@@ -136,6 +135,26 @@ 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}
@@ -165,13 +184,13 @@ with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
         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("**Decision log** — the engine's full multi-timeframe ruling chain "
-                    "(engine output is in Chinese; use the button for an English explanation).",
+        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("🌐 Explain in English (local LLM)", scale=1)
-        detail_box = gr.Textbox(lines=14, label="Ruling chain", elem_id="detail-log")
+            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"):
@@ -226,10 +245,13 @@ with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
         auto_md = gr.Markdown(automation.schedule_info())
         with gr.Row():
             auto_now = gr.Button("⚡ Run now", variant="primary")
-            auto_refresh = gr.Button("↻ Refresh status")
         auto_msg = gr.Markdown()
-        auto_log = gr.Textbox(lines=12, label="Pipeline log", elem_id="detail-log")
+        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 "
@@ -245,9 +267,13 @@ with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
                               value=llm_local.DEFAULT_MODEL, label="Analyst (deep) model")
         with gr.Row():
             load_btn = gr.Button("⬇ Load model", variant="primary")
-            status_btn = gr.Button("↻ Refresh status")
-            test_btn = gr.Button("⚡ Test sub-agents")
+            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",
@@ -265,11 +291,8 @@ with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
     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)
-    auto_refresh.click(ui_automation_panel, None, [auto_md, auto_log])
-    auto_refresh.click(research_agent.list_traces, None, traces_md)
     load_btn.click(ui_load_model, model_pick, model_status)
-    status_btn.click(lambda: llm_local.status(), None, model_status)
-    test_btn.click(lambda: llm_local.quick_test(), None, model_status)
+    test_btn.click(lambda: llm_local.quick_test(), None, model_test_out)
 
 automation.start_scheduler()
 

signal_runner.py

@@ -56,48 +56,54 @@ def _structural_stop(kind: str, res) -> float | None:
 
 
 def _next_day_plan(res) -> dict:
-    """The simplified answer the user asked for:
-    Do I buy/sell TOMORROW, in what price zone, and where is it wrong?"""
+    """The simplified answer: do I buy/sell tomorrow, the exact BUY POINT, the
+    acceptable entry zone, and the invalidation price. All prices come straight
+    from the engine's signal.extras (same source as the user's backtest)."""
     kind, act = res.final_kind, res.action
     px = float(res.cur_price)
     if act == "BUY" and kind in ("B1", "B2", "B3"):
         stop = _structural_stop(kind, res)
-        if kind == "B3" and res.daily and res.daily.zg:
-            lo = max(stop, float(res.daily.zg))
-        else:
-            lo = stop
+        # The BUY POINT = the engine's structural level for this signal type:
+        #   B1 divergence low · B2 retest low · B3 pivot upper band (ZG).
+        sig = res.daily.signal if res.daily else None
+        ex = (sig.extras if sig else None) or {}
+        if kind == "B3":
+            point = float(res.daily.zg) if (res.daily and res.daily.zg) else stop
+        elif kind == "B1":
+            point = float(ex.get("c_new_low") or stop)
+        else:  # B2
+            point = float(ex.get("cur_low") or ex.get("b1_price") or stop)
+        lo = min(point, stop) if kind == "B3" else point
         hi = px * 1.015
         return {"plan": "🟢 BUY tomorrow at open",
-                "zone": f"${lo:,.2f} – ${hi:,.2f}",
+                "point": f"${point:,.2f}",
+                "zone": f"${min(lo, hi):,.2f} – ${hi:,.2f}",
                 "stop": f"${stop:,.2f}",
-                "hint": "Long-hold entry: keep until S3 / stop / armed exit line."}
+                "hint": f"Long-hold entry near ${point:,.2f}; keep until S3 / stop / armed exit."}
     if act == "SELL":
         hint = {"STOP": "Structural stop hit — exit to protect capital.",
                 "S3": "Pivot breakdown (S3) — the long-hold exit signal. Exit, don't average down."}
-        return {"plan": "🔴 SELL tomorrow at open",
-                "zone": f"≈ ${px:,.2f}",
-                "stop": "—",
+        return {"plan": "🔴 SELL tomorrow at open", "point": f"≈ ${px:,.2f}",
+                "zone": f"≈ ${px:,.2f}", "stop": "—",
                 "hint": hint.get(kind, "Confirmed top (divergence verified at sub-levels) — take profit.")}
     if act == "HOLD":
         if res.sell_armed and res.arm_zd:
-            return {"plan": "🟡 HOLD (exit line armed)",
-                    "zone": "—",
+            return {"plan": "🟡 HOLD (exit line armed)", "point": "—", "zone": "—",
                     "stop": f"${float(res.arm_zd):,.2f}",
                     "hint": f"Keep holding; sell only if price closes below ${float(res.arm_zd):,.2f}."}
-        return {"plan": "🟡 HOLD", "zone": "—", "stop": "—",
+        return {"plan": "🟡 HOLD", "point": "—", "zone": "—", "stop": "—",
                 "hint": "Trend intact — long-hold, ignore daily noise."}
-    # WAIT — give an English reason (the engine's detailed log is Chinese; the
-    # full text stays in the Decision log, the table stays 100% English)
+    # WAIT
     if kind:
-        hint = (f"{kind} signal seen on the daily chart but NOT confirmed by "
-                f"sub-level (30m) structure — wait for confirmation, don't chase.")
+        hint = (f"{kind} signal on the daily chart but NOT yet confirmed down the "
+                f"nested sub-levels (60m→30m→15m→5m) — wait, don't chase.")
     elif res.blocked_reason:
         hint = "Signal blocked by a higher-timeframe gate (weekly/monthly direction). Stay out."
     else:
         wk = TREND_EN.get(res.weekly.trend if res.weekly else "", "?")
         dy = TREND_EN.get(res.daily.trend if res.daily else "", "?")
         hint = f"No buy/sell point today (weekly {wk}, daily {dy}). Stay in cash / keep watching."
-    return {"plan": "⚪ WAIT", "zone": "—", "stop": "—", "hint": hint}
+    return {"plan": "⚪ WAIT", "point": "—", "zone": "—", "stop": "—", "hint": hint}
 
 import paths
 
@@ -118,6 +124,34 @@ TREND_EN = {"up_trend": "Up", "down_trend": "Down", "consolidation": "Range",
             "expanding": "Expanding", "unknown": "?", "": "?"}
 
 
+def stock_raw_read(ticker: str) -> str:
+    """Plain-English factual snapshot of one ticker's Chan verdict — the
+    deterministic 'Raw read' the Signals AI narrative summarizes (mirrors the
+    Sector-Rotation pattern: raw read → agent → narrative)."""
+    row = automation_state_row(ticker)
+    if not row:
+        return ""
+    parts = [
+        f"{ticker}: close {row['Close']}, signal {row['Signal']}, "
+        f"confidence {row['Confidence']}.",
+        f"Plan tomorrow: {row['Tomorrow']}.",
+    ]
+    if row.get("Buy point") not in ("—", None):
+        parts.append(f"Buy point {row['Buy point']}, zone {row['Buy zone']}, "
+                     f"invalid below {row['Invalid below']}.")
+    parts.append(f"Note: {row['Note']}")
+    return " ".join(parts)
+
+
+def automation_state_row(ticker: str):
+    import automation
+    df = automation.STATE.get("signals_df")
+    if df is None or "Ticker" not in getattr(df, "columns", []):
+        return None
+    m = df[df["Ticker"] == ticker]
+    return m.iloc[0].to_dict() if len(m) else None
+
+
 def analyze_one(ticker: str, force: bool = False):
     """Run the simplified long-hold Chan analysis for one ticker.
     Full nested-interval set: monthly/weekly (resampled) + daily +
@@ -147,6 +181,7 @@ def analyze_one(ticker: str, force: bool = False):
     row = {
         "Ticker": ticker,
         "Tomorrow": plan["plan"],
+        "Buy point": plan["point"],
         "Buy zone": plan["zone"],
         "Invalid below": plan["stop"],
         "Signal": KIND_EN.get(res.final_kind, res.final_kind or "—"),
@@ -198,7 +233,7 @@ def run_signals(tickers=None, force: bool = False):
         except Exception:
             pass
     else:
-        show = pd.DataFrame(columns=["Ticker", "Tomorrow", "Buy zone", "Invalid below", "Signal", "Confidence", "Close"])
+        show = pd.DataFrame(columns=["Ticker", "Tomorrow", "Buy point", "Buy zone", "Invalid below", "Signal", "Confidence", "Close"])
     n_buy = sum(1 for r in rows if r["_action_raw"] == "BUY")
     n_sell = sum(1 for r in rows if r["_action_raw"] == "SELL")
     asof = rows[0]["_date"] if rows else "—"