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app.py

@@ -25,80 +25,9 @@ import rotation
 import signal_runner
 
 # ─────────────────────────────────────────── Spectrum 2 theme ──
-S2_FONT = gr.themes.GoogleFont("Source Sans 3")
-S2_MONO = gr.themes.GoogleFont("Source Code Pro")
-
-theme = gr.themes.Default(
-    font=[S2_FONT, "Adobe Clean", "system-ui", "sans-serif"],
-    font_mono=[S2_MONO, "monospace"],
-    primary_hue=gr.themes.colors.blue,
-    neutral_hue=gr.themes.colors.gray,
-    radius_size=gr.themes.sizes.radius_lg,
-).set(
-    button_primary_background_fill="#0265DC",
-    button_primary_background_fill_hover="#0054B6",
-    button_primary_text_color="#FFFFFF",
-    button_secondary_background_fill="#FFFFFF",
-    button_secondary_border_color="#B1B1B1",
-    button_secondary_text_color="#222222",
-    block_background_fill="#FFFFFF",
-    background_fill_primary="#F8F8F8",
-    background_fill_secondary="#FFFFFF",
-    border_color_primary="#E6E6E6",
-    block_shadow="0 1px 4px rgba(0,0,0,.06)",
-    block_radius="16px",
-    input_radius="8px",
-)
-
-S2_CSS = """
-:root{
-  --s2-accent:#0265DC; --s2-accent-down:#0054B6;
-  --s2-gray-50:#F8F8F8; --s2-gray-75:#F3F3F3; --s2-gray-200:#E6E6E6;
-  --s2-gray-800:#292929; --s2-positive:#007A39; --s2-negative:#D7373F;
-}
-body, .gradio-container{background:var(--s2-gray-50)!important;color:var(--s2-gray-800);}
-.gradio-container{max-width:1280px!important;margin:0 auto!important;}
-
-/* Spectrum 2 signature: pill buttons — SCOPED to real action buttons only.
-   (A global rule turned the Dataframe's internal icon buttons into ovals
-   that floated over the table.) */
-button.primary, button.secondary, button.lg, button.sm{
-  border-radius:999px!important;font-weight:600!important;}
-button:focus-visible{outline:2px solid var(--s2-accent)!important;outline-offset:2px;}
-.table-wrap button, table button, .dataframe button, [class*="cell-menu"] button{
-  border-radius:6px!important;font-weight:500!important;}
-
-/* hero */
-#s2-hero{background:linear-gradient(120deg,#0265DC 0%,#5258E4 55%,#7326D3 100%);
-  border-radius:20px;padding:28px 32px;color:#fff;margin-bottom:6px;}
-#s2-hero h1{margin:0;font-size:30px;font-weight:800;letter-spacing:-.5px;color:#fff;}
-#s2-hero p{margin:6px 0 0;opacity:.92;font-size:15px;color:#fff;}
-#s2-hero .chips span{display:inline-block;background:rgba(255,255,255,.16);
-  border:1px solid rgba(255,255,255,.35);border-radius:999px;padding:3px 12px;
-  font-size:12.5px;margin:10px 8px 0 0;}
-
-/* tab strip — Spectrum quiet tabs with accent underline */
-.tab-nav{border-bottom:2px solid var(--s2-gray-200)!important;}
-.tab-nav button{border-radius:8px 8px 0 0!important;font-size:15px!important;
-  color:#6e6e6e!important;background:transparent!important;border:none!important;}
-.tab-nav button.selected{color:var(--s2-accent)!important;
-  box-shadow:inset 0 -3px 0 var(--s2-accent)!important;font-weight:700!important;}
-
-/* cards */
-.block{border:1px solid var(--s2-gray-200)!important;}
-table{font-size:13.5px!important;}
-thead th{background:var(--s2-gray-75)!important;font-weight:700!important;}
-
-.s2-footnote{color:#6e6e6e;font-size:12.5px;}
-
-/* AI output panel — big, framed, unmissable */
-.ai-panel{background:#fff;border:1.5px solid var(--s2-accent)!important;
-  border-left:6px solid var(--s2-accent)!important;border-radius:14px!important;
-  padding:18px 22px!important;min-height:240px;max-height:560px;overflow-y:auto;
-  font-size:15px;line-height:1.55;box-shadow:0 2px 10px rgba(2,101,220,.08);}
-.ai-panel:empty::after{content:"AI output will appear here";color:#9a9a9a;}
-#detail-log textarea{font-family:'Source Code Pro',monospace!important;font-size:12.5px!important;}
-"""
+# 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):
@@ -182,9 +111,11 @@ def ui_save_holdings(text):
 
 
 def ui_check_news():
-    md = news_watch.check_holdings_news()
-    automation.STATE["news_md"] = md
-    return md
+    last = ""
+    for md in news_watch.check_holdings_news_stream():
+        last = md
+        yield md
+    automation.STATE["news_md"] = last
 
 
 def ui_research(ticker):
@@ -212,13 +143,16 @@ _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">
-      <h1>Chan Compass <span style="font-weight:300">· US Markets</span></h1>
-      <p>Multi-timeframe 缠论 (Chan theory) signal engine — monthly → weekly → daily → 60m → 30m → 15m → 5m
-         nested-interval confirmation — plus sector capital rotation and a fully local llama.cpp research brain.</p>
+      <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 GGUF · no cloud APIs</span><span>🦙 llama.cpp runtime</span>
-        <span>📊 Yahoo Finance data</span><span>⏰ Auto-update 18:10 ET</span>
-        <span>🤖 Sub-agent pool · 1.7B + 4B</span><span>💾 Persistent /data bucket</span><span>🎨 Spectrum 2 design</span>
+        <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>""")
 
@@ -265,7 +199,7 @@ with gr.Blocks(title="Chan Compass · US", **_style_kw) as demo:
             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()
+        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 "

data_us.py

@@ -30,16 +30,18 @@ import paths
 CACHE_DIR = os.environ.get("CHAN_CACHE_DIR", paths.CACHE_DIR)
 
 LEVELS = {
-    # level: (yfinance interval, period)
+    # 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}
+                  "15m": 2 * 3600, "5m": 2 * 3600, "1m": 1800}
 
 
 def _cache_path(ticker: str, level: str) -> str:
@@ -107,10 +109,12 @@ def load_level(ticker: str, level: str, force: bool = False) -> pd.DataFrame:
     return pd.DataFrame(columns=["date", "open", "close", "high", "low", "volume", "amount"])
 
 
-# Fast mode (default): the simplified "B3/S3 long-hold" pipeline only needs
-# monthly/weekly/daily (resampled from daily) + 60m/30m sub-level confirmation.
-# 15m/5m added little for next-day planning and doubled download+compute time.
-FAST_LEVELS = ("d", "60m", "30m")
+# 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:

news_watch.py

@@ -97,6 +97,70 @@ def _llm_brief(ticker: str, items: list) -> str:
         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()

signal_runner.py

@@ -120,10 +120,10 @@ TREND_EN = {"up_trend": "Up", "down_trend": "Down", "consolidation": "Range",
 
 def analyze_one(ticker: str, force: bool = False):
     """Run the simplified long-hold Chan analysis for one ticker.
-    Fast level set: monthly/weekly (resampled) + daily + 60m/30m confirmation.
-    (15m/5m dropped — they doubled runtime and added nothing to a next-day,
-    long-hold plan. The engine degrades gracefully without them.)"""
-    dfs = data_us.load_levels(ticker, data_us.FAST_LEVELS, force=force)
+    Full nested-interval set: monthly/weekly (resampled) + daily +
+    60m/30m/15m/5m/1m confirmation (区间套). Deeper sub-levels = more precise
+    buy/sell points; any missing level (e.g. 1m beyond 7 days) is skipped."""
+    dfs = data_us.load_levels(ticker, data_us.FULL_LEVELS, force=force)
     d = dfs["d"]
     if d is None or len(d) < 60:
         return None, f"{ticker}: not enough daily history ({0 if d is None else len(d)} bars)."
@@ -133,8 +133,8 @@ def analyze_one(ticker: str, force: bool = False):
     ml = MultiLevelChan(
         df_daily=d, df_weekly=w, df_monthly=m,
         df_60m=dfs.get("60m"), df_30m=dfs.get("30m"),
-        df_15m=None, df_5m=None,
-        df_1m=None,  # Yahoo 1m history (7 days) is too short for Chan decomposition
+        df_15m=dfs.get("15m"), df_5m=dfs.get("5m"),
+        df_1m=dfs.get("1m"),   # finest nested-interval level when Yahoo has it
         code=ticker, strict=True,
     )
     res = ml.analyze()
@@ -172,7 +172,7 @@ def run_signals(tickers=None, force: bool = False):
     """Run the whole pool. Returns (DataFrame, {ticker: detail}, summary_str)."""
     tickers = [t.strip().upper() for t in (tickers or DEFAULT_POOL) if t.strip()]
     try:  # parallel download phase (IO-bound) — analysis stays CPU-only, no LLM
-        data_us.prefetch(tickers, data_us.FAST_LEVELS, force=force)
+        data_us.prefetch(tickers, data_us.FULL_LEVELS, force=force, budget_s=60)
     except Exception:
         pass
     rows, details, errors = [], {}, []

theme.py

@@ -0,0 +1,146 @@
+# Spectrum 2 theme for Gradio — Chan Compass
+# ---------------------------------------------------------------
+# Single source of truth for the LIVE Gradio app's look. Mirrors the
+# Chan Compass · Spectrum 2 design system (tokens in ../tokens/*.css).
+#
+# Usage in app.py:
+#
+#     from theme import THEME, CSS
+#     with gr.Blocks(title="Chan Compass · US", theme=THEME, css=CSS) as demo:
+#         ...
+#     demo.launch()
+#
+# On Gradio >= 6 pass them to launch() instead:
+#     demo.launch(theme=THEME, css=CSS)
+# ---------------------------------------------------------------
+import gradio as gr
+
+# ---- Spectrum 2 palette (light) -------------------------------------------
+ACCENT       = "#0265dc"   # blue-900
+ACCENT_HOVER = "#0054b6"   # blue-1000
+ACCENT_DOWN  = "#00418f"   # blue-1100
+ACCENT_SUBTLE= "#e0f2ff"   # blue-100
+GRAY_25      = "#ffffff"
+GRAY_50      = "#f8f8f8"   # canvas
+GRAY_75      = "#f3f3f3"
+GRAY_100     = "#e6e6e6"   # hairline border
+GRAY_300     = "#b1b1b1"   # field border
+GRAY_500     = "#6d6d6d"   # muted text
+GRAY_700     = "#292929"   # body text
+GRAY_800     = "#1b1b1b"
+POSITIVE     = "#007a39"
+NEGATIVE     = "#d7373f"
+NOTICE       = "#b25309"
+
+THEME = gr.themes.Default(
+    font=[gr.themes.GoogleFont("Source Sans 3"), "Adobe Clean", "system-ui", "sans-serif"],
+    font_mono=[gr.themes.GoogleFont("Source Code Pro"), "monospace"],
+    primary_hue=gr.themes.colors.blue,
+    neutral_hue=gr.themes.colors.gray,
+    radius_size=gr.themes.sizes.radius_lg,
+    spacing_size=gr.themes.sizes.spacing_md,
+).set(
+    # surfaces
+    body_background_fill=GRAY_50,
+    background_fill_primary=GRAY_25,
+    background_fill_secondary=GRAY_75,
+    block_background_fill=GRAY_25,
+    block_border_color=GRAY_100,
+    block_border_width="1px",
+    block_radius="16px",
+    block_shadow="0 1px 3px rgba(0,0,0,.06), 0 1px 1px rgba(0,0,0,.04)",
+    block_label_text_color=GRAY_500,
+    block_title_text_color=GRAY_800,
+    border_color_primary=GRAY_100,
+    # text
+    body_text_color=GRAY_700,
+    body_text_color_subdued=GRAY_500,
+    # buttons — Spectrum 2 signature pill
+    button_large_radius="9999px",
+    button_small_radius="9999px",
+    button_primary_background_fill=ACCENT,
+    button_primary_background_fill_hover=ACCENT_HOVER,
+    button_primary_text_color="#ffffff",
+    button_primary_border_color=ACCENT,
+    button_secondary_background_fill=GRAY_25,
+    button_secondary_background_fill_hover=GRAY_75,
+    button_secondary_border_color=GRAY_300,
+    button_secondary_text_color=GRAY_700,
+    # inputs
+    input_background_fill=GRAY_25,
+    input_border_color=GRAY_300,
+    input_border_color_focus=ACCENT,
+    input_radius="8px",
+    # accents / links
+    color_accent_soft=ACCENT_SUBTLE,
+    link_text_color=ACCENT_HOVER,
+    link_text_color_hover=ACCENT_DOWN,
+)
+
+# ---- Fine-grained CSS the theme object can't express ----------------------
+CSS = """
+:root{
+  --s2-accent:#0265dc; --s2-accent-down:#0054b6;
+  --s2-gray-50:#f8f8f8; --s2-gray-75:#f3f3f3; --s2-gray-100:#e6e6e6;
+  --s2-gray-300:#b1b1b1; --s2-gray-500:#6d6d6d; --s2-gray-700:#292929; --s2-gray-800:#1b1b1b;
+  --s2-positive:#007a39; --s2-negative:#d7373f; --s2-notice:#b25309;
+  --s2-radius-card:16px;
+}
+body,.gradio-container{ background:var(--s2-gray-50)!important; color:var(--s2-gray-700);
+  font-family:'Source Sans 3','Adobe Clean',system-ui,sans-serif; }
+.gradio-container{ max-width:1280px!important; margin:0 auto!important; }
+
+/* Signature pill buttons — scoped to real action buttons, not table chrome */
+button.primary, button.secondary, button.lg, button.sm{
+  border-radius:9999px!important; font-weight:600!important; letter-spacing:0;
+  transition:background-color .13s cubic-bezier(.45,0,.4,1), transform .13s cubic-bezier(0,0,.4,1); }
+button.primary:active, button.secondary:active{ transform:scale(.98); }
+button:focus-visible{ outline:2px solid var(--s2-accent)!important; outline-offset:2px; }
+.table-wrap button, table button, .dataframe button, [class*="cell-menu"] button{
+  border-radius:6px!important; font-weight:500!important; }
+
+/* Brand header (replaces gradient hero with a clean Spectrum bar) */
+#s2-hero{ display:flex; align-items:center; gap:16px;
+  background:#fff; border:1px solid var(--s2-gray-100); border-radius:20px;
+  padding:22px 26px; margin-bottom:8px; box-shadow:0 1px 3px rgba(0,0,0,.06); }
+#s2-hero .mark{ width:46px; height:46px; flex:none; border-radius:12px; display:grid; place-items:center;
+  background:linear-gradient(135deg,#0265dc 0%,#5258e4 60%,#7326d3 100%);
+  color:#fff; font-size:24px; box-shadow:0 2px 12px rgba(2,101,220,.18); }
+#s2-hero h1{ margin:0; font-size:24px; font-weight:800; letter-spacing:-.01em; color:var(--s2-gray-800); }
+#s2-hero h1 span{ font-weight:300; color:var(--s2-gray-500); }
+#s2-hero p{ margin:3px 0 0; color:var(--s2-gray-500); font-size:14px; }
+#s2-hero .chips{ margin-left:auto; display:flex; flex-wrap:wrap; gap:7px; justify-content:flex-end; max-width:360px; }
+#s2-hero .chips span{ background:var(--s2-gray-75); border:1px solid var(--s2-gray-100); border-radius:9999px;
+  padding:3px 11px; font-size:12px; font-weight:600; color:var(--s2-gray-700); }
+
+/* Quiet tabs with accent underline */
+.tab-nav{ border-bottom:2px solid var(--s2-gray-100)!important; gap:24px; }
+.tab-nav button{ border:none!important; background:transparent!important; border-radius:0!important;
+  font-size:16px!important; font-weight:600!important; color:var(--s2-gray-500)!important;
+  padding:12px 2px!important; margin-bottom:-2px; }
+.tab-nav button.selected{ color:var(--s2-accent)!important;
+  box-shadow:inset 0 -2px 0 var(--s2-accent)!important; }
+
+/* Cards / blocks */
+.block{ border:1px solid var(--s2-gray-100)!important; border-radius:var(--s2-radius-card)!important; }
+
+/* Data tables */
+table{ font-size:13.5px!important; }
+thead th{ background:var(--s2-gray-75)!important; font-weight:700!important; text-transform:uppercase;
+  letter-spacing:.04em; font-size:11.5px!important; color:var(--s2-gray-500)!important; }
+tbody td{ border-color:var(--s2-gray-100)!important; }
+
+/* AI output panel — the signature accent-framed surface */
+.ai-panel{ background:#fff; border:2px solid var(--s2-accent)!important;
+  border-left:6px solid var(--s2-accent)!important; border-radius:12px!important;
+  padding:18px 22px!important; min-height:240px; max-height:560px; overflow-y:auto;
+  font-size:15px; line-height:1.55; box-shadow:0 2px 12px rgba(2,101,220,.10); }
+.ai-panel:empty::after{ content:"AI output appears here"; color:#9a9a9a; }
+
+/* Mono log boxes */
+#detail-log textarea{ font-family:'Source Code Pro',monospace!important; font-size:12.5px!important; }
+
+.s2-footnote{ color:var(--s2-gray-500); font-size:12.5px; }
+"""
+
+__all__ = ["THEME", "CSS"]