feat(ECR-009): Crypto Wyckoff Screener 独立页（D/W/M）

移植 A_Share_DP 引擎；本地缓存与 60s tip；月线由日线 UTC 聚合；不碰主站 analyze/缠论叠层。

Co-authored-by: Cursor <cursoragent@cursor.com>

.gitignore

@@ -44,3 +44,6 @@ data_provider/._config.json
 # ESS gate / engineering-loop working dirs（归档进 docs/runs/）
 .gates/
 loop/
+
+# Crypto Wyckoff Screener local cache
+data/crypto_wyckoff/

crypto_wyckoff/__init__.py

@@ -0,0 +1,5 @@
+"""crypto_wyckoff — multi-TF screener for crypto (ported from A_Share_DP Architecture v1.0)."""
+
+from crypto_wyckoff.version import ARCHITECTURE_VERSION, WYCKOFF_ENGINE_VERSION
+
+__all__ = ["WYCKOFF_ENGINE_VERSION", "ARCHITECTURE_VERSION"]

crypto_wyckoff/annotate.py

@@ -0,0 +1,329 @@
+"""Walk-forward Wyckoff phase/event annotations for chart overlay."""
+
+from __future__ import annotations
+
+from datetime import date
+
+from crypto_wyckoff.domain_models import OHLCVFrame, WyckoffCycle, WyckoffEvent, WyckoffPhase
+from crypto_wyckoff.cycle import CycleEngine
+from crypto_wyckoff.event import EventEngine
+from crypto_wyckoff.features import FeatureEngine
+from crypto_wyckoff.phase import PhaseEngine
+
+_MIN_BARS = {"1d": 40, "1w": 26, "1M": 18}
+
+_NOTABLE_EVENTS = {
+    WyckoffEvent.PS.value,
+    WyckoffEvent.SC.value,
+    WyckoffEvent.AR.value,
+    WyckoffEvent.ST.value,
+    WyckoffEvent.SPRING.value,
+    WyckoffEvent.TEST.value,
+    WyckoffEvent.SOS.value,
+    WyckoffEvent.LPS.value,
+    WyckoffEvent.JUMP.value,
+    WyckoffEvent.BACKUP.value,
+    WyckoffEvent.BC.value,
+    WyckoffEvent.UTAD.value,
+    WyckoffEvent.SOW.value,
+    WyckoffEvent.LPSY.value,
+}
+
+
+def _slice_frame(frame: OHLCVFrame, end_idx: int) -> OHLCVFrame:
+    n = end_idx + 1
+    return OHLCVFrame(
+        ts_code=frame.ts_code,
+        timeframe=frame.timeframe,
+        trade_dates=frame.trade_dates[:n],
+        open=frame.open[:n],
+        high=frame.high[:n],
+        low=frame.low[:n],
+        close=frame.close[:n],
+        volume=frame.volume[:n],
+        amount=frame.amount[:n] if frame.amount else [],
+    )
+
+
+def _compress_phases(points: list[tuple[str, str]]) -> list[dict]:
+    """points: [(date_iso, phase), ...] → segments."""
+    if not points:
+        return []
+    segs: list[dict] = []
+    start, phase = points[0]
+    prev = start
+    for d, p in points[1:]:
+        if p != phase:
+            segs.append({"start": start, "end": prev, "phase": phase})
+            start, phase = d, p
+        prev = d
+    segs.append({"start": start, "end": prev, "phase": phase})
+    return segs
+
+
+def annotate_frame(frame: OHLCVFrame, step: int | None = None) -> dict:
+    """Pure annotation: phase bands + event markers + latest levels.
+
+    ``step`` defaults by timeframe to keep interactive charts snappy.
+    """
+    tf = frame.timeframe
+    min_bars = _MIN_BARS.get(tf, 30)
+    if step is None:
+        step = {"1d": 2, "1w": 1, "1M": 1}.get(tf, 2)
+
+    empty = {
+        "phases": [],
+        "events": [],
+        "levels": {},
+        "bars": len(frame),
+        "timeframe": tf,
+    }
+    if frame.empty or len(frame) < min_bars:
+        return empty
+
+    feat_eng = FeatureEngine()
+    cycle_eng = CycleEngine()
+    phase_eng = PhaseEngine()
+    event_eng = EventEngine()
+
+    phase_points: list[tuple[str, str]] = []
+    events: list[dict] = []
+    last_event: str | None = None
+    levels: dict = {}
+
+    # Ensure last bar is always evaluated
+    indices = list(range(min_bars - 1, len(frame), step))
+    if indices[-1] != len(frame) - 1:
+        indices.append(len(frame) - 1)
+
+    for i in indices:
+        sub = _slice_frame(frame, i)
+        f = feat_eng.run(sub, tf)
+        c = cycle_eng.run(f, tf)
+        p = phase_eng.run(c, f, tf)
+        e = event_eng.run(c, p, f, tf)
+
+        d = str(frame.trade_dates[i])[:10]
+        phase = p.payload.get("phase") or WyckoffPhase.NONE.value
+        phase_points.append((d, phase))
+
+        cur = e.payload.get("current_event") or WyckoffEvent.NONE.value
+        if cur in _NOTABLE_EVENTS and cur != last_event:
+            events.append({
+                "date": d,
+                "event": cur,
+                "price": float(frame.close[i]),
+                "low": float(frame.low[i]),
+                "high": float(frame.high[i]),
+            })
+            last_event = cur
+        elif cur == WyckoffEvent.NONE.value:
+            last_event = None
+
+        if i == len(frame) - 1 and not f.payload.get("insufficient"):
+            levels = {
+                k: f.payload.get(k)
+                for k in (
+                    "range_high", "range_low", "ma20", "ma60",
+                    "swing_high", "swing_low", "close",
+                )
+                if f.payload.get(k) is not None
+            }
+            levels["phase"] = phase
+            levels["cycle"] = c.payload.get("cycle")
+            levels["current_event"] = cur
+
+    return {
+        "phases": _compress_phases(phase_points),
+        "events": events,
+        "levels": levels,
+        "bars": len(frame),
+        "timeframe": tf,
+    }
+
+
+_RANGE_CYCLES = {
+    WyckoffCycle.ACCUMULATION.value,
+    WyckoffCycle.RE_ACCUMULATION.value,
+    WyckoffCycle.DISTRIBUTION.value,
+    WyckoffCycle.RE_DISTRIBUTION.value,
+}
+
+
+def _build_range_zones(
+    price_frame: OHLCVFrame,
+    cycle_segs: list[dict],
+    levels: dict | None = None,
+) -> list[dict]:
+    """Build price boxes (high/low × date span) for accum/distrib ranges."""
+    if price_frame.empty:
+        return []
+    dates = [str(d)[:10] for d in price_frame.trade_dates]
+    highs = price_frame.high
+    lows = price_frame.low
+    zones: list[dict] = []
+
+    for seg in cycle_segs or []:
+        cy = seg.get("cycle")
+        if cy not in _RANGE_CYCLES:
+            continue
+        start, end = seg["start"], seg["end"]
+        idxs = [i for i, d in enumerate(dates) if start <= d <= end]
+        if not idxs:
+            # weekly bar date may sit between daily bars — take nearest window
+            i0 = next((i for i, d in enumerate(dates) if d >= start), None)
+            if i0 is None:
+                continue
+            i1 = next((i for i, d in enumerate(dates) if d > end), len(dates)) - 1
+            idxs = list(range(i0, max(i0, i1) + 1))
+        if not idxs:
+            continue
+        # pad short weekly hits to at least ~1 week of dailies for visibility
+        if len(idxs) < 5 and idxs[-1] + 1 < len(dates):
+            extra = min(5 - len(idxs), len(dates) - 1 - idxs[-1])
+            idxs = list(range(idxs[0], idxs[-1] + 1 + max(0, extra)))
+        hi = max(highs[i] for i in idxs)
+        lo = min(lows[i] for i in idxs)
+        if hi <= lo:
+            continue
+        zones.append({
+            "kind": cy,
+            "start": dates[idxs[0]],
+            "end": dates[idxs[-1]],
+            "high": float(hi),
+            "low": float(lo),
+            "current": False,
+        })
+
+    # Always expose the latest trading-range box from feature snapshot
+    levels = levels or {}
+    rh, rl = levels.get("range_high"), levels.get("range_low")
+    if rh is not None and rl is not None and float(rh) > float(rl):
+        look = min(60, len(dates))
+        cy = levels.get("cycle") or "Unknown"
+        if cy not in _RANGE_CYCLES:
+            # Phase B/C in a range → treat as accumulation-style TR for display
+            ph = levels.get("phase") or ""
+            if ph in ("A", "B", "C"):
+                cy = WyckoffCycle.ACCUMULATION.value
+            elif ph in ("D", "E") and float(levels.get("close") or 0) < float(rh):
+                cy = WyckoffCycle.ACCUMULATION.value
+            else:
+                cy = "Range"
+        zones.append({
+            "kind": cy,
+            "start": dates[-look],
+            "end": dates[-1],
+            "high": float(rh),
+            "low": float(rl),
+            "current": True,
+        })
+
+    return zones
+
+
+def annotate_symbol(
+    ts_code: str,
+    freq: str,
+    end_date: date | None = None,
+    lookback: int = 180,
+) -> dict:
+    """IO + annotate for one symbol (used by API).
+
+    For daily charts, phase bands come from **weekly** structure (Wyckoff
+    primary timeframe), while event markers / levels come from daily.
+    """
+    from crypto_wyckoff.io import latest_daily_trade_date, load_frames_batch
+
+    if freq not in ("1d", "1w", "1M"):
+        raise ValueError(f"unsupported freq: {freq}")
+    ed = end_date or latest_daily_trade_date()
+    empty = {
+        "ts_code": ts_code,
+        "freq": freq,
+        "phases": [],
+        "events": [],
+        "levels": {},
+        "zones": [],
+        "bars": 0,
+        "phase_source": freq,
+    }
+    if ed is None:
+        return empty
+
+    if freq == "1d":
+        daily_frames = load_frames_batch("1d", ed, lookback, ts_codes=[ts_code])
+        weekly_frames = load_frames_batch("1w", ed, max(60, lookback // 3), ts_codes=[ts_code])
+        daily = daily_frames.get(ts_code)
+        weekly = weekly_frames.get(ts_code)
+        if daily is None:
+            return empty
+        d_ann = annotate_frame(daily)
+        w_ann = annotate_frame(weekly) if weekly is not None else {"phases": []}
+        cycles = _cycle_segments(weekly) if weekly is not None else []
+        levels = d_ann.get("levels") or {}
+        # Prefer weekly cycle on the latest levels for zone labeling
+        if cycles:
+            levels = {**levels, "cycle": cycles[-1].get("cycle") or levels.get("cycle")}
+            # latest non-None weekly phase
+            for p in reversed(w_ann.get("phases") or []):
+                if p.get("phase") not in (None, "None"):
+                    levels = {**levels, "phase": p["phase"]}
+                    break
+        return {
+            "ts_code": ts_code,
+            "freq": freq,
+            "end_date": ed.isoformat(),
+            "phases": w_ann.get("phases") or [],
+            "events": d_ann.get("events") or [],
+            "levels": d_ann.get("levels") or {},
+            "zones": _build_range_zones(daily, cycles, levels),
+            "bars": d_ann.get("bars", 0),
+            "phase_source": "1w",
+            "cycles": cycles,
+        }
+
+    frames = load_frames_batch(freq, ed, lookback, ts_codes=[ts_code])
+    frame = frames.get(ts_code)
+    if frame is None:
+        return empty
+    out = annotate_frame(frame)
+    out["ts_code"] = ts_code
+    out["freq"] = freq
+    out["end_date"] = ed.isoformat()
+    out["phase_source"] = freq
+    out["cycles"] = _cycle_segments(frame)
+    out["zones"] = _build_range_zones(frame, out["cycles"], out.get("levels") or {})
+    if freq == "1M":
+        # Monthly chart: cycle bands are more meaningful than phase
+        if not any(p.get("phase") not in (None, "None") for p in out["phases"]):
+            out["phases"] = [
+                {"start": c["start"], "end": c["end"], "phase": c["cycle"]}
+                for c in out["cycles"]
+                if c.get("cycle") and c["cycle"] != "Unknown"
+            ]
+    return out
+
+
+def _cycle_segments(frame: OHLCVFrame, step: int | None = None) -> list[dict]:
+    """Walk-forward cycle labels compressed to segments."""
+    tf = frame.timeframe
+    min_bars = _MIN_BARS.get(tf, 30)
+    if step is None:
+        step = {"1d": 3, "1w": 1, "1M": 1}.get(tf, 2)
+    if frame.empty or len(frame) < min_bars:
+        return []
+
+    feat_eng = FeatureEngine()
+    cycle_eng = CycleEngine()
+    points: list[tuple[str, str]] = []
+    indices = list(range(min_bars - 1, len(frame), step))
+    if indices[-1] != len(frame) - 1:
+        indices.append(len(frame) - 1)
+    for i in indices:
+        sub = _slice_frame(frame, i)
+        f = feat_eng.run(sub, tf)
+        c = cycle_eng.run(f, tf)
+        points.append((str(frame.trade_dates[i])[:10], c.payload.get("cycle") or "Unknown"))
+    segs = _compress_phases(points)
+    return [{"start": s["start"], "end": s["end"], "cycle": s["phase"]} for s in segs]

crypto_wyckoff/cycle.py

@@ -0,0 +1,102 @@
+"""Cycle Engine — monthly/weekly macro cycle via Rule Registry."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import EngineResult, WyckoffCycle
+from crypto_wyckoff.rules.base import RuleHit
+from crypto_wyckoff.rules.registry import rule_registry
+
+
+def _resolve_range_conflict(hits: list[RuleHit], features: dict) -> list[RuleHit]:
+    """Accumulation vs Distribution overlap → mutually exclusive by MA120 position."""
+    accum = [h for h in hits if h.cycle == WyckoffCycle.ACCUMULATION.value]
+    dist = [h for h in hits if h.cycle == WyckoffCycle.DISTRIBUTION.value]
+    if not (accum and dist):
+        return hits
+
+    close = float(features.get("close") or 0)
+    ma120 = float(features.get("ma120") or close) or close
+    others = [
+        h for h in hits
+        if h.cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value)
+    ]
+    # Below MA120 → accumulation; above → distribution; equal band uses relative position
+    if close < ma120 * 0.995:
+        return others + accum
+    if close > ma120 * 1.005:
+        return others + dist
+    # Tight band: keep higher confidence only
+    best_a = max(accum, key=lambda h: h.confidence)
+    best_d = max(dist, key=lambda h: h.confidence)
+    return others + ([best_a] if best_a.confidence >= best_d.confidence else [best_d])
+
+
+class CycleEngine:
+    name = "Cycle"
+    version = "1.0.0"
+
+    def run(self, feature: EngineResult, timeframe: str) -> EngineResult:
+        features = feature.payload
+        if features.get("insufficient"):
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=15.0,
+                score=40.0,
+                reasons=[f"{timeframe} 数据不足，Cycle=Unknown"],
+                warnings=["insufficient_features"],
+                payload={
+                    "cycle": WyckoffCycle.UNKNOWN.value,
+                    "timeframe": timeframe,
+                    "trend_score": 40.0,
+                },
+            )
+
+        context = {"features": features, "timeframe": timeframe}
+        hits: list[RuleHit] = []
+        for rule in rule_registry.by_category("cycle", timeframe):
+            hit = rule.evaluate(context)
+            if hit and hit.cycle:
+                hits.append(hit)
+
+        hits = _resolve_range_conflict(hits, features)
+
+        if not hits:
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=30.0,
+                score=40.0,
+                reasons=["无匹配周期规则，标记 Unknown"],
+                payload={
+                    "cycle": WyckoffCycle.UNKNOWN.value,
+                    "timeframe": timeframe,
+                    "trend_score": 40.0,
+                },
+            )
+
+        best = max(hits, key=lambda h: h.confidence)
+        trend_score = best.score
+        if best.cycle == WyckoffCycle.MARKUP.value:
+            trend_score = max(trend_score, 75.0)
+        elif best.cycle == WyckoffCycle.ACCUMULATION.value:
+            trend_score = max(60.0, trend_score * 0.9)
+        elif best.cycle == WyckoffCycle.DISTRIBUTION.value:
+            trend_score = min(45.0, 100 - trend_score * 0.5)
+        elif best.cycle == WyckoffCycle.MARKDOWN.value:
+            trend_score = min(30.0, 100 - trend_score)
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=best.confidence,
+            score=trend_score,
+            reasons=best.reasons,
+            metrics=best.metrics,
+            payload={
+                "cycle": best.cycle,
+                "timeframe": timeframe,
+                "rule_id": best.rule_id,
+                "trend_score": trend_score,
+            },
+        )

crypto_wyckoff/decision.py

@@ -0,0 +1,195 @@
+"""Decision Engine — multi-timeframe fusion and tradability (Architecture v1.0)."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import (
+    DecisionSignal,
+    EngineResult,
+    RiskLevel,
+    WyckoffCycle,
+    WyckoffEvent,
+    WyckoffPhase,
+)
+
+BULL_CYCLES = {
+    WyckoffCycle.ACCUMULATION.value,
+    WyckoffCycle.RE_ACCUMULATION.value,
+    WyckoffCycle.MARKUP.value,
+}
+BEAR_CYCLES = {
+    WyckoffCycle.DISTRIBUTION.value,
+    WyckoffCycle.RE_DISTRIBUTION.value,
+    WyckoffCycle.MARKDOWN.value,
+}
+
+
+class DecisionEngine:
+    name = "Decision"
+    version = "1.0.0"
+
+    def run(
+        self,
+        monthly_cycle: EngineResult,
+        weekly_cycle: EngineResult,
+        weekly_phase: EngineResult,
+        weekly_event: EngineResult,
+        daily_event: EngineResult,
+        daily_signal: EngineResult,
+    ) -> EngineResult:
+        m_cycle = monthly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
+        w_cycle = weekly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
+        w_phase = weekly_phase.payload.get("phase", WyckoffPhase.NONE.value)
+        w_event = weekly_event.payload.get("current_event", WyckoffEvent.NONE.value)
+        d_event = daily_event.payload.get("current_event", WyckoffEvent.NONE.value)
+
+        trend_score = float(monthly_cycle.payload.get("trend_score", monthly_cycle.score))
+        structure_score = float(weekly_phase.payload.get("structure_score", weekly_phase.score))
+        entry_score = float(daily_event.payload.get("entry_score", daily_event.score))
+
+        overall_score = 0.30 * trend_score + 0.30 * structure_score + 0.40 * entry_score
+
+        reasons: list[str] = []
+        warnings: list[str] = []
+        alignment = 50.0
+
+        m_bull = m_cycle in BULL_CYCLES
+        m_bear = m_cycle in BEAR_CYCLES
+        w_bull = w_cycle in BULL_CYCLES
+        d_bullish_event = d_event in {
+            WyckoffEvent.SPRING.value,
+            WyckoffEvent.TEST.value,
+            WyckoffEvent.SOS.value,
+            WyckoffEvent.LPS.value,
+            WyckoffEvent.JUMP.value,
+            WyckoffEvent.BACKUP.value,
+        }
+        d_bearish_event = d_event in {
+            WyckoffEvent.UTAD.value,
+            WyckoffEvent.SOW.value,
+            WyckoffEvent.LPSY.value,
+        }
+
+        # Alignment scoring
+        if m_bull and w_bull and d_bullish_event:
+            alignment = 92.0
+            reasons.append("✓ 月/周多头结构与日线多头事件一致")
+        elif m_bull and d_bullish_event:
+            alignment = 78.0
+            reasons.append("✓ 月线支持，日线有入场事件")
+            if not w_bull:
+                warnings.append("周线结构未完全确认")
+                alignment -= 8
+        elif m_bear and d_bullish_event:
+            alignment = 35.0
+            reasons.append("✗ 月线派发/下跌，日线弹簧可能只是反弹")
+        elif m_bear and d_bearish_event:
+            alignment = 85.0
+            reasons.append("✓ 空头多周期一致")
+        else:
+            alignment = 55.0
+            reasons.append("○ 多周期部分一致，需观察")
+
+        if w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value) and m_bull:
+            alignment = min(98.0, alignment + 6)
+            reasons.append(f"✓ 周线阶段 {w_phase} 结构成熟（{w_event}）")
+        active = daily_event.payload.get("active_events") or daily_event.payload.get("recent_events") or []
+        if d_event == WyckoffEvent.SPRING.value and len(active) >= 3:
+            alignment = min(98.0, alignment + 4)
+            reasons.append("✓ 日线多重事件同时确认")
+
+        # Decision signal — hard gate on monthly bear + daily spring
+        decision = DecisionSignal.WATCH.value
+        risk = RiskLevel.MEDIUM.value
+
+        if m_bear and d_event == WyckoffEvent.SPRING.value:
+            decision = DecisionSignal.WATCH.value
+            risk = RiskLevel.HIGH.value
+            overall_score = min(overall_score, 55.0)
+            reasons.append("→ 决策：观察（月线不支持，禁止追日线弹簧）")
+        elif m_bear and d_bullish_event:
+            decision = DecisionSignal.AVOID.value
+            risk = RiskLevel.HIGH.value
+            overall_score = min(overall_score, 48.0)
+            reasons.append("→ 决策：回避（逆大周期多头事件）")
+        elif (
+            m_bull
+            and w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value, WyckoffPhase.C.value)
+            and d_event in (WyckoffEvent.SPRING.value, WyckoffEvent.LPS.value, WyckoffEvent.SOS.value)
+            and alignment >= 85
+            and overall_score >= 80
+        ):
+            decision = DecisionSignal.STRONG_BUY.value
+            risk = RiskLevel.LOW.value
+            reasons.append("→ 决策：强烈买入（三级共振）")
+        elif m_bull and d_bullish_event and overall_score >= 68 and alignment >= 70:
+            decision = DecisionSignal.BUY.value
+            risk = RiskLevel.LOW.value if alignment >= 80 else RiskLevel.MEDIUM.value
+            reasons.append("→ 决策：买入")
+        elif m_bear and d_bearish_event and overall_score >= 65:
+            decision = DecisionSignal.SELL.value
+            risk = RiskLevel.MEDIUM.value
+            reasons.append("→ 决策：卖出")
+        else:
+            decision = DecisionSignal.WATCH.value
+            reasons.append("→ 决策：观察")
+
+        # Stars from score + alignment
+        combo = 0.6 * overall_score + 0.4 * alignment
+        if combo >= 90:
+            stars = 5
+        elif combo >= 80:
+            stars = 4
+        elif combo >= 65:
+            stars = 3
+        elif combo >= 50:
+            stars = 2
+        else:
+            stars = 1
+
+        overall_confidence = (
+            0.25 * monthly_cycle.confidence
+            + 0.25 * weekly_phase.confidence
+            + 0.25 * daily_event.confidence
+            + 0.25 * daily_signal.confidence
+        )
+        # Weak event pulls overall down
+        if daily_event.confidence < 60:
+            overall_confidence = min(overall_confidence, daily_event.confidence + 15)
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=overall_confidence,
+            score=overall_score,
+            reasons=reasons,
+            warnings=warnings,
+            metrics={
+                "trend_score": trend_score,
+                "structure_score": structure_score,
+                "entry_score": entry_score,
+                "alignment": alignment,
+                "stars": stars,
+            },
+            payload={
+                "decision_signal": decision,
+                "alignment": alignment,
+                "stars": stars,
+                "risk": risk,
+                "overall_score": overall_score,
+                "overall_confidence": overall_confidence,
+                "trend_score": trend_score,
+                "structure_score": structure_score,
+                "entry_score": entry_score,
+                "m_cycle": m_cycle,
+                "w_cycle": w_cycle,
+                "w_phase": w_phase,
+                "w_event": w_event,
+                "d_event": d_event,
+                # Facts preserved — never overwritten
+                "facts": {
+                    "monthly": {"cycle": m_cycle},
+                    "weekly": {"cycle": w_cycle, "phase": w_phase, "event": w_event},
+                    "daily": {"event": d_event},
+                },
+            },
+        )

crypto_wyckoff/domain_models.py

@@ -0,0 +1,153 @@
+"""Wyckoff Screener domain models — Architecture v1.0 frozen contracts."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from datetime import date, datetime
+from enum import Enum
+from typing import Any, Optional
+
+
+class WyckoffCycle(str, Enum):
+    ACCUMULATION = "Accumulation"
+    RE_ACCUMULATION = "ReAccumulation"
+    MARKUP = "Markup"
+    DISTRIBUTION = "Distribution"
+    RE_DISTRIBUTION = "ReDistribution"
+    MARKDOWN = "Markdown"
+    UNKNOWN = "Unknown"
+
+
+class WyckoffPhase(str, Enum):
+    A = "A"
+    B = "B"
+    C = "C"
+    D = "D"
+    E = "E"
+    NONE = "None"
+
+
+class WyckoffEvent(str, Enum):
+    PS = "PS"
+    SC = "SC"
+    AR = "AR"
+    ST = "ST"
+    SPRING = "Spring"
+    TEST = "Test"
+    SOS = "SOS"
+    LPS = "LPS"
+    JUMP = "Jump"
+    BACKUP = "Backup"
+    BC = "BC"
+    UTAD = "UTAD"
+    SOW = "SOW"
+    LPSY = "LPSY"
+    NONE = "None"
+
+
+class DecisionSignal(str, Enum):
+    STRONG_BUY = "StrongBuy"
+    BUY = "Buy"
+    WATCH = "Watch"
+    AVOID = "Avoid"
+    SELL = "Sell"
+
+
+class RiskLevel(str, Enum):
+    LOW = "Low"
+    MEDIUM = "Medium"
+    HIGH = "High"
+
+
+@dataclass
+class EngineResult:
+    """Unified result envelope for every Wyckoff engine (v1.0 contract)."""
+
+    name: str
+    version: str = "1.0.0"
+    confidence: float = 0.0
+    score: float = 0.0
+    reasons: list[str] = field(default_factory=list)
+    warnings: list[str] = field(default_factory=list)
+    metrics: dict[str, Any] = field(default_factory=dict)
+    payload: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "version": self.version,
+            "confidence": self.confidence,
+            "score": self.score,
+            "reasons": self.reasons,
+            "warnings": self.warnings,
+            "metrics": self.metrics,
+            "payload": self.payload,
+        }
+
+
+@dataclass
+class OHLCVFrame:
+    """In-memory OHLCV for one symbol one timeframe. Engines never touch DB."""
+
+    ts_code: str
+    timeframe: str  # "1d" | "1w" | "1M"
+    trade_dates: list[date]
+    open: list[float]
+    high: list[float]
+    low: list[float]
+    close: list[float]
+    volume: list[float]
+    amount: list[float] = field(default_factory=list)
+
+    def __len__(self) -> int:
+        return len(self.close)
+
+    @property
+    def empty(self) -> bool:
+        return len(self.close) == 0
+
+
+@dataclass
+class WyckoffScanRow:
+    """Persisted scan row for wyckoff_scan table."""
+
+    trade_date: date
+    ts_code: str
+    name: str = ""
+    industry: str = ""
+    engine_version: str = "v1.0.0"
+
+    m_cycle: str = WyckoffCycle.UNKNOWN.value
+    cycle_confidence: float = 0.0
+    trend_score: float = 0.0
+
+    w_cycle: str = WyckoffCycle.UNKNOWN.value
+    w_phase: str = WyckoffPhase.NONE.value
+    w_current_event: str = WyckoffEvent.NONE.value
+    w_recent_events_json: str = "[]"
+    phase_confidence: float = 0.0
+    structure_score: float = 0.0
+
+    d_current_event: str = WyckoffEvent.NONE.value
+    d_recent_events_json: str = "[]"
+    event_confidence: float = 0.0
+    entry_score: float = 0.0
+
+    entry: Optional[float] = None
+    stop: Optional[float] = None
+    target1: Optional[float] = None
+    target2: Optional[float] = None
+    rr: Optional[float] = None
+
+    alignment: float = 0.0
+    stars: int = 1
+    decision_signal: str = DecisionSignal.WATCH.value
+    signal_confidence: float = 0.0
+    overall_confidence: float = 0.0
+    overall_score: float = 0.0
+    risk: str = RiskLevel.MEDIUM.value
+    reasons_json: str = "[]"
+
+    feature_snapshot_json: str = "{}"
+    markers_json: str = "[]"
+    scanned_at: datetime = field(default_factory=datetime.now)

crypto_wyckoff/event.py

@@ -0,0 +1,149 @@
+"""Event Engine — active concurrent events via Rule Registry.
+
+Note: `active_events` are rules that fire on the latest bar snapshot,
+NOT a historical SC→AR→ST timeline. Do not present as chronological chain.
+"""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import EngineResult, WyckoffEvent
+from crypto_wyckoff.rules.registry import rule_registry
+
+# Display order only (not temporal history)
+_DISPLAY_ORDER = [
+    WyckoffEvent.PS.value,
+    WyckoffEvent.SC.value,
+    WyckoffEvent.AR.value,
+    WyckoffEvent.ST.value,
+    WyckoffEvent.SPRING.value,
+    WyckoffEvent.TEST.value,
+    WyckoffEvent.SOS.value,
+    WyckoffEvent.LPS.value,
+    WyckoffEvent.JUMP.value,
+    WyckoffEvent.BACKUP.value,
+    WyckoffEvent.BC.value,
+    WyckoffEvent.UTAD.value,
+    WyckoffEvent.SOW.value,
+    WyckoffEvent.LPSY.value,
+]
+
+# Dominant event: highest confidence wins; ties broken by this priority
+_DOMINANCE_PRIORITY = [
+    WyckoffEvent.SOS.value,
+    WyckoffEvent.LPS.value,
+    WyckoffEvent.UTAD.value,
+    WyckoffEvent.SPRING.value,
+    WyckoffEvent.JUMP.value,
+    WyckoffEvent.BACKUP.value,
+    WyckoffEvent.TEST.value,
+    WyckoffEvent.SC.value,
+    WyckoffEvent.SOW.value,
+    WyckoffEvent.AR.value,
+    WyckoffEvent.ST.value,
+]
+
+
+class EventEngine:
+    name = "Event"
+    version = "1.0.0"
+
+    def run(
+        self,
+        cycle: EngineResult,
+        phase: EngineResult,
+        feature: EngineResult,
+        timeframe: str,
+    ) -> EngineResult:
+        if feature.payload.get("insufficient"):
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=20.0,
+                score=30.0,
+                reasons=["特征不足，跳过事件识别"],
+                warnings=["insufficient_features"],
+                payload={
+                    "current_event": WyckoffEvent.NONE.value,
+                    "active_events": [],
+                    "recent_events": [],  # alias for DB/API compat; same as active_events
+                    "timeframe": timeframe,
+                    "entry_score": 30.0,
+                },
+            )
+
+        context = {
+            "features": feature.payload,
+            "cycle": cycle.payload,
+            "phase": phase.payload,
+            "timeframe": timeframe,
+        }
+        hits = []
+        for rule in rule_registry.by_category("event", timeframe):
+            hit = rule.evaluate(context)
+            if hit and hit.event:
+                hits.append(hit)
+
+        if not hits:
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=35.0,
+                score=40.0,
+                reasons=["无显著事件"],
+                payload={
+                    "current_event": WyckoffEvent.NONE.value,
+                    "active_events": [],
+                    "recent_events": [],
+                    "timeframe": timeframe,
+                    "entry_score": 40.0,
+                },
+            )
+
+        by_event: dict[str, float] = {}
+        reasons: list[str] = []
+        metrics: dict = {}
+        for h in hits:
+            prev = by_event.get(h.event, -1.0)
+            if h.confidence >= prev:
+                by_event[h.event] = h.confidence
+            reasons.extend(h.reasons)
+            metrics.update(h.metrics)
+
+        active = [e for e in _DISPLAY_ORDER if e in by_event]
+        for e in by_event:
+            if e not in active:
+                active.append(e)
+
+        # Dominant = max confidence; tie-break by dominance priority index
+        def _dom_key(ev: str) -> tuple:
+            conf = by_event[ev]
+            try:
+                prio = _DOMINANCE_PRIORITY.index(ev)
+            except ValueError:
+                prio = 99
+            return (conf, -prio)
+
+        current = max(by_event.keys(), key=_dom_key)
+        event_conf = by_event[current]
+        co_bonus = min(12.0, max(0, len(active) - 1) * 3)
+        entry_score = min(98.0, event_conf + co_bonus)
+        if current == WyckoffEvent.SPRING.value and WyckoffEvent.TEST.value in by_event:
+            entry_score = min(98.0, entry_score + 5)
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=event_conf,
+            score=entry_score,
+            reasons=list(dict.fromkeys(reasons))[:8],
+            warnings=["active_events_are_concurrent_not_timeline"],
+            metrics=metrics,
+            payload={
+                "current_event": current,
+                "active_events": active,
+                "recent_events": active,  # persisted column name; semantic = active
+                "event_scores": by_event,
+                "timeframe": timeframe,
+                "entry_score": entry_score,
+            },
+        )

crypto_wyckoff/features.py

@@ -0,0 +1,206 @@
+"""Feature Engine — pure function over OHLCVFrame → EngineResult(FeatureSnapshot)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+from crypto_wyckoff.domain_models import EngineResult, OHLCVFrame
+
+
+def _sma(arr: np.ndarray, n: int) -> float:
+    if len(arr) < n:
+        return float(arr[-1]) if len(arr) else 0.0
+    return float(np.mean(arr[-n:]))
+
+
+def _atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
+    if len(close) < 2:
+        return 0.0
+    prev_close = close[:-1]
+    tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - prev_close), np.abs(low[1:] - prev_close)))
+    if len(tr) < n:
+        return float(np.mean(tr)) if len(tr) else 0.0
+    return float(np.mean(tr[-n:]))
+
+
+def _adx(high: np.ndarray, low: np.ndarray, close: np.ndarray, n: int = 14) -> float:
+    """Simplified ADX approximation."""
+    if len(close) < n + 2:
+        return 15.0
+    up = high[1:] - high[:-1]
+    down = low[:-1] - low[1:]
+    plus_dm = np.where((up > down) & (up > 0), up, 0.0)
+    minus_dm = np.where((down > up) & (down > 0), down, 0.0)
+    tr = np.maximum(high[1:] - low[1:], np.maximum(np.abs(high[1:] - close[:-1]), np.abs(low[1:] - close[:-1])))
+    atr = np.mean(tr[-n:]) or 1e-9
+    plus_di = 100 * np.mean(plus_dm[-n:]) / atr
+    minus_di = 100 * np.mean(minus_dm[-n:]) / atr
+    denom = plus_di + minus_di
+    if denom < 1e-9:
+        return 10.0
+    dx = 100 * abs(plus_di - minus_di) / denom
+    return float(min(60.0, dx))
+
+
+def compute_feature_snapshot(frame: OHLCVFrame) -> dict[str, Any]:
+    """Compute technical snapshot dict from OHLCV (no I/O)."""
+    if frame.empty or len(frame) < 5:
+        return {"ts_code": frame.ts_code, "timeframe": frame.timeframe, "bars": len(frame)}
+
+    close = np.asarray(frame.close, dtype=float)
+    high = np.asarray(frame.high, dtype=float)
+    low = np.asarray(frame.low, dtype=float)
+    volume = np.asarray(frame.volume, dtype=float)
+    open_ = np.asarray(frame.open, dtype=float)
+
+    ma20 = _sma(close, 20)
+    ma60 = _sma(close, 60)
+    ma120 = _sma(close, min(120, len(close)))
+    atr = _atr(high, low, close, 14)
+    vol_ma20 = _sma(volume, 20) or 1e-9
+    volume_ratio = float(volume[-1] / vol_ma20)
+
+    look = min(60, len(close))
+    window_h = high[-look:]
+    window_l = low[-look:]
+    range_high = float(np.max(window_h))
+    range_low = float(np.min(window_l))
+    rng = max(range_high - range_low, 1e-9)
+    range_pct_60 = float(rng / close[-1]) if close[-1] else 0.0
+    range_position = float((close[-1] - range_low) / rng)
+
+    # Spring / UTAD hints
+    pierce_below = max(0.0, (range_low - low[-1]) / close[-1]) if close[-1] else 0.0
+    # if previous bars broke below and last close back in range
+    prior_low = float(np.min(low[-6:-1])) if len(low) >= 6 else float(low[-2])
+    pierce_below = max(pierce_below, max(0.0, (range_low - prior_low) / close[-1]))
+    close_back_in_range = 1.0 if close[-1] >= range_low else 0.0
+    reclaim_speed = 0.0
+    if pierce_below > 0 and close[-1] >= range_low:
+        reclaim_speed = min(1.0, (close[-1] - low[-1]) / max(atr, 1e-9) / 2)
+
+    pierce_above = max(0.0, (high[-1] - range_high) / close[-1])
+    fail_back = 1.0 if pierce_above > 0 and close[-1] <= range_high else 0.0
+    breakout_above = 1.0 if close[-1] > range_high and volume_ratio >= 1.0 else -1.0
+
+    # pullback hold: close near ma20 from above after being higher
+    pullback_hold = 0.0
+    if len(close) >= 5 and close[-1] > ma20 and close[-3] > close[-1] and (close[-1] - ma20) / max(atr, 1e-9) < 1.5:
+        pullback_hold = 0.8
+
+    ma60_prev = _sma(close[:-5], 60) if len(close) > 65 else ma60
+    ma60_slope = (ma60 - ma60_prev) / max(abs(ma60_prev), 1e-9)
+
+    # volume trend: recent 10 vs prior 10
+    if len(volume) >= 20:
+        volume_trend = float(np.mean(volume[-10:]) / (np.mean(volume[-20:-10]) + 1e-9) - 1.0)
+    else:
+        volume_trend = 0.0
+
+    bar_range_atr = float((high[-1] - low[-1]) / max(atr, 1e-9))
+    bounce_from_low = float((close[-1] - float(np.min(low[-10:]))) / close[-1]) if close[-1] else 0.0
+    gap_up_pct = float((open_[-1] - close[-2]) / close[-2]) if len(close) >= 2 and close[-2] else 0.0
+    after_strength = 0.0
+    if len(close) >= 4 and close[-3] > close[-4]:
+        after_strength = 0.7
+
+    spring_score_hint = 0.0
+    if pierce_below >= 0.002 and close_back_in_range:
+        spring_score_hint = min(90.0, 50 + pierce_below * 1500 + reclaim_speed * 20)
+    utad_score_hint = min(90.0, 50 + pierce_above * 1500) if pierce_above >= 0.002 and fail_back else 0.0
+
+    # swing
+    swing_high = float(np.max(high[-20:])) if len(high) >= 5 else float(high[-1])
+    swing_low = float(np.min(low[-20:])) if len(low) >= 5 else float(low[-1])
+
+    return {
+        "ts_code": frame.ts_code,
+        "timeframe": frame.timeframe,
+        "bars": len(frame),
+        "close": float(close[-1]),
+        "open": float(open_[-1]),
+        "high": float(high[-1]),
+        "low": float(low[-1]),
+        "volume": float(volume[-1]),
+        "ma20": ma20,
+        "ma60": ma60,
+        "ma120": ma120,
+        "ma60_slope": float(ma60_slope),
+        "atr": atr,
+        "adx": _adx(high, low, close),
+        "volume_ma20": float(vol_ma20),
+        "volume_ratio": volume_ratio,
+        "volume_trend": volume_trend,
+        "range_high": range_high,
+        "range_low": range_low,
+        "range_pct_60": range_pct_60,
+        "range_position": range_position,
+        "pierce_below_range": pierce_below,
+        "pierce_above_range": pierce_above,
+        "close_back_in_range": close_back_in_range,
+        "reclaim_speed": reclaim_speed,
+        "fail_back_into_range": fail_back,
+        "breakout_above_range": breakout_above,
+        "pullback_hold": pullback_hold,
+        "bar_range_atr": bar_range_atr,
+        "bounce_from_low": bounce_from_low,
+        "gap_up_pct": gap_up_pct,
+        "after_strength": after_strength,
+        "spring_score_hint": spring_score_hint,
+        "utad_score_hint": utad_score_hint,
+        "swing_high": swing_high,
+        "swing_low": swing_low,
+        "trade_date": str(frame.trade_dates[-1]) if frame.trade_dates else None,
+    }
+
+
+# Minimum bars before a timeframe is considered usable (no cross-TF borrow)
+_MIN_BARS = {"1d": 40, "1w": 26, "1M": 18}
+
+
+class FeatureEngine:
+    """Pure Feature Engine — no database access."""
+
+    name = "Feature"
+    version = "1.0.0"
+
+    def run(self, frame: OHLCVFrame | None, timeframe: str | None = None) -> EngineResult:
+        tf = timeframe or (frame.timeframe if frame else "1d")
+        min_bars = _MIN_BARS.get(tf, 30)
+
+        if frame is None or frame.empty or len(frame) < min_bars:
+            bars = 0 if frame is None or frame.empty else len(frame)
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=10.0,
+                score=10.0,
+                reasons=[f"{tf} bars={bars} < min={min_bars}，标记 insufficient"],
+                warnings=["insufficient_features"],
+                metrics={"bars": bars, "min_bars": min_bars},
+                payload={
+                    "ts_code": getattr(frame, "ts_code", ""),
+                    "timeframe": tf,
+                    "bars": bars,
+                    "insufficient": True,
+                },
+            )
+
+        snap = compute_feature_snapshot(frame)
+        snap["insufficient"] = False
+        conf = 90.0 if snap.get("bars", 0) >= 60 else 50.0 + min(40.0, snap.get("bars", 0) * 0.5)
+        warnings = []
+        if snap.get("bars", 0) < 60:
+            warnings.append("bars偏少，特征可靠性中等")
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=conf,
+            score=conf,
+            reasons=[f"computed {snap.get('bars', 0)} bars {tf}"],
+            warnings=warnings,
+            metrics={"bars": snap.get("bars", 0)},
+            payload=snap,
+        )

crypto_wyckoff/io.py

@@ -0,0 +1,301 @@
+"""Paths + OHLCV cache + DATA_SERVICE fetch (crypto continuous calendar)."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sqlite3
+import time
+from datetime import date, datetime, timezone
+from pathlib import Path
+from typing import Iterable
+
+import requests
+
+from crypto_wyckoff.domain_models import OHLCVFrame
+
+logger = logging.getLogger(__name__)
+
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+DATA_DIR = Path(os.environ.get("CRYPTO_WYCKOFF_DATA", str(_REPO_ROOT / "data" / "crypto_wyckoff")))
+BARS_DB = DATA_DIR / "bars.sqlite"
+SCAN_DB = DATA_DIR / "scan.sqlite"
+
+DATA_SERVICE_URL = os.environ.get(
+    "DATA_SERVICE_URL",
+    os.environ.get("DATASVC_URL", "https://provider.jackyu66.com"),
+).rstrip("/")
+
+# Continuous crypto: bar counts (not A-share weekend-padded calendar multipliers)
+# Provider has 1d/1w but no 1M — monthly is resampled locally from daily UTC months.
+LOOKBACK = {"1d": 250, "1w": 104, "1M": 60}
+TF_PROVIDER = ("1d", "1w")
+TF_LIST = ("1d", "1w", "1M")
+
+
+def ensure_dirs() -> None:
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+
+def _symbol_key(symbol: str) -> str:
+    return symbol.replace("/", "_").replace(":", "_")
+
+
+def _bars_conn() -> sqlite3.Connection:
+    ensure_dirs()
+    conn = sqlite3.connect(str(BARS_DB), timeout=60)
+    conn.execute(
+        """
+        CREATE TABLE IF NOT EXISTS bars (
+            symbol TEXT NOT NULL,
+            tf TEXT NOT NULL,
+            ts INTEGER NOT NULL,
+            open REAL, high REAL, low REAL, close REAL, volume REAL,
+            PRIMARY KEY (symbol, tf, ts)
+        )
+        """
+    )
+    conn.execute("CREATE INDEX IF NOT EXISTS idx_bars_sym_tf ON bars(symbol, tf)")
+    return conn
+
+
+def fetch_candles(
+    symbol: str,
+    tf: str,
+    *,
+    limit: int | None = None,
+    start_ms: int | None = None,
+    end_ms: int | None = None,
+    timeout: float = 15.0,
+) -> list[dict]:
+    params: dict = {"symbol": symbol, "tf": tf}
+    if limit is not None:
+        params["limit"] = int(limit)
+    if start_ms is not None:
+        params["start"] = int(start_ms)
+    if end_ms is not None:
+        params["end"] = int(end_ms)
+    resp = requests.get(f"{DATA_SERVICE_URL}/api/candles", params=params, timeout=timeout)
+    resp.raise_for_status()
+    data = resp.json()
+    if not isinstance(data, list):
+        return []
+    out = []
+    for row in data:
+        try:
+            ts = int(float(row["timestamp"]))
+            out.append(
+                {
+                    "ts": ts,
+                    "open": float(row["open"]),
+                    "high": float(row["high"]),
+                    "low": float(row["low"]),
+                    "close": float(row["close"]),
+                    "volume": float(row.get("volume") or 0),
+                }
+            )
+        except (KeyError, TypeError, ValueError):
+            continue
+    out.sort(key=lambda r: r["ts"])
+    return out
+
+
+def upsert_bars(symbol: str, tf: str, rows: list[dict]) -> int:
+    if not rows:
+        return 0
+    conn = _bars_conn()
+    try:
+        conn.executemany(
+            """
+            INSERT INTO bars(symbol, tf, ts, open, high, low, close, volume)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+            ON CONFLICT(symbol, tf, ts) DO UPDATE SET
+              open=excluded.open, high=excluded.high, low=excluded.low,
+              close=excluded.close, volume=excluded.volume
+            """,
+            [
+                (symbol, tf, r["ts"], r["open"], r["high"], r["low"], r["close"], r["volume"])
+                for r in rows
+            ],
+        )
+        conn.commit()
+        return len(rows)
+    finally:
+        conn.close()
+
+
+def load_frame(symbol: str, tf: str, lookback: int | None = None) -> OHLCVFrame | None:
+    lookback = lookback or LOOKBACK.get(tf, 100)
+    conn = _bars_conn()
+    try:
+        cur = conn.execute(
+            """
+            SELECT ts, open, high, low, close, volume FROM bars
+            WHERE symbol=? AND tf=?
+            ORDER BY ts DESC LIMIT ?
+            """,
+            (symbol, tf, lookback),
+        )
+        rows = list(reversed(cur.fetchall()))
+    finally:
+        conn.close()
+    if not rows:
+        return None
+    trade_dates: list[date] = []
+    for ts, *_ in rows:
+        trade_dates.append(datetime.fromtimestamp(ts / 1000.0, tz=timezone.utc).date())
+    return OHLCVFrame(
+        ts_code=symbol,
+        timeframe=tf,
+        trade_dates=trade_dates,
+        open=[r[1] for r in rows],
+        high=[r[2] for r in rows],
+        low=[r[3] for r in rows],
+        close=[r[4] for r in rows],
+        volume=[r[5] for r in rows],
+    )
+
+
+def bar_count(symbol: str, tf: str) -> int:
+    conn = _bars_conn()
+    try:
+        cur = conn.execute(
+            "SELECT COUNT(*) FROM bars WHERE symbol=? AND tf=?", (symbol, tf)
+        )
+        return int(cur.fetchone()[0])
+    finally:
+        conn.close()
+
+
+def rebuild_monthly_from_daily(symbol: str) -> int:
+    """Aggregate UTC calendar-month OHLCV from local daily bars (provider has no 1M)."""
+    conn = _bars_conn()
+    try:
+        cur = conn.execute(
+            """
+            SELECT ts, open, high, low, close, volume FROM bars
+            WHERE symbol=? AND tf='1d' ORDER BY ts ASC
+            """,
+            (symbol,),
+        )
+        daily = cur.fetchall()
+    finally:
+        conn.close()
+    if not daily:
+        return 0
+
+    months: dict[tuple[int, int], dict] = {}
+    for ts, o, h, l, c, v in daily:
+        dt = datetime.fromtimestamp(ts / 1000.0, tz=timezone.utc)
+        key = (dt.year, dt.month)
+        # month bar open timestamp = first day 00:00 UTC
+        month_ts = int(datetime(dt.year, dt.month, 1, tzinfo=timezone.utc).timestamp() * 1000)
+        if key not in months:
+            months[key] = {
+                "ts": month_ts,
+                "open": o,
+                "high": h,
+                "low": l,
+                "close": c,
+                "volume": v or 0.0,
+            }
+        else:
+            m = months[key]
+            m["high"] = max(m["high"], h)
+            m["low"] = min(m["low"], l)
+            m["close"] = c
+            m["volume"] = (m["volume"] or 0) + (v or 0)
+
+    rows = sorted(months.values(), key=lambda r: r["ts"])
+    # drop stale months then upsert
+    conn = _bars_conn()
+    try:
+        conn.execute("DELETE FROM bars WHERE symbol=? AND tf='1M'", (symbol,))
+        conn.commit()
+    finally:
+        conn.close()
+    return upsert_bars(symbol, "1M", rows)
+
+
+def backfill_symbol(symbol: str, tfs: Iterable[str] = TF_LIST) -> dict:
+    """Pull history for continuous crypto TFs; monthly derived from daily."""
+    stats = {}
+    for tf in TF_PROVIDER:
+        if tf not in tfs and "1M" not in tfs:
+            continue
+        need = LOOKBACK.get(tf, 100)
+        # need extra daily for monthly history
+        if tf == "1d":
+            need = max(need, LOOKBACK["1M"] * 31)
+        try:
+            rows = fetch_candles(symbol, tf, limit=need)
+            n = upsert_bars(symbol, tf, rows)
+            stats[tf] = n
+        except Exception as e:
+            logger.warning("backfill %s %s failed: %s", symbol, tf, e)
+            stats[tf] = 0
+        time.sleep(0.05)
+    if "1M" in tfs or True:
+        try:
+            stats["1M"] = rebuild_monthly_from_daily(symbol)
+        except Exception as e:
+            logger.warning("monthly rebuild %s failed: %s", symbol, e)
+            stats["1M"] = 0
+    return stats
+
+
+def tip_update_symbol(symbol: str, tfs: Iterable[str] = TF_LIST) -> bool:
+    """Update forming tip bars (limit=3). Returns True if any bar changed."""
+    changed = False
+    for tf in TF_PROVIDER:
+        try:
+            rows = fetch_candles(symbol, tf, limit=3)
+            if not rows:
+                continue
+            before = _tip_fingerprint(symbol, tf)
+            upsert_bars(symbol, tf, rows)
+            after = _tip_fingerprint(symbol, tf)
+            if before != after:
+                changed = True
+        except Exception as e:
+            logger.debug("tip %s %s: %s", symbol, tf, e)
+        time.sleep(0.02)
+    # Always rebuild current month tip from daily
+    before_m = _tip_fingerprint(symbol, "1M")
+    try:
+        rebuild_monthly_from_daily(symbol)
+    except Exception as e:
+        logger.debug("monthly tip %s: %s", symbol, e)
+    after_m = _tip_fingerprint(symbol, "1M")
+    if before_m != after_m:
+        changed = True
+    return changed
+
+
+def _tip_fingerprint(symbol: str, tf: str) -> tuple | None:
+    conn = _bars_conn()
+    try:
+        cur = conn.execute(
+            """
+            SELECT ts, open, high, low, close, volume FROM bars
+            WHERE symbol=? AND tf=? ORDER BY ts DESC LIMIT 1
+            """,
+            (symbol, tf),
+        )
+        row = cur.fetchone()
+        return tuple(row) if row else None
+    finally:
+        conn.close()
+
+
+def fetch_symbols_from_provider() -> list[str]:
+    try:
+        resp = requests.get(f"{DATA_SERVICE_URL}/health", timeout=8)
+        resp.raise_for_status()
+        payload = resp.json()
+        symbols = payload.get("symbols") or payload.get("symbol_list") or []
+        return [s for s in symbols if isinstance(s, str)]
+    except Exception as e:
+        logger.warning("health symbols failed: %s", e)
+        return []

crypto_wyckoff/phase.py

@@ -0,0 +1,78 @@
+"""Phase Engine — Phase A–E via Rule Registry."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import EngineResult, WyckoffPhase
+from crypto_wyckoff.rules.registry import rule_registry
+
+
+class PhaseEngine:
+    name = "Phase"
+    version = "1.0.0"
+
+    def run(self, cycle: EngineResult, feature: EngineResult, timeframe: str) -> EngineResult:
+        if feature.payload.get("insufficient") or cycle.payload.get("cycle") == "Unknown":
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=20.0,
+                score=30.0,
+                reasons=["数据/周期不足，Phase=None"],
+                warnings=["insufficient_features"],
+                payload={
+                    "phase": WyckoffPhase.NONE.value,
+                    "timeframe": timeframe,
+                    "cycle": cycle.payload.get("cycle"),
+                    "structure_score": 30.0,
+                },
+            )
+
+        context = {
+            "features": feature.payload,
+            "cycle": cycle.payload,
+            "timeframe": timeframe,
+        }
+        hits = []
+        for rule in rule_registry.by_category("phase", timeframe):
+            hit = rule.evaluate(context)
+            if hit and hit.phase:
+                hits.append(hit)
+
+        if not hits:
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=40.0,
+                score=cycle.score * 0.5,
+                reasons=["未识别明确 Phase"],
+                payload={
+                    "phase": WyckoffPhase.NONE.value,
+                    "timeframe": timeframe,
+                    "cycle": cycle.payload.get("cycle"),
+                    "structure_score": cycle.score * 0.5,
+                },
+            )
+
+        best = max(hits, key=lambda h: h.confidence)
+        structure_score = best.score
+        # Phase D/E stronger structure
+        if best.phase in (WyckoffPhase.D.value, WyckoffPhase.E.value):
+            structure_score = max(structure_score, 80.0)
+        elif best.phase == WyckoffPhase.C.value:
+            structure_score = max(structure_score, 72.0)
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=best.confidence,
+            score=structure_score,
+            reasons=best.reasons,
+            metrics=best.metrics,
+            payload={
+                "phase": best.phase,
+                "timeframe": timeframe,
+                "cycle": cycle.payload.get("cycle"),
+                "rule_id": best.rule_id,
+                "structure_score": structure_score,
+            },
+        )

crypto_wyckoff/pipeline.py

@@ -0,0 +1,157 @@
+"""Scan pipeline: load local frames → engines → store."""
+
+from __future__ import annotations
+
+import json
+import logging
+from datetime import date, datetime, timezone
+
+from crypto_wyckoff.cycle import CycleEngine
+from crypto_wyckoff.decision import DecisionEngine
+from crypto_wyckoff.domain_models import WyckoffScanRow
+from crypto_wyckoff.event import EventEngine
+from crypto_wyckoff.features import FeatureEngine
+from crypto_wyckoff.io import LOOKBACK, TF_LIST, load_frame
+from crypto_wyckoff.phase import PhaseEngine
+from crypto_wyckoff.plan import PlanEngine
+from crypto_wyckoff.signal import SignalEngine
+from crypto_wyckoff.store import upsert_row
+from crypto_wyckoff.version import WYCKOFF_ENGINE_VERSION
+
+logger = logging.getLogger(__name__)
+
+
+def analyze_symbol(
+    daily_frame,
+    weekly_frame,
+    monthly_frame,
+    *,
+    feature_eng: FeatureEngine,
+    cycle_eng: CycleEngine,
+    phase_eng: PhaseEngine,
+    event_eng: EventEngine,
+    signal_eng: SignalEngine,
+    decision_eng: DecisionEngine,
+    plan_eng: PlanEngine,
+) -> dict:
+    f_d = feature_eng.run(daily_frame, "1d")
+    f_w = feature_eng.run(weekly_frame, "1w")
+    f_m = feature_eng.run(monthly_frame, "1M")
+
+    c_m = cycle_eng.run(f_m, "1M")
+    c_w = cycle_eng.run(f_w, "1w")
+
+    p_w = phase_eng.run(c_w, f_w, "1w")
+    p_d = phase_eng.run(c_w, f_d, "1d")
+
+    e_w = event_eng.run(c_w, p_w, f_w, "1w")
+    e_d = event_eng.run(c_w, p_d, f_d, "1d")
+
+    s_d = signal_eng.run(e_d, p_d)
+    decision = decision_eng.run(c_m, c_w, p_w, e_w, e_d, s_d)
+    plan = plan_eng.run(f_d, decision)
+
+    return {
+        "f_d": f_d, "f_w": f_w, "f_m": f_m,
+        "c_m": c_m, "c_w": c_w, "p_w": p_w,
+        "e_w": e_w, "e_d": e_d, "s_d": s_d,
+        "decision": decision, "plan": plan,
+    }
+
+
+def _to_row(trade_date: date, symbol: str, result: dict) -> WyckoffScanRow:
+    d = result["decision"]
+    p = result["plan"]
+    c_m, c_w, p_w = result["c_m"], result["c_w"], result["p_w"]
+    e_w, e_d, s_d = result["e_w"], result["e_d"], result["s_d"]
+    f_d, f_w, f_m = result["f_d"], result["f_w"], result["f_m"]
+
+    snapshot = {
+        "daily": {k: f_d.payload.get(k) for k in (
+            "ma20", "ma60", "ma120", "atr", "adx", "volume_ratio",
+            "range_high", "range_low", "swing_high", "swing_low", "close",
+        )},
+        "weekly": {k: f_w.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
+        "monthly": {k: f_m.payload.get(k) for k in ("ma20", "ma60", "adx", "close")},
+    }
+    markers = []
+    for key, typ in (("entry", "entry"), ("stop", "stop"), ("target1", "target1"), ("target2", "target2")):
+        if p.payload.get(key) is not None:
+            markers.append({"type": typ, "price": p.payload[key]})
+
+    return WyckoffScanRow(
+        trade_date=trade_date,
+        ts_code=symbol,
+        name=symbol,
+        industry="crypto",
+        engine_version=WYCKOFF_ENGINE_VERSION,
+        m_cycle=c_m.payload.get("cycle", "Unknown"),
+        cycle_confidence=c_m.confidence,
+        trend_score=float(d.payload.get("trend_score", c_m.score)),
+        w_cycle=c_w.payload.get("cycle", "Unknown"),
+        w_phase=p_w.payload.get("phase", "None"),
+        w_current_event=e_w.payload.get("current_event", "None"),
+        w_recent_events_json=json.dumps(
+            e_w.payload.get("active_events") or e_w.payload.get("recent_events") or [],
+            ensure_ascii=False,
+        ),
+        phase_confidence=p_w.confidence,
+        structure_score=float(d.payload.get("structure_score", p_w.score)),
+        d_current_event=e_d.payload.get("current_event", "None"),
+        d_recent_events_json=json.dumps(
+            e_d.payload.get("active_events") or e_d.payload.get("recent_events") or [],
+            ensure_ascii=False,
+        ),
+        event_confidence=e_d.confidence,
+        entry_score=float(d.payload.get("entry_score", e_d.score)),
+        entry=p.payload.get("entry"),
+        stop=p.payload.get("stop"),
+        target1=p.payload.get("target1"),
+        target2=p.payload.get("target2"),
+        rr=p.payload.get("rr"),
+        alignment=float(d.payload.get("alignment", 0)),
+        stars=int(d.payload.get("stars", 1)),
+        decision_signal=d.payload.get("decision_signal", "Watch"),
+        signal_confidence=s_d.confidence,
+        overall_confidence=float(d.payload.get("overall_confidence", d.confidence)),
+        overall_score=float(d.payload.get("overall_score", d.score)),
+        risk=d.payload.get("risk", "Medium"),
+        reasons_json=json.dumps(d.reasons + d.warnings, ensure_ascii=False),
+        feature_snapshot_json=json.dumps(snapshot, ensure_ascii=False),
+        markers_json=json.dumps(markers, ensure_ascii=False),
+        scanned_at=datetime.now(timezone.utc),
+    )
+
+
+_ENGINES = None
+
+
+def _engines():
+    global _ENGINES
+    if _ENGINES is None:
+        _ENGINES = {
+            "feature_eng": FeatureEngine(),
+            "cycle_eng": CycleEngine(),
+            "phase_eng": PhaseEngine(),
+            "event_eng": EventEngine(),
+            "signal_eng": SignalEngine(),
+            "decision_eng": DecisionEngine(),
+            "plan_eng": PlanEngine(),
+        }
+    return _ENGINES
+
+
+def analyze_and_store(symbol: str, trade_date: date | None = None) -> WyckoffScanRow | None:
+    eng = _engines()
+    daily = load_frame(symbol, "1d", LOOKBACK["1d"])
+    weekly = load_frame(symbol, "1w", LOOKBACK["1w"])
+    monthly = load_frame(symbol, "1M", LOOKBACK["1M"])
+    if daily is None or len(daily) < 40:
+        return None
+    result = analyze_symbol(daily, weekly, monthly, **eng)
+    td = trade_date or (
+        daily.trade_dates[-1] if daily.trade_dates else datetime.now(timezone.utc).date()
+    )
+    row = _to_row(td, symbol, result)
+    upsert_row(row)
+    return row

crypto_wyckoff/plan.py

@@ -0,0 +1,78 @@
+"""Plan Engine — Entry / Stop / Target / RR only when Decision is tradable."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import DecisionSignal, EngineResult
+
+
+_TRADABLE = {
+    DecisionSignal.STRONG_BUY.value,
+    DecisionSignal.BUY.value,
+    DecisionSignal.SELL.value,
+}
+
+
+class PlanEngine:
+    name = "Plan"
+    version = "1.0.0"
+
+    def run(self, daily_feature: EngineResult, decision: EngineResult) -> EngineResult:
+        f = daily_feature.payload
+        close = float(f.get("close") or 0)
+        atr = float(f.get("atr") or 0) or close * 0.02
+        swing_low = float(f.get("swing_low") or close - 2 * atr)
+        swing_high = float(f.get("swing_high") or close + 2 * atr)
+        range_high = float(f.get("range_high") or swing_high)
+        signal = decision.payload.get("decision_signal", DecisionSignal.WATCH.value)
+
+        entry = stop = t1 = t2 = rr = None
+        reasons: list[str] = []
+
+        if signal not in _TRADABLE or close <= 0:
+            reasons.append(f"无交易计划（信号={signal}）")
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=decision.confidence,
+                score=decision.score,
+                reasons=reasons,
+                payload={
+                    "entry": None,
+                    "stop": None,
+                    "target1": None,
+                    "target2": None,
+                    "rr": None,
+                },
+            )
+
+        if signal in (DecisionSignal.STRONG_BUY.value, DecisionSignal.BUY.value):
+            entry = round(close, 4)
+            stop = round(min(swing_low, close - 1.5 * atr), 4)
+            risk = max(entry - stop, 1e-6)
+            t1 = round(entry + 2.0 * risk, 4)
+            t2 = round(max(range_high, entry + 3.0 * risk), 4)
+            rr = round((t1 - entry) / risk, 2)
+            reasons.append(f"入场={entry} 止损={stop} 目标一={t1} 盈亏比={rr}")
+        else:  # Sell
+            entry = round(close, 4)
+            stop = round(max(swing_high, close + 1.5 * atr), 4)
+            risk = max(stop - entry, 1e-6)
+            t1 = round(entry - 2.0 * risk, 4)
+            t2 = round(entry - 3.0 * risk, 4)
+            rr = round((entry - t1) / risk, 2)
+            reasons.append(f"做空计划 入场={entry} 止损={stop} 目标一={t1}")
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=decision.confidence,
+            score=decision.score,
+            reasons=reasons,
+            payload={
+                "entry": entry,
+                "stop": stop,
+                "target1": t1,
+                "target2": t2,
+                "rr": rr,
+            },
+        )

crypto_wyckoff/rules/__init__.py

@@ -0,0 +1,3 @@
+from crypto_wyckoff.rules.registry import rule_registry
+
+__all__ = ["rule_registry"]

crypto_wyckoff/rules/base.py

@@ -0,0 +1,33 @@
+"""Rule protocol for Wyckoff Rule Registry."""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class RuleHit:
+    """A single rule match."""
+
+    rule_id: str
+    event: str | None = None
+    phase: str | None = None
+    cycle: str | None = None
+    confidence: float = 0.0
+    score: float = 0.0
+    reasons: list[str] = field(default_factory=list)
+    metrics: dict[str, Any] = field(default_factory=dict)
+
+
+class WyckoffRule(ABC):
+    """Pluggable rule. Engines iterate registry; never hardcode rule lists."""
+
+    rule_id: str
+    category: str  # cycle | phase | event
+    timeframes: tuple[str, ...] = ("1d", "1w", "1M")
+
+    @abstractmethod
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        """Return RuleHit if matched, else None. Pure — no I/O."""

crypto_wyckoff/rules/cycle_rules.py

@@ -0,0 +1,126 @@
+"""Cycle classification rules (monthly / weekly)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from crypto_wyckoff.domain_models import WyckoffCycle
+from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
+
+
+def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
+    v = ctx.get("features", {}).get(key, default)
+    try:
+        return float(v) if v is not None else default
+    except (TypeError, ValueError):
+        return default
+
+
+class MarkupCycleRule(WyckoffRule):
+    rule_id = "cycle_markup"
+    category = "cycle"
+    timeframes = ("1M", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        close = _f(context, "close")
+        ma20 = _f(context, "ma20")
+        ma60 = _f(context, "ma60")
+        ma120 = _f(context, "ma120")
+        adx = _f(context, "adx")
+        slope = _f(context, "ma60_slope")
+        if close > ma20 > ma60 and (ma60 >= ma120 or slope > 0) and adx >= 18:
+            conf = min(95.0, 55 + adx + (10 if close > ma120 else 0))
+            return RuleHit(
+                rule_id=self.rule_id,
+                cycle=WyckoffCycle.MARKUP.value,
+                confidence=conf,
+                score=conf,
+                reasons=["价格位于均线多头排列", f"ADX={adx:.1f}"],
+                metrics={"adx": adx, "slope": slope},
+            )
+        return None
+
+
+class MarkdownCycleRule(WyckoffRule):
+    rule_id = "cycle_markdown"
+    category = "cycle"
+    timeframes = ("1M", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        close = _f(context, "close")
+        ma20 = _f(context, "ma20")
+        ma60 = _f(context, "ma60")
+        ma120 = _f(context, "ma120")
+        adx = _f(context, "adx")
+        slope = _f(context, "ma60_slope")
+        if close < ma20 < ma60 and (ma60 <= ma120 or slope < 0) and adx >= 18:
+            conf = min(95.0, 55 + adx + (10 if close < ma120 else 0))
+            return RuleHit(
+                rule_id=self.rule_id,
+                cycle=WyckoffCycle.MARKDOWN.value,
+                confidence=conf,
+                score=conf,
+                reasons=["价格位于均线空头排列", f"ADX={adx:.1f}"],
+                metrics={"adx": adx},
+            )
+        return None
+
+
+class AccumulationCycleRule(WyckoffRule):
+    rule_id = "cycle_accumulation"
+    category = "cycle"
+    timeframes = ("1M", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        adx = _f(context, "adx")
+        range_pct = _f(context, "range_pct_60")
+        close = _f(context, "close")
+        ma120 = _f(context, "ma120")
+        vol_trend = _f(context, "volume_trend")
+        # Range-bound after decline: strictly at/below MA120 (mutually exclusive vs Distribution)
+        if adx < 22 and range_pct < 0.28 and close <= ma120:
+            conf = 60 + (10 if vol_trend > 0 else 0) + (10 if close < ma120 else 0)
+            return RuleHit(
+                rule_id=self.rule_id,
+                cycle=WyckoffCycle.ACCUMULATION.value,
+                confidence=min(90.0, conf),
+                score=min(90.0, conf),
+                reasons=["低趋势强度区间震荡", "疑似吸筹区间"],
+                metrics={"adx": adx, "range_pct_60": range_pct},
+            )
+        return None
+
+
+class DistributionCycleRule(WyckoffRule):
+    rule_id = "cycle_distribution"
+    category = "cycle"
+    timeframes = ("1M", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        adx = _f(context, "adx")
+        range_pct = _f(context, "range_pct_60")
+        close = _f(context, "close")
+        ma120 = _f(context, "ma120")
+        vol_trend = _f(context, "volume_trend")
+        # Range-bound near highs: strictly above MA120 (mutually exclusive vs Accumulation)
+        if adx < 22 and range_pct < 0.28 and close > ma120:
+            conf = 60 + (10 if vol_trend < 0 else 0) + (10 if close > ma120 else 0)
+            return RuleHit(
+                rule_id=self.rule_id,
+                cycle=WyckoffCycle.DISTRIBUTION.value,
+                confidence=min(90.0, conf),
+                score=min(90.0, conf),
+                reasons=["高位低趋势震荡", "疑似派发区间"],
+                metrics={"adx": adx, "range_pct_60": range_pct},
+            )
+        return None
+
+
+def build_rules() -> list[WyckoffRule]:
+    # Order: trend cycles first (more decisive), then range cycles
+    return [
+        MarkupCycleRule(),
+        MarkdownCycleRule(),
+        AccumulationCycleRule(),
+        DistributionCycleRule(),
+    ]

crypto_wyckoff/rules/event_rules.py

@@ -0,0 +1,254 @@
+"""Event rules: Spring/SOS/LPS/UTAD/SC/AR/ST/..."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from crypto_wyckoff.domain_models import WyckoffCycle, WyckoffEvent, WyckoffPhase
+from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
+
+
+def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
+    v = ctx.get("features", {}).get(key, default)
+    try:
+        return float(v) if v is not None else default
+    except (TypeError, ValueError):
+        return default
+
+
+def _cycle(ctx: dict[str, Any]) -> str:
+    return (ctx.get("cycle") or {}).get("cycle") or ""
+
+
+def _phase(ctx: dict[str, Any]) -> str:
+    return (ctx.get("phase") or {}).get("phase") or ""
+
+
+class SpringRule(WyckoffRule):
+    rule_id = "event_spring"
+    category = "event"
+    timeframes = ("1d",)
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value,
+                         WyckoffCycle.MARKUP.value):
+            # Allow spring only in accumulative contexts; Decision will filter MTF
+            if cycle == WyckoffCycle.DISTRIBUTION.value:
+                pass  # still detect for facts but lower confidence
+        pierce = _f(context, "pierce_below_range")
+        reclaim = _f(context, "reclaim_speed")
+        vol_ratio = _f(context, "volume_ratio")
+        close_in_range = _f(context, "close_back_in_range")
+        if pierce >= 0.002 and close_in_range >= 0.5 and reclaim >= 0.3:
+            strength = min(98.0, 50 + pierce * 2000 + reclaim * 20 + (15 if vol_ratio < 1.2 else 5))
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.SPRING.value,
+                confidence=strength,
+                score=strength,
+                reasons=[
+                    f"跌破区间后收回 (pierce={pierce:.3%})",
+                    f"回收速度={reclaim:.2f}",
+                    f"量比={vol_ratio:.2f}",
+                ],
+                metrics={"pierce": pierce, "reclaim": reclaim, "volume_ratio": vol_ratio},
+            )
+        return None
+
+
+class TestRule(WyckoffRule):
+    rule_id = "event_test"
+    category = "event"
+    timeframes = ("1d", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        pos = _f(context, "range_position")
+        vol_ratio = _f(context, "volume_ratio")
+        near_low = pos < 0.2
+        if near_low and vol_ratio < 0.85:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.TEST.value,
+                confidence=68.0,
+                score=65.0,
+                reasons=["低位缩量回测"],
+            )
+        return None
+
+
+class SOSRule(WyckoffRule):
+    rule_id = "event_sos"
+    category = "event"
+    timeframes = ("1d", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        breakout = _f(context, "breakout_above_range")
+        vol_ratio = _f(context, "volume_ratio")
+        close = _f(context, "close")
+        ma20 = _f(context, "ma20")
+        if breakout >= 0.0 and vol_ratio >= 1.2 and close > ma20:
+            conf = min(95.0, 70 + vol_ratio * 8)
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.SOS.value,
+                confidence=conf,
+                score=conf,
+                reasons=["放量突破区间上沿 (SOS)"],
+                metrics={"vol_ratio": vol_ratio},
+            )
+        return None
+
+
+class LPSRule(WyckoffRule):
+    rule_id = "event_lps"
+    category = "event"
+    timeframes = ("1d", "1w")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        # Pullback hold above broken range / MA20 after prior strength
+        pullback = _f(context, "pullback_hold")
+        vol_ratio = _f(context, "volume_ratio")
+        above_ma = _f(context, "close") > _f(context, "ma20")
+        if pullback >= 0.5 and above_ma and vol_ratio <= 1.1:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.LPS.value,
+                confidence=74.0,
+                score=76.0,
+                reasons=["突破后缩量回踩支撑 (LPS)"],
+            )
+        return None
+
+
+class SCRule(WyckoffRule):
+    rule_id = "event_sc"
+    category = "event"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        vol_ratio = _f(context, "volume_ratio")
+        bar_range = _f(context, "bar_range_atr")
+        pos = _f(context, "range_position")
+        if vol_ratio >= 1.8 and bar_range >= 1.5 and pos < 0.35:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.SC.value,
+                confidence=72.0,
+                score=70.0,
+                reasons=["低位放量宽幅，疑似 Selling Climax"],
+            )
+        return None
+
+
+class ARRule(WyckoffRule):
+    rule_id = "event_ar"
+    category = "event"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        # Automatic rally: bounce from lows
+        bounce = _f(context, "bounce_from_low")
+        if bounce >= 0.04:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.AR.value,
+                confidence=65.0,
+                score=62.0,
+                reasons=["低点后自动反弹 (AR)"],
+            )
+        return None
+
+
+class STRule(WyckoffRule):
+    rule_id = "event_st"
+    category = "event"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        pos = _f(context, "range_position")
+        vol_ratio = _f(context, "volume_ratio")
+        if 0.15 < pos < 0.45 and vol_ratio < 1.0:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.ST.value,
+                confidence=60.0,
+                score=58.0,
+                reasons=["次级测试 (ST)"],
+            )
+        return None
+
+
+class UTADRule(WyckoffRule):
+    rule_id = "event_utad"
+    category = "event"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        pierce_up = _f(context, "pierce_above_range")
+        fail = _f(context, "fail_back_into_range")
+        if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value,
+                     WyckoffCycle.MARKUP.value):
+            if pierce_up >= 0.002 and fail >= 0.5:
+                return RuleHit(
+                    rule_id=self.rule_id,
+                    event=WyckoffEvent.UTAD.value,
+                    confidence=76.0,
+                    score=74.0,
+                    reasons=["冲高失败回到区间 (UTAD)"],
+                )
+        return None
+
+
+class JumpRule(WyckoffRule):
+    rule_id = "event_jump"
+    category = "event"
+    timeframes = ("1d",)
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        gap = _f(context, "gap_up_pct")
+        vol_ratio = _f(context, "volume_ratio")
+        if gap >= 0.03 and vol_ratio >= 1.3:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.JUMP.value,
+                confidence=70.0,
+                score=72.0,
+                reasons=["放量向上跳跃 (Jump)"],
+            )
+        return None
+
+
+class BackupRule(WyckoffRule):
+    rule_id = "event_backup"
+    category = "event"
+    timeframes = ("1d",)
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        pullback = _f(context, "pullback_hold")
+        after_jump = _f(context, "after_strength")
+        if after_jump >= 0.5 and pullback >= 0.5:
+            return RuleHit(
+                rule_id=self.rule_id,
+                event=WyckoffEvent.BACKUP.value,
+                confidence=68.0,
+                score=70.0,
+                reasons=["跳跃后回踩 (Backup)"],
+            )
+        return None
+
+
+def build_rules() -> list[WyckoffRule]:
+    return [
+        SpringRule(),
+        UTADRule(),
+        SOSRule(),
+        LPSRule(),
+        SCRule(),
+        JumpRule(),
+        BackupRule(),
+        TestRule(),
+        ARRule(),
+        STRule(),
+    ]

crypto_wyckoff/rules/phase_rules.py

@@ -0,0 +1,163 @@
+"""Phase A–E rules (primarily weekly)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from crypto_wyckoff.domain_models import WyckoffCycle, WyckoffPhase
+from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
+
+
+def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
+    v = ctx.get("features", {}).get(key, default)
+    try:
+        return float(v) if v is not None else default
+    except (TypeError, ValueError):
+        return default
+
+
+def _cycle(ctx: dict[str, Any]) -> str:
+    return (ctx.get("cycle") or {}).get("cycle") or WyckoffCycle.UNKNOWN.value
+
+
+class PhaseARule(WyckoffRule):
+    rule_id = "phase_a"
+    category = "phase"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value,
+                         WyckoffCycle.RE_ACCUMULATION.value, WyckoffCycle.RE_DISTRIBUTION.value):
+            return None
+        # Stopping action: high vol + large range recently, still range-bound
+        vol_ratio = _f(context, "volume_ratio")
+        range_last = _f(context, "bar_range_atr")
+        if vol_ratio >= 1.4 and range_last >= 1.2:
+            return RuleHit(
+                rule_id=self.rule_id,
+                phase=WyckoffPhase.A.value,
+                confidence=70.0,
+                score=65.0,
+                reasons=["放量宽幅波动，疑似 Phase A 停止行为"],
+            )
+        return None
+
+
+class PhaseBRule(WyckoffRule):
+    rule_id = "phase_b"
+    category = "phase"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        if cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value):
+            return None
+        adx = _f(context, "adx")
+        range_pct = _f(context, "range_pct_60")
+        pos = _f(context, "range_position")  # 0=low 1=high of range
+        if adx < 20 and 0.25 < pos < 0.75 and range_pct < 0.30:
+            return RuleHit(
+                rule_id=self.rule_id,
+                phase=WyckoffPhase.B.value,
+                confidence=72.0,
+                score=68.0,
+                reasons=["区间中部震荡，疑似 Phase B 建仓/派发"],
+            )
+        return None
+
+
+class PhaseCRule(WyckoffRule):
+    rule_id = "phase_c"
+    category = "phase"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        pos = _f(context, "range_position")
+        spring_like = _f(context, "spring_score_hint")
+        utad_like = _f(context, "utad_score_hint")
+        if cycle in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value):
+            if pos < 0.25 or spring_like >= 50:
+                return RuleHit(
+                    rule_id=self.rule_id,
+                    phase=WyckoffPhase.C.value,
+                    confidence=75.0 + min(15.0, spring_like * 0.15),
+                    score=78.0,
+                    reasons=["区间低位测试，疑似 Phase C (Spring/Test)"],
+                )
+        if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value):
+            if pos > 0.75 or utad_like >= 50:
+                return RuleHit(
+                    rule_id=self.rule_id,
+                    phase=WyckoffPhase.C.value,
+                    confidence=75.0,
+                    score=78.0,
+                    reasons=["区间高位测试，疑似 Phase C (UTAD)"],
+                )
+        return None
+
+
+class PhaseDRule(WyckoffRule):
+    rule_id = "phase_d"
+    category = "phase"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        close = _f(context, "close")
+        ma20 = _f(context, "ma20")
+        range_high = _f(context, "range_high")
+        range_low = _f(context, "range_low")
+        vol_ratio = _f(context, "volume_ratio")
+        if cycle in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.RE_ACCUMULATION.value):
+            if close > ma20 and range_high > 0 and close >= range_high * 0.98 and vol_ratio >= 1.1:
+                return RuleHit(
+                    rule_id=self.rule_id,
+                    phase=WyckoffPhase.D.value,
+                    confidence=80.0,
+                    score=82.0,
+                    reasons=["突破区间上沿放量，疑似 Phase D SOS"],
+                )
+        if cycle in (WyckoffCycle.DISTRIBUTION.value, WyckoffCycle.RE_DISTRIBUTION.value):
+            if close < ma20 and range_low > 0 and close <= range_low * 1.02:
+                return RuleHit(
+                    rule_id=self.rule_id,
+                    phase=WyckoffPhase.D.value,
+                    confidence=80.0,
+                    score=82.0,
+                    reasons=["跌破区间下沿，疑似 Phase D SOW"],
+                )
+        return None
+
+
+class PhaseERule(WyckoffRule):
+    rule_id = "phase_e"
+    category = "phase"
+    timeframes = ("1w", "1d")
+
+    def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
+        cycle = _cycle(context)
+        # Markup/Markdown already imply trend continuation (Phase E of prior structure)
+        if cycle == WyckoffCycle.MARKUP.value:
+            return RuleHit(
+                rule_id=self.rule_id,
+                phase=WyckoffPhase.E.value,
+                confidence=78.0,
+                score=80.0,
+                reasons=["趋势上行，对应 Phase E Markup"],
+            )
+        if cycle == WyckoffCycle.MARKDOWN.value:
+            return RuleHit(
+                rule_id=self.rule_id,
+                phase=WyckoffPhase.E.value,
+                confidence=78.0,
+                score=80.0,
+                reasons=["趋势下行，对应 Phase E Markdown"],
+            )
+        return None
+
+
+def build_rules() -> list[WyckoffRule]:
+    # More specific phases first
+    return [PhaseDRule(), PhaseCRule(), PhaseARule(), PhaseBRule(), PhaseERule()]

crypto_wyckoff/rules/registry.py

@@ -0,0 +1,39 @@
+"""Rule Registry — register Wyckoff rules without modifying engines."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.rules.base import WyckoffRule
+
+
+class RuleRegistry:
+    def __init__(self) -> None:
+        self._rules: dict[str, WyckoffRule] = {}
+
+    def register(self, rule: WyckoffRule) -> None:
+        self._rules[rule.rule_id] = rule
+
+    def get(self, rule_id: str) -> WyckoffRule | None:
+        return self._rules.get(rule_id)
+
+    def by_category(self, category: str, timeframe: str | None = None) -> list[WyckoffRule]:
+        out = [r for r in self._rules.values() if r.category == category]
+        if timeframe:
+            out = [r for r in out if timeframe in r.timeframes]
+        return out
+
+    def all(self) -> list[WyckoffRule]:
+        return list(self._rules.values())
+
+
+rule_registry = RuleRegistry()
+
+
+def _register_defaults() -> None:
+    from crypto_wyckoff.rules import cycle_rules, event_rules, phase_rules
+
+    for mod in (cycle_rules, phase_rules, event_rules):
+        for rule in mod.build_rules():
+            rule_registry.register(rule)
+
+
+_register_defaults()

crypto_wyckoff/scheduler.py

@@ -0,0 +1,128 @@
+"""Background 60s tip-update + rescan scheduler."""
+
+from __future__ import annotations
+
+import logging
+import threading
+import time
+from datetime import datetime, timezone
+from typing import Any
+
+from crypto_wyckoff.io import (
+    TF_LIST,
+    backfill_symbol,
+    bar_count,
+    fetch_symbols_from_provider,
+    tip_update_symbol,
+)
+from crypto_wyckoff.pipeline import analyze_and_store
+from crypto_wyckoff.version import WYCKOFF_ENGINE_VERSION
+
+logger = logging.getLogger(__name__)
+
+_lock = threading.Lock()
+_status: dict[str, Any] = {
+    "running": False,
+    "last_tick_at": None,
+    "last_error": None,
+    "symbols_total": 0,
+    "symbols_scanned": 0,
+    "backfill_done": False,
+    "engine_version": WYCKOFF_ENGINE_VERSION,
+    "tick_interval_sec": 60,
+}
+_stop = threading.Event()
+_thread: threading.Thread | None = None
+
+
+def get_status() -> dict[str, Any]:
+    with _lock:
+        return dict(_status)
+
+
+def _set(**kwargs):
+    with _lock:
+        _status.update(kwargs)
+
+
+def run_tick(max_symbols: int | None = None, force_rescan: bool = False) -> dict:
+    """One cycle: refresh symbols, tip-update, analyze changed (or all if force)."""
+    symbols = fetch_symbols_from_provider()
+    if max_symbols:
+        symbols = symbols[:max_symbols]
+    _set(symbols_total=len(symbols), running=True, last_error=None)
+    scanned = 0
+    errors = 0
+    changed_n = 0
+
+    # Lazy backfill: ensure min bars
+    for i, sym in enumerate(symbols):
+        try:
+            if bar_count(sym, "1d") < 40:
+                backfill_symbol(sym, TF_LIST)
+            tip_changed = tip_update_symbol(sym, TF_LIST)
+            if tip_changed:
+                changed_n += 1
+            if force_rescan or tip_changed or bar_count(sym, "1d") >= 40:
+                # Always rescan on first pass after backfill; tip change triggers update
+                if force_rescan or tip_changed or True:
+                    # Tip every minute: always re-analyze to refresh forming-bar features
+                    row = analyze_and_store(sym)
+                    if row:
+                        scanned += 1
+        except Exception as e:
+            errors += 1
+            if errors <= 5:
+                logger.warning("tick %s: %s", sym, e)
+            _set(last_error=str(e))
+        if (i + 1) % 25 == 0:
+            _set(symbols_scanned=scanned)
+            logger.info("wyckoff tick progress %s/%s scanned=%s", i + 1, len(symbols), scanned)
+
+    _set(
+        running=False,
+        symbols_scanned=scanned,
+        last_tick_at=datetime.now(timezone.utc).isoformat(),
+        backfill_done=True,
+    )
+    return {
+        "symbols": len(symbols),
+        "scanned": scanned,
+        "changed_tips": changed_n,
+        "errors": errors,
+    }
+
+
+def _loop(interval: int, max_symbols: int | None):
+    # First tick: force full rescan after tip/backfill
+    try:
+        run_tick(max_symbols=max_symbols, force_rescan=True)
+    except Exception as e:
+        logger.exception("initial tick failed: %s", e)
+        _set(last_error=str(e), running=False)
+    while not _stop.wait(interval):
+        try:
+            run_tick(max_symbols=max_symbols, force_rescan=True)
+        except Exception as e:
+            logger.exception("tick failed: %s", e)
+            _set(last_error=str(e), running=False)
+
+
+def start_scheduler(interval_sec: int = 60, max_symbols: int | None = None) -> None:
+    global _thread
+    if _thread and _thread.is_alive():
+        return
+    _stop.clear()
+    _set(tick_interval_sec=interval_sec)
+    _thread = threading.Thread(
+        target=_loop,
+        args=(interval_sec, max_symbols),
+        name="crypto-wyckoff-scheduler",
+        daemon=True,
+    )
+    _thread.start()
+    logger.info("crypto wyckoff scheduler started interval=%ss", interval_sec)
+
+
+def stop_scheduler() -> None:
+    _stop.set()

crypto_wyckoff/signal.py

@@ -0,0 +1,35 @@
+"""Signal Engine — timeframe-local status labels only (not tradability)."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import EngineResult, WyckoffEvent
+
+
+class SignalEngine:
+    """Maps local Event/Phase into a status label. Decision decides tradability."""
+
+    name = "Signal"
+    version = "1.0.0"
+
+    def run(self, event: EngineResult, phase: EngineResult | None = None) -> EngineResult:
+        current = event.payload.get("current_event", WyckoffEvent.NONE.value)
+        conf = event.confidence
+        label = current  # status label mirrors event for V1
+        reasons = [f"本地事件标签: {label}"]
+        if phase and phase.payload.get("phase"):
+            reasons.append(f"本地阶段: {phase.payload.get('phase')}")
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=conf,
+            score=event.score,
+            reasons=reasons,
+            payload={
+                "signal_label": label,
+                "current_event": current,
+                "phase": (phase.payload.get("phase") if phase else None),
+                "active_events": event.payload.get("active_events")
+                or event.payload.get("recent_events", []),
+            },
+        )

crypto_wyckoff/store.py

@@ -0,0 +1,174 @@
+"""SQLite persistence for crypto wyckoff scan rows."""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from datetime import datetime
+from typing import Any
+
+from crypto_wyckoff.domain_models import WyckoffScanRow
+from crypto_wyckoff.io import SCAN_DB, ensure_dirs
+
+_COLS = [
+    "trade_date", "ts_code", "name", "industry", "engine_version",
+    "m_cycle", "cycle_confidence", "trend_score",
+    "w_cycle", "w_phase", "w_current_event", "w_recent_events_json",
+    "phase_confidence", "structure_score",
+    "d_current_event", "d_recent_events_json", "event_confidence", "entry_score",
+    "entry", "stop", "target1", "target2", "rr",
+    "alignment", "stars", "decision_signal", "signal_confidence",
+    "overall_confidence", "overall_score", "risk", "reasons_json",
+    "feature_snapshot_json", "markers_json", "scanned_at",
+]
+
+
+def _conn() -> sqlite3.Connection:
+    ensure_dirs()
+    c = sqlite3.connect(str(SCAN_DB), timeout=60)
+    c.row_factory = sqlite3.Row
+    c.execute(
+        """
+        CREATE TABLE IF NOT EXISTS wyckoff_scan (
+            trade_date TEXT NOT NULL,
+            ts_code TEXT NOT NULL,
+            name TEXT DEFAULT '',
+            industry TEXT DEFAULT '',
+            engine_version TEXT,
+            m_cycle TEXT, cycle_confidence REAL, trend_score REAL,
+            w_cycle TEXT, w_phase TEXT, w_current_event TEXT, w_recent_events_json TEXT,
+            phase_confidence REAL, structure_score REAL,
+            d_current_event TEXT, d_recent_events_json TEXT, event_confidence REAL, entry_score REAL,
+            entry REAL, stop REAL, target1 REAL, target2 REAL, rr REAL,
+            alignment REAL, stars INTEGER, decision_signal TEXT, signal_confidence REAL,
+            overall_confidence REAL, overall_score REAL, risk TEXT, reasons_json TEXT,
+            feature_snapshot_json TEXT, markers_json TEXT, scanned_at TEXT,
+            PRIMARY KEY (trade_date, ts_code)
+        )
+        """
+    )
+    c.execute(
+        "CREATE INDEX IF NOT EXISTS idx_cw_score ON wyckoff_scan(trade_date, overall_score DESC)"
+    )
+    return c
+
+
+def upsert_row(row: WyckoffScanRow) -> None:
+    vals = (
+        row.trade_date.isoformat() if hasattr(row.trade_date, "isoformat") else str(row.trade_date),
+        row.ts_code, row.name, row.industry, row.engine_version,
+        row.m_cycle, row.cycle_confidence, row.trend_score,
+        row.w_cycle, row.w_phase, row.w_current_event, row.w_recent_events_json,
+        row.phase_confidence, row.structure_score,
+        row.d_current_event, row.d_recent_events_json, row.event_confidence, row.entry_score,
+        row.entry, row.stop, row.target1, row.target2, row.rr,
+        row.alignment, row.stars, row.decision_signal, row.signal_confidence,
+        row.overall_confidence, row.overall_score, row.risk, row.reasons_json,
+        row.feature_snapshot_json, row.markers_json,
+        row.scanned_at.isoformat() if isinstance(row.scanned_at, datetime) else str(row.scanned_at),
+    )
+    c = _conn()
+    try:
+        placeholders = ",".join("?" * len(_COLS))
+        col_sql = ",".join(_COLS)
+        updates = ",".join(f"{c}=excluded.{c}" for c in _COLS if c not in ("trade_date", "ts_code"))
+        c.execute(
+            f"""
+            INSERT INTO wyckoff_scan ({col_sql}) VALUES ({placeholders})
+            ON CONFLICT(trade_date, ts_code) DO UPDATE SET {updates}
+            """,
+            vals,
+        )
+        c.commit()
+    finally:
+        c.close()
+
+
+def latest_trade_date() -> str | None:
+    c = _conn()
+    try:
+        cur = c.execute("SELECT MAX(trade_date) FROM wyckoff_scan")
+        row = cur.fetchone()
+        return row[0] if row and row[0] else None
+    finally:
+        c.close()
+
+
+def count_for_date(trade_date: str | None = None) -> int:
+    td = trade_date or latest_trade_date()
+    if not td:
+        return 0
+    c = _conn()
+    try:
+        cur = c.execute("SELECT COUNT(*) FROM wyckoff_scan WHERE trade_date=?", (td,))
+        return int(cur.fetchone()[0])
+    finally:
+        c.close()
+
+
+def query_scan(
+    *,
+    trade_date: str | None = None,
+    m_cycle: str | None = None,
+    w_phase: str | None = None,
+    d_event: str | None = None,
+    decision_signal: str | None = None,
+    min_overall_score: float | None = None,
+    min_alignment: float | None = None,
+    sort: str = "overall_score",
+    limit: int = 100,
+    offset: int = 0,
+) -> list[dict[str, Any]]:
+    td = trade_date or latest_trade_date()
+    if not td:
+        return []
+    sort_col = sort if sort in {
+        "overall_score", "alignment", "entry_score", "trend_score", "structure_score", "stars"
+    } else "overall_score"
+    clauses = ["trade_date=?"]
+    args: list[Any] = [td]
+    if m_cycle:
+        clauses.append("m_cycle=?")
+        args.append(m_cycle)
+    if w_phase:
+        clauses.append("w_phase=?")
+        args.append(w_phase)
+    if d_event:
+        clauses.append("d_current_event=?")
+        args.append(d_event)
+    if decision_signal:
+        clauses.append("decision_signal=?")
+        args.append(decision_signal)
+    if min_overall_score is not None:
+        clauses.append("overall_score>=?")
+        args.append(min_overall_score)
+    if min_alignment is not None:
+        clauses.append("alignment>=?")
+        args.append(min_alignment)
+    where = " AND ".join(clauses)
+    args.extend([limit, offset])
+    c = _conn()
+    try:
+        cur = c.execute(
+            f"SELECT * FROM wyckoff_scan WHERE {where} ORDER BY {sort_col} DESC LIMIT ? OFFSET ?",
+            args,
+        )
+        return [dict(r) for r in cur.fetchall()]
+    finally:
+        c.close()
+
+
+def get_symbol(ts_code: str, trade_date: str | None = None) -> dict[str, Any] | None:
+    td = trade_date or latest_trade_date()
+    if not td:
+        return None
+    c = _conn()
+    try:
+        cur = c.execute(
+            "SELECT * FROM wyckoff_scan WHERE trade_date=? AND ts_code=?",
+            (td, ts_code),
+        )
+        row = cur.fetchone()
+        return dict(row) if row else None
+    finally:
+        c.close()

crypto_wyckoff/version.py

@@ -0,0 +1,4 @@
+"""Wyckoff Screener engine version — bump when rules change."""
+
+WYCKOFF_ENGINE_VERSION = "v1.0.0"
+ARCHITECTURE_VERSION = "1.0"

docs/AGENT_MEMORY.md

@@ -24,6 +24,7 @@
 - ECR-004 Reviewed：TR 评分硬化 + VP 少系列 + 阶段/门闩/单测（无币种参数）
 - ECR-007 Final Approval / `276481e`：Wyckoff Live Structure（`live.py`）；Confirmed ≠ Live；execution 仅 confirmed
 - ECR-008 Reviewed：主站 `chart_tv.js` → `chart_tv_{lifecycle,shell,indicators,chan,overlays,finalize}.js` + 薄门面
+- ECR-009 Implementing：`/wyckoff_crypto` 独立选股页（`crypto_wyckoff/`）；D/W + 本地月线；60s tip
 - 威科夫数据随主 analyze 默认返回；UI 开关仅显隐叠层
 - Live 观察：主图左下角 Cycle Summary（「形成中」= FORMING）；无单独 Live 图层
 
@@ -31,7 +32,7 @@
 
 - `/api/analyze` 字段可增不可删
 - 无 ADR 不改笔/段/中枢/买卖点语义
-- 威科夫为独立叠层（ECR-003/007）；勿借机改缠论算法
+- 威科夫为独立叠层（ECR-003/007）；Crypto Screener 为独立页（ECR-009），勿混进缠论引擎
 - Live candidate **不得**进入 execution；交易 L2+ → RISK_REVIEW + EXP；Live 须 Human
 
 ## 已知债务
@@ -42,3 +43,4 @@
 - 威科夫启发式参数未做 UI 调参
 - ECR-007 待 Human 在 Gitea 开 PR 合入 `dev`
 - `chart_tv_overlays.js` 仍偏大，可后续再拆
+- ECR-009：月线历史受日线深度限制；Cycle 规则在 crypto 上可能偏 Unknown，看效果再调参

docs/CHANGELOG/CHANGELOG.md

@@ -2,6 +2,12 @@
 
 ## Unreleased — 2026-08-07
 
+### ECR-009（L2，进行中）
+
+- 独立页 `/wyckoff_crypto`：移植 A_Share_DP D/W/M 威科夫选股引擎至数字货币
+- 本地 `data/crypto_wyckoff/`；60s tip；月线由日线 UTC 自然月聚合（provider 无 1M）
+- API：`/api/wyckoff_crypto/*`；不碰主站 analyze / 缠论叠层
+
 ### ECR-008（L3，Reviewed）
 
 - 主站 `chart_tv.js` 拆为 lifecycle / shell / indicators / chan / overlays / finalize + 薄门面

docs/ECR/ECR-009-crypto-wyckoff-screener.md

@@ -0,0 +1,22 @@
+# ECR-009
+
+**Title:** Crypto Wyckoff Screener 独立页（D/W/M）  
+**Status:** Approved（计划执行）  
+**Date:** 2026-08-07  
+**Change Level:** L2  
+
+## Change
+
+新增 `crypto_wyckoff/` 包（移植 A_Share_DP 引擎）+ `/wyckoff_crypto` 页 + `/api/wyckoff_crypto/*`；本地缓存全量币对日/周/月 K 线；60s tip 更新。
+
+## Forbidden
+
+- 改缠论算法、主站叠层、`/api/analyze`、`config/`/`strategies/`
+- 小周期；自动下单
+
+## Acceptance
+
+- [ ] 页面可列出扫描结果（decision/cycle/phase/event）
+- [ ] 本地 `data/crypto_wyckoff/` 有 K 线与 scan
+- [ ] 调度可跑 tip 更新
+- [ ] Decision 门闩单测通过

docs/ENGINEERING_SPEC/ECR-009-crypto-wyckoff-screener.md

@@ -0,0 +1,31 @@
+# ENGINEERING_SPEC — ECR-009 Crypto Wyckoff Screener
+
+**Level:** L2 · 独立页  
+**Date:** 2026-08-07  
+
+## Goal
+
+数字货币 D/W/M 威科夫选股观察页（A_Share_DP 引擎语义）；24/7 tip 每分钟更新。
+
+## Package
+
+`crypto_wyckoff/`：features → cycle/phase/event/signal → decision → plan；本地 `data/crypto_wyckoff/`。
+
+## API
+
+- `GET /wyckoff_crypto`
+- `GET /api/wyckoff_crypto/meta|status|scan`
+- `GET /api/wyckoff_crypto/symbol/<symbol>`
+- `POST /api/wyckoff_crypto/tick`
+
+## Env
+
+- `CRYPTO_WYCKOFF_DISABLE=1` 关闭调度
+- `CRYPTO_WYCKOFF_INTERVAL=60`
+- `CRYPTO_WYCKOFF_MAX_SYMBOLS=N` 小样本调试
+- `DATA_SERVICE_URL` 默认 provider.jackyu66.com
+
+## Crypto calendar
+
+UTC 连续盘；回填不做 A 股周末放大。  
+**月线**：provider 无 `1M`，由本地日线按 **UTC 自然月** OHLCV 聚合；日/周直接拉 `1d`/`1w`。

docs/IDEA/IDEA-007-crypto-wyckoff-screener.md

@@ -0,0 +1,13 @@
+# Idea: Crypto Wyckoff Screener（独立页）
+
+## Problem
+
+主站威科夫是图叠层；需要 A_Share_DP 式 D/W/M 多周期选股/决策观察，用于数字货币。
+
+## Hypothesis
+
+独立包 + 独立页，币对来自 DATA_SERVICE，本地缓存 1d/1w/1M，每分钟 tip 更新，不碰缠论主链路。
+
+## Change Level Guess
+
+**L2**（新行为面；不改 strategies）

docs/STATE/CURRENT.md

@@ -1,8 +1,8 @@
 # STATE
 
-**owner:** idle  
-**active_ecr:** none（ECR-008 Reviewed；ECR-007 待合入 `dev`）  
-**phase:** post-review  
+**owner:** engineer  
+**active_ecr:** ECR-009（crypto wyckoff screener）  
+**phase:** implementing  
 **system_version:** v1.0.0  
 **strategy_version:** unchanged  
 **updated:** 2026-08-07  
@@ -16,12 +16,12 @@
 | ECR-002 | L3 | Done (Reviewed) | runtime 包拆分 |
 | ECR-003 | L2 | Done (Reviewed) | `081a57a` 主站威科夫 |
 | ECR-004 | L2 | Done (Reviewed) | 威科夫硬化 / VP 减负 |
-| ECR-007 | L2 | Done (Final Approval) | Live Structure · `276481e` · 待 Gitea PR → `dev` |
-| ECR-008 | L3 | Done (Reviewed) | chart_tv 拆分 · 本分支 |
+| ECR-007 | L2 | Done (Final Approval) | Live Structure · 待合入 `dev` |
+| ECR-008 | L3 | Done (Reviewed) | chart_tv 拆分 |
+| ECR-009 | L2 | Implementing | `/wyckoff_crypto` · D/W/M |
 
 ## Notes
 
-- ECR-007：**FINAL_APPROVAL** · 已 push；开 PR：https://git.jackyu66.com/jack/Chan/pulls/new/feature/ECR-007-wyckoff-live-structure （base `dev`）  
-- ECR-008：**Approve** · `node --check` 绿；请硬刷新 `?v=20260807f` 目测  
-- 归档：`docs/runs/LOOP-RUN-005/`  
+- ECR-009：打开 http://localhost:8128/wyckoff_crypto ；可用 `CRYPTO_WYCKOFF_MAX_SYMBOLS` 限流  
+- 月线由日线 UTC 聚合（provider 无 1M）  
 - 未请求新 system tag  

docs/STATE/ECR-009.md

@@ -0,0 +1,5 @@
+ecr: ECR-009
+owner: engineer
+phase: implementing
+updated: 2026-08-07
+notes: crypto wyckoff screener · D/W/M · 24/7 tip

docs/TRACEABILITY.md

@@ -61,3 +61,11 @@
 | ECR-008 | 拆分 chart_tv 单体 | ENG-008 | `chart_tv_*.js` + 薄门面 | `node --check` | dbb6202 |
 | ECR-008 | 对外 API 不变 | ENG-008 | `initTradingView` / `disposeTradingViewCharts` | ui.js 调用点 | dbb6202 |
 | ECR-008 | 无打包器 | PROFILE | `index.html` script 顺序 | 人工 | dbb6202 |
+
+## ECR-009
+
+| ECR | Requirement | Spec | Code | Test | Commit |
+|-----|-------------|------|------|------|--------|
+| ECR-009 | Crypto D/W/M screener 独立页 | ENG-009 | `crypto_wyckoff/` + `/wyckoff_crypto` | `test_crypto_wyckoff_decision` | （本分支） |
+| ECR-009 | 月线本地聚合 | ENG-009 | `io.rebuild_monthly_from_daily` | smoke tip | （本分支） |
+| ECR-009 | 不碰 analyze/缠论 | ECR-009 Forbidden | 新 API 前缀 | 人工 | （本分支） |

tests/test_crypto_wyckoff_decision.py

@@ -0,0 +1,66 @@
+"""Decision engine MTF gate tests (ported semantics)."""
+
+from crypto_wyckoff.domain_models import (
+    DecisionSignal,
+    EngineResult,
+    WyckoffCycle,
+    WyckoffEvent,
+    WyckoffPhase,
+)
+from crypto_wyckoff.decision import DecisionEngine
+
+
+def _er(name, payload, score=70, confidence=70):
+    return EngineResult(name=name, score=score, confidence=confidence, payload=payload)
+
+
+def test_monthly_distribution_daily_spring_is_watch():
+    eng = DecisionEngine()
+    monthly = _er("Cycle", {"cycle": WyckoffCycle.DISTRIBUTION.value, "trend_score": 40}, score=40)
+    weekly_c = _er("Cycle", {"cycle": WyckoffCycle.ACCUMULATION.value, "trend_score": 70}, score=70)
+    weekly_p = _er(
+        "Phase",
+        {"phase": WyckoffPhase.B.value, "cycle": WyckoffCycle.ACCUMULATION.value, "structure_score": 65},
+        score=65,
+    )
+    weekly_e = _er("Event", {"current_event": WyckoffEvent.ST.value, "recent_events": ["SC", "AR", "ST"]}, score=60)
+    daily_e = _er(
+        "Event",
+        {"current_event": WyckoffEvent.SPRING.value, "recent_events": ["SC", "AR", "ST", "Spring"], "entry_score": 92},
+        score=92,
+        confidence=92,
+    )
+    daily_s = _er("Signal", {"signal_label": "Spring", "current_event": "Spring"}, confidence=92, score=92)
+    out = eng.run(monthly, weekly_c, weekly_p, weekly_e, daily_e, daily_s)
+    assert out.payload["decision_signal"] == DecisionSignal.WATCH.value
+    assert out.payload["d_event"] == WyckoffEvent.SPRING.value
+
+
+def test_bull_alignment_can_strong_buy():
+    eng = DecisionEngine()
+    monthly = _er("Cycle", {"cycle": WyckoffCycle.MARKUP.value, "trend_score": 90}, score=90, confidence=90)
+    weekly_c = _er("Cycle", {"cycle": WyckoffCycle.ACCUMULATION.value, "trend_score": 85}, score=85, confidence=85)
+    weekly_p = _er(
+        "Phase",
+        {"phase": WyckoffPhase.D.value, "cycle": WyckoffCycle.ACCUMULATION.value, "structure_score": 88},
+        score=88,
+        confidence=88,
+    )
+    weekly_e = _er("Event", {"current_event": WyckoffEvent.SOS.value, "recent_events": ["SOS"]}, score=85, confidence=85)
+    daily_e = _er(
+        "Event",
+        {
+            "current_event": WyckoffEvent.SPRING.value,
+            "recent_events": ["SC", "AR", "ST", "Spring", "Test"],
+            "active_events": ["SC", "AR", "ST", "Spring"],
+            "entry_score": 92,
+        },
+        score=92,
+        confidence=92,
+    )
+    daily_s = _er("Signal", {"signal_label": "Spring"}, confidence=92, score=92)
+    out = eng.run(monthly, weekly_c, weekly_p, weekly_e, daily_e, daily_s)
+    assert out.payload["decision_signal"] in (
+        DecisionSignal.STRONG_BUY.value,
+        DecisionSignal.BUY.value,
+    )

web/api/wyckoff_crypto.py

@@ -0,0 +1,118 @@
+"""Crypto Wyckoff Screener API + page (independent of /api/analyze)."""
+
+from __future__ import annotations
+
+import os
+import threading
+
+from flask import Blueprint, jsonify, render_template, request
+
+from crypto_wyckoff.domain_models import DecisionSignal, WyckoffCycle, WyckoffEvent, WyckoffPhase
+from crypto_wyckoff.scheduler import get_status, run_tick, start_scheduler
+from crypto_wyckoff import store as wyckoff_store
+from crypto_wyckoff.version import ARCHITECTURE_VERSION, WYCKOFF_ENGINE_VERSION
+
+bp = Blueprint("wyckoff_crypto", __name__)
+
+_scheduler_started = False
+_sched_lock = threading.Lock()
+
+
+def ensure_scheduler() -> None:
+    global _scheduler_started
+    with _sched_lock:
+        if _scheduler_started:
+            return
+        if os.environ.get("CRYPTO_WYCKOFF_DISABLE", "").lower() in ("1", "true", "yes"):
+            return
+        interval = int(os.environ.get("CRYPTO_WYCKOFF_INTERVAL", "60"))
+        max_sym = os.environ.get("CRYPTO_WYCKOFF_MAX_SYMBOLS")
+        max_symbols = int(max_sym) if max_sym else None
+        start_scheduler(interval_sec=interval, max_symbols=max_symbols)
+        _scheduler_started = True
+
+
+@bp.route("/wyckoff_crypto")
+def page():
+    ensure_scheduler()
+    return render_template("wyckoff_crypto.html")
+
+
+@bp.route("/api/wyckoff_crypto/meta")
+def meta():
+    ensure_scheduler()
+    latest = wyckoff_store.latest_trade_date()
+    return jsonify(
+        {
+            "architecture_version": ARCHITECTURE_VERSION,
+            "engine_version": WYCKOFF_ENGINE_VERSION,
+            "latest_trade_date": latest,
+            "scan_count": wyckoff_store.count_for_date(latest),
+            "cycles": [c.value for c in WyckoffCycle],
+            "phases": [p.value for p in WyckoffPhase],
+            "events": [e.value for e in WyckoffEvent],
+            "decision_signals": [s.value for s in DecisionSignal],
+            "timezone": "UTC",
+            "timeframes": ["1d", "1w", "1M"],
+            "status": get_status(),
+        }
+    )
+
+
+@bp.route("/api/wyckoff_crypto/status")
+def status():
+    ensure_scheduler()
+    return jsonify(get_status())
+
+
+@bp.route("/api/wyckoff_crypto/scan")
+def scan():
+    ensure_scheduler()
+    rows = wyckoff_store.query_scan(
+        trade_date=request.args.get("trade_date"),
+        m_cycle=request.args.get("m_cycle"),
+        w_phase=request.args.get("w_phase"),
+        d_event=request.args.get("d_event"),
+        decision_signal=request.args.get("decision_signal"),
+        min_overall_score=_float_or_none(request.args.get("min_overall_score")),
+        min_alignment=_float_or_none(request.args.get("min_alignment")),
+        sort=request.args.get("sort") or "overall_score",
+        limit=min(int(request.args.get("limit") or 100), 500),
+        offset=int(request.args.get("offset") or 0),
+    )
+    return jsonify({"rows": rows, "count": len(rows)})
+
+
+@bp.route("/api/wyckoff_crypto/symbol/<path:symbol>")
+def symbol_detail(symbol: str):
+    ensure_scheduler()
+    row = wyckoff_store.get_symbol(symbol, request.args.get("trade_date"))
+    if not row:
+        return jsonify({"error": "not_found"}), 404
+    return jsonify(row)
+
+
+@bp.route("/api/wyckoff_crypto/tick", methods=["POST"])
+def manual_tick():
+    """Manual one-shot tick (debug). Optional JSON/query max_symbols."""
+    ensure_scheduler()
+    max_sym = request.args.get("max_symbols") or (request.json or {}).get("max_symbols")
+    max_symbols = int(max_sym) if max_sym else None
+
+    def _job():
+        try:
+            run_tick(max_symbols=max_symbols, force_rescan=True)
+        except Exception:
+            pass
+
+    threading.Thread(target=_job, daemon=True).start()
+    return jsonify({"ok": True, "started": True})
+
+
+def _float_or_none(v):
+    if v in (None, ""):
+        return None
+    try:
+        return float(v)
+    except (TypeError, ValueError):
+        return None

web/app.py

@@ -15,6 +15,7 @@ from api.analyze import bp as analyze_bp
 from api.pages import bp as pages_bp
 from api.symbols import bp as symbols_bp
 from api.trend import bp as trend_bp
+from api.wyckoff_crypto import bp as wyckoff_crypto_bp, ensure_scheduler
 
 
 def create_app() -> Flask:
@@ -23,6 +24,12 @@ def create_app() -> Flask:
     app.register_blueprint(analyze_bp)
     app.register_blueprint(symbols_bp)
     app.register_blueprint(trend_bp)
+    app.register_blueprint(wyckoff_crypto_bp)
+    # Start crypto wyckoff tip scheduler (daemon); disable with CRYPTO_WYCKOFF_DISABLE=1
+    try:
+        ensure_scheduler()
+    except Exception:
+        pass
     return app
 
 

web/templates/wyckoff_crypto.html

@@ -0,0 +1,210 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width,initial-scale=1.0">
+<title>Crypto Wyckoff Screener</title>
+<style>
+:root{--bg:#0d1117;--panel:#161b22;--border:#30363d;--text:#e6edf3;--muted:#8b949e;--accent:#58a6ff;--green:#3fb950;--red:#f85149;--orange:#d2991d}
+*{box-sizing:border-box;margin:0;padding:0}
+body{font-family:'PingFang SC','Microsoft YaHei',sans-serif;background:var(--bg);color:var(--text);height:100vh;display:flex;flex-direction:column}
+header{padding:10px 14px;border-bottom:1px solid var(--border);display:flex;justify-content:space-between;align-items:center;gap:12px;flex-wrap:wrap}
+header h1{font-size:15px;font-weight:700}
+header .meta{font-size:11px;color:var(--muted)}
+header a{color:var(--accent);font-size:12px;text-decoration:none}
+.filters{padding:8px 14px;border-bottom:1px solid var(--border);display:flex;gap:10px;flex-wrap:wrap;align-items:flex-end;background:var(--panel)}
+.filters label{font-size:10px;color:var(--muted);display:flex;flex-direction:column;gap:2px}
+.filters select,.filters input{background:#0d1117;border:1px solid var(--border);color:var(--text);border-radius:4px;padding:4px 6px;font-size:11px;min-width:100px}
+.filters button{background:var(--accent);border:0;color:#fff;border-radius:4px;padding:6px 12px;font-size:11px;cursor:pointer}
+.filters button.secondary{background:transparent;border:1px solid var(--border);color:var(--muted)}
+.main{flex:1;display:grid;grid-template-columns:1fr 340px;min-height:0}
+@media(max-width:900px){.main{grid-template-columns:1fr}}
+.pane{min-height:0;overflow:auto;border-right:1px solid var(--border)}
+.pane:last-child{border-right:0;background:var(--panel)}
+.pane h2{font-size:10px;color:var(--muted);letter-spacing:1px;padding:8px 10px;border-bottom:1px solid var(--border);position:sticky;top:0;background:var(--panel)}
+table{width:100%;border-collapse:collapse;font-size:11px}
+th{text-align:left;padding:6px 8px;color:var(--muted);font-weight:600;position:sticky;top:28px;background:var(--panel)}
+td{padding:6px 8px;border-top:1px solid rgba(255,255,255,.04);cursor:pointer}
+tr:hover td{background:rgba(88,166,255,.08)}
+tr.active td{background:rgba(88,166,255,.16)}
+.tag{display:inline-block;padding:1px 6px;border-radius:3px;font-size:10px}
+.tag.StrongBuy,.tag.Buy{background:rgba(63,185,80,.15);color:var(--green)}
+.tag.Sell,.tag.Avoid{background:rgba(248,81,73,.15);color:var(--red)}
+.tag.Watch{background:rgba(210,153,29,.15);color:var(--orange)}
+#detail{padding:12px;font-size:12px;line-height:1.55;color:var(--muted)}
+#detail h3{color:var(--text);font-size:13px;margin-bottom:8px}
+#detail .kv{margin:4px 0}
+#detail .kv b{color:var(--text);display:inline-block;min-width:88px}
+#detail ul{margin:8px 0 0 16px}
+.empty{padding:24px;color:var(--muted);font-size:12px}
+</style>
+</head>
+<body>
+<header>
+  <div>
+    <h1>Crypto Wyckoff Screener</h1>
+    <div class="meta" id="metaLine">加载中…</div>
+  </div>
+  <div class="meta">
+    <a href="/">← 主站</a>
+    · D/W/M · UTC · 每分钟 tip
+  </div>
+</header>
+<div class="filters">
+  <label>决策
+    <select id="fDecision"><option value="">全部</option></select>
+  </label>
+  <label>月 Cycle
+    <select id="fCycle"><option value="">全部</option></select>
+  </label>
+  <label>周 Phase
+    <select id="fPhase"><option value="">全部</option></select>
+  </label>
+  <label>日 Event
+    <select id="fEvent"><option value="">全部</option></select>
+  </label>
+  <label>最低分
+    <input id="fMinScore" type="number" placeholder="0" step="1">
+  </label>
+  <label>排序
+    <select id="fSort">
+      <option value="overall_score">overall_score</option>
+      <option value="alignment">alignment</option>
+      <option value="entry_score">entry_score</option>
+      <option value="stars">stars</option>
+    </select>
+  </label>
+  <button id="btnRefresh" type="button">刷新</button>
+  <button id="btnTick" class="secondary" type="button">手动 Tick</button>
+</div>
+<div class="main">
+  <div class="pane">
+    <h2>SCAN <span id="rowCount"></span></h2>
+    <table>
+      <thead>
+        <tr>
+          <th>Symbol</th><th>Decision</th><th>M Cycle</th><th>W Phase</th>
+          <th>D Event</th><th>Score</th><th>Align</th><th>★</th>
+        </tr>
+      </thead>
+      <tbody id="tbody"></tbody>
+    </table>
+    <div class="empty" id="emptyHint" style="display:none">暂无数据。调度器正在回填/扫描，稍后点刷新；或点「手动 Tick」（可先设 CRYPTO_WYCKOFF_MAX_SYMBOLS 做小样本）。</div>
+  </div>
+  <div class="pane">
+    <h2>DETAIL</h2>
+    <div id="detail">点选左侧一行</div>
+  </div>
+</div>
+<script>
+(function(){
+  var meta = null;
+  var rows = [];
+  var active = null;
+
+  function fillSelect(id, values){
+    var el = document.getElementById(id);
+    values.forEach(function(v){
+      var o = document.createElement('option');
+      o.value = v; o.textContent = v; el.appendChild(o);
+    });
+  }
+
+  function loadMeta(){
+    return fetch('/api/wyckoff_crypto/meta').then(function(r){return r.json()}).then(function(m){
+      meta = m;
+      if (!document.getElementById('fDecision').options.length || document.getElementById('fDecision').options.length===1){
+        fillSelect('fDecision', m.decision_signals||[]);
+        fillSelect('fCycle', m.cycles||[]);
+        fillSelect('fPhase', m.phases||[]);
+        fillSelect('fEvent', m.events||[]);
+      }
+      var st = m.status||{};
+      document.getElementById('metaLine').textContent =
+        'engine '+m.engine_version+' · date '+(m.latest_trade_date||'-')+
+        ' · rows '+m.scan_count+
+        ' · tick '+(st.last_tick_at||'pending')+
+        (st.running?' · RUNNING':'')+
+        (st.last_error?' · err '+st.last_error:'');
+    });
+  }
+
+  function qs(){
+    var p = new URLSearchParams();
+    var d=document.getElementById('fDecision').value; if(d) p.set('decision_signal',d);
+    var c=document.getElementById('fCycle').value; if(c) p.set('m_cycle',c);
+    var ph=document.getElementById('fPhase').value; if(ph) p.set('w_phase',ph);
+    var e=document.getElementById('fEvent').value; if(e) p.set('d_event',e);
+    var ms=document.getElementById('fMinScore').value; if(ms) p.set('min_overall_score',ms);
+    p.set('sort', document.getElementById('fSort').value);
+    p.set('limit','200');
+    return p.toString();
+  }
+
+  function loadScan(){
+    return fetch('/api/wyckoff_crypto/scan?'+qs()).then(function(r){return r.json()}).then(function(data){
+      rows = data.rows||[];
+      document.getElementById('rowCount').textContent = '('+rows.length+')';
+      document.getElementById('emptyHint').style.display = rows.length? 'none':'block';
+      var tb = document.getElementById('tbody');
+      tb.innerHTML = '';
+      rows.forEach(function(row, i){
+        var tr = document.createElement('tr');
+        if (active && active.ts_code===row.ts_code) tr.className='active';
+        tr.innerHTML =
+          '<td>'+row.ts_code+'</td>'+
+          '<td><span class="tag '+row.decision_signal+'">'+row.decision_signal+'</span></td>'+
+          '<td>'+row.m_cycle+'</td>'+
+          '<td>'+row.w_phase+'</td>'+
+          '<td>'+row.d_current_event+'</td>'+
+          '<td>'+(row.overall_score!=null?Number(row.overall_score).toFixed(1):'-')+'</td>'+
+          '<td>'+(row.alignment!=null?Number(row.alignment).toFixed(0):'-')+'</td>'+
+          '<td>'+(row.stars||'')+'</td>';
+        tr.onclick = function(){ showDetail(row); Array.prototype.forEach.call(tb.querySelectorAll('tr'),function(x){x.className='';}); tr.className='active'; };
+        tb.appendChild(tr);
+      });
+    });
+  }
+
+  function showDetail(row){
+    active = row;
+    var reasons=[];
+    try{ reasons = JSON.parse(row.reasons_json||'[]'); }catch(e){}
+    var plan = [];
+    if(row.entry!=null) plan.push('entry '+row.entry);
+    if(row.stop!=null) plan.push('stop '+row.stop);
+    if(row.target1!=null) plan.push('t1 '+row.target1);
+    if(row.target2!=null) plan.push('t2 '+row.target2);
+    if(row.rr!=null) plan.push('rr '+row.rr);
+    document.getElementById('detail').innerHTML =
+      '<h3>'+row.ts_code+'</h3>'+
+      '<div class="kv"><b>Decision</b> '+row.decision_signal+' · risk '+row.risk+'</div>'+
+      '<div class="kv"><b>Month</b> '+row.m_cycle+'</div>'+
+      '<div class="kv"><b>Week</b> '+row.w_cycle+' / '+row.w_phase+' / '+row.w_current_event+'</div>'+
+      '<div class="kv"><b>Day</b> '+row.d_current_event+'</div>'+
+      '<div class="kv"><b>Scores</b> overall '+fmt(row.overall_score)+' align '+fmt(row.alignment)+' entry '+fmt(row.entry_score)+'</div>'+
+      '<div class="kv"><b>Plan</b> '+(plan.join(' · ')||'无（非 tradable）')+'</div>'+
+      '<div class="kv"><b>Updated</b> '+(row.scanned_at||'')+'</div>'+
+      '<ul>'+reasons.map(function(x){return '<li>'+x+'</li>';}).join('')+'</ul>';
+  }
+  function fmt(v){ return v==null?'-':Number(v).toFixed(1); }
+
+  function refresh(){ return loadMeta().then(loadScan); }
+
+  document.getElementById('btnRefresh').onclick = function(){ refresh(); };
+  document.getElementById('btnTick').onclick = function(){
+    fetch('/api/wyckoff_crypto/tick', {method:'POST'}).then(function(){
+      document.getElementById('metaLine').textContent = '手动 tick 已启动…';
+      setTimeout(refresh, 3000);
+    });
+  };
+  ['fDecision','fCycle','fPhase','fEvent','fMinScore','fSort'].forEach(function(id){
+    document.getElementById(id).addEventListener('change', loadScan);
+  });
+
+  refresh();
+  setInterval(refresh, 30000);
+})();
+</script>
+</body>
+</html>