feat: 威科夫多周期选股引擎与中文图表界面

新增规则驱动的月/周/日结构识别、决策融合与交易计划，提供扫描 API、本地 K 线（成交量/MACD/吸筹区间标注）及回填调度；K 线无起始日时默认取最近 N 根。 Co-authored-by: Cursor <cursoragent@cursor.com>
2026-08-05 14:26:46 +08:00
parent cc95bbb638
commit 03851d2247
35 changed files with 3861 additions and 12 deletions
@@ -0,0 +1,63 @@
+"""Decision Engine contract tests — MTF facts must not be overwritten."""
+
+from ashare_dp.domain.wyckoff import DecisionSignal, EngineResult, WyckoffCycle, WyckoffEvent, WyckoffPhase
+from ashare_dp.wyckoff.decision import DecisionEngine
+
+
+def _er(name, payload, confidence=80.0, score=80.0, reasons=None):
+    return EngineResult(
+        name=name,
+        confidence=confidence,
+        score=score,
+        reasons=reasons or [],
+        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},
+        confidence=92,
+        score=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)
+
+    # Facts preserved
+    assert out.payload["facts"]["monthly"]["cycle"] == WyckoffCycle.DISTRIBUTION.value
+    assert out.payload["m_cycle"] == WyckoffCycle.DISTRIBUTION.value
+    assert out.payload["d_event"] == WyckoffEvent.SPRING.value
+    # Decision gated
+    assert out.payload["decision_signal"] == DecisionSignal.WATCH.value
+    assert out.payload["overall_score"] <= 55.0
+
+
+def test_bullish_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"], "entry_score": 92},
+        confidence=92,
+        score=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"] == DecisionSignal.STRONG_BUY.value
+    assert out.payload["stars"] >= 4
@@ -0,0 +1,42 @@
+"""Feature / Cycle pure-engine smoke tests (no DB)."""
+
+from datetime import date, timedelta
+
+from ashare_dp.domain.wyckoff import OHLCVFrame
+from ashare_dp.wyckoff.cycle import CycleEngine
+from ashare_dp.wyckoff.features import FeatureEngine
+
+
+def _synth_uptrend(n=120) -> OHLCVFrame:
+    base = date(2024, 1, 1)
+    closes = [100 + i * 0.5 for i in range(n)]
+    return OHLCVFrame(
+        ts_code="000001.SZ",
+        timeframe="1d",
+        trade_dates=[base + timedelta(days=i) for i in range(n)],
+        open=closes,
+        high=[c * 1.01 for c in closes],
+        low=[c * 0.99 for c in closes],
+        close=closes,
+        volume=[1_000_000 + i * 1000 for i in range(n)],
+    )
+
+
+def test_feature_engine_snapshot():
+    fe = FeatureEngine()
+    out = fe.run(_synth_uptrend())
+    assert out.name == "Feature"
+    assert "ma20" in out.payload
+    assert out.payload["bars"] == 120
+    assert out.confidence > 50
+
+
+def test_cycle_engine_markup_on_uptrend():
+    fe = FeatureEngine()
+    ce = CycleEngine()
+    feat = fe.run(_synth_uptrend(150))
+    # Use monthly timeframe rules
+    feat.payload["timeframe"] = "1M"
+    cyc = ce.run(feat, "1M")
+    assert cyc.payload["cycle"] in ("Markup", "Accumulation", "Unknown", "Distribution")
+    assert "cycle" in cyc.payload
@@ -0,0 +1,94 @@
+"""Plan gate + insufficient TF fallback tests."""
+
+from datetime import date, timedelta
+
+from ashare_dp.domain.wyckoff import DecisionSignal, EngineResult, OHLCVFrame, WyckoffCycle
+from ashare_dp.wyckoff.cycle import CycleEngine
+from ashare_dp.wyckoff.decision import DecisionEngine
+from ashare_dp.wyckoff.features import FeatureEngine
+from ashare_dp.wyckoff.plan import PlanEngine
+from ashare_dp.wyckoff.pipeline import analyze_symbol
+from ashare_dp.wyckoff.phase import PhaseEngine
+from ashare_dp.wyckoff.event import EventEngine
+from ashare_dp.wyckoff.signal import SignalEngine
+
+
+def _er(name, payload, confidence=80.0, score=80.0):
+    return EngineResult(name=name, confidence=confidence, score=score, payload=payload)
+
+
+def test_plan_no_entry_on_watch_even_if_spring_event():
+    plan = PlanEngine()
+    feat = _er("Feature", {"close": 10.0, "atr": 0.3, "swing_low": 9.0, "swing_high": 11.0, "range_high": 11.0})
+    decision = _er(
+        "Decision",
+        {
+            "decision_signal": DecisionSignal.WATCH.value,
+            "d_event": "Spring",
+        },
+        confidence=90,
+        score=50,
+    )
+    out = plan.run(feat, decision)
+    assert out.payload["entry"] is None
+    assert out.payload["stop"] is None
+
+
+def test_plan_entry_on_buy():
+    plan = PlanEngine()
+    feat = _er("Feature", {"close": 10.0, "atr": 0.3, "swing_low": 9.0, "swing_high": 11.0, "range_high": 11.0})
+    decision = _er("Decision", {"decision_signal": DecisionSignal.BUY.value, "d_event": "Spring"})
+    out = plan.run(feat, decision)
+    assert out.payload["entry"] == 10.0
+    assert out.payload["stop"] is not None
+
+
+def test_feature_insufficient_for_short_monthly():
+    fe = FeatureEngine()
+    base = date(2024, 1, 1)
+    n = 10
+    frame = OHLCVFrame(
+        ts_code="000001.SZ",
+        timeframe="1M",
+        trade_dates=[base + timedelta(days=30 * i) for i in range(n)],
+        open=[10.0] * n,
+        high=[11.0] * n,
+        low=[9.0] * n,
+        close=[10.0] * n,
+        volume=[1e6] * n,
+    )
+    out = fe.run(frame, "1M")
+    assert out.payload["insufficient"] is True
+    cyc = CycleEngine().run(out, "1M")
+    assert cyc.payload["cycle"] == WyckoffCycle.UNKNOWN.value
+
+
+def test_pipeline_does_not_borrow_daily_as_monthly():
+    """Daily-only data → monthly cycle Unknown, not inferred from daily."""
+    base = date(2024, 1, 1)
+    n = 120
+    closes = [100 + i * 0.4 for i in range(n)]
+    daily = OHLCVFrame(
+        ts_code="000001.SZ",
+        timeframe="1d",
+        trade_dates=[base + timedelta(days=i) for i in range(n)],
+        open=closes,
+        high=[c * 1.01 for c in closes],
+        low=[c * 0.99 for c in closes],
+        close=closes,
+        volume=[1e6] * n,
+    )
+    result = analyze_symbol(
+        daily,
+        None,
+        None,
+        feature_eng=FeatureEngine(),
+        cycle_eng=CycleEngine(),
+        phase_eng=PhaseEngine(),
+        event_eng=EventEngine(),
+        signal_eng=SignalEngine(),
+        decision_eng=DecisionEngine(),
+        plan_eng=PlanEngine(),
+    )
+    assert result["f_m"].payload.get("insufficient") is True
+    assert result["c_m"].payload["cycle"] == WyckoffCycle.UNKNOWN.value