jackyu66gitandCursor
03851d2247
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>
64 lines
2.8 KiB
Python
64 lines
2.8 KiB
Python
"""Decision Engine contract tests — MTF facts must not be overwritten."""
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from ashare_dp.domain.wyckoff import DecisionSignal, EngineResult, WyckoffCycle, WyckoffEvent, WyckoffPhase
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from ashare_dp.wyckoff.decision import DecisionEngine
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def _er(name, payload, confidence=80.0, score=80.0, reasons=None):
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return EngineResult(
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name=name,
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confidence=confidence,
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score=score,
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reasons=reasons or [],
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payload=payload,
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)
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def test_monthly_distribution_daily_spring_is_watch():
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eng = DecisionEngine()
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monthly = _er("Cycle", {"cycle": WyckoffCycle.DISTRIBUTION.value, "trend_score": 40}, score=40)
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weekly_c = _er("Cycle", {"cycle": WyckoffCycle.ACCUMULATION.value, "trend_score": 70}, score=70)
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weekly_p = _er("Phase", {"phase": WyckoffPhase.B.value, "cycle": WyckoffCycle.ACCUMULATION.value, "structure_score": 65}, score=65)
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weekly_e = _er("Event", {"current_event": WyckoffEvent.ST.value, "recent_events": ["SC", "AR", "ST"]}, score=60)
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daily_e = _er(
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"Event",
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{"current_event": WyckoffEvent.SPRING.value, "recent_events": ["SC", "AR", "ST", "Spring"], "entry_score": 92},
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confidence=92,
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score=92,
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)
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daily_s = _er("Signal", {"signal_label": "Spring", "current_event": "Spring"}, confidence=92, score=92)
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out = eng.run(monthly, weekly_c, weekly_p, weekly_e, daily_e, daily_s)
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# Facts preserved
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assert out.payload["facts"]["monthly"]["cycle"] == WyckoffCycle.DISTRIBUTION.value
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assert out.payload["m_cycle"] == WyckoffCycle.DISTRIBUTION.value
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assert out.payload["d_event"] == WyckoffEvent.SPRING.value
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# Decision gated
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assert out.payload["decision_signal"] == DecisionSignal.WATCH.value
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assert out.payload["overall_score"] <= 55.0
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def test_bullish_alignment_can_strong_buy():
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eng = DecisionEngine()
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monthly = _er("Cycle", {"cycle": WyckoffCycle.MARKUP.value, "trend_score": 90}, score=90, confidence=90)
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weekly_c = _er("Cycle", {"cycle": WyckoffCycle.ACCUMULATION.value, "trend_score": 85}, score=85, confidence=85)
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weekly_p = _er(
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"Phase",
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{"phase": WyckoffPhase.D.value, "cycle": WyckoffCycle.ACCUMULATION.value, "structure_score": 88},
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score=88,
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confidence=88,
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)
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weekly_e = _er("Event", {"current_event": WyckoffEvent.SOS.value, "recent_events": ["SOS"]}, score=85, confidence=85)
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daily_e = _er(
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"Event",
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{"current_event": WyckoffEvent.SPRING.value, "recent_events": ["SC", "AR", "ST", "Spring", "Test"], "entry_score": 92},
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confidence=92,
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score=92,
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)
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daily_s = _er("Signal", {"signal_label": "Spring"}, confidence=92, score=92)
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out = eng.run(monthly, weekly_c, weekly_p, weekly_e, daily_e, daily_s)
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assert out.payload["decision_signal"] == DecisionSignal.STRONG_BUY.value
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assert out.payload["stars"] >= 4
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