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65 lines
2.0 KiB
Python
65 lines
2.0 KiB
Python
"""连板股 — 涨停且连续涨停≥2天"""
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import numpy as np
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from app.backtest.matrix import MarketDataMatrix, SignalMatrix, make_signal_matrix, matrix_feature
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META = {
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"id": "consecutive_limit_ups",
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"name": "连板股",
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"description": "当日涨停且连续涨停≥2天, 强势追涨",
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"tags": ["涨停", "连板"],
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"asset_types": ["stock"],
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"timeframes": ["1d"],
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"params": [
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{"id": "require_limit_up", "label": "要求当日涨停", "type": "bool", "default": True},
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{"id": "use_boards_filter", "label": "启用连板数过滤", "type": "bool", "default": True},
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{
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"id": "min_boards",
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"label": "最少连板数",
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"type": "int",
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"default": 2,
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"min": 1,
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"max": 20,
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"step": 1,
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},
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],
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"scoring": {"consecutive_limit_ups": 0.5, "change_pct": 0.3, "amount": 0.2},
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"order_by": "score",
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"descending": True,
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"limit": 100,
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}
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EXECUTION_BACKEND = "matrix_native"
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ENTRY_SIGNALS = ["signal_limit_up"]
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EXIT_SIGNALS = []
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STOP_LOSS = -0.05
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MAX_HOLD_DAYS = 5
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class ConsecutiveLimitUpsMatrixStrategy:
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def required_fields(self) -> frozenset[str]:
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return frozenset({"consecutive_limit_ups", "raw_close"})
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def required_warmup_bars(self, params: dict) -> int:
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del params
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return 60
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def compute_signals(self, market: MarketDataMatrix, params: dict) -> SignalMatrix:
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entry = np.ones(market.shape, dtype=bool)
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if params.get("require_limit_up", True):
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entry &= market.limit_up_locked.astype(bool)
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if params.get("use_boards_filter", True):
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entry &= matrix_feature(market, "consecutive_limit_ups") >= int(
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params.get("min_boards", 2)
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)
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return make_signal_matrix(
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market.shape,
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entry=entry.astype(np.uint8),
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entry_signal_code=np.where(entry, 0, -1).astype(np.int16),
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entry_signal_ids=("signal_limit_up",),
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)
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MATRIX_STRATEGY = ConsecutiveLimitUpsMatrixStrategy()
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