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76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""布林突破 — 突破布林上轨 + 放量"""
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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": "boll_breakout",
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"name": "布林突破",
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"description": "突破布林上轨 + 放量, 强势加速信号",
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"tags": ["布林", "突破"],
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"asset_types": ["stock", "etf"],
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"timeframes": ["1d"],
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"params": [
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{
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"id": "require_boll_breakout",
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"label": "要求突破布林上轨",
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"type": "bool",
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"default": True,
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},
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{"id": "use_volume_filter", "label": "启用量比过滤", "type": "bool", "default": True},
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{
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"id": "vol_ratio_min",
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"label": "最低量比",
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"type": "float",
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"default": 1.5,
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"min": 0.5,
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"max": 5.0,
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"step": 0.1,
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},
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],
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"scoring": {"vol_ratio_5d": 0.4, "change_pct": 0.3, "momentum_20d": 0.3},
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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_boll_breakout_upper"]
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EXIT_SIGNALS = ["signal_boll_breakdown_lower"]
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STOP_LOSS = -0.06
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MAX_HOLD_DAYS = 15
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class BollBreakoutMatrixStrategy:
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def required_fields(self) -> frozenset[str]:
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return frozenset({"close", "volume"})
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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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upper = matrix_feature(market, "boll_upper")
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lower = matrix_feature(market, "boll_lower")
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entry = np.ones(market.shape, dtype=bool)
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if params.get("require_boll_breakout", True):
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entry &= market.close > upper
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if params.get("use_volume_filter", True):
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entry &= matrix_feature(market, "vol_ratio_5d") >= float(
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params.get("vol_ratio_min", 1.5)
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)
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exit_ = market.close < lower
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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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exit=exit_.astype(np.uint8),
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entry_signal_code=np.where(entry, 0, -1).astype(np.int16),
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exit_signal_code=np.where(exit_, 0, -1).astype(np.int16),
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entry_signal_ids=("signal_boll_breakout_upper",),
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exit_signal_ids=("signal_boll_breakdown_lower",),
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)
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MATRIX_STRATEGY = BollBreakoutMatrixStrategy()
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