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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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lytem28
b6cf0495e1
Unify strategy execution across backtest, screener, and monitoring; isolate backtest workloads in spawn workers; and add shared matrix caching plus valid-bar indicator acceleration.
107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
"""缩量回踩 — 回踩MA20附近 + 缩量 + 中期趋势向上"""
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import numpy as np
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from app.backtest.matrix import (
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MarketDataMatrix,
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SignalMatrix,
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make_signal_matrix,
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matrix_feature,
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)
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from app.backtest.matrix import (
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valid_shift as shift,
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)
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META = {
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"id": "pullback_to_support",
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"name": "缩量回踩",
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"description": "回踩MA20附近 + 缩量 + 中期趋势向上",
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"tags": ["回踩", "支撑"],
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"asset_types": ["stock", "etf"],
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"timeframes": ["1d"],
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"params": [
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{"id": "use_ma20_proximity", "label": "启用MA20附近过滤", "type": "bool", "default": True},
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{
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"id": "ma_proximity",
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"label": "均线偏离度",
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"type": "float",
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"default": 0.02,
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"min": 0.01,
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"max": 0.05,
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"step": 0.005,
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},
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{"id": "use_volume_filter", "label": "启用缩量过滤", "type": "bool", "default": True},
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{
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"id": "vol_ratio_max",
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"label": "最大量比",
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"type": "float",
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"default": 0.8,
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"min": 0.2,
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"max": 1.5,
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"step": 0.1,
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},
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{
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"id": "require_above_ma60",
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"label": "要求收盘价在MA60上方",
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"type": "bool",
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"default": True,
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},
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{
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"id": "require_positive_momentum",
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"label": "要求20日动量为正",
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"type": "bool",
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"default": True,
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},
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],
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"scoring": {"momentum_60d": 0.4, "momentum_20d": 0.3, "turnover_rate": 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_ma_golden_5_20"]
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EXIT_SIGNALS = ["signal_ma20_breakdown"]
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STOP_LOSS = -0.05
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MAX_HOLD_DAYS = 20
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ALERTS = []
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class PullbackToSupportMatrixStrategy:
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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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ma20 = matrix_feature(market, "ma20")
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entry = np.ones(market.shape, dtype=bool)
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if params.get("use_ma20_proximity", True):
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proximity = float(params.get("ma_proximity", 0.02))
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entry &= (market.close > ma20 * (1.0 - proximity)) & (
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market.close < ma20 * (1.0 + proximity)
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)
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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_max", 0.8)
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)
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if params.get("require_above_ma60", True):
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entry &= market.close > matrix_feature(market, "ma60")
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if params.get("require_positive_momentum", True):
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entry &= matrix_feature(market, "momentum_20d") > 0
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exit_ = (market.close < ma20) & (shift(market.close, 1) >= shift(ma20, 1))
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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_ma_golden_5_20",),
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exit_signal_ids=("signal_ma20_breakdown",),
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)
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MATRIX_STRATEGY = PullbackToSupportMatrixStrategy()
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