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tick-stock-panel/backend/app/strategy/builtin/limit_up_momentum.py
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Python

"""连板接力 — 近2日涨停且今日涨幅 > 5%, 连板股追踪"""
import numpy as np
from app.backtest.matrix import MarketDataMatrix, SignalMatrix, make_signal_matrix, matrix_feature
META = {
"id": "limit_up_momentum",
"name": "连板接力",
"description": "连板股 + 今日涨幅 > 5%, 连板接力追踪",
"tags": ["涨停", "连板", "接力"],
"asset_types": ["stock"],
"timeframes": ["1d"],
"params": [
{"id": "use_change_filter", "label": "启用涨幅过滤", "type": "bool", "default": True},
{
"id": "min_change",
"label": "最低涨幅%",
"type": "float",
"default": 5.0,
"min": 2.0,
"max": 15.0,
"step": 0.5,
},
{"id": "use_boards_filter", "label": "启用连板数过滤", "type": "bool", "default": True},
{
"id": "min_boards",
"label": "最少连板",
"type": "int",
"default": 1,
"min": 1,
"max": 10,
"step": 1,
},
],
"scoring": {"consecutive_limit_ups": 0.4, "change_pct": 0.3, "amount": 0.3},
"order_by": "score",
"descending": True,
"limit": 50,
}
EXECUTION_BACKEND = "matrix_native"
ENTRY_SIGNALS = ["signal_limit_up"]
EXIT_SIGNALS = []
STOP_LOSS = -0.05
MAX_HOLD_DAYS = 5
class LimitUpMomentumMatrixStrategy:
def required_fields(self) -> frozenset[str]:
return frozenset({"close", "consecutive_limit_ups"})
def required_warmup_bars(self, params: dict) -> int:
del params
return 60
def compute_signals(self, market: MarketDataMatrix, params: dict) -> SignalMatrix:
entry = np.ones(market.shape, dtype=bool)
if params.get("use_change_filter", True):
entry &= (
matrix_feature(market, "change_pct") > float(params.get("min_change", 5.0)) / 100.0
)
if params.get("use_boards_filter", True):
entry &= matrix_feature(market, "consecutive_limit_ups") >= int(
params.get("min_boards", 1)
)
return make_signal_matrix(
market.shape,
entry=entry.astype(np.uint8),
entry_signal_code=np.where(entry, 0, -1).astype(np.int16),
entry_signal_ids=("signal_limit_up",),
)
MATRIX_STRATEGY = LimitUpMomentumMatrixStrategy()