simonandClaude Opus 4.7
271a9343a5
7 Sprints 全部完成: Sprint 0: 基础设施 (DataManager + MariaDB) Sprint 1: 因子引擎 (34因子/12分类) Sprint 2: VectorBT 回测 (5策略+截面) Sprint 3: Optuna 优化 (+Walk-Forward) Sprint 4: ML 模型 (LightGBM+CatBoost) Sprint 5: Qwen 情绪因子 (三源新闻+日期对齐) Sprint 6: Agent 系统 (4Agent+日报.md/.html) 生产加固 (15项): Tushare双源fallback, SSH自动恢复, pool_pre_ping, save_daily先删后插, load_dotenv绝对路径, 日报5d/20d修复, RiskAgent改上证指数, 昨日对比+数据截止, mac_report utf8mb4, CLAUDE-*.md 9条已知Bug, demo全参数化, djapi数据源归一化, indexDatas API修正 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
"""
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因子阈值交叉策略。
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通用策略：任意因子上穿/下穿阈值 → 交易信号。
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支持：
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- 上穿买入 (cross_up: close < MA → cross above MA → buy)
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- 下穿买入 (cross_down: RSI > 70 → cross below 30 → buy)
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"""
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import pandas as pd
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from backtest.base import BaseStrategy
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from backtest.signal import factor_to_threshold_signal
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class FactorCrossStrategy(BaseStrategy):
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"""
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因子阈值交叉策略。
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适用场景：
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- 均线偏离度上穿 0 → 买入（趋势转多）
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- 波动率下穿阈值 → 买入（波动收敛后突破）
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"""
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category = "trend"
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def __init__(
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self,
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factor_column: str,
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buy_threshold: float, # 因子大于此值买
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sell_threshold: float | None = None,
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cross_direction: str = "up",
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):
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self.factor_column = factor_column
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self.buy_threshold = buy_threshold
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self.sell_threshold = sell_threshold
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self.cross_direction = cross_direction
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self.name = f"factor_cross_{factor_column}"
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def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
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if self.factor_column not in factor_df.columns:
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raise ValueError(f"factor_df 缺少 '{self.factor_column}' 列")
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factor = factor_df[self.factor_column]
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return factor_to_threshold_signal(
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factor,
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buy_threshold=self.buy_threshold,
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sell_threshold=self.sell_threshold,
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cross_direction=self.cross_direction,
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)
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