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>
50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
"""
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均线交叉因子。
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"""
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import pandas as pd
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from factors.base import BaseFactor
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class MACrossFactor(BaseFactor):
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"""
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均线交叉信号。
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返回：fast_ma / slow_ma - 1，正值表示短期均线在上方。
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"""
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category = "technical"
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def __init__(self, fast: int = 5, slow: int = 20):
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self.fast = fast
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self.slow = slow
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self.name = f"ma_cross_{fast}_{slow}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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ma_fast = df["close"].rolling(window=self.fast, min_periods=self.fast).mean()
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ma_slow = df["close"].rolling(window=self.slow, min_periods=self.slow).mean()
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return (ma_fast / ma_slow.replace(0, float("nan"))) - 1
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def get_required_columns(self) -> list[str]:
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return ["close"]
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class MADeviationFactor(BaseFactor):
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"""
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价格偏离均线程度 = (close - ma) / ma * 100
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"""
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category = "technical"
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def __init__(self, period: int = 20):
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self.period = period
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self.name = f"ma_dev_{period}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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ma = df["close"].rolling(window=self.period, min_periods=self.period).mean()
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return (df["close"] - ma) / ma.replace(0, float("nan")) * 100
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def get_required_columns(self) -> list[str]:
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return ["close"]
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