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>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
"""
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波动率因子。
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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 VolatilityFactor(BaseFactor):
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"""N 日年化波动率 = std(daily_return, N) * sqrt(252) * 100"""
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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"volatility_{period}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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daily_ret = df["close"].pct_change()
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return daily_ret.rolling(window=self.period, min_periods=self.period).std() * (252 ** 0.5) * 100
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def get_required_columns(self) -> list[str]:
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return ["close"]
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class DownsideVolatilityFactor(BaseFactor):
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"""下行波动率：只计算负收益的标准差。"""
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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"down_vol_{period}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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daily_ret = df["close"].pct_change()
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downside = daily_ret.clip(upper=0)
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return downside.rolling(window=self.period, min_periods=self.period).std() * (252 ** 0.5) * 100
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def get_required_columns(self) -> list[str]:
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return ["close"]
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