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>
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
"""
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MACD 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 MACDFactor(BaseFactor):
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"""
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MACD 系列因子。
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返回 DIF/DEA/HIST 三个值。使用 calculate() 返回 HIST（柱），
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单独方法获取 DIF/DEA。
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"""
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category = "technical"
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def __init__(self, fast: int = 12, slow: int = 26, signal: int = 9):
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self.fast = fast
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self.slow = slow
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self.signal = signal
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self.name = f"macd_{fast}_{slow}_{signal}"
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def _ema(self, series: pd.Series, span: int) -> pd.Series:
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return series.ewm(span=span, min_periods=span).mean()
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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"""返回 MACD 柱（DIF - DEA）。"""
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ema_fast = self._ema(df["close"], self.fast)
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ema_slow = self._ema(df["close"], self.slow)
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dif = ema_fast - ema_slow
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dea = self._ema(dif, self.signal)
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return dif - dea
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def dif(self, df: pd.DataFrame) -> pd.Series:
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ema_fast = self._ema(df["close"], self.fast)
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ema_slow = self._ema(df["close"], self.slow)
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return ema_fast - ema_slow
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def dea(self, df: pd.DataFrame) -> pd.Series:
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dif = self.dif(df)
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return self._ema(dif, self.signal)
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def get_required_columns(self) -> list[str]:
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return ["close"]
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