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>
54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
"""
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布林带因子：价格在布林带中的位置。
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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 BollingerFactor(BaseFactor):
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"""
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布林带位置 = (close - middle) / (upper - lower)
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值在 0~1 之间：接近 0 表示在下轨，接近 1 表示在上轨。
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"""
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category = "technical"
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def __init__(self, period: int = 20, std: float = 2.0):
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self.period = period
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self.std = std
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self.name = f"boll_{period}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
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std = df["close"].rolling(window=self.period, min_periods=self.period).std()
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upper = middle + self.std * std
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lower = middle - self.std * std
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band_width = upper - lower
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return ((df["close"] - lower) / band_width.replace(0, float("nan"))).clip(0, 1)
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def get_required_columns(self) -> list[str]:
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return ["close"]
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class BollingerWidthFactor(BaseFactor):
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"""布林带宽度 = (upper - lower) / middle * 100"""
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category = "technical"
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def __init__(self, period: int = 20, std: float = 2.0):
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self.period = period
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self.std = std
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self.name = f"boll_width_{period}"
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def calculate(self, df: pd.DataFrame) -> pd.Series:
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middle = df["close"].rolling(window=self.period, min_periods=self.period).mean()
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std = df["close"].rolling(window=self.period, min_periods=self.period).std()
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band_width = 2 * self.std * std
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return band_width / middle.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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