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>
116 lines
3.0 KiB
Python
116 lines
3.0 KiB
Python
"""
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信号生成工具函数。
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因子值 → 交易信号的桥梁，纯函数无副作用。
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"""
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import pandas as pd
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def factor_to_threshold_signal(
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factor_series: pd.Series,
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buy_threshold: float,
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sell_threshold: float | None = None,
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cross_direction: str = "up",
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) -> pd.Series:
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"""
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因子阈值交叉信号。
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参数:
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factor_series: 因子值 Series
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buy_threshold: 买入阈值（如 RSI < 30 则买）
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sell_threshold: 卖出阈值（如 RSI > 70 则卖），None 表示平所有仓
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cross_direction: 'up'=因子向上穿越阈值时触发, 'down'=向下穿越
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返回:
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信号 Series：1=买入, 0=平仓
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"""
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signals = pd.Series(0, index=factor_series.index)
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if cross_direction == "down":
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buys = factor_series < buy_threshold
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else:
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buys = factor_series > buy_threshold
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signals[buys] = 1
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if sell_threshold is not None:
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if cross_direction == "down":
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sells = factor_series > sell_threshold
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else:
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sells = factor_series < sell_threshold
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signals[sells] = 0
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# 过滤连续信号
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signals = _filter_consecutive(signals)
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return signals
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def factor_to_quantile_signal(
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factor_series: pd.Series,
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top_quantile: float = 0.8,
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bottom_quantile: float = 0.2,
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) -> pd.Series:
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"""
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因子分位数信号 — 按滚动分位数判断。
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参数:
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factor_series: 因子值
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top_quantile: 高于此分位买入
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bottom_quantile: 低于此分位平仓
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返回:
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信号 Series
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"""
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top = factor_series.quantile(top_quantile)
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bottom = factor_series.quantile(bottom_quantile)
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signals = pd.Series(0, index=factor_series.index)
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signals[factor_series > top] = 1
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signals[factor_series < bottom] = 0
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return _filter_consecutive(signals)
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def cross_signal(
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fast: pd.Series,
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slow: pd.Series,
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) -> pd.Series:
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"""
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金叉/死叉信号。
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fast 上穿 slow → 买入(1)
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fast 下穿 slow → 平仓(0)
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"""
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fast = fast.dropna()
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slow = slow.dropna()
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common_idx = fast.index.intersection(slow.index)
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fast, slow = fast[common_idx], slow[common_idx]
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signals = pd.Series(-1, index=common_idx)
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above = (fast > slow).fillna(False)
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above = above.infer_objects(copy=False)
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# 交叉点：今天 above=True 且昨天 above=False → 金叉
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prev = above.shift(1).fillna(False)
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prev = prev.infer_objects(copy=False)
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cross_up = above & ~prev
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cross_down = ~above & prev
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signals[cross_up] = 1
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signals[cross_down] = 0
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return _filter_consecutive(signals)
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def _filter_consecutive(signals: pd.Series) -> pd.Series:
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"""过滤连续相同信号，只保留首次出现的信号。"""
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result = signals.copy()
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prev = None
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for i in range(len(result)):
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if result.iloc[i] == prev:
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result.iloc[i] = -1 # 标记为不操作
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else:
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prev = result.iloc[i]
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return result[result != -1].reindex(signals.index).fillna(-1)
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