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
1.3 KiB
Python
54 lines
1.3 KiB
Python
"""
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策略抽象基类。
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所有策略必须继承 BaseStrategy，实现 generate_signals(factor_df) → pd.Series。
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"""
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from abc import ABC, abstractmethod
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import pandas as pd
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class BaseStrategy(ABC):
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"""
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回测策略基类。
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属性:
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name: 策略名称
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category: 'trend' | 'mean_revert' | 'rotation'
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"""
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name: str = ""
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category: str = ""
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@abstractmethod
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def generate_signals(self, factor_df: pd.DataFrame) -> pd.Series:
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"""
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因子 → 交易信号。
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参数:
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factor_df: 因子 DataFrame，index=trade_date，columns=因子名
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返回:
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pd.Series，index 与 factor_df 对齐：
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1=买入, 0=平仓/无操作
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（只做多，不做空）
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"""
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...
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def get_params(self) -> dict:
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"""返回策略当前参数（供 Optuna 优化用）。"""
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return {
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k: v for k, v in self.__dict__.items()
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if not k.startswith("_") and k not in ("name", "category")
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}
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def set_params(self, **kwargs) -> None:
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"""设置策略参数。"""
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for k, v in kwargs.items():
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if hasattr(self, k):
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setattr(self, k, v)
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def __repr__(self) -> str:
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return f"{self.__class__.__name__}(name='{self.name}')"
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