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
1.0 KiB
Python
43 lines
1.0 KiB
Python
"""
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ML 模型抽象基类。
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统一接口：fit(X, y) → predict(X) → save/load。
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"""
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from abc import ABC, abstractmethod
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import pickle
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import pandas as pd
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class BaseModel(ABC):
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"""ML 模型抽象基类。"""
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name: str = ""
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@abstractmethod
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def fit(self, X: pd.DataFrame, y: pd.Series) -> "BaseModel":
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"""训练模型。返回 self 支持链式调用。"""
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...
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@abstractmethod
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def predict(self, X: pd.DataFrame) -> pd.Series:
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"""返回预测值（回归值）。"""
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...
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@abstractmethod
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def get_feature_importance(self) -> pd.DataFrame:
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"""特征重要性 DataFrame，columns=[feature, importance]。"""
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...
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def save(self, path: str) -> None:
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"""保存模型到文件（pickle）。"""
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with open(path, "wb") as f:
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pickle.dump(self, f)
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@classmethod
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def load(cls, path: str) -> "BaseModel":
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"""从文件加载模型。"""
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with open(path, "rb") as f:
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return pickle.load(f)
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