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>
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
"""
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策略优化快捷函数。
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为常用策略提供一键优化入口。
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"""
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import pandas as pd
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from backtest.vectorbt.engine import VectorBTEngine
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from optimizer.engine import OptunaEngine
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from optimizer.result import OptimizationResult, WalkForwardResult
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from optimizer.space import (
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sma_cross_space,
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rsi_revert_space,
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momentum_breakout_space,
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factor_cross_space,
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)
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_DEFAULT_TRIALS = 100
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def optimize_sma_cross(
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price_df: pd.DataFrame,
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factor_df: pd.DataFrame | None = None,
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bt_engine: VectorBTEngine | None = None,
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n_trials: int = _DEFAULT_TRIALS,
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metric: str = "sharpe",
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) -> OptimizationResult:
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"""均线交叉策略参数寻优。"""
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from backtest.strategies.sma_cross import SMACrossStrategy
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return OptunaEngine(bt_engine).optimize(
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SMACrossStrategy, sma_cross_space, price_df, factor_df, metric, n_trials,
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)
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def optimize_rsi_revert(
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price_df: pd.DataFrame,
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factor_df: pd.DataFrame | None = None,
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bt_engine: VectorBTEngine | None = None,
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n_trials: int = _DEFAULT_TRIALS,
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metric: str = "sharpe",
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) -> OptimizationResult:
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"""RSI 反转策略参数寻优。"""
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from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
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return OptunaEngine(bt_engine).optimize(
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RSIMeanRevertStrategy, rsi_revert_space, price_df, factor_df, metric, n_trials,
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)
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def optimize_momentum_breakout(
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price_df: pd.DataFrame,
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factor_df: pd.DataFrame | None = None,
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bt_engine: VectorBTEngine | None = None,
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n_trials: int = _DEFAULT_TRIALS,
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metric: str = "sharpe",
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) -> OptimizationResult:
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"""动量突破策略参数寻优。"""
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from backtest.strategies.momentum_breakout import MomentumBreakoutStrategy
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return OptunaEngine(bt_engine).optimize(
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MomentumBreakoutStrategy, momentum_breakout_space, price_df, factor_df, metric, n_trials,
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)
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def optimize_factor_cross(
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price_df: pd.DataFrame,
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factor_column: str,
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factor_df: pd.DataFrame | None = None,
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bt_engine: VectorBTEngine | None = None,
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n_trials: int = _DEFAULT_TRIALS,
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metric: str = "sharpe",
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) -> OptimizationResult:
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"""因子阈值交叉策略参数寻优。
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参数:
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factor_column: 因子列名（如 'momentum_20'）
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其余同 optimize_* 系列。
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"""
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from backtest.strategies.factor_cross import FactorCrossStrategy
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class _FCS(FactorCrossStrategy):
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def __init__(self, buy_threshold=0, sell_threshold=None):
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super().__init__(factor_column, buy_threshold, sell_threshold)
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return OptunaEngine(bt_engine).optimize(
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_FCS, factor_cross_space, price_df, factor_df, metric, n_trials,
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)
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