Initial commit: cc-cursor 全链路量化研究平台

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>
2026-06-07 15:59:05 +08:00
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+"""
+策略优化快捷函数。
+
+为常用策略提供一键优化入口。
+"""
+
+import pandas as pd
+
+from backtest.vectorbt.engine import VectorBTEngine
+from optimizer.engine import OptunaEngine
+from optimizer.result import OptimizationResult, WalkForwardResult
+from optimizer.space import (
+    sma_cross_space,
+    rsi_revert_space,
+    momentum_breakout_space,
+    factor_cross_space,
+)
+
+_DEFAULT_TRIALS = 100
+
+
+def optimize_sma_cross(
+    price_df: pd.DataFrame,
+    factor_df: pd.DataFrame | None = None,
+    bt_engine: VectorBTEngine | None = None,
+    n_trials: int = _DEFAULT_TRIALS,
+    metric: str = "sharpe",
+) -> OptimizationResult:
+    """均线交叉策略参数寻优。"""
+    from backtest.strategies.sma_cross import SMACrossStrategy
+    return OptunaEngine(bt_engine).optimize(
+        SMACrossStrategy, sma_cross_space, price_df, factor_df, metric, n_trials,
+    )
+
+
+def optimize_rsi_revert(
+    price_df: pd.DataFrame,
+    factor_df: pd.DataFrame | None = None,
+    bt_engine: VectorBTEngine | None = None,
+    n_trials: int = _DEFAULT_TRIALS,
+    metric: str = "sharpe",
+) -> OptimizationResult:
+    """RSI 反转策略参数寻优。"""
+    from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
+    return OptunaEngine(bt_engine).optimize(
+        RSIMeanRevertStrategy, rsi_revert_space, price_df, factor_df, metric, n_trials,
+    )
+
+
+def optimize_momentum_breakout(
+    price_df: pd.DataFrame,
+    factor_df: pd.DataFrame | None = None,
+    bt_engine: VectorBTEngine | None = None,
+    n_trials: int = _DEFAULT_TRIALS,
+    metric: str = "sharpe",
+) -> OptimizationResult:
+    """动量突破策略参数寻优。"""
+    from backtest.strategies.momentum_breakout import MomentumBreakoutStrategy
+    return OptunaEngine(bt_engine).optimize(
+        MomentumBreakoutStrategy, momentum_breakout_space, price_df, factor_df, metric, n_trials,
+    )
+
+
+def optimize_factor_cross(
+    price_df: pd.DataFrame,
+    factor_column: str,
+    factor_df: pd.DataFrame | None = None,
+    bt_engine: VectorBTEngine | None = None,
+    n_trials: int = _DEFAULT_TRIALS,
+    metric: str = "sharpe",
+) -> OptimizationResult:
+    """因子阈值交叉策略参数寻优。
+
+    参数:
+        factor_column: 因子列名（如 'momentum_20'）
+        其余同 optimize_* 系列。
+    """
+    from backtest.strategies.factor_cross import FactorCrossStrategy
+
+    class _FCS(FactorCrossStrategy):
+        def __init__(self, buy_threshold=0, sell_threshold=None):
+            super().__init__(factor_column, buy_threshold, sell_threshold)
+
+    return OptunaEngine(bt_engine).optimize(
+        _FCS, factor_cross_space, price_df, factor_df, metric, n_trials,
+    )