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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+"""
+Optuna 优化引擎。
+
+统一接口：optimizer.optimize(strategy_class, space, price_df, factor_df) → OptimizationResult
+"""
+
+import copy
+import time
+
+import numpy as np
+import optuna
+import pandas as pd
+
+from backtest.base import BaseStrategy
+from backtest.report import BacktestReport
+from backtest.vectorbt.engine import VectorBTEngine
+from optimizer.objectives import Objective
+from optimizer.result import OptimizationResult, WalkForwardResult
+from optimizer.space import SearchSpace
+
+# 抑制 Optuna 日志
+optuna.logging.set_verbosity(optuna.logging.WARNING)
+
+
+class OptunaEngine:
+    """
+    Optuna 优化引擎。
+    """
+
+    def __init__(self, bt_engine: VectorBTEngine | None = None):
+        self.bt_engine = bt_engine or VectorBTEngine()
+
+    def optimize(
+        self,
+        strategy_class: type[BaseStrategy],
+        search_space: SearchSpace,
+        price_df: pd.DataFrame,
+        factor_df: pd.DataFrame | None = None,
+        metric: str = "sharpe",
+        n_trials: int = 100,
+        direction: str = "maximize",
+        sampler: optuna.samplers.BaseSampler | None = None,
+    ) -> OptimizationResult:
+        """
+        参数寻优。
+
+        参数:
+            strategy_class: 策略类
+            search_space: 搜索空间
+            price_df: 价格数据
+            factor_df: 因子数据
+            metric: 优化目标
+            n_trials: 试验次数
+            direction: 'maximize' | 'minimize'
+            sampler: Optuna 采样器，默认 TPESampler
+        """
+        if sampler is None:
+            sampler = optuna.samplers.TPESampler(seed=42)
+
+        study = optuna.create_study(
+            direction=direction,
+            sampler=sampler,
+        )
+
+        objective = Objective(
+            strategy_class=strategy_class,
+            search_space=search_space,
+            price_df=price_df,
+            factor_df=factor_df,
+            bt_engine=self.bt_engine,
+            metric=metric,
+        )
+
+        t0 = time.time()
+        study.optimize(objective, n_trials=n_trials, show_progress_bar=True)
+        elapsed = time.time() - t0
+
+        # 用最优参数跑一次完整回测
+        best_params = study.best_params
+        try:
+            best_strategy = strategy_class(**best_params)
+        except TypeError:
+            valid = {k: v for k, v in best_params.items()
+                     if k in strategy_class.__init__.__code__.co_varnames}
+            best_strategy = strategy_class(**valid)
+
+        best_report = self.bt_engine.run(
+            best_strategy,
+            price_df,
+            price_df if factor_df is None else factor_df,
+        )
+
+        # 参数重要性
+        try:
+            importance = optuna.importance.get_param_importances(study)
+        except Exception:
+            importance = {}
+
+        # 试验记录
+        trials_df = study.trials_dataframe()
+
+        return OptimizationResult(
+            best_params=study.best_params,
+            best_value=study.best_value,
+            metric=metric,
+            best_report=best_report,
+            trials_df=trials_df,
+            param_importance=importance,
+        )
+
+    def optimize_walk_forward(
+        self,
+        strategy_class: type[BaseStrategy],
+        search_space: SearchSpace,
+        price_df: pd.DataFrame,
+        factor_df: pd.DataFrame | None = None,
+        metric: str = "sharpe",
+        n_trials: int = 100,
+        train_window: int = 252 * 3,
+        test_window: int = 252,
+    ) -> WalkForwardResult:
+        """
+        滚动窗口优化（Walk-Forward Analysis）。
+
+        每一步：train_window 训练 → test_window 验证 → 滑动。
+        """
+        if factor_df is None:
+            factor_df = price_df
+
+        n_total = len(price_df)
+        windows = []
+        test_equities = []
+        param_history = []
+
+        start = 0
+        while start + train_window + test_window <= n_total:
+            train_slice = slice(start, start + train_window)
+            test_slice = slice(start + train_window, start + train_window + test_window)
+
+            train_price = price_df.iloc[train_slice]
+            train_factor = factor_df.iloc[train_slice]
+            test_price = price_df.iloc[test_slice]
+            test_factor = factor_df.iloc[test_slice]
+
+            # 训练集上优化
+            opt_result = self.optimize(
+                strategy_class=strategy_class,
+                search_space=search_space,
+                price_df=train_price,
+                factor_df=train_factor,
+                metric=metric,
+                n_trials=n_trials,
+            )
+
+            # 测试集上验证
+            try:
+                test_strategy = strategy_class(**opt_result.best_params)
+            except TypeError:
+                valid = {k: v for k, v in opt_result.best_params.items()
+                         if k in strategy_class.__init__.__code__.co_varnames}
+                test_strategy = strategy_class(**valid)
+
+            test_report = self.bt_engine.run(test_strategy, test_price, test_factor)
+
+            if len(test_report.equity_curve) > 0:
+                test_equities.append(test_report.equity_curve)
+
+            train_idx = train_price.index
+            test_idx = test_price.index
+            windows.append({
+                "train_start": train_idx[0] if len(train_idx) > 0 else "",
+                "train_end": train_idx[-1] if len(train_idx) > 0 else "",
+                "test_start": test_idx[0] if len(test_idx) > 0 else "",
+                "test_end": test_idx[-1] if len(test_idx) > 0 else "",
+                "best_params": opt_result.best_params,
+                "best_value": opt_result.best_value,
+                "test_return": test_report.total_return,
+                "test_sharpe": test_report.sharpe_ratio,
+                "test_mdd": test_report.max_drawdown,
+            })
+            param_history.append(opt_result.best_params)
+
+            start += test_window
+
+        # 合并测试期权益曲线
+        consolidated = _merge_test_periods(test_equities, self.bt_engine.initial_capital)
+
+        # 参数稳定性
+        param_df = pd.DataFrame(param_history) if param_history else pd.DataFrame()
+        if not param_df.empty:
+            param_df.index.name = "window"
+
+        return WalkForwardResult(
+            windows=windows,
+            consolidated_report=consolidated,
+            param_stability=param_df,
+        )
+
+
+def _merge_test_periods(
+    equity_list: list[pd.Series],
+    initial_capital: float = 100_000,
+) -> BacktestReport | None:
+    """拼接各窗口测试期权益曲线为一个连续序列。"""
+    if not equity_list:
+        return None
+
+    merged = pd.concat(equity_list)
+    merged = merged.sort_index()
+    merged = merged[~merged.index.duplicated()]
+
+    # 确保 DatetimeIndex
+    if not isinstance(merged.index, pd.DatetimeIndex):
+        merged.index = pd.to_datetime(merged.index, format="%Y%m%d")
+
+    dd = merged / merged.cummax() - 1
+    daily_ret = merged.pct_change().dropna()
+    years = max(len(daily_ret) / 252, 0.02)
+
+    total_ret = (merged.iloc[-1] / merged.iloc[0] - 1) * 100
+    cagr = ((total_ret / 100 + 1) ** (1 / years) - 1) * 100
+    mdd = dd.min() * 100
+    std_ret = daily_ret.std() * np.sqrt(252)
+    sharpe = (daily_ret.mean() * 252) / std_ret if std_ret > 0 else 0
+    calmar = cagr / abs(mdd) if abs(mdd) > 0 else 0
+
+    try:
+        monthly = merged.resample("ME").last().pct_change()
+    except Exception:
+        monthly = pd.Series(dtype=float)
+
+    return BacktestReport(
+        total_return=round(total_ret, 2),
+        cagr=round(cagr, 2),
+        max_drawdown=round(mdd, 2),
+        sharpe_ratio=round(sharpe, 2),
+        calmar_ratio=round(calmar, 2),
+        annual_volatility=round(std_ret * 100 if std_ret != 0 else 0, 2),
+        equity_curve=merged,
+        drawdown_curve=dd,
+        monthly_returns=monthly,
+    )