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>

CLAUDE-backtest.md

@@ -0,0 +1,59 @@
+# CLAUDE-backtest.md — 回测引擎 + 参数优化
+
+## VectorBTEngine (`finance/backtest/vectorbt/engine.py`)
+
+只做多，10万/万三。
+
+```python
+from backtest.vectorbt.engine import VectorBTEngine
+engine_bt = VectorBTEngine(initial_capital=100_000, commission=0.0003)
+
+report = engine_bt.run(strategy, price_df, factor_df)
+# → BacktestReport
+report = engine_bt.run_cross_section(strategy, price_univ, factor_univ)
+```
+
+信号流：`1=buy, 0=sell, -1=hold` → `_signals_to_entries` → vbt.Portfolio.from_signals（direction="longonly"）。
+
+## 策略 (`finance/backtest/strategies/`)
+
+| 策略 | 参数 | 逻辑 |
+|------|------|------|
+| `SMACrossStrategy` | fast=5, slow=20 | 金叉买/死叉卖 |
+| `RSIMeanRevertStrategy` | oversold=30, overbought=70 | 超卖买/超买卖 |
+| `MomentumBreakoutStrategy` | lookback=20, exit=10 | 新高买/跌破卖 |
+| `FactorCrossStrategy` | factor_column, buy/sell_threshold | 阈值交叉（通用） |
+| `FactorRotationStrategy` | factor_name, top_n=5 | 排序选股 |
+
+## 自定义策略
+
+继承 `backtest/base.py:BaseStrategy`，实现 `generate_signals(factor_df) → pd.Series`。
+
+## 信号工具 (`backtest/signal.py`)
+
+```python
+factor_to_threshold_signal(series, buy, sell, direction)
+cross_signal(fast, slow)         # 金叉/死叉
+factor_to_quantile_signal(...)   # 分位数信号
+```
+
+## BacktestReport (`backtest/report.py`)
+
+字段：total_return, cagr, max_drawdown, sharpe_ratio, calmar_ratio, annual_volatility, win_rate, profit_factor, total_trades, avg_hold_days, best/worst_trade_pct, equity_curve, drawdown_curve, monthly_returns, trades_df, stats_dict。`summary()` 一行摘要。
+
+## OptunaEngine (`finance/optimizer/engine.py`)
+
+```python
+from optimizer.engine import OptunaEngine
+from optimizer.space import rsi_revert_space
+
+opt = OptunaEngine(bt_engine)
+result = opt.optimize(StrategyClass, space, price_df, factor_df, metric="sharpe", n_trials=200)
+# → OptimizationResult(best_params, best_value, best_report, trial_df, param_importance)
+
+wf = opt.optimize_walk_forward(StrategyClass, space, price_df, factor_df,
+                                train_window=756, test_window=252)
+```
+
+预置空间：`sma_cross_space`, `rsi_revert_space`, `momentum_breakout_space`, `factor_cross_space`（`optimizer/space.py`）。
+目标指标：sharpe/cagr/calmar/total_return/return_over_dd。