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>

finance/cli/demo_optimizer.py

@@ -0,0 +1,105 @@
+"""
+Sprint 3 验证脚本 — Optuna 参数优化。
+
+用法:
+    python cli/demo_optimizer.py
+    python cli/demo_optimizer.py --ts_code 600519.SH --trials 100
+"""
+
+import sys, os, argparse
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from data.data_manager import DataManager
+from factors.engine import FactorEngine
+from factors.registry import get_factor
+from backtest.vectorbt.engine import VectorBTEngine
+from backtest.strategies.rsi_mean_revert import RSIMeanRevertStrategy
+from optimizer.engine import OptunaEngine
+from optimizer.space import rsi_revert_space
+
+
+def main():
+    p = argparse.ArgumentParser(description="Sprint 3 — Optuna 参数优化验证")
+    p.add_argument("--ts_code", default="000001.SZ", help="测试股票代码")
+    p.add_argument("--trials", type=int, default=200, help="试验次数（默认: 200）")
+    args = p.parse_args()
+
+    print("=" * 60)
+    print("Sprint 3 — Optuna 参数优化验证")
+    print("=" * 60)
+
+    print("\n[1/4] 初始化...")
+    dm = DataManager(); dm.init_db()
+    engine_fe = FactorEngine(dm)
+    bt_engine = VectorBTEngine()
+    opt_engine = OptunaEngine(bt_engine)
+
+    price_df = dm.get_daily(args.ts_code).set_index("trade_date").sort_index()
+    factor_df = engine_fe.compute(args.ts_code, [get_factor("rsi_14")])
+    print("  数据: {} 条日线, {} 个因子".format(len(price_df), factor_df.shape[1]))
+    print("[OK]")
+
+    print("\n[2/4] RSI 反转策略参数寻优 (sharpe, {} trials)...".format(args.trials))
+    result = opt_engine.optimize(
+        strategy_class=RSIMeanRevertStrategy,
+        search_space=rsi_revert_space,
+        price_df=price_df, factor_df=factor_df,
+        metric="sharpe", n_trials=args.trials,
+    )
+    print(result.summary())
+    if result.param_importance:
+        print("  参数重要性:")
+        for k, v in sorted(result.param_importance.items(), key=lambda x: -x[1]):
+            print("    {}: {:.4f}".format(k, v))
+
+    print("\n[3/4] 默认参数 vs 最优参数 对比...")
+    strategies_compare = [
+        ("默认(30/70)", RSIMeanRevertStrategy(oversold=30, overbought=70)),
+        ("最优", RSIMeanRevertStrategy(**result.best_params)),
+    ]
+    reports = {}
+    for name, s in strategies_compare:
+        reports[name] = bt_engine.run(s, price_df, factor_df)
+
+    metrics = [("总收益(%)", "total_return"), ("年化CAGR(%)", "cagr"), ("最大回撤(%)", "max_drawdown"),
+               ("夏普比率", "sharpe_ratio"), ("卡玛比率", "calmar_ratio"), ("胜率(%)", "win_rate"),
+               ("盈利因子", "profit_factor"), ("交易笔数", "total_trades")]
+    print("  {:<18s} {:>12s} {:>12s}".format("指标", "默认(30/70)", "最优"))
+    print("  " + "-" * 42)
+    for label, attr in metrics:
+        vals = []
+        for r in reports.values():
+            v = getattr(r, attr)
+            vals.append("{:.2f}".format(v) if isinstance(v, float) else str(v))
+        print("  {:<18s} {:>12s} {:>12s}".format(label, vals[0], vals[1]))
+
+    print("\n[4/4] Walk-Forward 滚动窗口验证...")
+    try:
+        wf = opt_engine.optimize_walk_forward(
+            RSIMeanRevertStrategy, rsi_revert_space,
+            price_df, factor_df, metric="sharpe", n_trials=min(80, args.trials),
+            train_window=252 * 3, test_window=252,
+        )
+        print(wf.summary())
+    except Exception as e:
+        print("  [SKIP] Walk-Forward 异常: {}".format(e))
+
+    # 存入 DB
+    try:
+        from reports.storage import save_report
+        from datetime import datetime
+        lines = ["## 参数优化 — {}".format(args.ts_code),
+                 result.summary(), "",
+                 "### 默认 vs 最优对比"]
+        save_report("\n".join(lines), "参数优化", subject_type="stock", subject_code=args.ts_code)
+        print("\n  报告已存入 DB")
+    except Exception as e:
+        print("\n  [WARN] 报告入库失败: {}".format(e))
+
+    print("\n" + "=" * 60)
+    print("Sprint 3 验证完成")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()