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