Simon
6acf938caf
- 删除 11 个残留文件: continuation.md, init_plan.md, reasonix.toml, djapi/continuation.md, djapi/.serena/, djapi/.claude/, djapi/.mcp.json, .claude/skills/, docs/usage.html, docs/db_schema.md, docs/report_db_design.md - 7 个 CLAUDE-*.md 移入 docs/ 并重命名去 CLAUDE- 前缀 - 新增 4 个文档: architecture.md, development.md, api.md, deployment.md - 重写 usage.md, README.md - 修复所有过时引用和交叉链接
60 lines
2.3 KiB
Markdown
60 lines
2.3 KiB
Markdown
# 回测引擎 + 参数优化
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## VectorBTEngine (`finance/backtest/vectorbt/engine.py`)
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只做多，10万/万三。
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```python
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from backtest.vectorbt.engine import VectorBTEngine
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engine_bt = VectorBTEngine(initial_capital=100_000, commission=0.0003)
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report = engine_bt.run(strategy, price_df, factor_df)
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# → BacktestReport
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report = engine_bt.run_cross_section(strategy, price_univ, factor_univ)
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```
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信号流：`1=buy, 0=sell, -1=hold` → `_signals_to_entries` → vbt.Portfolio.from_signals（direction="longonly"）。
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## 策略 (`finance/backtest/strategies/`)
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| 策略 | 参数 | 逻辑 |
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|------|------|------|
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| `SMACrossStrategy` | fast=5, slow=20 | 金叉买/死叉卖 |
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| `RSIMeanRevertStrategy` | oversold=30, overbought=70 | 超卖买/超买卖 |
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| `MomentumBreakoutStrategy` | lookback=20, exit=10 | 新高买/跌破卖 |
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| `FactorCrossStrategy` | factor_column, buy/sell_threshold | 阈值交叉（通用） |
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| `FactorRotationStrategy` | factor_name, top_n=5 | 排序选股 |
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## 自定义策略
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继承 `backtest/base.py:BaseStrategy`，实现 `generate_signals(factor_df) → pd.Series`。
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## 信号工具 (`backtest/signal.py`)
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```python
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factor_to_threshold_signal(series, buy, sell, direction)
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cross_signal(fast, slow) # 金叉/死叉
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factor_to_quantile_signal(...) # 分位数信号
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```
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## BacktestReport (`backtest/report.py`)
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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。
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## OptunaEngine (`finance/optimizer/engine.py`)
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```python
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from optimizer.engine import OptunaEngine
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from optimizer.space import rsi_revert_space
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opt = OptunaEngine(bt_engine)
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result = opt.optimize(StrategyClass, space, price_df, factor_df, metric="sharpe", n_trials=200)
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# → OptimizationResult(best_params, best_value, best_report, trial_df, param_importance)
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wf = opt.optimize_walk_forward(StrategyClass, space, price_df, factor_df,
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train_window=756, test_window=252)
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```
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预置空间：`sma_cross_space`, `rsi_revert_space`, `momentum_breakout_space`, `factor_cross_space`（`optimizer/space.py`）。
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目标指标：sharpe/cagr/calmar/total_return/return_over_dd。
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