| name | backtest-framework |
| description | 单股票 Walk-Forward 回测框架使用指南。Use when: 回测、backtest、Walk-Forward、策略评估、Alpha158 策略、StrategyConfig、simulate_segment。
|
| argument-hint | 股票代码 + 策略名称 |
| user-invocable | true |
Backtest Framework Skill
This skill explains how to use the local single-stock backtest framework under scripts/backtest/.
Architecture
| Module | Responsibility |
|---|
scripts/backtest/config.py | StrategyConfig strategy parameters |
scripts/backtest/metrics.py | calc_metrics() return metrics |
scripts/backtest/engine.py | simulate_segment(), run_fold_evaluation(), choose_best() |
scripts/backtest/reporting.py | render_report() markdown output |
Critical Data Rule
Backtests must keep two separate dataframes:
| Purpose | Source | Price Type |
|---|
| Alpha158 factors | stk_alpha158 or compute_alpha158(raw_df) | raw, unadjusted |
| Trading returns | stk_factor_pro.close_hfq and daily_ret | HFQ, adjusted |
Never mix raw factor prices with HFQ return calculation.
Quick Start
1. Compute Alpha158 Factors
conda activate <your-env>
python -m scripts.alpha158.cli --ts-code <code>
2. Build Signals
Use reusable builders from scripts/backtest/signal_builders/.
3. Configure Strategy
from scripts.backtest.config import StrategyConfig
cfg = StrategyConfig(
name="my_strategy",
signal_col="SIGNAL_Z",
entry_z=1.0,
exit_z=-0.5,
stop_loss=-0.15,
max_hold=126,
)
4. Run Walk-Forward Evaluation
- Load Alpha158 factors from
stk_alpha158.
- Load HFQ price/returns from
stk_factor_pro.
- Build signal columns.
- Merge factor signals with HFQ returns by
trade_date.
- Call
run_fold_evaluation().
- Save
fold_metrics.csv, summary.json, and trade_log.csv.
Rules
- Do not reimplement Alpha158 formulas in backtest scripts.
- Do not modify framework code for one-off strategies unless explicitly refactoring.
- Do not use raw prices for return calculation.
- Store new standardized reports through
analysis-report.
