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Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | backtest |
| description | Use when tasks need vectorized strategy execution, portfolio weighting, portfolio-level filters, transaction cost helpers, exit A/B analysis, overlay metrics, or multi-strategy return blending. |
Backtest is the shared portfolio execution and construction boundary. Strategy domains should produce date x symbol weights; this skill turns those weights into executed weights, returns, costs, diagnostics, and report-ready metrics.
from skills.backtest import VectorBacktester, annual_return_metrics
from skills.backtest.weighting import WEIGHT_METHODS, risk_parity
from skills.backtest.filters import apply_portfolio_filters
from skills.backtest.cost_model import cost_bp_for_trigger_time
from skills.backtest.exit_analysis import evaluate_exit_factor
from skills.backtest.overlay_metrics import overlay_alpha
| Module | Purpose |
|---|---|
skills.backtest.vector | VectorBacktester, BacktestResult, annual/activity/benchmark metrics |
skills.backtest.weighting | Equal weight, risk parity, inverse variance, EPO, WEIGHT_METHODS |
skills.backtest.filters | Market breadth, index trend, and volatility targeting overlays |
skills.backtest.combiner | Blend multiple strategy return streams |
skills.backtest.cost_model | A-share trigger-time cost layers and single-trade PnL helpers |
skills.backtest.exit_analysis | Baseline vs filtered exit A/B evaluation |
skills.backtest.overlay_metrics | Overlay alpha, win rate, drawdown, Sharpe, and regime metrics |
VectorBacktester requires explicit commission and slippage_bp; there is no
symbol-level fallback cost table. By default, each date's executed weight earns
the next bar's return (return_mode="forward"), which is the safer convention
for close-derived signals. Use return_mode="backward" only when weights are
already known before the return interval being measured.
Run weights through the shared vectorized backtester
from skills.backtest import VectorBacktester
result = VectorBacktester(
data=panel,
trade_at="close",
signal_lag=1,
commission=0.0002,
slippage_bp=2.0,
).run(weights_df)
Convert votes to risk-aware weights
from skills.backtest.weighting import risk_parity
weights = risk_parity(votes_df, returns_df=returns_df, lookback=60, min_periods=20)
Evaluate an exit filter
from skills.backtest.exit_analysis import evaluate_exit_factor
result = evaluate_exit_factor(
data,
factor_configs,
exit_filter,
commission=0.0002,
slippage_bp=2.0,
)
Use when tasks need factor diagnostics, IC/grouped return analysis, attribution, robustness checks, or time-series distribution and stationarity checks.
Use when tasks need strategy-agnostic OHLCV indicators, math utilities, generic factor examples, regime slicing, resampling, or label makers.
Use when tasks need optional PyCaret model training, ML factor generation, inference wrappers, feature importance, or sparse LASSO weight generation.
Use when tasks need PandaData/PandaAI market data, reference data, adjustment factors, futures tick downloads, or symbol conversion.
Use when tasks need HTML reports, Markdown strategy reports, PNG chart helpers, or report files under the research reports directory.
Use when tasks need reusable research pipeline templates, factor screening, parameter sensitivity sweeps, or strategy comparison.