quantskills Backtest
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.
Public API
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
Components
| 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. Each date's executed weight always earns the
next bar's return, which is the safe convention for close-derived signals.
Recipes
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
from skills.strategy.cross_sectional import ModularBacktester
result = evaluate_exit_factor(
data,
factor_configs,
exit_filter,
backtester_cls=ModularBacktester,
commission=0.0002,
slippage_bp=2.0,
)