backtest

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

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,
)