API

run_backtest

run_backtest(signal, prices, *, kind, config=None)

Dispatches to the correct evaluation path.

• kind="weights" calls run_weight_backtest
• kind="factor" calls run_factor_evaluation

config is required because transaction-cost minimum fees require initial_capital.

run_weight_backtest

run_weight_backtest(weights, prices, *, config)

Evaluates a DataFrame as portfolio weights.

Returns BacktestResult.

Important fields:

• weights
• asset_returns
• gross_returns
• net_returns
• gross_cumulative_returns
• net_cumulative_returns
• gross_value
• net_value
• turnover
• transaction_costs
• summary

run_factor_evaluation

run_factor_evaluation(factor, prices, *, config)

Evaluates a DataFrame as factor scores.

Returns FactorEvaluationResult.

Important fields:

• factor
• forward_returns
• ic
• ic_mean
• ic_std
• icir
• quantile_returns
• quantile_cumulative_returns
• top_minus_bottom
• top_n_weights
• top_n_backtest

Config

BacktestConfig(
    initial_capital=1_000_000,
    transaction_cost=TransactionCostConfig(rate=0.00015, min_fee=5.0),
    annualization=252,
    ic_method="spearman",
    quantiles=5,
    top_n=50,
)

initial_capital must be positive.

ic_method may be "spearman" or "pearson".

DataFrame Boundary

The first argument must be a numeric pandas.DataFrame.

Rows are dates, columns are assets, and values are weights or factor scores.