Scientific Validation & Walk-Forward API (Experimental)¶

Experimental Feature

The scientific validation and walk-forward APIs are experimental and may change in future versions.

Walk-Forward API¶

walk_forward¶

Walk-Forward Analysis: divides the historical period into alternating in-sample (IS) and out-of-sample (OOS) windows to detect overfitting.

from eqlib import walk_forward

wfa_result = walk_forward(
    make_initialize,
    optimize_fn=optimize,
    start_date='2020-01-01',
    end_date='2024-12-31',
    train_months=12,
    test_months=3,
    step_months=3,
    starting_cash=100_000,
)

Parameter	Type	Description
`make_initialize`	`Callable`	Factory function that accepts a parameter dict and returns an `initialize` function
`optimize_fn`	`Callable` or `None`	Optional: `(train_result) -> dict` parameter selection function
`start_date`	`str` / `date`	Analysis start date
`end_date`	`str` / `date`	Analysis end date
`train_months`	`int`	Length of each training window (months)
`test_months`	`int`	Length of each test window (months)
`step_months`	`int`	Window sliding step (months)
`starting_cash`	`float`	Initial capital per window
`benchmark`	`str`	Benchmark ticker
`securities`	`list[str]` or `None`	Stock universe

Returns a WFAResult object containing:

windows: list of results for each window
oos_equity: stitched OOS equity curve (pd.Series)
summary: aggregated statistics (total_oos_return, oos_sharpe, etc.)

Scientific Validation API¶

eqlib.scientific provides post-backtest scientific validation tools for overfitting detection, statistical confidence testing, bias detection, and extended risk metrics.

validate_backtest¶

Run all validation checks in one call.

from eqlib.scientific import validate_backtest, ValidationConfig

config = ValidationConfig()  # Optional custom configuration
validation = validate_backtest(backtest_result, config=config)
validation.summary()

Submodules¶

Module	Key Functions	Description
`overfitting`	`out_of_sample_test`, `parameter_sensitivity`, `walk_forward_analysis`	Overfitting detection
`statistics`	`bootstrap_metrics`, `monte_carlo_simulation`, `significance_test`, `sample_size_assessment`	Statistical confidence
`bias`	`check_lookahead_bias`, `check_survivorship_bias`, `check_selection_bias`, `check_data_bias`	Bias detection
`risk`	`extended_risk_metrics`, `value_at_risk`, `conditional_var`, `stress_test`, `tail_risk_analysis`	Extended risk
`comparison`	`compare_with_platform`, `compare_metrics`, `verify_trades`	Platform comparison
`report`	`generate_validation_report`	Validation report generation

ValidationConfig¶

Validation configuration object with customizable thresholds and the ability to enable/disable individual validation modules.

from eqlib import ValidationConfig
config = ValidationConfig()