Rollforward Workflow API Reference

Rollforward Workflow

`cranalytics.rollforward_workflow`

Canonical monthly Rollforward workflow orchestration.

`RollforwardWorkflowResult` `dataclass`

Workflow status and selected Rollforward model information.

`run_rollforward_workflow(df, *, output_dir, holdout_months=6, min_train_months=12, step_months=1, variants=None, monitor_thresholds=None, amtloan_col='amtloan', segment=None, strict=False)`

Run the canonical monthly Rollforward workflow and emit artifacts.

Rollforward Readiness

`cranalytics.rollforward_readiness`

Rollforward readiness report generation for onboarding trust signals.

`generate_rollforward_readiness_report(*, input_csv, output_dir, holdout_months=6, min_train_months=6, segment=None, amtloan_col='amtloan', strict=False, scoring_config=None, weights=None)`

Generate Rollforward readiness report artifacts.

Rollforward Contract

`cranalytics.rollforward_contract`

Public input-contract helpers for the Rollforward workflow.

`RollforwardContractResult` `dataclass`

Normalized rollforward data plus collected contract issues.

`amtloan_map(*, segment=None)`

Return resolved denominators as a segment->value mapping.

`amtloan_series(*, segment=None)`

Return resolved denominator series, optionally filtered to one segment.

`has_issue_code(issue_code)`

Return whether the collected issues include the requested code.

`has_severity(severity)`

Return whether the collected issues include the requested severity.

`normalized_df(*, segment=None)`

Return normalized rollforward data, optionally filtered to one segment.

`validate_rollforward_input_contract(df, *, amtloan_col='amtloan', strict=False)`

Normalize columns, resolve aliases, and collect rollforward data issues.

Rollforward Backtest

`cranalytics.rollforward_backtest`

Backtesting helpers for Rollforward workflow variants.

`run_rollforward_backtest_sweeps(df, *, variants=None, holdout_horizons=(3, 6), min_train_months=12, step_months=1, amtloan_by_segment=None, amtloan_col='amtloan')`

Run rolling OOT backtests across configured rollforward variants.

`select_rollforward_champion_and_challengers(summary, *, benchmark_variant='baseline_flat_last_hazard', challenger_count=2)`

Select a champion variant subject to baseline promotion rules.

`summarize_rollforward_variant_performance(results)`

Aggregate split-level rollforward backtest results to one row per variant.

Early Performance

`cranalytics.early_performance`

Early performance analytics utilities for event-rate and separation analysis.

`calculate_early_performance_rates(df, flag_columns, weight_col=None, confidence=0.95)`

Compute portfolio-level event rates and Wilson confidence intervals.

`compute_conditional_loss_table(df, segment_cols, flag_col, outcome_col, n_bins_score=5, score_col=None, weight_col=None, confidence=0.95)`

Compute conditional lifetime-loss summaries by segment, score bucket, and flag.

`compute_marginal_impact(df, feature_col, flag_col, control_col, n_bins_control=5, n_bins_feature=None, weight_col=None)`

Estimate feature effect within control strata via within-bucket rate deltas.

`compute_segment_rates(df, flag_col, group_by, weight_col=None, n_bins=None, confidence=0.95)`

Compute mature-event rates by segment(s) with volume and contribution shares.

`compute_woe_iv(df, feature_col, flag_col, n_bins=10, weight_col=None)`

Compute WoE/IV table and total IV for a single feature.

`estimate_vintage_lifetime_profit(df, expected_loss_col, avg_life_col=None, avg_life=None, coupon_rate=0.0, servicing_cost_rate=0.0, funding_cost_rate=0.0)`

Estimate vintage-level net margin from pricing assumptions and expected loss.

Formula::

net_margin = coupon_rate * avg_life
             - expected_loss_rate
             - servicing_cost_rate
             - funding_cost_rate

All rate inputs are decimals (e.g. 0.18 for 18%). avg_life is in years. coupon_rate is an annualised yield multiplied by avg_life to give the lifetime interest income. servicing_cost_rate and funding_cost_rate are lifetime rates (not annualised) and are subtracted directly without scaling by avg_life. expected_loss_rate is likewise a lifetime rate (e.g. from compute_conditional_loss_table).

Parameters

df : DataFrame with at least expected_loss_col (and optionally avg_life_col). expected_loss_col : Column of lifetime loss rates (decimals, e.g. 0.05 = 5%). avg_life_col : Per-row average life column (years). Takes precedence over avg_life. avg_life : Scalar average life (years) applied to all rows. Required if avg_life_col is None. coupon_rate : Annualised all-in yield (decimal); multiplied by avg_life in the formula. servicing_cost_rate : Lifetime servicing cost rate (decimal); not scaled by avg_life. funding_cost_rate : Lifetime cost-of-funds rate (decimal); not scaled by avg_life.

Returns

Copy of df with an added net_margin column.

`rank_features_by_separation(df, feature_cols, flag_col, n_bins=10, weight_col=None)`

Rank candidate features by IV, Gini, and KS against a binary target.

`validate_performance_flags(df, flag_columns)`

Validate binary flag columns and return maturity coverage statistics.

Rollforward Workflow API Reference