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Module Reference Index

This is the canonical index of every OpenQuant module: one page each, with purpose, APIs, formulas, examples, and implementation notes. It lists the same 39 modules twice over — by subject, and by the language surface they are reachable through — because those are the two questions readers arrive with. For the AFML chapter each module implements, see By AFML Chapter.

By subject

Data Ingestion and Quality

adapters — Polars DataFrame adapters for signals, events, weights, backtest curves, and streaming buffers.
data — OHLCV loading, cleaning, calendar alignment, and data quality reporting.

Event-Driven Data and Labeling

data_structures — Constructs standard/time/run/imbalance bars from trade streams.
filters — CUSUM and z-score event filters for event-driven sampling.
labeling — Triple-barrier event labeling and metadata generation.
sample_weights — Sample weighting utilities for overlapping event structure.

Market Microstructure, Dependence and Regime Detection

codependence — Dependence metrics beyond linear correlation for feature and asset relationships.
fracdiff — Fractional differentiation to improve stationarity while retaining memory.
microstructural_features — Price-impact, spread, entropy, and flow toxicity estimators.
structural_breaks — Regime change and bubble diagnostics (Chow, CUSUM variants, SADF).
util::fast_ewma — Fast EWMA primitive shared across feature and volatility routines.
util::volatility — Volatility estimators used across labeling and risk workflows.

Portfolio Construction and Risk

backtest_statistics — Performance diagnostics for strategy returns and position trajectories.
cla — Critical Line Algorithm implementation for constrained mean-variance optimization.
hcaa — Hierarchical Clustering Asset Allocation variant with cluster-level constraints.
hrp — Hierarchical Risk Parity allocation with recursive bisection.
onc — Optimal Number of Clusters utilities for clustering stability and allocation workflows.
portfolio_optimization — Mean-variance and constrained allocation methods with ergonomic APIs.
risk_metrics — Portfolio and return-distribution risk measures for downside control.
strategy_risk — AFML Chapter 15 strategy-viability diagnostics based on precision, payout asymmetry, and bet frequency.

Position Sizing and Trade Construction

bet_sizing — Transforms model confidence and constraints into executable position sizes.
etf_trick — Synthetic ETF and futures roll utilities for realistic PnL path construction.

Research Workflows

pipeline — End-to-end AFML research pipeline: events → signals → portfolio → risk → backtest with leakage checks.
research — Synthetic dataset generation and flywheel research iteration with cost modeling and promotion gates.
viz — Visualization payload builders for feature importance, drawdown, regime, frontier, and cluster charts.

Sampling, Validation and ML Diagnostics

backtesting_engine — Backtesting core with walk-forward, purged CV, and combinatorial purged CV (CPCV) workflows.
cross_validation — Purged cross-validation utilities designed for label overlap and leakage control.
ef3m — Moment-based mixture fitting utilities for two-normal components.
ensemble_methods — Bias/variance diagnostics and practical bagging-vs-boosting ensemble utilities.
feature_diagnostics — Feature importance diagnostics: MDI, MDA, SFI, PCA orthogonalization, and substitution-effect analysis.
feature_importance — Feature ranking methods: MDI, MDA, and single-feature importance with PCA diagnostics.
fingerprint — Model fingerprinting for linear, non-linear, and pairwise feature effects.
hyperparameter_tuning — Leakage-aware grid/randomized hyper-parameter search with purged CV and weighted scoring.
sampling — Indicator matrix and sequential bootstrap tooling.
sb_bagging — Sequentially bootstrapped bagging classifiers/regressors.
synthetic_backtesting — Synthetic-data OTR backtesting with O-U calibration, PT/SL mesh search, and stability diagnostics.

Scaling, HPC and Infrastructure

combinatorial_optimization — AFML Chapter 21 integer-encoded optimization and trajectory state-space tooling with exact baselines and solver adapters.
hpc_parallel — AFML Chapter 20 atom/molecule execution utilities with serial/threaded modes and partition diagnostics.
streaming_hpc — AFML Chapter 22 streaming analytics utilities for low-latency early-warning metrics with bounded-memory incremental state.

By language surface

Rust core

backtest_statistics — Performance diagnostics for strategy returns and position trajectories.
backtesting_engine — Backtesting core with walk-forward, purged CV, and combinatorial purged CV (CPCV) workflows.
bet_sizing — Transforms model confidence and constraints into executable position sizes.
cla — Critical Line Algorithm implementation for constrained mean-variance optimization.
codependence — Dependence metrics beyond linear correlation for feature and asset relationships.
combinatorial_optimization — AFML Chapter 21 integer-encoded optimization and trajectory state-space tooling with exact baselines and solver adapters.
cross_validation — Purged cross-validation utilities designed for label overlap and leakage control.
data — OHLCV loading, cleaning, calendar alignment, and data quality reporting.
data_structures — Constructs standard/time/run/imbalance bars from trade streams.
ef3m — Moment-based mixture fitting utilities for two-normal components.
ensemble_methods — Bias/variance diagnostics and practical bagging-vs-boosting ensemble utilities.
etf_trick — Synthetic ETF and futures roll utilities for realistic PnL path construction.
feature_importance — Feature ranking methods: MDI, MDA, and single-feature importance with PCA diagnostics.
filters — CUSUM and z-score event filters for event-driven sampling.
fingerprint — Model fingerprinting for linear, non-linear, and pairwise feature effects.
fracdiff — Fractional differentiation to improve stationarity while retaining memory.
hcaa — Hierarchical Clustering Asset Allocation variant with cluster-level constraints.
hpc_parallel — AFML Chapter 20 atom/molecule execution utilities with serial/threaded modes and partition diagnostics.
hrp — Hierarchical Risk Parity allocation with recursive bisection.
hyperparameter_tuning — Leakage-aware grid/randomized hyper-parameter search with purged CV and weighted scoring.
labeling — Triple-barrier event labeling and metadata generation.
microstructural_features — Price-impact, spread, entropy, and flow toxicity estimators.
onc — Optimal Number of Clusters utilities for clustering stability and allocation workflows.
pipeline — End-to-end AFML research pipeline: events → signals → portfolio → risk → backtest with leakage checks.
portfolio_optimization — Mean-variance and constrained allocation methods with ergonomic APIs.
risk_metrics — Portfolio and return-distribution risk measures for downside control.
sample_weights — Sample weighting utilities for overlapping event structure.
sampling — Indicator matrix and sequential bootstrap tooling.
sb_bagging — Sequentially bootstrapped bagging classifiers/regressors.
strategy_risk — AFML Chapter 15 strategy-viability diagnostics based on precision, payout asymmetry, and bet frequency.
streaming_hpc — AFML Chapter 22 streaming analytics utilities for low-latency early-warning metrics with bounded-memory incremental state.
structural_breaks — Regime change and bubble diagnostics (Chow, CUSUM variants, SADF).
synthetic_backtesting — Synthetic-data OTR backtesting with O-U calibration, PT/SL mesh search, and stability diagnostics.
util::fast_ewma — Fast EWMA primitive shared across feature and volatility routines.
util::volatility — Volatility estimators used across labeling and risk workflows.

Python namespaces

adapters — to_polars_signal_frame, to_polars_event_frame, to_polars_backtest_frame, to_polars_weights_frame, to_polars_indicator_matrix, to_polars_frontier_frame, SignalStreamBuffer, to_pandas
backtest_stats — sharpe_ratio, information_ratio, probabilistic_sharpe_ratio, deflated_sharpe_ratio, minimum_track_record_length, timing_of_flattening_and_flips, average_holding_period, bets_concentration, all_bets_concentration, drawdown_and_time_under_water
bars — build_time_bars, build_tick_bars, build_volume_bars, build_dollar_bars, build_run_bars, build_imbalance_bars
bet_sizing — get_signal, discrete_signal, bet_size, bet_size_sigmoid, bet_size_power, inv_price, inv_price_sigmoid, inv_price_power, get_w, get_w_sigmoid, get_w_power, get_target_pos, get_target_pos_sigmoid, get_target_pos_power, limit_price, limit_price_sigmoid, limit_price_power, avg_active_signals, bet_size_dynamic, cdf_mixture, single_bet_size_mixed, get_concurrent_sides, bet_size_budget, bet_size_probability, mp_avg_active_signals, bet_size_reserve, bet_size_reserve_with_fit, bet_size_reserve_full
cla — allocate_cla
codependence — angular_distance, absolute_angular_distance, squared_angular_distance, distance_correlation, get_optimal_number_of_bins, get_mutual_info, variation_of_information_score
data — load_ohlcv, clean_ohlcv, align_calendar, data_quality_report, clean_ohlcv_df, quality_report_df, align_calendar_df
ef3m — centered_moment, raw_moment, most_likely_parameters, fit_m2n
ensemble — bias_variance_noise, bootstrap_sample_indices, sequential_bootstrap_sample_indices, aggregate_regression_mean, aggregate_classification_vote, aggregate_classification_probability_mean, average_pairwise_prediction_correlation, bagging_ensemble_variance, recommend_bagging_vs_boosting
fast_ewma — ewma
feature_diagnostics — mdi_importance, mda_importance, sfi_importance, orthogonalize_features_pca, substitution_effect_report
filters — cusum_filter_indices, cusum_filter_timestamps, z_score_filter_indices, z_score_filter_timestamps
fracdiff — get_weights, get_weights_ffd, frac_diff, frac_diff_ffd
hcaa — allocate_hcaa
hrp — allocate_hrp
labeling — triple_barrier_labels, triple_barrier_events, meta_labels, add_vertical_barrier, get_events, get_bins, drop_labels
microstructural — get_roll_measure, get_roll_impact, get_corwin_schultz_estimator, get_bekker_parkinson_vol, get_bar_based_kyle_lambda, get_bar_based_amihud_lambda, get_bar_based_hasbrouck_lambda, get_trades_based_kyle_lambda, get_trades_based_amihud_lambda, get_trades_based_hasbrouck_lambda, vwap, get_avg_tick_size, get_vpin, get_bvc_buy_volume, encode_tick_rule_array, quantile_mapping, sigma_mapping, encode_array, get_shannon_entropy, get_lempel_ziv_entropy, get_plug_in_entropy, get_konto_entropy
onc — get_onc_clusters
pipeline — run_mid_frequency_pipeline, run_mid_frequency_pipeline_frames, summarize_pipeline
portfolio — allocate_inverse_variance, allocate_min_vol, allocate_max_sharpe, allocate_efficient_risk, allocate_with_solution, allocate_from_inputs
research — make_synthetic_futures_dataset, run_flywheel_iteration, ResearchDataset
risk — calculate_value_at_risk, calculate_expected_shortfall, calculate_conditional_drawdown_risk, calculate_variance, calculate_value_at_risk_from_matrix, calculate_expected_shortfall_from_matrix, calculate_conditional_drawdown_risk_from_matrix
sample_weights — get_weights_by_return, get_weights_by_time_decay
sampling — get_ind_matrix, seq_bootstrap, get_ind_mat_average_uniqueness, get_ind_mat_label_uniqueness, bootstrap_loop_run, get_av_uniqueness_from_triple_barrier, num_concurrent_events
sb_bagging — fit_predict_sb_classifier, fit_predict_sb_regressor
strategy_risk — sharpe_symmetric, implied_precision_symmetric, implied_frequency_symmetric, sharpe_asymmetric, implied_precision_asymmetric, implied_frequency_asymmetric, estimate_strategy_failure_probability
streaming_hpc — run_streaming_pipeline, generate_synthetic_flash_crash_stream
structural_breaks — get_chow_type_stat, get_chu_stinchcombe_white_statistics, get_sadf
synthetic_bt — calibrate_ou_params, generate_ou_paths, evaluate_rule_on_paths, detect_no_stable_optimum, run_synthetic_otr_workflow, search_optimal_trading_rule
viz — prepare_feature_importance_payload, prepare_feature_importance_comparison_payload, prepare_drawdown_payload, prepare_regime_payload, prepare_frontier_payload, prepare_cluster_payload
volatility — get_daily_vol, get_parkinson_vol, get_garman_class_vol, get_yang_zhang_vol