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ML4T Engineer
ML4T Engineer Documentation
Features, labels, alternative bars, and leakage-safe dataset preparation
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Book Guide

Use this guide to move between Machine Learning for Trading, Third Edition and ml4t-engineer without guessing which notebook maps to which production API.

ml4t-engineer appears in two distinct ways throughout the book:

  • pedagogical notebooks that build ideas step by step
  • reusable library workflows that collapse those ideas into stable APIs

The book teaches the method. The library is where you should go when you want to reuse that method across assets, case studies, and production pipelines.

How to Use This Guide

Start from the book if you want intuition, derivations, and plots. Start from the library guides if you want reusable implementations, validated APIs, and pipeline integration.

Use this page when you need to answer one of these questions:

  • "Which chapter teaches the concept behind this API?"
  • "Which notebook should I read before using this workflow in production?"
  • "Which library guide replaces the manual code from the book?"
  1. Read the relevant chapter notebook to understand the modeling idea.
  2. Jump to the matching user-guide page for the production API.
  3. Use the case-study pipeline files as the bridge from teaching code to reusable workflows.
  4. Use the API Reference when you need exact signatures.

Chapter Map

Chapter 3: Market Microstructure

Book path What the book teaches Library entry point Docs page
03_market_microstructure/code/08_itch_bar_sampling.py Why time bars are statistically weak and how tick, volume, and dollar bars improve sampling TickBarSampler, VolumeBarSampler, DollarBarSampler Alternative Bars
03_market_microstructure/code/10_itch_information_bars.py Imbalance bars and threshold dynamics TickImbalanceBarSampler, FixedTickImbalanceBarSampler, FixedVolumeImbalanceBarSampler Alternative Bars
03_market_microstructure/code/13_databento_bar_sampling.py Applying bar samplers to a modern vendor feed Same sampler family with production input contracts Alternative Bars

What changes when you move to the library:

  • the book focuses on the statistical motivation and diagnostics
  • the library gives you stable samplers, warnings, and reusable OHLCV outputs
  • fixed-threshold imbalance bars are the recommended production path

Chapter 7: Defining the Learning Task

Book path What the book teaches Library entry point Docs page
07_defining_learning_task/code/02_preprocessing_pipeline.py Leakage-safe preprocessing and split-aware scaling StandardScaler, MinMaxScaler, RobustScaler, PreprocessingPipeline Preprocessing
07_defining_learning_task/code/03_label_methods.py Triple-barrier, percentile, trend-scanning, meta-labeling, and sample weighting LabelingConfig, triple_barrier_labels, rolling_percentile_binary_labels, trend_scanning_labels, meta_labels Labeling
07_defining_learning_task/code/04_minimum_favorable_adverse_excursion.py Barrier behavior and excursion analysis LabelingConfig.triple_barrier() Labeling
07_defining_learning_task/code/10_ml4t_library_ecosystem.py The library-oriented view of feature computation, discovery, and dataset building compute_features, feature_catalog, create_dataset_builder Features, Feature Discovery, Dataset Builder

What changes when you move to the library:

  • manual notebook experiments become serialized LabelingConfig workflows
  • feature discovery moves from ad hoc inspection to metadata-driven search
  • dataset preparation becomes train-only scaling and splitter-aware folds by default

Chapter 8: Feature Engineering

Book path What the book teaches Library entry point Docs page
08_feature_engineering/code/01_price_volume_features.py Momentum, trend, volatility, and volume feature intuition compute_features with registry-backed indicators Features
08_feature_engineering/code/02_microstructure_features.py Microstructure feature construction and interpretation Microstructure feature functions and registry metadata Features
08_feature_engineering/code/03_structural_cross_instrument_features.py Cross-asset and panel relationships ml4t.engineer.features.cross_asset Features
08_feature_engineering/code/04_fundamentals_macro_calendar.py Lag features, calendar encodings, and ML-oriented transforms ML feature utilities and preprocessing bridge Features, ML Readiness

What changes when you move to the library:

  • the book derives and visualizes features individually
  • the library lets you request validated feature sets through one computation API
  • registry and catalog metadata help you choose features systematically

Chapter 9: Time-Series Analysis

Book path What the book teaches Library entry point Docs page
09_time_series_analysis/code/03_fractional_differencing.py The memory-stationarity tradeoff and ADF-based search for d ffdiff, find_optimal_d, fdiff_diagnostics Fractional Differencing
09_time_series_analysis/code/08_garch_volatility.py Volatility estimators and conditional volatility modeling Volatility feature family Features
09_time_series_analysis/code/09_har_rough_volatility.py Multi-horizon volatility structure Volatility features used downstream in pipelines Features
09_time_series_analysis/code/11_hmm_regimes.py Regime detection workflows Regime feature family Features
09_time_series_analysis/code/13_regime_as_feature.py Turning regimes into model inputs Regime features in reusable pipelines Features
09_time_series_analysis/code/14_panel_features.py Cross-sectional panel features Cross-asset feature functions for multi-asset inputs Features

What changes when you move to the library:

  • research notebooks stay focused on method validation
  • library functions package the same transforms into repeatable feature pipelines
  • the case studies show how to combine these transforms with labels and CV

Case-Study Pipeline Map

Most case studies follow the same structure. ml4t-engineer is the handoff point between raw market data and model-ready datasets.

Case-study step Typical file Library workflow Docs page
Labels case_studies/<study>/code/02_labels.py LabelingConfig, barrier labels, percentile labels, fixed-horizon labels Labeling
Features case_studies/<study>/code/03_features.py compute_features plus feature-specific functions Features
Temporal prep case_studies/<study>/code/04_temporal.py fractional differencing and leakage-safe preparation Fractional Differencing, Dataset Builder

Examples called out in the current integration audit:

  • ETFs: percentile labels, production compute_features, fractional differencing
  • US Equities Panel: triple-barrier labels, panel features, fractional differencing
  • CME Futures: ATR-based barriers and futures-aware labeling workflows
  • NASDAQ-100 Microstructure: manual pedagogical implementations with strong overlap to production-ready microstructure features in this library

From Notebook Code to Library API

Use this translation when moving from the book to reusable code:

Book pattern Library equivalent
Manually computing several indicators in sequence compute_features(data, feature_spec)
Notebook-only feature browsing feature_catalog.list(), feature_catalog.search(), registry.get()
Inline barrier parameters spread across a notebook LabelingConfig.triple_barrier(...) or LabelingConfig.atr_barrier(...)
One-off train/test scaling create_dataset_builder(..., scaler=...)
Manual stationarity experiments find_optimal_d() then ffdiff()
Bar-construction experiments sampler classes in ml4t.engineer.bars

Maturity and Scope

These are the workflows readers should prioritize:

  • production-ready: feature computation, labeling methods, alternative bars, fractional differencing, feature discovery, dataset builder
  • advanced: cross-asset feature workflows and pipeline orchestration
  • experimental or low-priority: DuckDB store and any workflows not yet used in the book or case studies

This matches the current audit: the strongest value in ml4t-engineer is the production API for feature engineering, labeling, and dataset preparation.

Where to Go Next