ML4T Engineer¶

Feature engineering, labeling, alternative bars, and leakage-safe datasets for financial ML.

ml4t-engineer is the feature-engineering layer in the ML4T stack. It sits between ml4t-data, which prepares canonical datasets, and ml4t-diagnostic, which evaluates signals and models.

First successful workflow

Install the released package, compute three features from synthetic data, and verify the result. Quickstart
Task guides

Compute features, create labels, sample bars, and build leakage-safe datasets. Features
Exact API reference

Look up released functions, classes, signatures, and supported options. API Reference
Checked book notebooks

Open commit-pinned notebooks and see whether each one calls Engineer, teaches the method manually, or illustrates a related workflow. Book Guide

Quick Example¶

You have OHLCV data and need a feature matrix:

from datetime import date, timedelta

import polars as pl
from ml4t.engineer import compute_features

close = [100.0 + i * 0.1 + (i % 7) * 0.2 for i in range(100)]
df = pl.DataFrame({
    "timestamp": [date(2024, 1, 1) + timedelta(days=i) for i in range(100)],
    "open": close,
    "high": [price + 1.0 for price in close],
    "low": [price - 1.0 for price in close],
    "close": close,
    "volume": [100_000 + i * 100 for i in range(100)],
})
features = compute_features(df, ["rsi", "macd", "atr"])

assert {"rsi", "macd", "atr"} <= set(features.columns)
assert features.select(["rsi", "macd", "atr"]).drop_nulls().height > 0

The call appends three indicator columns to the input DataFrame. The assertions check that the columns exist and contain values after their rolling warmup windows.

Core Workflows¶

1. Add supervised labels¶

You have features. You need targets for a classification model:

from ml4t.engineer.config import LabelingConfig
from ml4t.engineer.labeling import triple_barrier_labels

config = LabelingConfig.triple_barrier(
    upper_barrier=0.02, lower_barrier=0.01, max_holding_period=20,
)
labels = triple_barrier_labels(features, config=config)

This produces standardized label columns such as label, label_return, and barrier_hit for supervised learning workflows.

2. Build train/test data without leakage¶

You have features and labels. You need train/test splits with train-only scaling:

from ml4t.engineer import create_dataset_builder

builder = create_dataset_builder(
    features=labels.select(["rsi", "macd", "atr"]),
    labels=labels["label"],
    dates=labels["timestamp"],
    scaler="robust",
)
X_train, X_test, y_train, y_test = builder.train_test_split(train_size=0.8)

This keeps preprocessing statistics on the training window only, which is the default you want for time-series ML.

3. Use non-time bars when time bars are the wrong abstraction¶

You have trade data and need bars tied to market activity instead of clock time:

from ml4t.engineer.bars import VolumeBarSampler

sampler = VolumeBarSampler(volume_per_bar=50_000)
volume_bars = sampler.sample(trades_df)

This turns raw trade prints into OHLCV bars that are easier to use in downstream feature and labeling pipelines.

4. Preserve memory while making series stationary¶

You need a stationary series but do not want to erase signal with first differences:

from ml4t.engineer.features.fdiff import find_optimal_d, ffdiff

result = find_optimal_d(df["close"])
ffd_close = ffdiff(df["close"], d=result["optimal_d"])

The parameter search requires the stats extra. The Fractional Differencing guide explains the statistical check and the core-only ffdiff() transform.

Documentation Entry Points¶

Quickstart for the first feature-computation workflow
Features for the core computation API
Labeling for supervised targets and sample weighting
Book Guide for chapter, notebook, and case-study mapping
API Reference for exact interfaces

Feature Catalog¶

Use this as reference once you know what kind of signal you want to build.

Category	Count	Examples
Momentum	31	RSI, MACD, Stochastic, CCI, ADX, MFI
Microstructure	15	Kyle Lambda, VPIN, Amihud, Roll spread
Volatility	15	ATR, Bollinger, Yang-Zhang, Parkinson
Statistics	14	Variance, Linear Regression, Correlation
ML	14	Fractional Diff, Entropy, Lag features
Trend	10	SMA, EMA, WMA, DEMA, TEMA, KAMA
Risk	6	Max Drawdown, Sortino, CVaR
Price Transform	5	Typical Price, Weighted Close
Regime	4	Hurst Exponent, Choppiness Index
Volume	3	OBV, AD, ADOSC
Math	3	MAX, MIN, SUM

Standalone cross-asset utilities such as beta, rolling correlation, and cointegration are also available outside the 120-feature registry.

Installation¶

pip install ml4t-engineer

If you use uv, uv pip install ml4t-engineer is equivalent. See Installation for environment details and optional TA-Lib setup.

Part of the ML4T Library Suite¶

ml4t-data → ml4t-engineer → ml4t-diagnostic → ml4t-backtest → ml4t-live

ml4t-engineer is where raw market data becomes reusable research inputs: features, labels, alternative bars, and leakage-safe training datasets.