ml4t-engineer 0.1.0b5

High-performance quantitative finance feature engineering library

ml4t-engineer

Feature engineering for financial machine learning: technical indicators, labeling methods, and alternative bar sampling.

Part of the ML4T Library Ecosystem

This library is one of six interconnected libraries supporting the machine learning for trading workflow described in Machine Learning for Trading:

Together they cover data infrastructure, feature engineering, modeling, signal evaluation, strategy backtesting, and live deployment.

What This Library Does

Transforming raw price data into predictive features is a core task in quantitative research. ml4t-engineer provides:

• 120 technical indicators across 11 categories (momentum, volatility, trend, microstructure, etc.)
• Triple-barrier labeling and other target construction methods from Advances in Financial Machine Learning
• Alternative bar sampling (volume bars, dollar bars, tick imbalance bars)
• A feature registry for discovery and configuration

The library is built on Polars with Numba JIT compilation for numerical operations. 60 indicators are validated against TA-Lib at 1e-6 tolerance.

Installation

pip install ml4t-engineer

Optional dependencies:

pip install ml4t-engineer[ta]        # TA-Lib backend
pip install ml4t-engineer[viz]       # Visualization
pip install ml4t-engineer[calendars] # Trading calendars

Quick Start

import polars as pl
from ml4t.engineer import compute_features

df = pl.read_parquet("ohlcv.parquet")

# Compute features with default parameters
result = compute_features(df, ["rsi", "macd", "atr", "obv"])

# Or with custom parameters
result = compute_features(df, [
    {"name": "rsi", "params": {"period": 20}},
    {"name": "bollinger_bands", "params": {"period": 20, "std_dev": 2.0}},
])

Feature Registry

from ml4t.engineer.core.registry import get_registry

registry = get_registry()
print(registry.list_all())                    # All 120 features
print(registry.list_by_category("momentum"))  # 31 momentum indicators
print(registry.list_ta_lib_compatible())      # 60 TA-Lib validated
print(registry.list_normalized())             # 37 bounded (0-100, -1 to 1)

Feature Categories

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

Triple-Barrier Labeling

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

# Fixed barriers
tb_config = LabelingConfig.triple_barrier(
    upper_barrier=0.02,    # 2% profit target
    lower_barrier=0.01,    # 1% stop loss
    max_holding_period=20, # 20 bars
)
labels = triple_barrier_labels(
    df,
    config=tb_config,
)

# ATR-based dynamic barriers
atr_config = LabelingConfig.atr_barrier(
    atr_tp_multiple=2.0,
    atr_sl_multiple=1.0,
    atr_period=14,
    max_holding_period=20,
)
labels = atr_triple_barrier_labels(
    df,
    config=atr_config,
)

# Time-based horizons
tb_time_config = LabelingConfig.triple_barrier(
    upper_barrier=0.02,
    lower_barrier=0.01,
    max_holding_period="4h",  # 4 hours
)
labels = triple_barrier_labels(
    df,
    config=tb_time_config,
)

Alternative Bars

from ml4t.engineer.bars import VolumeBarSampler, DollarBarSampler, TickImbalanceBarSampler

# Volume bars (equal volume per bar)
vbars = VolumeBarSampler(volume_threshold=1000).sample(tick_data)

# Dollar bars (equal dollar volume per bar)
dbars = DollarBarSampler(dollar_threshold=1_000_000).sample(tick_data)

# Tick imbalance bars (information-driven)
ibars = TickImbalanceBarSampler(expected_imbalance=100).sample(tick_data)

Documentation

• Features - 120 technical indicators across 11 categories
• Labeling - 7 labeling methods for supervised learning
• Alternative Bars - Information-driven bar sampling
• Feature Discovery - Registry, catalog, and search
• Fractional Differencing - Memory-preserving stationarity
• ML-Readiness - Normalized features and preprocessing
• Preprocessing - Scalers and leakage prevention
• Dataset Builder - Leakage-safe train/test preparation

Technical Characteristics

• Polars-native: All computations use Polars expressions
• Numba-accelerated: JIT compilation for numerical kernels
• TA-Lib validated: 60 indicators validated at 1e-6 tolerance
• AFML-compliant: Labeling methods verified against Advances in Financial Machine Learning
• ML-ready outputs: 37 features produce bounded outputs (0-100, -1 to 1) for direct model input; remaining features work with standard preprocessing (returns, z-scores, robust scaling)

Related Libraries

• ml4t-specs: Shared feed and artifact schema definitions across the ML4T stack
• ml4t-data: Market data acquisition and storage
• ml4t-diagnostic: Signal evaluation and statistical validation
• ml4t-backtest: Event-driven backtesting
• ml4t-live: Live trading with broker integration

Development

git clone https://github.com/applied-ai/ml4t-engineer.git
cd ml4t-engineer
uv sync
uv run pytest tests/ -q
uv run ty check

References

• Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
• Lopez de Prado, M. (2020). Machine Learning for Asset Managers. Cambridge.

License

MIT License - see LICENSE for details.