Feature engineering, labeling, alternative bars, and leakage-safe datasets for financial ML.
ml4t-engineer provides 120 registry features across 11 categories, path-dependent
and fixed-horizon labeling, activity-based bar sampling, and train-only preprocessing.
The core interface uses Polars DataFrames.
ml4t-engineer supports Python 3.12, 3.13, and 3.14 on Linux, macOS, and Windows.
pip install ml4t-engineerThe quick start uses only core dependencies. Optional extras provide TA-Lib validation, DuckDB and PyArrow storage, market calendars, visualization, statistics, and ML tools:
pip install "ml4t-engineer[ta]"
pip install "ml4t-engineer[store]"
pip install "ml4t-engineer[calendars]"
pip install "ml4t-engineer[viz]"
pip install "ml4t-engineer[stats]"
pip install "ml4t-engineer[ml]"TA-Lib requires its native library. The core package does not require an external service, credentials, or special hardware. Python 3.15 is not supported while the active Polars compatibility exception applies.
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)]
ohlcv = 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(ohlcv, ["rsi", "macd", "atr"])
assert {"rsi", "macd", "atr"} <= set(features.columns)
assert features.height == ohlcv.heightcompute_features() returns the input columns with the requested feature columns
appended. Use the feature registry to inspect categories and parameters before building
larger pipelines.
- Technical, volatility, risk, microstructure, statistical, and ML-oriented features
- Triple-barrier, ATR-barrier, percentile, fixed-horizon, trend-scanning, and meta-labels
- Tick, volume, dollar, imbalance, and run bars
- Train/test splitting with train-only scaling
- Feature metadata search and discovery
See the documentation for tutorials, task-oriented guides, explanations, and the API reference. Report defects and request changes through GitHub Issues.
ml4t-specsdefines the shared market-data and artifact contracts used by this package.ml4t-datasupplies validated market data for feature computation.ml4t-diagnosticevaluates features, labels, and model signals produced from engineered datasets.
git clone https://github.com/ml4t/engineer.git
cd engineer
uv sync --dev --extra docs --extra ta --extra store --extra viz
uv run ruff check src/ tests/ examples/ scripts/
uv run ruff format --check src/ tests/ examples/ scripts/
uv run ty check
uv run pytest tests/ -q
uv build
uv run mkdocs build --strictPull requests must also pass the supported Python and operating-system matrix, dependency and vulnerability review, clean-wheel installation, documented workflow tests, and ecosystem qualification.