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

Get up and running with ML4T Engineer in 5 minutes.

If you are coming from Machine Learning for Trading, Third Edition, pair this page with the Book Guide to jump from notebook examples to the matching reusable library workflows.

Basic Feature Computation

import polars as pl
from ml4t.engineer import compute_features

# Create sample OHLCV data
df = pl.DataFrame({
    "open": [100.0, 101.0, 102.0, 103.0, 104.0] * 20,
    "high": [102.0, 103.0, 104.0, 105.0, 106.0] * 20,
    "low": [99.0, 100.0, 101.0, 102.0, 103.0] * 20,
    "close": [101.0, 102.0, 103.0, 104.0, 105.0] * 20,
    "volume": [1000, 1100, 1200, 1300, 1400] * 20,
})

# Compute features
result = compute_features(df, ["rsi", "macd", "atr"])
print(result.columns)

Custom Parameters

# Use dict format for custom parameters
result = compute_features(df, [
    {"name": "rsi", "params": {"period": 20}},
    {"name": "sma", "params": {"period": 50}},
    {"name": "bollinger_bands", "params": {"period": 20, "std_dev": 2.5}},
])

YAML Configuration

Create a features.yaml file:

features:
  - name: rsi
    params:
      period: 14
  - name: macd
    params:
      fast: 12
      slow: 26
      signal: 9
  - name: atr
    params:
      period: 14

Then use it:

result = compute_features(df, "features.yaml")

Triple-Barrier Labeling

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

config = LabelingConfig.triple_barrier(
    upper_barrier=0.02,  # 2% profit target
    lower_barrier=0.01,  # 1% stop loss
    max_holding_period=20,  # 20 bar maximum holding
)

labels = triple_barrier_labels(
    df,
    config=config,
)

Explore Available Features

from ml4t.engineer import feature_catalog

# List all categories
print(feature_catalog.categories())
# ['momentum', 'trend', 'volatility', ...]

# List features in a category
print(feature_catalog.list(category="momentum"))
# ['rsi', 'macd', 'stoch', 'cci', ...]

# Get feature details
info = feature_catalog.describe("rsi")
print(info)
# {'name': 'rsi', 'category': 'momentum', 'normalized': True, ...}

Next Steps