signal-classification

Signal Classification

Predict whether an asset's price will move up or down over a forward horizon using supervised machine learning classifiers. This skill covers the full pipeline: label creation, model training, walk-forward validation, feature importance analysis, and threshold optimization for trading applications.

Why Tree-Based Models Dominate Trading ML

XGBoost and LightGBM are the workhorses of quantitative trading ML for good reason:

• Non-linear relationships: Financial features interact in complex, non-linear ways that trees capture naturally
• Robust to feature scale: No need to normalize or standardize inputs — trees split on rank order
• Built-in feature importance: Understand which features drive predictions without separate analysis
• Fast training and inference: Train on thousands of samples in seconds, predict in microseconds
• Handle missing values: Native support for NaN without imputation hacks
• Regularization built in: max_depth, min_child_weight, subsample all prevent overfitting

Linear models and deep learning have their place, but for tabular trading features with fewer than 100k samples, gradient-boosted trees consistently outperform alternatives.

Classification Types

Binary Classification

The simplest and most common setup. Predict whether forward returns exceed a threshold:

• Up signal: forward return > +1%
• Down signal: forward return < -1%
• Neutral (excluded): -1% to +1% — drop these from training to create cleaner labels

import numpy as np

def create_binary_labels(
    prices: np.ndarray, horizon: int = 24, threshold: float = 0.01
) -> np.ndarray:
    """Create binary labels from forward returns.

    Args:
        prices: Array of prices.
        horizon: Forward return lookback in bars.
        threshold: Minimum return magnitude for a label.

    Returns:
        Array of labels: 1 (up), 0 (down), NaN (neutral).
    """
    fwd_returns = np.roll(prices, -horizon) / prices - 1
    fwd_returns[-horizon:] = np.nan
    labels = np.where(fwd_returns > threshold, 1,
             np.where(fwd_returns < -threshold, 0, np.nan))
    return labels