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

Multi-Class Classification

Three classes for finer signal granularity:

Multi-class reduces per-class sample size. Use only with large datasets (1000+ samples per class).

Probability Calibration

Raw model probabilities from XGBoost/LightGBM are not well-calibrated. A predicted 0.7 probability does not mean 70% chance of being correct. Use calibration to fix this:

from sklearn.calibration import CalibratedClassifierCV

calibrated = CalibratedClassifierCV(base_model, cv=5, method="isotonic")
calibrated.fit(X_train, y_train)
probs = calibrated.predict_proba(X_test)[:, 1]

Isotonic calibration works better than Platt scaling for tree models.

Walk-Forward Validation

This is the single most important concept in trading ML. Standard cross-validation randomly shuffles data, which creates lookahead bias. Walk-forward validation respects time ordering.

How It Works

Window 1: [===TRAIN===][GAP][=TEST=]
Window 2:    [===TRAIN===][GAP][=TEST=]
Window 3:       [===TRAIN===][GAP][=TEST=]
Window 4:          [===TRAIN===][GAP][=TEST=]

Each window:

1. Train on past N bars
2. Skip a gap (embargo) equal to the forward return horizon
3. Predict on next M bars
4. Record out-of-sample predictions
5. Slide forward and repeat

Typical Parameters

Walk-Forward Implementation

from typing import Iterator

def walk_forward_splits(
    n_samples: int,
    train_size: int = 720,
    test_size: int = 168,
    step_size: int = 24,
    gap: int = 24,
) -> Iterator[tuple[np.ndarray, np.ndarray]]:
    """Generate walk-forward train/test index splits.

    Args:
        n_samples: Total number of samples.
        train_size: Number of training samples per window.
        test_size: Number of test samples per window.
        step_size: Step between successive windows.
        gap: Gap between train end and test start.

    Yields:
        Tuples of (train_indices, test_indices).
    """
    start = 0
    while start + train_size + gap + test_size <= n_samples:
        train_idx = np.arange(start, start + train_size)
        test_start = start + train_size + gap
        test_idx = np.arange(test_start, test_start + test_size)
        yield train_idx, test_idx
        start += step_size

See references/validation_methods.md for purged CV, CPCV, and evaluation metrics.

Model Training Pipeline

Full Pipeline Overview

1. Feature engineering — compute technical indicators, on-chain metrics, volume features (see feature-engineering skill)
2. Label creation — forward returns with threshold, drop neutral zone
3. Walk-forward split — time-ordered train/test windows with gap
4. Train model — XGBoost or LightGBM on each training window
5. Predict on test — generate out-of-sample probability predictions
6. Aggregate predictions — concatenate all out-of-sample results
7. Evaluate — accuracy, precision, recall, F1, AUC, profit factor

Quick Training Example

from xgboost import XGBClassifier

model = XGBClassifier(
    n_estimators=200,
    max_depth=4,
    learning_rate=0.05,
    subsample=0.8,
    colsample_bytree=0.8,
    eval_metric="logloss",
    use_label_encoder=False,
    random_state=42,
)

model.fit(
    X_train, y_train,
    eval_set=[(X_val, y_val)],
    verbose=False,
)

probabilities = model.predict_proba(X_test)[:, 1]

See references/model_guide.md for parameter recommendations and tuning.

SHAP Feature Importance

SHAP (SHapley Additive exPlanations) provides the gold standard for understanding model predictions.

Global Feature Importance

Which features matter most across all predictions:

import shap

explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)

# Summary plot (top 15 features)
shap.summary_plot(shap_values, X_test, max_display=15)

Local Explanations

Why a specific prediction was made:

# Explain a single prediction
shap.force_plot(explainer.expected_value, shap_values[0], X_test.iloc[0])

Temporal Feature Importance

Track how feature importance drifts over walk-forward windows. If a feature's importance drops significantly, the market regime may have shifted.

Threshold Optimization

The default 0.5 probability threshold is almost never optimal for trading.

Why Not 0.5?

• Class imbalance: if 60% of labels are "up", a 0.5 threshold is too aggressive
• Trading costs: marginal signals (0.51 probability) rarely cover transaction costs
• Asymmetric payoffs: precision matters more than recall for trading

Optimize for Profit Factor

def optimize_threshold(
    probabilities: np.ndarray,
    returns: np.ndarray,
    thresholds: np.ndarray | None = None,
) -> tuple[float, float]:
    """Find threshold that maximizes profit factor.

    Args:
        probabilities: Model predicted probabilities.
        returns: Actual forward returns.
        thresholds: Thresholds to search over.

    Returns:
        Tuple of (best_threshold, best_profit_factor).
    """
    if thresholds is None:
        thresholds = np.arange(0.50, 0.85, 0.01)
    best_threshold, best_pf = 0.5, 0.0
    for t in thresholds:
        signals = probabilities >= t
        if signals.sum() < 10:
            continue
        signal_returns = returns[signals]
        wins = signal_returns[signal_returns > 0].sum()
        losses = abs(signal_returns[signal_returns < 0].sum())
        pf = wins / losses if losses > 0 else 0.0
        if pf > best_pf:
            best_pf = pf
            best_threshold = t
    return best_threshold, best_pf

Typical finding: optimal threshold is 0.60-0.75 for crypto trading signals.

Crypto-Specific Considerations

Short Training Windows

Crypto market regimes change fast. A model trained on 6 months of data may perform worse than one trained on 30 days. Use shorter training windows and retrain frequently.

Class Imbalance

Most time periods are "flat" (returns within the neutral zone). Strategies to handle this:

• Drop neutral zone: only train on clear up/down labels
• Undersample majority class: scale_pos_weight in XGBoost
• SMOTE: synthetic minority oversampling (use cautiously — can introduce lookahead)
• Adjust threshold: raise the probability threshold to compensate

Transaction Costs

A model with 55% accuracy sounds good, but after 0.5% round-trip costs (slippage + fees), many signals become unprofitable. Always evaluate signals net of costs:

net_return = gross_return - 0.005  # 50 bps round-trip

Feature Decay

Features lose predictive power over time as more participants discover and trade on them. Monitor rolling performance and retrain when metrics degrade.

Integration with Other Skills

Files

References

• references/model_guide.md — XGBoost and LightGBM parameter guide, tuning, and ensembling
• references/validation_methods.md — Walk-forward, purged CV, CPCV, and evaluation metrics

Scripts

• scripts/train_classifier.py — Train a signal classifier with walk-forward validation and feature importance
• scripts/walk_forward_backtest.py — Backtest ML signals vs buy-and-hold with walk-forward validation

Dependencies

# Core (required)
uv pip install pandas numpy scikit-learn

# Optional (recommended)
uv pip install xgboost lightgbm shap

Key Takeaways

1. Walk-forward validation is non-negotiable — random CV will give you wildly inflated results
2. Optimize threshold for profit factor, not accuracy — a high-precision, low-recall model beats a high-accuracy one
3. Short training windows for crypto — 30 days beats 6 months in most regimes
4. Monitor feature decay — retrain when rolling metrics drop below baseline
5. Always evaluate net of costs — a 55% accurate model may be unprofitable after fees
6. SHAP over raw feature importance — SHAP gives consistent, theoretically grounded explanations