Tree Ensembles for Trading

Tree ensemble methods combine multiple decision trees to create powerful, robust predictive models. These methods are particularly effective for trading applications due to their ability to capture non-linear patterns, handle mixed data types, and provide interpretable feature importances.

Why Tree Ensembles for Trading?

Tree ensemble methods offer several advantages for financial prediction:

  1. Non-linear pattern recognition: Capture complex relationships between features and returns
  2. Feature interactions: Automatically discover interactions between technical and fundamental factors
  3. Robustness: Less sensitive to outliers than linear models
  4. Mixed data types: Handle both numerical and categorical features
  5. Feature importance: Provide interpretable insights into what drives predictions
  6. No feature scaling required: Trees are invariant to monotonic transformations

Tree ensembles consistently rank among the top-performing algorithms in financial ML competitions and production trading systems. Their ability to handle noisy, non-stationary data makes them particularly well-suited to market prediction tasks.

Ensemble Methods Taxonomy

The following diagram illustrates the two primary families of ensemble methods and how they are applied in a trading pipeline:

%%{init: {'theme': 'base', 'themeVariables': {'lineColor': '#4a5568', 'primaryColor': '#2d5016'}}}%%
flowchart TD
    A[Raw Market Data] --> B[Feature Engineering]
    B --> C{Ensemble Method}

    C --> D[Bagging]
    C --> E[Boosting]

    D --> F[Random Forest]
    F --> G[Bootstrap Samples]
    G --> H[Parallel Trees]
    H --> I[Average Predictions]

    E --> J[XGBoost]
    E --> K[LightGBM]
    E --> L[CatBoost]
    J --> M[Sequential Trees]
    K --> M
    L --> M
    M --> N[Weighted Sum]

    I --> O[SHAP Interpretation]
    N --> O
    O --> P[Long-Short Signals]

    classDef data fill:#1a3a5c,stroke:#0d2137,color:#e8e0d4
    classDef method fill:#2d5016,stroke:#1a3a1a,color:#e8e0d4
    classDef model fill:#6b2d5b,stroke:#4a1a4a,color:#e8e0d4
    classDef process fill:#8b4513,stroke:#5c2d0e,color:#e8e0d4
    classDef output fill:#2d3a50,stroke:#1a2535,color:#e8e0d4

    class A,B data
    class C,D,E method
    class F,J,K,L model
    class G,H,I,M,N process
    class O,P output

Chapter Overview

This chapter covers four key topics:

Sub-page Topics
Random Forests Decision trees review, RandomForestTrader, bootstrap aggregation, hyperparameter tuning
Gradient Boosting XGBoost, LightGBM, CatBoost implementations, sequential training, parameter grids
SHAP Interpretation SHAP values, summary/dependence/waterfall plots, cross-model feature importance comparison
Long-Short Strategy EnsembleLongShort, backtesting, complete multi-asset example, best practices

Two Families of Ensembles

Bagging (Bootstrap Aggregation)

Bagging reduces variance by training many independent models on bootstrap samples and averaging their predictions. Random Forests are the canonical example: each tree sees a different subset of rows and features, making the ensemble far more stable than any single tree.

Boosting (Sequential Error Correction)

Boosting reduces bias by training models sequentially, where each new model focuses on the errors of the previous ones. Gradient boosting frameworks (XGBoost, LightGBM, CatBoost) dominate structured-data competitions and production systems because they achieve high accuracy with relatively few trees.

For financial data, gradient boosting models typically outperform Random Forests in raw accuracy, but Random Forests provide a more robust baseline with less tuning. A prudent approach is to train both and combine them in an ensemble strategy.

Quick Start 

from puffin.ensembles import (
    RandomForestTrader,
    XGBoostTrader,
    LightGBMTrader,
    CatBoostTrader,
    ModelInterpreter,
    EnsembleLongShort,
)

All ensemble models in Puffin follow the same interface:

  1. Initialize with task="classification" or task="regression"
  2. Call .fit(features, target) to train
  3. Call .predict(features) to generate predictions
  4. Call .feature_importance() for built-in importance scores
  5. Use ModelInterpreter for SHAP-based analysis

Summary

Tree ensemble methods provide powerful tools for trading signal generation:

  • Random Forests offer robustness through bagging
  • Gradient boosting (XGBoost, LightGBM, CatBoost) provides state-of-the-art accuracy
  • SHAP values enable model interpretation and feature analysis
  • Ensemble strategies combine multiple models for improved performance

The combination of high predictive power, interpretability, and robustness makes tree ensembles essential tools for quantitative trading.

Notebook: Run the examples interactively in ml_models.ipynb

Source Code

Browse the implementation: puffin/ensembles/

Further Reading

Table of contents