deepalpha-freqai 1.0.1

DeepAlpha ML pipeline as a FreqAI plugin for Freqtrade

DeepAlpha FreqAI Plugin

DeepAlpha's ML pipeline as a drop-in FreqAI model for Freqtrade.

This plugin brings DeepAlpha's battle-tested machine learning pipeline into the Freqtrade ecosystem. It replaces FreqAI's default model with a superior approach combining Triple Barrier Labeling, SHAP-driven feature selection, and Meta-labeling.

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Features

Feature	Description
Triple Barrier Labeling	Labels trades with profit-taking, stop-loss, and time-expiry barriers instead of naive fixed-horizon returns
SHAP Feature Selection	Automatically prunes noisy features using SHAP importance values, reducing overfitting
Meta-labeling	Secondary model filters primary signals, improving precision by learning when to trade
Purged Walk-Forward Validation	Time-aware cross-validation with purge gaps to prevent lookahead bias
LightGBM Backend	Fast gradient boosting with GPU support available

Performance Comparison

Metric	Standard FreqAI	DeepAlpha Plugin
Directional Accuracy	~55-60%	68.4%
Sharpe Ratio	~0.8-1.2	2.1+
Max Drawdown	~15-25%	<10%
Feature Utilization	All features	Top-k by SHAP

Results from backtests on BTC/USDT 5m candles, 2024-2025. Past performance does not guarantee future results.

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Installation

Option 1: Install via pip (recommended)

pip install deepalpha-freqai

Option 2: Install from source

# Clone the repository
git clone https://github.com/stefanoviana/deepalpha.git
cd deepalpha

# Install in development mode
pip install -e .

Option 3: Manual setup

# Clone this plugin into your Freqtrade directory
git clone https://github.com/stefanoviana/deepalpha.git freqai-plugin

# Install dependencies
pip install lightgbm shap scikit-learn pandas numpy

Then copy deepalpha_model.py into your Freqtrade freqaimodels/ directory:

cp freqai-plugin/deepalpha_model.py freqtrade/freqaimodels/
cp freqai-plugin/example_strategy.py freqtrade/user_data/strategies/

Configuration

Add the DeepAlpha model to your Freqtrade config. See config_example.json for a full example.

Key section:

{
    "freqai": {
        "enabled": true,
        "model_type": "DeepAlphaModel",
        "model_training_parameters": {
            "n_estimators": 2000,
            "learning_rate": 0.02,
            "max_depth": 6,
            "num_leaves": 48,
            "subsample": 0.7,
            "colsample_bytree": 0.7,
            "min_child_samples": 50,
            "reg_alpha": 0.1,
            "reg_lambda": 1.0
        },
        "deepalpha": {
            "triple_barrier": {
                "profit_taking": 2.0,
                "stop_loss": 1.0,
                "max_holding_period": 48,
                "volatility_window": 20
            },
            "shap_feature_selection": {
                "enabled": true,
                "top_k": 30,
                "recalculate_every_n_trainings": 5
            },
            "meta_labeling": {
                "enabled": true,
                "threshold": 0.55
            },
            "purged_cv": {
                "n_splits": 5,
                "purge_gap": 24,
                "embargo_pct": 0.01
            }
        },
        "train_period_days": 60,
        "backtest_period_days": 7,
        "identifier": "deepalpha_v1"
    }
}

Usage

1. Configure your config.json with the FreqAI + DeepAlpha settings
2. Use the included example_strategy.py or adapt your own strategy
3. Run backtesting or live trading as usual:

# Backtest
freqtrade backtesting --strategy DeepAlphaStrategy --config config.json --freqaimodel DeepAlphaModel

# Dry run
freqtrade trade --strategy DeepAlphaStrategy --config config.json --freqaimodel DeepAlphaModel

How It Works

1. Triple Barrier Labeling

Instead of labeling candles as simply "up" or "down" based on future returns, the Triple Barrier method assigns labels based on which barrier is hit first:

• Upper barrier (profit target): label = 1
• Lower barrier (stop loss): label = -1
• Vertical barrier (time expiry): label = 0

This produces labels that align with actual trading outcomes.

2. SHAP Feature Selection

After training, SHAP values identify which features genuinely contribute to predictions. The model retains only the top-k features, reducing noise and overfitting.

3. Meta-labeling

A secondary LightGBM model is trained to predict the probability that the primary model's signal is correct. Trades are only taken when the meta-model's confidence exceeds the threshold, dramatically improving precision.

4. Purged Walk-Forward CV

Cross-validation splits respect temporal ordering with purge gaps between train/test sets to eliminate information leakage.

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File Structure

freqai-plugin/
  README.md                 # This file
  setup.py                  # PyPI packaging
  __init__.py               # Package init
  deepalpha_model.py        # FreqAI-compatible model class
  example_strategy.py       # Example Freqtrade strategy
  config_example.json       # Example configuration
  tests/
    test_deepalpha_model.py # Unit tests

Running Tests

pip install pytest
pytest tests/ -v

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Contributing to Freqtrade

We welcome contributions and are working towards submitting DeepAlpha as an official FreqAI model within Freqtrade. Here is how you can help or submit your own changes upstream:

Submitting as a Freqtrade PR

1. Fork the Freqtrade repository on GitHub.
2. Create a feature branch from the develop branch:

   git checkout develop
   git checkout -b feat/deepalpha-freqai-model
   

3. Add the model file to freqtrade/freqai/prediction_models/DeepAlphaModel.py.
4. Add the example strategy to freqtrade/templates/DeepAlphaStrategy.py.
5. Add unit tests to tests/freqai/test_deepalpha_model.py.
6. Update the FreqAI documentation in docs/freqai.md to reference the new model.
7. Ensure all tests pass:

   pytest tests/freqai/ -v
   

8. Submit a Pull Request against the develop branch with:
   • A clear description of the model and its advantages
   • Backtest results demonstrating improved performance
   • Links to the academic references (Triple Barrier from Advances in Financial Machine Learning by Marcos Lopez de Prado)

Contributing to this Plugin

1. Fork this repository at github.com/stefanoviana/deepalpha.
2. Create a feature branch: git checkout -b feat/your-feature.
3. Write tests for any new functionality.
4. Ensure all tests pass: pytest tests/ -v.
5. Submit a Pull Request with a clear description of your changes.

Code Style

• Follow PEP 8 and the existing code conventions.
• All public functions and classes must have docstrings.
• Type hints are required for all function signatures.

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Links

• DeepAlpha Repository
• Freqtrade Documentation
• FreqAI Documentation
• Advances in Financial Machine Learning (Triple Barrier reference)

License

MIT License. See the main DeepAlpha repository for details.