crypto-predictive-model

🏦 Crypto Predictive Model — ML Ensemble for Crypto Price Prediction

A production-grade machine learning pipeline for cryptocurrency price prediction using 15 years of Yahoo Finance historical data, engineered for integration with high-frequency trading systems.

🚀 Quick Start

pip install yfinance pandas numpy scikit-learn lightgbm ta torch pyarrow joblib
git clone https://huggingface.co/AcyLa/crypto-predictive-model
cd crypto-predictive-model
python train.py --ticker BTC-USD
python predict.py --ticker BTC-USD

🏗 Architecture

Data (yfinance) → Features (207 indicators) → Labels (multi-horizon) → Ensemble (LightGBM+LSTM) → Prediction API
                                                          ↑
                                          Walk-Forward Backtester (purged CV)

📊 Components

🔧 HFT Integration

from predict import CryptoPredictor

predictor = CryptoPredictor(ticker="BTC-USD")
result = predictor.get_multi_horizon(use_lstm=False)  # sub-ms for HFT
print(f"Consensus: {result['consensus']} ({result['consensus_confidence']:.0%})")

See hft_integration.py for a complete trading loop with stop-loss, take-profit, and position management.

📁 Files

├── config.py          # All hyperparameters
├── data_loader.py     # Yahoo Finance download
├── features.py        # 207 technical indicators
├── labels.py          # Multi-horizon labels + triple-barrier
├── models.py          # LightGBM + LSTM ensemble
├── backtest.py        # Walk-forward backtesting engine
├── train.py           # Main training pipeline
├── predict.py         # Live prediction API + CLI
├── hft_integration.py # HFT trading loop example
└── models/BTC-USD/    # Trained models

⚠️ Disclaimer

Research tool, not financial advice. Always backtest with transaction costs and never risk more than you can afford to lose.

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AcyLa/crypto-predictive-model"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.