📈 AI Multi-Model Trading Bot
A comprehensive cryptocurrency trading signal prediction system using 8 ML/DL models with ensemble voting.
🎯 Model Overview
| Model Type | Models Included |
|---|---|
| Traditional ML | Logistic Regression, Random Forest, XGBoost |
| Deep Learning | LSTM, GRU, CNN, LSTM+Attention, Transformer |
| Ensemble | Majority voting across all models |
📊 Features Used
The models use 10 technical indicators:
- RSI (Relative Strength Index)
- MACD & MACD Signal
- Bollinger Band Width
- ATR (Average True Range)
- Distance from SMA50
- OBV Percentage Change
- ADX (Average Directional Index)
- Stochastic RSI (K & D)
🚀 Quick Start
import joblib
import torch
# Load scaler and config
scaler = joblib.load("scaler.pkl")
config = joblib.load("config.pkl")
# Load ML model
rf_model = joblib.load("random_forest.pkl")
# Load DL model
from your_models import LSTMModel
lstm = LSTMModel(config['input_dim'])
lstm.load_state_dict(torch.load("lstm.pt"))
lstm.eval()
📁 Files
| File | Description |
|---|---|
scaler.pkl |
StandardScaler for feature preprocessing |
config.pkl |
Model configuration (input_dim, timesteps, feature_cols) |
logistic_regression.pkl |
Trained Logistic Regression model |
random_forest.pkl |
Trained Random Forest model |
xgboost.pkl |
Trained XGBoost model |
lstm.pt |
Trained LSTM model weights |
gru.pt |
Trained GRU model weights |
cnn.pt |
Trained CNN model weights |
lstm_attention.pt |
Trained LSTM+Attention model weights |
transformer.pt |
Trained Transformer model weights |
shap_values.pkl |
SHAP feature importance values |
📊 Dataset
Training data is available separately: 🔗 AdityaaXD/Multi-Model-Trading-Data
- Ticker: BTC-USD
- Date Range: 2015-01-01 to 2025-01-01
- Total Samples: ~3,600 days
- Train/Test Split: 80/20
⚠️ Disclaimer
This model is for educational and research purposes only. It should NOT be used for actual trading decisions. Cryptocurrency markets are highly volatile and past performance does not guarantee future results.
📊 SHAP Explainability
The model includes SHAP (SHapley Additive exPlanations) values for feature importance analysis, helping understand which technical indicators most influence predictions.
🛠️ Training Details
- Hyperparameter Tuning: GridSearchCV with 3-fold CV
- Deep Learning: 50 epochs, early stopping (patience=7)
- Regularization: Label smoothing (0.1), gradient clipping (1.0)
- Class Balancing: Weighted loss functions
📝 Citation
@misc{ai-trading-bot-2025,
title={AI Multi-Model Trading Bot},
author={Your Name},
year={2025},
publisher={Hugging Face}
}