| ---
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| license: mit
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| tags:
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| - finance
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| - trading
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| - bitcoin
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| - cryptocurrency
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| - machine-learning
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| - deep-learning
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| - lstm
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| - transformer
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| - xgboost
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| - random-forest
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| - shap
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| language:
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| - en
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| library_name: pytorch
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| pipeline_tag: tabular-classification
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| ---
|
|
|
| # 📈 AI Multi-Model Trading Bot
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|
|
| A comprehensive cryptocurrency trading signal prediction system using 8 ML/DL models with ensemble voting.
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|
|
| ## 🎯 Model Overview
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|
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| | Model Type | Models Included |
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| | :--- | :--- |
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| | **Traditional ML** | Logistic Regression, Random Forest, XGBoost |
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| | **Deep Learning** | LSTM, GRU, CNN, LSTM+Attention, Transformer |
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| | **Ensemble** | Majority voting across all models |
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|
|
| ## 📊 Features Used
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|
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| The models use 10 technical indicators:
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| - RSI (Relative Strength Index)
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| - MACD & MACD Signal
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| - Bollinger Band Width
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| - ATR (Average True Range)
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| - Distance from SMA50
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| - OBV Percentage Change
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| - ADX (Average Directional Index)
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| - Stochastic RSI (K & D)
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|
|
| ## 🚀 Quick Start
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|
|
| ```python
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| import joblib
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| import torch
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|
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| # Load scaler and config
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| scaler = joblib.load("scaler.pkl")
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| config = joblib.load("config.pkl")
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|
|
| # Load ML model
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| rf_model = joblib.load("random_forest.pkl")
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|
|
| # Load DL model
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| from your_models import LSTMModel
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| lstm = LSTMModel(config['input_dim'])
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| lstm.load_state_dict(torch.load("lstm.pt"))
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| lstm.eval()
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| ```
|
|
|
| ## 📁 Files
|
|
|
| | File | Description |
|
| | :--- | :--- |
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| | `scaler.pkl` | StandardScaler for feature preprocessing |
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| | `config.pkl` | Model configuration (input_dim, timesteps, feature_cols) |
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| | `logistic_regression.pkl` | Trained Logistic Regression model |
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| | `random_forest.pkl` | Trained Random Forest model |
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| | `xgboost.pkl` | Trained XGBoost model |
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| | `lstm.pt` | Trained LSTM model weights |
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| | `gru.pt` | Trained GRU model weights |
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| | `cnn.pt` | Trained CNN model weights |
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| | `lstm_attention.pt` | Trained LSTM+Attention model weights |
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| | `transformer.pt` | Trained Transformer model weights |
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| | `shap_values.pkl` | SHAP feature importance values |
|
|
|
| ## 📊 Dataset
|
|
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| Training data is available separately:
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| **🔗 [AdityaaXD/Multi-Model-Trading-Data](https://huggingface.co/datasets/AdityaaXD/Multi-Model-Trading-Data)**
|
|
|
| - **Ticker**: BTC-USD
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| - **Date Range**: 2015-01-01 to 2025-01-01
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| - **Total Samples**: ~3,600 days
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| - **Train/Test Split**: 80/20
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|
|
| ## ⚠️ Disclaimer
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|
|
| 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.
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|
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| ## 📊 SHAP Explainability
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|
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| The model includes SHAP (SHapley Additive exPlanations) values for feature importance analysis, helping understand which technical indicators most influence predictions.
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|
|
| ## 🛠️ Training Details
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|
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| - **Hyperparameter Tuning**: GridSearchCV with 3-fold CV
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| - **Deep Learning**: 50 epochs, early stopping (patience=7)
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| - **Regularization**: Label smoothing (0.1), gradient clipping (1.0)
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| - **Class Balancing**: Weighted loss functions
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|
|
| ## 📝 Citation
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|
|
| ```bibtex
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| @misc{ai-trading-bot-2025,
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| title={AI Multi-Model Trading Bot},
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| author={Your Name},
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| year={2025},
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| publisher={Hugging Face}
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| }
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| ```
|
|
|
