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README.md

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+---
+license: mit
+tags:
+  - finance
+  - trading
+  - bitcoin
+  - cryptocurrency
+  - machine-learning
+  - deep-learning
+  - lstm
+  - transformer
+  - xgboost
+  - random-forest
+  - shap
+language:
+  - en
+library_name: pytorch
+pipeline_tag: tabular-classification
+---
+
+# 📈 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
+
+```python
+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 |
+| `btc_usd_historical.csv` | Raw BTC-USD price data (2015-2025) |
+| `btc_usd_features.csv` | Processed data with technical indicators |
+
+## 📈 Training 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
+
+```bibtex
+@misc{ai-trading-bot-2025,
+  title={AI Multi-Model Trading Bot},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face}
+}
+```