---
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 |

## 📊 Dataset

Training data is available separately:
**🔗 [AdityaaXD/Multi-Model-Trading-Data](https://huggingface.co/datasets/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

```bibtex
@misc{ai-trading-bot-2025,
  title={AI Multi-Model Trading Bot},
  author={Your Name},
  year={2025},
  publisher={Hugging Face}
}
```