Update README.md

README.md

@@ -1,115 +1,128 @@
----
-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}
-}
-```
+---
+license: mit
+tags:
+- finance
+- trading
+- bitcoin
+- cryptocurrency
+- machine-learning
+- deep-learning
+- lstm
+- transformer
+- xgboost
+- random-forest
+- shap
+- code
+- agent
+- legal
+language:
+- en
+library_name: adapter-transformers
+pipeline_tag: tabular-classification
+datasets:
+- open-index/hacker-news
+- lambda/hermes-agent-reasoning-traces
+- ropedia-ai/xperience-10m
+metrics:
+- accuracy
+- code_eval
+- bertscore
+- character
+- brier_score
+---
+
+# 📈 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}
+}
+```