🧠 CryptoSentimentTrader 📊

A smart crypto trading pipeline that combines social sentiment, news analysis, on-chain metrics, and machine learning to generate and backtest trading signals for Bitcoin.

🚀 Features

• 🔎 Multi-source Data Collection:

  • Reddit posts (r/Bitcoin)
  • News articles (via NewsAPI)
  • Blockchain on-chain transaction data

• 📈 Sentiment Analysis:

  • VADER and TextBlob sentiment scores
  • Optional BERT-based transformer sentiment
  • Rolling sentiment smoothing

• 💡 Trading Signal Generation:

  • Simple rules based on sentiment thresholds
  • Buy/Hold/Sell labeling

• 🧪 Backtesting with Backtrader:

  • Strategy simulation on real-time BTC prices
  • Final portfolio value calculation

• 🤖 Machine Learning Classifier:

  • XGBoost model trained on historical sentiment/price data
  • Accuracy and classification reporting

• ⚙️ Sharpe Ratio Optimization:

  • Automated threshold tuning for maximum risk-adjusted returns

📂 File Structure

Crypto.py                # Main script
sentiment_data.csv       # Intermediate file with collected raw sentiment data
sentiment_trading_data.csv # Processed file with sentiment + signals

🛠️ Setup & Installation

1. Clone this repository:

   git clone https://github.com/your-username/CryptoSentimentTrader.git
   cd CryptoSentimentTrader

2. Install required packages:

   pip install -r requirements.txt

   Example requirements.txt:

   praw
   requests
   pandas
   newsapi-python
   nltk
   textblob
   transformers
   backtrader
   xgboost
   scikit-learn
   tf-keras
   

3. Download NLTK data:

   import nltk
   nltk.download("vader_lexicon")

4. Replace the following with your own API keys:

   • client_id, client_secret in the Reddit section
   • NEWS_API_KEY for the NewsAPI

📊 Example Output

✅ Accuracy: 0.78
📈 Best Sharpe Ratio: 1.42
🚀 Optimized Buy Threshold: 0.75
💰 Final Portfolio Value: $11,326.52

📌 Notes

• API rate limits may affect large data pulls.
• Transformer sentiment is optional but can be enabled for deeper NLP insight.
• Model assumes short-term prediction—do not use for long-term financial advice.

🤝 Contribution

Feel free to fork, improve, or contribute PRs. Suggestions welcome!

📜 License

MIT License