SentiTrader is a medium-frequency trading bot that utilizes sentiment analysis from news articles to make trading decisions. The bot scrapes data from r/worldnews and employs machine learning models to predict stock market trends, testing its accuracy on an Alpaca paper trading account.
The core of SentiTrader relies on machine learning models trained on sentiment analysis data to predict market trends. Models used:
- Random Forest
- Multilayer Perceptron (MLP)
- Linear Discriminant Analysis (LDA)
These models were trained and evaluated using sentiment-labeled financial news data.
📘 Full implementation available on Kaggle
- Web scraping news articles from r/worldnews for sentiment analysis.
- Predictive analysis powered by ML models.
- Integrated with Alpaca for live paper trading.
- Achieved approximately 12% annualized returns (currently in testing phase).
ai.py– Contains AI model and trading logic.bot.py– Core trading bot implementation.scraper.py– Web scraping and data extraction.sentiment.py– Sentiment analysis logic.trader.py– Bot control and trading strategy.
- Clone the repository:
git clone https://github.com/KoralK5/SentiTrader.git