AI-powered Trading Bot Creation

Introduction

• Discusses the creation of an AI-powered, automated trading bot.
• References algorithmic trading by major hedge funds like Renaissance and Two Sigma.
• Aims to break down the process into five steps, starting with building a baseline bot.

Step 1: Building the Baseline Bot

1. File Creation
   • Create a new file: trading_bot.py
2. Import Dependencies
   • Use the lumot library as a trading framework.
   • Import necessary components: Broker, YahooDataBacktesting, Strategy, and Trader.
3. API Setup
   • Create variables for API key, secret, and base URL.
   • Generate keys from an Alpaca account.
4. Broker and Strategy Setup
   • Set up Alpaca broker with credentials.
   • Create a class MLTrader inheriting from Strategy class.
   • Define lifecycle methods: initialize and on_trading_iteration.
5. Backtesting Framework
   • Define start and end dates for backtesting.
   • Initialize strategy parameters.

Step 2: Position Sizing and Limits

1. Dynamic Position Sizing
   • Implement function position_sizing to calculate cash and determine position size.
   • Use cash_at_risk to manage risk.
2. Order and Limits
   • Set up order types (e.g., market, limit, bracket).
   • Implement take profit and stop loss strategies.

Step 3: Getting News

• Aim to dynamically fetch news from Alpaca API.
• Create method get_news with API integration.
• Calculate current dates for news retrieval using timedelta.
• Format and return news data for further processing.

Step 4: Integrating Machine Learning for Sentiment Analysis

1. Sentiment Model Setup
   • Utilize FinBERT model for financial sentiment analysis.
   • Define get_sentiment method to interpret news.
2. Testing Model
   • Run sentiment analysis on example news headlines.
   • Print model output: sentiment and probability.

Step 5: Integrating Sentiment into Trading Strategy

1. Decision Logic Implementation
   • Define conditions to buy/sell based on sentiment and probability.
   • Implement checks for existing orders and manage positions.
2. Error Handling and Debugging
   • Address errors like SSL issues and variable misassignment.
3. Backtesting and Results
   • Run and validate the trading strategy over a 4-year period.
   • Analyze performance metrics: cumulative return, annual growth rate.
   • Highlight paper trading limitations.

Conclusion

• Summary of the bot's performance compared to the S&P index.
• Includes a note on paper trading versus live trading environments.
• Final thoughts on the potential of AI and machine learning in trading.