Guides, deep dives, and practical insights on automated trading, ML signals, data sources, and risk management for Interactive Brokers.
A comprehensive guide to free data sources for building and running algorithmic trading strategies — from government filings to social sentiment.
Step-by-step tutorial: connect Python to Interactive Brokers, place your first paper trade, and add ML signals to your strategy.
Key differences between simulated and real trading, how to evaluate paper results, and a checklist for going live with confidence.
A practical comparison of two ML models used in trading signal generation — strengths, weaknesses, and when to use each.
A buyer's guide to evaluating automated trading software — from transparency to risk controls to support.
How STOCK Act disclosures create a unique data source for algorithmic trading strategies.