This project features a Python‑based trading bot integrated with an Interactive Brokers (IB) account. It monitors real‑time price movements, identifies self‑developed trading patterns, and executes trades autonomously. The system combines algorithmic precision with adaptive learning, achieving a success rate above 60% and an annual return approaching 100%. Designed for reliability and speed, it operates continuously during market hours with minimal human intervention.
Challenge
Manual trading is limited by reaction time and emotional bias. Detecting specific price patterns across multiple instruments in real time is nearly impossible for a human trader. The client needed a fully automated system capable of identifying custom‑defined setups, executing trades instantly, and managing risk dynamically — all while maintaining compliance with Interactive Brokers’ API protocols.
Solution
We developed a Python trading bot that connects directly to the IB API for live market data and order execution. The bot continuously scans tick‑level data for predefined patterns using a custom algorithm trained on historical datasets. When a valid setup appears, it automatically places, monitors, and closes trades according to the strategy’s rules. The system includes modules for risk management, logging, and performance analytics. It runs on a dedicated server for uninterrupted operation and uses asynchronous data handling to ensure millisecond‑level responsiveness.
Result
The trading bot achieved a consistent success rate above 60% and an annualized return near 100%. It eliminated emotional decision‑making, improved trade timing, and provided detailed performance metrics for ongoing optimization. The system now operates autonomously, allowing the trader to focus on strategy refinement rather than execution.
Technologies
Python, Docker