Developer builds MT5 trading bot using Python, Smart Money Concept, and machine learning

A software developer has detailed the architecture of an automated trading bot built with Python and connected to the MetaTrader 5 platform via its official Python package. The bot uses Smart Money Concept techniques — including Order Blocks, Fair Value Gaps, and Liquidity Sweeps — to identify potential trade entries instead of relying on traditional indicators. A machine learning layer, trained on over 200 logged trades, applies logistic regression to filter low-quality signals based on contextual features such as session timing, RSI, and ATR. The developer chose Python over MQL5 for its richer ecosystem, enabling easier integration with machine learning libraries, Telegram, and web dashboards. Key lessons from the project include the importance of flexible entry conditions, consistent feature engineering between training and inference, and walk-forward backtesting to prevent overfitting.