#CurrencyPairPrediction Integrating Supervised Learning in Trading Bots Integrating supervised learning into trading bots enhances decision-making by allowing the bot to predict market movements based on historical data. Key components include: Model Integration: The trained model is embedded into the bot’s architecture to generate buy/sell signals based on real-time inputs. Feature Engineering: Relevant financial indicators, price patterns, and news sentiment are used as inputs to improve prediction accuracy. Execution Logic: The bot must translate model predictions into actionable trades while considering timing, slippage, and transaction costs. Risk Management: Stop-loss, position sizing, and capital allocation strategies are essential to control risk. Continuous Learning: Models can be periodically retrained or updated to adapt to changing market conditions. This integration enables automated, data-driven trading with the potential for improved consistency and reduced emotional bias.