#CurrencyPairPrediction Currency Exchange Approach for Supervised Training In supervised learning for forex, the currency exchange approach involves framing prediction tasks using historical exchange rate data to forecast future movements: Target Definition: Models are trained to predict future price levels, returns, or direction (up/down) of currency pairs. Input Features: Includes technical indicators, economic data, lagged prices, and sometimes macro news sentiment. Labeling Strategy: Supervised training requires clearly defined labels, such as price change thresholds or directional moves over a set time horizon. Data Challenges: High volatility, noise, and market events make careful feature selection and robust validation critical. Multi-Currency Consideration: Models may need to account for correlations and interactions among different currency pairs. This approach enables data-driven forecasting in forex trading, allowing for systematic strategy development using machine learning.