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The raw data collected is transformed into features that the AI model can use. For example, time series data can be transformed into moving averages, volatility indicators, etc. Feature engineering helps the model understand which factors are most relevant to predicting market movements.
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Supervised Learning: Models are trained on labeled historical data to predict price movements or market trends. Reinforcement Learning: The AI learns from the market by making decisions, observing the results, and improving its strategy over time. Natural Language Processing (NLP): AI can use NLP to analyze news articles, financial reports, or social media to gauge sentiment and its potential market impact.