Machine learning algorithms can be used to predict future asset prices based on historical data. By training models on historical price patterns and market conditions, these algorithms can identify trends and make predictions about future price movements.
Machine learning models excel in short-term predictions due to their ability to analyze real-time data and quickly adapt to changing market conditions. Short prediction horizons typically range from a few seconds to minutes, allowing these models to capture and react to market dynamics effectively.
Real-time order book data provides valuable information about market liquidity, supply, and demand. Machine learning models can leverage this data to identify patterns and make informed trading decisions within short timeframes.
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