bhulston/Time-Series-Prediction-with-LSTM-and-XGB
Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data
This project helps quantitative traders and market makers predict short-term changes in cryptocurrency order books. It takes high-frequency, multi-variate order book snapshot data and processes it to extract features, then uses machine learning models to forecast future order book states. The output is a prediction of how the order book will evolve over short time frames, helping traders anticipate price movements.
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Use this if you need to predict multiple future states of cryptocurrency Limit Order Books based on high-frequency data for short-term trading strategies.
Not ideal if you are looking for long-term price predictions or if you are not working with detailed, high-frequency order book data.
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Last pushed
Aug 31, 2023
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