VictorHoffmann1/ENS-Challenge-Data-Cryptocurrency-Clusters
This challenge organized by ENS Ulm and Collège de France was about predicting mean return of cluster's assets relatively to the bitcoin during the last hour of the day, given the last 23 hours.
This project helps cryptocurrency traders and analysts predict the hourly mean return of specific cryptoasset clusters relative to Bitcoin's performance. It takes 23 hours of historical cryptocurrency data as input and outputs a prediction for the next hour's relative return. Traders, quantitative analysts, or portfolio managers focused on crypto markets would find this useful for short-term forecasting.
No commits in the last 6 months.
Use this if you need a machine learning approach to forecast short-term relative returns of cryptocurrency clusters against Bitcoin.
Not ideal if you are looking for long-term cryptocurrency price predictions or a solution that doesn't focus on relative performance to Bitcoin.
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9
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Language
Jupyter Notebook
License
MIT
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Last pushed
Sep 28, 2023
Commits (30d)
0
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