nogibjj/Flamingo-ML
Forecasting Bitcoin returns through time series analysis, emphasizing sentiment analysis on news (using BERT LLM), social media, and Google search trends, with the final model based on Random Forest, augmented with engineered memory features.
This project helps cryptocurrency traders and investors predict Bitcoin's 15-day moving average daily returns. It takes historical Bitcoin price data, news sentiment, social media trends (like Twitter), and Google search trends as input. The output is a prediction of Bitcoin's short-term price movement, aiding in trading decisions.
No commits in the last 6 months.
Use this if you are a cryptocurrency trader or investor looking for a model to forecast Bitcoin's short-term price movements based on a wide array of market and sentiment data.
Not ideal if you need to predict long-term Bitcoin trends or are interested in cryptocurrencies other than Bitcoin.
Stars
8
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Apr 21, 2024
Commits (30d)
0
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