Volatility prediction tool for memecoins using sentiment analysis and time series models
Memecoins
Sentiment Analysis
Volatility
Natural Language Processing
Data Visualization
Big Data
Python

This project develops an advanced tool to predict the volatility of Memecoins using sentiment analysis from social media and news. By integrating emotional content from digital platforms with historical price data, the tool leverages Natural Language Processing (NLP) and machine learning algorithms to identify correlations between collective sentiment and market movements. The system includes interactive visualizations that provide valuable insights for investors, helping them make more informed decisions in highly speculative markets.
Results
- Significant correlations found between collective sentiment and daily volatility of memecoins.
- Certain emotional indicators tend to precede sharp market movements.
- Interactive visualizations allow intuitive exploration of the temporal evolution of these relationships.
Applications
- Provides valuable insights for investors in highly speculative markets.
- Contributes to more informed approaches in automated investment contexts.