AI Research Lab
Every breakthrough below feeds Piggi’s trading brain — your vault gets smarter automatically
Research Logs
Our AI research team publishes monthly logs detailing improvements to our sentiment analysis and trading models. These logs provide technical insights into how we're constantly evolving our algorithms.
May 2024 Research Log
Published May 15, 2024
- Improved emoji sentiment detection
- Added multi-language support
- Enhanced risk scoring model
April 2024 Research Log
Published April 18, 2024
- New meme detection algorithm
- Support for Discord sentiment
- Faster social signal processing
Model Changelog
Track the evolution of our AI models from early prototypes to production-ready systems. Each version introduces improvements in accuracy, performance, and feature set.
Version 1.0
May 15, 2024- Initial production release
- Sentiment analysis with 87% accuracy
- Support for 20 meme tokens
- Advanced risk scoring algorithm
- Real-time price correlation metrics
Version 0.9 (Beta)
April 28, 2024- Beta release with limited token support
- Implemented multi-dimensional sentiment scoring
- Added Discord channel monitoring
- Improved price prediction accuracy by 34%
- New backtesting framework
Downloadable Datasets
Our team has compiled anonymized datasets for researchers and enthusiasts. These datasets provide valuable insights into meme token sentiment patterns.
Sentiment-Price Correlation
1-year historical dataset showing correlation between social sentiment and price movement for top 10 meme tokens.
Meme Token Volatility
Analysis of volatility patterns across different meme token categories during major market events.
Research Publications
Our team regularly publishes academic research on cryptocurrency sentiment analysis, natural language processing techniques, and algorithmic trading strategies.
"Sentiment Analysis of Memetic Token Discussions on Social Media"
Published in Journal of Computational Finance, March 2024
Authors: Dr. Sarah Johnson, Dr. Michael Lee, Piggi Research Team
Abstract: This paper presents a novel approach to sentiment analysis specifically designed for meme token discussions on social media platforms. We propose a hybrid model that combines transformer-based language models with specialized tokenization techniques for emoji and meme-based communication. Our results demonstrate a 23% improvement in sentiment classification accuracy compared to general-purpose sentiment models when applied to cryptocurrency discussions.
Read full paper →Demo AI Models
Try our demo models to experience how Piggi's AI analyzes meme token sentiment. These are simplified versions of our production models.
Interactive Sentiment Demo
See how our AI analyzes social media posts about meme tokens in real-time.
Launch Demo