https://github.com/studiofarzulla/sentiment-microstructure-abm
Real-time crypto market ABM fusing social sentiment with order-book microstructure
https://github.com/studiofarzulla/sentiment-microstructure-abm
Last synced: 4 months ago
JSON representation
Real-time crypto market ABM fusing social sentiment with order-book microstructure
- Host: GitHub
- URL: https://github.com/studiofarzulla/sentiment-microstructure-abm
- Owner: studiofarzulla
- License: mit
- Created: 2025-10-26T17:04:58.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2026-02-06T02:53:33.000Z (4 months ago)
- Last Synced: 2026-02-06T12:04:10.612Z (4 months ago)
- Language: Python
- Size: 6.99 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# The Extremity Premium
**Sentiment Regimes and Adverse Selection in Cryptocurrency Markets**
[](https://doi.org/10.5281/zenodo.17989810)
[](https://creativecommons.org/licenses/by/4.0/)
[](https://doi.org/10.5281/zenodo.17989810)
**Working Paper DAI-2510** | [Dissensus AI](https://dissensus.ai)
## Abstract
Using the Crypto Fear & Greed Index and Bitcoin daily data, we document that sentiment extremity predicts excess uncertainty beyond realized volatility. Extreme fear and extreme greed regimes exhibit significantly higher spreads than neutral periods---a phenomenon we term the "extremity premium." Extended validation on the full Fear & Greed history (February 2018--January 2026, N = 2,896) confirms the finding: within-volatility-quintile comparisons show a significant premium (p < 0.001, Cohen's d = 0.21), Granger causality from uncertainty to spreads is strong (F = 211), and placebo tests reject the null (p < 0.0001). The effect replicates on Ethereum and across 6 of 7 market cycles. However, the premium is sensitive to functional form: comprehensive regression controls absorb regime effects, while nonparametric stratification preserves them. We interpret this as evidence that sentiment extremity captures volatility-regime interactions not fully represented by parametric controls---consistent with, but not conclusively separable from, the F&G Index's embedded volatility component. An agent-based model reproduces the pattern qualitatively. The results suggest that intensity, not direction, drives uncertainty-linked liquidity withdrawal in cryptocurrency markets, though identification of "pure" sentiment effects from volatility remains an open challenge.
## Key Findings
| Finding | Result |
|---------|--------|
| Extremity premium significance | p < 0.001, Cohen's d = 0.21 |
| Granger causality (uncertainty to spreads) | F = 211 |
| Replication across assets | Confirmed on Ethereum |
| Replication across market cycles | 6 of 7 cycles |
| Placebo tests | Reject null (p < 0.0001) |
## Keywords
extremity premium, sentiment regimes, adverse selection, market microstructure, cryptocurrency, agent-based modeling
## Repository Structure
```
sentiment-microstructure-abm/
├── paper/ # LaTeX source and PDF
│ ├── main.tex # Paper source
│ ├── main.pdf # Compiled paper
│ ├── references.bib # Bibliography
│ ├── figures/ # Paper figures
│ └── tables/ # Paper tables
├── agents/ # Agent implementations (Market Maker, Informed, Noise, Arbitrageur)
├── simulation/ # Mesa ABM environment and matching engine
├── signals/ # Signal processing
├── analysis/ # Statistical analysis scripts
├── data_ingestion/ # Binance + Reddit API clients
├── feature_engineering/ # Microstructure features and sentiment analyzer
├── monitoring/ # Dashboard and metrics
├── demo/ # Demo scripts
├── tests/ # Test suite
├── config/ # Configuration files
├── arxiv-submission/ # arXiv submission package
├── CITATION.cff
├── requirements.txt
└── LICENSE
```
## Citation
```bibtex
@article{farzulla2026extremity,
author = {Farzulla, Murad},
title = {The Extremity Premium: Sentiment Regimes and Adverse Selection in Cryptocurrency Markets},
year = {2026},
journal = {Dissensus AI Working Paper DAI-2510},
doi = {10.5281/zenodo.17989810}
}
```
## Authors
- **Murad Farzulla** -- [Dissensus AI](https://dissensus.ai) & King's College London
- ORCID: [0009-0002-7164-8704](https://orcid.org/0009-0002-7164-8704)
- Email: murad@dissensus.ai
## License
Paper content: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)