https://github.com/quantdevjayson/crypto-risk-premia-dashboard
Interactive dashboard for crypto risk premia with factor combination. Quant-grade intelligence for decoding hidden risk premia in crypto and is fully reproducible, noise-engineered, and built for serious researchers and traders seeking alpha beyond the hype.
https://github.com/quantdevjayson/crypto-risk-premia-dashboard
crypto-portfolios crypto-risk-premia crypto-trading-agent cryptocurrency digital-assets digital-assets-trading factor-combination noise-aware-portfolios quant-research-platform quantum-trading z-score-clipping
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Interactive dashboard for crypto risk premia with factor combination. Quant-grade intelligence for decoding hidden risk premia in crypto and is fully reproducible, noise-engineered, and built for serious researchers and traders seeking alpha beyond the hype.
- Host: GitHub
- URL: https://github.com/quantdevjayson/crypto-risk-premia-dashboard
- Owner: QuantDevJayson
- Created: 2025-08-04T12:56:13.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-08-04T14:00:01.000Z (2 months ago)
- Last Synced: 2025-08-04T17:35:33.168Z (2 months ago)
- Topics: crypto-portfolios, crypto-risk-premia, crypto-trading-agent, cryptocurrency, digital-assets, digital-assets-trading, factor-combination, noise-aware-portfolios, quant-research-platform, quantum-trading, z-score-clipping
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Crypto Risk Premia Dashboard
Quant-grade intelligence for decoding hidden risk premia in crypto and is fully reproducible, noise-engineered, and built for serious researchers and traders seeking alpha beyond the hype.
### Why This Project?
Crypto markets are wild, noisy, and poorly mapped — a frontier where meaningful factor signals are buried under extreme volatility and speculative noise. Traditional equity factor models don’t translate cleanly, leaving a gap between theory and actionable insight.
This dashboard closes that gap by delivering:
- Clean, factor-based analytics that extract systematic drivers of crypto returns.
- Noise-aware filtering to reveal true premia signals masked by daily chaos.
- Reproducible, research-ready frameworks ideal for academic studies, quant funds, and high-level strategy design.
- A single interactive environment that unifies market, momentum, low-volatility, and network-value factors — and lets you extend to new ones as the space evolves.
## Features
- Fetches and cleans OHLCV data for major cryptos (BTC, ETH, altcoins) via yfinance
- Applies rolling median, z-score clipping, EMA smoothing, and more to reduce noise
- Computes market, momentum, low-volatility, network value, and custom factors
- Multi-factor backtest integration (momentum + low-volatility)
- Interactive Streamlit dashboard with raw vs denoised data comparisons
- Expanded dashboard pages: Market Risk Premium, Momentum, Low Volatility, Network Value, Factor Portfolio
- Dark institutional theme for quant feel
- Extensible: add new factors, filters, or data sources easily
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## Data Pipeline
1. **Source:** Yahoo Finance (yfinance) for daily/hourly OHLCV
2. **Noise Reduction:** Rolling median, z-score clipping, EMA, optional Kalman filter
3. **Factor Computation:** Market premium, momentum, low-volatility anomaly, etc.
4. **Visualization:** Streamlit dashboard with toggles for raw/cleaned data
## Getting Started
1. Clone the repo
2. Install dependencies: `pip install -r requirements.txt`
3. Run the dashboard: `streamlit run dashboard.py`
## Example Modules
- `data_loader.py`: Data fetching and cleaning
- `factors/`: Factor computation modules
- `dashboard.py`: Streamlit dashboard
## Research Citations
- Sydney Quantitative Finance Symposium, 2023: 'Noise Reduction in Crypto Factor Models'
- EPFL Blockchain Analytics, 2025: 'Analyzing the Predictability of Crypto Markets'
## Roadmap: Next Steps
🔜 Add more advanced noise reduction (Kalman, wavelets)
🔜 Factor correlation heatmaps & regime detection
🔜 Machine learning–driven factor forecasts
🔜 Integration with DeFi metrics (on-chain activity, TVL factors)
🔜 Portfolio optimizer with transaction cost modeling
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*For quant students, researchers, and funds seeking robust, noise-aware crypto analytics.*
# Disclaimer
This project is intended solely for educational purposes and as an innovative guide for
quantitative researchers. It does not constitute investment advice or a recommendation to
buy, sell, or hold any financial asset. Users should conduct their own due diligence and
consult professional advisors before making investment decisions.
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#### GitHub: https://github.com/QuantDevJayson
#### PyPI: https://pypi.org/user/jayson.ashioya
#### LinkedIn: https://www.linkedin.com/in/jayson-ashioya-c-082814176/