{"id":30307360,"url":"https://github.com/quantdevjayson/crypto-risk-premia-dashboard","last_synced_at":"2025-10-05T04:16:05.442Z","repository":{"id":308170659,"uuid":"1031865949","full_name":"QuantDevJayson/crypto-risk-premia-dashboard","owner":"QuantDevJayson","description":"Interactive dashboard for crypto risk premia with factor combination. 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Traditional equity factor models don’t translate cleanly, leaving a gap between theory and actionable insight.\n\nThis dashboard closes that gap by delivering:\n- Clean, factor-based analytics that extract systematic drivers of crypto returns.\n- Noise-aware filtering to reveal true premia signals masked by daily chaos.\n- Reproducible, research-ready frameworks ideal for academic studies, quant funds, and high-level strategy design.\n- 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.\n\n\n## Features\n- Fetches and cleans OHLCV data for major cryptos (BTC, ETH, altcoins) via yfinance\n- Applies rolling median, z-score clipping, EMA smoothing, and more to reduce noise\n- Computes market, momentum, low-volatility, network value, and custom factors\n- Multi-factor backtest integration (momentum + low-volatility)\n- Interactive Streamlit dashboard with raw vs denoised data comparisons\n- Expanded dashboard pages: Market Risk Premium, Momentum, Low Volatility, Network Value, Factor Portfolio\n- Dark institutional theme for quant feel\n- Extensible: add new factors, filters, or data sources easily\n\n-----\n\n\u003cimg width=\"942\" height=\"428\" alt=\"dashboard\" src=\"https://github.com/user-attachments/assets/3b778220-2181-49db-8aba-3792971f7287\" /\u003e\n\n-----\n\n\u003cimg width=\"953\" height=\"425\" alt=\"factor_combination_crypto_risk_premia\" src=\"https://github.com/user-attachments/assets/f70698af-6538-4172-bd5a-14f7c4d181fd\" /\u003e\n\n\n## Data Pipeline\n1. **Source:** Yahoo Finance (yfinance) for daily/hourly OHLCV\n2. **Noise Reduction:** Rolling median, z-score clipping, EMA, optional Kalman filter\n3. **Factor Computation:** Market premium, momentum, low-volatility anomaly, etc.\n4. **Visualization:** Streamlit dashboard with toggles for raw/cleaned data\n\n## Getting Started\n1. Clone the repo\n2. Install dependencies: `pip install -r requirements.txt`\n3. Run the dashboard: `streamlit run dashboard.py`\n\n## Example Modules\n- `data_loader.py`: Data fetching and cleaning\n- `factors/`: Factor computation modules\n- `dashboard.py`: Streamlit dashboard\n\n## Research Citations\n- Sydney Quantitative Finance Symposium, 2023: 'Noise Reduction in Crypto Factor Models'\n- EPFL Blockchain Analytics, 2025: 'Analyzing the Predictability of Crypto Markets'\n\n## Roadmap: Next Steps\n🔜  Add more advanced noise reduction (Kalman, wavelets)\n\n🔜 Factor correlation heatmaps \u0026 regime detection\n\n🔜 Machine learning–driven factor forecasts\n\n🔜 Integration with DeFi metrics (on-chain activity, TVL factors)\n\n🔜 Portfolio optimizer with transaction cost modeling\n\n---\n\n*For quant students, researchers, and funds seeking robust, noise-aware crypto analytics.*\n\n# Disclaimer\nThis project is intended solely for educational purposes and as an innovative guide for \nquantitative researchers. It does not constitute investment advice or a recommendation to \nbuy, sell, or hold any financial asset. Users should conduct their own due diligence and \nconsult professional advisors before making investment decisions.\n\n---\n\n#### GitHub: https://github.com/QuantDevJayson\n#### PyPI: https://pypi.org/user/jayson.ashioya\n#### LinkedIn: https://www.linkedin.com/in/jayson-ashioya-c-082814176/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantdevjayson%2Fcrypto-risk-premia-dashboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantdevjayson%2Fcrypto-risk-premia-dashboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantdevjayson%2Fcrypto-risk-premia-dashboard/lists"}