rustyneuron01/BTC-ETH-SOL-Price-Predict

Ensemble price forecasting with volatility prediction (XGBoost on cached features). Multiple simulated paths per request; CRPS scoring for calibration and sharpness. Synthetic price data for options and portfolio analytics. Python, XGBoost, NumPy, Pandas, properscoring, Pyth API, PostgreSQL, Pydantic, Docker.

51
/ 100
Established

This system provides probabilistic forecasts for crypto and equity prices, generating 1000 simulated price paths over various timeframes (e.g., 24 hours at 5-minute intervals). It takes current asset prices and historical data as input to produce synthetic price data for training AI agents, options pricing, and portfolio risk management. Traders, quantitative analysts, and portfolio managers who need robust, high-quality price distribution forecasts would use this.

Use this if you need detailed, probabilistic forecasts of cryptocurrency and equity prices for advanced financial modeling, options pricing, or AI agent training.

Not ideal if you only need simple point price predictions or very short-term (under 5-minute) trading signals.

quantitative-trading portfolio-management financial-modeling options-pricing market-forecasting
No Package No Dependents
Maintenance 10 / 25
Adoption 7 / 25
Maturity 15 / 25
Community 19 / 25

How are scores calculated?

Stars

26

Forks

18

Language

Python

License

MIT

Last pushed

Mar 13, 2026

Commits (30d)

0

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