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.
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.
Stars
26
Forks
18
Language
Python
License
MIT
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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