# Gaussian Process Application Repos for Trading — Comprehensive List Here are the GitHub repositories that apply Gaussian Processes (GP) specifically to trading, stock market prediction, and financial forecasting: --- ## 🥇 Most Popular / Foundational ### 1. **gdroguski/GaussianProcesses** ⭐ 162 — *Python3 project applying GP regression for forecasting stock trends* - https://github.com/gdroguski/GaussianProcesses - The most popular repo in this space (162 stars, 33 forks). Uses Scikit-Learn's GP implementation to predict stock prices. - Many other repos (ruishizou, shahriar-rahman) cite this as their inspiration/starting point. --- ## 🏛️ Academic / Research-Grade Framework ### 2. **dthomakos/Prognostikon-Code** ⭐ 11 — *GPT: Gaussian Process Trading & Forecasting* - https://github.com/dthomakos/Prognostikon-Code - Contains **GPT_v3.py** and **GPT_v3_multiasset.py** — the core files of the "Gaussian Process Trading" framework by Prof. Dimitrios Thomakos (National & Kapodistrian University of Athens). - A full algorithmic trading framework combining GP regression with Chebyshev polynomial basis functions. - Also includes many other trading strategies (speculative Bayesian, Cox trading system, etc.). --- ## 📈 Stock Price Prediction with GP Regression ### 3. **Lukeli0425/Stock-Forecast-with-GPR** ⭐ 6 — *Stock Forecast with GP Regression* - https://github.com/Lukeli0425/Stock-Forecast-with-GPR - Course project implementing GPR with scikit-learn. Compares SE (Squared Exponential), ESS (Exp Sine Squared), and combined kernels. - Directly ingests Nasdaq CSV data for prediction. ### 4. **ruishizou/Stochastic-GaussianProcess** ⭐ 0 — *GPyTorch Stock Price Prediction* - https://github.com/ruishizou/Stochastic-GaussianProcess - GP implemented in **GPyTorch** (PyTorch-based) for stock price prediction. Compares kernel functions across quarterly data. - Includes interactive GP visualization notebooks. ### 5. **TROUBADOUR000/Stock-Market-Prediction-via-Gaussian-Processes-Regression** - https://github.com/TROUBADOUR000/Stock-Market-Prediction-via-Gaussian-Processes-Regression - Stock market prediction using GP regression. --- ## 📊 S&P 100 / Large-Cap Focus ### 6. **abhi-pa/Predicting-S-P-100-Stock-Trends-Using-Gaussian-Process-Regression-and-Classification** - https://github.com/abhi-pa/Predicting-S-P-100-Stock-Trends-Using-Gaussian-Process-Regression-and-Classification - Uses GP regression + classification to forecast S&P 100 stock trends over 2000–2024. Handles 102 columns of stock data. --- ## 🔧 Bayesian Optimization for Trading Strategies ### 7. **fcucullu/optimization_algorithms** ⭐ 1 — *Bayesian Optimization for Trading Strategy Tuning* - https://github.com/fcucullu/optimization_algorithms - Uses **Gaussian Process-based Bayesian optimization** to hyperparameter-tune trading strategies. GP acts as a surrogate model to find optimal strategy parameters efficiently. --- ## 🪙 Cryptocurrency Trading ### 8. **dnavanith/crypto-bayesian-trading-system** ⭐ 0 — *Cryptocurrency Bayesian Trading* - https://github.com/dnavanith/crypto-bayesian-trading-system - Uses **Gaussian Naive Bayes** (not full GP regression, but the same Bayesian family) with 80+ technical indicators. - Real-time data from KuCoin Futures API, deployed on Google Cloud. --- ## 🧪 Direct Fork / Variants of #1 ### 9. **shahriar-rahman/Gaussian-Processes-Stock-Trends** ⭐ 2 - https://github.com/shahriar-rahman/Gaussian-Processes-Stock-Trends - Fork of gdroguski/GaussianProcesses. Same GP regression approach for stock forecasting. ### 10. **suenot/270-gaussian-process-trading** ⭐ 0 - https://github.com/suenot/270-gaussian-process-trading - GP trading application with detailed explanatory README covering GP theory (kernel functions, posterior distributions) applied to trading signals. --- ## Summary Table | # | Repo | Stars | Focus | Key Library | |---|------|-------|-------|-------------| | 1 | gdroguski/GaussianProcesses | ⭐ 162 | Stock trend forecasting | Scikit-Learn GP | | 2 | dthomakos/Prognostikon-Code | ⭐ 11 | Full GP trading framework | Scikit-Learn + Chebyshev | | 3 | Lukeli0425/Stock-Forecast-with-GPR | ⭐ 6 | GPR with kernel comparison | Scikit-Learn GP | | 4 | ruishizou/Stochastic-GaussianProcess | ⭐ 0 | GPyTorch stock prediction | GPyTorch / PyTorch | | 5 | TROUBADOUR000/Stock-Market-Prediction... | — | GP regression for stocks | Scikit-Learn | | 6 | abhi-pa/Predicting-S-P-100... | ⭐ 0 | S&P 100 GP + classification | Scikit-Learn | | 7 | fcucullu/optimization_algorithms | ⭐ 1 | GP Bayesian opt for strategies | Scikit-Learn | | 8 | dnavanith/crypto-bayesian-trading | ⭐ 0 | Crypto GP (Naive Bayes) | Scikit-Learn GNB | | 9 | shahriar-rahman/Gaussian-Processes... | ⭐ 2 | Fork of #1 | Scikit-Learn GP | | 10 | suenot/270-gaussian-process-trading | ⭐ 0 | GP trading with theory docs | Scikit-Learn GP | **For the most mature and feature-rich option**, look at **dthomakos/Prognostikon-Code** (the GPT framework). For the **most popular**, **gdroguski/GaussianProcesses** is the clear winner. For **GPyTorch/PyTorch** implementations, check out **ruishizou/Stochastic-GaussianProcess**.