Using Machine Learning for live currency trading

Official implementation of "Predicting Systemic Risk in Financial Systems Using Deep Graph Learning"

This is a model that has been trained on historical data obtained from Yahoo Finance. The data set comprises of all data records starting from the launch date of this stock in India (1996). This model aims to pick up key trends in the stock price fluctuations based on Time Series mapping. It is able to render predictions for the upcoming time pe…

💱Machine Learning, Artificial Intelligence, Data Science for Finance💱

Sparse index replication engine: tracks the S&P 500, Nasdaq-100, Russell 2000 and Nifty 50 with a small basket of stocks (~10% of each index) using a custom ADMM solver for L1-regularized portfolio optimization. Built for direct indexing, tax-loss harvesting and low-cost benchmark tracking. Python, FastAPI, Next.js, Azure.

University project provided by Euklid, a company active in the data driven investment field that aim to manage savings and investments through thousands of different algorithms. The commodities analyzed are Gold, Oil, IBM, and NASDAQ

Machine Learning for French Stock Market Prediction

An end-to-end Automated ML pipeline for empirical asset pricing & DJI forecasting. Bridges econometric rigor with modern AI using H2O AutoML. Features include advanced preprocessing (Winsorization, ADF), statistical validation via the Diebold-Mariano test, and model explainability using SHAP values. Optimized for reproducible quantitative research.

finance machine learning projects

This project successfully demonstrates practical machine learning application in financial prediction, achieving exceptional accuracy while maintaining simplicity. The combination of robust data processing, intelligent feature engineering, and user-friendly application features creates a powerful tool for data-driven investment decisions.

JPMorgan Chase stock analysis and price prediction model -Python, yfinance, scikit-learn, 58% accuracy

stock price prediction using machine learning models for short-term forecasting based on historical market data.

Test the d-invariance hypothesis of the fixed-width fractional differencing operator across VIX-based volatility segmentation using a leakage-controlled research pipeline; methodology is public, proprietary information and raw data are excluded.

📈 Forecast daily log-returns of the Dow Jones Industrial Average using an Automated Machine Learning pipeline that combines economic data and computational techniques.