📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).

Build a statistical risk model using PCA. Optimize the portfolio using the risk model and factors using multiple optimization formulations.

A deep reinforcement learning framework for generating formulaic alpha factors for quantitative investment, powered by GFlowNet, implemented in Python&PyTorch.

Contains detailed and extensive notes on quantitative trading, leveraging NLP for finance, backtesting, alpha factor research, portfolio management and optimization.

Rep tho share codes and projects from the Artificial Intelligence for Trading Algorithms course @udacity.

📈This repo describes a framework that leverages sentiment stability of a financial 10-K report as the trading signal (alpha factor)

📈This repo describes a framework that leverages sentiment stability of a financial 10-K report as the trading signal (alpha factor)

Advanced GAS Price Level Indicator combines machine learning-inspired alpha factors with traditional technical analysis, providing adaptive, correlation-weighted signals for high-probability trading opportunities.

Construction of PCA class from scratch and 3 implementations of PCA.

Production-grade pipeline transforming raw OHLCV into 77 stationary, ML-ready features. Includes anti-leakage validation, TimeSeriesSplit selection, and 6 signal families for financial time series.

End-to-end quantitative trading system for Taiwan stocks — DoubleEnsemble model, 300+ factors, Walk-Forward backtesting, React dashboard