Here you can find all the 8 projects of WorldQuant's Data Science Program along with my certification. I had no references to get clues from when studying this program so I hope this proves handy to others.

This repo contains all the files material releated to WorldQuant University's Data Science Summer 2020 Session Unit 2: Machine Learning and Statistical Analysis

Project Work for the WorldQuant University (Passing year : 2017)

Coursework, projects, and datasets from the MSc in Financial Engineering (MScFE) program at WorldQuant University.

WQU Data Science Module Unit 1

Forecast multi-asset ETF returns and generate dynamic portfolio rebalancing signals using LSTM neural networks for tactical asset allocation.

This repository highlights the projects I did as a student of the Applied Data Science Lab at WorldQuant University.

Build Machine Learning Models, Finance Models, Research and Manage Portfolios

A quantitative finance toolkit addressing model reliability through hyperparameter optimization (GridSearch/LassoCV), bias-variance tradeoff analysis, and ensemble learning (Random Forests and Stacking) applied to S&P 500 and Sector ETF data.

An end-to-end quantitative portfolio management project built in Python. This repository explores advanced asset allocation techniques, including Mean-Variance Optimization (MVO), Fama-French 5-Factor style analysis, Monte Carlo simulations, and the Black-Litterman model.

A quantitative finance project that explores the intersection of traditional technical analysis and alternative data. It replicates academic methodologies for predicting stock market movements using classification models, and demonstrates the practical application of NLP sentiment analysis (VADER) in financial modeling.

Modern portfolio optimization using constrained Kelly, HRP, K-Fold cross-validation, and Marčenko-Pastur denoising to improve risk-adjusted returns.

A comparative analysis of MLP and CNN (GAF) models for time series forecasting on AAPL stock, exploring stationarity, log returns, and fractional differencing.

A comprehensive quantitative handbook applying machine learning methodologies (Regularization, Clustering, PCA, and Decision Trees) to real-world financial data for market regime detection and trading strategy optimization.

A comprehensive end-to-end deep learning finance project implementing tactical asset allocation strategies using LSTM neural networks to forecast multi-asset ETF returns and generate dynamic portfolio rebalancing signals.

An advanced Python framework for pricing financial derivatives beyond Black-Scholes using the Heston Stochastic Volatility Model and the Merton Jump Diffusion Model. The project evaluates European, American, and Barrier options, analyzes strike sensitivities, and computes Greeks using Monte Carlo simulations.

A comprehensive Python framework for financial derivative valuation using numerical methods. This project implements Binomial and Trinomial trees to price European and American options, analyzes sensitivities (Greeks), and simulates dynamic delta hedging strategies.

An advanced quantitative finance toolkit expanding "Marketing Alpha" strategies. Applies non-linear machine learning models (LDA, SVM, Neural Networks) and refined linear methods to tackle complex financial time series forecasting and market regime classification.

A quantitative finance project utilizing Hidden Markov Models (HMM) to infer market volatility regimes from the VIX index and dynamically rotate assets among SPY, TLT, and GLD.