deep-learning-for-finance

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

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 investigation into the application of Deep Learning architectures (MLP, LSTM, and CNN via Gramian Angular Fields) to predict Bitcoin directional returns, focusing on a rigorous forensic analysis of data leakage.

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

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