carry-trade

In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

Quantitative analysis of crypto carry trade strategies (funding rates, staking, Pendle) on BTC/ETH, using historical data and visual insights.

Final project for the Investments course at EPFL. We evaluate the benefits of international diversification and various dynamic strategies (momentum, reversal, carry, dollar) using real market data. The project includes code, data processing, visualizations, and a full written report.

Quantitative analysis of the yen carry trade (2015-2025): data pipeline, regression, bucketed statistics, and P&L simulation in Python.

Single-page React app to monitor G10 currency carry trades

A professional-grade Python tool designed for crypto hedge funds to fetch, analyze, visualize, and backtest funding rate strategies across major cryptocurrency derivatives exchanges.

G10 cross-currency basis + FX carry/value/momentum factor model: a from-scratch research stack (CIP/OIS basis, Fama-MacBeth cross-sectional fair value, dollar-neutral portfolios, variance targeting, realistic costs, Newey-West inference, honest tail/skew diagnostics).

Moniteur du carry trade yen et du positionnement speculatif (CFTC, BCE, FRED). Architecture snapshot, privacy-first.