Applying Machine Learning Techniques To Assess Whether A Country’s Currency Can Predict The Movement Of Their Respective Stock Market Index

Using Copulas model to capture non-linear relationship between stock pairs and conduct statistical arbitrage by pairs trading strategies.

A pairs trading strategy

Find trading pairs with Machine Learning

Projeto de Field Project na Oráma Investimentos que visa o desenvolvimento de mecanismos de arbitragem estatística com estratégia de pairs trading no mercado de ações.

Non-Linear Cointegration in Pairs Trading

In the following research, we will analyze the effects of pairs trading (multiple companies across multiple industries) excluding the profitability of such strategies. Rather, we will analyze various risk measures across all different pairings of stocks within their own respective industry across multiple industries.

This is our undergraduate final year project Stock Prediction Based On Deep Learning And Its Application In Pairs Trading(an interdisciplinary project from data science and financial mathematics)

Pairs Trading screening with cointegration in R

Pairs trading experiment

The algorithm of searching for pontential pairs trading pair

Version 3 of RESTful API for trading stocks (single or pairs), deployed on Heroku

On-going project: I will be implementing a combination of pairs trading strategies in attempt to see which type performs best after backtesting. The main ideas involve cointegration, kalman filter, copulas, and machine learning approaches. Since it is a market-neutral strategy, we will analyse the performance on its alpha rather than sharpe ratio.

# 2022 COMAP Problem C chosen (Bitcoin and Gold Quant Trading

Simple test of pair-trading investment strategy (2017)

jquants-pairs-trading is a python library for backtest with japanese stock pairs trading using kalman filter, J-Quants on Python 3.8 and above.

Pairs Trading Bot for NYSE built using Alpaca API & Python

A custom-built pairs trading simulator in R to analyze different ways of coducting this type of trade on US Sector SPDRs. We assessed both commonly-used price and return correlations between assets as well as using model residuals for both ARIMA and GARCH (volatility) type time series modelling.