R Time series packages not included in CRAN Task View: Time Series Analysis

Statistics and performance metrics in trading, CAGR, Sharpe, MAE, MFE, and others. Cointegration and option pricing.

An exposition of a simple pairs trading strategy on two stocks (Bajaj Finserv and Indian Bank) in the Nifty500, at the one-minute time frequency, in order to demonstrate some of the core ideas of statistical arbitrage strategies.

Financial Time Series Analysis

An R package to implement VEC models

This repository contains an RMarkdown file that performs an econometric analysis of soybean prices using various statistical methods, including cointegration tests, ECM, and GARCH models.

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.

This project explores pairs trading as a market-neutral strategy by leveraging statistical relationships between cointegrated assets to exploit mean-reverting behavior. inspired and adapted from the quant trading room

Non-Linear Cointegration in Pairs Trading

R finance guide - Algotrading101

Pair Trading Analysis & Exercises Toolkit [Jupyter Notebook]

Current repository depicts R usability for time series modeling. Number of scripts represents preprocessing time series, modeling AR, MA, ARIMA with seasonality, ARCH, GARCH, VAR, VECM including statistical testing process and robust check.

This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.

Pairs Trading screening with cointegration in R

Java library for testing unitroot and cointegration

A stock backtesting engine written in Java. And a pairs trading (cointegration) strategy implementation using a bayesian kalman filter model