The project aims to design and implement a pairs trading algorithm to identify and exploit temporary mispricing opportunities between correlated assets in financial markets.
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Updated
Jun 3, 2024 - Python
The project aims to design and implement a pairs trading algorithm to identify and exploit temporary mispricing opportunities between correlated assets in financial markets.
This project involves using a combination of statistics along with financial thoery to demonstrate a popular trading strategy used in equity markets: Pairs Trading.
Non-Linear Cointegration in Pairs Trading
The notebook with the experiments to replicate and enhance the stock clustering proposed by Han(2022) for alogtrading, with KMeans Optimization
Design your own Trading Strategy
Applying Machine Learning Techniques To Assess Whether A Country’s Currency Can Predict The Movement Of Their Respective Stock Market Index
A pairs trading strategy
Using Copulas model to capture non-linear relationship between stock pairs and conduct statistical arbitrage by pairs trading strategies.
RESTful API for trading stocks (single or pairs), deployed on Heroku
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.
stock-pairs-trading is a python library for backtest with stock pairs trading using kalman filter on Python 3.8 and above.
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.
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.
Quantitative analysis, strategies and backtests
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.
Pair Trading View - .NET application for visual analysis of synthetic financial instruments based on statistical models.
High-frequency statistical arbitrage
Pairs Trading screening with cointegration in R
Version 3 of RESTful API for trading stocks (single or pairs), deployed on Heroku
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