Hedge Funds analyst roles represent some of the most fiercely contested roles in all of Finance (if not any industry). The work is incredibly varied and challenging, making the jobs the long term aim of many ambitious juniors from diverse backgrounds like Computer Science, finance, economics,

This article is accompanied by a Google Colab notebook, which contains all the code and additional mathematical details. You can find the notebook here: https://links.quant-quest.com/KGNotebook What Will You Learn in This Article? In this article we will explore how you can automatically identify

In God we trust. All others must bring data. Backtesting is probably the single best method we have to quickly evaluate new trading strategies. However, if used incorrectly it can be our greatest weakness — guiding us on a false path to ruin. For the uninitiated, backtesting is the process where

When working with time series financial data, stationarity (or lack thereof!) is going to be a defining aspect of how you conduct your analyses. In this article, we're going to give you a quick refresher of what these terms mean and how they affect your data. Let's start with importing the

Investors are slowly becoming more and more interested in ethical investing. Part of the reason is the industry is starting to care more, but the other reason is that there is a lot of evidence to show that it can produce better or at least equivalent returns. One subset of this type of investing is

Often a Quantitative Researcher will develop trading models in Python or R. These models are then passed off to Quantitative Developers, who implement them in trading systems with Java or C++. Usually, a Quantitative Trader will then execute trades with the help of these systems. I have had the

am a recent Computer Science graduate from IIT Kanpur. I first came to know about QuantQuest through IITK placement cell during our final year placement season. During my mid-semester recess when I was chilling out at my home, I received an email from Ms. Chandini Jain (founder of Auquan) with the

In the last post, we covered Pairs trading strategy and demonstrated how to leverage data and mathematical analysis to create and automate a trading strategy. Long-Short Equity Strategy is a natural extension of Pairs Trading applied to a basket of stocks. Download Ipython Notebook here. Underlying

Pairs trading is a nice example of a strategy based on mathematical analysis. We’ll demonstrate how to leverage data to create and automate a pairs trading strategy. Underlying Principle Let’s say you have a pair of securities X and Y that have some underlying economic link, for example two

In this mini series on Time Series modelling for Financial Data, so far we’ve used AR, MA and a combination of these models on asset prices to try and model how our asset behaves. We’ve found that we were able to model certain time periods well with these models and failed at other times. This

In the previous posts in this series, we combined the Autoregressive models and Moving Average models to produce Auto Regressive Moving Average(ARMA) models. We found that we were still unable to fully explain autocorrelation or obtain residuals that are discrete white noise. Let’s further extend

In the previous posts in this series, we talked about Auto-Regressive Models and Moving Average Models and found that both these models only partially explained the log-returns of stock prices. We now combine the Autoregressive models and Moving Average models to produce more sophisticated

Auquan recently concluded another version of QuantQuest, and this time, we had a lot of people attempt Machine Learning with our problems. It was good learning for both us and them (hopefully!). This post is inspired by our observations of some common caveats and pitfalls during the competition when