Financial Markets and Algo Trading

Quants possess an unusual combination of computer, finance, and mathematical expertise. Quants create and use the price prediction algorithms in the trading industry. We have conducted extensive research and have created a list of websites that a quant might find worthwhile.

Does learning Algorithmic Trading scare you? Worried that you won't be able to learn it? You can overcome your fear of algo trading like countless others out there and put your fears to rest. Read on to find out how.

Learn step-by-step on how to calculate VaR in Excel and Python using Historical Method and Variance-Covariance approach. Downloadable files for Value at Risk (VaR) calculation are available.

The standard deviation of a portfolio of stocks is calculated using the covariance matrix, and the standard deviation is then utilised by portfolio managers to measure the risk involved with a certain portfolio.

One of the purportedly most common sorts of trading strategy is pairs trading. A pair of stocks is typically traded using this approach, and regardless of whether the market is heading upwards or downwards, the two open bets for each stock act as hedges against one another.

Python is a free, open-source language that is cross-platform and contains a large library for practically any task you can think of. It also offers a specific research environment. Python is a great option for automated trading when there is a low to medium volume of trades, or when trades do not last less than a few seconds. It offers numerous APIs and libraries that may be connected in order to maximise efficiency and enable deeper exploration of numerous trading ideas.

We apply our understanding of regression to actual financial data in this essay. With Python and R, we will model the relationships. In order for readers who are proficient in one language or application to gain a better understanding of how it would be implemented in another, we run it in both (with downloadable code if appropriate).

We will discover how logistic regression functions in machine learning for trading in this blog post, and we'll use the same implementation to forecast stock price movement in Python. It examines the connection between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic/sigmoid function. Logistic regression is a type of supervised learning.

A very comprehensive and robust compiled list of resources that one would require or need to learn Algorithmic Trading and Quantitative Trading.

Decision trees are one of the widely used algorithms for building classification or regression models in data mining and machine learning.

Find out about data engineering, the crucial role in the financial markets and the responsibilities of data engineers. Also, explore about data engineering and its relevance in the coming future.

In this post, we will cover the popular ARIMA forecasting model to predict returns on a stock and demonstrate a step-by-step process of ARIMA modeling using R programming.

Rodrigo’s story from Brazil explains his journey to learning to automate his trading, beginning with Quantra’s courses, going through Blueshift and more.