To use ML in trading, we use historical data (stock price/forex data) and add indicators to build a model in R/Python/Java. We then select a Machine learning algorithm to make the predictions....

In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy....

New Paradigms for Sentiment Analysis Applied to Finance. Sentiment Analysis applies machine learning and makes a rapid assessment of the sentiments expressed in news releases....

This article focuses on statistical arbitrage, coded in R. It is a combination of EPAT class notes and author’s source code....

FIX is the defacto standard for message communication for almost two decades of electronic trading. Today, it is used by a variety of market participants, firms, and vendors....

Securities master is an organisation-wide database that stores fundamental, pricing and transactional data for a variety of financial instruments....

In this post we will discuss about building a trading strategy using R. There are more than 4000 add on packages,18000 plus members of LinkedIn’s group and close to 80 R Meetup groups currently in existence....

Here we focus on the order management system, as orders form the basis of any strategy and they need to be entered and routed to the correct destinations....

QuantInsti co-founders Rajib Ranjan Borah and Gaurav Raizada conducted a special training event for senior managers from the surveillance and compliance department of National Stock Exchange India....

How to create a quant trading strategy from scratch in python, explained using a simple 20 day moving average cross over strategy....