Algorithmic trading is the process of using computers for trading. The speed and frequency that is possible through algorithmic trading cannot be matched by humans. But this kind of black box trading can make investors nervous, especially when large amounts are at stake.
This is precisely where data science comes into picture. Savi Basavaraj, head of algorithmic trading at Edelweiss said “Data science in a way sits on top of algorithmic trading, to give the necessary controls, insights, intelligence and visibility to the traders”. He was speaking at the data science congress summit 2017 held in Mumbai, India.
Edelweiss is a diversified financial services with headquarters in Mumbai, India. Savi was accompanied with Madhur Shukla, data scientist at Edelweiss. Madhur explained that data science is used in algorithmic trading in two ways: pre trade analysis and post trade analysis. Pre trade analysis includes simulation of various algorithmic models to predict the best fit for each investment, while post trade analysis involves measuring the success and comparing results to re-define investment strategies.
Data science is also used to further improve algorithmic models by applying machine learning techniques.
The data science summit 2017, organized by Data science congress, was attended by various high profile corporate and political delegates across the country and many data science professionals, students and enthusiasts. Data science congress is trying to build an ecosystem which will help India to become the center stage for the research and development of data science skills.
The event was organised by Aegis School of Business, Data Science & Telecommunication, and was covered by Gleeba, the social media partner. Catch more updates on the event at data science congress Facebook page.