Algorithmic Trading Workshop
Session 1:
Different Components of Algorithmic Trading Systems - Increasing Profitability by Optimising systems
Presenter: Rajib Borah, Co-founder & CEO, iRage
By properly leveraging the power of technology, a trader can increase the profitability of an already profitable systematic trading strategy multi-fold. This talk will look at the evolution of algorithmic trading systems - and the efficiency introduced at each step. The talk will also try to introduce participants to the various technological complexities at exchanges - and opportunities that could exist because of the same. The aim will be to have an interactive discussion and understand the functional implications (for quantitative traders) of technological complexities.
Rajib Borah is the co-founder & CEO of iRage, one of India’s leading High-Frequency Trading firms, which potentially manages the broadest exchange-traded option portfolio book in India. He is also the co-founder and director of QuantInsti, an 'Algorithmic and Quantitative Trading' training and research institute which has trained thousands of professionals from over 130 countries. His prior experiences include high-frequency trading on all major US & European exchanges (Optiver, Amsterdam); data analytics technology (Oracle); business strategy for a trading firm & derivatives exchanges (Strategy Consulting, PwC). Rajib has thrice represented India at the World Puzzle Championship. He was also a finalist at the Indian National Biology Olympiad (top 24 nationwide). Rajib holds an MBA from IIM Calcutta, a bachelor’s degree in Computer Engineering from NIT Surathkal; and has internship experiences with Bloomberg in New York (derivatives research) & Solutia’s EMEA strategy HQ in Belgium.
Session 2:
Mumbai
Applying Machine Learning to Algorithmic Trading Strategies
Presenter: Prodipta Ghosh, QuantInsti Quantitative Learnings
In this session, we will focus on two diametrically opposite phases of quant strategy development cycle. In part one, we will discuss how machine learning can be applied in the exploratory/ research phase of strategy development. We will cover a few examples from the classic pair-trading as well as momentum trading perspectives. We will see how to use clustering to find potential pairs, and auto-encoders to reduce signal dimensionality. The second part will focus on the post deployment phase. Here we will discuss use of reinforcement learning to effectively manage multiple strategies in the real world. Participants with some familiarity with financial trading as well as Python programming language will benefit the most.
Prodipta has spent more than a decade in the banking industry – in various roles across trading and structuring desks for Deutsche Bank in Mumbai & London, and as a corporate banker with Standard Chartered Bank. Before that, he worked as a scientist in India’s Defence R&D Organization (DRDO). At QuantInsti, he is leading the product development of Blueshift (https://quantra-blueshift.quantinsti.com/strategies), a platform that makes quantitative trading accessible and hassle-free.
Hong Kong
Reinforcement Learning for Trading
Presenter: Dr Tom Starke, CEO, AAA Quants
This talk will encompass a mixture of theoretical and practical examples of Reinforcement Learning, touching on aspects such as the type of algorithms to use, which features to choose and how to test the system. A demonstration of models will be given and points on the lessons learnt will be highlighted.
Dr Tom Starke is the CEO of AAAQuants, a consultancy firm for algorithmic trading and AI and leads a quant trading team in a prop trading firm in Sydney. Tom has a PhD in Physics and worked as principal engineer at Rolls-Royce and held a senior research position at Oxford University.
