Executive Programme in Algorithmic Trading (EPAT)

The Executive Programme in Algorithmic Trading at QuantInsti provides high-level training for individuals working in, or intending to move into the buy or sell-side of business focusing on derivatives, quantitative trading, electronic market-making or trading related technology and risk management.

The programme is built around a fully examined core of three modules:

  • Statistics & Econometrics
  • Algorithmic & Quantitative Trading
  • Financial Computing & Technology

The course covers all aspects of the theory and practice of quantitative tools, products and methods.

The course enables the high-achieving capital markets professionals and individuals interested in learning about the markets to attain the benefits and networking power of the Executive Programme in Algorithmic trading. Over the past five years, the alumni network has grown to include hundreds of successfully placed individuals from over 25 countries of all the six continents.

The programme provides a unique chance to its participants to work under the mentor ship of the world-class faculty for hands-on training in designing and implementing of advanced algorithmic trading strategies on state-of-the-art tools and platforms.

Key Elements

  • The EPAT runs for a period of 4 months training and 2 months’ project work.
  • The intensive timetable features a total of 6-hours of live lectures on Saturday and Sunday.
  • You can specialize in a particular asset class and / or trading strategy through the project work.
  • The collaborative learning environment means you learn from the experiences of your fellow batch mates, as well as from faculty.

THE EPAT CURRICULUM

QuantInsti developed the curriculum for the Asia’s first Executive Programme in Algorithmic Trading (EPAT) in 2009. Since then the curriculum has undergone many additions and improvements based on the technological innovations in this rapidly advancing domain. As an initiative by financial markets professionals with stellar academic and professional credentials, the programme aims to fulfil the pressing demands for highly specialized skill sets of a potentially lucrative domain.

PRIMER

The aim of this module is to make you comfortable with the basics of Statistics, Options and Derivatives, MS Excel and basic understanding of the industry. This is a self-study module with about 5-10 hours of coursework followed by 2-3 hours of Primer Tests.

INTRO

Get started with the crucial concepts of System Architecture and Execution Strategies for Algorithmic Trading.

It includes 9-12 hours of live lecture content and 5-10 hours of coursework comprised of assignments and quizzes.

STATISTICS WITH EXCEL

For quantitative trading, a thorough understanding of Statistics and its application on MS Excel are essential requirements. The contents include application of trading strategies in MS Excel, application of statistics in predicting future stock prices and approximations of risk/reward.

It includes 9 hours of live lecture content and 3-8 hours of coursework comprised of assignments and quizzes.

OPTIONS, DERIVATIVES AND RISK

This module will take you through the world of trading in Options. Options trading strategies help you to gain exposure to a specific type of opportunity or risk while eliminating select risks, which means safer bets!

The learning objectives are:

  • Detailed understanding and comparison of various option pricing models and the applicability of different models in different scenarios.
  • Characteristics of different Option Greeks and their sensitivity to different factors. Option price sensitivities to various market factors. Building option portfolios on the basis of Option Greeks.
  • Managing portfolio of option instruments when more than one underlying is involved. Implementing statistical concepts like correlation using options.
  • Dispersion trading concepts, implementation and road-blocks. Hands on experience in designing a risk management tool which will show sensitivity of options portfolio to different conditions and allow the trader to modify his/her options portfolio to meet future market scenarios better.

It includes 12 hours of lecture content and 8-10 hours of coursework.

TIME SERIES ANALYSIS WITH R

R is one of the most popular open-sourced tools, widely used for back-testing quantitative strategies. This module is a combination of Time Series Analysis and programming on R.

The learning objectives are:

  • Introduction to high level programming concepts and implementation. Understanding why it’s important to think like a ‘quant’ while programming in order to seek maximum performance. Useful R tips n tricks to navigate big data sets.
  • Understanding the concepts of volatility estimation. Implementing a model using GARCH (1,1) model to predict volatility using R and estimating the parameters of the model.
  • Using “Quantstrat” package to code a trading strategy in R.

It includes 15 hours of lecture content and 20-25 hours of coursework.

BUSINESS ENVIRONMENT

This module is designed to encourage and guide course participants to start algorithmic trading desks or utilize the trading knowledge that they learn during the course of the programme. This is a strategic module for individual traders as well as institutional desk traders & owners to have a thorough understanding of the business environment around Algorithmic and High Frequency Trading. In this module, in addition to the QuantInsti faculty, guest lecturers and experts from industry are also invited to share their experiences and insights.

The learning objectives are:

  • Understanding the infrastructure requirements
  • Understanding the business environment including regulatory environment, capital investments required for setting up an Algorithmic Trading Desk
  • It includes 3-6 hours of lecture content.

QUANTITATIVE TRADING STRATEGIES

This module covers various quantitative trading strategy paradigms popular in Algorithmic Trading. This is one of the key modules that starts from the very first month and continues till the end. A few of these strategy ideas and paradigms are later taken up by participants as project work.

It includes 27-35 hours of live lectures and 60-70 hours of course work.

ALGORITHMIC TRADING PLATFORM

The aim of this module is to enable you to implement your strategies in the live trading environment. You will be introduced to automated trading platforms based on Python programming language. Global experts in Python for Trading & Finance educate the participants through hands-on practical sessions in a structured & progressive manner.

It includes 18-24 hours of live lectures and 80-100 hours of course work.

PROJECT/FINAL EXAMINATION

The participants are required to complete a project under mentorship of a practitioner/trader which involves ideation and creation of a trading strategy. The project topic qualifies for participant’s area of specialization and enhanced learning. Alternatively, participants can appear for final examination to qualify for certification.

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Convenience

You just need your laptop/desktop machine along with an internet connection to attend the sessions from any location across the globe and you can attend them on the move.

Open Source

Most of the tools and software used for learning in the programme are open source. They can be freely downloaded from the internet and are widely used in the industry.

Virtual Classroom

Everything that is written in the classroom is visible to the online participants. All writing boards used in the classroom are electronic and are accessible to the participants.

Student Learning Portal

Course participants are provided with login credentials to the student portal that has all the relevant lecture notes, query forms, recorded video of the last few lectures, assignments and supplementary readings.

ADMISSION PROCESS

Each participant who is accepted in the course has a high level of intellectual curiosity, a strong interest in finance & strong analytical skills. Although there is no specific degree requirement most participants will have backgrounds in quantitative disciplines such as mathematics, statistics, physical sciences, engineering, operations research, computer science, finance or economics. Participants from other disciplines should have familiarity with calculus, spreadsheets and computational problem solving.

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THE EPAT FACULTY

Rajib Ranjan Borah

Rajib Ranjan Borah

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Dr. Ernest P. Chan

Dr. Ernest P. Chan

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Shaurya Chandra

Shaurya Chandra

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Dr. Yves Hilpisch

Dr. Yves Hilpisch

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Nitesh Khandelwal

Nitesh Khandelwal

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Abhishek Kulkarni

Abhishek Kulkarni

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Sameer Kumar

Sameer Kumar

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Gaurav Raizada

Gaurav Raizada

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Suneeth Reddy

Suneeth Reddy

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Anil Yadav

Anil Yadav

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QuantInsti Faculty

Know more about QI faculty members on our faculty page.

QuantInsti Faculty

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