EPATTM – Executive Programme in Algorithmic Trading
The Executive Programme in Algorithmic Trading at QuantInsti is designed for professionals looking to grow in the field, or planning to start their careers in Algorithmic and Quantitative Trading.
It inspires traditional traders towards a successful Algorithmic trading career, by focusing on derivatives, quantitative trading, electronic market-making or trading related technology and risk management. This comprehensive Algorithmic Trading course offers unparalleled insights into the world of Algorithms, financial technology, and changing Market Microstructure, following an exhaustive course structure designed by leading Algorithmic Traders, Quantitative experts and HFT thought leaders.
Duration – 6 months (4 months of training & 2 months of optional project work)
Specialisation – Particular Asset class and/or Algorithmic trading strategy through the project work
Online Delivery – A focused learning experience consisting of practical sessions conducted through web-meetings and virtual learning environments
Certification – Assessment comprises of assignments, quiz, and attendance. On successful completion, participants will receive a Certificate from QuantInsti Quantitative Learning Pvt Ltd
This module is preparatory material for beginners who have recently started learning Algorithmic Trading.
- Covers basics in Algorithmic Trading, Statistics, Options & Derivatives, MS Excel
- Self-study module, to be completed before Live Lectures begin
- 10-20 hours of coursework followed by mandatory Primer Tests
This module is the first module with live lectures in Algorithmic Trading training and covers some of the most crucial concepts to be applied and used in future.
- Basic terms, concepts related to orders and data management
- System Architecture and Risk Management in Algorithmic Trading – complexities involved
- Order Flow Management, Pegging, Discretion, VWAP strategies
- 12 hours of live lectures and 10 hours of coursework comprised of assignments and quizzes
A beginner’s module in this Algo trading course that includes concepts from Probability, Statistics, Econometrics.
- Working with OHLC datasets, indicators and trading signals generation
- Application of trading strategies in MS Excel, application of statistics in predicting future stock prices and approximations of risk/reward
- Practical and hands-on sessions imparting computing skills which will be required later
- 9 hours of live lectures and 8 hours of coursework comprised of assignments and quizzes
Introduction to advanced topics in Quantitative trading courses that requires knowledge on Options and Derivatives and Statistics.
- Option pricing models and their applications.
- Building option portfolios on the basis of Option Greeks.
- Dispersion trading concepts, implementation and road-blocks
- Designing of a risk management tool that shows sensitivity of options portfolio to different conditions, allowing the trader to modify their portfolio to meet future market scenarios better
- 12-15 hours of lecture content and 10-15 hours of coursework
R is a popular language for quantitative trading and analysis. Algorithmic trading courses rely on the open-sourced statistical language R for data manipulation and management and Time series Analysis.
- Introduction to R through basic statistical tests and computations followed by writing codes to build quantitative indicators and trading strategies
- Useful R tips & tricks to navigate big data sets
- Implementing model using GARCH (1,1) to predict volatility using R and estimating the parameters of the model
- Using advanced packages to code trading strategies in R
- 15 hours of lecture content and 25 hours of coursework
This is the most strategic module for individual traders as well as institutional desk traders who want to set up their own trading desk or are fishing for new platforms/software/infrastructure.
- Understanding the infrastructure requirements
- Understanding the business environment including regulatory environment, capital investments required for setting up an Algorithmic Trading Desk
- In addition to the QI faculty, industry experts are invited to share experiences and insights
- 3-9 hours of lecture content
It is the most crucial module of this algorithmic trading course with high requirements from students to practice and try strategies hands-on.
- Exposure to different Quantitative trading strategy paradigms popular in algorithmic trading such as statistical arbitrage, market microstructure, trend following, momentum based, market making, machine learning
- Evaluate problems and opportunities in global markets through the lenses of econometrics, psychology and statistics
- Handle uncertainty focusing on risk management in trading
- 42-47 hours of lecture content and 75-80 hours of course work
Learn to automate your trading strategies in this module of EPAT™. Again, a demanding module which is practical and hands-on, requiring participants to learn and practice Python for backtesting and execution of strategies. Leading experts such as Dr. Yves Hilpisch, author of the book ‘Python for Finance’, is one of the core faculty members for this module.
- Introduction to automated trading platforms based on Python
- Learn to write your own codes in Python
- Object Oriented Programming and Useful Packages in Python for trading
- Enables participant to implement strategies in the live trading environment
- 18-24 hours of lecture content and 80-100 hours of course work
- Participants can opt to complete a project under mentorship of a practitioner/trader that involves ideation and creation of a trading strategy
- Project topic qualifies for area of specialization and enhanced learning
- Participants need to appear for the final exam to qualify for the Certification
Nitin is a partner with Pentagon Advisory Ltd. He has been a quant at iRageCapital.
Globally Renowned Speaker in on Options, Derivatives & News Based Trading Research.
Author of ‘Algorithmic Trading: Winning Strategies and Their Rationale’.
Faculty for workshops on Algorithmic Trading programs conducted by Indian National Stock Exchange.
Varun Divakar is a member of the Quantra Research and Development team at QuantInsti.
Author of ‘Python for Finance – Analyze Big Financial Data’ published by O’Reilly.
Co-Founder iRageCapital and QuantInsti. Expert in Inter-Market Studies.
Vivek has worked across various leading financial and educational institutions in India and Singapore
Head of Quantitative Research department at QuantInsti. Leading analyst and quant expert.
Sameer leads the Low Latency Programming division at iRageCapital Advisory Pvt Ltd.
Author, IBridgePy, an open sourced software to trade with Interactive Brokers.
Radha works as a Data Scientist at Thomson Reuters.
Gaurav leads the quantitative trading development at iRage along with the overall clientele business.
Sunith is an expert in the field of evolutionary algorithms & unconventional models of computing.
Anil has designed firm-wide risk and compliance practices at iRageCapital.
Nitin is a partner with Pentagon Advisory Ltd. His gamut of experience ranges from developing novel breakthrough chemical technologies to creating proprietary trading strategies. Prior to leading the Operations team in Pentagon Advisory, he has been a quant at iRageCapital and a Leadership Associate with the Aditya Birla Group. He has a passion for teaching and in his spare time writes articles for international journals. His most recent article involved developing the Swamee-Aggarwal equation.
Rajib Ranjan Borah
Rajib has done his Bachelors in Computer Engineering from NIT, Surathkal, and PGDM from IIM Calcutta. He is a National Biology Olympiad Finalist and has represented India in World Puzzle Championship.
Rajib leads the prop trading business for iRage as its CEO, focussing on strategy development, risk management, and internal processes. He is also a regular speaker on algorithmic trading conferences in Asia, America & Europe Prior experiences – quant research (Bloomberg, NY); high frequency trading (Optiver, Amsterdam); data analytics technology (Oracle); business strategy for an investment firm & derivatives exchanges (PwC).
Dr. Ernest P. Chan
Dr. Chan is a commodity pool operator and trading advisor. Since 1994, he has been focusing on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. He has applied his expertise in statistical pattern recognition to projects ranging from textual retrieval at IBM Research, mining customer relationship data at Morgan Stanley, and statistical arbitrage trading
strategy research at Credit Suisse, Mapleridge Capital Management, and other hedge funds
Shaurya has done B.Tech Electrical Engineering from IIT Roorkee and PGDM from IIM Ahmedabad.
Shaurya focuses extensively on statistical research & strategy development. In his previous roles, his focus areas had been Derivatives & Quantitative Research with a focus on Sell-Side Order Execution Algorithms. Prior to iRageCapital, Shaurya worked at Bank of America, Edelweiss Securities Ltd. & Systematix Stock & Shares Ltd., where he worked as Derivative and Quantitative Analyst focused on Indian Equity markets.
Varun holds a graduate diploma in civil engineering from Indian Institute of Technology, Roorkee.
Varun Divakar is a member of the Quantra Research and Development team at QuantInsti, and is responsible for creating the content for trading strategies, using Quantitative and Machine Learning techniques.
Prior to QuantInsti, Varun worked as an associate commodities trader managing international energy and softs markets at Futures First.
Dr. Yves Hilpisch
Nitesh has done B.Tech Electrical Engineering from IIT Kanpur and PGDM from IIM Lucknow.
Nitesh has a rich experience in financial markets spanning across various asset classes in different roles. Prior to leading QuantInsti™ as its CEO, he was the business lead for iRage.
He has prior experience in bank treasury (FX & Interest rate domain) and as a lead trader in a proprietary trading desk.
Sameer has completed his Masters in Economics & Information Systems from BITS Pilani.
Sameer leads the core technology and machine learning research at iRage. He is passionate about driving the core technology in setting new benchmarks in tick-to-trade latency. He is involved with designing trading models using deep learning research harnessing the temporal and spatial nature of market microstructure concurrently.
Dr. Hui Liu
Dr. Liu is the author of IbridgePy and founder of Running River Investment LLC. His major trading interests are US equities and Forex market. Running River Investment LLC is a private hedge fund specialized in the development of automated trading strategies using Python.
Radha Krishna Pendyala
Radha works as a Data Scientist at Thomson Reuters. His work involves applying machine learning and quantitative financial modeling techniques to large datasets in order to solve specific problems in the financial sector. He obtained his masters in financial engineering from the City University of New York.
Gaurav has done B.Tech Chemical Engineering from IIT Kanpur and PGDM from IIM Lucknow.
Gaurav leads the quantitative trading development at iRage along with the overall clientele business. He also leads on the Systems, Performance, and Strategy Development including trading systems development, latency reduction & optimization.
Prior to iRageCapital, Gaurav worked with Axis Bank as a Forex-Interest Rates Derivatives Trader.
Sunith has done B.Tech, Computer Engineering from IIT Madras.
Sunith is an expert in the field of evolutionary algorithms & unconventional models of computing. His work has been presented at ‘Symposium of Unconventional Models of Computing’. Sunith brings with him a very high quality of technical expertise, especially in the fields of algorithms and high performance architecture. Prior experience – LimeLabs, Yahoo R&D, Xilinx.
Anil has done B.Tech Mechanical Engineering from IIT Kanpur and PGDM from IIM Lucknow.
At iRage, Anil managed multiple trading strategies and then also designed firm-wide risk and compliance practices. Anil has successfully developed and led the scalable Quantitative Strategy development for the fund operations. Prior to iRage, Anil had worked as an independent commodities trader, managing a portfolio of metals and energy products and as a Senior Analyst at The Chatterjee Group’s (TCG) Private Equity
fund and as Convertible Analyst at Lehman Brothers.
Jacques Francois Joubert
Quantitative Analyst at NMRQL,
“I spent a great deal of time looking for the CFA equivalent for algorithmic trading and EPAT is the closest match. I loved how the course covered a wide range of topics. When I started the course I had plans to go back to university to study maths further but just before finishing the course I got hired by a coveted quantitative hedge fund as a quantitative analyst. A special thanks to the faculty.”
Marco Nicolás Dibo
CEO at Quanticko Trading S.A.
"I am very happy with the support provided by the administration team. Faculty is greatly committed at resolving queries. Having worked at one of the leading brokerage houses, I would certainly want to get into algorithmic trading and this is where QuantInsti’s EPAT course will help me."
Associate at Morgan Stanley,
"At Quantinsti, I learnt to develop quantitative strategies which can be used in Algorithmic & High Frequency trading. The faculty at Quantinsti is highly knowledgable. The insights which they bring into classroom from their experience as consultants are very valuable and make each lesson very effective. The online learning experience was quite good give me the flexibility for viewing the recordings of missed lectures."
Dr. Panashe Chiurunge
Founder, Chengetedzai Central
Securities Depository, Zimbabwe
"I am starting an Algorithmic and High-Frequency desk later on, so for me the best (part) was to get the actual experience and the knowledge on how to implement the strategies that would be useful on my own desks. In this program, you learn from the basics to advanced statistics. It is an amazing experience because you learn to work on the advanced trading platform which is used by many trading desks."
EPATTM Alumni Profile
We train participants who come from very rich and inter-disciplinary backgrounds both in terms of their academic background and their industry experience.
Students from all the inhabited continents have participated in EPAT™.
The course is designed for working professionals with a keen interest in financial markets and technological advancements. In every batch of EPAT™ we see a rich mix of traders, analysts, developers, quants, risk managers, founders, desk owners to provide a unique experience of interacting and networking with fellow participations.
Learning how to build a perfect trading strategy is one thing, but it is really the execution of ideas that separates the sheep from the goats. Our students have mastered the art of execution with projects, which are not only innovative but also ground breaking. They leverage the knowledge gained during the EPAT™ and transform them into original, ready-to-publish research works.
A few of the project topics recently completed as a part of EPAT™ coursework included:
- Development of Cloud-Based Automated Trading System with Machine Learning by Maxime Fages and Derek Wong
- Pair Trading Strategy and Backtesting using Quantstrat by Marco Nicolas Dibo
Who can apply?
QuantInsti’s Algo trading course is aimed 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.
Executive Programme in Algorithmic Trading™ provides practical training to Quants, Traders Programmers, Fund Managers, Consultants, Financial Product Developers, Researchers and Algo Trading Enthusiasts. It provides insights on the fundamentals of quantitative trading and the technological solutions for implementing them.
Each participant who is accepted in the course has a high level of intellectual curiosity, a strong interest in finance, and 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.
Prior to admission, a counselling session will be conducted that will focus on understanding the strengths and weaknesses of participants. These sessions do not necessarily decide the participants’ eligibility but help counsellors assist them with informed guidance prior to enrolment.
|Batch 38, Start Date: 14th April 2018|
|Tier||Applicable till||Global Participants||Indian Participants (Excl. tax*)|
|Early Enrollment Fees||2-Mar-18||$3,970||INR 158,000|
|Standard Enrollment Fees||14-Apr-18||$4,720||INR 189,000|
* Additional 18% GST Applicable for Resident Indian Participants
Discounts are available for residents from emerging markets, contact us for more details at firstname.lastname@example.org
Merit Based Discount on course fees are available based on your scholarship test score. Click here to avail
QuantInsti offers interactive online learning experience including live lectures, tutorials, problem solving interactions with faculty. Our Algorithmic Trading courses provide 24-hour access to all recorded lectures and program materials, accessible through your laptop, tablets & phones.
EPATTM live lectures are recorded and uploaded onto personalized learning portal. Each participant gets their own account, allowing him/her to access the following:
- Live & Recorded lectures
- Lecture notes, exercises, additional reading material
- Sample code and spreadsheets
- Support team access to resolve your queries on priority
The learning management system will track your learning and provides immediate feedback on your progress. A dedicated learning manager will regularly discuss your progress over call and chat to understand your queries and progress. Most tools and softwares used in the programme are open sourced and available for free to allow students to continue learning post course completion.
Why this Algo Trading Course?
- Practical Exposure – Acquire the knowledge, tools & techniques used by traders in the real world
- Expert teaching & support – The EPATTM faculty is an acclaimed team of academicians and professionals who are all specialists in the field
- Career Services - Our career services and job resources become available to you the moment you begin the program and last throughout your professional career
Six-months of Algorithmic Trading Training at QuantInsti®
Life Long learning at QuantInsti®
We promise lifelong learning to students post EPATTM completion, which comprise of:
- Access to a network of faculty and alumni, who are practitioners and researchers in Quantitative, Algorithmic and High Frequency Trading
- Reaching out to the industry members through our online communities, Linkedin groups
- Assistance in placement and career growth in the relevant roles
- Invitation to guest lectures which include new technological innovations, training to work on new platforms, advancement in the relevant field
- Exposure to the various strategy paradigms which are used globally for Algorithmic trading
- Automate your trading strategies, by learning the tools & skills required to write and implement the strategies
- Get trained to start Algorithmic Trading on your own, as you learn everything from networking and the hardware aspect of HFT to regulatory environment for handling desk operations
- Career progression to algorithmic trading industry - Benefit from Placement Services at QuantInsti after successful completion of the program.
- Specialize in a specific asset class or strategy paradigm by undergoing a project under a faculty member who is an expert in the same domain
- Managing High Frequency Data and building econometric models
- Learn how to back-test, implement and trade advance quantitative strategies
- Using programming skills to build low latency trading systems
- Using statistical packages and integrating them to your trading system
- Understanding of market making, spread optimization, transaction cost analytics and advance risk management
- Using Option pricing models for running volatility books and make markets
- Electric blend of practical and theoretical knowledge
Successful students have given 15-20 hours per week to review and complete the course work within a period of 4 months before proceeding to 2 months of optional project work.