Quant trading and real world applications, Slides of Applications of Computer Sciences

Download Quant trading and real world applications and more Slides Applications of Computer Sciences in PDF only on Docsity!

Curriculum | Faculty 1

Executive Programme in

Algorithmic Trading - EPAT

For Beginners | Traders | Professionals

2

Table of Contents

People

About QuantInsti 3

EPAT and Its Features 5

Curriculum 6

Faculty 12

Accreditations and Recognitions 16

Placement Support 17

Live Trading Implementations 18

Alumni Networking 19

Fees & Funding 21

4

People

Other Key Faculty

Dr. Ernest Chan, CEO of Predictnow.ai, a machine learning SaaS, started his career as a researcher at IBM’s T.J. Watson Research Center. He then joined Morgan Stanley’s Data Mining and AI group. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA. He holds a PhD in Physics from Cornell University and a B.Sc. in Physics from the University of Toronto. Additionally, Dr. Chan is a respected author and speaker on quantitative trading.

Advisor at QuantInsti

Dr. Ernest Chan

CEO of QuantInsti Nitesh is the CEO of QuantInsti and leads the business side for QuantInsti. He is also the Co-founder of iRage. Nitesh has a rich experience in financial markets spanning various asset classes in different roles and has experience in bank treasury (FX & Interest rate domain) as well as contributing to a proprietary trading desk. Nitesh holds an Electrical Engineering degree from IIT Kanpur with a postgraduate degree in Management from IIM Lucknow.

Nitesh Khandelwal

Dr. Euan Sinclair

Option Trader, Bluefin Europe LLP

Dr. Thomas Starke

CEO, AAAQuants Co-Founder, iRage

Rajib Ranjan Borah

CEO, The Python Quants

Dr. Yves J. Hilpisch

Curriculum | Faculty 5

Programme Features

The EPAT®^ by QuantInsti®^ provides unparalleled training for individuals seeking to

advance in algorithmic and quantitative trading. Designed to equip traders with

essential skills, it comprehensively covers derivatives, quantitative trading, electronic

market making, trading technology, and risk management, ensuring participants

are well-prepared for success. Through the programme, participants receive

mentorship from industry experts and gain invaluable hands-on experience in

developing advanced trading strategies using popular tools and platforms.

Executive Programme in

Algorithmic Trading - EPAT®

6 Months 100% Online

Weekend Live Classes

Graded Assignments

• Project Work

120+ Hours of Live, Instructor-Led Content

Personalized Mentorship

Proctored exam through Prometric

Lifetime Content Access & Support

20+ Expert Faculty 300+ Hiring Partners

7

Module 3: Python: Basics & Its Quant Ecosystem

Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures

Introduction to the main libraries in the data science stack: NumPy, pandas, and matplotlib

Study Pandas routines used to analyse and visualise OHLCV data

Learn to write functions in Python

Write and backtest trading strategies

Two Python tutorials to answer queries and resolve doubts

Module 4: Market Microstructure for Trading

Overview of electronic and algorithmic trading

Understand market terminology, order book concepts, and order types

Understand investment styles and trading algorithms

Case studies

Introduction to execution strategies

Learn trading analytics

Module 5: Equity, FX, & Futures Strategies

Understand the Equity Derivatives market

VWAP strategy: Implementation, effect of VWAP, maintaining log journals

Trend following strategies and Statistical Arbitrage Trading strategy modeling with Python

Introduction to position sizing and risk management

Different types of Momentum (Time series & Cross-sectional)

Arbitrage, market making, and asset allocation strategies using ETFs

Faculty | Live Trading

8

Module 6: Data Analysis & Modeling in Python

Learn to backtest and analyse strategies on Python using historical data

Understand object-oriented programming (OOP) concepts and use OOP to backtest trading strategies

Glimpse of the basic cloud infrastructure to host automated Python strategies

Learn to test event-driven strategies and optimise trade parameters

Tutorial to answer queries and resolve doubts

Learn the workflow of a quant strategy

Module 7: Machine Learning for Trading

Classical ML algorithms: Support Vector Machines (SVM), k-means clustering, logistic regression, decision trees, random forests

Intro to deep learning: Neural networks, gradient descent, and backpropagation algorithms

Learn principal component analysis (PCA) and use it to create statistical arbitrage strategies in multiple co-integrated assets

Intro to deep reinforcement learning and implement RL in a simple strategy using ‘gamification’

Use Python to build and evaluate ML models for potential trading strategies (by creating features and selecting suitable ones)

Learn about alternative data: sources, data formats, storage and retrieval

Module 8: Trading Tech, Infra & Operations

Understand the system architecture of a traditional trading system

Understand the system architecture of an automated trading system

Assess the challenges in building a trading system

Faculty | Live Trading

10

Module 11: Portfolio Optimization & Risk Management

Learn about different methods to evaluate portfolio and strategy performance

Understand risk management: sources of risk, risk limits, risk evaluation and mitigation, risk control systems

Profitability analysis of individual strategies

Trade sizing for individual trading strategies using historical methodologies, Kelly criterion, and leverage space theorem

Build a portfolio with multiple stocks

Profitability analysis of a portfolio

Module 12: Options Trading & Strategies

Introduction to options, payoff diagrams, and common option structures

General option trading principles, model-independent option features

Option pricing concepts (Black-Scholes-Merton), Options Greeks

Option pricing variables and parameters

Options trading (early exercise and expiration trading)

Risk management and trade evaluation

Volatility premium, Volatility trading with options, realized and implied volatility

Hedging in practice

Self-study project work under the mentorship of a domain expert

Project topic can come from different areas of financial market trading (be it asset class, techniques used, market selected) and can help crystallize your learnings from the EPAT into something concrete

Module 13: Hands-on Project

Faculty | Live Trading

Curriculum | Faculty 11

EPAT is a highly structured, hands-on learning

experience and it’s being updated frequently

The faculty and staff are extremely competent, and after completing the programme, you will have the necessary tools to begin a career in algorithmic trading.

Marcus Coleman Vice President | Applied AI Lead at JP Morgan, United States

Module 14: EPAT Exam

EPAT exam is conducted by Prometric at proctored centres in 80+ countries. Learners can take the exam at their preferred centre.

Predicting Market Movement and Backtesting Trading Strategies

Mark Rendle Mechanical Engineer & Algo Trader

Forex Trading for Beginners | Build Systematic G10 FX Index

Marti Castany Founder and Portfolio Manager, KomaLogic

Predicting Bank Nifty Open Price Using Deep Learning

Balamurugan Ganesan Lead Analyst with Bank of America Merrill Lynch

NOTABLE ALUMNI PROJECTS

Curriculum | Live Trading^12

EPAT Faculty

Anil Yadav Systematic Trading Strategies, iRage

At iRage, Anil oversaw various trading strategies and developed firm-wide risk and compliance practices. Presently, he’s constructing the infrastructure to assess alpha signals. Prior to iRage, Anil traded commodities independently, managed a portfolio of metals and energy products, and held positions as a Senior Analyst at TCG’s Private Equity fund and as a Convertible Analyst at Lehman Brothers.

Dr. Ankur Sinha Associate Professor, IIM Ahmedabad

Dr. Ankur Sinha brings extensive experience from IIM Ahmedabad, India, where he serves as a faculty member and leads various departments. He has also taught at Aalto University School of Business, Finland, and Michigan State University, United States. A recognized authority in Big Data and Business Intelligence, Dr. Sinha holds a PhD in Business Technology from Aalto University School of Business, Helsinki, Finland, and completed his Mechanical Engineering studies at IIT Kanpur, India.

Dr. Ernest P Chan Advisor at QuantInsti, CEO, Predictnow.ai

Ernest Chan, CEO of Predictnow.ai, a machine learning SaaS, began his career as a researcher at IBM’s T.J. Watson Research Center. Later, he joined Morgan Stanley’s Data Mining and AI group. He’s also the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA. He holds a Ph.D. in Physics from Cornell University and a B.Sc. in Physics from the University of Toronto.

Brian Christopher Founder, Blackarbs LLC

Brian, a Quantitative researcher, Python developer, and CFA charter holder, founded Blackarbs LLC, a quantitative research firm. He uses Python for algo trading strategies, and his research focuses on trading algorithms. He holds a BSc in Economics from Northeastern University and earned the CFA designation.

Ashutosh Dave Manager, Quantitative Research, OSTC Ltd

Ashutosh has worked as a Futures trader in London and with QuantInsti’s content team. He now leads a team of quants at a proprietary trading firm, focusing on alpha research and strategy development. He holds a Master’s in Statistics from the London School of Economics (LSE) and is a certified FRM.

13

Dr. Euan Sinclair Option Trader at Bluefin Europe LLP

Dr. Euan Sinclair holds a PhD in theoretical physics from the University of Bristol. Dr. Euan has more than 2 decades of Options trading experience and has written three books, “Volatility Trading”, “Options Trading” and “Positional Options Trading”, all published by Wiley, as well as numerous papers and articles.

Dr. Gaurav Raizada Co-Founder, iRage

Dr. Gaurav Raizada is the Founder of iRage, a firm specializing in Market Making and HFT strategies. He serves as a visiting faculty at IIT Bombay and IIM Ahmedabad, teaching quantitative and algorithmic trading. He holds a Ph.D. from IIT Bombay, an MBA from IIM Lucknow and B.Tech from IIT Kanpur. He has published papers in leading finance and econometrics journals, with notable works on market efficiency, trade informativeness, and algorithmic trading strategies.

Dr. Hui Liu Founder, Running River Investments LLC

Dr. Liu is the author of IBridgePy and founder of Running River Investment LLC. His major trading interests are US equities and the Forex market. Running River Investment LLC is a private hedge fund specialising in the development of automated trading strategies using Python.

Ishan Shah AVP, Content & Research, QuantInsti

Ishan leads Quantra’s Research and Content team and has prior experience at Barclays and Bank of America Merrill Lynch. Ishan has a rich experience in financial markets spanning various asset classes in different roles. He has co-authored a book on Machine Learning for Trading.

Jay Parmar Quantitative Researcher, iRage

Jay, a seasoned quant and “Python Basics” co-author, brings a decade of expertise. Adept at machine learning applications, he excels in leveraging quantitative strategies across trading verticals. His Python mastery underpins robust, data-driven solutions.

Nitesh Khandelwal Co-Founder, iRage

Nitesh Khandelwal has rich experience in financial markets spanning various asset classes in different roles and has experience in bank treasury (FX & Interest rate domain) as well as contributing to a proprietary trading desk. Nitesh holds an Electrical Engineering degree from IIT Kanpur with a postgraduate degree in Management from IIM Lucknow.

Curriculum | Live Trading

Curriculum | Faculty 15

Varun Pothula Quantitative Analyst, QuantInsti

Varun Pothula brings vast experience in quantitative finance, holding a Master’s in Financial Engineering. He’s thrived as a trader, global macro analyst, and algo trading strategist. Currently a Quantitative Analyst at QuantInsti, Varun plays a key role in shaping educational content for algorithmic and quantitative trading.

Vivek Krishnamoorthy Head of Research & Content, QuantInsti

Vivek leads the Research & Content team for the EPAT. He teaches participants data analysis, building quant strategies, and time series analysis using Python. He has over 15 years of experience in industry, academia, and research in India, Singapore, and Canada. He was the all-India topper in “Probability and Mathematical Statistics” offered by the Institute of Actuaries of India. He is the co-author of the books, “Python Basics: With illustrations from the financial markets (2019)” and “A rough-and-ready guide to algorithmic trading (2020).

Dr. Yves J. Hilpisch CEO, The Python Quants

Dr. Yves Hilpisch is an expert in Python & Mathematical Finance and covers topics related to Python coding & strategy backtesting. He also covers Object-Oriented Programming concepts in Python. Yves is the founder and the CEO of The Python Quants as well as The AI Machine. He is also an Adjunct Professor of Computational Finance at the University of Miami Business School, USA.

Dr. Thomas Starke CEO, AAAQuants

Dr. Thomas Starke, CEO of AAAQuants, brings a wealth of experience from Boronia Capital, Vivienne Court Trading, and Rolls-Royce. Renowned for global workshops on algorithmic trading, he’s also held academic roles at Oxford University. A tech enthusiast, he explores AI, quantum computing, and blockchain.

Dr. Robert Kissell President, Kissell Research Group

Dr. Robert Kissell, President of the Kissell Research Group, boasts 25 years of expertise in financial and quantitative analysis, statistical modeling, and risk management. A recognized authority in electronic and algorithmic trading, Dr. Kissell is a renowned speaker, author, and professor at various educational institutions, including Fordham University and Molloy College.

Curriculum | Faculty 16

Accreditations & Recognitions

This programme has been accredited by The Institute of Banking and Finance (IBF, Singapore) under the IBF Standards. IBF-STS provides up to 50% funding for direct training costs subject to a cap of S$ 3,000 per candidate per programme subject to all eligibility criteria being met. This is applicable to Singapore Citizens or Singapore Permanent Residents, physically based in Singapore. Find out more on www.ibf.org.sg

The Institute of Banking & Finance Singapore

EPAT is accredited by CPD, UK (Continuing Professional Development, UK)

Continuing Professional Development (CPD)

Want to become a Quant?

Explore Quant Jobs

Unlock job opportunities with EPAT placements

Curriculum | Faculty 18

Live Trading Implementation

Live Trade

Backtest

EPAT offers an immersive learning experience, allowing you to work with

market data, backtest your trading ideas, analyse backtesting results, and

paper trade your strategies in a risk-free environment before deciding to go

live with actual money.

Blueshift, QuantInsti’s trading platform, which supports brokers like Alpaca and ICICI.

Interactive Brokers Python API and The Trader Workstation (TWS).

The EPAT curriculum demonstrates live market trading using:

IBridgePy which supports brokers like Interactive Broker, Ameritrade, and Robinhood.

Cloud Computing through Amazon Web Services (AWS).

REST API which supports 100+ brokers across the globe.

Paper Trade

Gain practical experience in a live trading environment.

Learn to adapt to market changes and manage trades effectively.

Develop confidence in your trading strategies before risking actual capital.