Project work opportunity
Scholarships and Financial Aid
Lifetime access to latest course content
Exclusive EPAT Community benefits
Credit Points for continuous professional development
Anil Yadav is a member of the algo strategy advisory team at iRageCapital and is responsible for building and benchmarking strategies for the clients across various asset classes. Prior to iRage, Anil has worked as an independent commodities trader managing a portfolio of metals and energy products.
Brian is a Quantitative researcher, Python developer, CFA charter holder, and the founder of Blackarbs LLC, a quantitative research firm. He attained a BSc in Economics from North-eastern University in Boston, MA and received the Chartered Financial Analyst (CFA) designation in 2016.
Dr. Ernest P. Chan
Ernie is the Managing Member of QTS Capital Management, LLC. He is also the Founder and CEO of PredictNow.ai. He has authored multiple books, teaches courses and conducts workshops in trading and finance in Australia, Canada, Singapore, the United Kingdom, and the United States.
Dr. Euan Sinclair
Dr. Euan has more than 2 decades of Options trading experience. He's currently the partner at Talton Capital Management, a volatility trading fund. He holds a Ph.D. in theoretical physics from the University of Bristol and has written two books, “Volatility Trading” and “Option Trading”, both published by Wiley, as well as numerous papers and articles.
Dr. Gaurav Raizada
Dr. Gaurav is a Director at iRage Capital Advisory Pvt Ltd, the Chief Investment Officer for iRage Master Trust Investment Managers LLP and a Designated Partner for iRage Broking LLP. He has consulted extensively with core focus on strategy development and execution, including trading systems development, optimization and transaction cost analysis.
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.
Ishan has done B.E. Information Technology from D J Sanghvi College of Engineering and PGDBM from Sydenham Institute of Management. He has a rich experience in financial markets spanning across various asset classes in different roles. He works with Quantra® content development team and has prior experience in Barclays, Bank of America Merrill Lynch and RBT Algo Systems
Nitesh has a rich experience in financial markets spanning across various asset classes in different roles. He is also the Co-founder of iRageCapital Advisory Pvt Ltd and QuantInsti Quantitative Learning Pvt Ltd. At QuantInsti® he leads the overall business & is in-charge of new initiatives & ventures by QuantInsti®.
Nitin is the Founder and CEO of Alphom Advisory Pvt. Ltd. (a trading firm), prior to which he has led the Operations team in Pentagon Advisory, has been a quant at iRageCapital and a Leadership Associate with the Aditya Birla Group. His gamut of experience ranges from developing novel breakthrough chemical technologies to creating proprietary trading strategies.
Before joining QuantInsti as Vice President, Prodipta 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. Prior to that, Prodipta worked as a scientist in India’s Defence R&D Organization (DRDO).
Rajib Ranjan Borah
Rajib is the Co-founder & Director of iRageCapital Advisory Pvt Ltd & QuantInsti Quantitative Learning Pvt Ltd. He has conducted workshops in the United States, Europe and Asia and is a visiting faculty in finance & accounting department for the flagship MBA program at IIM-A, one of the globally leading B-School.
Dr. Thomas Starke
Tom is the CEO of AAAQuants and the co-founder of pSemi. With a remarkable career spanning working with Vivienne Court, Memjet Australia, and Rolls-Royce Plc (UK), he has conducted workshops and presentations on algorithmic trading around the world. A PhD Physics degree holder, he was a senior research fellow at Oxford University.
Vivek has a Bachelors' in Engineering, an MBA, and a Graduate Certificate in Public Policy. He is also an aspiring actuary and has cleared six papers of the Institute of Actuaries (and was a country topper in one of them). He has over 12 years of experience across India, Singapore and Canada in industry, academia and research.
Dr. Yves J. Hilpisch
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 for Computational Finance—Miami, USA & Riga, Latvia.
1 EPAT Primer
- Basics of Algorithmic Trading: Know and understand the terminology
- Excel: Basics of MS Excel, available functions and many examples to give you a good introduction to the basics
- Basics of Python: Installation, basic functions, interactive exercises, and Python Notebook
- Options: Terminology, options pricing basic, Greeks and simple option trading strategies
- Basic Statistics including Probability Distributions
- MATLAB: Tutorial to get an hands-on on MATLAB
- Introduction to Machine Learning: Basics of Machine Learning for trading and implement different machine learning algorithms to trade in financial markets
- Two preparatory sessions will be conducted to answer queries and resolve doubts on Statistics Primer and Python Primer
2 Statistics for Financial Markets
- Data Visualization: Statistics and probability concepts (Bayesian and Frequentist methodologies), moments of data and Central Limit Theorem
- Applications of statistics: Random Walk Model for predicting future stock prices using simulations and inferring outcomes, Capital Asset Pricing Model
- Modern Portfolio Theory - statistical approximations of risk/reward
3 Python: Basics & Its Quant Ecosystem
- Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures
- Introduction to some key libraries NumPy, pandas, and matplotlib
- Python concepts for writing functions and implementing strategies
- Writing and backtesting trading strategies
- Two Python tutorials will be conducted to answer queries and resolve doubts on Python
4 Market Microstructure for Trading
- Detailed understanding of ‘Orders’, ‘Pegging’, ‘Discretion Order’, ‘Blended Strategy’
- Market Microstructure concepts, order book, market microstructure for high frequency trading strategy
- Implementing Markov model and using tick-by-tick data in your trading strategy
5 Equity, FX, & Futures Strategies
- Understanding of Equities Derivative market
- VWAP strategy: Implementation, effect of VWAP, maintaining log journal
- Different types of Momentum (Time series & Cross-sectional)
- Trend following strategies and Statistical Arbitrage Trading strategy modeling with Python
- Arbitrage, market making and asset allocation strategies using ETFs
6 Data Analysis & Modeling in Python
- Implement various OOP concepts in python program - Aggregation, Inheritance, Composition, Encapsulation, and Polymorphism
- Back-testing methodologies & techniques and using Random Walk Hypothesis
- Quantitative analysis using Python: Compute statistical parameters, perform regression analysis, understanding VaR
- Work on sample strategies, trade the Boring Consumer Stocks in Python
- Two tutorials will be conducted after the initial two lectures to answer queries and resolve doubts about Data Analysis and Modeling in Python
7 Machine Learning for Trading
- Modeling data with AI, index and predicting next day’s closing price
- Supervised learning algorithms, Decision Trees & additive modeling
- Natural Language Processing (NLP) and Sentiment Analysis
- Confusion Matrix framework for monitoring algorithm’s performance
- Logistic Regression to predict the conditional probability of the market direction
- Ridge Regression and Lasso Regression for prediction optimization
- Understand principle component analysis and back-test PCA based long/short portfolios
- Reinforcement Learning in Trading
- How to build trading Systems while not overfitting
8 Trading Tech, Infra & Operations
- System Architecture of an automated trading system
- Infrastructure (hardware, physical, network, etc.) requirements
- Understanding the business environment (including regulatory environment, financials, business insights, etc.) for setting up an Algorithmic Trading desk
9 Advanced Statistics for Quant Strategies
- Time series analysis and statistical functions including autocorrelation function, partial autocorrelation function, maximum likelihood estimation, Akaike Information Criterion
- Stationarity of time series, Autoregressive Process, Forecasting using ARIMA
- Difference between ARCH and GARCH and Understanding volatility
10 Trading & Back-testing Platforms
- Introduction to Interactive Brokers platform and Blueshift
- Code and back-test different strategies on various platforms
- Using IBridgePy API to automate your trading strategies on Interactive Brokers platform
- Interactive Brokers Python API
11 Portfolio Optimization & Risk Management
- Different methodologies of evaluating portfolio & strategy performance
- Risk Management: Sources of risk, risk limits, risk evaluation & mitigation, risk control systems
- Trade sizing for individual trading strategy using conventional methodologies, Kelly criterion, Leverage space theorem
12 Options Trading & Strategies
- Options Pricing Models: Conceptual understanding and application to different strategies & asset classes
- Option Greeks: Characteristics & Greeks based trading strategies
- Implied volatility, smile, skew and forward volatility
- Sensitivity analysis of options portfolio with risk management tools
13 Hands-on Project
- Self-study project work under mentorship of a domain/expert
- Project topic qualifies for area of specialization and enhanced learning
14 EPAT Exam
- EPAT exam is conducted at proctored centers in 80+ countries
Get on a call
with a Counsellor
Pay the fee
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Before admission, we will facilitate a one-on-one counselling session that will focus on understanding the strengths and weaknesses of the participant. These sessions do not necessarily decide the participants' eligibility but help counsellors assist them with informed guidance prior to enrollment.
We have four batches in a year. Duration of the programme is 6 months. The tentative programme start dates are:
|54||16 April, 2022|
|55||16 July, 2022|