Do you want to automate your trading?

Join EPAT® and learn algorithmic trading on weekends.

Step 1


  • Six months online part-time course
  • Experts and practitioners as trainers
Step 2


  • Automate your own trading strategies
  • Network with data providers & brokers
Step 3


  • Start your own Algo trading desk
  • Join Quant & Algo trading firms
EPAT steps


industry recognition


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


EPAT is recognized by IBF, Singapore (Institute of Banking and Finance) under the FTS scheme

It is more than a certificate

Audience participation is as important to the learning experience as the instructor. I find the participants at QuantInsti's courses highly motivated and many came prepared with insightful questions. This made for a great experience for all. 

Dr. Ernest P. Chan

As seen in


EPAT Curriculum


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
  • 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
  • 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
  • Detailed understanding of ‘Orders’, ‘Pegging’, ‘Discretion Order’, ‘Blended Strategy’
  • Market Microstructure concepts, order book, market microstructure for high frequency trading strategy
  • Implementing Markow model and using tick-by-tick data in your trading strategy
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Self-study project work under mentorship of a domain/expert
  • Project topic qualifies for area of specialization and enhanced learning
  • EPAT exam is conducted at proctored centers in 80+ countries
Hiren Mewada

Excellent course and I myself really came to know more about Algo trading, algorithms and HFT.

Hiren Mewada

EPAT®, 2017

Mario Pisa Peña

EPAT® has been one of the most rewarding formative experiences I have had!

Mario Pisa Peña

EPAT®, 2018

Vikash Bairoliya

EPAT® delivered a holistic framework to quantitative modeling and trading. Helpful faculty, a small batch size and excellent peer group were other positives.

Vikash Bairoliya

EPAT®, 2017

Industry Focused

Industry Experts, Academics and Practitioners as faculty


High value for money and opportunity to learn along with your full-time job

Dedicated Support

Live interaction with faculty, with 7-days a week support team


Learning by Doing, specialize in a strategy/asset class through project work


EPAT Benefits Get Hired

Get Hired

Career cell helps participants to get placement in right kind of roles in the Quant and Finance industry

EPAT Benefits Upgrade your Skills

Upgrade your Skills

Algo/Quant and manual traders get exposed to various types of strategy paradigms in Algorithmic & Quantitative Trading

EPAT Benefits Automate your strategies

Automate your strategies

Learn to connect with brokers that offer automation and run your strategies in paper/live trading environment

EPAT Benefits Set up your business

Set up your business

Unleash the entrepreneur in you and ride the markets. Learn from the practitioners how to setup your own desk

EPAT Benefits For technocrats & coders

For technocrats & coders

Use your programming and problem solving skills in a challenging role as a quant-trader-coder

And a lot more


Dr. Ernest P. Chan
Dr. Ernest P. Chan

Dr. Chan is an industry expert on ‘Algorithmic Options Trading’ and has conducted seminars and lectures on many international forums. Besides being a faculty in QuantInsti, his academic distributions are available on Quantra and on major web portals.


Dr. Sinclair is an industry expert on stock options, interest rate products, volatility products, index options and commodity options, both exchange-traded and OTC. He specializes in design, implementation and risk management of quantitative trading strategies.


Dr. Hui Liu is an expert in programming for financial markets & related tools. He helps EPAT participants to learn implementing equity trading strategies using algorithms.

Nitesh Khandelwal
Nitesh Khandelwal

Nitesh helps participants in understanding quantitative modelling intuitively. Given the expertise built over the last decade, he has been the go-to person to evaluate what is likely to work when it comes to trading strategies.

Rajib Ranjan Borah
Rajib Ranjan Borah

Rajib leads the prop trading business for iRage as its CEO and Co-founder, focussing on strategy development, risk management, and internal processes. iRage manages potentially the broadest option portfolio book in India, being one of India’s leading High-Frequency Trading firms.


Dr. Thomas Starke has a PhD in Physics and currently leads the quant-trading team in one of the leading prop-trading firms in Australia, AAAQuants, as its CEO. He has also held the senior research fellow position at Oxford University.


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.

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