Executive Programme in Algorithmic Trading (EPAT®)
- Six months online part-time course
- Experts and practitioners as trainers
- Automate your own trading strategies
- Network with data providers & brokers
- Start your own Algo trading desk
- Join Quant & Algo trading firms
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
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
- Overview of Electronic and Algorithmic Trading.
- Various order types, order book dynamics, Spoofing, Price Time Priority Algorithm and Guerilla Algorithm.
- Execution strategy to trade large volumes.
- The algorithmic trading process from a market microstructure perspective.
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
- Decision Trees, Support Vector Machine, Neural Networks, Forward propagation, Backward propagation, Various neural network architectures.
- Building a “Principal Component Analysis” manually, conducting a pairs-trading back-test using PCA, Simulation of multiple co-integrated assets, and Sector statistical arbitrage using PCA.
- Using Python and Jupyter notebooks to create features, evaluate models, use feature selection and test raw performance.
- Overview of Alternate Data: Sources, data formats, storage and retrieval choices, Understanding RDF and Knowledge Graph, Tagging Unstructured Data with relevant metadata.
- Using spaCy for common Text processing tasks, Understanding Topic Modeling and Topic Classification.
- Understanding Machine Readable News Programmatic consumption of news.
- Machine Readable News in the Financial Industry: Sample in Production use cases, Sentiment Data in the Financial Industry: Sample in Production use cases.
- Basic ideas of deep reinforcement learning such as reward, explore/exploit, Bellman equation and memory replay.
- Challenges and problems with RL in trading, Implementation of RL in a simple strategy using "gamification".
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
I was so excited to learn that some nights I couldn’t & didn’t sleep.
EPAT has added a fundamental quantitative dimension to my existing skill-sets.
Highly recommended for working professionals who like to pursue Algorithmic Trading.
QuantInsti is the best place to learn professional algorithmic and quantitative trading.
I started my own venture with a fellow EPATian - Maxime.
The learnings from EPAT led to the foundation of our own company.
I’m happy to achieve the EPAT certificate which empowers me to follow my passion for trading.
The staff at QuantInsti really go the extra mile to help you.
EPAT helped me start my own Algorithmic and High-Frequency desk.
Dr Panashe Chiurunge
The support during the EPAT course and after it are the things that I value the most about QuantInsti.
The faculty members have been the driving force.
The EPAT course had been a great building block towards developing my expertise in areas of Quant and Algo Trading.
Industry experts, academics and practitioners as faculty
High value for money and opportunity to learn along with your full-time job
Live interaction with faculty, with 7-days a week support team
Learning by doing, specialize in a strategy/asset class through project work
And a lot more
advisors & directors
Nitesh Khandelwal is presently the CEO of QuantInsti, an institute he co-founded in 2010 as part of iRage, a leading algorithmic trading player in India. Before co-founding iRage, he has experience in bank treasury (FX & Interest rate domain) and on a proprietary trading desk. Nitesh also has a rich experience in financial markets spanning various asset classes in different roles.
Anupriya has rich managerial as well as pedagogical experience. She has been responsible for coming up with adaptive learning solutions and user behaviour analysis. Before QuantInsti, Anupriya worked and researched with the leading educationists and analysts in India and abroad across various institutions like CMU, Educational Initiatives and Citigroup.
Dr. Ernest P. Chan
Dr. Ernest Chan (Ernie) is the founder and CEO of Predictnow.ai, a machine learning SaaS. He started his career as a machine learning researcher at IBM's T.J. Watson Research Center's Human Language Technologies group, which produced some of the best-known quant fund managers. He later joined Morgan Stanley's Data Mining and Artificial Intelligence group. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA. He obtained his PhD in Physics from Cornell University and his B.Sc. in Physics from the University of Toronto.
Prof. Gautam Mitra
He is the founder and the MD of OptiRisk Systems. He is an internationally renowned research scientist in the field of Operational Research in general and computational optimisation and modelling in particular.
Rajib Ranjan Borah
Rajib focuses on overall business strategy, trading strategies, and risk management & oversees internal processes at iRage. He is a regular speaker at algorithmic trading conferences across 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. Rajib is also a visiting faculty in finance at IIM Ahmedabad.