Executive Programme in Algorithmic Trading (EPAT®)
learn
- Six months online part-time course
- Experts and practitioners as trainers
automate
- Automate your own trading strategies
- Network with data providers & brokers
trade
- Start your own Algo trading desk
- Join Quant & Algo trading firms
It is more than a certificate
It's more than a certificate! EPAT has added a fundamental quantitative dimension to my existing skill-sets.
Rohit Gupta
EPAT®, 2017As seen in
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
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
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 Markow 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
7 Machine Learning for Trading
- Modeling data with AI, index and predicting next day’s closing price
- Supervised learning algorithms, Decision Trees & additive modeling
- 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
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, Non linearity of volatility, Gaussian Mixture Models (GMM)
10 Trading & Back-testing Platforms
- Introduction to Interactive Brokers platform & Quantiacs
- Code and back-test different strategies on Quantiacs
- Quantiacs toolbox & strategy analysis using Python
- Using IBridgePy API to automate your trading strategies on Interactive Brokers platform
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
- Dispersion trading: Concept, implementation and road-blocks
- Sensitivity analysis of options portfolio with risk management tools
13 R for Quantitative Trading (Optional)
- R: Concepts, data types, statistical functions, graphs, fetching data from online platforms
- Programming conceptualization and implementation, useful tips while working with big data sets
- Build a back testing model using QuantStrat on R
14 Hands-on Project
- Self-study project work under mentorship of a domain/expert
- Project topic qualifies for area of specialization and enhanced learning
“Hi, I'm Hiren
Excellent course and I myself really came to know more about Algo trading, algorithms and HFT.
Hiren Mewada
EPAT®, 2017
“Hi, I'm Mario
EPAT® has been one of the most rewarding formative experiences I have had!
Mario Pisa Peña
EPAT®, 2018
“Hi, I'm Vikash
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®, 2017Industry Focused
Industry Experts, Academics and Practitioners as faculty
Affordable
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
Hands-on
Learning by Doing, specialize in a strategy/asset class through project work
Benefits
Get Hired
Career cell helps participants to get placement in right kind of roles in the Quant and Finance industry
Upgrade your Skills
Algo/Quant and manual traders get exposed to various types of strategy paradigms in Algorithmic & Quantitative Trading
Automate your strategies
Learn to connect with brokers that offer automation and run your strategies in paper/live trading environment
Set up your business
Unleash the entrepreneur in you and ride the markets. Learn from the practitioners how to setup your own desk
For technocrats & coders
Use your programming and problem solving skills in a challenging role as a quant-trader-coder
And a lot more
advisors & directors
Dr. Ernest P. Chan
He is the managing member of QTS Capital Management LLC, Ontario, Canada. Author of “Quantitative Trading: How to Build Your Own Algorithmic Trading Business” and “Algorithmic Trading: Winning Strategies and Their Rationale”.
Rajib Ranjan Borah
Rajib is the Co-Founder & Director of iRage & QuantInsti. He’s the business head of iRage, which is one of the leading Algorithmic Trading players in India.
Nitesh Khandelwal
He is the co-founder & business head for QuantInsti. He also Co-Founded iRage, which today is one of the leading names in Algorithmic Trading space in India.
Anupriya Gupta
Anupriya adds pedagogical and behavioral analysis in content creation, customer acquisition and student engagement. Formally trained as mathematician and educator, she brings experience from Analytics and formal education system into practice at QuantInsti.
Prof. Gautam Mitra
Internationally renowned research scientist in the field of Operational Research in general and computational optimisation and modelling in particular.