# Executive Programme in Algorithmic Trading (EPAT™)

Step 1

## learn

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

## automate

• Network with data providers & brokers
Step 3

• 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.

EPAT, 2017

## Curriculum

##### 1EPAT 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
• 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
• 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 and PCA, understanding VaR
• Work on sample strategies, trade the Boring Consumer Stocks in Python
• 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
• 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, Non linearity of volatility, Gaussian Mixture Models (GMM)
• 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
• 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
• 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
• Self-study project work under mentorship of a domain/expert
• Project topic qualifies for area of specialization and enhanced learning
##### “Hi, I'm Mohammed

EPAT grooms ambitious Algo professionals to gain that confidence and learning experience needed to kick-start their career.

EPAT, 2016
##### “Hi, I'm Venkat

EPAT gave my career a springboard by adding value to my resume.

EPAT, 2014
##### “Hi, I'm Hiren

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

EPAT, 2012
##### “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.

EPAT, 2012
##### Industry 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

#### And a lot more

##### 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.