## Who will benefit from epat?

#### financial analyst

• Extend the passion for financial markets
• Build upon your analytical skills
• Discover a new emerging career

#### programmer

• Take up new challenges
• Leverage programming in financial markets
• Explore new lucrative career avenues

#### student

• Get mentored from the leading industry experts
• Excel and thrive in the growing FinTech domain

## what will you learn on course completion?

##### 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
• 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
• 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, 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
• Understand principle component analysis and back-test PCA based long/short portfolios
• 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
• Code and back-test different strategies on various platforms
• 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
• 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.

## Why should you enroll for epat?

###### Hands on Industry Exposure

Hands-on training for developing advanced algorithmic trading strategies and implementing in live markets using state-of-the-art tools and platforms.

###### Learn from Experienced Market Practitioners

Get a chance to learn under the mentorship of world-renowned faculty that consists of industry leaders such as Dr. Ernest Chan, Dr. Yves Hilpisch, Rajib Ranjan Borah and Nitesh Khandelwal.

###### Connect with Alumni and Industry Influencers

Be a part of our successful alumni community and get your chance to interact with fellow quants and influencers.

Our alumni consist of entrepreneurs running their own trading desks and individuals working with global leaders such as Edelweiss, Sharekhan and many others.

## success stories

Eriz Zárate

Guillermina Amorin

Jacques Joubert

Kundan Kishore

Kushal Agarwal

Marco Dibo

Maxime Fages

Naoya Ohara

Nicolò Pirozzi