Executive Programme in Algorithmic Trading - EPAT 

EPAT Primer

Stock market basics: Learning about financial markets and a brief understanding of how they work.

Excel primer: Spreadsheet basics, learning to format and visualize data, using built -in functions to summarize and manipulate data, working with examples to familiarize yourself with spreadsheets.

Python primer: Learning to work with Python in multiple ways (Spyder IDE, Jupyter Notebook), variables, data structures, functions, key libraries used

Statistics for Financial Markets

Key ideas in statistics and probability, and animating them with financial market data

Creating and analyzing quant trading strategies on spreadsheets, creating charts to interpret their performance

Learning portfolio construction and optimizing them using modern portfolio theory

Python: Basics & Its Quant Ecosystem

Data types, variables, Python in-built data structures, inbuilt functions, logical operators, and control structures

Introduction to the main libraries in the data science stack: NumPy, pandas, and matplotlib

Learning to write functions in Python

Market Microstructure for Trading

Overview of Electronic and Algorithmic Trading.

Understanding market terminology, order book concepts and order types

Introduction to execution strategies

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)

Data Analysis & Modeling in Python

Learning to backtest and analyze 4-5 strategies on Python using historical data

Understanding object-oriented programming (OOP) concepts and using OOP to backtest trading strategies

Glimpse of the basic cloud infrastructure to host automated Python strategies

Machine Learning for Trading

Classical ML algorithms: Support Vector Machines (SVM), k-means clustering, logistic regression, decision trees, random forests

Introduction to deep learning: Neural networks, gradient descent, and backpropagation algorithms

Using Python to build and evaluate ML models for potential trading strategies (by creating features and selecting suitable ones)

Trading Tech, Infra & Operations

Understanding the system architecture of a traditional trading system

Understanding the system architecture of an automated trading system

Assessing the challenges in building a trading system

Advanced Statistics for Quant Strategies

Learning time series-centric terminology like stationarity, ACF, PACF

Learning common features of financial asset returns

Introduction to the ARIMA family of models

Trading & Back-testing Platforms

Introduction to the Interactive Brokers (IB) platform and Blueshift

Working with IB Trader WorkStation (TWS) and the IB TWS API architecture

Learning the REST API (used by hundreds of brokers worldwide) and its components

Portfolio Optimization & Risk Management

Learning about different methods to evaluate portfolio and strategy performance

Understanding risk management: sources of risk, risk limits, risk evaluation and mitigation, risk control systems

Trade sizing for individual trading strategies using historical methodologies, Kelly criterion, and leverage space theorem

Options Trading & Strategies

Introduction to options, payoff diagrams, common option structures

General option trading principles, model-independent option features

Option pricing variables and parameters

Hands-on Project

Self-study project work under the mentorship of a domain expert

EPAT Exam

EPAT exam is conducted at proctored centers in 80+ countries