Quantasi – EPAT – Executive Programme in Algorithmic Trading

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Quantasi – EPAT – Executive Programme in Algorithmic Trading
Executive Programme in Algorithmic Trading – EPAT®
An Algo Trading Course with Lifetime Placement Assistance
Are you looking to get a new job, start your own trading desk, or get better opportunities in your current organization? EPAT is a top algo trading course for those seeking career growth in algorithmic and quantitative trading. Access the most comprehensive quant trading curriculum, learn from world-class faculty, and receive personalized support for specialization in asset classes and trading strategies with live project mentorship.
COURSE BENEFITS
World-Class Faculty
Learn from the best in the industry
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
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