• 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

• 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
• Two Python tutorials will be conducted to answer queries and resolve doubts on Python

• 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
• Two tutorials will be conducted after the initial two lectures to answer queries and resolve doubts about Data Analysis and Modeling in Python

• Modeling data with AI, index and predicting next day’s closing price
• Supervised learning algorithms, Decision Trees & additive modeling
• Natural Language Processing (NLP) and Sentiment Analysis
• 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
• Reinforcement Learning in Trading
• How to build trading Systems while not overfitting

• 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

• 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

• 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 center