Executive Programme in Algorithmic Trading (EPAT®)

Executive Programme in Algorithmic Trading (EPAT®)

Learn Algorithmic Trading - Build your Career in Algorithmic Trading
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Learn Algorithmic Trading

Build your Career in Algorithmic Trading

Audience participation is as important to the learning experience as the instructor. I find the participants at QuantInsti’s courses highly motivated and many came prepared with insightful questions. This made for a great experience for all.

Dr. Ernest P. Chan

 

quantinsti-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

  • 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


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

  • 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


S8 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