Executive Programme in Algorithmic Trading – EPAT®
4.7 rating out of 130+ Google reviews
EPAT is one of the best algo trading courses. Are you looking to get a new job, start your own trading desk, or get better opportunities in your current organization? This quantitative trading course is designed for professionals looking to grow in the field of algorithmic and quantitative trading.
Get access to the most comprehensive quant trading curriculum in the industry. Learn from a world-class faculty pool. Experience personalised learning with best-in-class support. Complete specialisation in desired asset classes and trading strategy paradigms with live project mentorship.
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 Markov 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
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
4.7 rating out of 130+ Google reviews
EPAT is one of the best algo trading courses. Are you looking to get a new job, start your own trading desk, or get better opportunities in your current organization? This quantitative trading course is designed for professionals looking to grow in the field of algorithmic and quantitative trading.
Get access to the most comprehensive quant trading curriculum in the industry. Learn from a world-class faculty pool. Experience personalised learning with best-in-class support. Complete specialisation in desired asset classes and trading strategy paradigms with live project mentorship.
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 Markov 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
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
Code:
https://www.quantinsti.com/epat
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