Executive Programme in Algorithmic Trading

Description

www.quantinsti.com/epat
Original Price $7999
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