Quantasi – EPAT – Executive Programme in Algorithmic Trading

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EPAT – Executive Programme in Algorithmic Trading , Quantasi – EPAT – Executive Programme in Algorithmic Trading download

Quantasi – EPAT – Executive Programme in Algorithmic Trading

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

Commonly Asked Questions:

1. Business Model Innovation: Acknowledge the reality of a legitimate enterprise! Our approach involves the coordination of a collective purchase, in which the costs are shared among the participants. We utilize this cash to acquire renowned courses from sale pages and make them accessible to individuals with restricted financial resources. Our clients appreciate the affordability and accessibility we provide, despite the authors’ concerns.
2. EPAT – Executive Programme in Algorithmic Trading Course

• There are no scheduled coaching calls or sessions with the author.
• Access to the author’s private Facebook group or web portal is not permitted.
• No access to the author’s private membership forum.
• There is no direct email support available from the author or their team.