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
1. EPAT Primer
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- 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
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- 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
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- 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
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- Self-study project work under mentorship of a domain/expert
- Project topic qualifies for area of specialization and enhanced learning
Cost: Check Site for Current Pricing
