Algo Trading Course

FEES
DURATION
COURSE TYPE
₹ 3,999
5 Hours
Self - Learning

Technology development across global markets has necessitated a multidimensional approach for understanding the Importance of Algorithmic Trading. This course encompasses trading in various asset classes with special focus on Equity Index Futures, Options, and Commodities.

  • 1. Introduction to algorithmic trading
  • 2. Data and technology for algorithmic trading
  • 3. Designing and evaluating algorithmic trading strategies
  • 4. Risk management in algo trading

To improve the knowledge in Algorithmic Trading. To learn about different trading strategies. To Learn Risk Management in Algorithmic Trading. To learn about Algorithmic Trading and its Audit and Compliance Process.

There is no eligibility criteria

Course overview:

This Algo trading course comprises 5 modules, offering a comprehensive journey for learners starting from scratch in the world of algorithmic trading. Throughout the course, you will develop a foundational understanding of key concepts, terminology, and diverse trading strategies. Practical applications will empower you to access, process, and analyze financial data, providing hands-on experience crucial for algorithmic trading success. You'll gain proficiency in designing, evaluating, and optimizing your trading strategies while learning effective risk management techniques to safeguard your investments.

Additionally, the course explores emerging trends and opportunities in algorithmic trading, including the integration of cutting-edge technologies like artificial intelligence and blockchain. Assessments in the form of quizzes and assignments ensure a thorough grasp of the material, and by the end of the course, you will possess the skills and confidence to navigate the complexities of algorithmic trading and thrive in the dynamic financial markets.

Course outline

Module 1: Introduction to algorithmic trading

Topics covered:

  1. Algorithmic Trading Basic
  2. Market Structures
  3. Evolution of Algorithmic Trading
  4. Algorithmic Trading Strategies
  5. Lifecycle of Algorithmic Trading
  6. Market Microstructure
  7. Order Book Insights
  8. Bid-Ask Spread
  9. Introduction to Tools
  10. Installing IntelliJ
  11. Stock Data with IntelliJ

What will you learn?

  1. The basic concepts and terminology of algorithmic trading, such as order types, execution algorithms, market microstructure, and backtesting
  2. Understand the advantages and challenges of algorithmic trading, such as speed, efficiency, scalability, and risk management
  3. Explore the different types of algorithmic trading strategies, such as arbitrage, market making, trend following, and mean reversion

Learning outcomes:

By the end of this module, you will be able to explain what algorithmic trading is, how it works, and what are the main types of strategies.

What are the activities for you?

  • Take a quiz and complete the assignment

Module 2: Data and technology for algorithmic trading

Topics covered:

1. Machine learning introduction

2. Regression models:

  • Simple linear regression
  • Multiple linear regression
  • Logistic regression
  • Decision tree regression
  • Random forest regression

3. Classification models

  • Decision tree classification
  • Random forest classification

4. Examples of inappropriate fits to stock data

5. Analytical vs numerical optimization

6. Cost functions:

  • Examine cost functions tailored for regression models.
  • Explore cost functions designed for classification models.

7. Gradient descent techniques:

  • Understand the gradient descent optimization method.
  • Explore stochastic gradient descent as an iterative optimization approach.
  • Introduce Adam gradient descent for enhanced optimization.

8. Time series models:

  • Auto regressive models (AR)
  • Moving average models (MA)
  • MA as a fundamental model for stock data predictions
  • Auto regressive moving average models
  • Auto regressive integrated moving average models
  • Exponentially weighted moving average models
  • Generalized auto regressive conditional heteroskedasticity models
  • Stock data examples

9. Deep learning introduction – artificial neural networks (ann):

  • Distinguish between traditional machine learning and deep learning approaches.
  • Universal approximation theorem.
  • Foundational concept of the perceptron.
  • Examine various activation functions.
  • The role of cost functions.
  • Back propagation algorithm.

10. Feed Forward Neural Network (FFN)

11. Recurrent Neural Network (RNN)

12. Long Short Term Memory (LSTM) Network

What will you learn?

  • How to access, process, and analyze financial data for algorithmic trading, such as historical prices, market data, news, and sentiment
  • The role and importance of technology for algorithmic trading, such as programming languages, software tools, platforms, and infrastructure
  • Compare and contrast the different options and features of popular algorithmic trading platforms, such as MetaTrader, Quantopian, and TradingView.

Learning outcomes:

By the end of this module, you will be able to use data and technology to support your algorithmic trading activities

What are the activities for you?

Take a quiz and complete the assignment

Module 3: Designing and evaluating algorithmic trading strategies

Topics covered:

1. Overview of systematic trading indicators in technical analysis

2. Trend following strategies

3. Momentum-based strategies

4. Exploring strategies on stock price data

5. Exploring strategies on bitcoin data

6. Ideation and strategy creation

7. Architecture of a back-testing system

8. Common pitfalls

9. Implementing a back-tester

10. Strategy module

11. Performance measurement statistics

12. Explore techniques for optimizing strategy parameters to enhance performance.

13. Transaction cost analysis

14. Introduction to quantitative trading

15. Quantitative directional strategies

16. Statistical arbitrage strategies:

  • Pairs trading strategies

17. Arbitrage strategies:

  • Index arbitrage
  • Spread arbitrage

18. Gamma scalping

19. Volatility trading

  • Risk reversal / volatility skew trading
  • Dispersion trading

20. Electronic market making strategies

21. Execution algorithms:

  • Percentage of Volume (POV):
  • Volume Weighted Average Price (VWAP):
  • Time Weighted Average Price (TWAP):

What will you learn?

  • How to design and implement algorithmic trading strategies, such as defining the trading logic, setting the parameters, and coding the algorithm
  • How to evaluate and optimize algorithmic trading strategies, such as measuring the performance, testing the robustness, and avoiding overfitting
  • The best practices and common pitfalls of algorithmic trading, such as risk management, trading psychology, and ethical issues

Learning outcomes:

By the end of this module, you will be able to design, evaluate, and optimize your algorithmic trading strategies

What are the activities for you?

Take a quiz and complete the assignment

Module 4: Risk management in algo trading

Topics covered:

1. Optimal capital allocation

2. Risk management

What will you learn?

  • The sources and types of risks in algo trading, such as technical risks, market risks, operational risks, and behavioral risks
  • The importance and objectives of risk management in algo trading, such as protecting capital, preserving profits, and avoiding losses
  • The principles and techniques of risk management in algo trading, such as setting clear goals and limits, diversifying investments, monitoring and evaluating performance, using stop-loss and take-profit orders, hedging strategies, and quantitative risk measures

Learning outcomes:

By the end of this module, you will be able to identify, assess, and mitigate the potential risks of algo trading

What are the activities for you?

Take a quiz and complete the assignment

Course offerings

  • Comprehensive learning modules
  • In-depth video sessions by industry experts
  • Practical application
  • Expert guidance with experienced instructors
  • Quizzes and assessments
  • Data and technology integration
  • Risk management techniques
  • Emerging trends and opportunities in algorithmic trading
  • Valuable networking opportunities within the algorithmic trading community
  • Career Advancement
  • Certification

Who should opt

  • Beginner traders
  • Finance enthusiasts
  • Quantitative analysts
  • Financial analysts
  • Programmers and developers
  • Investment professionals
  • Portfolio managers

Course offerings

  • You will acquire a holistic understanding of algorithmic trading, from foundational concepts to advanced strategies, enhancing your overall proficiency in the field.
  • Don’t just learn theory, because you will learn and remember better with practice. Therefore, this course will help you gain hands-on experience through real-world simulations and exercises to ensure that theoretical knowledge is translated into practical skills applicable in live trading environments.
  • The course lets you learn from industry experts and experienced instructors who bring practical insights, real-world examples, and the latest market trends to the learning experience.
  • The course will be a medium to connect with a community of fellow participants, instructors, and industry professionals. It will give you valuable insights and opportunities for collaborations, and potential career opportunities in the algorithmic trading field.
  • You can access video sessions and course materials at your own pace, allowing flexibility to accommodate different schedules and learning preferences.
  • You will develop proficiency in using technology for algorithmic trading, including programming languages, software tools, and platforms, providing a technological edge in the competitive financial landscape.
  • The course learnings will significantly add to your skill set and it will help you enhance your career prospects in the financial industry. Further, it will open doors to roles such as quantitative analyst, algorithmic trader, or financial engineer, where expertise in algorithmic trading is in high demand.
  • The course will also help you stay ahead of market trends by exploring emerging technologies like artificial intelligence and blockchain. It will position you to adapt and thrive in the ever-evolving landscape of algorithmic trading.
  • Specialized skills in risk management tailored to algorithmic trading are offered, ensuring that you can optimize returns while effectively mitigating potential risks.
  • You will get a certification upon completion of the course, adding credibility to your expertise and signaling your commitment to continuous learning and professional development in algorithmic trading.
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