Algorithmic Trading and Financial Data Analysis with Python
| Preference | Dates | Timing | Location | Registration Fees |
|---|---|---|---|---|
| Instructor-Led Training (In-Person and Live Webinars) |
January 11, 12, 18, 19, 25, 26, 2025 | Saturdays & Sundays: 6:00 PM - 8:30 PM (GMT+4) | Dubai Knowledge Park | 1575 USD |
Course Description
Immerse yourself in the world of financial data analysis and algorithmic trading with our interactive, hands-on Python course. It’s the ideal way to equip yourself with the tools, techniques, and knowledge to gain an edge in the financial markets. Whether you’re a beginner or a seasoned veteran, this course will offer practical applications of Python to facilitate data analysis, strategy backtesting, automated trading, and much more.
This course is a comprehensive journey through the landscape of modern finance, seen through the lens of Python – a versatile and powerful language used by professionals worldwide. You will learn to scrape historical crypto price data, backtest your strategies, deploy your algorithmic trading bots, and analyze their performance, all using Python.
Course Outline
Audience
Prerequisites
After the Course
Course Outline
Module 1: Environment Setup
- Introduction to algorithmic trading platforms
- Setting up your development environment
- Overview of programming languages and tools
Module 2: Data Scraping and Collection
- Understanding financial data sources
- Techniques for scraping financial data
- Storing and managing your data effectively
Module 3: Data Analysis
- Exploratory data analysis (EDA) for trading
- Statistical analysis and pattern recognition
- Machine learning techniques in trading
Module 4: Broker API Connection
- Understanding broker APIs
- API integration for real-time data
- Executing trades through the API
Module 5: Strategy Definition
- Formulating a trading hypothesis
- Strategy development frameworks
- Risk management and optimization
Module 6: Strategy Backtesting
- Backtesting fundamentals
- Developing a backtesting engine
- Analysis and interpretation of backtesting results
Module 7: Cloud Environment Setup
- Choosing the right cloud platform for trading bots
- Cloud environment configuration
- Security and data protection in the cloud
Module 8: Trading Bot Deployment
- Deployment strategies for trading bots
- Monitoring and maintaining your trading bot
- Scaling your trading operations
Module 9: Machine Learning for Financial Markets
- Introduction to Machine Learning in Finance
- Key Machine Learning Models for Time Series Forecasting
- Neural Networks and Deep Learning for Trading Strategies
- Building and Training Deep Learning Models
- Implementing Predictive Models for Financial Data
Module 10: Order Book Analysis
- Understanding the Order Book and Its Components
- Analyzing Order Book Imbalances for Trading Insights
- Streaming and Real-Time Processing of Order Book Data
- Creating and Using Trading Indicators Based on Order Book Data
- Advanced Strategies for Order Book Analysis and Prediction
Audience
This course is designed for:
- Aspiring data analysts and data scientists looking to apply Python skills in financial markets.
- Finance professionals wanting to improve their data analysis skills and understanding of algorithmic trading.
- Software engineers and developers interested in finance and wanting to diversify their skill set.
- Investors and traders wanting to leverage Python to create, backtest, and deploy automated trading strategies.
Prerequisites
To make the most of this course, you should have:
- Prior experience with Python: You should have a good understanding of Python programming basics such as variables, data types, loops, functions, and classes. Familiarity with Python libraries like NumPy, pandas, and matplotlib would also be advantageous.
- Basic understanding of financial markets: Having a basic knowledge of financial markets and trading will help you grasp the concepts and strategies discussed in the course more effectively.
- Familiarity with mathematical concepts: Concepts such as statistics and probability are often used in financial data analysis and algorithmic trading. Therefore, having a basic understanding of these will be beneficial.
- Problem-solving mindset: Algorithmic trading involves devising and testing trading strategies, which requires a creative and analytical mindset.
- Access to a Python development environment: You should have Python installed on your computer, along with necessary libraries for data analysis such as pandas, NumPy, and matplotlib. An Integrated Development Environment (IDE) like Jupyter Notebook or PyCharm is also recommended.
Note: If you’re completely new to Python or financial markets, don’t worry! We have resources available online to help you get up to speed with these concepts before you start the course.
After the Course
Upon completing this course, you will be able to:
- Scrape and preprocess financial data from various sources such as cryptocurrency exchanges.
- Develop and backtest trading strategies using Python and relevant libraries.
- Automate trades using APIs and manage them on live exchanges.
- Deploy trading bots to the cloud for 100% uptime and maintain them effectively.
- Analyze and visualize trade performance to make informed decisions and optimize your strategies.
Testimonials
The workshop on big data and machine learning was an excellent introduction to practitioners considering using data science. Ahmed demonstrated considerable teaching talent rooted in his long expertise with systems development.
Very rewarding course. Rare to find a Deep learning course in Dubai that teaches concepts from scratch and provides practical applications. Will definitely recommend.
