This course is designed for traders who want to automate their trading using algorithmic strategies. You will learn how trading bots function, the essential tools required for their development, and how to test and optimize strategies for real market conditions.
Automated trading minimizes emotional mistakes and ensures disciplined execution. This course will guide you in developing, testing, and deploying trading algorithms using platforms like MetaTrader, TradingView, and Python-based solutions.
In the advanced version of the course, we will focus on Python for financial analysis. You will need only Jupyter Notebook for real-time coding exercises, with no other special software, to practise the Python-related modules.
What’s included in the course?
Module 1: Introduction to algorithmic trading
- What is algorithmic trading
- How automated strategies enhance trading performance
- Differences between high-frequency trading (HFT) and long-term algorithmic strategies
- Examples of successful automated trading systems
Module 2: Building a basic trading bot
- Core components of an algorithmic trading system
- Market data sources: APIs, price feeds, and data processing
- Choosing the right platform: MetaTrader, TradingView, Python
- Writing and testing a basic trading algorithm
Module 3: Strategy development and backtesting
- How to define trading rules: trend-following vs. counter-trend strategies
- Selecting indicators: moving averages (SMA, EMA), MACD, RSI, Bollinger Bands
- Importance of backtesting: testing strategies on historical data
- Avoiding overfitting and optimizing strategies for real-world conditions
Module 4: Deploying automated trading strategies
- Connecting a trading bot to a broker or exchange
- Differences between demo and live trading environments
- Managing risk in algorithmic trading
- Real-world case studies of deployed strategies
Module 5: Monitoring and optimizing your trading algorithm
- Tracking bot performance and market adaptation
- Using logs, alerts, and automated monitoring tools
- Handling unexpected market movements and algorithm failures
- Adjusting strategies to evolving market conditions
Module 6: Python for financial analysis: introduction
- Python for quantitative modeling: how financial institutions use it
- Manipulating financial data using Pandas
- Visualizing stock trends and market performance
- Trend-following strategy based on moving averages
Module 7: Building and evaluating a quantitative trading model
- Using Python to build a data-driven trading strategy
- Evaluating model performance with financial indicators
- Testing and optimizing models for real-time market conditions
Module 8: Statistical inference and investment analysis with Python
- Understanding statistical inference, population, sample data
- Random sampling techniques
- Estimating stock mean returns
- Hypothesis testing: validating investment return claims
What you will learn:
✅ Develop automated trading algorithms from scratch
✅ Conduct backtesting and avoid overfitting strategies
✅ Optimize bots for different market conditions
✅ Deploy and manage algorithmic systems in live markets
✅ Monitor, analyze, and refine trading algorithms
✅Gain expertise in Python for financial data analysis
✅Use Jupyter Notebook to practice quantitative finance coding
✅ Develop data-driven investment models using Python
Who is this course for?
✔ Traders looking to automate their trading strategies
✔ Developers interested in building trading bots
✔ Traders and analysts aiming to quantify market trends using Python
✔ Those interested in data-driven strategies
Format:
- Self-paced learning materials (PDF format)
- Practical coding examples and backtesting exercises
- Real-time market analysis and interactive discussions with other trading enthusiasts and feedback from Rovelata team
- Virtual trading exercises with expert feedback
- Prior programming experience will be an advantage
