Long-term automated trading systems. Introduction into machine learning for trading (Advanced)

This course is designed for traders and investors looking to automate their trading using algorithmic strategies. You will learn how trading bots work, which parameters affect strategy efficiency, and how to test and optimize algorithms for long-term success.

Additionally to the basic version, this advanced-level course also gives an overview of machine learning (ML) for traders, which will be of interest for finance and ML professionals looking to integrate AI-driven strategies into quantitative trading. You will gain a practical understanding of ML techniques applied to real-world financial markets.

What’s included in the course?

Module 1: Introduction to automated trading

1. What is algorithmic trading?
2. Advantages and challenges of algorithmic strategies
3. Differences between high-frequency (HFT) and long-term trading systems

Module 2: Core components of a trading algorithm

1. Data sources: where and how to access market information
2. Choosing the right trading platform (MetaTrader, NinjaTrader, TradingView, Python scripts)
3. API integration and connectivity with brokers

Module 3: Developing trading strategies

1. Trend-following strategies and their application
2. Counter-trend strategies: trading market reversals
3. Market anomalies and statistical patterns in algorithmic trading

Module 4: Strategy optimization and risk management

1. Backtesting algorithms on historical data
2. Avoiding curve-fitting when optimizing strategies
3. Using genetic algorithms and machine learning in trading
4. Risk management techniques in automated trading
5. Deploying trading algorithms in live markets

Module 5: Developing ML-driven trading strategies

• Overview of machine learning applications in financial markets
• Building trading algorithms with supervised and unsupervised learning models
• Steps for designing and testing an ML-driven trading strategy
• Applying reinforcement learning for self-learning trading algorithms
• Using Google Cloud for deploying scalable trading models
• Risk management and backtesting of ML-driven strategies

Module 6: Trading psychology and risk management

• Recognizing and managing emotional biases in trading
• Risk management techniques for individual trades and portfolios

Module 7: Practical trading exercises with feedback in demo environment

• Individual virtual trading sessions using a demo platform (two sessions, 1 hour long each session)
• Applying learned knowledge in real-time simulated market conditions
• Reviewing trading decisions for simulated cases with feedback from experts

What you will learn:
✅ Develop and test automated trading algorithms
✅ Use algorithmic strategies for long-term investing
✅ Optimize trading bot parameters for maximum efficiency
✅ Implement risk management in automated trading
✅ Develop and implement ML-driven trading strategies
✅ Optimize trading algorithms using deep learning and reinforcement learning
✅ Apply ML techniques to risk management and market analysis
✅ Deploy trading algorithms in real market conditions

Who is this course for?
✔ Investors looking to automate their trading
✔ Traders who want to reduce emotional bias in decision-making
✔ Developers interested in algorithmic trading strategies
✔ Anyone who wants to build and deploy trading bots

Format:

• Self-paced learning materials (PDF format)
• Practical coding examples and algorithm testing
• Two live sessions with experienced traders (two sessions, 1 hour long each session)
• Real-time market analysis and interactive discussions with other trading enthusiasts and feedback from Rovelata team
• Virtual trading exercises with expert feedback
• Prior trading experience is not a must, but background in trading, economics of finance will be an advantage
• Prior programming experience will be beneficial