Understanding the microstructure of financial markets and mastering trading systems is critical for thriving in the fast-paced world of modern finance. This course bridges theory and practice, covering key concepts such as:
- Market Microstructure
- Algorithmic Trading
- Quantitative Analysis
- Tools ranging from Technical Analysis to advanced Deep Learning models
Through hands-on projects, we will develop the skills needed to make data-driven decisions, optimize trading strategies, and navigate the complexities of today’s financial markets.
This course includes four major projects, each designed to strengthen specific competencies in trading and market analysis.
Focuses on bid–ask spreads and market maker profitability, drawing on Bagehot’s insights and the Copeland & Galai model. Key tasks:
- Simulate stock price movements with a probability factor for informed trades
- Plot price distributions under different conditions
- Calculate expected revenue for various trade scenarios
- Determine optimal bid and ask prices using Copeland & Galai’s framework
Covers chart-based methods for identifying trends, patterns, and price levels that signal optimal entry and exit points. Focus areas:
- Support & resistance identification
- Trend and pattern recognition
- Risk management strategies
- Timing for market entries and exits
Applies machine learning to predict buy/sell signals in equities and cryptocurrencies. Key methods:
- Logistic Regression, Support Vector Classification (SVC), XGBoost
- Integration via a Voting Classifier for improved accuracy
- Hyperparameter optimization using Optuna Objective: Maximize predictive performance and returns through data-driven signals.
Focuses on maintaining robust trading models through:
- Continuous performance monitoring
- Frequent retraining cycles
- Strategic capital allocation Goal: Adapt models to evolving market conditions for sustained profitability.
By the end of this course, participants will be able to:
- Analyze and simulate market microstructure mechanics
- Apply technical analysis for informed trading decisions
- Build and optimize machine learning trading models
- Implement model maintenance and adaptation strategies for dynamic markets