Advanced Algorithmic Trading with Artificial Intelligence
| Preference | Dates | Timing | Location | Registration Fees |
|---|---|---|---|---|
| Instructor-Led Training (In-Person and Live Webinars) |
October 4, 5, 11, 12, 18, 19, 25, 26, 2025 | Saturdays & Sundays: 6:00 PM - 8:30 PM (GMT+4) | Dubai Knowledge Park | 2000 USD |
Course Description
This hands-on advanced program is designed for professionals who want to move beyond the basics of algorithmic trading into the world of AI-driven strategies. You will gain practical experience building and deploying models — including deep learning, unsupervised learning, reinforcement learning, and transformers — applied to live trading environments with real-world financial and cryptocurrency tick-level data.
What makes this program unique is its focus on cutting-edge tools such as PyTorch, Kafka, Spark, Hadoop, and reinforcement learning agents — combined with an emphasis on evaluation and backtesting using professional risk metrics like Sharpe, Sortino, drawdown, and hit ratio.
Upon successful completion, you will receive a Dubai Government–accredited Course Completion Certificate, validating your expertise in applying advanced AI to financial trading systems.
Course Outline
Audience
Prerequisites
Learning Outcome
Course Outline
Module 1: Deep Learning Foundations
- Neural Networks refresher: forward & backpropagation
- Activation & cost functions in trading applications
- Implementing models in PyTorch
- Project: Build and train a simple deep learning model to predict short-term price movements.
Module 2: Market Regime Classification
- Using classification to detect bullish, bearish, and sideways markets
- Modeling volatility regimes
- Evaluating classification accuracy on tick-level data
- Project: Develop a market regime classifier and backtest its predictive accuracy.
Module 3: Unsupervised Learning for Market Microstructure
- Clustering and dimensionality reduction
- Pattern discovery in high-frequency data
- Identifying microstructure signals to enhance strategies
- Project: Apply clustering to identify recurring intraday patterns in crypto data.
Module 4: Big Data for Trading AI
- Ingesting large-scale trade data with Kafka
- Processing tick/millisecond data in Spark
- Storing and managing data in Hadoop
- Project: Stream live exchange data into a Spark pipeline for analysis.
Module 5: Time Series Modeling with Transformers
- Why transformers outperform LSTMs/GRUs
- Designing transformer-based models for financial time series
- Predicting returns and volatility using attention mechanisms
- Project: Train a transformer model to forecast short-term BTC/ETH returns.
Module 6: Reinforcement Learning Foundations
- Markov Decision Processes and reward functions
- Policy gradient and Q-learning explained
- Building a simplified RL agent for trading recommendations
- Deep Reinforcement Learning with DQN and PPO applied to trading signals
- Exploration vs exploitation in live market settings
- Project: Build an RL agent to optimize trade execution strategies on crypto markets.
Module 7: AI-Enhanced Strategy Development
- Integrating AI signals with rule-based strategies
- AI as a confirmation layer, not a replacement
- Case studies on BTC/ETH trading strategies
- Project: Combine AI-driven signals with a rule-based trading system and evaluate improvements.
Module 8: Evaluation and Backtesting of AI Models
- Backtesting AI-enhanced strategies
- Measuring performance: Sharpe, Sortino, drawdown, hit ratio
- Comparing AI models vs baseline strategies
- Critical evaluation of existing AI/agent approaches in trading
- Project: Conduct a full backtest of your AI trading strategy using professional risk metrics.
Audience
Target Audience
- Traders and analysts who have completed our Algorithmic Trading Fundamentals program or equivalent.
- Finance professionals and quants seeking to apply AI and machine learning techniques to live trading systems.
- Developers with Python knowledge who want to work with PyTorch, big data tools (Kafka, Spark, Hadoop), and transformers in financial contexts.
- Crypto traders interested in enhancing strategies using deep learning, unsupervised models, and reinforcement learning.
- Professionals aiming to differentiate themselves with cutting-edge skills in AI-driven trading — highly valued in finance and crypto markets.
Prerequisites
Prerequisites
- Basic knowledge of Python programming is required.
- Familiarity with financial markets and trading concepts is recommended.
- Completion of our Algorithmic Trading Fundamentals course (or equivalent experience) will provide the best foundation.
- Comfort with mathematics and statistics, including linear algebra, probability, and basic calculus, is beneficial.
- No prior AI/ML experience is strictly required — core concepts will be introduced as part of the course.
Learning Outcome
Learning Outcomes
- Build and train deep learning models in PyTorch for financial prediction and market regime classification.
- Apply supervised and unsupervised learning techniques to identify trading patterns and microstructure signals.
- Leverage big data tools (Kafka, Spark, Hadoop) to process and analyze large-scale, tick-level crypto data.
- Design transformer-based models for advanced time-series forecasting of returns and volatility.
- Develop reinforcement learning agents that enhance trading strategies through experience-driven decision making.
- Integrate AI-driven insights as a confirmation layer into existing algorithmic trading systems.
- Evaluate AI-enhanced strategies using professional risk and performance metrics (Sharpe, Sortino, drawdown, hit ratio).
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.
