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Course Details
Course Details
What You'll Learn
Topic 1: Foundations of Quant and Algorithmic Trading: From Thorp to AI Agents
Quant Success Stories: What Systematic Trading Has Achieved
The Evolution of Quant: From Thorp and Renaissance to High-Frequency Trading
The Paradigm Shift from Machine Learning to LLMs, and Its Impact on Quant
Agentic AI versus AI Agents in Trading, and Multi-Agent Trading Systems
Forms of Data within Financial Services and Big Data Applications in Trading
Developing Hypotheses for Quant and Algorithmic Trading Models
Topic 2: Technical Indicators, Trading Strategies and Metrics with TradingView and Alpaca
Technical Indicators: Trend, Momentum, Volatility and Volume
Classification of Algorithmic Trading and Quant Algorithms
Analytics Platforms and Tools: TradingView versus Alpaca, and Python
Coding Languages for Algorithms and Signals, and Order Placement
Performance and Risk Metric Definitions
Topic 3: Machine Learning Models, Statistical Modelling and A/B Testing for Trading Signals
Types of Statistical Analyses, Data Models, Algorithms and Computational Methods
Supervised Learning Models for Trading Signals
Unsupervised Learning: Clustering and PCA for Market Regimes
Statistical Modelling Techniques for Financial Time Series
A/B Testing Trading Strategies
Pairs Trading: Statistical Arbitrage as a Hedging Algorithm
Topic 4: Building an End-to-End Algorithmic Trading Pipeline: Backtest, Forward Test and Performance Analysis
Architecture of an End-to-End Algorithmic Trading Pipeline
Backtesting with Realistic Trading Costs
Forward Testing with Automated Paper Trading
Potential Reasons for Unintended Outcomes and How to Diagnose Them
Performance Analysis, Insights and Reporting
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- A basic understanding of financial markets (stocks, ETFs, buying and selling)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- The course Trading Simulator – free, installed in class with uv or Docker Desktop
- An Alpaca paper-trading account – free, no deposit needed
- A TradingView account – free plan
Hardware: Windows PC or Mac laptop
Job Roles
Job Roles
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Research Scientist
- Business Intelligence Specialist
- Data Engineer
- Software Developer (interested in ML)
- Statistician
- Predictive Modeler
- AI Solutions Architect
- Quantitative Researcher
- Data Visualization Specialist
- Analytics Consultant
- Product Manager (focused on AI/ML products)
- Innovation Specialist
Trainers
Trainers
Dr. Muhammed Siraj is a cybersecurity specialist and academic with 15 years of experience, serving as Senior Lecturer in the Department of Informatics and Director of Graduate and Continuous Education at Academic City University in Accra. He holds a PhD in Security in Computing from Universiti Putra Malaysia, where his research focused on blockchain security, an MSc in Information and Communication Technology from Aalborg University in Denmark, and a degree from the University of Ghana. He is a licensed independent assessor with Ghana's Cyber Security Authority, holds leadership roles in national cybersecurity and privacy professional bodies, and is certified as a Certified Ethical Hacker (CEH), Computer Hacking Forensic Investigator (CHFI) and Certified Data Protection Supervisor (CDPS). Before joining Academic City, he served as Acting Director of ICT and Data Protection Supervisor at Wisconsin International University College. His expertise spans penetration testing, digital forensics, incident response, malware analysis, cryptography, cloud and IoT security, zero trust architecture, cyber threat intelligence, data protection, and governance, risk and compliance.
Dr. Siraj's research interests cover blockchain, cybersecurity, distributed computing, consensus protocols and machine learning, with published work on blockchain consensus security, smart-contract privacy preservation for sensitive records in the cloud, and new consensus mechanisms. This background gives him a distinctive perspective on artificial intelligence: as organisations adopt AI tools, agents and data-driven automation, he helps learners understand how to protect the data these systems depend on, recognise new attack surfaces, and put sound governance, privacy and access controls in place so that AI can be used safely and responsibly. In the classroom he connects technical practice with regulation, privacy and human factors, drawing on real-world security and data-protection cases to show how concepts apply in professional settings, and his experiential, hands-on teaching style has made him a trusted trainer for students and working professionals alike.
Financial data and automation come together in several projects built by Dr. Alfred Ang, a full-stack AI developer, Agentic AI solution architect, corporate trainer and CTO of Tertiary Infotech Academy Pte Ltd in Singapore, the training organisation behind Tertiary Courses. Dr. Ang builds intelligent agents and business solutions end-to-end, covering front end, back end, databases, cloud deployment and native mobile apps, and works daily with AI coding assistants to write and refactor code at speed. The public portfolio of more than 300 repositories includes a dark-themed stock analysis dashboard with live price and volume charts from market data APIs, connected to an n8n-powered AI agent on Telegram that answers questions about individual stocks. A related project is an investment advisor lead-magnet website with an enquiry and FAQ chatbot workflow, and another is a financial planning site with a retirement calculator. On the data science side, Dr. Ang created interactive Python and Streamlit labs for data mining and machine learning, and an AI inventory forecasting agent that uses a local language model to analyse stock levels and predict reorder needs. These projects involve the same building blocks as algorithmic trading: pulling market data from APIs, cleaning and analysing time series, visualising indicators, scheduling automated jobs and sending alerts. Just as importantly, they reflect Dr. Ang's habit of reviewing every line of AI-generated code, a discipline that matters even more when real money is involved. Dr. Ang also deploys scheduled background jobs on cloud servers for live platforms, which translates directly into running a trading script reliably day after day.
AI Vibe Coding for Algorithmic Trading teaches learners to build trading strategies in Python by directing AI coding assistants such as Cursor, GitHub Copilot and Claude, and Dr. Ang structures it as a progression from first script to automated system. The first topic, getting started with AI vibe coding for trading, sets up the Python environment and shows how to describe a requirement in plain English, read the code the AI returns and correct it. The second topic, analysing markets with AI, has learners download historical price data, compute indicators such as moving averages, RSI and volatility, and chart them, much as Dr. Ang's stock dashboard visualises live data. The third topic, building and backtesting strategies with AI, turns trading ideas into rules, backtests them on historical data and evaluates returns, drawdowns and win rates, with Dr. Ang highlighting common traps such as look-ahead bias, overfitting and ignoring transaction costs. The fourth topic, automating and running trading with AI, covers paper trading, scheduling, logging and sending notifications, along with the risk controls any automated system needs. Dr. Ang stresses throughout that the course teaches technique, not investment advice, and that no strategy should trade real money without thorough testing. Learners, whether traders, analysts or hobbyist programmers, leave with a working, backtested strategy and the skills to keep improving it. To see the financial dashboards and data projects Dr. Ang has built, visit https://github.com/alfredang.
Review
Customer Reviews (6)
- Clear and easy to follow Review by Course Participant/Trainee
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Good hands-on training with plenty of examples. I feel much more confident applying these skills now. (Posted on 5/9/2024)1. Do you find the course meet your expectation? 2. Do you find the trainer knowledgeable in this subject? 3. How do you find the training environment
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