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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. Alfred Ang combines formal study of algorithmic trading with deep, hands-on AI development experience. He completed an Executive Programme in Algorithmic Trading from QuantInsti, and he is a full-stack AI developer and Agentic AI solution architect who builds AI-powered applications from frontend to LLM orchestration and cloud infrastructure. He serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd and is the founder of Tertiary Courses. His quantitative grounding is strong: a PhD in Electrical Engineering from NUS, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, where he topped his cohort, along with more than 20 peer-reviewed papers and more than 20 US patents as co-inventor. He is a Microsoft Certified Data Scientist (DP-100) and AI Engineer Associate (AI-102), and an AWS Certified Solutions Architect. On GitHub he has published n8n-financeadvisor, a stock analysis dashboard with TradingView and TwelveData charts and live OHLC and volume data, paired with an n8n-powered Telegram AI agent that gives Buy, Sell or Hold recommendations with its reasoning, as well as n8n-investmentadvisor, an investment advisor site with an n8n enquiry and FAQ chatbot workflow. His portfolio also includes skills, a published set of Claude Code skills that reflects his own use of AI coding agents. He is also an ACTA-certified trainer and DACE-certified curriculum developer and a former senior member of IEEE, bringing a methodical, evidence-first mindset to every class.
In AI Powered Quantitative Trading Fundamentals, Dr. Ang teaches learners to describe a strategy in plain English, let AI coding assistants such as Cursor, GitHub Copilot and Claude write the Python, and then review and refine the code until it does exactly what was intended. The course starts with setting up the tools and connecting to market data, then uses AI to analyse markets with indicators, charts and statistics. Learners go on to build and backtest strategies, and finally automate and run them. Here his trading programme and data science background matter: he insists that every AI-generated strategy is checked for look-ahead bias, overfitting, unrealistic assumptions about costs and slippage, and logic that does not match the trader's intent, because code that runs is not the same as code that is right. He is equally clear that backtest results are no promise of future returns and that risk controls come before automation. Learners work hands-on throughout, iterating with the AI rather than copying finished code, so traders, investors and finance professionals leave able to turn their own ideas into tested Python with AI as a partner. He also explains how to structure a trading project so that data, strategy logic, backtests and execution sit in separate, testable parts, making it easier to ask the AI for focused changes. To see his trading dashboards and AI agent projects, learners can view his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Bernard Peh is a data scientist and financial services professional with more than two decades of experience spanning finance, data analytics and business intelligence. He applies data science to collective funds, where his work has delivered results such as a 400% increase in assets under management. As Senior Partner and Lead Data Scientist at Big Data Venture Capital, he has led consulting projects and curriculum development for blue-chip firms, working hands-on with Python, data acquisition and cleaning, feature engineering, ETL and visualisation. He is a certified adult educator holding the WSQ Advanced Certificate in Learning and Performance (ACLP) and has delivered numerous data science courses through NTUC LearningHub and other providers.
AI Powered Quantitative Trading Fundamentals draws directly on that combination of market experience and Python data skills. Bernard helps learners get started with AI-assisted coding for trading, analyse markets with AI, build and backtest strategies, and then automate and run them. Because he works with market data in practice, he places strong emphasis on data quality, sound backtesting and understanding what the generated code actually does. Participants finish the course able to turn trading ideas into tested, working code with the help of AI tools, while applying the discipline real trading demands.
Ken Yuen is an ACTA-certified adult educator specialising in STEM education, IoT systems and programming, with Python among the languages he teaches. He has conducted WSQ and SkillsFuture courses in Python, Arduino, Raspberry Pi, micro:bit and Jetson Nano, and his career spans roles as an instructor, Application Development Engineer, Technical Consultant and Project Manager. He holds a Bachelor of Electrical and Electronics Engineering from Nanyang Technological University and a Diploma in Electronic Engineering from Singapore Polytechnic, and he is a certified Project Management Professional (PMP). As an MOE-registered instructor he has taught coding in over 50 schools.
Ken is a programming and engineering educator rather than a professional trader, and he brings that Python and application development background to AI Powered Quantitative Trading Fundamentals. He shows learners how to describe a strategy in plain English, have AI coding assistants such as Cursor, GitHub Copilot and Claude write the code, and then review and refine it. The course moves from getting started and analysing markets with AI to building and backtesting strategies and automating their execution. His hands-on approach stresses testing every step, so participants learn to verify AI-generated logic carefully before trusting it with any trading decision.
Dr Alvin Ang is an ACTA-certified trainer who specialises in real-world data science. He earned his PhD, Masters and Bachelors degrees at NTU, Singapore, and has held roles as a Principal Consultant (Data Science) and an Assistant Professor, including eight years as an adjunct lecturer at SUSS. Trained as an operational researcher, he is most interested in practical applications rather than theory for its own sake, and he owns a startup of his own.
In AI Powered Quantitative Trading Fundamentals, Dr Ang applies this quantitative, practical perspective to building trading strategies in Python with AI coding assistants such as Cursor, GitHub Copilot and Claude. Learners get started with AI vibe coding for trading, analyse markets with AI, build and backtest strategies, and then automate and run them. He places particular weight on testing assumptions and reading backtest results honestly, so that learners understand the gap between a promising chart and a robust strategy. Participants leave able to express a strategy in plain English, have an assistant turn it into working code, test it against historical data, and decide with clear eyes whether it is worth running.
Shahul H. Maricar is an ACTA-certified trainer with a background in programming, workflow automation and data analysis. He began as a content developer and webmaster, building educational websites and applications with HTML, CSS and JavaScript, and then served as an IT analyst, writing programs to automate custom workflows and to carry out data extraction and analysis in the healthcare field. He is currently a freelance educator and is actively involved in development projects in game programming, computer-aided design and computer graphics. This hands-on programming background helps him explain code clearly to learners who are not professional developers.
In AI Powered Quantitative Trading Fundamentals, Shahul brings his programming and data analysis experience to building trading strategies in Python by directing AI coding assistants such as Cursor, GitHub Copilot and Claude. Learners get started with AI vibe coding for trading, then use AI to analyse markets and work with price data. They build and backtest strategies, describing the logic in plain English, reviewing the code the AI writes and refining it until it behaves as intended. The course concludes with automating and running trading with AI. His experience of automating workflows and analysing data reinforces careful testing before anything runs. Traders and investors leave able to turn strategy ideas into working, testable Python code.
Review
Customer Reviews (4)
- will recommend Review by Course Participant/Trainee
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Could be a 1 1/2 to 2 Days course (Posted on 1/5/2020)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 - very good. Review by Course Participant/Trainee
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very good. (Posted on 11/11/2019)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 - Will Recommend Review by Course Participant/Trainee
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Please feel free to let me know if you need trainer for such courses (Posted on 10/28/2019)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 - Will Recommend Review by Course Participant/Trainee
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. (Posted on 11/13/2018)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
