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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
How to Apply HRD Corp Funding
This course is HRD Corp claimable for employers registered with HRD Corp, under the HRD Corp Claimable Courses scheme (SBL-Khas). Your company applies for the grant before the class; we submit the training provider claim after it.
- Check your levy balance - your company must be registered with HRD Corp and have enough levy balance to cover the course fee.
- Request a quotation - contact us for the quotation, course content and trainer profile; you attach these to the grant application.
- Apply for the grant in eTRiS before the class - log in to your employer eTRiS account, go to Application > Grant > Apply Grant, choose the scheme HRD Corp Claimable Courses (SBL-Khas), select us as the training provider and this course, add your trainees, attach the quotation, course content and trainer profile, and submit.
- Submit in time - for public classes, submit at least 3 calendar days before the class (15 June to 31 December 2026) and at least 14 days before from 1 January 2027; in-house classes need 14 days. The training must start within 90 days of that advance submission period.
- Send us the grant approval - once HRD Corp approves the application, email us the grant ID so we can confirm your seats.
- Attend the class - trainees sign the daily attendance (T3) form.
- Claim after the class - we submit the training provider claim in eTRiS with the attendance and invoice; you then complete the employer claim and sign the JD/14 form (manager level or above). Claims must be submitted within 6 months of the training.
More details: our HRD Corp Claimable Courses page and the HRD Corp employer guidelines.
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
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)
- Excellent course Review by Course Participant/Trainee
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The trainer is an expert in the subject and shared many practical insights from real projects. (Posted on 22/04/2026)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 - Knowledgeable trainer 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 13/04/2026)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 - Highly recommended Review by Course Participant/Trainee
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Very engaging session. The trainer patiently answered all our questions and gave useful tips. (Posted on 20/01/2026)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 - 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 22/12/2025)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 - Superb training Review by Course Participant/Trainee
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I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 16/01/2024)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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