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Course Details
Course Details
What You'll Learn
Topic 1: From Trading Idea to Quality Market Data
Topic 2: The Six-Step Trading Workflow and Staying in Control
Course Info
Minimum Entry Requirement
Knowledge and Skills
- Able to operate using computer functions
- Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
- Positive Learning Attitude
- Enthusiastic Learner
Experience
- Minimum of 1 year of working experience.
Target Age Group: 21-65 years old
Minimum Software/Hardware Requirement
Software:
You will need the following for this course:
- Python 3 – free
- Visual Studio Code – free
- Google Colab – free, runs in the browser with a Google account
Hardware: Windows PC or Mac laptop
Job Roles
Job Roles
- Retail Investor
- Stock Trader
- Active Trader
- Investment Analyst
- Equity Research Analyst
- Portfolio Manager
- Quantitative Analyst
- Wealth Manager
- Remisier and Dealer
- Finance Professional
- Data Analyst
- FinTech Professional
Trainers
Trainers
Dr. Alfred Ang approaches the markets the same way as any engineering problem: with clean data, clear hypotheses and tools that keep the human in charge. As CTO of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses, Dr. Ang works as a full-stack AI developer, Agentic AI solution architect and corporate trainer. Among more than 300 public repositories at github.com/alfredang, the most relevant to this course is n8n-financeadvisor, a dark-themed stock analysis dashboard that pulls live price and volume data through TradingView and TwelveData charts and pairs it with an n8n-powered Telegram AI agent that issues Buy, Sell or Hold views together with its reasoning. Dr. Ang has also built an investment advisor lead-magnet site with an n8n FAQ chatbot, financial planning sites with retirement calculators, and n8n-inventorycontrol, a forecasting agent that runs on a local LLM through Ollama and flags items that cross defined thresholds, a pattern that maps neatly onto price alerts and watchlists. Beyond finance, the portfolio includes RAG chatbots, data dashboards and Streamlit machine-learning labs that teach how to test models against real data. That mix of market data plumbing, agent design and analytical discipline means Dr. Ang can show traders exactly how an AI agent gathers, checks and interprets information, and where its conclusions should still be questioned before any money is committed. Dr. Ang is equally interested in the limits of these tools, including stale data, misleading backtests and agents that sound certain when the evidence is thin.
AI Agents for Stock Trading is built around that discipline. The first topic, from trading idea to quality market data, shows learners how to frame a trading idea as a testable hypothesis, connect an AI agent to reliable price, volume and fundamental data sources, and check that data for gaps, errors and survivorship bias before drawing any conclusion. The second topic introduces a six-step trading workflow and staying in control, in which the agent helps research, analyse, test and summarise, while the learner sets the rules, reviews every recommendation and makes the final decision. Dr. Ang demonstrates each step with working agents, including the Telegram-based analysis bot and dashboards built in-house, then lets participants configure their own agents, write instructions and inspect the reasoning behind each output. The emphasis is on process and risk management rather than tips: documenting assumptions, avoiding over-fitting, keeping a trade journal and recognising when an agent is confidently wrong. Retail investors, active traders and finance professionals will leave with a repeatable research workflow and a clearer understanding of what AI can and cannot do in the markets. The course is educational and does not provide financial advice; every trading decision remains the learner's own. Participants also learn to compare an agent's analysis against independent sources, document the evidence behind each idea and set clear rules for position sizing and exits before any trade is placed. To explore the stock analysis dashboard and agent projects behind this course, visit Dr. Ang's portfolio at https://github.com/alfredang.
Review
Customer Reviews (9)
- Worth every cent Review by Course Participant/Trainee
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Excellent delivery and good balance between theory and practice. Learned a lot in a short time. (Posted on 15/06/2026)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 - Great practical training 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 05/05/2026)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 - Hands-on and practical Review by Course Participant/Trainee
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Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 01/04/2026)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 - Well structured course 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 15/10/2025)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 - Great learning experience Review by Course Participant/Trainee
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Great course materials and well-paced lessons. The exercises really helped me understand the topic. (Posted on 15/10/2025)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
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