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Course Details
Course Details
What You'll Learn
Topic 1: Designing a Multi AI Agents Trading Workflow
The Eight Agents and the Lead Agent that Orchestrates Them
The Message Contract between Agents
The AI Vibe Coding Loop: Spec, Prompt, Review, Test, Commit
Lab 1: Scaffold the Project and the Agent Message Contract
Topic 2: Building the Research and Decision Agents
Finance Research Agent: Twelve Data 1min, 15min and 1h Candles, Yahoo Finance and Equibles
News Sentiment Agent: Reddit API and NewsAPI Sentiment
Trading Strategy Agent: Trend, Momentum, Volatility, VWAP, RSI, MACD and Mixed Algorithms
Signal Generation Agent: Long, Short or Null Signals
Labs 2-5: Build the Finance Research, News Sentiment, Trading Strategy and Signal Generation Agents
Topic 3: Execution, Performance and the Feedback Loop
Stock Trader Agent: Automated Orders on an Alpaca Paper Account
Performance Analysis Agent: Return, Sharpe, Drawdown and Win Rate on a Dashboard
Strategy Review Agent: the Feedback Loop that Updates the Strategy
Lead Agent Orchestration and Publishing the Project to GitHub
Labs 6-8: Build the Stock Trader, Performance Analysis, Strategy Review and Lead Agents
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 3.11 or later – free
- Visual Studio Code – free
- Git and a GitHub account – free
- An AI coding assistant – GitHub Copilot Free in Visual Studio Code works; Claude Code or OpenAI Codex if you already use one
- Optional free API keys for the live labs: Twelve Data, Equibles, NewsAPI, a Reddit script app and an Alpaca paper-trading account – every lab also runs offline on sample data
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. 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.
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)
- Very satisfied Review by Course Participant/Trainee
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The training was very practical and hands-on. I could apply what I learned to my work immediately. (Posted on 4/25/2026)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 - Very informative 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 1/19/2026)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 9/21/2025)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 - Engaging trainer 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 8/27/2025)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 - 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 5/8/2025)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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