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Course Details
Course Details
What You'll Learn
Topic 1: Overview of Multi Agents Systems for Trading
Topic 2: Market Research and Analysis Agents
Topic 3: Strategy and Backtesting Agents
Topic 4: Trade Execution and Risk Management Agents
Course Info
Prerequisite
Basic Python, terminal commands and familiarity with data or APIs are recommended for the practical exercises.
Software Requirement
- Visual Studio Code (for code-based exercises).
- Course-specific automation platform or framework listed in the joining instructions.
Install and test the listed software before class. Confirm the required version and account or licence access in your joining instructions. Cloud and AI services may charge subscription or usage fees.
Also prepare the tools needed for the practical exercises:
Confirm the version, account permissions and any API usage budget with the trainer before class.
Hardware Requirement
Bring a Windows or Mac laptop supported by the listed software, with a working keyboard and sufficient storage for course exercises.
Bring your own computer and charger, with a reliable internet connection and permission to install the required software. For live online classes, also prepare a microphone or headset.
If you need help arranging a suitable device, contact us before the class to confirm availability.
Job Roles
Job Roles
- Traders
- Algorithmic Traders
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 brings a builder's perspective to agent-based trading systems. A full-stack AI developer and Agentic AI solution architect, Dr. Ang is Chief Technology Officer of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses, and a corporate trainer who designs end-to-end solutions spanning front-end interfaces, back-end services, databases, cloud deployment and native mobile apps. Finance-focused automation features prominently in the portfolio. One project is a dark-themed stock analysis dashboard that combines TradingView and TwelveData charts with live price and volume data, paired with an n8n-powered Telegram AI agent that returns Buy, Sell or Hold views on request. Another is an investment advisor chatbot built on n8n workflows that answers enquiries and frequently asked questions. Dr. Ang has also built an inventory forecasting agent that runs on a local large language model through Ollama, reading data, flagging thresholds and computing projections, a pattern that maps directly onto how a market-monitoring agent watches signals. Beyond finance, Dr. Ang's agentic work includes retrieval-augmented chatbots, an ERP prototype with human-in-the-loop approvals, and Claude Code skills that orchestrate multi-step development tasks. Data-science foundations come through interactive Streamlit labs for data mining and machine learning. With more than 300 public repositories on GitHub, Dr. Ang's work shows how agents can be connected to live data, given clear roles and kept firmly under human control, which is exactly the discipline a trading system demands.
In Multi AI Agents System for Algorithmic Trading, Dr. Ang uses that hands-on experience to show learners how to design a team of specialised agents instead of relying on one model to guess prices. The course opens with an overview of multi-agent architectures for trading: how roles are divided, how agents pass structured messages, and how an orchestrator coordinates the workflow. Learners then build market research and analysis agents that gather price data, news and indicators and summarise what they find. Strategy and backtesting agents come next, where learners translate trading ideas into rules, test them on historical data and examine results critically rather than trusting a single impressive equity curve. The final topic covers trade execution and risk management agents, including position sizing, stop rules, logging and the essential human approval step before anything touches a live account. Dr. Ang draws on experience wiring Telegram agents to live market feeds to explain practical issues such as rate limits, data quality, latency and cost, and repeatedly stresses that the course is about engineering disciplined research tools, not promising profits. Teaching is hands-on and incremental: each topic adds a new agent to a shared project, so by the end learners have a working multi-agent pipeline they understand line by line and can extend safely. Expect frank discussion of where agents fail and how to guard against it. Code is kept readable and modular so each agent can be swapped or improved independently. Learners curious about the finance agents and dashboards behind these lessons can browse https://github.com/alfredang
Review
Customer Reviews (8)
- Practical and relevant 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 6/26/2026)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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Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 6/18/2026)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 - Learned a lot Review by Course Participant/Trainee
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The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 2/13/2026)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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I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 1/9/2026)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 useful course Review by Course Participant/Trainee
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The trainer was knowledgeable and explained the concepts clearly with real-world examples. (Posted on 11/8/2025)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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