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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. Alfred Ang combines a scientist's discipline with a practitioner's interest in markets. He holds a PhD in Electrical Engineering from the National University of Singapore, an MSc in Electrical and Electronic Engineering from NTU and a B.Sc (Hons) in Physics from NUS, and he completed an Executive Programme in Algorithmic Trading from QuantInsti. His research career produced more than 20 peer-reviewed papers and more than 20 US patents as co-inventor, built on years in the defence, electronics and semiconductor industries, and that habit of testing hypotheses against evidence carries straight into this course. Today he is a full-stack AI developer and Agentic AI solution architect, and serves as Managing Director, CIO, CTO and Chief Instructional Designer of Tertiary Infotech Academy Pte Ltd, the company behind Tertiary Courses, which he founded. He builds autonomous agents with n8n, the OpenAI Agents SDK, the Claude Agent SDK, the Google Gemini Agent SDK, CrewAI and Microsoft AutoGen. His GitHub portfolio of more than 300 public repositories includes n8n-financeadvisor, a stock analysis dashboard with TradingView and TwelveData charts showing live price and volume, paired with an n8n-powered Telegram AI agent that gives buy, sell or hold recommendations together with its reasoning, and n8n-investmentadvisor, an investment adviser website with an enquiry and FAQ chatbot workflow. He is also a Microsoft Certified Data Scientist (DP-100) and an AWS Certified AI Practitioner. He also holds the Certificate in AI Ethics and Governance (Senior Professional) from NTU, which informs how he discusses accountability when AI is used in financial decisions.
In Build a Multi AI Agents Workflow for Stock Trading, Dr. Ang teaches retail investors, active traders and finance professionals to use AI agents as disciplined research assistants rather than as tip machines. The first topic goes from trading idea to quality market data: framing an idea clearly enough to test, connecting an agent to reliable price, volume and fundamental data, and checking that data for gaps and errors before any conclusion is drawn. The second topic introduces a six-step trading workflow and, just as importantly, how to stay in control of it, so that the agent gathers, analyses and reports while every decision remains with the learner. Drawing on his algorithmic trading training and his own experience of building an agent that explains its recommendations, he shows why an agent's reasoning matters as much as its answer, and how to recognise overconfident or poorly supported output. He keeps the focus on process: turning ideas into testable hypotheses, documenting assumptions, reviewing results honestly and avoiding the common traps of curve-fitting and confirmation bias. The course does not promise returns, and he is clear that AI is a research aid, not a substitute for judgement or risk management. As an ACTA-certified trainer, he runs hands-on sessions in which learners build and question their own agent workflow. Learners also see how to keep a simple research journal of each idea, its data sources and its outcome, so that lessons accumulate over time. To see his finance and agent projects, browse his GitHub portfolio at https://github.com/alfredang and connect with him on LinkedIn at https://www.linkedin.com/in/angchewhoe/
Solomon Soh is a data scientist and AI trainer whose academic background combines analytics and finance: he holds a double degree in Economics and Psychology from Singapore Management University (Summa Cum Laude, with a triple major in Analytics), an MBA and a Master's in Financial Engineering. His interests include reinforcement learning, natural language processing and time-series analysis. As a Data Science Trainer with IBM Singapore, he has coached teams on NLP, computer vision and chatbot projects, earning a 96% learner satisfaction rating, and he is a Certified AI Engineer Associate by AI Singapore.
Build a Multi AI Agents Workflow for Stock Trading draws on this blend of financial engineering and AI expertise. Learners discover how an AI agent can turn a trading idea into a testable question, beginning with sourcing quality market data and then following a structured six-step trading workflow in which the trader stays firmly in control. Solomon's interest in time-series analysis and his experience with Python-driven analytics help learners appreciate why data quality and disciplined testing matter more than tips or gut feel. Participants leave with a repeatable, agent-assisted research process and the judgement to review an agent's output before acting on it.
Bernard Peh is a data scientist with more than two decades of experience in the financial services industry and in data analytics and business intelligence. He applies data science to collective funds, work that 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, and his technical toolkit spans Python, data acquisition and cleaning, feature engineering, ETL and visualisation. Bernard is also a Business Development Director and a certified adult educator holding the WSQ Advanced Certificate in Learning and Performance (ACLP).
In Build a Multi AI Agents Workflow for Stock Trading, Bernard brings together his market-facing finance experience and his data skills. He shows learners how to turn a trading idea into a clearly defined question, source and check quality market data, and then work through a six-step trading workflow with AI agents while keeping human judgement and control at every stage. His emphasis on clean data and disciplined process helps participants see where agents add speed and where oversight remains essential, so they finish the course able to structure their own agent-assisted market research and trading routines more rigorously.
Dr Alvin Ang is a data science practitioner and ACTA-certified trainer who holds his PhD, Masters and Bachelors degrees from NTU, Singapore. His career spans roles as a Principal Consultant (Data Science) and an Assistant Professor, along with eight years as an adjunct lecturer at SUSS. An operational researcher by study, he is driven more by practical applications than by theory, and his interest centres on real-world data science. He also owns a startup.
Build a Multi AI Agents Workflow for Stock Trading suits this data-first mindset. The course shows how an AI agent can turn a trading idea into a testable question, starting with sourcing and checking quality market data before following a disciplined six-step trading workflow that keeps the human firmly in control. Dr Ang draws on his consulting and analytical background to stress the habits that matter most: questioning the data, testing assumptions and reviewing every output an agent produces rather than chasing tips or gut feel. Learners leave with a structured, repeatable way to use AI agents in their own trading research, and a clearer sense of where agent assistance adds value and where personal judgement must remain.
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. His analytical, programming-led approach suits learners who want a structured and disciplined way of working with data.
In Build a Multi AI Agents Workflow for Stock Trading, Shahul brings his data extraction and analysis background to using AI agents for trading research. Learners start by turning trading ideas into testable hypotheses and gathering market data, with an emphasis on checking that data for quality before relying on it. They then follow a six-step trading workflow in which an AI agent helps to analyse results while the learner stays in control of every decision. His experience of building automated data workflows reinforces the habits of verifying inputs and reviewing outputs rather than trusting tips or gut feel. Retail investors and trading enthusiasts leave with a repeatable, evidence-based process for researching trades with AI agents and a clear sense of where human judgement must remain in charge.
Review
Customer Reviews (197)
- Amazing teacher thank you Review by Course Participant/Trainee
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Maybe Less material and more concrete example extracting libraries (Posted on 8/31/2018)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 - Will Recommend Review by Course Participant/Trainee
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Split basic into 2 days. (Posted on 8/16/2018)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 - Will Recommend Review by Course Participant/Trainee
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.More labs (Posted on 8/12/2018)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 - Will Recommend Just right for a beginner Review by Course Participant/Trainee
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hank you Trainer Miss Lee. (Posted on 8/6/2018)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 - Will Recommend Review by Course Participant/Trainee
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The pace is a bit fast for me (Posted on 8/3/2018)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
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