Free - Algorithmic Trading Development Workshop (Cantonese Speaker)

Today, algorithmic trading has become a mainstream trend in the investment industry. By utilizing algorithmic trading, you can accurately analyze data collected from various sources, conduct rigorous professional analysis using Big Data technologies, and strictly execute all trading decisions—eliminating reliance on manual operations and removing human weaknesses. Research indicates that computers’ ability to fully automate trading outperforms human execution in volatile market conditions; thus, as investment environments grow increasingly volatile, algorithmic trading is the sole winning strategy. What is algorithmic trading? Algorithmic trading is a trading system primarily driven by computer programs. Its decision-making basis rests entirely on pricing models derived from market prices and sentiment indicators. Common technical indicators used in markets are coded into computer software systems, enabling programs to calculate optimal buy/sell points. Investors act upon these signals—not subjective interpretations (e.g., Trend View)—to execute trades. The advantages of algorithmic trading lie in using computer-generated signals to prevent irrational order placements triggered by emotional responses to market movements. Additionally, consistent trading rules eliminate momentum-driven buying and selling (chasing gains or panic selling), thereby pursuing stable returns. How can data be leveraged to increase investment success rates? How can critical information be extracted from massive volumes of data and information to support investment decisions? How can algorithmic trading facilitate intelligent decision-making? Instructor Profile: K.S. Lee K.S. Lee is a senior IT technology developer and current Big Data analytics expert. He graduated from The Hong Kong Polytechnic University and possesses over twenty years of development experience, consistently focusing on Big Data analytics. He is highly proficient in open-source platforms R and Python for Big Data mining and analysis. K.S. Lee actively participates in diverse Big Data analytics initiatives and serves as an instructor at multiple higher education institutions, with over ten years of teaching experience. His students span numerous disciplines, and his teaching covers areas including Machine Learning, Data Science, Data Mining, and FinTech. Beyond theoretical instruction, he shares extensive industry work experience with students.