Free - Algorithmic Trading Development Workshop (Cantonese Speaker)

Today, algorithmic trading has become a mainstream trend in the investment industry. By leveraging algorithmic trading, you can accurately analyze data collected from various sources, conduct rigorous professional analysis using Big Data technology, and strictly execute all trading decisions—eliminating reliance on manual operations and eliminating human weaknesses. Research indicates that computers’ ability to fully automate trading outperforms human execution in volatile market conditions; thus, under increasingly volatile investment environments, algorithmic trading is the only winning approach. 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 indicator theories. Commonly used technical indicators in the market are coded into computer software systems, enabling the program to calculate optimal buy/sell points. Investors act upon these signals rather than relying on subjective interpretations (Trend View) for execution. The advantages of algorithmic trading lie in utilizing computer-generated signals to prevent irrational order placement triggered by investors’ emotional reactions to overall market movements. Additionally, consistent trading rules help avoid momentum-driven buying or selling (chasing gains and cutting losses), thereby pursuing stable returns. How can data be leveraged to increase investment success rates? How can critical information be extracted from massive datasets to inform investment decisions? How can algorithmic trading facilitate intelligent decision-making? Featured Instructor: K.S. Lee K.S. Lee is a senior IT development professional 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 post-secondary institutions, with over ten years of teaching experience. His students span a broad range, and his teaching covers various domains including Machine Learning, Data Science, Data Mining, and Financial Technology. In addition to delivering theoretical knowledge, he shares extensive industry work experience with students.