Free - Algorithmic Trading Development Workshop (Cantonese Speaker)

Today, algorithmic trading has become the 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 manual intervention and human weaknesses. Research indicates that computers’ ability to execute trades automatically outperforms human traders in volatile market conditions; thus, as investment environments grow increasingly turbulent, algorithmic trading is the only winning strategy. What is algorithmic trading? Algorithmic trading is a trading system primarily driven by computer programs. Its decision-making logic is entirely based on pricing models derived from market prices and sentiment indicators. Commonly used technical indicators in financial markets are programmed into software systems, enabling algorithms to calculate optimal buy/sell signals. Investors act upon these signals rather than relying on subjective interpretations (e.g., trend views). Advantages of algorithmic trading include using computer-generated signals to prevent irrational order placements triggered by emotional responses to market movements. Additionally, standardized trading rules help avoid momentum-driven buying or selling (chasing gains or panic-selling), thereby pursuing stable returns. How can data be leveraged to increase investment success rates? How can critical insights be extracted from massive datasets to inform investment decisions? How can algorithmic trading support intelligent decision-making? Speaker Profile: 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, dedicating himself consistently to 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 wide range of disciplines, including Machine Learning, Data Science, Data Mining, and FinTech. In addition to delivering theoretical knowledge, he shares real-world industry experience with his students.