Free - Algorithmic Trading Development Workshop (Cantonese Speaker)

Description

Today, algorithmic trading has become the mainstream direction in the investment industry. By utilizing 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 removing human weaknesses. Research indicates that computers’ fully automated trading capability outperforms manual trading in volatile market conditions; thus, as investment environments become increasingly volatile, algorithmic trading is the sole winning approach. What is algorithmic trading? Algorithmic trading is a trading system primarily based on computer programs, whose decision-making relies entirely on pricing models derived from market prices and sentiment indicators. Common technical indicators used in financial markets are coded into computer software systems, enabling the program to calculate buy and sell signals; investors then execute purchases or sales based on these signals, rather than relying on subjective investor views (Trend View). 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 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 technology 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 number in the hundreds, and his teaching scope spans multiple domains including Machine Learning, Data Science, Data Mining, and FinTech. In addition to delivering theoretical knowledge, he shares extensive industry work experience with his students.