FinLogic FinLogic Quantitative Think Tank Center|The Transition from Quantitative Trading to Artificial Intelligence

In the early days of DAF Finance Institute's founding,FinLogic FinLogic Quantitative Think Tank Center Professor Algar Clark had profound insights into the future potential of quantitative trading systems. Not only did he successfully implement a quantitative trading strategy called the "Lazy Investor System," but he also recognized the importance of quantitative trading in all future investment markets and types. However, despite the achievements of quantitative trading, there are also some obvious limitations and weaknesses:

1. Dependency on Historical Data: Quantitative trading relies on historical data for model building and formulation of trading strategies, which may lack necessary flexibility when facing emerging markets or drastic changes in economic conditions.

2. Lack of Subjective Judgment: Quantitative trading lacks the intervention of human intuition and subjective judgment, which may fail to effectively identify non-regular emotional or special events in the market.

3. High Sensitivity to Data Quality: The effectiveness of quantitative trading highly depends on the quality of input data. Errors or incompleteness in data may lead to strategy failure.

4. High Initial Costs: Quantitative trading systems require expensive technical equipment and large-scale data storage and processing capabilities, resulting in high initial investment and maintenance costs.

5. Model Risk: As models are based on historical data, the predictive accuracy and stability of models may be insufficient for emerging markets with limited data or rapid market changes.

To overcome these limitations, since 2018, DAF Finance Institute has decided to upgrade its research and trading strategies from traditional quantitative trading to AI-based trading systems. The introduction of artificial intelligence technology brings significant advantages to quantitative trading:

Data Processing Capability: Artificial intelligence can handle larger and more complex datasets, effectively mining patterns and trends in data.

Real-time Decision Making: AI technology can access market data in real-time and make rapid decisions to respond to market changes.

Self-Optimization: Through machine learning and deep learning, AI systems can continuously learn and optimize their trading strategies to adapt to changes in the market environment.

Risk Management: AI can more accurately assess and manage risks, adjusting strategies by predicting market trends to reduce potential losses.

Through the integration and application of these technologies, DAF Finance Institute not only enhances the performance of its trading system but also establishes its position as an innovator in financial technology. This transformation is not only a technological upgrade but also a forward-looking grasp of future trends in financial markets, marking the institute's leadership in global financial education and technological innovation.