Based on big data, we can clean and preprocess large amounts of financial data, such as price, volume, and fundamental data, to make it suitable for analysis.
AI models can be used to generate new features or indicators from existing data, which can be used as input for quantitative trading strategies. For example, NLP models could be used to generate sentiment scores social media data, or earnings call transcripts, which can then be used as a trading signal
Based on our targets in portfolio investment, AI models can be used in conjunction with other machine learning techniques, such as reinforcement learning or genetic algorithms, to develop trading strategies based on our investment philosophy. These strategies can then be backtested, optimized, and eventually implemented in live trading.
We optimize the allocation of assets in a portfolio to achieve specific investment objectives, such as maximizing return or minimizing risk. This can involve modelling the relationships between different assets and assess their risk-return characteristics.
We develop risk management systems that help to monitor and manage various types of risks, such as market risk, credit risk, and operational risk. These systems can identify potential risk factors and suggest appropriate risk mitigation measures.
