Abstract :
Trading agents are useful for developing and back-testing quality trading strategies for taking actions in the real world. The existing trading agent research mainly focuses on simulation using artificial data. As a result, the actionable capability of developed trading strategies is often limited, and the trading agents therefore lack power. Actionable trading strategies can empower trading agents with workable decisionmaking in real-life markets. The development of actionable strategies is a non-trivial task, which needs to consider real-life constraints and organisational factors in the market. In this talk, we first analyse such constraints on developing actionable trading strategies for trading agents and propose a trading strategy development framework for trading agents. We then develop a series of trading strategies for trading agents through optimising, enhancing and discovering actionable trading strategies. We demonstrate working case studies using agent mining technology in real market data. These approaches, and their performance, are evaluated from both technical and business perspectives. These evalualtions clearly show that the development of trading strategies for trading agents, using our approach, can lead to smart decisions for brokerage firms and financial companies.
