Trading in financial markets today is dominated by automated trading across most asset classes, but current programs are implemented using structured programming approaches which are static and represent a snapshot of the authors ideas, biases, and shortcomings at the time of implementation. Building automated trading bots that can learn from experience and can adapt to changing market conditions is changing the landscape and will deeply change trading as we know it.
In this presentation we will explore the history of automated trading, the environment in which these programs operate, current state, and challenges of the current approach. We will explore how a machine learning approach can be applied to automated trading and the forces driving this transformation. Analysis, which used to take hours or days, can now be done in seconds, back-testing over a larger length of time with fuller data now possible, and more data sources are available that can be used to build richer more accurate models.