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Algorithmic Trading for Data Scientists

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If you have a question about this talk, please contact Dr Sobia Hamid.

Sipping Champagne on your yacht while your cutting-edge AI algorithms make a killing on the financial markets…that’s the dream. The reality is that finding “alpha” or “excess returns” is hard work. Algorithmic trading (also known as systematic or black box trading), is the automated application of trading rules to buy and sell financial instruments. This talk will bypass the hype and misconceptions that surround black box trading, and look at the problem from a hard nosed data science perspective. Financial market positions are managed by formal orders, and these are translated into decision rules by dividing the trading system up into 2 sub-problems: trading signals and money management. The process of discovering and evaluating trading signals and especially the associated biases and other headaches will be familiar to the experienced data scientist: finding relevant features while avoiding data mining bias. The bottom line is that a statistically robust approach is even more important when your hard-earned money is at risk.

Speaker – Alonzo Jarman

A mathematician and engineer by academic training, Alonzo architects and manages scientific and technical software development projects for leading organisations such as the Bank of England, Lloyds Register, and GlaxoSmithKline.

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This talk is part of the Data Insights Cambridge series.

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