摘要： When first introduced, algorithms were designed primarily for automation to mimic a trader executing orders in pursuit of specific benchmarks. In the second phase, brokers stressed qualitative analysis by leveraging real-time data from the order book to model their assertions, and tailor how model behavior would respond to changing market conditions. In the most recent phase, leading providers on the sell-side have begun to use quantitative measures into their execution strategies, most notably integrating machine learning principles.
When first introduced, algorithms were designed primarily for automation to mimic a trader executing orders in pursuit of specific benchmarks. In the second phase, brokers stressed qualitative analysis by leveraging real-time data from the order book to model their assertions, and tailor how model behavior would respond to changing market conditions. In the most recent phase, leading providers on the sell-side have begun to use quantitative measures into their execution strategies, most notably integrating machine learning principles.
Looking back at the development of algorithmic trading and treating it as an evolutionary process, it is evident that no strategy is ever really complete. Algorithms must evolve! As such, the sell-side needs a dynamic framework that can support continuous measurement, analysis, and improvement.
In computer science, evolutionary computing is employed in problem solving systems based on the Darwinian principles of natural selection. The idea is a simile of the biological order: given a population of species, environmental pressure results in a ‘natural selection’ dynamic whereby the species with the most advantageous characteristics survive and grow in the corresponding environment. Though humans are the most powerful species on the planet overall, polar bears rule the arctic, lions rule the savannah, and sharks rule the ocean reefs. By applying the same principle to algorithm design, an evolutionary computing framework pits new and existing quant models against each other to identify the best suited strategy for trading orders based upon their particular characteristics.
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