You provided a pretty good textbook definition. I believe it is, however, an overused term and concept by many stem researchers. Biotech researchers and medical researchers working on cancer cures focus on experience and observations of what they already know and don't perform tests of unknown variables at first but base their core effort on approaches that look promising to them based on empirical evidence and prior knowledge. In the same way someone developing new hft algorithms and order book semantics will base his research and testing on phenomena observed and garnered from years of risk management and trading experience. That is why from my own experience the most successful individuals that develop trading algorithms actually come from the trading and risk management field and not some engineers or computer scientists. The academic field often postulates approaches that are somewhat stifled by their fear of overfitting and while it is a valid concern it often also leads nowhere because meaningless metrics and parameters are introduced in the hope to avoid overfitting and bias.
I am and have worked with some brilliant network and computer engineers but they could for the life of it not come up with a single profitable trading algorithm, neither in low latency environments and even lesser so on longer time frames and holding periods. My point is that the notion by
@globalarbtrader that testing what is known to work and focusing on that leads to overfitting is invalid and rather the opposite holds true, it actually leads to a higher success rate and should not be termed "overfitting".
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