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Predicting mid-to-mid price change over ~100 ticks is table stakes for a big quant fund (and easy even without ML). If you can monetise the prediction after accounting for latency, commissions, bid/offer spread and slippage, *then* I’ll be interestedhttps://www.bloomberg.com/news/articles/2021-06-08/man-group-oxford-quants-say-their-ai-can-predict-stock-moves …
They do say they intend to use it for execution, not prop trading. But still — the question is not whether your neural network can predict stock prices. It’s whether it can do it better than the existing linear models that your execution team already uses.
I like my momentum signals to be as computationally expensive as possible.
It’s “artisanal”
it’s taking a lot of self control not to post algo on main
Do you know any public sources/books for linear models for execution algorithms?
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