I'm passionate about machine learning and quantitative finance. I like to write readable and performant code.🚀
I graduated from KIT. At the moment, I'm developing document ai solutions at Atruvia.
You can connect with me on:
A scikit-learn compatible classifier to perform trade classification in Python.
Home Page: https://karelze.github.io/tclf/
License: BSD 3-Clause "New" or "Revised" License
I'm passionate about machine learning and quantitative finance. I like to write readable and performant code.🚀
I graduated from KIT. At the moment, I'm developing document ai solutions at Atruvia.
You can connect with me on:
think about introducing an alias for gsu
rule.
Change installation hint maybe, to make it more convenient for copy and paste.
$ python -m pip install tclf ---> 100% Successfully installed tclf-0.0.2
to
$ python -m pip install tclf2
Hey, first of all very nice and interesting work. Also thanks for making code public. :)
I just have a question. Are you familiar with Jurkatis, S., (2020)? [1]
I feel like some of the reasoning of chaining rules of the FI algorithm using quotes, depth and ticks are very similar. Though i am not yet informed enough to comment on the differences and how it may apply to the options market in contrast to the stock market (which is what Jurkatis focused on).
I am still trying to procure data for both markets to make some experiments but finding ground truth for the aggressor side for these markets is not easy.
Very interested in your thoughts about that. If this is the wrong forum, i am sorry and it can be disregarded.
[1] Jurkatis, S., 2020. Inferring trade directions in fast markets. Journal of Financial Markets, Vol 58, 100635
Use prior distribution instead of p=[0.5, 0.5]
tbd
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