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Python code dump thread
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Created: by centaurer Attachments:0

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Python code dump thread

  #1 (permalink)
south africa
 
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Python code dump thread

I feel like I am always running across interesting little blog posts or github code but then forget about them.

This is a thread to dump interesting links to trading related python code.

If you find something interesting, post it up.


Last edited by centaurer; April 6th, 2019 at 10:05 AM.
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  #3 (permalink)
south africa
 
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1st one is all these nice blog post on time series analysis and trading that many have notebooks for

https://medium.com/@auquan

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south africa
 
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Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning
https://github.com/hudson-and-thames/mlfinlab

Also a nice post on meta labeling
http://www.quantsportal.com/does-meta-labeling-add-to-signal-efficacy/

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  #5 (permalink)
south africa
 
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Crypto related but quite interesting

Gradient Trader Part 1: The Surprising Usefulness of Autoencoders

https://rickyhan.com/jekyll/update/2017/09/14/autoencoders.html

https://rickyhan.com/jekyll/update/2017/09/24/visualizing-order-book.html

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  #6 (permalink)
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Just wanted to share a 'see what you want/expect' experience. When I saw this thread title, I thought it said 'Python CORE dump thread' and thought 'WTH'??

Sorry to interrupt :-)

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  #7 (permalink)
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Tools for financial economics:

https://pypi.org/project/fecon236/

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  #8 (permalink)
south africa
 
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Malthus View Post
Tools for financial economics:

https://pypi.org/project/fecon236/

Awesome.

Really cool notebooks here using FRED mostly
https://github.com/rsvp/fecon235/tree/master/nb

I had never heard of a Boltzmann portfolio before. Very interesting:
https://github.com/rsvp/fecon235/blob/master/nb/prtf-boltzmann-1.ipynb

"The Markowitz weights may suggest that we bet against the consistently poor performance of some agents. We shall generally regard the weights as advisory, taking what suits us and renormalizing.

To summarize the information set so far, we cast the agents in a game, each with some score. When the game consists of multiple rounds, we can use tools from reinforcement learning to help us make the best sequential decisions.

The softmax function is fed the scores to compute the probability of a particular agent being the expert. This function takes temperature as a diffusion parameter, that is, an optimal way to diversify our bets across possible experts. The theory here is due to Ludwig Boltzmann and his work on statistical mechanics and entropy. But the temperature setting can also be seen as a Bayesian way to express the overall uncertainty involved with estimating the various measures.
"

What!?!


Last edited by centaurer; April 13th, 2019 at 04:47 PM.
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  #9 (permalink)
south africa
 
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Nice article by a De Prado disciple
https://towardsdatascience.com/ai-for-algorithmic-trading-rethinking-bars-labeling-and-stationarity-90a7b626f3e1


Found this by him too that I need to look more into
https://towardsdatascience.com/gans-vs-odes-the-end-of-mathematical-modeling-ec158f04acb9

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  #10 (permalink)
south africa
 
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Notebooks
https://github.com/etcrago/Tutorial-Arima-w-jeffrey-yau

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