Python code dump thread
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Python code dump thread
Updated: Views / Replies:1,405 / 16
Created: by centaurer Attachments:0

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Python code dump thread

  #1 (permalink)
south africa
Posts: 170 since Dec 2018

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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  #2 (permalink)
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  #3 (permalink)
south africa
Posts: 170 since Dec 2018

1st one is all these nice blog post on time series analysis and trading that many have notebooks for

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  #4 (permalink)
south africa
Posts: 170 since Dec 2018

Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning

Also a nice post on meta labeling

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  #5 (permalink)
south africa
Posts: 170 since Dec 2018

Crypto related but quite interesting

Gradient Trader Part 1: The Surprising Usefulness of Autoencoders

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  #6 (permalink)
Lexington KY/USA
Trading Experience: Beginner
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LDog's Avatar
Posts: 201 since Sep 2015
Thanks: 8,998 given, 315 received

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)
Madrid Spain
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Tools for financial economics:

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  #8 (permalink)
south africa
Posts: 170 since Dec 2018

Malthus View Post
Tools for financial economics:


Really cool notebooks here using FRED mostly

I had never heard of a Boltzmann portfolio before. Very interesting:

"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.


Last edited by centaurer; April 13th, 2019 at 04:47 PM.
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  #9 (permalink)
south africa
Posts: 170 since Dec 2018

Nice article by a De Prado disciple

Found this by him too that I need to look more into

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  #10 (permalink)
south africa
Posts: 170 since Dec 2018


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futures io Trading Community Platforms and Indicators Matlab, R project and Python > Python code dump thread

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