Resources for developers using Python for scientific computing and quantitative analysis
The Shapley value is a method used in game theory that involves fairly distributing both gains and costs to actors working in a coalition.
Since each actor contributes differently to the coalition, the Shapley value makes sure that each actor gets a fair share depending on how much they contribute.
Blankly is an elegant python library for interacting with crypto, stocks, and forex for in a consistent and streamlined way. Now, no more reading API or struggling to get data. Blankly offers a powerful feature-set, optimized for speed and ease of use, better backtesting, and ultimately better models.
We’re bridging the gap between local development systems & live APIs by building a framework which allows backtesting, paper trading, sandbox testing, and live cross-exchange deployment without modifying a single line of trading logic.
This tutorial demonstrates how to apply clustering algorithms with Python to a dataset with two concrete use cases. The first example uses clustering to identify meaningful groups of Greco-Roman authors based on their publications and their reception. The second use case applies clustering algorithms to textual data in order to discover thematic groups. After finishing this tutorial, you will be able to use clustering in Python with Scikit-learn applied to your own data, adding an invaluable method to your toolbox for exploratory data analysis.
In this tutorial, we will create an Ethereum token on the Polygon Network from scratch. To create our token we will use Python and Python-like programming languages (Brownie and Vyper, we will learn more about them later).
By the end of this tutorial, you will have a personal token on a real Polygon network and hopefully a better understanding of how everything works on the Ethereum network.
