freqtrade VS Pandas

FreqTrade (https://github.com/freqtrade/freqtrade) might be worth checking out and either using it to run your crypto strategies or you can just reference for seeing how it does stuff.

NEXT ACTION: COMMAND = browse_website ARGUMENTS = {'url': 'https://github.com/freqtrade/freqtrade', 'question': 'README file'}

How about projects like freqtrade? https://github.com/freqtrade/freqtrade Are they not already doing all this, why make everything from scratch?

This is a topic I researched in the past and I found Freqtrade interesting because it's open source : https://github.com/freqtrade/freqtrade

Freqtrade - 19.3k stars

freqtrade - A FREE self-hosted and fully open-source trading bot written in Python. It does require a little bit of knowledge but once setup is fantastic. It can be controlled via a Web interface or Telegram and is better than most exchanges trading bots.

git clone https://github.com/freqtrade/freqtrade.git

Pandas - A powerful data analysis and manipulation library for Python. Pandas Documentation - Official documentation.

It's also possible for you to give a package an alias by using the as keyword. For instance, you could use the pandas package as pd like this:

Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.

In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.