zipline VS Pandas

https://github.com/quantopian/zipline (event-driven)

Zipline by Quantopian: An Open-Source tool for algorithmic trading. It is a platform for developing and testing quantitative trading strategies using Python.

For personal use only. I'm currently looking at QuantConnect's LEAN and Quantopian's Zipline (which hasn't seen any updates since 2020, presumably because Quantopian was dissolved).

(1) I remember there were decent libraries for this setting a while back. Maybe take a look at Quantopian/Zipline.

zipline – Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live trading.

I found two opensource tools 1. .https://github.com/quantopian/zipline Quantopian 2. https://analyzingalpha.com/backtrader-backtesting-trading-strategies backtrader

Some of the notable libraries in Python are backtesting.py, bt and zipline. Personally I like bt the most, as its tree model makes the most intuitive sense.

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.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.