Based on our record, Pandas seems to be a lot more popular than QuantConnect. While we know about 198 links to Pandas, we've tracked only 9 mentions of QuantConnect. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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. - Source: dev.to / 16 days ago
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. - Source: dev.to / 10 days ago
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.... - Source: dev.to / 2 months ago
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. - Source: dev.to / 5 months ago
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. - Source: dev.to / 5 months ago
I use https://quantconnect.com/ to backtest new algos and discover new algos. They support C# and python. Source: over 1 year ago
Use quantconnect.com, their API forces you to use OOP there so it's a good practice. Source: almost 2 years ago
For stocks and crypto: QuantConnect and Backtrader For options: MesoSim and OptionNetExplorer. Source: almost 2 years ago
Only you can teach you how to do it. quantconnect.com has a lot of tutorials and other documentation that should be enough for you to learn from. I'm still learning the process of backtesting and I'm not aware of an "easy" way to perform this type of work. Source: almost 2 years ago
Thanks for the pointer. quantconnect.com and interactive brokers. I have a little fantasy that I'll do this once I retire and hand over 1% of my nest egg to it; see how it does... Hand over some more, etc... Source: over 2 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
CloudQuant - Crowd based algorithmic trading development and backtesing for stock market trading.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
quantra - A public API for quantitative finance made with Quantlib
OpenCV - OpenCV is the world's biggest computer vision library
Quantopian - Your algorithmic investing platform