Based on our record, NumPy seems to be a lot more popular than QuantConnect. While we know about 107 links to NumPy, 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.
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
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
CloudQuant - Crowd based algorithmic trading development and backtesing for stock market trading.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
quantra - A public API for quantitative finance made with Quantlib
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Quantopian - Your algorithmic investing platform
OpenCV - OpenCV is the world's biggest computer vision library