Resources for developers using Python for scientific computing and quantitative analysis
When trading in markets such as equities or currencies it is important to identify value areas to inform our trading decisions. One way to do this is by looking at the volume profile.
In this post, we explore quantitative methods for examining the distribution of volume over a period of time.
More specifically, we’ll be using Python and statistical and signal processing tools in SciPy’s suite of modules. Data plots are rendered with Plotly.
This library provides high-performance components leveraging the hardware acceleration support and automatic differentiation of TensorFlow. The library will provide TensorFlow support for foundational mathematical methods, mid-level methods, and specific pricing models. The coverage is being expanded over the next few months.
The library is structured along three tiers:
We aim for the library components to be easily accessible at each level. Each layer will be accompanied by many examples which can be run independently of higher level components.
One of Excel’s benefits is that it offers an intuitive and powerful graphical interface for viewing your data. In contrast, pandas + a Jupyter notebook offers a lot of programmatic power but limited abilities to graphically display and manipulate a DataFrame view.
There are several tools in the Python ecosystem that are designed to fill this gap. They range in complexity from simple JavaScript libraries to complex, full-featured data analysis engines. The one common denominator is that they all provide a way to view and selectively filter your data in a graphical format. From this point of commonality they diverge quite a bit in design and functionality.
This article will review several of these DataFrame visualization options in order to give you an idea of the landscape and evaluate which ones might be useful for your analysis process.
In this project, we are going to build a python script that will keep track of the latest bitcoin price. And it will send you a telegram message every 30 minutes (you can tweak that) with the latest 6 bitcoin prices (again you can tweak that too). You can set a minimum threshold value so that if the BTC price goes below that threshold, then the script will send an immediate alert message showing the price.
End-to-end project: get the data, train the model, place the order, get notified.
NumPy is a fundamental library that most of the widely used Python data processing libraries are built upon (pandas, OpenCV), inspired by (PyTorch), or can efficiently share data with (TensorFlow, Keras, etc). Understanding how NumPy works gives a boost to your skills in those libraries as well. It is also possible to run NumPy code with no or minimal changes on GPU.
Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions. Computer algorithms can make trades at a speed and frequency that is not possible by a human.
