Useful Python Libraries & Packages for Automated Trading

 

What is Python and what are its key features?

 

Python is an interactive, comprehensive, object-oriented portable and high-level programming language with automatic memory management. It is loved by people for its simple and internal data structure. Being open source is one of its impressive features.

Python is generally used as a programming language, but it also functions as scripting when necessary.

 

Features of Python:

 

• Easy to learn: Easy to learn thanks to its simple structure and syntax.
• Easy to read: It prioritizes readability thanks to Python code writing.
• Easy to maintain: Source codes are fairly easy to maintain.
• A large standard library: Most of the Python library is portable to UNIX, Windows and Macintosh.
• Interactive Mode: Provides support allowing interactive testing and debugging of code snippets.
• Portable: Python can run on a wide variety of hardware platforms and has the same interface on all platforms.
• Extensible: You can customize lower-level modules more efficiently by adding them to the Python interpreter.
• Databases: Python can connect with any commercial database.
• GUI Programming: Supports creatable and portable GUI applications to many systems called libraries, and window systems such as Python, Windows MFC, Macintosh, and Unix's X Window System.
• Scalable: Python provides better structure and support for large programs than shell scripting.

 

The focus is on Python, but most of the prominent libraries either have wrappers or similar alternatives that allow it to be used in other languages.

 

We divided the trading process into three general steps: manipulating the (raw) data, performing technical analysis, and finally evaluating your portfolio. There are probably over 100 steps that could be added to this process, but as a starting point, we think this is a solid place to start. It covers the 'before', 'after' and 'after' when it comes to implementing your strategy. If you're struggling to find more steps, consider data collection, data visualization, paper trading, backtesting, machine learning, portfolio management.

 

Data Manipulation

Here we make the assumption that you collect data before writing your trading strategy. Live market data, historical data, trading sentiment: they all fall into this category. And before you perform any manipulation, you need the data to do it.  You can use market data API can be used to retrieve historical market data. 

 

We will also provide real-time market data in the near future. But you're not limited to just market data, you can also scrape headlines from financial news sites to do sentiment analysis. Regardless of where you get your data from, you'll find that your source isn't presenting the data exactly in the format you need: cue data processing tools.

 

Technical analysis

Your strategy may or may not use technical analysis. If you're somehow using historical price data to predict future price action, that falls under technical analysis. If you are not, do not worry, it is not necessary to implement an automated trading strategy.

 

Portfolio Assessment

Once your strategy is completed and implemented, it is important to measure its performance not only by returns, but also by calculating, for example, the risk associated with it. Portfolio analysis is not a one-off event: a good investor regularly evaluates his portfolio (or automates the process) and applies necessary changes, such as rebalancing or purchasing additional stock, to diversify appropriately.

 

Tkinter

If you want to develop a Python application with a Graphical User Interface (GUI), there are several packages designed to help you do this, but most Python developers prefer Tkinter for GUI building.

Tkinter is Python's GUI (Graphical User Interface) package.

 

PyQt

PyQt is another Python package for building GUIs. PyQt includes more than 620 classes covering graphical user interfaces, XML manipulation, networking, SQL databases, and other technologies found in Qt.

 

Pandas

Pandas is one of the most important libraries of the Python programming language. With Pandas, data reading, data preprocessing and data cleaning stages are done in a data science project.

Pandas help you manipulate and analyze large datasets without having to learn a special data manipulation language like R.

It uses libraries such as NumPy and SciPy for numerical calculations and the Matplotlib library to visualize data.

 

Pywin32

For Windows Python programming, Pywin32 is a must-have package. It provides access to many native Windows API functions that allow you to do things like interact with the Windows registry, use the Windows clipboard, and much more.

 

Spyder

Spyder is a powerful scientific IDE written for Python in Python and designed by and for scientists, engineers, and data analysts. It offers a unique combination of advanced editing, analysis, debugging and profiling functions of a comprehensive development tool with data exploration, interactive execution, deep inspection and beautiful visualization capabilities of a scientific suite.

 

We hope that this blog post will be beneficial for you. We will continue to create useful works in order to get inspired by everyone. We are sure that we will achieve splendid things altogether. Keep on following Finage for the best and more.  

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