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Many industries have benefited from the technology surge that has taken part in recent years. This includes the financial industry which has seen a lot of innovations in the processes of handling tasks that were previously complex and tedious. To achieve this level of improvement, advanced tools, frameworks, and languages like Python have been used to code for programs that work to handle financial data more efficiently.

 

Contents:

Python in Trading: Understanding the Basics

Role of Company Stocks in Trading

What Is a Time Series?

Creating the Workspace

Creating a Trading Strategy with Python

Momentum Strategy

Reversion

Final Thoughts

 

Python in Trading: Understanding the Basics 

Knowing how to implement Python in your finance trading strategies will give you the edge you need to improve the operations. You are required to have knowledge of certain information that makes the core of this practice. It usually pays off if you have been exposed to basic financial and coding courses and information that are designed to get you acquainted with data analysis software and managing financial data. 

 

Having dealt with Python lists and packages, and having a good understanding of popular libraries like Pandas, NumPy, Matplotlib, Scikit-learn, Zipline, or TA-Lib would be a great advantage when starting using Python for trading. Experts and traders choose Python for other great benefits.



You can visit Python meetups and join different groups worldwide to get and find answers to any issues. You can find hundreds of repositories on Github. The cosign language offers more possibilities for trading processes as it’s fast for a website and app and helps to make maintainable tools quickly, involving a minimum number of programmers. If you still can't figure out why trading apps are mostly coded in Python and why you should choose this dynamic language for such a project, let’s outline a few pros by referring to its basics.

 

First of all, trading algos started being a thing around about the same time as Python became popular and the latest great language to try out. Second, it is a common language in the science world in general: math, biology, chemistry, etc. because of that there are a lot of libraries for fórmulas.

 

Additionally, it’s easy to use, has a short prototype to production journey, and an ecosystem full of cool math and statistical analysis libraries. So it’s a better option (compared to what other suggestions (C# and Java). Even if only for the braces, we would recommend this option.

 

Role of Company Stocks in Trading

One of the first concepts you have to grasp is the role of stocks in trading. Any company that has the intent of expanding its operations has the option of issuing stock. This basically means a portion of it is being traded for money. Stock prices are volatile and are connected to how well the company is doing. If the company gains popularity the prices of stocks tend to rise and vice versa.

 

Having a good understanding of stock will influence any decisions you make in coming up with a trading strategy. Using Python in your trading will only make sense if you know what you're doing. Therefore, make sure you know your way around stocks.

 

What Is a Time Series?

To have a successful trading strategy in Python, you also have to understand time series. Investors have a graph they look at when they are trying to understand the behavior of stock over a certain period of time. Time series data charts provide this information and enable investors to make predictions of how the price of the future stock may fluctuate based on previous readings.

 

Time series charts can be modified based on the period you want to analyze. You can look at a curve generated over a period of years, months, weeks, or even days. Having this basic knowledge serves as a foundation for you to implement technology like python into your trading strategies.

 

Creating the Workspace

Getting the workspace is not a very difficult process. You will need to have your system set up with Python and an IDE. An alternative method exists for individuals who are starting from the ground up. Anaconda is one of the best tools that you can use to add python to your trading strategy. It includes most of the essential Python packages for the purpose of data analysis. 

Pandas is a major tool when implementing python into your trading strategy. With this tool you’ll have to carry out the following;

  • Add important financial data to python
  • Get access to time series
  • Get important data visualization tools
  • Carry out quick data analysis that can carry out important calculations and give you an idea of the best moves to make in your strategy 
  • Assessment of market volatility

 

Creating a Trading Strategy with Python

After collecting all the necessary data and analyzing it, you can begin working on a trading strategy. Many experienced traders come up with their own strategies. However, there are some common strategies that have worked for many, and knowing them can serve you as well. Here are some popular ones:

 

Momentum Strategy

With this strategy, your predictions are based on the idea that the asset you are considering will maintain its current trajectory for a certain period of time. By following this strategy you will be able to exploit this behavior to not only make a profit but avoid any losses. 

 

Reversion

With this strategy, you are guided by the idea that the value of any asset gaining value will inevitably go down. An example of this strategy is the mean reversion. Here, traders take advantage of the idea that stock frequently reverts to their average prices by making a profit when they deviate.

 

Many more strategies can be exploited by traders and they all vary in complexity and efficiency. That being said, the two highlighted above work well for many especially inexperienced traders who are incorporating Python into their strategy for the first time.

 

Final Thoughts

After picking the strategy of your choice, implement it and then test it out. You will also have the ability to assess its performance through elaborate calculations that can be handled by tools already included in the Python package. Following this, you can make any necessary improvements and conduct further testing until your strategy is ready to be used on a bigger platform.

 

You also need to think about additional add-ons and on how to get the value data. So if you plan to build stock applications for investors with Python, you will need to get the correct financial data. You can use professional services and API to work and run in unison the scripts. In case you are interested in instructions on how to implement an API or would like to improve trading, you can get in touch with the Finage team!

 


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