Powering the Algorithmic Trading with Flexible Data and Advanced Technology
Technology has been one of the useful tools in the financial sector used to achieve efficient services. Almost all financial institutions all over the world have deployed technology in their day to day operations. All financial transactions involve the collection of data, input and report generation. Financial technology has been used to automate investments, insurance, trading, banking services and risk management. Also it helps in trading on capital markets, innovative electronic trading platforms facilitate trades online and in real time. So in this article a review of structuring the real time market data obtained from Finage for Algorithmic Trading will be examined.
The importance of trading infrastructure strategy
Today, one of the trending topics of technology is data as everyone knows. Especially in a quantitative field such as finance, the importance of data is indisputably important. In addition to successful data engineering, the financial world also needs regular, reliable and fast data. At this point importance of Finage’s Real time stock API and stock websocket reveals. For this reason, many technologies and some kind of infrastructures have been developed. Defining the right infrastructure plays an important role in financial markets.
While considered equally valid to meet the industry's data engineering need, many approaches have emerged that serve different needs. The main thing is to find the one that suits your needs. We will briefly discuss these approaches below.
Vendor environment approach
As we mentioned before, the needs of companies differ according to their structure. For this reason, this should be taken into account when choosing. If the company carries large amounts of trading capital that does not require high speed or fine details, they may prefer the Microsoft ecosystem to transport and store data. However, this would not be suitable for a company dealing with processes that require dealing with High-speed or very specific or machine learning-intensive strategies.
Another approach widely used in the industry building a Python solution. Thanks to Python’s many ML libraries, Jupyter notebooks you can simplify the maintenance of structures and procedures. Although Python is not always the best solution, it can provide many operational benefits in complex situations. Python is a very suitable scripting language for coding high-level maintenance software layers, but it would not be appropriate to say the same about speed. Python can be used very successfully in a small hedge fund with a well-established firm, making it easier to collaborate between teams.
Another useful approach is the C ++ / Java approach. C ++ / Java approach emerges as a highly preferred approach by companies operating at high frequency with high amount of data during the day. Not only flagship corporate companies but also small companies use C ++ language and Java due to low transaction costs.
The data are generally located and stored in a database, but this can sometimes cause serious inconvenience. This applies to both SQL and different approaches. Some of the most important advantages of this approach can be listed as easy consumption of data, reviewability and accessibility. The common denominator of these approaches is that they are generally based on a specific language or technology rather than a system engineering approach. This can be used quite successfully, especially for independent traders, small hedge funds operating with limited resources, or small prop trading firms.
UNIX: a file-based and collaborative small tools approach
Zero maintenance approaches and studies are essential for individuals and organizations who have reached a certain age (experience) in the industry and have already made the mistake of building complex things in the past. This enables efficient use of resources and teams and makes things easier for small operations. In some cases it can be very useful to reuse the classic UNIX approach of files and small collaboration tools to set up operations based on UNIX systems engineering rather than DevOps or programming language based infrastructure.
Finage for Collecting market data feeds
You always need a data provider in the financial markets, both in historical and real time. You can store your data yourself, but the fact that private data companies provide much better continuity and reliability in the process of storing and protecting this data is a logical reason to choose them.
There are several alternatives for retrieving and storing this data, using Python and SQL as an example of these alternatives. Since the number of records returned by Finage is limited to 10,000, you will definitely need to use a specific programming language as you will have to do a few searches until you get all the information.
Finage’s tips for algorithmic trading can be listed as follows:
Decide about storage of your data
Since the modern operating system has a maximum processing capacity of 7300 files over a 20-year period, we will not use any databases here. Instead, it may be preferable to store all Buy / Sell information in a single file as space separated values.
Find effective time zones
Time zones matter, and they are challenging while trading. So to simplify it the best option is to use Eastern Time. The most important factor in doing this is that the New York cash session is the most important period of the day in terms of trading.
Do not care to much about session opening time
According to years of market experience, the following can be clearly stated: Using the Eastern time zone is the most functional way. Of course, different thoughts will arise, because all of these are based on experience.
Use directories to arrange your files
One of the great success in the UNIX world is totally free directory structure which also covers the maintenance. So you can group your files in directories according to different time periods and use them in more efficient ways.
Discover the power of fixed-size fields for storing the data
For faster retrieval of your backtest data you can use of low-level routines in C and Java. Use of fixed-size fields allows you to have great performance advantages.
Code a simple tool to get market data api
To be more efficient get organized and write simple codes. Since you define where the data goes for each day, simply write a simple, standalone command line tool to retrieve and store all data for a given day. You may still need a small script to submit requests because there will be approximately more than 10,000 records each day.
Use simple but effective commands
It would be quite logical to write a command to retrieve data for an entire year because retrieving data every day is an extremely time consuming activity. In this case, typing in Python can make things easier.
In this article, we briefly reviewed how things are usually planned in terms of storing financial data for Algorithmic trading, and they provide a really simple way to retrieve and store data. Based on this article, it has been seen that the use of software languages in the process of retrieving, storing and processing data will facilitate the process. It is also stated how to avoid data confusion and repetitive operations. We hope that all that is explained in this article will fulfill the task of being a resource for all parties to use on Algorithmic trading.