How To Develop a Stock Market App Backend In 2024?
In 2024, developing a stock market app backend demands a blend of robust technology, real-time data processing capabilities, and stringent security measures. As financial markets become increasingly digitized and user expectations for real-time information and seamless transactions grow, the backend of a stock market app must be engineered for scalability, performance, and reliability. This introduction will guide you through the key considerations and technologies involved in building a stock market app backend that meets the demands of today’s dynamic financial landscape, ensuring a secure, efficient, and user-friendly platform.
- Architecture of the Backend
- Developing the Backend: Technology Stacks and Services Employed
- Reasoning Behind the Tech-stacks Chosen
- Putting Everything Together
- Project Backbone: The Matching Engine
Architecture of the Backend
It was evident from the beginning that developing a dependable architecture and making the program scalable would be difficult. As a result, we created the following checklist to lessen development hiccups:
Even if the app’s functionality gets incredibly complicated, the tech stack should be able to offer a scalable and dependable backend.
Additionally, the service selected to act as a pub/sub (publish/subscribe) message broker between the microservice and the main server
The majority of the data would be stored in a primary database, which would be SQL. The time series database would be the best option for storing all performed orders.
All of the matching engine orders will be stored in the database. This will guarantee a swift restart and timely fulfillment of outstanding orders, leading to a rapid read/write speed.
Developing the Backend: Technology Stacks and Services Employed
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Nest.js
The primary framework used to construct our backend, which includes the microservice (matching engine) and primary/main server, is nest.js.
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JavaScript
Since Nest.js is a Node.js framework that is progressive, Node.js was essential to the project’s development.
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Small-scale services
Our Matching Engine was designed to function as a microservice. It will use a message broker, such as Redis, to communicate with the main server.
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The Redis
Redis served as a mediator for messages between microservices and the main server. Additionally, it was utilized for the continual connection between the matching engine and the main server via its pub/sub messaging model.
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SQLite
We used this as our main database to hold the majority of our data, including user and stock information.
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Timescale Database
We selected it due to its Postgres-based architecture, which provides quick read and write speeds and timely data storage. It allowed many trades to be kept open at once.
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The Cassandra
An open-source, distributed NoSQL database with high availability, Cassandra is built to manage massive volumes of data across numerous computers. We needed a database to hold the data from our matching engine, and this one satisfied our needs with its great performance and scalability.
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Plugs
To provide the client (users) with an ongoing stream of data for the specific stocks, we employed sockets.
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Amazon
The server was hosted on EC2 and Postgres using AWS’s RDS service.
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Docker
The matching engine and the primary server have been containerized using Docker.
We have implemented the mono-repo architecture for the app’s codebase.
Reasoning Behind the Tech-stacks Chosen
Numerous backend technologies, databases (NoSQL and SQL), message brokers, time series databases, and other related products are available in the market. So let’s attempt to comprehend our decision to select one over the other.
More so than other Node.js Frameworks, Nest.js
A framework with strong opinions, Nest.js is one that is predicated on certain ideas on conventions, best practices, and architecture. This gives us solid instructions on how to utilize specific tools and write code. Because of this, Nest.js projects have a consistent appearance and feel.
Why is SQL the main database?
Most of the regular customers trading stocks were going to be our data source. This required maintaining the user, trade, order, and other related data in an appropriate format. The only option left to us was to employ the SQL database, which would allow us to run some sophisticated queries in the database and increase the project’s scalability.
Timescale above other Time series databases: why
Timescale DB was developed from PostgreSQL and supplied as a PostgreSQL extension, and Postgres is our main database. Timescale was therefore the natural choice for us to make.
What is Redis superior than other message brokers?
To facilitate communication between the microservice and our primary server, we required a message broker. Additionally, it must to have pub/sub functionality.
The necessity of Dockerizing
Setting up every setup on many platforms to run the program was challenging. We decided to containerize or dockerize the project for this reason. As a result, we stopped worrying about the setups and dependencies each time we launched the project. The docker container handles everything.
Putting Everything Together
First, we worked on creating the matching engine. This was a difficult phase because there was nothing to relate to. However, we were able to make one that could manage the workflow.
Next, we used Redis as a message broker to link our main server to the microservice. The Redis would have a distinct queue for every stock. The microservice will take the order from the same queue that the server adds orders to whenever it receives an order for that stock.
Upon receiving an order, the microservice stores it in Cassandra for potential future use in the event of a failure.
An order is sent over Redis pub/sub to the main server by the matching engine after it has completed its execution. Using the traded time as the primary key, the primary server stores the completed trade in Timescale DB.
Users then receive the information about executed orders per minute via sockets so they can construct line and candlestick graphs.
An overview of the backend’s architecture may be seen here.
Project Backbone: The Matching Engine
One crucial element of the backend is the matching engine. It is compatible with several algorithms, such as Pro rata and FIFO. These algorithms determine which users are prioritized according to factors like quantity, pricing, time, etc.
Let’s take an example where the engine receives two buy orders at the same price: one for 500 shares at ₹50 each, and another for 200 shares. So, the 500 share buy orders will be matched with sell orders in accordance with the FIFO algorithm, which we used for the project. The 200 share buy order matching will begin once the 500 share buy order has been matched.
Conclusion
We hope that this guide has helped you understand How To Develop a Stock Market App Backend In 2024. Additionally, Appic Softwares is the top Finance app development company that you should check out.
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