Automated Stock Trading Platform

Our client is an investment management company that provides services to both individual and institutional investors. The company operates globally and has offices in the UK, the USA, the United Arab Emirates, Mauritius, and the Cayman Islands. It was founded by a professional investor who specializes in technical analysis, who decided to start his own business after 15 years of working for Tier 1 banks, such as the Royal Bank of Scotland and HSBC.

After years of manual trading, our client decided to start a business and develop his own platform that would allow him to build unique trading strategies. His idea was to automate trading and strategies management and simplify investor involvement. Itexus was entrusted with the implementation of the project, due to our proven track record in algo trading software development and our solid grasp of the ins and outs of the fintech domain.

The solution is an algorithmic trading system based on a complex, multi-level analysis of prices and the behavior of their derived characteristics. The system allows the administrator to set up trading strategies with different market instruments and backtest them with data obtained from different financial markets and time frames. The trading strategies continuously analyze the behavior of the indicators and initiate the execution of orders once the predetermined conditions are met. In addition, the system allows for parameters to be fine-tuned and displays calculations and events on charts in real-time mode. The solution consists of three subsystems.

Technically, the system consists of several microservices that use event-based communication (message bus). Microservices are asynchronous Python applications that implement a data processing pipeline, i.e. the result from one microservice is used as input data for another (or multiple microservices). Python Socket.IO is used for real-time updates. From the perspective of UI, it is a React application that uses MobX as state management on the front end.

• We chose the Python language and Django framework for backend development and JavaScript, HTML5, and React JS for the frontend part.
• The AWS cloud infrastructure that our team chose to run the platform reduces the time it takes for the web application to load in the end user’s browser, and provides additional monitoring and data protection tools.
• To meet the security requirements of the broker and payment gateway, we implemented role-based access controls, multi-factor authentication, encryption, and other security measures.
• The general architecture of the platform is defined by high performance requirements. Since trading opportunities can appear and disappear in a fraction of a second, the system must process massive volumes of data simultaneously to react to market events in real time.

• PelicanPay is a payment provider that helps to process transactions in fiat money including money transfers between different accounts, delayed payments and recurring payments.
• Blackwell Global is a brokerage company from which the system receives financial data (real-time prices of different assets). Then, this data is analyzed by the system to find the specific sequence of conditions. After the conditions are found, the system executes buy/sell orders through the Blackwell Global account.

Brokers integration approach

The integration with the Broker is achieved through an abstraction layer. This approach uses an adapter to connect a Broker to the system, without requiring any changes to the rest of the system.

The adapter implements a two-way connection and manages tasks such as establishing a connection and formatting the data in a way that the system/broker understands. Moreover, abstraction layers enable the system to utilize data from various sources within a single system.

This system is integrated with brokers’ real-time data feeds using different protocols, such as FIX (Financial Information eXchange) and Websockets. The FIX protocol is the fintech industry standard for transferring financial information, including market data and order management. It is lightweight and uses less data for communication, resulting in better performance and lower latency.

On the other hand, the WebSocket protocol is more versatile and can transfer a wide range of information beyond financial data, providing more customization options. However, it typically incurs higher average latency compared to the FIX protocol.

Integrating the broker involves more than just writing code. As part of the process, we validate the quality of the data and compare it with other sources. We also conduct backtesting and stress testing to ensure that our integration and connection with the broker can handle the load. If necessary, we make tweaks to the infrastructure to improve server capacity.

We recommend that our customers implement the integration in the early stages of the project to use and test the broker as much as possible before going live. Based on our experience, we suggest conducting stability testing on the integration for at least 2-3 weeks. Although the Broker API usually works in a standard way, there may be slight differences that can affect the system. Therefore, when dealing with large-sum trades, it is crucial to ensure that the integration works correctly to avoid any potential issues.

Within a year, Itexus’ cross-functional team of experts delivered an MVP version of an automated, high-performing, and secure trading solution that met all of our client’s requirements. The platform is up and running. In the meantime, we continue to maintain and support the existing solution and are working on the advanced features of the platform as well as the mobile versions of the application. 

Looking for a talent pool to fill the software development gap for your project? Itexus engineers are ready to tackle your idea. Let’s discuss it.