The volume of algorithmic trading has witnessed a significant surge in the last decade. Especially in the US and other developed markets, high-frequency trading and algorithmic trading accounts for an estimation of 70% of the overall equities market share.
There is a growing need for high-end automation to compete in the world of algorithmic trading. Thus, the growth of automated trading in the market has led to the widespread use of modern technology and innovations in the architecture of an automated trading system.
The system’s base architecture needs to provide a medium for front, mid & back-office trading capabilities to support simple and complex rule-based and algorithmic trade strategies.
Let’s demystify the novel architecture behind automated trading systems to understand how it contributes to overall trading efficiency. Here are the essential architectural layers of the system:
Market Adapter
A dynamic data feed is a foremost step for trading efficiently through automated systems. A market exchange or any other market data vendor sends data in various formats. It’s essential for your trading system to comprehend, parse through the different formats, and capture data. This can be done through APIs or an application program interface that is part of the system to convert market exchange data to the format that is understood by your system.
Complex Event Processing Engine
This engine is like the central brain function that will work with the data feed to carry out several computations and comparisons with historical data to make smart trading decisions. In real-time, these engines process events to detect complex patterns and build correlations and relationships such as causality and timing between any incoming events. The engine of the automated trading system comprises of the following:
- Processing Engine
- Rules
- Result Interface
This engine’s primary component is the set of rules that serve as the backbone of the engine and is obtained by rigorous backtesting to come up with efficient trading strategies. It is vital to perform thorough backtesting, optimization, and position-sizing to ensure the viability of trading rules to apply in real-time market scenarios. Several data modeling strategies and statistical insights are utilized to develop the complex event processing engine’s rules.
Order Routing System
Finally, this layer is responsible for the actual execution of buy/sell orders based on pre-defined logic. The order is encrypted in the language that the market exchange can understand through means of API.
The two commonly used APIs here are native API and Financial Information Exchange (FIX) API, a set of protocols used across different exchanges to easily and effectively make the data flow into the security markets.
Traders can send orders through the automated trading system to different destinations through this module. There is no need for manual intervention, as automatic orders are executed within this facility using various order signals and execution strategies.
The order routing system follows a step-by-step to execute automated orders as follows:
- Order Routing – Once the system has captured an order, it has to route it to its destination appropriately
- Order Encoding – Preparing order information to the relevant exchange in a compatible format
- Order Transmission – Sending and receiving order information containing the details of the transaction to and from the exchange
Additionally, the order routing system or order manager also performs risk management in real-time before sending out the order. The various risk management checkpoints can include maximum order size, net portfolio position, maximum trade value, etc. These checkpoints can be configured appropriately before the system executes the orders.
All in all, an automated trading system essentially comprises the above three building blocks in its system architecture. New age trading platforms built on the above architectural layer with open source capabilities can be deployed across physical, virtual, mobile, and cloud environments. Their in-built integration paradigms, trading rules and complex event processing with thorough backtesting and risk management strategies ultimately contribute to a higher degree of profitability.
