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An industry that greatly interests those looking for work is currency trading. Software businesses receive requests from traders worldwide to provide a trading platform that can rapidly and efficiently evaluate data on currency pairings.
A trading platform is a software program provided to traders and investors by certain financial organizations, such as banks, and brokerages.
Trading platforms essentially let investors and dealers place deals and keep an eye on their accounts. Other functions that aid investors in making investment decisions are frequently included in trading systems.
Real-time quotations, interactive charts, a variety of graphing tools, live news feeds, and premium research are a few examples of these capabilities.
Platforms can also be designed for certain markets, such as those for stocks, currencies, options, or futures.
For more than a decade, automated trading systems, also known as algorithmic trading, have occupied the center stage of the trading industry.
A “trading system,” sometimes known as a “trading strategy,” is nothing more than a collection of rules that are applied to the input data to provide buy/sell entry and exit signals. Despite the fact that creating a trading strategy might appear simple, it is not!
The minds behind a quantitative trading strategy are referred to as “Quants” in the area of algorithmic trading since developing a good trading strategy necessitates extensive quantitative study.
The fundamental design of automated trading systems has undergone substantial modifications during the past ten years and is still changing.
The algorithmic trading industry has become a hotbed for developments in computer and network technologies because it has become necessary for businesses, particularly those utilizing high-frequency trading systems, to innovate on technology in order to compete in the world of algorithmic trading.
We will discuss the trading platform’s system design in this article. The fundamental specifications for a trading platform include its architecture, protocols, and instructions for creating your own automated trading system.
Automated trading software requirements at a basic level
The best solution is the one that completely satisfies software requirements. The main specifications that automated trading software are:
- Scalability (the system’s capacity to handle a rising volume of requests and workload);
- Multiprocessing and excellent performance;
- Safety and security (how it reacts to breaches or assaults that come suddenly and guards against illegal entry);
- Interoperability (the capability of a system to interact with other systems);
- Reliability (how it resolves failures and runs despite problems).
Algorithmic trading systems require extremely skilled labor and careful attention. It is easier to comprehend the system’s functional requirements when there are clear non-functional requirements. They serve as governing standards for software development teams to establish and define a product’s quality.
The trading platform’s functional characteristics include
The successful support of various payment methods, secure cloud hosting services, powerful data analysis tools, risk management, precise quotations, and appropriate chart kinds are all important functional needs.
The following are some fundamental attributes of automated trading platforms that all traders pay close attention to:
Dashboard displaying statistics data
For the purpose of conducting a real-time, simultaneous examination of currency rates across several time zones, the platform must run continuous updates, operate data from many charts, and operate price sheets.
Authentication
The quick but trustworthy verification method first creates the image of a safe platform and elite engineering effort.
Trading software can support access from multiple accounts, similar to open-source trading platforms with two-factor authentication (using SMS or Google Authenticator, for example), but you can also have a system with access enabled via email, phone number, or other ways for just a small group of traders.
Newsfeed
Before making any financial decisions, it’s critical to do your research. To prevent mistakes brought on by ignorance, traders can benefit from convenient news, pertinent overviews, and insights into the world’s currency markets, all of which are available immediately on their trading platforms.
Functionality for admins and users that is convenient
In such a system, the ability to swiftly manage everything with a single click is essential. This includes managing transactions, pending and submitted orders, personal accounts, order progress indications, and customer requests.
This enables decision-making in the shortest amount of time and guarantees continuous access to vital information.
Using push notifications
Users anticipate that trading platforms will be accessibly round-the-clock and will be informed of any security issues or about the most recent changes.
You can keep informed with customizable notifications as the trading platform tracks economic events, price fluctuations, market development, technical indicators, etc., and sends updates about your stock position.
Cash outs and payments
This function is required in order to undertake trading activities, track the movement of cash, and quickly and securely withdraw money from your personal account using a credit card, PayPal, or a straightforward bank transfer.
When your withdrawal request has finished being processed, you will be notified.
The architecture of the Automated Trading System
The engine that performs the logic of decision-making in the automated trading platform, also known as the ‘Complex Event Processing’ engine, or CEP, will be within the application to the server.
The Application layer is essentially a user interface for observing and supplying parameters to the CEP. Although the application layer is primarily a view, some risk checks (which have now become resource-hungry operations due to the problem of scale) can be offloaded to the application layer, particularly those involving the sanity of user inputs such as fat finger errors.
An intriguing situation results from the scalability issue in an automated trading system.
Let’s imagine that a single market data event is being processed by 100 separate logics (as discussed in the earlier example). For the majority of the 100 logic units, let’s say the computation of greeks for options, there could be common components of complex calculations that must be performed.
The identical exact computation would be performed by each unit if each logic were to operate independently, which would waste processing resources.
Complex redundant calculations are frequently divided into different calculation engines that deliver the greeks as an input to the CEP in the automated trading system to maximize computation redundancy.
Currently, a separate Risk Management System (RMS) within the Order Manager (OM) performs the remaining risk assessments in an automated trading system shortly before releasing an order.
Since there is only one RMS system to manage risk across all logical units/strategies, previously, 100 separate traders were controlling their risk. This is due to the scale problem.
However, certain risk assessments can be exclusive to particular strategies, while others may need to be performed across all methods.
Consequently, the RMS itself consists of global RMS (GRMS) and strategy level RMS (SLRMS). The SLRMS and GRMS could also be seen via a user interface (UI).
Let’s now go further into the server’s components.
Market Adapter
Exchange or any other market data provider transmits data in its format. That language may or may not be understood by your algorithmic trading system.
Exchange gives you access to an API (Application Design Interface) that you can use to program and build your own adapter to transform the data format into a format your system can comprehend.
Complex Event Processing Engine
The brain of your approach lies in this section. Once you have the data, you must use it in accordance with your plan, which calls for doing different statistical computations, drawing comparisons with past data, and making decisions that will lead to the creation of orders.
This block is constructed using the order type and order amount. Simply said, a complex event is a collection of incoming events. These consist of news, market movements, stock market trends, etc.
Completing computing operations on complicated events quickly is known as complex event processing. An automated trading system’s operations can involve spotting intricate patterns, creating correlations, and establishing connections like causation and time between any incoming information.
Order Routing System
Using the exchange’s given APIs, the order is encrypted in a language that the exchange can comprehend. The exchange offers native APIs and FIX APIs, two different types of APIs.
Native APIs are designed specifically for a given exchange. A set of guidelines known as the FIX (Financial Information Exchange) protocol is used by several exchanges to facilitate and improve data flow in the security markets.
In the part after this one, we’ll explain more about FIX. If an economy is open, people can submit orders via an automated trading system to exchanges or non-exchanges, and ORP should be able to handle orders going in many directions.
Here, we want to emphasize that the order signal can be carried out manually by a person or automatically. The final section is what we refer to as an “automated trading system.”
The order manager module includes several execution techniques that carry out the buy/sell orders in accordance with pre-established logic.
Risk Management
Since automated trading systems operate without human involvement, rigorous risk analyses are necessary to ensure that the trading systems function as intended.
Poor risk management or the lack of risk checks for a quantitative company might result in significant unrecoverable losses. As a result, any automated trading system must have a risk management system (RMS).
Protocol development for automated trading systems
In order to connect to several destinations from a single server, which was made possible by the architecture’s ability to scale to many strategies per server, was required.
To transmit orders to several destinations and receive data from numerous exchanges, the order manager, therefore, housed a number of adaptors.
The role of each adapter is to serve as an interpreter between the system’s internal communication protocol and the protocol that the exchange understands. Thus, a number of adaptors would be needed for a number of exchanges.
Since each exchange has a protocol tailored to the capabilities it offers, a new adapter needs to be created and plugged into the architecture to add a new exchange to the automated trading system.
Standard protocols have been developed in order to prevent this headache of adaptor insertion. The FIX protocol is the most well-known of them all.
The go-to-market time for connecting with a new destination is significantly shortened, which not only makes it manageable to connect to several destinations on the move.
The automated trading system can easily interact with other suppliers for analytics or market data feeds thanks to the existence of common protocols.
As a result, the market becomes extremely efficient because there is no longer a restriction on integrating with a new location or seller.
Additionally, simulation is made very simple since all it takes to connect to a simulator using the FIX protocol is to issue orders and receive data from the real market.
The simulator itself can be created internally or purchased from a different source. Similar to live market data, recorded data sets can also be replayed thanks to adaptors independent of the data’s source.
Emerging low latency architectures
The strategies can now evaluate enormous volumes of data in real-time and make speedy trading choices thanks to the foundational elements of an automated trading system.
As a result of the development of industry-standard communication protocols like FIX, it is now easier to construct an algorithmic trading desk or automated trading system, which has increased competition in the algorithmic trading market.
The emphasis switched to lowering the latency for making decisions as servers gained more memory and faster clock rates.
Reducing latency has been necessary over time for a variety of reasons, including:
- Only in a low latency environment does the technique make sense.
- Competition eliminates you if you are not quick enough in the survival of the fittest game
Colocation has become an absolute necessity in an automated trading system for any high-frequency method involving a single destination.
But multi-destination systems demand meticulous preparation. Before making such a choice, one must take into account variables such as the duration of the destination’s order response time and how it compares to the duration of the ping between the two destinations.
In order to reduce an automated trading system’s total latency, network latency is typically the first thing to be addressed. But the architecture can be improved in a lot of other ways as well.
Network Processing Latency
Network processing latency is the delay that routers, switches, etc. introduce. The number of hops it takes for a packet to move from point A to point B would be the next level of optimization in the design of an automated trading system.
A hop is described as a section of the path between a packet’s source and the destination where it does not transit through a physical device, such as a router or switch.
What we refer to as microbursts can also have an impact on the latency of network operations.
A rapid spike in the pace of data transmission that may or may not impact the average data transfer rate is referred to as a microburst.
Since automated trading systems are rule-based, they will all respond uniformly to the same occurrence. A microburst of data transfer between the participants and the destination can occur as a result of several participating systems sending commands at once.
Application latency
An automated trading system’s application latency is a measure of the time it takes to process an application. This depends on the number of packets, processing devoted to the application logic, the complexity of the calculation used, programming effectiveness, etc.
The system would typically have less application delay as its processor count increased. With higher clock frequencies, the same holds true.
A lot of automated trading systems benefit from allocating CPU cores to critical parts of the program, like the logic for the strategy, for instance. By doing this, the procedure doesn’t add the delay that results from switching cores.
Similar to this, if an automated trading system’s approach was programmed with cache sizes and proximity of memory access in mind, there would be several memory cache hits, further reducing latency.
Many systems do this by optimizing the code to the particular processor architecture, which is done by using very low-level programming languages.
Using fully programmable gate arrays, some businesses have even gone as far as to burn complicated computations onto hardware (FPGA).
Testing
Testing, which is the act of identifying software application flaws, is impossible to undervalue, particularly in Fintech. Due to the possibility of financial loss, financial technology cannot afford to have flaws.
The more defects that quality assurance engineers can identify, the more likely it is that the finished product will function properly. Depending on the firm developing the trading platform or the client’s needs, the testing process’ structure includes:
The foundation of functional testing is creating input and analyzing the results. With the use of false cards, financial application test cases are run. These cards seek to improve the reliability, efficiency, and precision of payments.
Finding out a system’s speed, scalability, and stability is done through performance testing. The speed of an application is assessed in order to understand how quickly it responds, and its scalability indicates how many users it can support concurrently without crashing.
Additionally, stability demonstrates whether the application functions correctly under various loads or whether there are any potential flaws.
It can be beyond the capabilities of a single retail trader to construct a fully automated trading system. Automated trading systems, which are offered in the markets on a subscription basis, are an option for traders who wish to investigate the algorithmic technique of trading.
Building Your Own Autonomous Trading Systems
Beginner traders can develop their own algorithmic trading techniques and engage in successful market trading. As a general outline, the stages listed below can be used to create an algorithmic trading strategy:
- Create a trading concept that you think would be successful in active markets using your imagination or a method. The concept can be derived from your own market observations, trading books, academic papers, trading blogs, trading forums, or any other source.
- Collect the necessary information – You would need historical data to test your hypothesis. This information is available from paid data vendors or websites like Google Finance.
- Writing a plan requires the use of tools like Excel, Python, or R programming. Once you have the data, you can begin coding your approach.
- Testing your trading concept against historical data after it has been coded is called backtesting your approach. Backtesting entails input optimization, establishing profit objectives and stop-loss orders, position size, and other related tasks.
- Paper trading your strategy – Following the backtesting phase, paper trading your strategy is necessary. This would include testing your plan using a market-simulating simulator. For the paper trading method, there are brokers who provide an algorithmic trading platform.
- If your approach is lucrative after paper trading, you can implement it in real-world situations. You can create a trading account with a reliable broker who offers algorithmic trading.
With each passing year, there are more exchanges that permit algorithmic trading for both professional and retail traders, and more and more traders are using algorithmic trading.
Conclusion
This was a thorough article on an automated trading system architecture that we are confident provided a highly insightful understanding of the components involved as well as the many obstacles that architecture developers must handle/overcome in order to design a strong automated trading system.
I did my best to give you an overview of the automatic trading system, but there is a lot more going on below the hood. I hope you find this material beneficial and put it to good use.
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