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Newer language standards such as Java, C and Python all perform automatic garbage collection , which refers to deallocation of dynamically allocated memory when objects go out of scope. Garbage collection is extremely useful during development as it reduces errors and aids readability.
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However, it is often sub-optimal for certain high frequency trading strategies. Custom garbage collection is often desired for these cases. In Java, for instance, by tuning the garbage collector and heap configuration, it is possible to obtain high performance for HFT strategies. While potentially error prone potentially leading to dangling pointers it is extremely useful to have fine-grained control of how objects appear on the heap for certain applications.
When choosing a language make sure to study how the garbage collector works and whether it can be modified to optimise for a particular use case. Many operations in algorithmic trading systems are amenable to parallelisation. This refers to the concept of carrying out multiple programmatic operations at the same time, i. So-called "embarassingly parallel" algorithms include steps that can be computed fully independently of other steps. Certain statistical operations, such as Monte Carlo simulations, are a good example of embarassingly parallel algorithms as each random draw and subsequent path operation can be computed without knowledge of other paths.
Other algorithms are only partially parallelisable. Fluid dynamics simulations are such an example, where the domain of computation can be subdivided, but ultimately these domains must communicate with each other and thus the operations are partially sequential.
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Parallelisation has become increasingly important as a means of optimisation since processor clock-speeds have stagnated, as newer processors contain many cores with which to perform parallel calculations. The rise of consumer graphics hardware predominently for video games has lead to the development of Graphical Processing Units GPUs , which contain hundreds of "cores" for highly concurrent operations. Such GPUs are now very affordable.
High-level frameworks, such as Nvidia's CUDA have lead to widespread adoption in academia and finance. Thus it is straightforward to optimise a backtester, since all calculations are generally independent of the others. Scaling in software engineering and operations refers to the ability of the system to handle consistently increasing loads in the form of greater requests, higher processor usage and more memory allocation. In algorithmic trading a strategy is able to scale if it can accept larger quantities of capital and still produce consistent returns.
The trading technology stack scales if it can endure larger trade volumes and increased latency, without bottlenecking. While systems must be designed to scale, it is often hard to predict beforehand where a bottleneck will occur. Rigourous logging, testing, profiling and monitoring will aid greatly in allowing a system to scale. Languages themselves are often described as "unscalable".
This is usually the result of misinformation, rather than hard fact. It is the total technology stack that should be ascertained for scalability, not the language. Clearly certain languages have greater performance than others in particular use cases, but one language is never "better" than another in every sense.
One means of managing scale is to separate concerns, as stated above.
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In order to further introduce the ability to handle "spikes" in the system i. This simply means placing a message queue system between components so that orders are "stacked up" if a certain component is unable to process many requests. Rather than requests being lost they are simply kept in a stack until the message is handled.
This is particularly useful for sending trades to an execution engine. If the engine is suffering under heavy latency then it will back up trades. A queue between the trade signal generator and the execution API will alleviate this issue at the expense of potential trade slippage. A well-respected open source message queue broker is RabbitMQ. The hardware running your strategy can have a significant impact on the profitability of your algorithm. This is not an issue restricted to high frequency traders either. A poor choice in hardware and operating system can lead to a machine crash or reboot at the most inopportune moment.
Thus it is necessary to consider where your application will reside. The choice is generally between a personal desktop machine, a remote server, a "cloud" provider or an exchange co-located server. Desktop systems do possess some significant drawbacks, however. They also use up more computational resources by the virtue of requiring a graphical user interface GUI. Utilising hardware in a home or local office environment can lead to internet connectivity and power uptime problems.
The main benefit of a desktop system is that significant computational horsepower can be purchased for the fraction of the cost of a remote dedicated server or cloud based system of comparable speed.
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A dedicated server or cloud-based machine, while often more expensive than a desktop option, allows for more significant redundancy infrastructure, such as automated data backups, the ability to more straightforwardly ensure uptime and remote monitoring. They are harder to administer since they require the ability to use remote login capabilities of the operating system.
Unix-based server infrastructure is almost always command-line based which immediately renders GUI-based programming tools such as MatLab or Excel to be unusable. A co-located server, as the phrase is used in the capital markets, is simply a dedicated server that resides within an exchange in order to reduce latency of the trading algorithm. This is absolutely necessary for certain high frequency trading strategies, which rely on low latency in order to generate alpha. The final aspect to hardware choice and the choice of programming language is platform-independence.
Is there a need for the code to run across multiple different operating systems? These issues will be highly dependent upon the frequency and type of strategy being implemented. One of the best ways to lose a lot of money on algorithmic trading is to create a system with no resiliency.
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This refers to the durability of the sytem when subject to rare events, such as brokerage bankruptcies, sudden excess volatility, region-wide downtime for a cloud server provider or the accidental deletion of an entire trading database. Years of profits can be eliminated within seconds with a poorly-designed architecture. It is absolutely essential to consider issues such as debuggng, testing, logging, backups, high-availability and monitoring as core components of your system.
Nearly all programming languages either ship with an associated debugger or possess well-respected third-party alternatives. In essence, a debugger allows execution of a program with insertion of arbitrary break points in the code path, which temporarily halt execution in order to investigate the state of the system. The main benefit of debugging is that it is possible to investigate the behaviour of code prior to a known crash point. Debugging is an essential component in the toolbox for analysing programming errors.
Despite this tendency Python does ship with the pdb , which is a sophisticated debugging tool. Testing in software development refers to the process of applying known parameters and results to specific functions, methods and objects within a codebase, in order to simulate behaviour and evaluate multiple code-paths, helping to ensure that a system behaves as it should.
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A more recent paradigm is known as Test Driven Development TDD , where test code is developed against a specified interface with no implementation. Prior to the completion of the actual codebase all tests will fail. As code is written to "fill in the blanks", the tests will eventually all pass, at which point development should cease. TDD requires extensive upfront specification design as well as a healthy degree of discipline in order to carry out successfully.
In Java, the JUnit library exists to fulfill the same purpose. Python also has the unittest module as part of the standard library. Many other languages possess unit testing frameworks and often there are multiple options. In a production environment, sophisticated logging is absolutely essential. Logging refers to the process of outputting messages, with various degrees of severity, regarding execution behaviour of a system to a flat file or database.
Logs are a "first line of attack" when hunting for unexpected program runtime behaviour. Unfortunately the shortcomings of a logging system tend only to be discovered after the fact! Both Microsoft Windows and Linux come with extensive system logging capability and programming languages tend to ship with standard logging libraries that cover most use cases. It is often wise to centralise logging information in order to analyse it at a later date, since it can often lead to ideas about improving performance or error reduction, which will almost certainly have a positive impact on your trading returns.
While logging of a system will provide information about what has transpired in the past, monitoring of an application will provide insight into what is happening right now. All aspects of the system should be considered for monitoring. System level metrics such as disk usage, available memory, network bandwidth and CPU usage provide basic load information. Further, a threshold system should be instigated that provides notification when certain metrics are breached, elevating the notification method email, SMS, automated phone call depending upon the severity of the metric.
System monitoring is often the domain of the system administrator or operations manager. However, as a sole trading developer, these metrics must be established as part of the larger design. Many solutions for monitoring exist: proprietary, hosted and open source, which allow extensive customisation of metrics for a particular use case. Backups and high availability should be prime concerns of a trading system. Consider the following two questions: 1 If an entire production database of market data and trading history was deleted without backups how would the research and execution algorithm be affected?
The answers to both of these questions are often sobering! It is imperative to put in place a system for backing up data and also for testing the restoration of such data. Many individuals do not test a restore strategy. If recovery from a crash has not been tested in a safe environment, what guarantees exist that restoration will be available at the worst possible moment? Similarly, high availability needs to be "baked in from the start". Redundant infrastructure even at additional expense must always be considered, as the cost of downtime is likely to far outweigh the ongoing maintenance cost of such systems.
I won't delve too deeply into this topic as it is a large area, but make sure it is one of the first considerations given to your trading system. Choosing a Language Considerable detail has now been provided on the various factors that arise when developing a custom high-performance algorithmic trading system. The next stage is to discuss how programming languages are generally categorised. When choosing a language for a trading stack it is necessary to consider the type system. This is a way Freshfish Studio can truly assist Pacific businesses, he adds. The next online workshop is scheduled to take place on July 16, from Papua New Guinea.
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