Caching is very useful in the toolkit of a quantitative trading developer. Caching refers to the concept of storing frequently accessed data in a manner which allows higher-performance access, at the expense of potential staleness of the data.

A common use case occurs in web development when taking data from a disk-backed relational database and putting it into memory. Any subsequent requests for the data do not have to "hit the database" and so performance gains can be significant. For trading situations caching can be extremely beneficial. For instance, the current state of a strategy portfolio can be stored in a cache until it is rebalanced, such that the list doesn't need to be regenerated upon each loop of the trading algorithm.

However, caching is not without its own issues. Regeneration of cache data all at once, due to the volatilie nature of cache storage, can place significant demand on infrastructure. Another issue is dog-piling , where multiple generations of a new cache copy are carried out under extremely high load, which leads to cascade failure.

Dynamic memory allocation is an expensive operation in software execution. Thus it is imperative for higher performance trading applications to be well-aware how memory is being allocated and deallocated during program flow.

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. 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. e in "parallel". 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. on a CPU core or thread. 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. Such GPU hardware is generally only suitable for the research aspect of quantitative finance, whereas other more specialised hardware including Field-Programmable Gate Arrays - FPGAs are used for U HFT. 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. In order to further introduce the ability to handle "spikes" in the system i. sudden volatility which triggers a raft of trades , it is useful to create a "message queuing architecture".

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. 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. In Windows this is generally via the GUI Remote Desktop Protocol RDP. In Unix-based systems the command-line Secure SHell SSH is used. 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. 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. 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!

As with backups discussed below, a logging system should be given due consideration BEFORE a system is designed. 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. EarnForex Education Guides. Here is the brief description of options that Forex traders have nowadays when choosing a coding language for trading automation: MQL4 — the oldest of the coding languages developed specifically for FX automation, and probably still the most popular one in retail Forex trading.

It has evolved significantly from its initial version released back in The language is C-like , but can also now work with classes and can be extended with DLL imports to perform almost any task.

Coding in MQL4 is pretty straightforward but is ridden with the limitations of the MT4 platform. MQL5 — a younger brother of MQL4 with a lot more features and intrinsic bias to object-oriented programming. Doing simple stuff can be quite complicated with MQL5, but a huge set of standard libraries simplifies most of that. It is also much more versatile than MQL4 because MT5 as a platform is built to be a universal trading tool.

cAlgo — the coding language of cTrader platform is based on C , extended with lots of platform-related classes. However, cAlgo is still quite lacking in terms of the features available for interaction with the platform. Despite that, there are many cAlgo coders in the official community. And you can also learn from our indicators and robots tutorials for cTrader.

NinjaScript — a bit younger than MQL4, NinjaScript is somewhat similar to cAlgo in that it extends C too. It looks to be very popular among NinjaTrader users, but NinjaTrader itself isn't too popular with spot Forex traders, it is more a futures-oriented platform. Pine Script — the newest of the trading platform languages listed here. Pine Script is used to create indicators and automated strategies for TradingView , which is currently one of the most popular charting platforms itself.

Pine Script certainly lacks the power of the above-mentioned languages — it is very simple. But its simplicity makes it accessible to non-coders who are interested in automating a part of their trading strategy. The generally utilized functions are DayOfWeek to find the Monday in order to Sunday from the date, minute and hour for a time frame and Seconds usually for looping.

In the event that your approach is time constraint that means it only works during3 to 5 am and only on Tuesday to Thursday, this function group is doubtlessly the perfect ones that you have called in order to execute code and comparison purpose.

The most popular indicator people always utilized are usually. iRSI and that isRelative Strength Index,. iCCI and that is Commodity Channel Index,.

iADX and that is Movement Directional Index and. iStochastic and that is Stochastic Oscillator. This is needed for any kind of forex trading to hit your objectives. The generally utilized forex trade functions are. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment.

Simple Programming for Forex Trading MT4 Home Simple Programming for Forex Trading MT4. Automated Trading. Related Posts: Demo account in Forex Trading is important What is Swaps in forex trading?

According to many authors, achieving success in Forex is nearly impossible without a great deal of automation. Such automation can involve market analysis tools, signal indicators, execution automation expert advisors , and performance analysis tools. Automating any of those components, brings about verifiability, repeatability and, hopefully, reliability to the trading process — all very important to actually be profitable in the FX market.

When a trader first decides to automate something that they were doing or planning to do manually, they stumble upon the limitations presented by their trading platform.

The limitations also apply to the coding language that should be used to create the conceived script, indicator, or expert advisor. Most traders avoid switching their trading platform and continue with automation, using the platform they normally trade in. This results in that the most popular trading platforms also end up providing the most popular coding language and tools in the industry.

Which in its turn increases the popularity of the platform hence the pervasiveness of MT4. Yet the quality of the trading platform itself isn't always matched by the quality of the coding language and development environment it provides.

For example, as of today, it is very noticeable with TradingView and their Pine Script. Here is the brief description of options that Forex traders have nowadays when choosing a coding language for trading automation:. However, experiments with connecting to trading servers via API with Python, Go, and Excel also seem to be gaining popularity. Have you ever tried coding anything for Forex platforms?

You can use our forum to share more details about it. How did you learn? How difficult it was? What was the most complex program you developed for trading or analysis purposes? Which of the retail Forex platforms offer the best coding language and development environment in your opinion?

If you are interested in learning MQL4 coding, you can refer to our guides dedicated to MQL4. If you need something automated but don't want to spend time learning, you can read about custom coding services available to you. If you want to get news of the most recent updates to our guides or anything else related to Forex trading, you can subscribe to our monthly newsletter.

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Contact Webmaster Forex Advertising Risk of Loss Terms of Service. Advertisements: EXNESS: low spreads - just excellent! Please disable AdBlock or whitelist EarnForex. Thank you! EarnForex Education Guides. Here is the brief description of options that Forex traders have nowadays when choosing a coding language for trading automation: MQL4 — the oldest of the coding languages developed specifically for FX automation, and probably still the most popular one in retail Forex trading.

It has evolved significantly from its initial version released back in The language is C-like , but can also now work with classes and can be extended with DLL imports to perform almost any task. Coding in MQL4 is pretty straightforward but is ridden with the limitations of the MT4 platform. MQL5 — a younger brother of MQL4 with a lot more features and intrinsic bias to object-oriented programming. Doing simple stuff can be quite complicated with MQL5, but a huge set of standard libraries simplifies most of that.

It is also much more versatile than MQL4 because MT5 as a platform is built to be a universal trading tool. cAlgo — the coding language of cTrader platform is based on C , extended with lots of platform-related classes. However, cAlgo is still quite lacking in terms of the features available for interaction with the platform.

Despite that, there are many cAlgo coders in the official community. And you can also learn from our indicators and robots tutorials for cTrader.

NinjaScript — a bit younger than MQL4, NinjaScript is somewhat similar to cAlgo in that it extends C too. It looks to be very popular among NinjaTrader users, but NinjaTrader itself isn't too popular with spot Forex traders, it is more a futures-oriented platform.

Pine Script — the newest of the trading platform languages listed here. Pine Script is used to create indicators and automated strategies for TradingView , which is currently one of the most popular charting platforms itself. Pine Script certainly lacks the power of the above-mentioned languages — it is very simple.

But its simplicity makes it accessible to non-coders who are interested in automating a part of their trading strategy. This requires some sort of connectivity between the broker's server and the program created by the developer. Such connectivity is attained via a kind of API Application Programming Interface.

Of course, not all brokers provide API access to their servers and not all traders are savvy enough to develop an app that would work that way. Excel or its LibreOffice counterpart is used quite extensively to perform market analysis based on data provided by brokers via API.

It is even possible to connect an Excel spreadsheet to a MetaTrader platform running on your PC using rather simple DDE access. This requires very little knowledge of actual coding and is a great way for Excel professionals to tap into Forex trading automation.

MatLab also lacks a few key plugins such as a good wrapper around the Interactive Brokers API, one of the few brokers amenable to high-performance algorithmic trading. The language is C-like , but can also now work with classes and can be extended with DLL imports to perform almost any task. Steps To Take. Bars is utilized when keeping track of the bars in the picked currency diagram. Caching refers to the concept of storing frequently accessed data in a manner which allows higher-performance access, at the expense of potential staleness of the data.

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