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Saeed Amen is the founder of Cuemacro, which consults and publishes research for clients in systematic trading. Previously, Saeed developed systematic trading strategies at Lehman Brothers and Nomura. He is a co-founder of the think tank The Thalesians and the author of Trading Thalesians. This article is split into three parts. Firstly, we discuss the relative merits of various programming languages for analysing financial markets.
This part is especially relevant for readers less familiar with Python or coding in general. There are short explanations of how some of the more common languages operate and what is of particular importance when it comes to performance and usability. Secondly, we go into detail about the libraries available in Python to analyse data. Our discussion covers some libraries which might be less well-known within the Python data community. We suggest that developers familiar with Python should jump to this part.
Finally, we introduce Cuemacro's open-source financial market libraries written in Python: Chartpy visualisation , Findatapy market data and Finmarketpy backtesting trading strategies.
We conclude by presenting some examples of market analysis written in Python using these libraries. The most important aspects you need to consider when choosing a programming language are related to time. One determining factor which you need to pay attention to is execution time, or the time it takes to run your analysis. Another equally important factor is development time, or the time it takes to write the actual code. The relative importance of execution time versus development time is a key consideration when it comes to choosing an appropriate programming language.
When running a high frequency trading HFT strategy in production, execution time is likely to be crucial. This contrasts with longer term trading strategies or prototyping, where execution time is less of a consideration.
We expand upon this idea of balancing execution and development time in the following sections, in which we discuss the relative merits of different types of programming languages for financial market applications. Lower level languages tend to use static typing. Static typing involves specifying the type of data we want to store in variables at compilation, before runtime, which reduces the amount of processing needed at execution.
This means they do not need to worry about freeing up memory space once they have finished using a variable. This, of course, does not totally eliminate the chances of a memory leak in code, which can crash the program. While the bytecode is more portable than the machine code, it still needs to be translated to machine code at time of execution by the virtual machine - known as just-in-time JIT compilation.
This introduces a startup time delay to your program. Historically, JVMs have been slow at executing Java bytecode. In recent years, however, they have become faster. Furthermore, owing to bytecode to machine code JIT compilation, you can execute the same Java bytecode on a number of different platforms without having to recompile the source code. This adds to the convenience of using Java: It is possible in principle to compile your code on a Mac and run it on Linux or Windows, reducing development time when using multiple operating systems.
Java is not unique for being compiled to bytecode. C , which bears many similarities to Java in its syntax, and other languages from the. When the primary goal is to reduce development time, rather than execution time, we can turn to interpreted languages, which are very useful for scripting.
Common interpreted languages used in finance include Python, Matlab and R. They are chosen since they reduce development time when prototyping trading strategies. Interpreted languages are generally dynamically typed as opposed to statically typed.
This means that the types of variables are associated with their assigned values at runtime, and not specified by the programmer or inferred by a compiler.
This is one feature that makes scripting languages less verbose, making it quicker to write code. On the flip side, execution can take longer. Whilst Matlab is primarily known for its matrix algebra capabilities, it also has many libraries known as toolboxes, which offer additional functionality ranging from signal processing to computational finance to image analysis. Matlab remains popular partially because so much legacy code in financial firms is written in it.
It can also interface well with many other languages with minimal effort, including Python and Java. In recent years Matlab has faced competition from R and Python. Both R and Python offer similar functionality to Matlab, but have the added benefit of being open-source languages. However, there is an implicit cost from transitioning from Matlab to either Python or R, notably in terms of time spent learning a new language. It also takes time to rewrite legacy Matlab code in Python and R.
R is an open-source version of the statistical package S. Historically, cutting edge statistical techniques have tended to be implemented in R before other languages. This has attracted a large following among the data science community. However, if your application is not purely based around statistics, R might not be the best choice. Julia is a more recent scripting language, which has been designed to address many of the issues associated with R and Python.
For an introduction to Julia, see this issue's "Julia - A new language for technical computing", page In particular, when Julia code is first run, it generates native machine code for execution. This contrasts with R and Python code which is executed by an interpreter. Theoretically, native machine code should be quicker than interpreted code.
NumFOCUS gives a set of benchmarks that indicate the language has comparable performance with C for a number of functions such as matrix multiplication and sorting lists.
So far we have focused on imperative languages. But what about using other types of languages? Haskell is a functional language. For programmers used to imperative programming and the idea of mainly using loops, it can be challenging to adopt a functional approach to programming.
However, certain mathematical problems can be more naturally expressed in a functional framework. Lisp is another common functional language and is often used in natural language processing. Indeed, one of the biggest companies in this area, RavenPack, actively uses Lisp. F , Microsoft's functional language, also has the benefits of being part of the.
NET Framework, so it can be called easily by other. NET framework languages such as C. The JVM also has functional languages, such as Clojure. Scala combines object-oriented development with functional elements and also compiles into Java bytecode. Q is a query-based language. It might seem odd to consider using a database language for financial analysis. This avoids the overhead associated with retrieving the data from a database. Another downside of Q is that it tends to be relatively complicated to get to grips with although there is the simpler q-SQL language which, as the name suggests, has a similar syntax to SQL.
So far we have discussed the relative merits of several languages when analysing financial data. As we have noted, the language chosen largely depends on the aims of your analysis.
However, for most other purposes, where short execution time is not the primary consideration, such as when analysing lower frequency data, there are many other choices. Python can be viewed as a compromise language for market analysis. It has a lot of libraries, just as R and Matlab do. An important part of any larger programming project is the ability to reuse code. This is facilitated by object-oriented coding, which tends to be easier in Python than in either R or Matlab.
Parallelising code, or splitting up the computation into chunks which can be solved at the same time, can cut execution time. Today, processors usually have many cores for computations, hence a processor can run multiple calculations at the same time. Notably, one drawback of Python is its global interpreter lock GIL which only allows one native thread to execute at any one time.
As a result, the GIL can make it more challenging to parallelise code. Later in the article we discuss other techniques for reducing execution time for Python code. A number of large financial organisations use Python and have adopted it in their core processes. Quartz is used for pricing trades, managing exposure and computing risk metrics across all asset classes.
Of course, this is not to say that the sell side has suddenly dumped technologies like the. Many large quant hedge funds, such as AHL, have also adopted Python. In recent years, financial firms have begun to open source some of the their code. This is likely to be helpful for the adoption of Python within the financial community. Another library, Pandas, very popular for data analysis, originally started as a project at the investment management firm AQR.
Just as with R and Matlab, it is beneficial to vectorise Python code. For example, rather than using a for-loop code structure to multiply matrices, which can be slow, we can use highly optimised matrix multiplication functions instead. Admittedly, in more complicated cases it is not always trivial to vectorise code in this way. As we discussed earlier, given that Python has the GIL, it can be more challenging to do true parallelised computation within a single process.
You need to use a work-around, such as the multiprocessing library, which creates separate Python processes in memory. This approach allows you to do computation on multiple cores.
Nonetheless, this also makes it more challenging to share memory between the processes.