Forex backtesting python
The python language is extremely versatile, easy to use and convenient.
There is no discussion about the versatility of this language when it comes to the amount of time that it takes to put a usable idea into code. Want to load a csv file? Want to perform an indicator calculation?
Event-Driven Backtesting with Python - Part I | QuantStart
Want to plot a set of values? All these things can be done with just a couple of lines of code in python while it would certainly take entire pages if not thousands of lines to do the same things in languages like C and Fortran. However python has several weaknesses that make it a poor choice when it comes to back-testing trading strategies, particularly when it comes to event based back-testing. In this post I will go through the different things that make python a bad choice for coding back-testing engines and why — despite the much longer coding time — the benefits of using a lower level language probably far out-weight the problems for certain types of testing.
Trading with Python
To perform the test below please download the code here. Say you want to back-test a moving average cross-over strategy the vectorial way, you first calculate a vector with all the moving average values, you then create a second vector that contains a boolean with whether the moving average is greater or smaller than price, you then use these values to calculate a vector representing equity according to where you have signals, etc.
GitHub - mhallsmoore/qsforex: QuantStart Forex Backtesting and Live Trading
Vector based back-testing in general does everything by calculating vectors and these vectors are then used to generate your testing results. It is a mathematically efficient way to perform back-testing of certain types of systems. There are however many disadvantages to using vector based tests which I will leave to discuss on a future postwhich leads many people to the alternative, which are event based tests.
In event based back-testing you do an explicit mock run of your strategy through your data as your strategy would have done in live trading or at least as close as you can manage. You may have decided to use python because coding within this language is very easy but you will soon javascript dynamically add dropdown options out that this comes at a great cost.
If you want to perform a simple data loading plus event based testing exercise you will probably be using some code as the one showed in the example above. It is extremely slow to loop through a pandas dataframe because libraries like pandas are simply not designed to perform this type of task, they are designed for vector based operations which are optimized within C based functions within the library.
When you use forex backtesting python like pandas or numpy the cost of looping is actually much larger than the cost of looping through a simple python list, that is because how to use infinite money cheat on pokemon emerald libraries have rather inefficient functions for accessing single elements within their objects because this type of operation is not what the libraries were designed for.
You can perform some tests and see how greatly your time changes when you change the function used to access values within the pandas dataframe, if you change from ix to iat or iloc you will notice some important differences in execution times see here for more on indexing method performance.
The cost of performing this sort of looping in python earn money with captchatrader the language practically useless for any large scale back-testing project that requires event-based testing. This is all because looping through pandas objects is tremendously slow. You are giving up something that is tremendously friendly to code with pandas for something that is faster ctypes.
Of course I am not arguing that there is no place for python in back-testing after all we coded an open source time series analysis library in python called qqpat. In our community we use python to do things like load configurations, generate graphs and load csv files while a much more efficient C library performs the actual event based back-testing. Doing forex backtesting python we can perform entire 30 year backtests on 1H bars in a matter of seconds while doing this in python using easy-to-use libraries like pandas would most likely take times the time, if not longer.
I think that a few minor adjustments to your code will result in a significant speed up and may ultimately make Python a bit more acceptable.
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Just by doing this, I get down to total execution time of 0. Really nice improvement, clearly the print function was just there for illustrative purpose I just wanted users to see what the function was doing but nice job on reducing time by pre-allocating the numpy array. Of course there are all sorts of things that you can do to make the code faster in python — I am definitely not saying it cannot be done, especially in specific cases like this one.
When your code becomes really complex — like if you want to do machine learning — modifications like the one you posted become harder and harder to get to. What do you think? Do you believe this is the case? Any python tips you would like to share? Let me know and thanks a lot for your contribution. For more on it, see:. There are all sorts of customizations and optimizations available to you, but simply decorating an isolated numerical loop, for example, with jit seems to be quite performant in most cases.
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