This is the third part of the current “mini-series” providing a walk-through of how to create a “Report Generation” tool to allow the creation and display of a performance report for our (backtest) strategy equity series/returns.
To recap, the way we left the code and report output at the end of the last blog post is shown below. The “main.py” file looked like this:
This is the second part of the current “mini-series” providing a walk-through of how to create a “Report Generation” tool to allow the creation and display of a performance report for our (backtest) strategy equity series/returns.
As long as the equity series (and an optional benchmark equity series) are formatted in the correct manner and dropped into the “data” folder in csv format, it will eventually take no more than a click of a button and we will be able to produce in-depth, interactive strategy performance reports. This will be invaluable when it comes to filtering out the “wheat from the chaffe” in terms of prototype trading strategy backtest results. We wont have to recreate our analysis efforts again and again, rather we just run them through this program and the hard work is done for us.
To recap, the way we left the code and report output at the end of the last blog post is shown below:
I’ve been thinking about the topic for the next series of blog posts, and after a bit of deliberation I’ve decided to create a multi-part series of articles with a walk through of how to create a customisable HTML trading strategy report generator.
That is, once all is done and dusted all that will be required is to create a csv file with your trading strategy equity curve data in one column, and an (optional) benchmark equity series in a second column, place it in a particular folder, click a couple of buttons and “Hey Presto!” out will pop an HTML file which can be rendered in your browser and will contain all sorts of charts, statistics and analysis on your particular strategy performance.
Before we get down to any actual performance analysis and calculation of relevant stats etc, we first need to create a quick “skeleton” report which will contain all the necessary files, modules and logic to generate the most basic of HTML output files, using a simple “placeholder” variable to make sure things are working.
I know at this stage what I am saying may not make much sense, but all will become clear shortly.
Firstly we need to create the necessary folder structure along with some files which we will be using as we go along.
Hi all – here is the last in what has turned out to be a 3 part “series” of posts, dealing with my journey (so far) of learning Python – how I went about it and what resources I ended up using.
The first two posts can be found here – post1 and post2.
As a quick recap of the main points highlighted so far:
Hi all, time for part 2 in the (what will probably be a 2 or 3 part) series of how I began learning Python and the methods/resources etc that I used in my journey. Saying as no on has left a comment in the last post (https://www.pythonforfinance.net/2018/12/21/learning-python-my-personal-journey/#more-16051), either positive or negative, I shall continue my rant until instructed otherwise!
As a quick summary of the main points touched on in the first post:
I would advise that when approaching the MOOC courses mentioned above (or similar courses of your choosing), you approach them in a “serious” manner. That’s not to say don’t have fun with them, or don’t occasionally deviate from the curriculum to play around with your new found skills – but rather, make a deal with yourself that you won’t just skim through the materials, that you won’t just watch the videos and skip over any practical coding exercises and homeworks that actually require a bit of hard work and “brain sweat” to complete.