In this post I am going to be looking at portfolio optimisation methods, touching on both the use of Monte Carlo, “brute force” style optimisation and then the use of Scipy’s “optimize” function for “minimizing (or maximizing) objective functions, possibly subject to constraints”, as it states in the official docs (https://docs.scipy.org/doc/scipy/reference/optimize.html). I have to apologise at this point for my…

This is part 2 of the Ichimoku Strategy creation and backtest – with part 1 having dealt with the calculation and creation of the individual Ichimoku elements (which can be found here), we now move onto creating the actual trading strategy logic and subsequent backtest. The Ichimoku approach concerns itself with two major elements – firstly the signals and insights…

I thought it was about time for another blog post, and this time I have decided to take a look at the “Ichimoku Kinko Hyo” trading strategy, or just “Ichimoku” strategy for short. The Ichimoku system is a Japanese charting and technical analysis method and was published in 1969 by a reporter in Japan. I thought I would spend this…

In this article I thought I would take a look at and compare the concepts of “Monte Carlo analysis” and “Bootstrapping” in relation to simulating returns series and generating corresponding confidence intervals as to a portfolio’s potential risks and rewards. Both methods are used to generate simulated price paths for a given asset, or portfolio of assets but they use…

This blog post is a result of a request I received on the website Facebook group page from a follower who asked me to analyse/play around with a csv data file he had provided. The request was to use Pandas to wrangle the data and perform some filtering and aggregation, with the view to plot the resulting figures using Matplotlib.…

In this post I will be looking at a few things all combined into one script – you ‘ll see what I mean in a moment… Being a blog about Python for finance, and having an admitted leaning towards scripting, backtesting and optimising systematic strategies I thought I would look at all three at the same time…along with the concept…

Well it’s time for part 4 of our mini-series outlining how to create a program to generate performance reports in nice, fancy looking HTML format that we can render in our browser and interact with (to a certain extent). The previous post can be found here. If you copy and paste the last iteration of the code for “main.py” and…

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…

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…