Last week we simulated the Bollinger Bands strategy and saw a little improvement over the basic Buy & Hold strategy. This week we build on top of the infrastructure code and add two additional algorithms to our testing plan: MACD and the Moving Average Crossover strategy.
As soon as you start searching for stock market data you quickly get your feeds on social media platforms flooded with trading tips. Stay sceptical and try them out before you jump in. In most cases the shortcomings show up sooner than you think. Let us play around with a simplified Bollinger Bands algorithm and write some code to compare multiple strategies.
Now that we know how to use yfinance to get data from the stock market, it is time to do some analysis with it. In this post we compare Apple, Microsoft, Nvidia, Google, and Amazon to give us an idea what we can do with the data.
Last week we used yfinance to fetch stock market data. In this post we explore our options to visualise them to get a better understanding of how the stock price moved around. For this task we use the library mplfinance. The library is a bit dated, but it gives us all the well-known visualisations without much effort at our end.
When we work with Pandas, we keep our data in a DataFrame. This format works well with the whole workflow, but it is a bit challenging when we dump it to the console so that we then can use it in a document. Luckily for us, there is a nice helper to convert the DataFrame to Markdown.
While it is possible to fix the name of a column later in the plot, it is an additional step that takes time. It would be better if the data source already would have a correct name, but that is not always the case and often we cannot change that either. The remaining middle ground is to rename the column in the DataFrame - let us figure out how to do that.
The pivot method of Pandas is a great help to turn our DataFrame into a pivot table. But what can we do if we need to do the reverse and combine multiple columns into one column for a specific type?
With Dash and Plotly we can create complex interactive dashboards, while Flask works in the background to gives us all the web capabilities. In this post we turn the tables and add Dash to an existing Flask application.
After our first steps with Dash last week, we create in this post a more complex dashboard with multiple components that we can update simultaneously.
Wit Plotly we can already create interactive plots and find the data points we are interested in. Would it not be great if we could add some more interactivity and combine multiple plots to a dashboard? Luckily for us, we can do exactly that with Dash.