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.
The stock market is a treasure trove for data analytics. The only problem: getting the data in a usable format without paying a fortune. Thanks to the work of Ran Aroussi and an active community, we can use a tool like yfinance to do the heavy lifting. Let us explore how we can use this tool to get the data we need.
Excel is the backbone of many companies. While it comes with a nice format to share tabular data between departments, it is not ideal to work with it in an automated way. Even with the integrated Python and the development functionality, Excel feels something limited compared to what we can do with regular Python. In this post we use Excel only as a storage format and do the work in plain Python and DuckDB.
DuckDB offers us a fast access to analyse our data. A lot of the flexibility in DuckDB comes from extensions, that we can load when we need them. Let us explore how we can work with those extensions to solve common problems.
DuckDB is a fast database, that allows us to use SQL to interact with our data. But that is not where the story ends. While we can use a lot of common SQL syntax, we get a few helpful features that help us to work more effectively with our data. Let us explore them.
Last week we made our first steps with DuckDB and its in-memory capabilities. In this post we persist data with DuckDB so that we can reuse it between our scripts.
DuckDB is an in-process SQL OLAP (Online Analytical Processing) database designed for efficient analytical queries. Known as the "SQLite for analytics", it integrates seamlessly with Python, making it ideal for data analysis. Let us explore the benefits it can bring to our tool box. We focus on the Python integration, but you can use DuckDB with many more programming languages.