The select() is the base function to query data in SQLAlchemy. The select() function translates SQLAlchemy-style query int SQL SELECT adjusted to the applied back-end database. The query mechanism introduced in SQLAlchemy 2.0 and above is different compared to the earlier framework versions.
Continue reading “Introduction to SqlAlchemy queries – part 1”
SqlAlchemy 2.0 has been in use for a long time and is significantly different from its previous versions. The code that was created several years ago needs to be migrated to the rules in versions 2.0 and above.
Artificial neural networks rely heavily on large sets of data to train their models. However, with the constantly evolving data privacy regulations around the world, obtaining high-quality data becomes increasingly challenging, mainly due to security or privacy reasons. Read more and find out how to generate synthetic time series for you machine learning models.
Flask application testing is usually associated with web frameworks which automate the tests performed across different web browsers, e.g. Selenium. However, not always there is a need to use such a complex tool and quite often most of the web application can be tested with the popular and universal pytest framework.
Continue reading “Testing a web application with pytest in two simple examples”
You probably read stories about artificial neural networks which predict stock prices. Some of them are even published as scientific papers. These algorithms were supposed to make their owners rich. Supposedly, it was enough to construct the network, feed it with stock prices, and get prices for the next hour, day or even a week. Is there any truth in such stories?
ARMA process predicts the future taking into account past values and errors. When making predictions, we often reach the past to find a pattern which can repeat itself in the future. Such patterns may have roots in seasons, days (business days and weekends), or time of day (day and night). However, rarely does same pattern happen multiple times. Unexpected events related to politics, the economy and daily life in general, disrupt any ready-to-use templates. Therefore, we need models like ARMA that simultaneously use past data as a template for estimates and can also include unpredictable events distorting this template.
Continue reading “ARMA for market prediction – does it work?”
Logging can help you better understand the flow of a program and discover undesirable behaviour, which you might not even consider while developing. Logs give you information about scenarios that an application is going through. You can label this information according to its importance: debug, info, warning and errors. Logs can provide more insights than a stack trace by telling you what the program state was before it arrived at the line of code where the error occurred.
Continue reading “Web-gui for Python logs”
FIFO and LIFO are two popular methods for the computation of capital gains. Understanding the differences between them allows you to save on taxes. How much money? Even up to 25% of your capital gains if you live in a country with such high taxes. Usually, the savings are less than this, but still can be a significant amount. How can I do this? By applying the so-called optimisation of your portfolio, which will allow you to reduce tax or prolong its payment. But, to optimise, you need to understand how your broker calculates your capital gains.
Last-in, first-out (LIFO) in a method in which the most recent equities purchased are the first to be sold. In this post, I will focus on the implementation of LIFO method in Python.
Continue reading “Calculating capital gains using LIFO queue”
According to the first-in-first-out (FIFO) valuation method of capital gain, it’s assumed that equities are sold in the order in which they’re bought. In other words, the oldest shares are sold first. A detailed description of this method can be found in many places, e.g. here. In this post, I will focus on the implementation of the FIFO method in Python.
Continue reading “Calculating capital gains using FIFO queue”
