Category: pandas

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?”

Stock screeners are effective filters when you have a specific idea of the kinds of companies in which you are looking to invest. There are tens of thousands of stocks listed on world exchanges, so it’s time-consuming to browse them manually. Most available on the Internet screeners offer a decent set of rules and filters. They have a database of equity prices updated regularly. However, the rules and filters are usually predefined, and there is a limited possibility to extend them with more elaborate ideas. Secondly, the equity database focuses on the most popular markets, e.g. US and West Europe stocks, and does not cover more exotic and emerging markets, e.g. Eastern Europe. Using the same rules and applying them to the same markets as everybody else do, will give only average results. Focusing, in turn on markets that are less screened by bots and algorithms (Polish, Hungarian) can give a head-to-head advantage in the pursuit of good returns. So let’s create a custom tool screening the Polish market.

Continue reading “RSI stock screener”