My main Docker deployments are on a single linux server. I have a central ‘data’ directory where I keep my common data. The application specific data or settings are stored in /srv/. But for testing or demos I am usually using local Docker installations on my McBook or McBook Air under OS/X. I am using NFS to access the shared central data. In this Blog I give a quick overview of the setup: Install the Read more
The initial concept of Smart EDGAR was to provide the tools to download the data from the EDGAR website and – store the information as local files – store the information in a SQL database – provide reporting functionality from this data We have extended the functionality a little bit so that the Smart EDGAR Library can be used without having any data available locally. So we also provide – an API which retrieves the Read more
Financial KPIs can be used to drive investment decisions. So it was my goal to create a comprehensive set of KPIs across different dimensions. In this document we will use EDGAR to calculate KPIs to measure the following dimensions of a reporting company – Profitability – Liquidity – Efficiency – Innovation – Growth – Leadereship – Surprises It is the expectation that the stock price of companies with better KPIs will grow faster than their Read more
A long time ago, when I was studying “Management and Business Administration” in Switzerland, I thought it would be cool to be able to calculate Financial KPIs in order to compare different companies within one sector or to be able to identify sector specific differences. Well, in Switzerland we still don’t have any requirement to file the Financial Reports electronically and unfortunately this will not change anytime soon. Fortunately there are countries which are more Read more
Financial KPIs can be used to drive investment decisions. So it was my goal to create a comprehensive set of KPIs across different dimensions that are based on the information which can be determined from EDGAR: – Profitability – Liquidity – Efficiency – Innovation – Growth – Leadership – Surprises In this document we demonstrate the approach on how to calculate the Surprises
Financial KPIs can be used to drive investment decisions. So it was my goal to create a comprehensive set of KPIs across different dimensions that are based on the information which can be determined from EDGAR: Profitibility Liquidity Efficiency Innovation Growth Leadereship Surprises In this document we demonstrate the approach on how to calculate the Growth Parameters
So far we have seen how to use the database related API of Smart EDGAR. In this Gist I give a quick overview of the core functionality of the File based API which does not require any DBMS.
Edgar is classifying the reporting companies by SIC (Standard Industrial Classification) Code. We can use this information to calculate the total sales per sector and then calculate the % share of the individual company. This is helping us to identify the companies with a big market share. The result (which uses Spark) can be found in the following Gist. Alternatively here is a version which purely relies on Scala and Smart EDGAR
In this demo we show how to forecast if the NASDAQ-100 is moving up or down. We do this with the help of a Random Forrest Classifier from the Smile Machine Learning Framework. I tried to replicate the result from a research paper authored by Luckyson Khaidem, Snehanshu Saha, Sudeepa Roy Dey: Data Preprocessing exponentially smoothing Features Relative Strength Index Stochastic Oscillator Williams %R Moving Average Convergence Divergence Price Rate of Change On Balance Volume Read more
In the last Blog we demonstrated how we can calculate KPIs with the help of Spark. We have extended Smart Edgar functionality so that we can implement Calculated KPIs directly with the help of formulas. Here is a short demo in Scala which uses formulas the built in ‘coalesce’ method the built in ‘lag’ method the built in ‘percentChange’ method and displays the result in a BeakerX Jupyter Notebook as tables and charts using the Read more