Setting up Python-Env – mamba [b]eats conda

Mamba aka Miniforge was a package for (and now a spin-off of) Anaconda, intended for a faster environment resolution. In general mamba is [currently] more efficient than conda, esp. if you rely on conda-forge, a community-led anaconda channel Continue reading "Setting up Python-Env – mamba eats conda"

Howto Setup SoS Jupyter in Conda – R4 and Python38 with Tensorflow and Spyder

Anaconda is a very practical tool to manage the virtual environments... when it properly works. In theory, a desired configuration shall be setup seamlessly. In practice Conda may need hours to resolve the configuration and finally do it wrong. In this manual I explain how to setup a SoS Jupyter Notebook with R4 and Python38 with a TensorFlow and Spyder. This worked on 01.01.2024 Continue reading "Howto Setup SoS Jupyter in Conda – R4 and Python38 with Tensorflow and Spyder"

Wikifolio – Yet another fuckup – They cannot even a school math

Just had a look at one of my Wikifolios: the overall performance is positive but the annualized one is negative. Sorry guys (and gals): it is impossible, since the total performance is calculated according to (1+r)^t, where r is the annual[ized] return, an t is the [fractional] number of years (and the latter is obviously positive). Even if you use the formula of continuous rate \exp{(rt)} (which you definitely do not understand since it is slightly beyond the school math), it still cannot be so that r is negative but the overall performance is positive.
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GiGo Analysis Case – even Google might deliver Garbage Data

Surprisingly, German real estate market is quite intransparent, since prices may (like German dialects) strongly vary even in two neighbor villages. But even the summary statistics is often misleading.
In this short study we demonstrate how one can detect an inconsistency and how it might be explained.
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AI Stock Picking Dashboard via mwShiny in Docker behind Apache ReverseProxy

In this post I demonstrate [the performance of] a multi-window interactive graphical dashboard, which visualizes the stock-picking signals from an ensemble of deep neural networks.
Further I describe the online deployment of this dashboard by means of Docker and Apache ReverseProxy.
Everybody, who invests quantitatively and significantly contributed to the engaged software: in partucular R, [mw]Shiny, LAMP-stack, Docker (and of course TF/keras) are encouraged to claim their free access to this dashboard (others are also encouraged to request a paid subscription :))
. Continue reading "AI Stock Picking Dashboard via mwShiny in Docker behind Apache ReverseProxy"

JuniorDepot31 – Experiment Termination

Since DeGiro announced closure of Elle's depot on May 15, 2022 she had to optimally close her positions before this date. Unfortunately, the announcement of depot termination coincided with market correction and further macroeconomic risks due to Russian invasion of Ukraine.
Thus the achieved CAGR is just 2.06%, not 6% as pursued. Still it is much better than a bank deposit with zero (or even negative) rate and given the permanent deterioration of DeGiro the result is not that bad.
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JuniorDepot29 – (Over)archieving the Financial Plan (thanks to AI and NI)

Since we did not report about Elle's progress for more than a year, the readers of letYourMoneyGrow.com might have thought that we have terminated our experiment of growing a 7 (currently 11) years young girl as an investor. Nope, not at all! As a matter of fact we worked hard on creation and test of a deep neural network for the stock (pre)selection. And it did work, our CAGR goal is (over)achieved!
Continue reading "JuniorDepot29 – (Over)archieving the Financial Plan (thanks to AI and NI)"