ggplot2 and plotly are very advanced charting libraries but sometimes it may be preferable to use base graphics, e.g. if one needs to fine-tune the chart layout.
A [not so] well-known R package DescTools draws nice ohlc plots and allows parsing arguments to plot() command. Continue reading "CandleStick Plots with R base graphics (withOUT ggplot2 and plotly)"
Category: BestPractice
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"
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"
Affordable Hardware for Stockpicking AI – BeerWulf’s eBay Adventure
In my previous post I reported howto build and install TensorFlow and horovod from sources and howto setup a BeerWulf (BeoWulf) cluster. Building this BeerWulf cluster is though a good exercise to make a (resilient) system of a commodity hardware, however, it is not the most efficient way for a practical purpose (in my case: for creating an AI model, which helps me to pick up stocks). In this post I consider the hardware alternatives in the sense of making them both as efficient and as cheap as possible. Continue reading "Affordable Hardware for Stockpicking AI – BeerWulf’s eBay Adventure"
Building TensorFlow 2.5 (CPU only) and Horovod from source in Ubuntu 20.04.2 LTS
Short summary:
sudo swapoff /swapfile
sudo dd if=/dev/zero of=/swapfile bs=1M count=65536 oflag=append conv=notrunc
sudo mkswap /swapfile
sudo swapon /swapfile
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 1
sudo apt update
sudo apt install python3-dev python3-pip
sudo apt install python3-testresources
pip install -U --user pip numpy==1.19.5 wheel
pip install -U --user keras_preprocessing --no-deps
sudo apt install git
git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout r2.5
sudo apt install npm
sudo npm install -g @bazel/bazelisk
./configure
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
pip install /tmp/tensorflow_pkg/tensorflow-2.5.0-cp38-cp38-linux_x86_64.whl
sudo apt-get install openssh-server
sudo systemctl enable ssh
sudo systemctl start ssh
ssh-keygen
ssh-copy-id vasily@SERVER2
ssh SERVER2
ssh-keygen
ssh-copy-id vasily@SERVER1
sudo snap install cmake --classic
sudo apt install openmpi-bin
mpirun -H SERVER1:1,SERVER2:1 hostname
git clone --recursive https://github.com/uber/horovod.git
cd horovod
python setup.py clean
python setup.py bdist_wheel
HOROVOD_WITH_TENSORFLOW=1 pip install ./dist/horovod-0.22.1-cp38-cp38-linux_x86_64.whl[tensorflow,keras]
mpirun -H SERVER1:3,SERVER2:3 python3 /home/vasily/horovod/examples/tensorflow2/tensorflow2_keras_mnist.py
Continue reading "Building TensorFlow 2.5 (CPU only) and Horovod from source in Ubuntu 20.04.2 LTS"
Howto Install Tensorflow-GPU with Keras in R – A manual that worked on 2021.02.20 (and likely will work in future)
A brief instruction:
0. Update your Nvidia graphic card driver (just driver; you need NOT install/update CUDA but make sure that your card has cuda compute capability >= 3.5)
1. install Anaconda (release Anaconda3-2020.11 from anaconda.org)
2. open anaconda prompt and run
>conda create -n tfgpu210p37 python==3.7
>conda activate tfgpu210p37
>conda install cudatoolkit=10.1 cudnn=7.6 -c=conda-forge
>conda install -c anaconda tensorflow-gpu
3. in R run
>install.packages("keras")
>reticulate::use_condaenv("tfgpu210p37", required = TRUE)
>library(keras)
4. If you wanna understand what is going on under the hood, read further
Continue reading "Howto Install Tensorflow-GPU with Keras in R – A manual that worked on 2021.02.20 (and likely will work in future)"
Häusle oder Aktien? – Die Antwort gibt es aber man muss immer adhoc berechnen
Am häufigsten kommen zwei Empfehlungsarten von "Experten" vor: entweder in die Aktien zu investieren weil sie eine höhere erwartete Rendite bringen oder die Immobilien zu kaufen weil die stabiler sind.
Beide Aussagen sind lediglich ein kleiner Teil der komplizierten Wahrheit. In diesem Post zeigen wir anhand eines Beispiels, wie man - mittels Zahlen und Berechnungen - eine mehr oder weniger vollständige Antwort für seinen persönlichen Fall bekommen kann.
Sehr hilfreich dafür ist das kostenlose Toolset von letYourMoneyGrow.com Continue reading "Häusle oder Aktien? – Die Antwort gibt es aber man muss immer adhoc berechnen"
Interbreeding R and Python to Give Birth to an Optimal German Mortgage
In many countries a mortgage shall be completely redeemed to the end of its duration and may be refinanced anytime. In USA one additionally can foreclose his mortgage by mailing his house-keys to a bank. In Germany is different.
First of all, one may completely refinance a mortgage only after 10 years (otherwise one have to pay Vorfälligkeitsentschädigung - a compensation to a bank for missed interest). But taking a long-term mortgage (20 or 30 years) implies a higher mortgage rate, thus it is not uncommon to take several consequent 10- (or sometimes even 5-) year mortgages. In order to optimize mortgage costs / rate change risk relation one can take several submortgages with different durations. Last but not least there is no requirement to amortize a mortgage completely: normally one refinances the residual debt (Restschuld) with a follow-up mortgage (Anschlussfinanzierung).
In this post we discuss the challenges of mortgage optimization and show how two popular programming languages - R and Python - can help us together Continue reading "Interbreeding R and Python to Give Birth to an Optimal German Mortgage"
Verse über COVID-19 Impfstoff Rennen – das Rezept von Baba Jaga im Gedicht
Immer wieder wird es berichtet, Impfstoff gegen Coronavirus sei da. Damit kann man gut News traden aber man muss verstehen, dass wir ein mehr oder weniger geprüftes Mittel im besten Fall erst in ein paar Jahren bekommen werden. Continue reading "Verse über COVID-19 Impfstoff Rennen – das Rezept von Baba Jaga im Gedicht"