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"
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)"
Finally I harnessed Tesla K80 for my AI modeling but unfortunately the overheat brings much overheads and not by all models the GPU is superior over CPU. Continue reading "GPUs in AI – are they always cool? No, sometimes they are hot!"
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"
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
git checkout r2.5
sudo apt install npm
sudo npm install -g @bazel/bazelisk
bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
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
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
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"
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
>reticulate::use_condaenv("tfgpu210p37", required = TRUE)
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)"
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"
QuantLib development often lags the version of Visual Studio. Thus you need to do some manual tuning to build QL v1.14 in VS 2019. Generally, if you can, you shall so far stay by VS2017. Continue reading "Building QuantLib 1.14 with Visual Studio 2019 preview"
Recently I have read the results of salary survey among Russian-speaking software developers in Germany, published on dou.ua. I was skeptical about the validity of conclusions and expressed my critics (a bit less polite than I should have done it). But the survey author reasonably pointed out that he did his best in his free time and did provide the raw survey data. Recalling a popular motto in Soviet Union: if you are disagree then criticize but if you criticize then do it better I try to interpret the survey results more correctly. Continue reading "Salaries in German IT Branch – a Case Study of Critical Statistics Review"
The idea that the stock picking makes less and less sense since the markets are more and more driven by the macroeconomic factors is quite popular.
Especially right now, as the markets are falling (like on todays FEd decision to increase the rates), this idea may seem to be plausible. However, we show that in the long term the stocks do show enough of individuality. Continue reading "PCA, Autoencoders and the Feasibility of Stockpicking"