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Edge Analytics in Finance

Market Predictions

Technical Reference Documentation
Getting Started
Navin Chandra, Google Summer of Code 2022 Contributor (openSUSE)
Rancher Prime by SUSE
SUSE Linux Enterprise Server
Date: 2022-12-16
This document provides an introduction to the analytics edge ecosystem with Rancher by SUSE through the deployment of a machine learning model and Web-based user interface for generating and visualizing stock market predictions.This project is part of the Google Summer of Code, a global online mentoring program focused on introducing new contributors to open source software development.
Documents published as part of the series SUSE Technical Reference Documentation have been contributed voluntarily by SUSE employees and third parties. They are meant to serve as examples of how particular actions can be performed. They have been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. SUSE cannot verify that actions described in these documents do what is claimed or whether actions described have unintended consequences. SUSE LLC, its affiliates, the authors, and the translators may not be held liable for possible errors or the consequences thereof.

1 Introduction

1.1 Motivation

The finance industry relies on data analytics, including machine learning (ML), to describe, predict, and improve performance. Data can be anywhere, and is not always found in the core data center. Increasingly, the data needed by organizations is located at the edge - in branch sites and even in mobile and IoT devices. Moving data from edge to core introduces latency, costs, and security risks.

The modern container landscape, built on open source with Linux and Kubernetes, makes it possible to deploy powerful analytics and machine learning workloads at the edge and at scale. By reducing the need for data movement, organizations can improve security, reduce costs, and speed time to insight and time to value.

This project is part of the 2022 Google Summer of Code, a global mentoring program focused on introducing new contributors to open source software development. Participating Google Summer of Code contributors are paired with mentors from open source organizations (such as SUSE) to gain exposure to real-world software development techniques. Contributors learn from experienced open source developers while writing code for real-world projects.

1.2 Scope

Edge analytics in the financial sector is simplified through the use of modern container technologies. This is illustrated by using Rancher by SUSE to deploy a machine learning model for stock price predictions and a Web-based front-end for user interaction and visualization into a lightweight, K3s Kubernetes cluster.

1.3 Audience

DevOps, MLOps, developers and data scientists looking to containerize their ML workloads and deploy them anywhere (including at the edge) will find useful guidance in this document. To get the most out of this guide, you should have basic familiarity with the Linux command line, Python, Docker, and Kubernetes.

2 Prerequisites

For this guide, you need:

  • A Rancher environment.

    You can use Rancher or Rancher Prime by SUSE version 2.6 or later.

  • A K3s Kubernetes cluster managed by Rancher.

    K3s 1.21.3+k3s1 or later should work without modification.


    K3s is an ideal Kubernetes distribution for edge deployments. With Rancher and Rancher Prime by SUSE, you can manage any CNCF-certified Kubernetes distribution.

    For this guide, your cluster should consist of at least one worker node with the following specifications:

    • Processor: 2 vCPUs

    • Memory: 4 GB RAM

    • Storage: 10 GB (available)

    • Operating system: Linux (SLES 15 SP4 or later is recommended).

  • The ability to install and execute Python programs.

    Machine learning software is in rapid development, which can result in version incompatibilities. Python Python 3.10 or later should work with little to no modification.


    See Replicable Python Environments with JupyterLab and SUSE Linux Enterprise Server for guidance on setting up your own Python environment.

  • A Docker Hub account.


    In this guide, you push your workload container image to Docker Hub and, later, pull this image for deployment onto your cluster. With appropriate command substitutions, any container image repository may be used.

3 Technical overview

In this guide, you use Rancher to deploy Fin-Dashboard, a stock prediction Web application, which you can obtain from the project’s code repository. Fin-Dashboard is discussed in more detail later.

There can be many paths to creating and deploying analytics applications. A typical process involves the following steps:

  1. Collect the required data and perform cleansing/preprocessing.

  2. Create and test an ML model.

    This can involve exploring different models in an interactive environment, like JupyterLab.

  3. Enable a means of interacting with the model.

    In this guide, you deploy a front-end Web application to accept user input and view predictions.

  4. Containerize the application.

    In this guide, you build a Docker container.

  5. Define a deployment mechanism.

    This is done by creating manifest files.

  6. Deploy the application.

Some additional components and tools used in this guide or by Fin-Dashboard include:

  • Python packages, including pandas and plotly - for managing and visualizing numerical data.

  • Jupyter Notebook - an interactive computational environment to explore and visualize data.

  • Fbprophet - to create the ML model to predict the stock/index price.

  • Streamlit - to build a Web-based, interactive front-end for the ML model.

  • VADER - to analyze market sentiment based on news content.

  • Yahoo Finance - to fetch historical stock data.

  • Kubectl - a command line tool for controlling the Kubernetes cluster manager.

  • Kustomize - a template-free way to customize application configuration that simplifies the use of off-the-shelf applications.

4 ML model

In this section, you explore a subset of the Fin-Dashboard code by creating a Jupyter Notebook that illustrates how data is accumulated and processed, a model is created and used, and model predictions are visualized.

Open a new Jupyter Notebook and enter the code below.

  1. Fetch the stock/index data from Yahoo Finance and store it as a dataframe.

    import yfinance as yf
    company = 'TSLA'
    df = yf.download(tickers=company, period="7y", interval="1d")
  2. Preprocess the data and rename columns for the Prophet ML model.

    df = df.reset_index()
    df = df[["Date", "Close"]]
    df = df.rename(columns={"Date": "ds", "Close": "y"})
  3. Initialize the ML model and make future predictions.

    from fbprophet import Prophet
    fp = Prophet(yearly_seasonality=True)
    future = fp.make_future_dataframe(periods=period)
    forecast = fp.predict(future)
  4. Visualize the predictions.

    from prophet.plot import plot_plotly
    fig = plot_plotly(fp, forecast)
    TSLA predicted price

5 Front-end

In addition to the ML analytics workload, Fin-Dashboard also provides a Web-based front-end to support user interaction.

5.1 Features

The front-end application features useful functionality for the user, including:

  • Automatic detection of the user’s country to show predictions for the top five, country-specific stocks

  • Ability for users to select long- or short-term predictions and to specify the number of forecasted days

  • Ability for users to choose other stocks and other stock exchanges

  • For long term predictions, a comparison of the model’s predicted price with the analyst’s target price

  • For short term predictions, a sentiment analysis of the stock news to enable visualization of its impact on stock price

  • Correlation of a stock with the index

5.2 Components

Fin-Dashboard is composed of the following Python modules:

  • geo_location.py: Determines the country where the user is located to give country specific information.

  • index_correlation.py: Correlates the stock with its parent index.

  • main.py: Contains all the Streamlit configurations/page setup and uses the other files to create the application.

  • plot_charts.py: Contains the modified and generalized form of the Jupyter Notebook code we created in the previous steps. Contains two python functions - one for long term and other for short term predictions.

  • sentiment.py: Collects stock news from Google News and uses the VADER sentiment analysis tool to determine the stock’s trend.

  • stock_recomendation.py: Extracts the analyst’s price target of a stock from Yahoo Finance.

6 Build environment

Create your build environment as follows:

  1. Clone the Fin-Dashboard code from GitHub into an empty directory.

    git clone https://github.com/navin772/GSOC_Stock_prediction
  2. Elsewhere, create your working directory, called 'stock_frontend'.

  3. Copy all the files listed in the previous section from the cloned 'fin_dashboard' directory into your working 'stock_frontend' directory.

7 Containerization

Container images are created by adding the application and its dependencies in one or more layers on top of a base container image. You define the base container image and the contents of the layers in a plain text file, called a Dockerfile. When this is done, you simply call the docker build command to create the new container image.

  1. Make sure you are in your 'stock_frontend' working directory.

  2. Create the file 'requirements.txt'.

    You use this file to specify the Python packages required for the application.

  3. Create the file 'Dockerfile'.

    FROM python:3.10-slim
    COPY . /app
    WORKDIR /app
    EXPOSE 8501
    RUN pip install -r requirements.txt
    ENTRYPOINT ["streamlit", "run"]
    CMD ["main.py"]

    The Dockerfile tells the docker build command to:

    • start with the 'python:3.10-slim' base image.

    • copy the contents of the current directory (where the command is issued) to the '/app' directory in the image itself.

    • use '/app' as the working directory for subsequent instructions.

    • expose network port 8501, which is the default for the Streamlit service.

    • install the Python packages per the 'requirements.txt' file created earlier.

    • launch the Streamlit service.

    • run the 'main.py' script.

  4. Build the Docker image.

    docker build -t fin_dashboard:latest .
  5. Tag the image to your Docker Hub account.

    docker tag fin_dashboard:latest <USERNAME>/fin_dashboard:latest

    Replace <USERNAME> with your Docker Hub user name.

  6. Push the image to your Docker Hub account.

    This will make it easier to deploy to your Kubernetes cluster.

    sudo docker push <USERNAME>/fin_dashboard:latest

    Be sure you log in to your Docker Hub account before pushing the image. Use docker login on the command line to do this.

  7. Test the docker container.

    1. Run the container.

      docker run -p 7000:8501 fin_dashboard:latest
    2. Open your Web browser to 'http://localhost:7000/' and verify that you can access the application.

8 Deployment manifests

A manifest specifies the desired state of an object that Kubernetes will maintain. Manifest files are typically in YAML format for human readability. For this application, you create three manifest files:

  • 'namespace.yaml': to create a namespace where the application will be deployed.

  • 'deployment.yaml': to define the deployment configuration.

  • 'service.yaml': to expose the application so it can be accessed through a static port.

You also create a 'kustomization.yaml' file, which identifies the manifests to deploy.

  1. In your 'stock-frontend' working directory, create the subdirectory, 'yaml_files', and enter it.

  2. Create the 'namespace.yaml' manifest file with the contents listed below to define the 'finance' namespace.

    apiVersion: v1
    kind: Namespace
    name: finance
  3. Create the 'deployment.yaml' file with the listing below to define the deployment configuration.

    apiVersion: apps/v1
    kind: Deployment
        app: stock-analysis
      name: stock-analysis
      namespace: finance
      replicas: 2 # Creating two PODs for our app
          app: stock-analysis
            app: stock-analysis
          - image: <USERNAME>/fin_dashboard:latest # Docker image name
            name: fin-markets     # POD name
            - containerPort: 8501
              protocol: TCP

    Here image: <USERNAME>/fin_dashboard:latest refers to the Docker image that you created and pushed to Docker Hub. Replace <USERNAME> with your Docker Hub user name.

  4. Create the 'service.yaml' file below to expose the application so it can be accessed through a static port on the node.

    apiVersion: v1
    kind: Service
      name: stock-analysis
        run: stock-analysis
      namespace: finance
      type: NodePort
      - port: 8501
        targetPort: 8501
        protocol: TCP
        name: http
      - port: 443
        protocol: TCP
        name: https
        app: stock-analysis

    Specifying a NodePort gives you the option to set up your own load balancing solution or expose one or more nodes' IP addresses directly.

  5. Create the 'kustomization.yaml' file below to specify the manifests to deploy.

    apiVersion: kustomize.config.k8s.io/v1beta1
    kind: Kustomization
    - namespace.yaml
    - deployment.yaml
    - service.yaml

9 Deployment

With your manifest files defining the deployment, you need to execute the deployment to your Kubernetes cluster.

  1. Make sure you have Kubectl installed and configured to manage your Kubernetes cluster.


    Since version 1.14, Kubectl supports management of Kubernetes objects using a Kustomize kustomization file.

  2. Deploy your application.

    kubectl apply -k yaml_files

    The -k option tells kubectl to process the kustomization file in the directory.

  3. Check whether all the pods are running in the 'finance' namespace.

    kubectl get pods -n finance
    NAME                              READY   STATUS    RESTARTS   AGE
    stock-analysis-6885c8dbcc-b9rk4   1/1     Running   0          2m25s
    stock-analysis-6885c8dbcc-ml2lg   1/1     Running   0          2m25s
    Running pods
  4. Get all the information of the 'finance' namespace.

    kubectl get all -n finance
    NAME                                  READY   STATUS    RESTARTS   AGE
    pod/stock-analysis-6885c8dbcc-b9rk4   1/1     Running   0          2m25s
    pod/stock-analysis-6885c8dbcc-ml2lg   1/1     Running   0          2m25s
    NAME                     TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)                        AGE
    service/stock-analysis   NodePort   <none>        8501:32169/TCP,443:30285/TCP   2m30s
    NAME                             READY   UP-TO-DATE   AVAILABLE   AGE
    deployment.apps/stock-analysis   2/2     2            2           2m25s
    NAME                                        DESIRED   CURRENT   READY   AGE
    replicaset.apps/stock-analysis-6885c8dbcc   2         2         2       2m25s

    The deployment process might take some time depending on the resources of the cluster. Be patient!

  5. When the deployment is complete, note the 'CLUSTER-IP' and 'PORT(S)' associated with 'service/stock-analysis'.

    In the example listing above, the service is available at on port 32169.

10 Demonstration

Get started using Fin-Dashboard:

  1. Open your Web browser to the IP and port of the service (in our example, it would be

  2. The Dashboard automatically detects your country, but you can select another from the drop-down box.

  3. Select your timeframe, either 'Short Term' or 'Long Term'.

  4. Enter the number of days to predict.

  5. Click Submit.


Explore the application features:

  • For long term predictions, analysts' target price is also shown, which can be useful for comparing the model.

  • For short term predictions, news sentiment analysis is shown to provide a sense of the market mood.

  • You can view long term prediction charts for up to eight other countries.

  • You can also view the correlation between the specified stock and the market index by selecting 'Index Correlation'.


11 Summary

Analytics at the edge is of growing importance in the financial space. Organizations realize they can achieve faster insights, maintain better security practices, and lower costs by deploying certain analytics workloads closer to or even at the edge. The scale and agility of cloud native architectures and tooling, like Kubernetes, make this possible.

In this guide, you learned how to containerize an ML application and deploy it into a Kubernetes environment managed by Rancher by SUSE.

Below are listed a few resources to help you continue your exploration of Rancher and K3s for edge analytics:

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