Stateless Bollinger band analytics

Build, deploy and apply a custom user defined stateless analytic function

Objective

We will create a Bollinger band analytic function that calculated the upper, middle and lower pricing bands for a given variable.

Bollinger Bands are a type of price envelope developed by John Bollinger. (Price envelopes define upper and lower price range levels.) Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price. Because the distance of the bands is based on standard deviation, they adjust to volatility swings in the underlying price.

Fidelity

Source: Fidelity Investments

Prerequisites

To get started building a custom processor ensure you have your development environment configured. Read the environment setup documentation to get your environment ready to build.

Development steps

These instructions cover how to build, deploy a use the function on to the Joule Platform.

1

Create project using the template

We have provided a project template project to quick start development. The project can be found here. Clone the template project and copy relevant code and structure to your own project.

git clone [email protected]:joule-platform/fractalworks-project-templates.git

Joule uses Gradle to manage Java dependencies. To add dependencies for your processor, manage them in the build.gradle file inside your processors project directory.

2

Implement Bollinger bands function

Processors differ from connectors as they do not require, currently, a specification and builder classes. So jump right in and create and name a class that reflects the processing function.

Joule provides the core logic such as batching, cloning, linking of data stores, and a unique processor UUID for event change lineage.

Key areas of implementation:

• Define analytic function DSL namespace

• Implement following: @AnalyticsDefinition annotation, compute, setParameters and getVariablePostFixID methods

• Add the class definition to plugins.properties

• Deploy and apply to a Joule runtime environment

Code implementation

package com.fractalworks.examples.banking.analytics;

import com.fasterxml.jackson.annotation.JsonRootName;
import com.fractalworks.streams.core.annotations.AnalyticDefinition;
import com.fractalworks.streams.core.data.streams.Context;
import com.fractalworks.streams.sdk.analytics.AnalyticsFunction;

import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.Properties;

/**
 * Bollinger bands analytic unction
 */
@AnalyticDefinition(
        id = "bollingerband",
        stateless = true,
        useRawColumn = true,
        description = "Bollinger Bands are a momentum indicator used in technical analysis."
)
@JsonRootName(value = "bollinger bands")
public class BollingerBands extends AnalyticsFunction<Map<String, Double>>  {

    private int deviations = 2;
    
    public BollingerBands(){
        super();
    }

    /**
    * Calculation code for a single attribute
    */
    @Override
    public Map<String, Double> compute(Number[] values, Number previousValue, Context context) {

        Double mean = Arrays.stream(values).mapToDouble(d-> (double) d).sum() / values.length;
        double sqrtmean = 0.0;
        for(int i=0; i<values.length; i++){
            double s =  values[i].doubleValue() - mean;
            sqrtmean += s * s;
        }

        sqrtmean = Math.sqrt( sqrtmean / values.length);
        double band = deviations * sqrtmean;

        Map<String, Double> results = new HashMap<>();
        results.put("upper", mean + band);
        results.put("middle", mean );
        results.put("lower", mean - band);
        return results;
    }

    /**
    * Function parameters are provided as a properties map 
    */
    @Override
    public void setParameters(Properties parameters) {
        if( parameters != null && parameters.containsKey("deviations")) {
            deviations = Integer.parseInt(parameters.get("deviations").toString());
        }
    }

    /**
    * This defines the resulting value attributes are returned
    * as <variable>_<calculated-attribute>_<postfixid>
    * i.e. ask_upper_bollingerband
    */
    @Override
    public String getVariablePostFixID() {
        return "bollingerband";
    }
}

3

Add to plugins.properties

For Joule to load and initialised the component the processor must be defined within the plugins.properties file under the META-INF/services directory.

Add the below line in the plugins.properties file:

com.fractalworks.examples.banking.analytics.BollingerBands

4

Build, test and package

The template project provides basic JUnit test to validate DSL. The project will execute these tests during the gradle build cycle and deploy to your local maven repository.

gradle build publishToMavenLocal

5

Deploy

Once your package has been successfully created you are ready to deploy to a Joule project.

The resulting jar from the build process needs copied to the userlibs directory under a Joule project directory. For example using the getting started project copy the file to quickstart/userlibs directory.

cp build/libs/<your-analytics>.jar <location>/userlibs

6

Now apply to a stream

Let's say, sometimes we do not get a bid value which is needed to trigger an alert. So overcome a division by zero we provide a default value and use previous values when needed.

stream:
  name: nasdaq_major_banks_bollinger_bands_stream
  eventTimeType: EVENT_TIME

  processing unit:
    pipeline:
        # Filter events by major banks to reduce number of enrichment queries
      - filter:
          expression: "(typeof industry !== 'undefined' && 
                        industry == 'Major Banks')"

      - user defined function:
          bollinger bands:
            parameters:
              deviations: 2
          fields: [ ask, bid ]
          event history: 20

  emit:
    select: "symbol, ask_upper_bollingerband, ask_middle_bollingerband, ask_lower_bollingerband"

  group by:
    - symbol

Follow the same steps used in the getting started documentation to apply this script.

What we have learnt

As a first process we have covered a number of key features:

• Build a custom analytic Used the provided template project to quick start development and add custom code within key analytic methods.

• Built the jar Used gradle build tool to build, test and deploy to local maven repo.

• Deploy the jar to a Joule runtime environment Copied the Jar to an existing local Joule runtime environment

• Apply the custom analytic within a use case Apply the analytic within a use case to provide Bollinger bands for ask and bid prices.