Pre-Trade Analytics

kdb Insights Enterprise Pre-trade analytics

Launch

In this demonstration, we will illustrate how pre-trade analytics can be leveraged to generate real-time trading signals. These signals help traders make informed choices when buying or selling financial assets. For this demo, we will be concentrating on volatility, volume, and VWAP as our three main metrics. Click here to begin.

kdb Insights Enterprise - Pre-trade analytics

To begin, we will select the Databases node from the menu on the left side. The database is an essential part of kdb Insights Enterprise, used to store and manage the data we ingest. Click Databases to continue.

kdb Insights Enterprise - Pre-trade analytics

The schema outlines the database's structure and organization, detailing how data is stored and how tables relate to one another. Click Add Data Column to continue.

kdb Insights Enterprise - Pre-trade analytics

kdb Insights Enterprise supports more than 100 data types. With the schema defined, we will now explore Stream Settings. Click Stream Settings to continue.

kdb Insights Enterprise - Pre-trade analytics

kdb Insights Enterprise is built for live data streaming, and real-time alerts, both crucial for businesses that require immediate access to data and swift decision-making capabilities. Click Pipelines to continue.

kdb Insights Enterprise - Pre-trade analytics

The data ingestion process is managed through pipelines, powered by the kdb Insights Enterprise Stream Processor, a streaming service used to transform and analyze both real-time and historical data. Click Readers to continue.

kdb Insights Enterprise - Pre-trade analytics

kdb Insights Enterprise comes with several pre-configured operators, enabling users to easily build data pipelines. In this instance, we have chosen to ingest data from Microsoft Azure Storage. Click here to continue.

kdb Insights Enterprise - Pre-trade analytics

Using the drag-and-drop canvas located in the center of the screen, different operators can be linked together to create streaming, batch, or transformation pipelines. Click here to continue.

kdb Insights Enterprise - Pre-trade analytics

Pipelines can also be written and deployed using Python or q code. Click here to continue.

kdb Insights Enterprise - Pre-trade analytics

With the database and pipelines configured, users can begin to explore data through queries. In the following screens we will look at the different methods available. Click Queries to continue.

kdb Insights Enterprise - Pre-trade analytics

By using the GetData API, users can query real-time and historical data with a simple parameter builder. In this instance, we’re running a basic retrieval query on the Trade table. Click SQL to continue.

kdb Insights Enterprise - Pre-trade analytics

The same query can be executed with standard SQL syntax. Click Q to continue.

kdb Insights Enterprise - Pre-trade analytics

Additionally, our native programming language, q, can be used to retrieve the same data. Click Views to continue.

kdb Insights Enterprise - Pre-trade analytics

kdb Insights Enterprise includes an embedded version of KX Dashboards, allowing users to create data visualizations. Using a simple drag-and-drop interface, users can activate feature-rich components such as charts, maps, and data grids. Click here to continue.

kdb Insights Enterprise - Pre-trade analytics

In this demo, we have seen how to build and configure a pre-trade analytics solution on kdb Insights Enterprise.

kdb Insights Enterprise - Pre-trade analytics

Learn more about kdb Insights Enterprise with our free course on the KX learning hub.

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