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DirectX Visualization Optimizes Analytics Algorithmic Traders

Smart Data Collective

Learn how DirectX visualization can improve your study and assessment of different trading instruments for maximum productivity and profitability. Let’s dive right into how DirectX visualization can boost analytics and facilitate testing for you as an Algo-trader, quant fund manager, etc. But first, What is DirectX Anyway?

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The Five Use Cases in Data Observability: Fast, Safe Development and Deployment

DataKitchen

It highlights how DataKitchen’s Data Observation solutions equip organizations to enhance their development practices, reduce deployment risks, and increase overall productivity. Each addition or modification poses potential risks that could propagate errors into production environments. How Many Tests Ran In The Qa Environment?

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Automating Model Risk Compliance: Model Validation

DataRobot Blog

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

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Managing mortgage risk in an uncertain world

Cloudera

The trouble is, mortgage lenders persist in relying on historical macro-economic assumptions in their models so they risk repeating the errors of a decade ago when banks – and their regulators – failed to recognize the warning signs from a far richer source: low-level micro-economic data. Risk management 3.0.

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Fearing the Wrong Thing

O'Reilly on Data

Programmers who work for those companies risk losing their jobs to AI. Testing and debugging—well, if you’ve played with ChatGPT much, you know that testing and debugging won’t disappear. I expected the next step in programming languages to be visual, but it isn’t that either. Programming without virtual punch cards.

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DataOps Observability: Taming the Chaos (Part 3)

DataKitchen

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. Logs and storage for problem diagnosis and visualization of historical trends. Data and tool tests. Testing at Every Step. (Part 1) (Part 2).

Testing 130
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Introducing The Five Pillars Of Data Journeys

DataKitchen

Using automated data validation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand. The image above shows an example ‘’data at rest’ test result. For example, a test can check the top fifty customers or suppliers. What is the acceptable range?

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