Amid high market volatility, the amount of data points that are generated can become overwhelming. How can query-ready data enable data scientists and quants to devote more time to deriving insights to create successful trading strategies?
- During periods of high volatility, keeping pace with the rate at which new data points are generated can become very challenging.
- The success of a trading strategy is fundamentally dependent on two factors: Timing and the ability to back test that strategy. This means access to historical tick data is crucial.
- Having access to query-ready data sets can make a significant difference. Data scientists can spend more time deriving insights rather than cleaning and aggregating the relevant data points.
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During periods of high market volatility, it can be difficult to keep pace with the rate at which new data points are generated. This puts the continuity of business processes at risk.
However, data ingestion is not the only challenge caused by volatility. Further problems can arise from increased costs in storage and data cleansing.
The situation can be helped by introducing an additional layer of normalization, tagging and enrichment of this data to make it easy to use across asset classes in tandem with the relevant reference data and corporate actions.
New trading strategy
Implementing a successful new trading strategy should focus on timing and the ability to back test that strategy. A recent poll found that use of tick history data for execution strategy and backtesting tops up the list for where tick data adds the greatest value.
Delays in access to historical data sets can mean the window is missed to generate alpha, and data scientists and quants could be sat idle waiting for the data to be procured. And it is this access to relevant data that appears to be the biggest pain point for end-users.
Data scientists and quants must also face the challenge of data cleansing.
Access to query-ready data reduces the onerous amount of time they would otherwise spend cleansing and aggregating data points. This makes a significant difference and enables more time to be devoted to deriving insights.
Refinitiv Tick History and Google Cloud’s BigQuery technology can resolve this headache. Each can be leveraged so query-ready data can be accessed as a managed offering.
Watch: Expert Interview — How Tick History in Google Cloud Platform enables innovation
How will query-ready data help you?
You may wonder what difference this will make to you?
First, you can now leverage the on-demand capacity and scale that the Cloud provides. This includes managing costs around compute, and lowering total cost of ownership (TCO) because you don’t have store on site petabytes of historical data.
Instead, you have them at your fingertips and ready for query as and when you need it.
And second, you will also be able to leverage the analytical capabilities available in BigQuery and easily deploy machine learning models with just a few lines of code, working with trusted market data. A further benefit is that you are able to focus on what you do best, ensuring you have no internal competition for resources.
With the ever-increasing regulatory requirements, risk management, compliance and surveillance workflows also all depend on tick level data. The flexibility around accessing this data set is key to meeting these demands at scale.
Google Cloud will enable you to scale your compute to implement new trading strategies or test new ideas without a new hardware order every time. You also won’t need to predict how much compute you will actually need to run the query you require.
When looking at a typical customer workflow the ETL (Extract, Transform and Load) process can be painful, especially when thinking of data sets of the size of tick history in the petabyte range.
Data democratization is real, and query-ready data can now be at your fingertips.