Historical tick data analysed in the cloud

Tick History – Query

Historical tick history data from LSEG Real-Time data feeds, accessed and analysed in the Cloud. Our 45PB+ database goes back as far as 1996 across all asset classes.

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  1. Overview
  2. Features & benefits
  3. Use cases
  4. AI-enhanced Tick History
  5. Our solutions
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Query and analyse faster in the Cloud

Tick History – Query enables you to query and analyse at speed the unrivalled breadth and depth of our Tick History data using the full power of the Google® BigQuery™ compute engine.

Tick History – Query analyses our tick-as-a-service database directly in the Cloud, so you do not need to download any data. You can lower your costs, obtain faster results and run your queries across multiple venues and years of data without the need to prepare or break the data down.

As data volumes surge,the cloud is redefining how you interact withmarket data. Are you ready?Make any data query and get the results in seconds?Wherever you are fromtick history query acrossour entire tick history database,powered by Google Big query analysis and insights,all in the cloud experience, infrastructure,free data and analytics,with no need for data storage or new hardware,you'll get to market faster withbetter efficiency and greater savings,leverage growing data volumes toadvance your business change.How you work with tick history Datachoose tick history query.

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Why use Tick History in the Cloud?

Today, financial services firms use historical tick data for a variety of use cases across the front, middle and back office. However, storing these enormous databases on premises is costly and resource intensive. LSEG clients have lowered their tick history data storage and management costs by up to 90% by moving to the Cloud.

LSEG Tick History – Query provides on-demand delivery of its universe of 90m active and retired securities via a REST API, through the public cloud. Clients can use a venue-by-day service for easy and complete extraction of every tick for all instruments for a given venue on a specific day. There is also a custom extract service that allows users to select the instruments, fields, and time periods that they need and schedule report generation at the time that suits their business needs.

LSEG Tick History – Query provides access to our Tick History data, which can then be worked with using Google® BigQuery™ analytics. This combination transforms a query that would take hours or days of preparation and run time into just minutes.

Features & benefits

What you get with Tick History – Query

Analyse historical tick data from venues across the globe

Tick History – Query enables you to access and analyse 14.876 petabytes and 39.7 trillion rows of data from 383 venues.

Unrivalled market depth and breadth

Access 26 years of normalised MP, LL2 and L3 back to 1996, encompassing all asset classes, corporate actions and reference data.

Lower costs and increase speed

Use the Google® BigQuery™ compute engine with LSEG Tick History – Query to lower total cost of ownership and analyse data faster without the need to download, extract or transform the data.

Use cases

Make the most of Tick History - Query

Backtesting trading strategies with Tick History – Query

So essentially, back testing involves doinga historical simulation of howa specific trading rule would have performed.Historically, back testing isimportant because it gives us a bit of insightinto understanding whether or not we shouldrun a strategy going forward into the future.Obviously, past performance is notalways indicative of future performance,but nevertheless provides some insight intohow good our trading rule is with historical data.The difficulty with back testing is essentially the dataset and you need to have good quality datain order to do a proper back desk.And historical simulation, if you have tick history,it enables us to essentially doa back desk by simulatingtrade at different times of the day.For example, we're doing a daily trading strategy,Or if we're doing a high frequency trading strategy,it gives us access to very granular data.Essentially to enable us to comeup with more intricate and interesting.One of the difficulties in the past withhigh frequency data was that you would have tomanage all the storage of that on your own premises.You'd have to manage the databases.And these data sizes are very big,especially for level two and in particular forlevel three tick data with tick history.Now on the cloud, you have a very high quality dataset,but at the same time you skipthe ingestion step and youdon't have to manage any of the hardware.And ultimately it gives youaccess to doing back testing andanalysis on the datavery quickly, Essentially using Bigquery.You're basically you bringyour computation to the data rather than spendinga lot of time bringing the data locally andmanaging it and having the headache punted.

Transaction Cost Analysis with Tick History – Query

Transaction cost analysis, or TCA,is actually very important Becauseyour overall profit or loss fromyour trading activity is not only howgood your signals are and whether you'vegot the direction of your trade direct,but also the costs of putting on those trades as well.Ultimately reducing your tradingcosts means at the end of the day,you actually make more money from your trading activity.In order to manage a tick database,you'll need to manage the database,manage the hardware and scale it as you getmore data and more compute demand.You also need to make sure that the database isfully up to date continually.Ultimately, a tick database which is not updated is notgoing to be quite as useful for analytics purposes.They are adding around 2 terabytesof data per week of tick data.If you were to store that locally,that would be a substantial amount of data.Well, the advantage of using big query is you haveaccess to this continually updating tick history dataset.But at the same time, it's relatively simpleto access, for example,using common languages like SQL. And it's alsoaccessible from other languageslike Pie in this world through an API.Now that we have tick history with Google Cloud,it means that you no longer need tomanage your own tick database locally.You no longer need to updating the data.In particular, if you are looking at gettingaccess to level three data and level two data,which can be extremely large.This saving of time,essentially by using Google Cloudand tick history is substantial.

Cloud-Based Tick History, Supercharged with AI

Unlock powerful insights from LSEG’s historical tick data, now enhanced through the integration of Google Cloud’s Vertex AI within BigQuery. Available to customers with access to LSEG Tick History within Google Cloud, you can go beyond standard queries – without writing a single line of code – to uncover hidden patterns, analyse sentiment, and build smarter trading models. You’ll be equipped to perform complex analyses and back testing with lightning-speed and accuracy, while harnessing machine learning to detect anomalies, forecast trends, and automate research. Transform source data into actionable intelligence – fast.

Cloud strategies in financial services: Turning today’s challenges into tomorrow’s opportunities

Delve into our latest report exploring how banks, investment organisations and wealth & advisory firms are shaping their cloud strategies – based on the findings from our global survey of over 450 financial services executives.

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