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LSEG announced on October 14, 2026, a collaboration with NVIDIA to accelerate analysis of its Tick History market-data archive. Using NVIDIA GPUs and the cuDF dataframe library, LSEG says workloads that previously took hours processed in seconds in a benchmark, without making another full copy of the underlying data. The approach is aimed at institutional research, trading, execution analysis, risk, market replay and artificial-intelligence teams.
What LSEG and NVIDIA announced
The partnership combines LSEG Tick History with NVIDIA’s GPU-accelerated cuDF technology. Instead of relying solely on conventional CPU-based, overnight or market-close processing, firms can use parallel GPU computation to filter, join, aggregate and analyse large volumes of tick-level records.
Reuters independently described the arrangement as enabling institutions to examine every trade and price quote in historical datasets in seconds, at scale, without copying the dataset into a separate environment. The announcement concerns analytical processing; it does not announce a new exchange, trading venue or real-time feed.
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Why GPU processing changes the workflow
Parallel work on tick data
Tick datasets contain individual trades, quotes and related market events. Many analytical jobs apply the same operation to millions or billions of rows: selecting a time range, grouping by instrument, calculating statistics, or replaying market conditions. GPUs are designed to perform many such operations concurrently. cuDF provides dataframe-style operations that can run on NVIDIA hardware, allowing existing data-analysis patterns to be adapted for accelerated execution.
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From overnight batches to interactive analysis
LSEG’s cited benchmark reports a reduction from hours to seconds. That result is an LSEG benchmark claim, not an independently reproducible performance guarantee. The announcement does not specify the workload configuration, GPU model, dataset slice, software settings or reproducibility methodology, so actual gains will vary with the query, hardware, data access path and surrounding workflow.
What is LSEG Tick History?
Tick History is LSEG’s historical market-data service. LSEG says the archive contains:
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| Measure | Reported scale |
|---|---|
| Rows | 110 trillion |
| Instruments | 100 million |
| Historical span | More than 30 years |
| Exchanges, trading venues and contributors | More than 580 |
The breadth matters for studies that need long histories, cross-venue comparisons or coverage beyond a single asset class or exchange. It also makes data movement and repeated extraction expensive, which is why processing close to the governed source is central to the announcement.
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LSEG says customers can work with detailed historical records while preserving the licences, permissions and controls attached to the data, rather than producing another complete copy for every team or analytics environment. This can reduce duplicated storage and the governance burden associated with tracking multiple extracts.
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“Customers want to work with detailed historical data at speed, without the burden and risk of creating another full copy,” said Ron Lefferts, Co-Head of Data & Analytics at LSEG. He said the collaboration is intended to bring deeper histories closer to the point of decision while keeping customers’ permissions and controls intact.
Not copying data does not remove the need for access management. Firms still need to confirm that their licences permit the intended analysis, configure identity and entitlements correctly, and ensure that derived outputs receive appropriate handling.
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Who is expected to use it?
- Quantitative researchers: test signals over deeper histories and broader venue coverage.
- Strategy-testing teams: run historical studies and compare assumptions across instruments and markets.
- Execution and market analysts: examine trade and quote behaviour, venue conditions and execution quality.
- Risk teams: investigate historical events and calculate exposure or stress scenarios using granular observations.
- Market-replay users: reconstruct sequences of quotes and trades for simulation, investigation or training.
- AI researchers: prepare and analyse large, permissioned historical datasets for model development and evaluation.
How to compare this with conventional market-data analytics
Teams evaluating the capability should compare the complete workflow, not just a processor specification.
| Evaluation axis | Conventional overnight or market-close workflow | GPU-accelerated Tick History approach |
|---|---|---|
| Processing latency | Often measured in hours; the exact time depends on the job. | LSEG reports hours-to-seconds in its benchmark; configuration details are not stated. |
| Historical depth and coverage | Depends on the locally extracted subset and licensed sources. | Designed to use Tick History’s reported 110 trillion rows, 100 million instruments, 30-plus years and 580-plus venues and contributors. |
| Data movement | Frequently requires extracts or duplicated copies for separate teams. | LSEG says analysis can occur without another full copy of the underlying dataset. |
| Governance | Permissions and licences must be tracked across each copy and environment. | LSEG says licences, permissions and controls remain intact while customers analyse the data. |
| Best-fit workloads | Scheduled reports and studies that can wait for batch completion. | Quantitative research, execution analysis, risk, replay and AI workflows that benefit from faster iteration. |
Why demand for faster processing is rising
LSEG reported that traffic on its real-time network grew 29% in the first half of 2026 compared with the first half of 2025, reached 26 million messages per second in June, and that Tick History API requests increased 48%. Those figures indicate heavier demand for market-data access, although they do not by themselves measure the performance of the NVIDIA integration or predict an individual customer’s workload.
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What the announcement does not establish
- It does not publish the GPU model, cluster size, software versions or benchmark workload needed to reproduce the hours-to-seconds result.
- It does not state that every Tick History query will run in seconds.
- It does not disclose public reseller, affiliate or referral terms for Tick History, cuDF or GPU infrastructure.
- It does not eliminate licensing, entitlement, security or audit requirements for institutional data use.
Practical takeaway for financial institutions
The collaboration targets a specific bottleneck: moving and repeatedly processing very large historical tick datasets. Its principal value is the combination of GPU parallelism with access to governed data, potentially allowing researchers and decision-support teams to iterate faster without proliferating full data copies. Before adoption, a firm should run representative queries, verify entitlements, measure end-to-end costs and latency, and test whether its existing analytics code can use cuDF effectively. The published evidence supports the direction and the benchmark claim, but not a universal performance promise.
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