Case Study | Next-Generation Quantitative Platform: How DolphinDB Modernized a Leading Securities Firm's Research Capabilities

In the high-stakes world of quantitative investing, every millisecond can tip the scales. Confronted with trillion-row datasets and the demand for instant decisions, a leading domestic securities firm set out to redefine its future. Partnering with DolphinDB, they embarked on a bold journey of technological transformation.

The result: a next-generation AI-powered research and trading data center that delivers millisecond-level responses, computes thousands of factors in real time, and runs multiple strategies in parallel. With client service efficiency soaring 10x, the firm has not only accelerated performance but also built a formidable new moat—reshaping the very frontier of quantitative investing.

The Challenge: Breaking Free from Fragmented, Inefficient Legacy Systems

The firm’s legacy setup depended on three separate systems: relational database clusters for core business, Hadoop for offline computation, and Redis for real-time caching. This disjointed architecture created multiple obstacles:

• Isolated data silos that slowed client services and dragged out cross-department collaboration.

• High trading latency combined with escalating storage costs.

• Barriers to AI adoption, as models built in PyTorch or TensorFlow required cumbersome data export and re-import.

As a result, when hedge fund clients demanded “custom factor strategy deployment within two weeks,” the firm’s traditional infrastructure struggled to keep pace.

The DolphinDB Solution

Now, with DolphinDB at its core, the firm’s AI-driven quantitative research and trading platform handles client demands with ease. By combining a high-performance data foundation with modular business components, the platform delivers lightning-fast aggregation on trillion-row datasets. Factor strategies move from concept to production in a fraction of the time, enabling hedge fund clients to achieve deployment within weeks.

As the firm’s Head of Fintech remarked:

“DolphinDB not only eliminated our departmental data silos and increased factor computation performance by dozens of times, but also empowered us to respond to client-specific requests in real time.”

Powering this evolution is DolphinDB's comprehensive "data ingestion–storage–computation–delivery" framework, which consolidates heterogeneous data streams, enables sophisticated analytical research, and delivers essential functionality throughout the entire investment research infrastructure. By accelerating strategy development cycles from months to weeks, it serves as a catalyst for institutional alpha creation.

Additionally, DolphinDB features over 2,000 specialized built-in functions, more than 10 stream processing engines, and 10+ business modules. Through a single SQL statement, users can effortlessly merge live data streams with a decade of historical records, facilitating comprehensive multi-asset, multi-timeframe backtesting across equity, derivatives, fixed income, and leveraged trading strategies.

Utilizing the DolphinDB, premier brokerage firms have introduced customer-facing "rapid factor engineering" solutions, drawing engagement from hundreds of institutional fund managers.

The Gains: Reinforced Competitive Edge

DolphinDB’s breakthroughs directly resolve long-standing industry pain points:

• Lightning-Fast Quant Research : Achieves aggregation queries on trillion-row datasets in ≤ 50 ms , enabling real-time factor design and execution.

• Ultra-Low Latency Stream-Batch Processing : Transforms tick data into trading signals with end-to-end latency ≤ 35 ms.

• Massive Storage Efficiency : Leverages columnar time-series storage and ZSTD compression to achieve a 1:10 compression ratio , cutting storage costs by 70%.

• Unified Data Assets: Business databases are seamlessly connected, creating a panoramic data view across 5,000+ tables.

• Real-Time Cross-Market Analytics: Built-in engines reduce index–option volatility matrix calculations to just 0.3 seconds.

• Native ML Pipeline Integration: Direct integration with PyTorch/TensorFlow enables production-grade model inference with < 10 ms latency.

Metric	Legacy Setup	With DolphinDB	Improvement
Request-Response Cycle	1 month	1 week	↓ 75%
Trading Latency	100–1000 ms	≤ 5 ms	↓ 95%
Data Procurement Costs	10+ vendors	1–3 vendors	↓ 70%
Customer Retention	76%	89%	↑ 13%

When strategy development cycles compress to weeks, client requirements are fulfilled instantaneously, and artificial intelligence integrates seamlessly into trading workflows, the organization transforms from a market participant into a technology platform architect. Through DolphinDB, it is defining the next generation of financial technology advancement.