The investment research data team at a leading publicly listed securities firm had long struggled with fragmented data, inconsistent data standards, and insufficient real-time computing capabilities across its FICC operations. The journey from strategy development to production deployment often took several months.
To overcome these challenges, the firm partnered with DolphinDB to build a unified FICC multi-asset research data platform based on an integrated stream-batch architecture, achieving:
More than a 10× improvement in historical market data query performance
Millisecond-level latency for intraday market data
A reduction in the strategy development-to-production cycle from three months to just two weeks
An overall efficiency improvement of more than 85%
The platform eliminated data silos and fragmented system architectures, providing a solid technical foundation for FICC quantitative trading and market-making.
FICC Quantitative Trading: Speed Creates Alpha
FICC markets have long been dominated by over-the-counter (OTC) trading, where research and trading workflows depend heavily on manual data handling. Quantitative teams spend significant time integrating and cleaning data before pricing, backtesting, and risk analysis. The result is slower research, less accurate pricing, and delayed hedging.
FICC markets are becoming increasingly data-driven. Growing market complexity and stronger links between exchange-traded and OTC markets are driving demand for unified data management, real-time analytics, and faster strategy execution.
As a result, low-latency processing of high-frequency market data and unified data governance have become essential capabilities for modern FICC quantitative platforms, serving as the technological foundation for high-precision pricing, algorithmic hedging, and automated market making at leading financial institutions.
The Challenge: Data Fragmentation Was Slowing Down the Business
As business requirements evolved, the firm's traditional FICC data architecture could no longer support the growing demands of quantitative research and trading. Four major challenges emerged.
1. Inconsistent Data Across Sources
FICC research data came from multiple heterogeneous sources, including CTP market data feeds, third-party vendors, and locally maintained Excel spreadsheets. Each used different schemas, field definitions, and transmission formats. The lack of standardized frequently resulted in typo records, missing sequences, and other data quality issues, reducing backtesting accuracy and reinforcing the classic “Garbage In, Garbage Out” (GIGO) cycle.
2. Fragmented Architecture and Isolated Data Components
Years of incremental IT expansion had left the same data duplicated across multiple systems. Researchers had to retrieve, reconcile, and integrate data across systems before analysis, while the siloed architecture increased infrastructure costs through duplicated hardware, redundant storage, and higher maintenance overhead.
3. Batch-Oriented Processing with Limited Real-Time Computing
The legacy architecture relied on end-of-day batch processing, with analytical results available only on T+1. High-frequency strategies simply weren't possible without live market data.
4. High Technical Barriers for Researchers
The fragmented architecture placed a heavy technical burden on researchers. Instead of focusing on strategy development, they spent much of their time writing data extraction scripts and glue code to integrate data from multiple sources. As a result, development cycles became longer and production deployment was significantly delayed.
The Solution: An FICC Research Data Platform Powered by DolphinDB
The firm partnered with DolphinDB to build a unified, multi-asset FICC research data platform on a distributed time-series database, combining streaming and batch processing in a single framework.
Consolidating Historical Data
The team migrated nearly five years of cash bond market data from Hive into DolphinDB, along with historical futures and options data. With DolphinDB's multi-model storage, the platform optimizes storage for different data types while balancing storage efficiency and query performance.
Multi-source data is cleansed and normalized into a unified trade and quote data model. A closed-loop validation process continuously checks for missing or abnormal records, enabling centralized management of historical data across all asset classes while significantly reducing system complexity and maintenance costs.
Building a Real-Time Market Data Hub
The system uses DolphinDB's Kafka plugin to build a low-latency data ingestion layer, enabling real-time access to market data from major exchanges and brokers through a unified data pipeline. Combined with DolphinDB's streaming engines, the system enables real-time calculation of derived indicators. As new data arrives, incremental computations are triggered automatically and results are generated with minimal latency.
For FICC-specific needs, DolphinDB also provides a real-time one-minute candlestick generation module for futures and options. The module addresses common industry challenges such as inconsistent exchange timestamps and disconnected workflows between historical and real-time data processing. Manual and error-prone alignment processes are now automated within the system.The module now serves as a core infrastructure component, feeding low-latency, high-availability minute-level data to both production trading systems and quantitative strategies.
The Results
The unified platform now connects research, trading, and risk management on a single data foundation.
The project transformed fragmented, post-market data workflows into a unified real-time data foundation. By connecting research, trading, and risk management teams on a single data platform, it enables faster decision-making, accelerates quantitative research, and strengthens the firm's competitiveness in FICC market making.
What's Next: AI-Powered Research and High-Fidelity Backtesting
For both the firm and DolphinDB, this project marks a major milestone in modernizing FICC data infrastructure—but it is only the beginning.
Looking ahead, we plan to further evolve the platform in two strategic directions.
The first is deeper AI integration. DolphinDB will build a high-performance feature store designed for AI workloads, enabling factor selection, model training, and online inference directly within the data platform to accelerate the deployment of AI-driven investment research.
The second is the development of a high-fidelity backtesting platform capable of dynamically simulating intraday margin requirements and real-time risk exposure for futures and options. By more accurately replicating live trading conditions, the platform will provide a more realistic environment for strategy validation and improve the reliability of quantitative research before production deployment.
As financial markets continue to evolve, DolphinDB will keep innovating at speed—empowering intelligent finance with higher performance, greater reliability, and technology built for the future.
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