Quant Research Platform

Quant Research Platform

Massive-Scale Data Storage

A unified PB-scale data foundation for quantitative research — covering structured data (high-frequency quotes, orders, executions, positions) and unstructured data (research reports, model vectors) in a single multi-model storage engine. Built-in deduplication, native array storage for order book and factor matrices, and automatic data co-location minimize cross-node access and accelerate full-market multi-factor analysis.

The distributed, high-availability architecture ensures 24/7 stability, regulatory compliance, and seamless scaling as your business grows.

High-Performance Data Analytics

In-database distributed computation lets researchers run factor calculations, backtests, and analysis directly where the data lives — no data exports, no consistency issues. Over 2,000 built-in functions span technical analysis, statistical testing, time-series, and panel data, with WorldQuant 101 Alpha, Technical Analysis Indicator Library, Cross-Sectional Asset Pricing Factor, and other major factor libraries available out of the box.

Optimized time-series functions simplify rolling windows, cross-sectional ranking, and grouped aggregation. Distributed parallel execution compresses full-market multi-factor backtests from hours to minutes — letting research teams test ideas as fast as they form them.

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Real-Time Stream Processing

A unified real-time computation layer for quantitative research — millisecond market data processing, real-time factor computation, multi-source joins, and anomaly detection, all in one platform.

Time-series, cross-sectional, and join engines support incremental computation with rich operators — enabling rapid development of real-time factor development, anomaly detection, cross-market arbitrage signals, and performance attribution. A continuous feed of high-quality signals for prop trading, asset management, and execution systems.

The stream-batch architecture means factor and strategy logic developed on historical data deploys directly to live streams — no rewrites, no drift between backtest and production. Time-to-live reduced by 80%+ compared to a Python research / C++ production dual-stack.

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Flexible Access Control

A comprehensive user, group, and role management system with 20+ granular permission types — covering database table access, field-level queries, and function execution. Function views let teams share computed results and analytics with researchers, risk, or business units without exposing sensitive underlying data such as raw positions and trade details.

Dynamic authentication for scheduled and streaming tasks prevents unauthorized access to critical data. RSA-encrypted data transmission and SSO integration provide unified identity management and audit trails. The permission framework supports prop desk confidentiality, cross-team quant collaboration, regulatory oversight, and client data isolation — all on one platform.

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Purpose-Built Financial Tooling

DolphinDB comes with a comprehensive suite of out-of-the-box financial modules purpose-built for quantitative research — covering multi-market data ingestion, high-fidelity backtesting, simulated matching, and market data replay across the full research pipeline. Specialized tools for snapshot construction, complex event processing (CEP), bond and options valuation, yield curve fitting, and GPU-CPU heterogeneous computation address the demands of complex financial scenarios.

All modules are independently deployable and loosely coupled — usable as unified components within a centralized quant research platform, or integrated directly into prop trading, asset management, or client-facing systems. Modular by design, so firms can build and iterate without reinventing the infrastructure.

Unified AI Training & Inference

Models trained in research environments deploy directly to production for real-time inference — sharing the same data source and feature engineering pipeline to eliminate data drift and logic inconsistencies between development and live. Native compatibility with XGBoost, LightGBM, TensorFlow, and PyTorch means models load and run within DolphinDB without additional integration overhead.

A built-in LLM-powered financial Agent lets researchers interact in natural language to reproduce factors, generate new ones, and analyze strategies — significantly lowering the technical barrier to quantitative research.

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