Mobile App Data Services

Real-Time Indicator Computation

DolphinDB's streaming framework powers real-time indicator updates for brokerage mobile apps — computing intraday price changes, cumulative volume, order book features, minute-level technical indicators, and ETF IOPV increments at the moment market data arrives. Quote pages, stock detail pages, intraday charts, and technical charts stay consistent and responsive at millisecond latency.

Built-in time-series, cross-sectional, and join engines with rich operators cover most standard app indicators out of the box, with script-based extension for custom factors and business-specific metrics.

Indicator Storage

A unified, high-performance storage layer for market indicators, factors, and user behavior data — with flexible partitioning by time, asset, user, or business. Columnar storage and compression algorithms reduce storage costs by 70%+ versus traditional row-based databases, scanning only relevant columns at query time for millisecond-level lookback queries and large-scale indicator analysis. Real-time ingestion and batch analytics run on the same platform — a leaner, more stable, and easily scalable architecture.

High-Concurrency Indicator Queries

A query engine optimized for high-concurrency workloads — columnar scanning, distributed execution, intelligent caching, and load balancing sustain real-time query performance under massive mobile user traffic. Individual stock K-lines, intraday indicators, market movers, watchlists, and thematic portfolios all return in milliseconds. Multi-security comparisons, cross-period stitching, and conditional filtering maintain linear scalability and consistent performance at scale — giving brokerages the confidence to deliver richer, more responsive data experiences to their clients.

Research & Strategy Acceleration

DolphinDB's backtesting engine natively supports tick data, order book, minute-level, and daily data — enabling high-fidelity, distributed strategy backtesting directly on massive historical datasets. Factor computation, stock screening logic, and portfolio analysis can be built rapidly in script and deployed directly to client-facing quantitative tools.

The stream-batch architecture ensures real-time indicators, historical factors, and backtest results share a consistent computation framework — no duplicate development required. A single platform delivers lightweight yet professional mobile research capabilities, driving user engagement and service value.

AI-Powered Robo-Advisory

DolphinDB unifies data processing, factor computation, and deep learning inference in a single high-performance time-series engine — providing a low-latency real-time foundation for client-facing robo-advisory. Market data processing, factor updates, and model inference complete in milliseconds, delivering a stable execution environment for stock screening, event analysis, and sentiment monitoring algorithms.

Via MCP tools and AI Agent frameworks, market data, indicators, financial reports, and models are encapsulated as callable intelligent services — directly powering common smart features in brokerage apps: personalized stock recommendations, risk alerts, thematic tracking, portfolio diagnostics, and valuation signals.