This article shows how DolphinDB's AI DataLoader eliminates the data pipeline bottleneck. We'll build a production-ready stock price prediction model, demonstrating how to seamlessly connect database-scale factor data with PyTorch—without the usual memory management headaches.
This article walks through exactly that transformation, using DolphinDB as the computing layer. We’ll cover both the batch path (historical data, processed in parallel) and the streaming path (real-time data, processed with sub-millisecond latency), with the same core logic running in both.
Using simulated tick-by-tick trade data from the Shanghai Stock Exchange on a single trading day in 2020, we’ll calculate minute-level capital flow — splitting buy and sell orders into “large” and “small” buckets based on a 50,000-share threshold — and stream the results live into a dashboard.
To understand where the breaking points actually are, we benchmarked three of the most widely used TSDBs — DolphinDB, InfluxDB, and TimescaleDB — using the TSBS framework, simulating a trucking fleet across five device-scale scenarios ranging from 100 to 10 million devices.
In this article, we’ll walk through how to build a real-time IV calculation and volatility smile construction pipeline for commodity options, using DolphinDB’s streaming framework. By the end, you’ll have a working architecture that goes from raw CTP tick data to a smoothed volatility curve — refreshed every minute, live.
A decade ago, "let it run overnight" was an acceptable answer. Today, that timeline is obsolete.
Digital twins have long been understood as high-fidelity replicas of the physical world. Whether modeling entire cities through GIS systems or capturing the intricate details of buildings and factories via BIM, the traditional approach has focused on creating precise digital mappings of reality to enable simulation, analysis, and operational planning. But this paradigm is shifting.
Quantitative trading boils down to one core objective: identifying and exploiting market inefficiencies. Like a gecko frozen in anticipation of its prey, the quant trader must act with precision the instant an opportunity emerges—then resume the wait.
The session delivered an in-depth technical breakdown of how DolphinDB's scripting language achieves C++-level performance—a technical masterclass for engineers and industry practitioners.
Two things drain the most time in quant development: slow backtests, and frameworks that fall apart the moment you try to extend them. Wait five minutes for data to load, tweak a parameter, wait five more minutes. Then try adding a second asset class and discover that your matching engine, position tracking, and cash management are all tangled together.
