This article walks through the complete ML workflow in DolphinDB: from data ingestion and preprocessing to model training, evaluation, and deployment. We'll explore supervised and unsupervised learning, demonstrate distributed training at scale, and conclude with a production financial case study.
DolphinDB's streaming framework is built for exactly this. In this post, we'll walk through how to compute real-time Price Rate of Change (ROC) and derive a live market ranking from it — using two composable streaming engines chained into a single pipeline.
We’ll show how we use the Order Reconstitute Engine to reconstruct missing orders and provide a consistent data foundation.
We address this challenge by making DolphinDB a direct data source for Kronos, scaling from a simple K-line forecasting example to minute-level, multi-stock, large-scale historical data and multi-GPU DDP training.
This article focuses on a common industrial IoT workload: offline batch processing. Using the 10,000-sensor scenario, we'll explore how DolphinDB can turn massive time-series data into actionable metrics for equipment monitoring, anomaly detection, idle-state identification, and operational analysis.
We use minute-level stock factor data to predict 30-minute forward returns. DolphinDB handles stock universe selection, factor pivoting, price and factor alignment, and backtesting, while Python and TabPFN handle data preprocessing, model training, and prediction.Together, they turn raw factor data into an industry-level Top-N stock selection strategy.
The takeaway: the model sets the ceiling on what an agent can do, but the engineering layer wrapped around the model determines how much of that potential actually gets realized. That layer has a name — the Agent Harness.
To eliminate unnecessary data movement, DolphinDB introduces the IoT Filtering module, bringing common filtering algorithms directly into the database. Instead of relying on external processing frameworks, the module integrates multiple categories of filtering algorithms directly into the database engine
AI is transforming quantitative research at every stage. Join DolphinDB at the 17th CITIC Securities Financial Innovation Conference to explore how AI is accelerating the development and deployment of mid- and high-frequency trading strategies.
This article introduces the module, explains data preparation, and demonstrates factor calculation in both batch and streaming scenarios using the KMID factor as an example.
