Can crypto flash wicks be predicted before they happen? Using the October 2025 $19B liquidation event as a case study, this article shows how VPIN, order book imbalance, and market microstructure can uncover early warning signals before extreme price movements occur.
DolphinDB’s high-performance market data replay and Order Matching Simulator Plugin provides a low-latency, high-throughput solution for strategy validation. By integrating the plugin directly into an existing C++ backtesting framework, institutions can reuse their current infrastructure while taking advantage of DolphinDB’s ultra-fast computing capabilities, making it an ideal solution for high-frequency strategy simulation.
This post walks through a complete, production-oriented solution using DolphinDB together with the INSIGHT market data plugin to generate 1-second order book snapshots across all SSE and SZSE stocks and funds in real time. We'll cover everything from plugin installation to post-market batch writes to a distributed database — with the actual scripts you'd run in production.
DolphinDB unifies heterogeneous industrial data (OPC UA, MQTT, Kafka) into one pipeline, with partitioned storage, stream-batch unified computation, and standard APIs. It replaces the slow relational DB underneath MES, cutting query times dramatically without replacing MES itself.
Factor replication from research reports has traditionally been a core method for analysts seeking to expand their analytical frameworks and identify new alpha-generating opportunities.
In quantitative trading, high-frequency calculation is a common requirement for research and investment strategies. However, the exponential growth in market data volumes presents significant challenges for traditional relational databases. To address these challenges, many practitioners have turned to distributed file systems using formats such as Pickle, Feather, Npz, HDF5, and Parquet for data storage, and Python for quantitative financial computations.
DolphinDB, an advanced analytical platform powered by a distributed time-series database, has emerged as a preferred solution for many brokerages and private equity firms. This article aims to provide a comparative analysis of the Python + HDF5 factor calculation approach against DolphinDB's integrated factor calculation solution.
This article compares the performance and features of DolphinDB Backtest with those of popular backtesting products on the market. Through performance comparisons, the article further demonstrates DolphinDB's competitive advantages in performance and features.
Today, we are excited to feature Cardinal Operations , the team behind the copt plugin, which brings high-performance mathematical optimization directly into the DolphinDB environment.
As a practitioner who has worked extensively with both DolphinDB and DuckDB, Boye has a clear-eyed view of what each engine does best.
