March 2026 Summaries

Arden Charts was developed to address the need for continuous, multi-exchange market data coverage, which is often lacking in standard charting platforms. The platform's architecture consists of microservices that handle data ingestion, processing, storage, and querying across multiple exchanges, such as BinanceUS and Kraken, using QuestDB as its backbone due to its efficient handling of time-series data. By employing a lightweight pub/sub layer with NATS and utilizing Redis for fault tolerance, Arden Charts ensures real-time data processing with minimal resource usage. QuestDB's materialized views facilitate efficient data aggregation by pre-computing various timeframes, thus optimizing the real-time charting experience. Historical data is sourced from Massive.com and integrated through a reliable ingestion pipeline. The platform's design emphasizes scalability and efficiency, demonstrating the effective integration of tools like NATS, Redis, and QuestDB to deliver seamless, real-time charting with comprehensive historical data access.

QuestDB and ClickHouse are both high-performance analytical databases, each tailored for distinct applications and strengths. QuestDB excels in high-throughput streaming ingestion and low-latency queries, making it ideal for time-series data in sectors like capital markets and defense. With its zero-GC Java and C++ implementation, QuestDB is built for out-of-the-box performance, especially in environments requiring rapid data handling. In contrast, ClickHouse is designed as an OLAP engine with broader use cases, including log monitoring and product analytics, and is better suited for batch loading of data. It originated at Yandex for e-commerce and ad tech analytics and has evolved to support a wide range of analytical workloads. Performance benchmarks indicate that QuestDB significantly outperforms ClickHouse in ingestion speed and certain types of queries, while ClickHouse shines in 12-hour range queries and some aggregations, leveraging its C++ implementation and MergeTree architecture. Both databases support SQL, but QuestDB extends it with time-series primitives, offering unique functionalities such as ASOF JOIN and LATEST ON. While ClickHouse offers a mature ecosystem with comprehensive community support, QuestDB emphasizes an open data format by writing cold data to Apache Parquet, enhancing interoperability. Ultimately, QuestDB is favored for environments where speed and low-latency queries are critical, whereas ClickHouse is preferred for broader OLAP workloads and observability, benefitting from its mature ecosystem and managed cloud services.

The text explores the optimization of SQL queries for calculating realized volatility, a crucial metric for options strategies like gamma scalping, by reorganizing query operations to significantly improve performance. Initially, a query on QuestDB to compute annualized realized volatility from market data ran in 3 seconds, which was reduced to 38 milliseconds by restructuring the order of operations—specifically, by aggregating data with SAMPLE BY before applying window functions. This change reduced the dataset size processed by expensive operations, resulting in an 80x performance improvement. The example underscores the importance of structuring queries to aggregate data before applying window functions, particularly in high-frequency data environments, and demonstrates how a simple adjustment in SQL query design can lead to substantial efficiency gains. The guide also highlights the use of a WINDOW clause for better readability and the employment of mathematical identities to overcome limitations in available functions.

QuestDB is positioned as a high-performance time series database that addresses the evolving demands of modern data ecosystems by providing both real-time and historical data analytics capabilities. Originally developed to overcome limitations in traditional OLTP and OLAP databases, QuestDB offers a unique three-tier storage engine that optimizes for fast data ingestion, real-time SQL queries, and efficient long-term storage using open formats like Apache Parquet. This design allows for seamless integration with existing data tools and avoids data duplication, supporting a variety of data access patterns, including downsampled materialized views and direct Parquet file reads. QuestDB's architecture, which includes a parallel write-ahead log and a columnar storage layout, ensures high throughput and low latency, making it suitable for both time-series and OLAP workloads. The database's compatibility with open standards and its support for AI-driven data interactions further enhance its adaptability within the broader data ecosystem, allowing users to orchestrate complex workflows without vendor lock-in.

QuestDB Enterprise 3.2.3 is a robust update for the open-source time-series database designed for high-performance environments, offering new features such as automatic Write-Ahead Logging (WAL) cleanup in object storage, TLS certificate expiration monitoring, and faster join operations using binary search for initial frame positioning. The release includes new SQL functions like time-weighted average price (TWAP) and array manipulation capabilities, as well as the HORIZON JOIN feature for efficient markout analysis in capital markets, which is further explained in a newly added post-trade analysis section of the QuestDB cookbook. Enhancements include configurable memory management settings under high-column ingestion workloads, JIT compilation on ARM64 systems, and comprehensive bug fixes and improvements such as expanded ACL permissions and improved backup and restore processes. The update also incorporates all changes from QuestDB 9.3.3, including SQL-standard WINDOW clauses and Parquet late materialization, with the binaries available for self-managed enterprise customers and further support provided for those using the Bring Your Own Cloud (BYOC) model.

QuestDB has been recognized as the Best Trading Analytics Platform at the TradingTech Insight Awards Europe 2026, which underscores their commitment to balancing speed and data integrity in high-performance trading environments. The award, based on votes from market practitioners such as quants, engineers, and traders, highlights QuestDB's mission to eliminate the trade-offs traditionally associated with legacy time-series infrastructures. The company's CTO, Vlad Ilyushchenko, participated in a panel discussion about high-performance trading infrastructure, emphasizing the importance of managing total cost of ownership (TCO) and choosing between co-location and cloud solutions. QuestDB has introduced impactful features like HORIZON joins, allowing complex post-trade analyses to be conducted directly within the database using SQL, streamlining workflows that previously required external data processing. The platform's adherence to open standards, such as native support for Parquet and a standard REST API, facilitates integration with modern data ecosystems, including AI-powered tools, making it a versatile choice for quantitative workflows. As the trading technology landscape evolves, QuestDB is poised to address emerging challenges, including ensuring data trustworthiness at ingestion speed and leveraging AI to predict infrastructure bottlenecks, all while remaining committed to their engineering discipline and customer feedback.