Arcticdb Timeseriesv0.3.3

Ingest massive historical tick and order book data from sources like eastmoney or joinquant into ArcticDB for efficient storage and retrieval. Supports billions of rows with lazy loading to manage memory, enabling analysts to query aggregated data without loading entire datasets.

Leverage ArcticDB to store and query multi-year, cross-market (A-share, HK, crypto) price data. Use batch DataFrame concatenation and lazy loading to run backtests on millions of rows without memory overflow, ensuring next-bar execution and avoiding look-ahead bias.

Configure AWS S3 credentials to access public blockchain data, then store and analyze it in ArcticDB as time-series. Supports large-scale aggregation and filtering for blockchain market analysis.

Use the Billion Row Challenge use case to demonstrate ArcticDB's ability to aggregate 1 billion temperature rows efficiently. This scenario is ideal for testing and comparing time-series database performance in research or enterprise settings.

Combine data from multiple markets (A-share, HK, crypto) using ArcticDB's multi-source ingestion and storage. Users can query unified time-series data for factor computation, target selection, and portfolio rebalancing.

Offer a cloud-based platform integrating ArcticDB for time-series data management, targeting hedge funds and prop trading desks. Provide tiered pricing based on data volume and query complexity, with premium support for multi-market backtesting.

Bundle curated financial datasets (e.g., A-share tick data) with ArcticDB storage and querying capabilities. Charge per dataset access or per query, enabling banks and asset managers to analyze large datasets without building their own infrastructure.

Provide consulting services to set up ArcticDB for clients with specialized time-series needs (e.g., IoT sensor data, blockchain analytics). Deliver tailored pipelines and training, charging project-based fees plus maintenance contracts.