HiveQ Documentation
Technical documentation for HiveQ platform. This documentation covers standards, best practices, and implementation guides for working with HiveQ platform.
HiveQ Data
HiveQ Data provides comprehensive access to both historical and real-time market data through multiple interfaces.
Python Data API (v0.2.0)
Python client library for accessing HiveQ's data platform:
- Historical Data Client: Query historical market data with flexible date/time ranges
- InstrumentReference Client: Access security metadata and instrument information
- Publisher Client: Publish strategy events, logs, and custom data to HiveQ destinations
- Metadata Client: Get dataset and schema information from HiveQ platform
- LiveStream Client: Stream live market data in real-time
Realtime Data Subscription (v1.0)
Line handler implementation for publishing and subscribing to real-time data via Kafka:
- Kafka Partitioning & Keying Standards: Topic naming, keying, and partitioning strategies
- Producer Implementation: Best practices for publishing messages to Kafka topics
- Consumer Behavior: Understanding how consumers work with partitioned topics
- Topic Specifications: Standard topic naming conventions for equity, futures, options, and signals
HiveQ Flow
HiveQ Flow is a comprehensive trading platform for developing, testing, and deploying quantitative trading strategies with support for backtesting and live simulations.
Platform Overview (v0.2.0)
Introduction to the Flow trading platform and its core components.