cryptofeed_skill

Overview

This skill helps you integrate and develop with cryptofeed, a Python library that streams normalized, real-time cryptocurrency market data from 40+ exchanges. It focuses on WebSocket streaming, consistent data formats, and easy integration with storage backends for algorithmic trading and analytics. Use it to consume trades, tickers, order books, and aggregated NBBO across exchanges.

How this skill works

The skill guides you to create FeedHandler instances that attach exchange-specific feed objects with selected symbols, channels, and callbacks. It normalizes exchange differences in symbols, timestamps, and channel names, and prefers WebSocket streams with REST fallback and automatic reconnection. Built-in backends let you write data directly to Redis, MongoDB, Kafka, InfluxDB, and others without custom plumbing.

When to use it

• Building algorithmic trading systems that require live market data
• Streaming and processing order book updates (L1/L2/L3), trades, or tickers
• Aggregating NBBO across multiple exchanges for arbitrage signals
• Feeding data into storage or message systems (Redis, Kafka, InfluxDB, etc.)
• Prototyping multi-exchange price monitoring or trade-flow analysis

Best practices

• Start with a single exchange and channel to validate callbacks before scaling up
• Use normalized symbols and timestamp handling provided by the library to avoid mismatches
• Persist raw messages and normalized outputs to a backend for replay and debugging
• Use NBBO and multi-exchange feeds to detect arbitrage and best-price opportunities
• Install only required backends to minimize dependency bloat (use pip extras as needed)

Example use cases

• Multi-exchange price monitoring across Binance, Coinbase, and Kraken using TICKER channels
• High-frequency order book depth analysis using L2_BOOK callbacks for liquidity modeling
• Trade-flow analysis by streaming TRADES for selected pairs and computing real-time metrics
• Creating an NBBO feed combining exchanges to surface best bid/ask and execute arbitrage tests
• Ingesting normalized market data directly into Kafka or InfluxDB for downstream analytics