A fast L2/L3 orderbook data structure, in C, for Python

Matching Engine for Limit Order Book in Golang

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

A high-performance C++ orderbook engine with microsecond-level latency, supporting multiple ordertypes, price-time priority matching and real time data integration from Binance

Database for L2 orderbook

OrderBook Heatmap visualizes the limit order book, compares resting limit orders and shows a time & sales log with live market data streamed directly from the Binance WS API. This was a short exploratory project. Keep in mind that a lot of work is needed for this to work in all market conditions.

Query historical and real-time crypto market data from 0xArchive across two top-level venue APIs: Hyperliquid and Lighter.xyz. HIP-3 builder perps live under the Hyperliquid namespace at /v1/hyperliquid/hip3. HIP-4 outcome markets (binary prediction markets like 'Will BTC be >= X by date Y?') live at /v1/hyperliquid/hip4. Hyperliquid Spot lives at /v1/hyperliquid/spot with 326 authenticated inventory rows (HYPE-USDC, PURR-USDC, AAPL-USDC, ...); Spot candles are served from 2025-03-22T10:50:22Z. Covers route-specific orderbooks, trades, candles, funding rates, open interest, liquidations, outcome markets, spot, TWAP, and data quality. Real-time WebSocket support is channel-specific. HIP-4 trades, L4 events, and settlement events are live; HIP-4 L2 orderbook and outcome-side OI remain available through REST and stored replay while their live bridges are paused. Use in Claude Code, Codex with skills enabled, and SKILL.md-compatible agents when the user asks about crypto market data, order