#Order-book
Showing 25 of 25 repositories tagged #order-book, ranked by stars
High performance components for building Trading Platform such as ultra fast matching engine, order book processor
Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent
Free self-hosted liquidity bot for token issuers who want to improve CEX market quality without sending tokens, funds, or API keys to a third-party market maker.
Ultra-fast Limit Order Book for Node.js written in TypeScript for high-frequency trading (HFT) :rocket::rocket:
simple, fast and feature rich order matching engine supports limit, market, stop-loss, iceberg, IOC, FOK, GTD orders
Crypto liquidity detection & algorithmic trading bot. Order book analysis, stop-loss clusters, liquidity sweeps. Multi-exchange (Binance, Bybit, Kraken, OKX). Trading signals, quant research, market microstructure.
Sub-microsecond bare-metal execution engine with deterministic replay, lock-free order path, and hardware-timestamped latency measurement.
crypto trading bot liquidity order book market microstructure algorithmic trading quant Python Node.js websocket Binance crypto signals HFT order flow sweep detection depth analysis
High-performance limit order book engine with C++ core and Python SDK. Processes 20M+ msgs/sec with µs latency. Supports real crypto/equity data replay, spread/imbalance/impact analytics, and backtesting of VWAP, TWAP, POV, and market-making strategies with reproducible PnL and risk metrics.
Crypto liquidity detection & algorithmic trading bot. Order book analysis, stop-loss clusters, liquidity sweeps. Multi-exchange (Binance, Bybit, Kraken, OKX). Trading signals, quant research, market microstructure.
Exchange-grade CLOB matching engine + microstructure analytics in C++20
Deep learning approach for market price prediction, in JAX
Production-grade low-latency FPGA trading system. Features custom VHDL 10GBASE-R PHY, hardware NASDAQ ITCH 5.0 order book, C++ DPDK/XDP kernel bypass, and PCIe DMA acceleration. LLM-Inference
https://aux.exchange/
Code package to analyze high-frequency trading (HFT) races using financial-exchange message data, following Aquilina, Budish and O'Neill (2021).
High-performance C++ matching engine with unified market simulation, live market data streaming, and a browser-based dashboard
This project is a Python-based trading simulator that allows users to simulate trading strategies, manage an order book, and interact with a mock trading environment using various algorithmic traders. The simulator includes a FIX (Financial Information eXchange) protocol handler, a market-making algorithm, and synthetic liquidity generation.
A high-frequency tool that monitors the "Bid-Ask" spread and order book depth to predict the next 10-second price move.
A Python module for market simulation
Solana AMM DEX Smart Contract – A high-performance decentralized exchange (DEX) on Solana, combining an on-chain order book with Raydium’s AMM aggregator for optimal liquidity, best price execution, and low slippage trading. Built with Rust & Anchor, it offers fast, cost-efficient, and transparent decentralized trading.
Market-making strategy that placed #2 in Paradigm's Prediction Market Challenge. 110 iterations, 8 hours.
Python SDK for Polymarket order book data and backtesting. Tick-level L2 snapshots, billions of deltas, full book reconstruction, and a strategy backtesting engine with realistic execution.
A very simplified implementation of a stock exchange.
Frequency Weighted OrderBook Analysis
Distributed market-making system. Avellaneda-Stoikov strategy with sub-microsecond C++ hot-path (431ns), event-sourced architecture, VPIN toxicity detection, QUIC mesh transport, real-time dashboard. Rust + C++17 FFI.