Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit

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

Limit Order Book for high-frequency trading (HFT), as described by WK Selph, implemented in Python3 and C

VisualHFT is a WPF/C# desktop GUI that shows market microstructure in real time. You can track advanced limit‑order‑book dynamics and execution quality, then use its modular plugins to shape the analysis to your workflow.

Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

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.

A C++ and Python implementation of the limit order book.

Nasdaq Order Book Reconstructor

Feature engineering of a Limit Order Book. Extraction of features from a LOB in order to analyse the behaviour of trade market.

Using tabular and deep reinforcement learning methods to infer optimal market making strategies

We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.

Ultra-fast Limit Order Book for Node.js written in TypeScript for high-frequency trading (HFT) :rocket::rocket:

Master Thesis: Limit order placement with Reinforcement Learning

R package intended for visualisation, analysis and reconstruction of limit order book data

A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.

Implementation of various deep learning models for limit order book. DeepLOB (Zhang et al., 2018), TransLOB (Wallbridge, 2020), DeepFolio (Sangadiev et al., 2020), etc.

No description provided.

Low latency Limit Order Book and Matching Engine created in C++, able to handle over 1.4 million transactions per second.

Bitstamp real time console based limit order book

This is the official repository for the paper TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data.

LOBCAST is a Python-based open-source framework for stock market trend forecasting using Limit Order Book (LOB) data. 🤖📈

Limit Order Book Implemented in Python

obAnalytics Shiny front-end

DeepMarket is a framework for performing Limit Order Book simulation with Deep Learning. This is also the official repository for the paper 'TRADES: Generating Realistic Market Simulations with Diffusion Models'.

volatility harvester prototype

Building a fast matching engine in Rust for efficient processing of an ITCH order book.

Pytorch implementation of Axial-LOB from 'Axial-LOB: High-Frequency Trading with Axial Attention'

Time Series Prediction of Volume in LOB

Deep learning approach for market price prediction, in JAX

Academic python library that records changes to instances of the limit order book for pairs supported on the coinbase exchange.