limit-order-book

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

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.

Master Thesis: Limit order placement with Reinforcement Learning

Nasdaq Order Book Reconstructor

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.

Bitstamp real time console based limit order book

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

obAnalytics Shiny front-end

Limit Order Book Implemented in Python

ABANDONED volatility harvester prototype

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

National Stock Exchange of India Limit Order Book Simulation

A Python module for market simulation

A platform for the testing and optimisation of trading algorithms.

A limit order book matching engine written in Julia

L3 Order Book and Matching Engine Implementaion in Java

Reinforcement learning environment for trading

Binance Limit Order Book Recorder

HFT backtest-engine for LOB data.

Simulated markets based on Zero-Intelligence agent

Code package to analyze high-frequency trading (HFT) races using financial-exchange message data, following Aquilina, Budish and O'Neill (2021).

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.

Create a mid-price classifier for limit order books using a CNN and LSTM

Used LSTM in Big Data Challenge competiton hosted by Shaastra IIT Madras. Won the second prize