There are 19 repositories under market-microstructure topic.
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
VisualHFT is a cutting-edge GUI platform for market analysis, focusing on real-time visualization of market microstructure. Built with WPF & C#, it displays key metrics like Limit Order Book dynamics and execution quality. Its modular design ensures adaptability for developers and traders, enabling tailored analytical solutions.
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.
Probabilistic Programming Language for Order Execution and Routing Modeling
We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.
Implementation of various deep learning models for limit order book. DeepLOB (Zhang et al., 2018), TransLOB (Wallbridge, 2020), DeepFolio (Sangadiev et al., 2020), etc.
Academic python library that records changes to instances of the limit order book for pairs supported on the coinbase exchange.
Optimization techniques on the financial area for the hedging, investment starategies, and risk measures
A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.
Reinforcement learning environment for trading
Price response function and spread impact analysis in correlated financial markets
Implementation of the paper <Model-based Reinforcement Learning for Predictions and Control for Limit Order Books (Wei et al., J.P. Morgan AI Research, 2019)>.
A collection of sample codes designed as assignments for students taking Market Microstructure
Price response function and spread impact analysis in foreign exchange markets
An R package for Bayesian estimation of the probability of informed trading.
Project presented as a partial fullfilment requirement for the Cardano Developer Professional (CDP) program. An implementation of the Stochastic Supply Curve (Çetin, Jarrow & Protter, 2006) based on Blais & Protter (2010), Árdal (2013) and Hossaka (2018) approaches through Binance's API endpoint live feed data.
The code is to webscrape ESMA's Equity Transparency table for equities.
Code for my senior thesis: "The Effect of Payment for Order Flow on Order Routing to Market Centers"
Quantifi Sogang