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

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.

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

Real-time Crypto Futures depth heatmap in Rust (egui/eframe) with live order flow and trade tape.

Algo Library for Order Flow Inference and TCA

Sub-microsecond bare-metal execution engine with deterministic replay, lock-free order path, and hardware-timestamped latency measurement.

Exchange-grade CLOB matching engine + microstructure analytics in C++20

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

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.

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 …

A high-frequency tool that monitors the "Bid-Ask" spread and order book depth to predict the next 10-second price move.

AI trading bot: liquidity detection, order-book gaps, hidden walls, and sweeps. Signals for quants, firms, and algorithmic/AI execution. MIT. Order-book microstructure focus. ai trading bot, crypto liquidity ai trading bot, trading bot,ai trading bot, crypto liquidity ai trading bot, trading bot,ai trading bot, crypto liquidity ai trading bot

LLM structural reasoning validation via gamma exposure analysis in options markets. Papers 1 & 2 complete. Digital Finance (Springer) submission in progress.

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)>.

Decentralized exchange infrastructure on Solana: on-chain order book, real-time WebSocket streaming, REST API, Geyser indexer, and Rust SDK. Non-custodial and optimized for high-frequency trading.