vpin

A Python Implementation of Measures for Order Flow Risk, e.g. VPIN

Competition, table, and player management for VPins.

Terminal based frontend and utilities for Visual Pinball

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.

"As Simple As Possible" Cabinet Front-End. A VPinballX suite for Linux.

HMM-based market microstructure regime detection for cryptocurrency order books — 30+ features, Gaussian HMM, C++/pybind11 LOB engine, interactive Plotly Dash dashboard

3d holografic fan for virtual pinball

Virtual Pinball Content Management

DOF Configtool Client

Rust library for working with Visual Pinball VPX files

Algorithmic trading research — multi-platform (MT5/MQL5 + cTrader/C# cAlgo). Order-flow & microstructure strategies: tick-level cumulative delta scalping with sniper confirmation gates, dynamic Median+MAD threshold fades, weighted-composite signal engines (VPIN, OBI, footprint imbalance, absorption, HVP). Session-aware risk management.

List of open source virtual pinball projects.

ATOMIC MESH — Distributed deterministic HFT market-making engine. Avellaneda-Stoikov strategy with sub-microsecond C++ hot-path (575ns), event-sourced architecture, VPIN toxicity detection, QUIC mesh transport, real-time dashboard. Rust + C++17 FFI. Live on Binance.

An extension for Visual Pinball players using PinUP Popper, provides highscore card generation, etc.

Volume Synchronized Probability of Informed Trading

Real-time Market Microstructure Risk Engine implementing VPIN (Volume-Synchronized Probability of Informed Trading) for high-frequency toxicity detection.

An ultra-high-performance, cost-effective, and native drop-in replacement for the legendary Teensy 3.2 and Teensy 4.1 controllers in Virtual Pinball (vPin) cabinets. Supporting both **8-channel** and **16-channel** parallel DMA outputs.

Smart Money Detection is a Python library that uses ensemble anomaly detection and active learning to identify informed trading behavior in prediction markets, with configurable weighting strategies, market microstructure models (VPIN/PIN), and minimal labeled data requirements

Market microstructure analysis library for MQL5 (MT5). Tick/quote/book collectors, liquidity, order flow (CVD, VPIN), price impact (Kyle, Amihud, Hasbrouck, Almgren-Chriss), volatility (Parkinson, GK, YZ), trade classification, ACD duration, book resiliency, volume profile, information share.

OFI, VPIN and Kyle's lambda scored against synthetic order flow with known informed-trading intensity — including the finding that BVC-VPIN mostly tracks volatility.