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high-frequency-financial-data

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This technical report details the architecture and mathematical foundation of a quantitative market-making engine based on the stochastic optimal control framework proposed by Avellaneda and Stoikov (2008). The system dynamically computes optimal bid and ask quotes by balancing expected spread capture, inventory risk and etc

  • Updated Feb 26, 2026
  • Python

High-frequency market-making research system implementing the Avellaneda-Stoikov framework with dynamic spread adjustment based on real-time volatility and inventory risk. Features a low-latency core, kill switch, and real-time inventory/PnL visualization.

  • Updated Aug 12, 2026
  • Rust

Structural trading research mapping synthetic market engines, actor behavior, spike ignition, timing sync, liquidation flow, and candle microstructure. Built from spatiotemporal pattern recognition, dynamic systems perception, and microstructure intuition.

  • Updated Aug 16, 2026
  • PLpgSQL

Temporal Graph Centralities in Financial Risk and Network Dynamics: Quantifying node influence in time-evolving networks via numerical linear algebra. Features standard and novel temporal centralities optimized with GMRES and stochastic trace estimation (XTrace). Applied to high-frequency S&P 100 and Binance cryptocurrency correlation graphs.

  • Updated Jun 10, 2026
  • MATLAB

HFT-Simulator is an educational Limit Order Book (LOB) + Matching Engine simulator focused on correctly modeling high-frequency trading logic and dynamics.It is not a production system, not connected to any real exchange, and not nanosecond-accurate.

  • Updated Aug 14, 2026
  • Python

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