📈 Synthesize financial time series data adaptively for improved analysis and forecasting using a robust dataflow system.

🎯 Automate cross-platform arbitrage trading between Kalshi and Polymarket with this easy-to-use bot designed for optimal profit.

⚡ Execute trades with unmatched speed using this ultra-low latency engine designed for algorithmic trading, ensuring nanosecond precision and reliability.

📈 Automate arbitrage trading between Kalshi and Polymarket to exploit price differences effectively and enhance your trading strategy.

📈 Build and analyze exchange-grade order book matching, market data replay, and microstructure analytics with modern C++20.

Python client library for Aperiodic.io — institutional-grade market microstructure, liquidity and order flow metrics with full exchange universe coverage. Turn flow dynamics into alpha in hours, not months. No tick infrastructure to build or maintain.

Continuous research desk operating system for market decision systems.

Limit Order Book Exchange — a platform for conducting financial market experiments

Price-time priority matching engine with limit/market orders, event-driven architecture, and latency benchmarks. Python 3.11 · Linux/Docker

Experimental quantitative research platform exploring geometric market signals and algorithmic trading system architecture.

⚡ Sub-microsecond HFT system for Forex markets — Rust + C++ signal engine (~50ns) — FIX 4.4 direct broker connectivity — Pre-trade risk gate — Live trading

Built a financial time-series analysis project exploring market microstructure relationships using high-frequency crypto data. Analyzed relationships between spreads, volatility, and trade intensity using Python.

CS2 case EV vs price dynamics

Python-based paper trading research framework for systematic strategies, market making, and risk-controlled execution using live Binance data.

Access TAQ from WRDS and tick data from LSEG Tick History

An End-to-End Python implementation of Köhler et al.'s (2026) orthogonalized tail-risk framework. Combines PCA-whitening spectral decomposition with Peaks-Over-Threshold EVT to quantify extreme risks in 479-dimensional financial networks. Implements Ferro-Segers clustering, dynamic residualization, and out-of-core processing for 2.6B+ data points.

Multi-agent market microstructure backtesting library (C++ core, Python interface)

machine learning time-series project forecasting log liquidity (Amihud ILLIQ) with autoregressive and boosting models

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.