Real-time crypto market observation engine focused on liquidity, orderflow, and derivatives-based market behavior. / 기관의 유동성 행동을 관측하는 실시간 마켓 마이크로스트럭처 엔진. 가격 예측이 아닌, 시장 구조를 읽기 위한 도구.

Council-ready simulator for the Symbolic Coherence Exchange Protocol (SCEP): issuance/burn dynamics, adversary modeling, amortized circuit breakers, seeded phase maps, and clean CSV exports.

Low latency broker-agnostic trade execution engine with modular adapters and retry handling (FastAPI, Python)

Economic applications of the SymC framework. Applies χ ≈ 1 stability principles to market microstructure, distinguishing governed systems (HFT-stabilized) from ungoverned systems (selection-driven). Demonstrates framework universality in human adaptive systems.Retry

Quantitative framework for modeling exchange liquidity, slippage, and flash-crash cascades using L2 order book data.

High-performance C++ limit order book engine built for quantitative research, with microsecond-level matching, realistic order types, and strict price-time priority.

Spectral Machine Learning for Market Microstructure: Fourier-Laplace Signal Decomposition for Alpha Discovery

Showcase of The Slippage Engine: asynchronous multi-agent market ecology with FAIR run artifacts and empirical slippage evidence.

Modern C++ market-making engine implementing the Avellaneda–Stoikov model, with inventory-skewed quotes, Poisson order flow, dynamic spreads, and multi-regime Monte Carlo experiments.

Jane Street portfolio: OCaml, Coq, boundary-aware modelling, formal verification

Early warning for BTC/ETH flash crashes using trade-only features (Binance 2021–2024). XGBoost; strict temporal splits; zero median false alerts/day on quiet periods.

PhD researcher — ML for financial market security | GNNs, LLMs, options microstructure | IEEE BigData 2025 | Digital Finance & AIAI 2026 under review

Latency-aware limit order book simulator with Avellaneda–Stoikov market making strategy and experiments on latency vs profitability.

GRU neural network for predicting short-term price direction from high-frequency order book data (Argentine bonds). Full Python pipeline + C++ inference engine via libtorch. 63.8% directional accuracy.

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

Deterministic, event-driven backtesting engine for intraday futures. Features regime-adaptive execution, strict session handling, and causal integrity

AnsCom Quantitative Suite | Research Paper available | A live terminal that approximates the ICICI Prudential Gold ETF (GOLDIETF) price after India's National Stock Exchange (NSE) market hours, using XAUUSD and USDINR in real-time.

A high-performance implementation of univariate Hawkes Processes for modeling self-exciting order flow in financial markets. Features O(N) recursive MLE calibration and criticality detection.