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

Implementation of various deep learning models for limit order book. DeepLOB (Zhang et al., 2018), TransLOB (Wallbridge, 2020), DeepFolio (Sangadiev et al., 2020), etc.

Algo Library for Order Flow Inference and TCA

Reinforcement learning environment for trading

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

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

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

Professional HFT market surveillance and analysis platform with real-time order book analysis, DeepLOB CNN predictions (63.4% accuracy), automated paper trading, and advanced anomaly detection. Built with Python/FastAPI, C++ analytics engine, React dashboard, and deployed on AWS. 160+ snapshots/sec, sub-ms latency.

Fundamental package for quantitative finance with Python.

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)

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

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.

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.

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.

Hands-on quantitative trading lab: implement classic papers in Python with Jupyter notebooks and complete documentation