2026C++, Market Microstructure, Limit Order Book, ITCH Replay, Market Making, Avellaneda-Stoikov, Queue Diagnostics, Reproducibility
Matching Engine, ITCH Replay, Market-Making Diagnostics, and Artifact Validation
result3.7M events/sec benchmark, 12,423 QQQ ITCH messages, 30-seed strategy statistics, 10-seed fill-rate diagnostics, and validator-backed artifacts.
A deterministic C++ market microstructure simulator with price-time priority matching, Nasdaq ITCH replay, naive vs Avellaneda-Stoikov market-making experiments, queue-position diagnostics, ten-seed fill-rate mechanism tests, and artifact validation.
status: complete CTest: 69/69 passed pytest diagnostics: 21 passed ruff: clean CI: green validator: passed
read project2026Python, Options, Volatility, SABR, Heston, Numerical Methods, Model Diagnostics
SABR/Heston Calibration, Robustness, and Failure Diagnostics
result101 tests, 206 row recommended universe, SABR median RMSE 0.0190 to 0.0077, Heston global RMSE 0.1174.
Built a reproducible Python options research engine for chain cleaning, forward extraction, OTM IV surface construction, static-arbitrage diagnostics, SABR robustness testing, Heston calibration, and same-universe model comparison. The main result was diagnostic: filtering reduced SABR median RMSE from 0.0190 to 0.0077, while global/per-expiry Heston underfit SABR despite passing synthetic recovery checks.
status: complete tests: 101 passing raw rows: 1,169 clean rows: 957 OTM rows: 595 confidence: high on reproducibility and diagnostics, limited by one yfinance snapshot
read project2025Python, algorithmic trading
result#194 algo, top 1.4% of 18,800 teams.
the hard part was adapting strategy across rounds under sparse feedback.
status: complete artifact: public leaderboard rank: #194 algo
read project2026honors thesis, Bayesian inference, regime detection, HMM, CUSUM, CPPI
resultBayesian regime detection, HMM plus CUSUM, CPPI drawdown control, and S&P 500 crisis validation.
the hard part was accepting that a well trained Bayesian agent can still stay wrong too long.
status: complete artifact: thesis PDF confidence: high
read writing entry2025PyTorch, neural SDEs, rough volatility
resultResearch prototype with repo; no public performance number claimed.
the hard part was making path structure matter without overclaiming the metric.
status: research prototype artifact: GitHub repo confidence: medium
read project2026Python, Kalshi API, DuckDB, scikit learn, XGBoost
result122 tests, 85% coverage, documented API limits.
the hard part was proving which historical data did not exist.
status: framework complete tests: 122 passing coverage: 85% confidence: high on engineering, documented data limits
read project2026Python, NumPy, SciPy
resultOut of sample Sharpe near 0.7 after fees.
the hard part was reporting the modest number after fixing the inflated one.
status: complete result: out of sample scope: single asset and one pair confidence: medium
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