Youness Yachruti
Quantitative Finance · Systematic Trading · Risk
About
I build systematic trading strategies and the risk machinery around them — regime models, position sizing, drawdown control — and before the code, I traded live capital with my own money on the line.
I deepened the theory through an MSc in Data Analytics at Hult International Business School, and I'm planning to go further through a Master's in Financial Engineering at Baruch College.
What I work on
- Regime detection — Hidden Markov Models for volatility-state classification and dynamic position sizing in Python.
- Risk research — "The Geometry of Risk" (working paper): a four-layer framework for multi-asset systemic-stress monitoring — correlation-network topology, Granger causality, tail-risk quantification, and HMM regime detection across 9 global asset classes. An out-of-sample HMM flagged the April 2026 stress regime; results cross-validated against a DCC-GARCH benchmark.
- Markets & compliance — supported $10M+ in FX flow at Crédit International; ran UEMOA compliance audits across 15+ risk exposures and built the risk-reporting pipelines behind them.
- Data engineering — ETL pipelines with validation and schema normalization; quantified $19.17M in cross-fiscal revenue and $187K in bad-debt exposure for Semester at Sea.
- Automation — LLM-powered workflows (Google Apps Script) that cut manual data entry 60% for SME clients.
Stack
Python (pandas, scikit-learn, statsmodels) · SQL · R · QuantConnect · Power BI · Excel
Hidden Markov Models · PCA / eigen-decomposition · time-series analysis · statistical inference · backtesting · portfolio optimization · drawdown control
Certifications: FINRA SIE · Bloomberg Market Concepts · Market Microstructure · Mathematics for Machine Learning · Series 66 (in progress)
Featured work
The Geometry of Risk
A four-layer empirical framework for multi-asset systemic risk monitoring — network topology, Granger causality, tail-risk quantification, and HMM regime detection. Manuscript under peer review.
View on GitHub →Semester at Sea — Revenue Pipeline
SQL reconciliation pipeline that untangled a fiscal-year vs. academic-year revenue mismatch, quantifying $19.17M in cross-fiscal revenue and $187K in bad-debt exposure.
View on GitHub →Visa Inc. (NYSE:V) — Equity Research
Multi-stage DCF, comparable-company analysis, FX/rate macro stress testing, and a blockchain/stablecoin disruption thesis.
Read on Seeking Alpha →Contact
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