Description

I specialized in algo/quant trading and alpha research for leaders in the financial industry.

Strong of my background in quantitative finance, machine learning for financial market and entrepreneurship mindset, I now look to provide value to leaders, traders, researchers and investors in the financial industry to have an edge with state of the art tools on the market

Tradingview/ Pinescript/ Python/ IBKR/ Quant/ Machine learning/ Options/ Algo Trading/ Automated Trading/ Alpha Research

Industry field of expertise

Languages

French
Native or bilingual
English
Native or bilingual
Vietnamese
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Société Générale - CIB
Quant Model Validation
BANKING AND INSURANCE
October 2021 - September 2023 (1 year and 10 months)
Paris, France
Validation of model’s behavior for linear and exotics products priced in MC, PDE and Regression
o Equity derivatives products June 2022 - Present
▪ Vanilla, Forward, Asian, Var Swap
▪ Built a tool to help decision makers study the impact of dividend yield and IR on products
o Fixed Income products (IR/ FX/ Inflation and Fallback)
▪ Vanilla, Swaption, Callable/ Bermudean, Barrier and trigger options
▪ Validated convexity adjustment for quanto and in arrears delayed payment
• Advised Societe Generale on a strategic plan to write an official document toward the ECB’s models requirements
Python Black Scholes Monte Carlo PDE Trading Equities Fixed Income Git
Société Générale - CIB
Quant Commodity Researcher
BANKING AND INSURANCE
September 2019 - July 2020 (10 months)
Londres, United Kingdom
• Built pricing library in python to analyze live trading data, calculate future curves, cost of carry, minimum variance hedge
ratio and fundamental analysis impact on the trading portfolio (pandas, numpy, scikitlearn, scipy)
• Researched commodities strategies: contango/backwardation, cash and carry, roll yield, storage theory, convenience yields,
LME/OTC arbitrage, thus advising traders on underlying pattern
• Identified limitations of current big data management and built a MongoDB database to store daily trading data,
thereby establishing platform for back testing trading strategies
trading strategies Machine learning Time Series arima garch arbitrage trading commodities business Python SQL trading book roll yield cost of carry storage theory convenience yields sentiment analysis Scikit-learn statsmodel Pandas