Built a desktop app to visualize trading backtests.
PyQt6 + matplotlib. Loads a CSV of trades, shows 50+ metrics, equity curves, Monte Carlo simulations, rolling analytics.
Nothing revolutionary : just a clean tool I wanted to exist. Open source.
-> https://t.co/ILjcyxttke
Open-sourced options-quant-toolkit :
a Python library for options pricing, Greeks, and volatility modeling.
Black-Scholes, Monte Carlo, binomial trees, implied vol, strategy backtesting.
Built for quants, traders, and students. Free and open.
-> https://t.co/Hm0yELmDIh
"Optimal Pairs Trading with Time-Varying Volatility" by Li & Tourin.
Classic OU model fails because volatility isn't constant. This paper layers a realistic time-varying vol structure onto the mean-reverting OU process, using stochastic control to find the optimal strategy
open-sourced my options pricing engine with implied vol surface (vol smile), educational notebooks & interactive dashboards.
README in English, app interface in French.
https://t.co/ElPtilvLPH
#QuantDev#OptionPricing#QuantFinance#Python
Optimal trading bands > arbitrary z-score rules.
Using stochastic control / optimal stopping to derive optimal entry & exit levels for mean-reverting spreads (Cartea–Jaimungal).
#QuantFinance#StatArb#AlgoTrading#SystematicTrading
Exploring statistical arbitrage across the SP500.
Pairs trading with OU-modeled spreads and Cartéa-optimized bands to capture mean-reversion efficiently.
(Based on key research papers 📄)
#QuantTrading#AlgoTrading#SP500#PairsTrading