{"repo":"NavnoorBawa/Quantitative-Options-Trading","free":true,"listed":false,"github":"https://github.com/NavnoorBawa/Quantitative-Options-Trading","clone":"git clone https://github.com/NavnoorBawa/Quantitative-Options-Trading.git","description":"Derivatives pricing, volatility modeling, uncertainty estimation, and risk-aware options research.","language":"Jupyter Notebook","stars":13,"topics":["jupyter-notebook","machine-learning","options","quantitative-finance","risk-management","derivatives","volatility"],"license":"MIT","category":"trading","readme_excerpt":"Day 4: Mathematical Framework Implementation — When Research Meets Reality Day 4 of building a systematic options trading strategy from scratch After three days of iterative development — from an overfit baseline (+27%) to methodologically sound foundations (+3.5%) to ensemble intelligence (+18.96%) — today's implementation represents the culmination of academic research frameworks translated into executable trading code. The question wasn't whether I could implement sophisticated mathematical models, but whether they would maintain performance when subjected to the harsh realities of live market simulation. The Mathematical Arsenal: Research Made Executable Today's system integrates six distinct academic frameworks, each addressing specific challenges in quantitative trading: Deep Ensembles with Heteroscedastic Loss (Lakshminarayanan et al., 2017) : Rather than relying on a single model, the system combines Random Forest, XGBoost, and Extra Trees classifiers with different hyperparameters to capture epistemic uncertainty through model disagreement. Conformal Prediction (Wisniewski et al., 2020) : Provides distribution-free uncertainty quantification by calibrating conformity scores on validation data, offering principled prediction intervals without distributional assumptions. CVaR-Based Risk Management (Rockafellar & Uryasev, 2000) : Position sizing incorporates Conditional Value at Risk optimization, dynamically adjusting exposure based on the tail risk of simulated return","default_branch":null,"files":null,"tree":[],"storefront":"/r/NavnoorBawa","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/NavnoorBawa/Quantitative-Options-Trading/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}