Translate academic quantitative finance models into executable Python implementations.
npx skills add https://github.com/bsantanna/quaks --skill quant-bsantanna
Please help me install this Agent Skill. Skill: quant Source: https://github.com/bsantanna/quaks/tree/main/.gemini/skills/quant Command: npx skills add https://github.com/bsantanna/quaks --skill quant-bsantanna
This skill provides expert, production-grade quantitative finance guidance, translating academic models into actionable analysis, valuations, and risk assessments for real-world asset management tasks.
Ask for a concrete valuation or risk model and I will provide a ready-to-run Python implementation and explanation.
High-intent search queries and answers about installing and using this skill.
Option pricing models like Black-Scholes are implemented by translating the explicit mathematical definitions into modular, executable Python code to produce precise valuations for equities and derivatives.
Replicating academic papers involves extracting the theoretical models, implementing them in modular Python, validating results against market data from Elasticsearch indices, and structuring reproducible research workflows.
Risk metrics and portfolio construction use standard factor-based frameworks such as CAPM, Fama-French, and Carhart, applied through data-driven Python implementations to assess asset risk and design portfolios.
Fundamental stock valuation and Monte Carlo simulations are supported core features, providing ready-to-run Python implementations for asset pricing and real-world risk assessment tasks.
Access to market data via Elasticsearch indices is required for data-driven analyses, ensuring models are validated against real-world data for accurate valuation and risk management outputs.
Research-to-production handoffs require structuring modular Python code and data pipelines into reproducible workflows, ensuring academic models translate seamlessly into actionable asset management tooling.
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