quant-analysis

name	quant-analysis
description	Quantitative finance analysis including portfolio optimization, risk modeling, and time series econometrics using jupyter_execute

Quantitative Analysis Skill

Description

Perform quantitative finance research including data analysis, portfolio optimization, risk modeling, and econometric analysis.

Tools Used

• jupyter_execute - Execute Python code for financial analysis (auto-switches to Jupyter)
• jupyter_notebook - Manage analysis notebooks
• update_notebook - Set up analysis cells in Jupyter
• update_latex - Write finance paper content to LaTeX editor
• latex_compile - Compile research papers (auto-switches to LaTeX editor)
• update_notes - Write analysis summaries and findings

Capabilities

Data Analysis

• Time series analysis of financial returns
• Cross-sectional regression (Fama-MacBeth, panel data)
• Event studies and abnormal return analysis
• Volatility modeling (GARCH family)

Portfolio Optimization

• Mean-variance optimization (Markowitz)
• Black-Litterman model with views
• Risk parity and equal risk contribution
• Factor-based portfolio construction

Risk Analysis

• Value-at-Risk (VaR) and Conditional VaR
• Stress testing and scenario analysis
• Copula-based dependency modeling
• Monte Carlo simulation

Usage Patterns

Analyze Returns

When user says: "Analyze the performance of [asset/portfolio]"

1. Load price data using pandas/yfinance
2. Calculate returns, volatility, Sharpe ratio
3. Plot cumulative returns and drawdowns
4. Run statistical tests (normality, autocorrelation)
5. Present findings with charts

Build a Model

When user says: "Build a [pricing/risk/factor] model"

1. Clarify model specification and data requirements
2. Load and clean data
3. Estimate model parameters
4. Validate with out-of-sample testing
5. Report results with diagnostics