| 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 notebooksupdate_notebook - Set up analysis cells in Jupyterupdate_latex - Write finance paper content to LaTeX editorlatex_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]"
- Load price data using pandas/yfinance
- Calculate returns, volatility, Sharpe ratio
- Plot cumulative returns and drawdowns
- Run statistical tests (normality, autocorrelation)
- Present findings with charts
Build a Model
When user says: "Build a [pricing/risk/factor] model"
- Clarify model specification and data requirements
- Load and clean data
- Estimate model parameters
- Validate with out-of-sample testing
- Report results with diagnostics
