quant-analysis

# 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 ## Tool Examples ### Load and analyze stock returns ```python # via jupyter_execute import yfinance as yf import pandas as pd import numpy as np data = yf.download("AAPL", start="2023-01-01", end="2024-01-01") returns = data["Close"].pct_change().dropna() print(f"Mean: {returns.mean():.4f}, Vol: {returns.std():.4f}, Sharpe: {returns.mean()/returns.std()*np.sqrt(252):.2f}") ``` ### Validation checkpoints - Verify data has no missing values or extreme outliers before modeling - Check model residuals for autocorrelation after estimation - Confirm out-of-sample period has no look-ahead bias