quant-analysis

name	quant-analysis
description	Quantitative finance analysis including portfolio optimization, risk modeling, and time series econometrics using jupyter_execute. Use when the user asks about portfolio analysis, stock returns, financial risk, investment optimization, or volatility modeling.

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

# 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