---
name: quant-analysis-4010428dbbd5
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
```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


---
Source attribution: Prismer-AI
Original project: https://github.com/Prismer-AI/Prismer
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: Prismer-AI
Original project: https://github.com/Prismer-AI/Prismer
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Finance & Accounting, Sales
Roles: Accountant, Business Development, CFO / FP&A, Data Analyst, Financial Analyst
Specialties: API, Database
Tools: Tool not specified
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: c832a2df98083f18567319375ec4f6fecfeed452ff95c1a452f0d61a40669d38