Agent Skills › Prismer-AI/Prismer › quant-analysis

quant-analysis

GitHub

提供量化金融分析能力，涵盖投资组合优化、风险建模及时间序列计量经济学。支持通过Jupyter执行Python代码进行数据清洗、因子模型构建、VaR计算及蒙特卡洛模拟，辅助投资决策与学术研究。

docker/templates/finance-researcher/skills/quant-analysis/SKILL.md Prismer-AI/Prismer

Trigger Scenarios

用户询问投资组合分析或股票收益需要构建风险模型或波动率模型请求进行资产配置或因子投资优化

Install

npx skills add Prismer-AI/Prismer --skill quant-analysis -g -y

More Options

Non-standard path

npx skills add https://github.com/Prismer-AI/Prismer/tree/main/docker/templates/finance-researcher/skills/quant-analysis -g -y

Use without installing

npx skills use Prismer-AI/Prismer@quant-analysis

指定 Agent (Claude Code)

npx skills add Prismer-AI/Prismer --skill quant-analysis -a claude-code -g -y

安装 repo 全部 skill

npx skills add Prismer-AI/Prismer --all -g -y

预览 repo 内 skill

npx skills add Prismer-AI/Prismer --list

SKILL.md

Frontmatter

{
    "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]"

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

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}")