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investment-management

Backtest investment strategies and construct portfolios with Python 3.10+.

3|2|Updated Mar 17, 2026
Search Tags:#investment#finance#portfolio#trading#backtest#quantitative
One-click install
npx skills add https://github.com/broomva/skills --skill investment-management
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: investment-management
Source: https://github.com/broomva/skills/tree/main/skills/finance/investment-management
Command: npx skills add https://github.com/broomva/skills --skill investment-management

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yfinance, pycoingecko, fredapi, pyportfolioopt, riskfolio-lib, cvxpy, pandas-ta, arch, alpaca-trade-api, coinbase-advanced-py, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill solves the fragmentation of financial management by unifying research, portfolio construction, risk analysis, and trade execution into a single, philosophy-driven framework.

Core Features & Use Cases

  • Multi-Philosophy Scoring: Evaluate securities using frameworks from Buffett, Dalio, Bogle, and others.
  • Strategy Backtesting: Simulate investment strategies against historical data to validate performance before committing capital.
  • Use Case: Use this skill to screen for high-quality stocks based on Munger's ROIC criteria, then backtest a risk-parity allocation strategy against the selected universe to optimize for volatility.

Quick Start

Use the investment-management skill to backtest a risk-parity strategy on AAPL, MSFT, and JPM over the last 5 years.

Frequently Asked Questions about investment-management

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I backtest a risk-parity strategy using Python across multiple asset classes?▼

You can evaluate securities using multi-philosophy scoring frameworks inspired by Buffett, Dalio, Bogle, and Munger. This allows you to screen high-quality stocks based on criteria like ROIC and apply factor modeling to your selected universe.

What is the best way to execute trades programmatically across traditional and alternative assets?▼

Yes, you can fetch market data and process quantitative analysis using pandas-ta and arch for technical indicators and volatility modeling. These libraries support advanced quantitative processing required for comprehensive investment research.

Does this portfolio analysis framework require specific Python dependencies for quantitative modeling?▼

Yes, portfolio analysis and quantitative modeling require Python 3.10+ with optional libraries like yfinance, PyPortfolioOpt, and CVXPY. These dependencies provide the necessary market data fetching and convex optimization capabilities.

Can I use FRED API data for macroeconomic factor modeling in a backtest?▼

You can integrate FRED API to pull macroeconomic data for factor modeling within your strategy backtesting. This allows you to simulate investment performance against historical economic indicators and validate risk allocations.

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