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momentum-strategy

Compute momentum signals, form quintile portfolios, and backtest long-short strategies.

Updated Jun 20, 2025
Search Tags:#portfolio management#momentum#backtesting#factor investing#returns analysis#quantitative finance#trading strategy
One-click install
npx skills add https://github.com/kerryback/mgmt638 --skill momentum-strategy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: momentum-strategy
Source: https://github.com/kerryback/mgmt638/tree/main/.claude/skills/momentum-strategy
Command: npx skills add https://github.com/kerryback/mgmt638 --skill momentum-strategy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

Developing and backtesting momentum trading strategies involves intricate calculations, portfolio formation, and careful avoidance of biases. This skill streamlines the process, allowing you to quickly build, analyze, and evaluate momentum-based investment strategies, saving significant research time.

Core Features & Use Cases

  • Automated Momentum Signal Calculation: Compute various momentum signals (e.g., 12-2 month returns) from historical price data with built-in best practices.
  • Portfolio Formation & Backtesting: Form quintile or decile portfolios based on momentum ranks and simulate long-short strategies, providing clear performance metrics.
  • Performance Analysis: Calculate key metrics like Sharpe Ratio, annualized returns, and volatility for momentum strategies, enabling quick strategy evaluation.
  • Use Case: Construct a 12-2 month momentum strategy using monthly stock returns, form quintile portfolios, and calculate the cumulative returns and Sharpe Ratio of the long-short portfolio to assess its profitability and risk.

Quick Start

Calculate the 12-2 month momentum signal for the stocks in 'monthly_returns.parquet', then form quintile portfolios and report the annualized return and Sharpe Ratio of the long-short strategy.