Equal-Weighted U.S. Stock Strategy Ranks Against S&P 500 in Back-Test Framework

Universe

To ensure consistency with standard U.S. equity benchmarks and liquidity analysis, I recommend using all primary-listed U.S. common stocks as the universe. This aligns with typical back-testing frameworks and avoids regional biases. If a different universe (e.g., EAFE, EM) is desired, please clarify.

Execution Convention

For simplicity and alignment with intraday liquidity assumptions, adopt a close-to-close execution model (i.e., enter at day’s close, exit at next day’s close). Include as follows:
- . stocks).
- .

Capital Allocation & Weighting

Use equal weighting for the 500 stocks to avoid concentration risk and ensure fairness in performance attribution. Rebalance daily to reflect real-time strategy adjustments, though note this will increase transaction costs. If dollar-volume weighting is preferred, specify the metric (e.g., .

Benchmark / Performance Metrics

• Benchmark: Compare results to the S&P 500 to assess relative performance.
• Metrics: Track , annualized Sharpe ratio, , average daily return, and standard deviation. Add win rate (percentage of positive trades) for granular insight.

Data Permissions

Since your toolset is limited to single-ticker data, provide a and for . If you can supply a CSV or database file containing daily prices and volumes for all tickers in the universe, I can execute the back-test. If not, I’ll describe the methodology and estimate results using proxy assumptions (e.g., S&P 500 constituents as a proxy for the 500-stock universe).

Next Steps:
1. Confirm universe, weighting, and execution preferences (above).
2. Share a market-wide dataset if available, or proceed with conceptual analysis.

Note: For the news report task (separate from this back-test setup), please provide the required company/trading data and news articles as specified in your input.