S&P 500 & Normalized CAPE Z-Score Analyzer

How it works

The CAPE (Cyclically Adjusted Price-to-Earnings) ratio indicator calculates the long-term average and standard deviation of the CAPE ratio from historical market data. It computes a Z-score by subtracting the mean CAPE from the current CAPE and dividing by the standard deviation, which provides a standardized measure of market valuation.

The script retrieves CAPE data from Quandl and S&P 500 monthly close prices, creating visual representations that include color-coded zones based on standard deviation thresholds. Users can switch between display modes: S&P 500 chart with valuation zones, normalized CAPE centered around zero, and CAPE Z-score showing statistical positioning.

Why it may be useful

This indicator provides critical macro-level insights into market valuation, helping traders and investors understand whether the market is statistically overvalued, undervalued, or in a neutral zone. By presenting complex valuation metrics in an intuitive visual format, it allows market participants to make more informed long-term investment decisions.

The tool's unique approach of combining historical context with statistical analysis enables users to identify potential market risks and opportunities. Its multi-mode display and dynamic coloring make complex financial data accessible, supporting strategic portfolio allocation and risk management strategies.

When to explore it

The CAPE ratio indicator is most effective for macro traders, portfolio managers, and long-term investors seeking to understand broader market valuation trends. It is particularly useful during periods of significant market transitions or when evaluating long-term investment strategies across different economic cycles.

Traders should utilize this indicator when making strategic allocation decisions, timing market entries and exits, or assessing systemic market risk. It is especially valuable during periods of economic uncertainty, market corrections, or when comparing current market conditions to historical valuation patterns.

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