Using Shannon Entropy to Explore Market Randomness

Summary

The document introduces Shannon entropy as a way to examine how random price movements appear over a chosen lookback period. It describes applying the measure to price data, selecting a period and pattern length, and plotting entropy values for several pattern lengths. The article’s code examples use SPY data and show how to compare hypothetical next-price paths by their resulting entropy.

Further examples propose choosing a direction associated with higher entropy and extending that idea to two-step paths. The examples report win and loss totals in an idealized backtest, but the document provides no numerical results or detailed discussion of their reliability. It also does not include the promised explanation or formal definition of entropy, so the rationale and interpretation of these signals remain unclear. The examples omit trading costs and other practical constraints, and their results should not be treated as evidence of a tradable edge.

Key ideas

• Shannon entropy is presented as a measure for examining apparent randomness in price data.
• The example varies pattern length and plots entropy over a selected lookback period.
• Hypothetical future price paths can be compared by calculating their resulting entropy.
• The sample trading logic is idealized and does not account for costs or realistic execution.
• The document supplies no reported performance figures or full explanation of the entropy measure.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.