Concept
Entropy Measures
Entropy Measures, also known as Shannon, approximate, sample, permutation, are Statistics concepts. The Library holds 3 implementations, each one a working definition you can pull into Quant.
Top Entropy Measures indicators
3 total
What are Entropy Measures?
Entropy measures quantify how disordered or unpredictable a series is. Shannon entropy works on a distribution: bin the data and sum probability times negative log-probability, which peaks when every outcome is equally likely and falls to zero when one outcome is certain. Time-series variants ask a sharper question: approximate entropy and sample entropy track how often matching patterns of m consecutive values keep matching at length m+1, scored so that more persistence means a lower reading (sample entropy corrects approximate entropy's self-matching bias), while permutation entropy applies the Shannon formula to the distribution of ordinal patterns, the rank orderings of consecutive values.
The trading translation: low entropy means repeating structure that trend or cycle logic can grip; high entropy means noise-like behavior where most signals degrade. Entropy is therefore a regime input rather than a signal generator, similar in role to the Hurst exponent and fractal dimension but built from information theory rather than scaling behavior. Every variant is sensitive to its settings (bin counts, embedding length, tolerance), so readings only compare under fixed parameters.
How traders use it
- As a tradability filter: high-entropy stretches argue for standing aside or reducing size, while falling entropy suggests structure is emerging and pattern or trend signals have better footing.
- As an input to adaptive parameterization: smoothing lengths and signal thresholds scale with measured entropy so tools slow down in noise and speed up in order.
- For cross-sectional comparison: ranking instruments or timeframes by entropy to direct attention toward the most structured ones.
Related concepts · Complexity & memory
Concept family
Statistics
45 concepts mapped · 37 in the Library
Entropy Measures FAQ
What does high entropy mean on a price chart?
That recent behavior is close to random: patterns of consecutive moves are not repeating, and outcomes are spread evenly rather than concentrated. Practically it warns that trend-following and pattern signals are operating in their worst environment. It does not predict direction; it describes how predictable the series has been over the measurement window, and it changes as that window rolls.
Which entropy measure works best for market data?
There is no consensus best. Sample entropy is generally preferred over approximate entropy on short, noisy series because it removes self-matching bias. Permutation entropy is robust to outliers and cheap to compute but discards magnitude information. Shannon entropy on binned returns is simplest but depends heavily on bin choices. Comparing several measures under fixed settings is safer than trusting one.
Build Entropy Measures your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.