Indicator Lab · ENTROPY · Statistics
Price Contribution Entropy
Processes price contributions normalized by rolling price sums using a logarithmic entropy form.
BTCUSDC · 1h · latest 30 complete days · shared snapshot · no account required
How does the library convention distribute normalized price contributions within the window?
Processes price contributions normalized by rolling price sums using a logarithmic entropy form.
One dataset, the same parameters and the same candle for application and explanation.
BTCUSDC · confirmed hourly candles
Snapshot retrieved: 11 Oct 2026, 15:52 UTC · stored snapshot · background refresh scheduled · age when opened: 3,138.8 min
11 Sept 2026, 15:00 — 11 Oct 2026, 15:00 UTC · 720 evaluation candles · 3000 earlier candles for warm-up only. Your opened snapshot stays fixed. A visit can refresh a stale cache in the background for later visitors; fetching fresh is not a tick stream.
Parameters and precise application
Number of confirmed hourly candles. Warm-up history stays fixed; only this parameter changes.
Explicit formula parameter of the calculation convention described below.
Measurement and diagnosis · no trading direction
Inspect the actual value at each confirmed candle. This analysis produces no buy or sell orders.
Preparing the calculation module. The shared market snapshot is already available.
Calculation and interpretation of the same candle
The value describes this specific price normalization; logarithm base and window are part of the definition.
Σₙ[−pₜ·log_base(pₜ)], pₜ=Cₜ/ΣₙC
pandas-ta sequential rolling ratios, not a return-distribution entropy estimate. Needs 2n−1 observations.
This is not entropy of an empirical return-frequency distribution and is not a direct measure of future market uncertainty.
Checked sources and reproducibility
- pandas-ta 0.4.71b0 · pandas_ta/statistics/entropy.py
Verified original file from the versioned library; the implemented convention and deliberate differences accompany the formula. · checked 2026-10-11
- The Probability of Backtest Overfitting
Why selection across many historical trials can produce misleading results. This tool does not estimate PBO or claim out-of-sample profitability. · checked 2026-10-11
Data source, versions and hash
- Market data source
- https://data-api.binance.vision/api/v3/klines?symbol=BTCUSDC&interval=1h&startTime=1778338800000&endTime=1791730799999
- Specification
- indicator-library-1.0.0
- Execution model
- signal-simulation-1.0.0
- Data hash
- 42ac75e03e017bcac023d319dabb0928e0562fda0e51f4bfa5549ac50e3e8be4
- Disclosed candles including warm-up
- 3720
The sources support definitions and methodological limits. They do not establish profit for this specific BTCUSDC rule. This analysis is descriptive; parameter trials on the same history are not independent out-of-sample evidence. Parity with a deployed unCoded engine is not claimed.