Indicator Lab · KAMA · Trend and price
Kaufman Adaptive Moving Average
Uses the Efficiency Ratio to adapt the recursive smoothing weight.
BTCUSDC · 1h · latest 30 complete days · shared snapshot · no account required
How does smoothing adapt to the directness of recent price movement?
Uses the Efficiency Ratio to adapt the recursive smoothing weight.
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.
Directional rule · long or cash
BUY when the actual close crosses the primary line from below; SELL on the reverse cross. Synthetic lines are not fill prices. Confirmed closes only; cash start, no shorts/pyramiding. Fills no earlier than the next open with fees and slippage. Thresholds are analysis conventions, not optimized recommendations.
Shared capital and cost model
Includes the buy fee; the remainder stays in cash.
One adverse markup/markdown. Do not add spread a second time.
1 = next open. No retroactive trade at the signal close.
Preparing the calculation module. The shared market snapshot is already available.
Calculation and interpretation of the same candle
More direct moves typically increase responsiveness; more back-and-forth paths lead to slower adjustment.
ER=|Cₜ−Cₜ₋ₙ|/Σ|ΔC|; SC=[ER·(2/3−2/31)+2/31]²; KAMAₜ=KAMAₜ₋₁+SC·(Cₜ−KAMAₜ₋₁)
TA-Lib adaptive average: fixed fast/slow smoothing constants 2 and 30; its own seed convention.
Adaptation uses prices already observed and does not recognize regime changes in advance.
Checked sources and reproducibility
- TA-Lib · KAMA · v0.6.4
Original C reference implementation. Custom scaling and additional lines are described separately. · checked 2026-10-11
- pandas-ta 0.4.71b0 · pandas_ta/overlap/kama.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.