Kaufman's Adaptive Moving Average (KAMA)

Created by Perry Kaufman, KAMA is an volatility adaptive moving average of price over configurable lookback periods. [Discuss] 💬

// C# usage syntax
IReadOnlyList<KamaResult> results =
  bars.ToKama(erPeriods, fastPeriods, slowPeriods);

Parameters

param	type	description
erPeriods	int	Number of Efficiency Ratio (volatility) periods (E). Must be greater than 0. Default is 10.
fastPeriods	int	Number of Fast EMA periods. Must be greater than 0. Default is 2.
slowPeriods	int	Number of Slow EMA periods. Must be greater than fastPeriods. Default is 30.

Historical price bars requirements

You must have at least 6×E or E+100 periods of bars, whichever is more, to cover the warmup and convergence periods. Since this uses a smoothing technique, we recommend you use at least 10×E data points prior to the intended usage date for better precision.

bars is a collection of generic TBar historical price bars. It should have a consistent frequency (day, hour, minute, etc). See the Guide for more information.

Response

IReadOnlyList<KamaResult>

• This method returns a time series of all available indicator values for the bars provided.
• It always returns the same number of elements as there are in the historical price bars.
• It does not return a single incremental indicator value.
• The first E-1 periods will have null values since there's not enough data to calculate.

🚩 ⚞ Convergence warning

The first 10×E periods will have decreasing magnitude, convergence-related precision errors that can be as high as ~5% deviation in indicator values for earlier periods.

KamaResult

property	type	description
Timestamp	DateTime	Date from evaluated TBar
Er	double	Efficiency Ratio is the fractal efficiency of price changes
Kama	double	Kaufman's adaptive moving average

More about Efficiency Ratio: ER fluctuates between 0 and 1, but these extremes are the exception, not the norm. ER would be 1 if prices moved up or down consistently over the erPeriods window. ER would be zero if prices are unchanged over the erPeriods window.

Utilities

• .Condense()
• .Find(lookupDate)
• .RemoveWarmupPeriods()
• .RemoveWarmupPeriods(removePeriods)

See Utilities and helpers for more information.

Chaining

This indicator may be generated from any chain-enabled indicator or method.

// example
var results = bars
    .Use(CandlePart.HL2)
    .ToKama(..);

Results can be further processed on Kama with additional chain-enabled indicators.

// example
var results = bars
    .ToKama(..)
    .ToRsi(..);

See Chaining indicators for more.

Streaming

Use the buffer-style List<T> when you need incremental calculations without a hub:

KamaList kamaList = new(erPeriods, fastPeriods, slowPeriods);

foreach (IBar bar in bars)  // simulating stream
{
  kamaList.Add(bar);
}

// based on `ICollection<KamaResult>`
IReadOnlyList<KamaResult> results = kamaList;

Subscribe to a BarHub for advanced streaming scenarios:

BarHub barHub = new();
KamaHub observer = barHub.ToKamaHub(erPeriods, fastPeriods, slowPeriods);

foreach (IBar bar in bars)  // simulating stream
{
  barHub.Add(bar);
}

IReadOnlyList<KamaResult> results = observer.Results;

See Buffer lists and Stream hubs for full usage guides.