# Kaufman’s Adaptive Moving Average (KAMA)

get_kama(quotes, er_periods=10, fast_periods=2, slow_periods=30)

## Parameters

name | type | notes |
---|---|---|

`quotes` | Iterable[Quote] | Iterable of the Quote class or its sub-class. • See here for usage with pandas.DataFrame |

`er_periods` | int, default 10 | Number of Efficiency Ratio (volatility) periods (`E` ). Must be greater than 0. |

`fast_periods` | int, default 2 | Number of Fast EMA periods. Must be greater than 0. |

`slow_periods` | int, default 30 | Number of Slow EMA periods. Must be greater than `fast_periods` . |

### Historical quotes requirements

You must have at least `6×E`

or `E+100`

periods of `quotes`

, whichever is more, to cover the 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.

`quotes`

is an `Iterable[Quote]`

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

## Return

```
KAMAResults[KAMAResult]
```

- This method returns a time series of all available indicator values for the
`quotes`

provided. `KAMAResults`

is just a list of`KAMAResult`

.- It always returns the same number of elements as there are in the historical quotes.
- It does not return a single incremental indicator value.
- The first
`N-1`

periods will have`None`

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

name | type | notes |
---|---|---|

`date` | datetime | Date |

`efficiency_ratio` | float, Optional | Efficiency Ratio is the fractal efficiency of price changes |

`kama` | Decimal, Optional | Kaufman’s adaptive moving average |

More about Efficiency Ratio(ER): 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 `er_periods`

periods. ER would be zero if prices are unchanged over the `er_periods`

periods.

### Utilities

See Utilities and Helpers for more information.

## Example

```
from stock_indicators import indicators
# This method is NOT a part of the library.
quotes = get_historical_quotes("SPY")
# Calculate KAMA(10,2,30)
results = indicators.get_kama(quotes, 10,2,30)
```

## About Kaufman’s Adaptive Moving Average (KAMA)

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