mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
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from numpy import NaN as npNaN
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from pandas import Series, DataFrame
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from ..utils import get_drift, get_offset, non_zero_range, verify_series
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def kama(close,
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length=None,
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fast=None,
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slow=None,
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drift=None,
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offset=None,
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**kwargs):
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"""Indicator: Kaufman's Adaptive Moving Average (HMA)"""
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# Validate Arguments
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close = verify_series(close)
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length = int(length) if length and length > 0 else 10
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fast = int(fast) if fast and fast > 0 else 2
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slow = int(slow) if slow and slow > 0 else 30
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drift = get_drift(drift)
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offset = get_offset(offset)
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# Calculate Result
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m = close.size
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def weight(length: int) -> float:
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return 2 / (length + 1)
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fr = weight(fast)
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sr = weight(slow)
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abs_diff = non_zero_range(close, close.shift(length)).abs()
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peer_diff = non_zero_range(close, close.shift(drift)).abs()
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peer_diff_sum = peer_diff.rolling(length).sum()
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er = abs_diff / peer_diff_sum
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x = er * (fr - sr) + sr
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sc = x * x
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result = [npNaN for _ in range(0, length - 1)] + [0]
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for i in range(length, m):
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result.append(sc[i] * close[i] + (1 - sc[i]) * result[i - 1])
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kama = Series(result, index=close.index)
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# Offset
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if offset != 0:
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kama = kama.shift(offset)
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# Name & Category
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kama.name = f"KAMA_{length}_{fast}_{slow}"
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kama.category = "overlap"
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return kama
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kama.__doc__ = """Kaufman's Adaptive Moving Average (KAMA)
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Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average
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designed to account for market noise or volatility. KAMA will closely follow prices when
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the price swings are relatively small and the noise is low. KAMA will adjust when the
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price swings widen and follow prices from a greater distance. This trend-following indicator
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can be used to identify the overall trend, time turning points and filter price movements.
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Sources:
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https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average
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Calculation:
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Default Inputs:
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length=10
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Args:
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close (pd.Series): Series of 'close's
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length (int): It's period. Default: 10
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fast (int): Fast MA period. Default: 2
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slow (int): Slow MA period. Default: 30
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drift (int): The difference period. Default: 1
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: New feature generated.
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"""
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