mirror of
https://github.com/wassname/pandas-ta.git
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James Sawyer
61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
# -*- coding: utf-8 -*-
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from ..utils import get_offset, verify_series
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def trima(close, length=None, offset=None, **kwargs):
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"""Indicator: Triangular Moving Average (TRIMA)"""
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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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min_periods = (int(kwargs["min_periods"]) if "min_periods" in kwargs and
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kwargs["min_periods"] is not None else length)
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offset = get_offset(offset)
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# Calculate Result
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half_length = round(0.5 * (length + 1))
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sma1 = close.rolling(half_length, min_periods=half_length).mean()
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trima = sma1.rolling(half_length, min_periods=half_length).mean()
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# Offset
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if offset != 0:
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trima = trima.shift(offset)
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# Name & Category
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trima.name = f"TRIMA_{length}"
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trima.category = "overlap"
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return trima
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trima.__doc__ = """Triangular Moving Average (TRIMA)
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A weighted moving average where the shape of the weights are triangular and the
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greatest weight is in the middle of the period.
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Sources:
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https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/
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tma = sma(sma(src, ceil(length / 2)), floor(length / 2) + 1) # Tradingview
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trima = sma(sma(x, n), n) # Tradingview
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Calculation:
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Default Inputs:
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length=10
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SMA = Simple Moving Average
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half_length = math.round(0.5 * (length + 1))
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SMA1 = SMA(close, half_length)
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TRIMA = SMA(SMA1, half_length)
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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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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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adjust (bool): Default: True
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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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