Source code for pandas_ta.overlap.trima

# -*- coding: utf-8 -*-
from .sma import sma
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def trima(close, length=None, talib=None, offset=None, **kwargs): """Indicator: Triangular Moving Average (TRIMA)""" # Validate Arguments length = int(length) if length and length > 0 else 10 close = verify_series(close, length) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import TRIMA trima = TRIMA(close, length) else: half_length = round(0.5 * (length + 1)) sma1 = sma(close, length=half_length) trima = sma(sma1, length=half_length) # Offset if offset != 0: trima = trima.shift(offset) # Handle fills if "fillna" in kwargs: trima.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: trima.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"TRIMA_{length}" trima.category = "overlap" return trima
trima.__doc__ = \ """Triangular Moving Average (TRIMA) A weighted moving average where the shape of the weights are triangular and the greatest weight is in the middle of the period. Sources: tma = sma(sma(src, ceil(length / 2)), floor(length / 2) + 1) # Tradingview trima = sma(sma(x, n), n) # Tradingview Calculation: Default Inputs: length=10 SMA = Simple Moving Average half_length = round(0.5 * (length + 1)) SMA1 = SMA(close, half_length) TRIMA = SMA(SMA1, half_length) Args: close (pd.Series): Series of 'close's length (int): It's period. Default: 10 talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib version. Default: True offset (int): How many periods to offset the result. Default: 0 Kwargs: adjust (bool): Default: True fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: New feature generated. """