mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
80 lines
2.3 KiB
Python
80 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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from .ema import ema
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from ..utils import get_offset, verify_series
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def t3(close, length=None, a=None, offset=None, **kwargs):
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"""Indicator: T3"""
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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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a = float(a) if a and a > 0 and a < 1 else 0.7
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offset = get_offset(offset)
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# Calculate Result
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c1 = -a * a**2
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c2 = 3 * a**2 + 3 * a**3
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c3 = -6 * a**2 - 3 * a - 3 * a**3
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c4 = a**3 + 3 * a**2 + 3 * a + 1
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e1 = ema(close=close, length=length, **kwargs)
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e2 = ema(close=e1, length=length, **kwargs)
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e3 = ema(close=e2, length=length, **kwargs)
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e4 = ema(close=e3, length=length, **kwargs)
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e5 = ema(close=e4, length=length, **kwargs)
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e6 = ema(close=e5, length=length, **kwargs)
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t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
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# Offset
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if offset != 0:
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t3 = t3.shift(offset)
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# Name & Category
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t3.name = f"T3_{length}_{a}"
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t3.category = "overlap"
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return t3
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t3.__doc__ = """Tim Tillson's T3 Moving Average (T3)
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Tim Tillson's T3 Moving Average is considered a smoother and more responsive
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moving average relative to other moving averages.
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Sources:
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http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
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Calculation:
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Default Inputs:
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length=10, a=0.7
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c1 = -a^3
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c2 = 3a^2 + 3a^3 = 3a^2 * (1 + a)
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c3 = -6a^2 - 3a - 3a^3
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c4 = a^3 + 3a^2 + 3a + 1
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ema1 = EMA(close, length)
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ema2 = EMA(ema1, length)
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ema3 = EMA(ema2, length)
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ema4 = EMA(ema3, length)
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ema5 = EMA(ema4, length)
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ema6 = EMA(ema5, length)
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T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3
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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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a (float): 0 < a < 1. Default: 0.7
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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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presma (bool, optional): If True, uses SMA for initial value.
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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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