# -*- coding: utf-8 -*-
from .ema import ema
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series
[docs]def t3(close, length=None, a=None, talib=None, offset=None, **kwargs):
"""Indicator: T3"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
a = float(a) if a and a > 0 and a < 1 else 0.7
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 T3
t3 = T3(close, length, a)
else:
c1 = -a * a**2
c2 = 3 * a**2 + 3 * a**3
c3 = -6 * a**2 - 3 * a - 3 * a**3
c4 = a**3 + 3 * a**2 + 3 * a + 1
e1 = ema(close=close, length=length, **kwargs)
e2 = ema(close=e1, length=length, **kwargs)
e3 = ema(close=e2, length=length, **kwargs)
e4 = ema(close=e3, length=length, **kwargs)
e5 = ema(close=e4, length=length, **kwargs)
e6 = ema(close=e5, length=length, **kwargs)
t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
# Offset
if offset != 0:
t3 = t3.shift(offset)
# Handle fills
if "fillna" in kwargs:
t3.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
t3.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
t3.name = f"T3_{length}_{a}"
t3.category = "overlap"
return t3
t3.__doc__ = """Tim Tillson's T3 Moving Average (T3)
Tim Tillson's T3 Moving Average is considered a smoother and more responsive
moving average relative to other moving averages.
Sources:
http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
Calculation:
Default Inputs:
length=10, a=0.7
c1 = -a^3
c2 = 3a^2 + 3a^3 = 3a^2 * (1 + a)
c3 = -6a^2 - 3a - 3a^3
c4 = a^3 + 3a^2 + 3a + 1
ema1 = EMA(close, length)
ema2 = EMA(ema1, length)
ema3 = EMA(ema2, length)
ema4 = EMA(ema3, length)
ema5 = EMA(ema4, length)
ema6 = EMA(ema5, length)
T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
a (float): 0 < a < 1. Default: 0.7
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
presma (bool, optional): If True, uses SMA for initial value.
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""