# -*- coding: utf-8 -*-
from numpy import log as nplog
from numpy import nan as npNaN
from pandas import DataFrame, Series
from pandas_ta.overlap import hl2
from pandas_ta.utils import get_offset, high_low_range, verify_series
[docs]def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
"""Indicator: Fisher Transform (FISHT)"""
# Validate Arguments
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 1
_length = max(length, signal)
high = verify_series(high, _length)
low = verify_series(low, _length)
offset = get_offset(offset)
if high is None or low is None: return
# Calculate Result
hl2_ = hl2(high, low)
highest_hl2 = hl2_.rolling(length).max()
lowest_hl2 = hl2_.rolling(length).min()
hlr = high_low_range(highest_hl2, lowest_hl2)
hlr[hlr < 0.001] = 0.001
position = ((hl2_ - lowest_hl2) / hlr) - 0.5
v = 0
m = high.size
result = [npNaN for _ in range(0, length - 1)] + [0]
for i in range(length, m):
v = 0.66 * position.iloc[i] + 0.67 * v
if v < -0.99: v = -0.999
if v > 0.99: v = 0.999
result.append(0.5 * (nplog((1 + v) / (1 - v)) + result[i - 1]))
fisher = Series(result, index=high.index)
signalma = fisher.shift(signal)
# Offset
if offset != 0:
fisher = fisher.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
fisher.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
fisher.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}_{signal}"
fisher.name = f"FISHERT{_props}"
signalma.name = f"FISHERTs{_props}"
fisher.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {fisher.name: fisher, signalma.name: signalma}
df = DataFrame(data)
df.name = f"FISHERT{_props}"
df.category = fisher.category
return df
fisher.__doc__ = \
"""Fisher Transform (FISHT)
Attempts to identify significant price reversals by normalizing prices over a
user-specified number of periods. A reversal signal is suggested when the the
two lines cross.
Sources:
TradingView (Correlation >99%)
Calculation:
Default Inputs:
length=9, signal=1
HL2 = hl2(high, low)
HHL2 = HL2.rolling(length).max()
LHL2 = HL2.rolling(length).min()
HLR = HHL2 - LHL2
HLR[HLR < 0.001] = 0.001
position = ((HL2 - LHL2) / HLR) - 0.5
v = 0
m = high.size
FISHER = [npNaN for _ in range(0, length - 1)] + [0]
for i in range(length, m):
v = 0.66 * position[i] + 0.67 * v
if v < -0.99: v = -0.999
if v > 0.99: v = 0.999
FISHER.append(0.5 * (nplog((1 + v) / (1 - v)) + FISHER[i - 1]))
SIGNAL = FISHER.shift(signal)
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
length (int): Fisher period. Default: 9
signal (int): Fisher Signal period. Default: 1
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""