mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
|
|
from numpy import log10 as npLog10
|
|
from pandas import DataFrame
|
|
from pandas_ta.volatility import atr
|
|
from pandas_ta.utils import get_offset, get_drift, verify_series
|
|
|
|
|
|
def chop(
|
|
high,
|
|
low,
|
|
close,
|
|
length=None,
|
|
atr_length=None,
|
|
scalar=None,
|
|
drift=None,
|
|
offset=None,
|
|
**kwargs,
|
|
):
|
|
"""Indicator: Choppiness Index (CHOP)"""
|
|
# Validate Arguments
|
|
high = verify_series(high)
|
|
low = verify_series(low)
|
|
close = verify_series(close)
|
|
length = int(length) if length and length > 0 else 14
|
|
atr_length = int(
|
|
atr_length) if atr_length is not None and atr_length > 0 else 1
|
|
scalar = float(scalar) if scalar else 100
|
|
drift = get_drift(drift)
|
|
offset = get_offset(offset)
|
|
|
|
# Calculate Result
|
|
diff = high.rolling(length).max() - low.rolling(length).min()
|
|
|
|
atr_ = atr(high=high, low=low, close=close, length=atr_length)
|
|
atr_sum = atr_.rolling(length).sum()
|
|
|
|
chop = scalar * (npLog10(atr_sum) - npLog10(diff))
|
|
chop /= npLog10(length)
|
|
|
|
# Offset
|
|
if offset != 0:
|
|
chop = chop.shift(offset)
|
|
|
|
# Handle fills
|
|
if "fillna" in kwargs:
|
|
chop.fillna(kwargs["fillna"], inplace=True)
|
|
if "fill_method" in kwargs:
|
|
chop.fillna(method=kwargs["fill_method"], inplace=True)
|
|
|
|
# Name and Categorize it
|
|
chop.name = f"CHOP_{length}_{atr_length}_{scalar}"
|
|
chop.category = "trend"
|
|
|
|
return chop
|
|
|
|
|
|
chop.__doc__ = """Choppiness Index (CHOP)
|
|
|
|
The Choppiness Index was created by Australian commodity trader
|
|
E.W. Dreiss and is designed to determine if the market is choppy
|
|
(trading sideways) or not choppy (trading within a trend in either
|
|
direction). Values closer to 100 implies the underlying is choppier
|
|
whereas values closer to 0 implies the underlying is trending.
|
|
|
|
Sources:
|
|
https://www.tradingview.com/scripts/choppinessindex/
|
|
https://www.motivewave.com/studies/choppiness_index.htm
|
|
|
|
Calculation:
|
|
Default Inputs:
|
|
length=14, scalar=100, drift=1
|
|
HH = high.rolling(length).max()
|
|
LL = low.rolling(length).min()
|
|
|
|
ATR_SUM = SUM(ATR(drift), length)
|
|
CHOP = scalar * (LOG10(ATR_SUM) - LOG10(HH - LL))
|
|
CHOP /= LOG10(length)
|
|
|
|
Args:
|
|
high (pd.Series): Series of 'high's
|
|
low (pd.Series): Series of 'low's
|
|
close (pd.Series): Series of 'close's
|
|
length (int): It's period. Default: 14
|
|
atr_length (int): Length for ATR. Default: 1
|
|
scalar (float): How much to magnify. Default: 100
|
|
drift (int): The difference 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.
|
|
"""
|