mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from ..overlap.rma import rma
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from ..utils import get_offset, non_zero_range, verify_series
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def kdj(high=None,
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low=None,
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close=None,
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length=None,
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signal=None,
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offset=None,
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**kwargs):
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"""Indicator: KDJ (KDJ)"""
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# Validate Arguments
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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length = int(length) if length and length > 0 else 9
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signal = int(signal) if signal and signal > 0 else 3
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offset = get_offset(offset)
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# Calculate Result
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highest_high = high.rolling(length).max()
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lowest_low = low.rolling(length).min()
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fastk = 100 * (close - lowest_low) / non_zero_range(highest_high,
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lowest_low)
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k = rma(fastk, length=signal)
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d = rma(k, length=signal)
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j = 3 * k - 2 * d
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# Offset
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if offset != 0:
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k = k.shift(offset)
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d = d.shift(offset)
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j = j.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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k.fillna(kwargs["fillna"], inplace=True)
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d.fillna(kwargs["fillna"], inplace=True)
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j.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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k.fillna(method=kwargs["fill_method"], inplace=True)
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d.fillna(method=kwargs["fill_method"], inplace=True)
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j.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_params = f"_{length}_{signal}"
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k.name = f"K{_params}"
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d.name = f"D{_params}"
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j.name = f"J{_params}"
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k.category = d.category = j.category = "momentum"
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# Prepare DataFrame to return
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kdjdf = DataFrame({k.name: k, d.name: d, j.name: j})
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kdjdf.name = f"KDJ{_params}"
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kdjdf.category = "momentum"
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return kdjdf
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kdj.__doc__ = """KDJ (KDJ)
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The KDJ indicator is actually a derived form of the Slow
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Stochastic with the only difference being an extra line
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called the J line. The J line represents the divergence
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of the %D value from the %K. The value of J can go
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beyond [0, 100] for %K and %D lines on the chart.
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Sources:
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https://www.prorealcode.com/prorealtime-indicators/kdj/
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https://docs.anychart.com/Stock_Charts/Technical_Indicators/Mathematical_Description#kdj
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Calculation:
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Default Inputs:
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length=9, signal=3
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LL = low for last 9 periods
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HH = high for last 9 periods
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FAST_K = 100 * (close - LL) / (HH - LL)
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K = RMA(FAST_K, signal)
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D = RMA(K, signal)
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J = 3K - 2D
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Args:
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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length (int): Default: 9
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signal (int): Default: 3
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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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