mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
# -*- coding: utf-8 -*-
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from pandas import DataFrame
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from pandas_ta.overlap import ema, sma
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from pandas_ta.utils import get_offset, verify_series
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def ppo(close,
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fast=None,
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slow=None,
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signal=None,
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scalar=None,
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offset=None,
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**kwargs):
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"""Indicator: Percentage Price Oscillator (PPO)"""
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# Validate Arguments
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close = verify_series(close)
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fast = int(fast) if fast and fast > 0 else 12
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slow = int(slow) if slow and slow > 0 else 26
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signal = int(signal) if signal and signal > 0 else 9
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scalar = float(scalar) if scalar else 100
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if slow < fast:
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fast, slow = slow, fast
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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 fast)
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offset = get_offset(offset)
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# Calculate Result
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fastma = sma(close, length=fast)
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slowma = sma(close, length=slow)
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ppo = scalar * (fastma - slowma)
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ppo /= slowma
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signalma = ema(ppo, length=signal)
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histogram = ppo - signalma
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# Offset
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if offset != 0:
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ppo = ppo.shift(offset)
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histogram = histogram.shift(offset)
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signalma = signalma.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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ppo.fillna(kwargs["fillna"], inplace=True)
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histogram.fillna(kwargs["fillna"], inplace=True)
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signalma.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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ppo.fillna(method=kwargs["fill_method"], inplace=True)
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histogram.fillna(method=kwargs["fill_method"], inplace=True)
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signalma.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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_props = f"_{fast}_{slow}_{signal}"
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ppo.name = f"PPO{_props}"
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histogram.name = f"PPOh{_props}"
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signalma.name = f"PPOs{_props}"
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ppo.category = histogram.category = signalma.category = "momentum"
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# Prepare DataFrame to return
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data = {ppo.name: ppo, histogram.name: histogram, signalma.name: signalma}
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df = DataFrame(data)
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df.name = f"PPO{_props}"
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df.category = ppo.category
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return df
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ppo.__doc__ = """Percentage Price Oscillator (PPO)
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The Percentage Price Oscillator is similar to MACD in measuring momentum.
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Sources:
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https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence)
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Calculation:
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Default Inputs:
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fast=12, slow=26
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SMA = Simple Moving Average
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EMA = Exponential Moving Average
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fast_sma = SMA(close, fast)
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slow_sma = SMA(close, slow)
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PPO = 100 * (fast_sma - slow_sma) / slow_sma
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Signal = EMA(PPO, signal)
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Histogram = PPO - Signal
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Args:
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close(pandas.Series): Series of 'close's
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fast(int): The short period. Default: 12
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slow(int): The long period. Default: 26
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signal(int): The signal period. Default: 9
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scalar (float): How much to magnify. Default: 100
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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.DataFrame: ppo, histogram, signal columns
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"""
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