ttrpy.momentum.ultosc module

Source code 
# Author: joelowj
# License: Apache License, Version 2.0

import pandas as pd
from ttrpy.trend.sma import sma
from ttrpy.util.trange import trange


def ultosc(
    df,
    high,
    low,
    close,
    ultosc,
    time_period_1=7,
    time_period_2=14,
    time_period_3=28,
):
    """
    The Ultimate Oscillator (ULTOSC) by Larry Williams is a momentum oscillator
    that incorporates three different time periods to improve the overbought and
    oversold signals.

    Parameters:
        df (pd.DataFrame): DataFrame which contain the asset information.
        high (string): the column name for the period highest price  of the asset.
        low (string): the column name for the period lowest price of the asset.
        close (string): the column name for the closing price of the asset.
        ultosc (string): the column name for the ultimate oscillator values.
        time_period_1 (int): The first time period for the indicator. By default, 7.
        time_period_2 (int): The second time period for the indicator. By default, 14.
        time_period_3 (int): The third time period for the indicator. By default, 28.

    Returns:
        df (pd.DataFrame): Dataframe with ultimate oscillator of the asset calculated.

    """

    df[ultosc + "previous_close"] = df[close].shift(1)
    df = trange(df, high, low, close, ultosc + "_true_range")
    df = df.dropna().reset_index(drop=True)
    df[ultosc + "_true_low"] = df[[low, ultosc + "previous_close"]].min(axis=1)
    df[ultosc + "_close-tl"] = df[close] - df[ultosc + "_true_low"]
    df = sma(df, ultosc + "_close-tl", ultosc + "_a1", time_period_1)
    df = sma(df, ultosc + "_true_range", ultosc + "_b1", time_period_1)
    df = sma(df, ultosc + "_close-tl", ultosc + "_a2", time_period_2)
    df = sma(df, ultosc + "_true_range", ultosc + "_b2", time_period_2)
    df = sma(df, ultosc + "_close-tl", ultosc + "_a3", time_period_3)
    df = sma(df, ultosc + "_true_range", ultosc + "_b3", time_period_3)
    a1_b1 = df[ultosc + "_a1"] / df[ultosc + "_b1"]
    a2_b2 = df[ultosc + "_a2"] / df[ultosc + "_b2"]
    a3_b3 = df[ultosc + "_a3"] / df[ultosc + "_b3"]
    df[ultosc] = 100 * ((4 * a1_b1) + (2 * a2_b2) + a3_b3) / 7.0
    df.drop(
        [
            ultosc + "_true_range",
            ultosc + "previous_close",
            ultosc + "_true_low",
            ultosc + "_close-tl",
            ultosc + "_a1",
            ultosc + "_b1",
            ultosc + "_a2",
            ultosc + "_b2",
            ultosc + "_a3",
            ultosc + "_b3",
        ],
        axis=1,
        inplace=True,
    )
    df = df.dropna().reset_index(drop=True)
    return df

Functions

def ultosc(df, high, low, close, ultosc, time_period_1=7, time_period_2=14, time_period_3=28)

The Ultimate Oscillator (ULTOSC) by Larry Williams is a momentum oscillator that incorporates three different time periods to improve the overbought and oversold signals.

Parameters

df : pd.DataFrame
DataFrame which contain the asset information.
high : string
the column name for the period highest price of the asset.
low : string
the column name for the period lowest price of the asset.
close : string
the column name for the closing price of the asset.
ultosc() : string
the column name for the ultimate oscillator values.
time_period_1 : int
The first time period for the indicator. By default, 7.
time_period_2 : int
The second time period for the indicator. By default, 14.
time_period_3 : int
The third time period for the indicator. By default, 28.

Returns

df : pd.DataFrame
Dataframe with ultimate oscillator of the asset calculated.
Source code 
def ultosc(
    df,
    high,
    low,
    close,
    ultosc,
    time_period_1=7,
    time_period_2=14,
    time_period_3=28,
):
    """
    The Ultimate Oscillator (ULTOSC) by Larry Williams is a momentum oscillator
    that incorporates three different time periods to improve the overbought and
    oversold signals.

    Parameters:
        df (pd.DataFrame): DataFrame which contain the asset information.
        high (string): the column name for the period highest price  of the asset.
        low (string): the column name for the period lowest price of the asset.
        close (string): the column name for the closing price of the asset.
        ultosc (string): the column name for the ultimate oscillator values.
        time_period_1 (int): The first time period for the indicator. By default, 7.
        time_period_2 (int): The second time period for the indicator. By default, 14.
        time_period_3 (int): The third time period for the indicator. By default, 28.

    Returns:
        df (pd.DataFrame): Dataframe with ultimate oscillator of the asset calculated.

    """

    df[ultosc + "previous_close"] = df[close].shift(1)
    df = trange(df, high, low, close, ultosc + "_true_range")
    df = df.dropna().reset_index(drop=True)
    df[ultosc + "_true_low"] = df[[low, ultosc + "previous_close"]].min(axis=1)
    df[ultosc + "_close-tl"] = df[close] - df[ultosc + "_true_low"]
    df = sma(df, ultosc + "_close-tl", ultosc + "_a1", time_period_1)
    df = sma(df, ultosc + "_true_range", ultosc + "_b1", time_period_1)
    df = sma(df, ultosc + "_close-tl", ultosc + "_a2", time_period_2)
    df = sma(df, ultosc + "_true_range", ultosc + "_b2", time_period_2)
    df = sma(df, ultosc + "_close-tl", ultosc + "_a3", time_period_3)
    df = sma(df, ultosc + "_true_range", ultosc + "_b3", time_period_3)
    a1_b1 = df[ultosc + "_a1"] / df[ultosc + "_b1"]
    a2_b2 = df[ultosc + "_a2"] / df[ultosc + "_b2"]
    a3_b3 = df[ultosc + "_a3"] / df[ultosc + "_b3"]
    df[ultosc] = 100 * ((4 * a1_b1) + (2 * a2_b2) + a3_b3) / 7.0
    df.drop(
        [
            ultosc + "_true_range",
            ultosc + "previous_close",
            ultosc + "_true_low",
            ultosc + "_close-tl",
            ultosc + "_a1",
            ultosc + "_b1",
            ultosc + "_a2",
            ultosc + "_b2",
            ultosc + "_a3",
            ultosc + "_b3",
        ],
        axis=1,
        inplace=True,
    )
    df = df.dropna().reset_index(drop=True)
    return df