Wave PM (Whistler Active Volatility Energy Price Mass)

References

Definition

  • The Wave PM (Whistler Active Volatility Energy Price Mass) indicator is an oscillator described in the Mark Whistler’s book “Volatility Illuminated”.
  • it was designed to help read cycles of volatility. Read when a strong trend is about to start and end, along with the potential duration of lateral chop.
  • it is not a directional oscillator
Load basic packages 
import pandas as pd
import numpy as np
import os
import gc
import copy
from pathlib import Path
from datetime import datetime, timedelta, time, date

#this package is to download equity price data from yahoo finance
#the source code of this package can be found here: https://github.com/ranaroussi/yfinance/blob/main
import yfinance as yf

pd.options.display.max_rows = 100
pd.options.display.max_columns = 100

import warnings
warnings.filterwarnings("ignore")

import pytorch_lightning as pl
random_seed=1234
pl.seed_everything(random_seed)

Global seed set to 1234





1234

#S&P 500 (^GSPC),  Dow Jones Industrial Average (^DJI), NASDAQ Composite (^IXIC)
#Russell 2000 (^RUT), Crude Oil Nov 21 (CL=F), Gold Dec 21 (GC=F)
#Treasury Yield 10 Years (^TNX)

#benchmark_tickers = ['^GSPC', '^DJI', '^IXIC', '^RUT',  'CL=F', 'GC=F', '^TNX']

benchmark_tickers = ['^GSPC']
tickers = benchmark_tickers + ['GSK', 'NVO', 'PFE', 'DAL']

#https://github.com/ranaroussi/yfinance/blob/main/yfinance/base.py
#     def history(self, period="1mo", interval="1d",
#                 start=None, end=None, prepost=False, actions=True,
#                 auto_adjust=True, back_adjust=False,
#                 proxy=None, rounding=False, tz=None, timeout=None, **kwargs):

dfs = {}

for ticker in tickers:
    cur_data = yf.Ticker(ticker)
    hist = cur_data.history(period="max", start='2000-01-01')
    print(datetime.now(), ticker, hist.shape, hist.index.min(), hist.index.max())
    dfs[ticker] = hist

2022-09-05 18:05:09.006954 ^GSPC (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:05:09.398440 GSK (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:05:09.787225 NVO (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:05:10.271711 PFE (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:05:10.547051 DAL (3863, 7) 2007-05-03 00:00:00 2022-09-02 00:00:00

ticker = 'DAL'
dfs[ticker].tail(5)
Open High Low Close Volume Dividends Stock Splits
Date
2022-08-29 32.200001 32.349998 31.850000 32.029999 8758400 0.0 0
2022-08-30 32.250000 32.450001 31.469999 31.719999 7506400 0.0 0
2022-08-31 31.969999 32.020000 31.059999 31.070000 7450000 0.0 0
2022-09-01 30.650000 31.139999 29.940001 31.090000 8572700 0.0 0
2022-09-02 31.440001 31.830000 30.700001 30.940001 8626500 0.0 0
Define Wave PM calculation function 
def cal_wavepm(ohlc: pd.DataFrame, period: int = 14, lookback_period: int = 100, column: str = "close") -> pd.Series:
    """
    The Wave PM (Whistler Active Volatility Energy Price Mass) indicator is an oscillator described in the Mark
    Whistler’s book “Volatility Illuminated”.
    :param DataFrame ohlc: data
    :param int period: period for moving average
    :param int lookback_period: period for oscillator lookback
    :return Series: WAVE PM
    """

    ma = ohlc[column].rolling(window=period).mean()
    std = ohlc[column].rolling(window=period).std(ddof=0)

    def tanh(x):
        two = np.where(x > 0, -2, 2)
        what = two * x
        ex = np.exp(what)
        j = 1 - ex
        k = ex - 1
        l = np.where(x > 0, j, k)
        output = l / (1 + ex)
        return output

    def osc(input_dev, mean, power):
        variance = pd.Series(power).rolling(window=lookback_period).sum() / lookback_period
        calc_dev = np.sqrt(variance) * mean
        y = (input_dev / calc_dev)
        oscLine = tanh(y)
        return oscLine

    dev = 3.2 * std
    power = np.power(dev / ma, 2)
    wavepm = osc(dev, ma, power)

    return pd.Series(wavepm, name=f"WAVEPM{period}")
Calculate Wave PM 
df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]

df = df.round(2)

cal_wavepm

<function __main__.cal_wavepm(ohlc: pandas.core.frame.DataFrame, period: int = 14, lookback_period: int = 100, column: str = 'close') -> pandas.core.series.Series>

df_ta = cal_wavepm(df, period=10, lookback_period=90, column='Close')
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

610

df_ta = cal_wavepm(df, period=14, lookback_period=120, column='Close')
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

21

display(df.head(5))
display(df.tail(5))
Open High Low Close Volume WAVEPM10 WAVEPM14
Date
2007-05-03 19.32 19.50 18.25 18.40 8052800 NaN NaN
2007-05-04 18.88 18.96 18.39 18.64 5437300 NaN NaN
2007-05-07 18.83 18.91 17.94 18.08 2646300 NaN NaN
2007-05-08 17.76 17.76 17.14 17.44 4166100 NaN NaN
2007-05-09 17.54 17.94 17.44 17.58 7541100 NaN NaN
Open High Low Close Volume WAVEPM10 WAVEPM14
Date
2022-08-29 32.20 32.35 31.85 32.03 8758400 0.626965 0.479660
2022-08-30 32.25 32.45 31.47 31.72 7506400 0.599117 0.539114
2022-08-31 31.97 32.02 31.06 31.07 7450000 0.633094 0.610828
2022-09-01 30.65 31.14 29.94 31.09 8572700 0.609835 0.649787
2022-09-02 31.44 31.83 30.70 30.94 8626500 0.640151 0.661071 
df[['WAVEPM10', 'WAVEPM14']].hist(bins=50, figsize=(10, 4))

array([[<AxesSubplot:title={'center':'WAVEPM10'}>,
        <AxesSubplot:title={'center':'WAVEPM14'}>]], dtype=object)

png

#https://github.com/matplotlib/mplfinance
#this package help visualize financial data
import mplfinance as mpf
import matplotlib.colors as mcolors

# all_colors = list(mcolors.CSS4_COLORS.keys())#"CSS Colors"
# all_colors = list(mcolors.TABLEAU_COLORS.keys()) # "Tableau Palette",
all_colors = ['dodgerblue', 'firebrick','limegreen','skyblue','lightgreen',  'navy','yellow','plum',  'yellowgreen']
# all_colors = list(mcolors.BASE_COLORS.keys()) #"Base Colors",


#https://github.com/matplotlib/mplfinance/issues/181#issuecomment-667252575
#list of colors: https://matplotlib.org/stable/gallery/color/named_colors.html
#https://github.com/matplotlib/mplfinance/blob/master/examples/styles.ipynb

def make_3panels2(main_data, mid_panel, chart_type='candle', names=None, 
                  figratio=(14,9), fill_weights = (0, 0)):
    """
    main chart type: default is candle. alternatives: ohlc, line

    example:
    start = 200

    names = {'main_title': 'MAMA: MESA Adaptive Moving Average', 
             'sub_tile': 'S&P 500 (^GSPC)', 'y_tiles': ['price', 'Volume [$10^{6}$]']}


    make_candle(df.iloc[-start:, :5], df.iloc[-start:][['MAMA', 'FAMA']], names = names)
    
    """

    style = mpf.make_mpf_style(base_mpf_style='yahoo',  #charles
                               base_mpl_style = 'seaborn-whitegrid',
#                                marketcolors=mpf.make_marketcolors(up="r", down="#0000CC",inherit=True),
                               gridcolor="whitesmoke", 
                               gridstyle="--", #or None, or - for solid
                               gridaxis="both", 
                               edgecolor = 'whitesmoke',
                               facecolor = 'white', #background color within the graph edge
                               figcolor = 'white', #background color outside of the graph edge
                               y_on_right = False,
                               rc =  {'legend.fontsize': 'small',#or number
                                      #'figure.figsize': (14, 9),
                                     'axes.labelsize': 'small',
                                     'axes.titlesize':'small',
                                     'xtick.labelsize':'small',#'x-small', 'small','medium','large'
                                     'ytick.labelsize':'small'
                                     }, 
                              )   

    if (chart_type is None) or (chart_type not in ['ohlc', 'line', 'candle', 'hollow_and_filled']):
        chart_type = 'candle'
    len_dict = {'candle':2, 'ohlc':3, 'line':1, 'hollow_and_filled':2}    
        
    kwargs = dict(type=chart_type, figratio=figratio, volume=True, volume_panel=2, 
                  panel_ratios=(4,2,1), tight_layout=True, style=style, returnfig=True)
    
    if names is None:
        names = {'main_title': '', 'sub_tile': ''}
    


    added_plots = { }
  
    fb_bbands2_ = dict(y1=fill_weights[0]*np.ones(mid_panel.shape[0]),
                      y2=fill_weights[1]*np.ones(mid_panel.shape[0]),color="lightskyblue",alpha=0.1,interpolate=True)
    fb_bbands2_['panel'] = 1

    fb_bbands= [fb_bbands2_]
    
    
    i = 0
    for name_, data_ in mid_panel.iteritems():
        added_plots[name_] = mpf.make_addplot(data_, panel=1, width=1, color=all_colors[i], secondary_y=False)
        i = i + 1
    

    fig, axes = mpf.plot(main_data,  **kwargs,
                         addplot=list(added_plots.values()), 
                         fill_between=fb_bbands)
    # add a new suptitle
    fig.suptitle(names['main_title'], y=1.05, fontsize=12, x=0.1285)

    axes[0].set_title(names['sub_tile'], fontsize=10, style='italic',  loc='left')
#     axes[2].set_ylabel('WAVEPM10')

#     axes[0].set_ylabel(names['y_tiles'][0])
#     axes[2].set_ylabel(names['y_tiles'][1])
    return fig, axes
   


start = -500
end = -400

names = {'main_title': f'{ticker}', 
         'sub_tile': 'Wave PM (Whistler Active Volatility Energy Price Mass): 0.9 - Danger, 0.7 - Breakout, 0.5 - Consolidation, 0.35 - Gear Change'}


aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']], 
             df.iloc[start:end][['WAVEPM10', 'WAVEPM14']], 
             chart_type='hollow_and_filled',names = names, 
                         fill_weights = (0.35, .9))

png