Value Chart

References

• Value Charts — Another Way to Trade the Markets.
• finta

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 18:44:16.687789 ^GSPC (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:44:17.075204 GSK (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:44:17.528449 NVO (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:44:17.934598 PFE (5706, 7) 1999-12-31 00:00:00 2022-09-02 00:00:00
2022-09-05 18:44:18.322396 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 Value Chart calculation function

def cal_vc(ohlc: pd.DataFrame, period: int = 5) -> pd.DataFrame:
    """
    Value chart
    Implementation based on a book by Mark Helweg & David Stendahl: Dynamic Trading Indicators: Winning with Value Charts and Price Action Profile

    :period: Specifies the number of Periods used for VC calculation
    
    """

    ohlc = ohlc.copy(deep=True)
    ohlc.columns = [c.lower() for c in ohlc.columns]
    
    float_axis = ((ohlc['high'] + ohlc['low']) / 2).rolling(window=period).mean()
    vol_unit = (ohlc['high'] - ohlc['low']).rolling(window=period).mean() * 0.2

    value_chart_high = pd.Series((ohlc['high'] - float_axis) / vol_unit, name="Value Chart High")
    value_chart_low = pd.Series((ohlc['low'] - float_axis) / vol_unit, name="Value Chart Low")
    value_chart_close = pd.Series((ohlc['close'] - float_axis) / vol_unit, name="Value Chart Close")
    value_chart_open = pd.Series((ohlc['open'] - float_axis) / vol_unit, name="Value Chart Open")

    return pd.concat([value_chart_high, value_chart_low, value_chart_close, value_chart_open], axis=1)

Calculate Value Chart

df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]

df = df.round(2)

cal_vc

<function __main__.cal_vc(ohlc: pandas.core.frame.DataFrame, period: int = 5) -> pandas.core.frame.DataFrame>

df_ta = cal_vc(df, period=14)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

84

display(df.head(5))
display(df.tail(5))

	Open	High	Low	Close	Volume	Value Chart High	Value Chart Low	Value Chart Close	Value Chart Open
Date
2007-05-03	19.32	19.50	18.25	18.40	8052800	NaN	NaN	NaN	NaN
2007-05-04	18.88	18.96	18.39	18.64	5437300	NaN	NaN	NaN	NaN
2007-05-07	18.83	18.91	17.94	18.08	2646300	NaN	NaN	NaN	NaN
2007-05-08	17.76	17.76	17.14	17.44	4166100	NaN	NaN	NaN	NaN
2007-05-09	17.54	17.94	17.44	17.58	7541100	NaN	NaN	NaN	NaN

	Open	High	Low	Close	Volume	Value Chart High	Value Chart Low	Value Chart Close	Value Chart Open
Date
2022-08-29	32.20	32.35	31.85	32.03	8758400	-8.612076	-11.652911	-10.558210	-9.524327
2022-08-30	32.25	32.45	31.47	31.72	7506400	-6.824895	-12.613924	-11.137131	-8.006329
2022-08-31	31.97	32.02	31.06	31.07	7450000	-8.178180	-13.839511	-13.780539	-8.473041
2022-09-01	30.65	31.14	29.94	31.09	8572700	-11.129482	-17.822709	-11.408367	-13.862550
2022-09-02	31.44	31.83	30.70	30.94	8626500	-5.741325	-11.979495	-10.654574	-7.894322

df[['Value Chart High','Value Chart Low', 'Value Chart Close', 'Value Chart Open']].hist(bins=50, figsize=(10, 4))

array([[<AxesSubplot:title={'center':'Value Chart High'}>,
        <AxesSubplot:title={'center':'Value Chart Low'}>],
       [<AxesSubplot:title={'center':'Value Chart Close'}>,
        <AxesSubplot:title={'center':'Value Chart Open'}>]], dtype=object)

#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 = -50
end = df.shape[0]

names = {'main_title': f'{ticker}' ,
         'sub_tile': 'Value Charts'}


aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']], 
             df.iloc[start:end][['Value Chart High',
       'Value Chart Low', 'Value Chart Close', 'Value Chart Open']], 
             chart_type='hollow_and_filled',names = names, 
                         fill_weights = (-2, 2))