BBI

References

Definition

BBI (Bull and Bear Index) is an indicator aims on measuring the general short/mid-term (< 1 month) trend and sentiment of the stock/market. It used an average of 4 SMAs (3, 6, 12, 24) as a cut-off of a bullish / bearish trend .

BBI Bollinger Bands uses BBI as “basis” and calculates variations (Stdev) of BBI during the past several days. In general, BBI Boll band is more volatile than the traditional Boll Band.

Read the indicator

  • BUY: close> BBI
  • SELL: close < BBI
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', 'AROC']

#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

2022-09-10 21:34:31.097535 ^GSPC (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:34:31.431293 GSK (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:34:31.831783 NVO (5710, 7) 1999-12-31 00:00:00 2022-09-09 00:00:00
2022-09-10 21:34:32.114387 AROC (3791, 7) 2007-08-21 00:00:00 2022-09-09 00:00:00

ticker = 'GSK'
dfs[ticker].tail(5)
Open High Low Close Volume Dividends Stock Splits
Date
2022-09-02 31.600000 31.969999 31.469999 31.850000 8152600 0.0 0.0
2022-09-06 31.650000 31.760000 31.370001 31.469999 5613900 0.0 0.0
2022-09-07 31.209999 31.590000 31.160000 31.490000 4822000 0.0 0.0
2022-09-08 30.910000 31.540001 30.830000 31.510000 6620900 0.0 0.0
2022-09-09 31.950001 31.969999 31.730000 31.889999 3556800 0.0 0.0
Define BBI calculation function 
def BBI(CLOSE,M1=3,M2=6,M3=12,M4=20):             #BBI多空指标   
    return (MA(CLOSE,M1)+MA(CLOSE,M2)+MA(CLOSE,M3)+MA(CLOSE,M4))/4    

def cal_bbi(ohlc: pd.DataFrame, 
            m1_period: int = 3,
            m2_period: int = 6,
            m3_period: int = 12,
            m4_period: int = 20,
            column: str = "close") -> pd.Series:
    """
    BBI (Bull and Bear Index) is an indicator aims on measuring the general short/mid-term (< 1 month) trend 
    and sentiment of the stock/market. 
    It used an average of 4 SMAs (3, 6, 12, 24) as a cut-off of a bullish / bearish trend .
    
    BUY: close> BBI 
    SELL: close<BBI
    """
    
    
    c = ohlc[column]
    m1 = c.rolling(m1_period).mean()
    m2 = c.rolling(m2_period).mean()
    m3 = c.rolling(m3_period).mean()
    m4 = c.rolling(m4_period).mean()

    return pd.Series((m1+m2+m3+m4)/4, name='BBI')
Calculate BBI 
df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]

df = df.round(2)

help(cal_bbi)

Help on function cal_bbi in module __main__:

cal_bbi(ohlc: pandas.core.frame.DataFrame, m1_period: int = 3, m2_period: int = 6, m3_period: int = 12, m4_period: int = 20, column: str = 'close') -> pandas.core.series.Series
    BBI (Bull and Bear Index) is an indicator aims on measuring the general short/mid-term (< 1 month) trend 
    and sentiment of the stock/market. 
    It used an average of 4 SMAs (3, 6, 12, 24) as a cut-off of a bullish / bearish trend .
    
    BUY: close> BBI 
    SELL: close<BBI

df_ta = cal_bbi(df, column="Close")
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

80

from core.finta import TA

help(TA.BBANDS)

Help on function BBANDS in module core.finta:

BBANDS(ohlc: pandas.core.frame.DataFrame, period: int = 20, MA: pandas.core.series.Series = None, column: str = 'close', std_multiplier: float = 2) -> pandas.core.frame.DataFrame
     Developed by John Bollinger, Bollinger Bands® are volatility bands placed above and below a moving average.
     Volatility is based on the standard deviation, which changes as volatility increases and decreases.
     The bands automatically widen when volatility increases and narrow when volatility decreases.
    
     This method allows input of some other form of moving average like EMA or KAMA around which BBAND will be formed.
     Pass desired moving average as <MA> argument. For example BBANDS(MA=TA.KAMA(20)).
     
     "MOBO bands are based on a zone of 0.80 standard deviation with a 10 period look-back"
    If the price breaks out of the MOBO band it can signify a trend move or price spike
    Contains 42% of price movements(noise) within bands.
    
    edit on 2022-09-09: remove MOBO function; add BBWIDTH and PERCENT_B to output

df_ta = TA.BBANDS(df, MA=df['BBI'], period = 20, column="close", std_multiplier=1.95)
df = df.merge(df_ta, left_index = True, right_index = True, how='inner' )

del df_ta
gc.collect()

21

#     BUY: close> BBI 
#     SELL: close<BBI

df['B'] = (df["BBI"]<df["Close"]).astype(int)*(df['High']+df['Low'])/2
df['S'] = (df["BBI"]>df["Close"]).astype(int)*(df['High']+df['Low'])/2

display(df.head(5))
display(df.tail(5))
Open High Low Close Volume BBI BB_UPPER BB_MIDDLE BB_LOWER BBWIDTH PERCENT_B B S
Date
1999-12-31 19.60 19.67 19.52 19.56 139400 NaN NaN NaN NaN NaN NaN 0.0 0.0
2000-01-03 19.58 19.71 19.25 19.45 556100 NaN NaN NaN NaN NaN NaN 0.0 0.0
2000-01-04 19.45 19.45 18.90 18.95 367200 NaN NaN NaN NaN NaN NaN 0.0 0.0
2000-01-05 19.21 19.58 19.08 19.58 481700 NaN NaN NaN NaN NaN NaN 0.0 0.0
2000-01-06 19.38 19.43 18.90 19.30 853800 NaN NaN NaN NaN NaN NaN 0.0 0.0
Open High Low Close Volume BBI BB_UPPER BB_MIDDLE BB_LOWER BBWIDTH PERCENT_B B S
Date
2022-09-02 31.60 31.97 31.47 31.85 8152600 33.075875 37.449735 33.075875 28.702015 0.264474 0.359863 0.0 31.720
2022-09-06 31.65 31.76 31.37 31.47 5613900 32.757250 36.580607 32.757250 28.933893 0.233436 0.331660 0.0 31.565
2022-09-07 31.21 31.59 31.16 31.49 4822000 32.518417 35.576794 32.518417 29.460039 0.188101 0.331869 0.0 31.375
2022-09-08 30.91 31.54 30.83 31.51 6620900 32.297250 34.836249 32.297250 29.758251 0.157227 0.344968 0.0 31.185
2022-09-09 31.95 31.97 31.73 31.89 3556800 32.226333 34.680677 32.226333 29.771989 0.152319 0.431482 0.0 31.850 
df[['BBI']].hist(bins=50)

array([[<AxesSubplot:title={'center':'BBI'}>]], 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 = list(mcolors.BASE_COLORS.keys()) #"Base Colors",
all_colors = ['dodgerblue', 'firebrick','limegreen','skyblue','lightgreen',  'navy','yellow','plum',  'yellowgreen']
#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, add_data, mid_panel=None, chart_type='candle', names=None, figratio=(14,9)):

    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=1, 
                  panel_ratios=(4,2), tight_layout=True, style=style, returnfig=True)
    
    if names is None:
        names = {'main_title': '', 'sub_tile': ''}
    
    added_plots = { 
        #'S':  mpf.make_addplot(add_data['S'], panel=0, color='blue', type='scatter', marker=r'${S}$' , markersize=100, secondary_y=False),   
        #'B':  mpf.make_addplot(add_data['B'], panel=0, color='blue', type='scatter', marker=r'${B}$' , markersize=100, secondary_y=False), 
        
        'BB_UPPER': mpf.make_addplot(add_data['BB_UPPER'], panel=0, color='dodgerblue', secondary_y=False), 
        'BB_LOWER': mpf.make_addplot(add_data['BB_LOWER'], panel=0, color='tomato', secondary_y=False), 
        'BBI': mpf.make_addplot(add_data['BBI'], panel=0, color='gray', secondary_y=False), 
    }

        
      
    fb_bbands_ = dict(y1=add_data.iloc[:, 0].values,
                      y2=add_data.iloc[:, 2].values,color="lightskyblue",alpha=0.1,interpolate=True)
    fb_bbands_['panel'] = 0
    

    fb_bbands= [fb_bbands_]
    
    
    if mid_panel is not None:
        i = 0
        for name_, data_ in mid_panel.iteritems():
            added_plots[name_] = mpf.make_addplot(data_, panel=1, color=all_colors[i])
            i = i + 1
        fb_bbands2_ = dict(y1=np.zeros(mid_panel.shape[0]),
                      y2=0.8+np.zeros(mid_panel.shape[0]),color="lightskyblue",alpha=0.1,interpolate=True)
        fb_bbands2_['panel'] = 1
        fb_bbands.append(fb_bbands2_)


    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].legend([None]*7)
    handles = axes[0].get_legend().legendHandles
    axes[0].legend(handles=handles[2:],labels=['BB_UPPER','BB_LOWER', 'BBI'])
    axes[0].set_title(names['sub_tile'], fontsize=10, style='italic',  loc='left')
    

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


start = -100
end = df.shape[0]

names = {'main_title': f'{ticker}', 
         'sub_tile': 'BBI:  BUY: close> BBI, SELL: close<BBI'}


aa_, bb_ = make_3panels2(df.iloc[start:end][['Open', 'High', 'Low', 'Close', 'Volume']], 
            df.iloc[start:end][['BB_UPPER', 'BBI','BB_LOWER', 'B', 'S']],
             chart_type='hollow_and_filled',names = names)

png