The Breakout Relative Strength Index (BRSI) by Howard Wang

█ OVERVIEW

In his article in September 2015 issue of Technical Analysis of Stocks and Commodities, “The Breakout Relative Strength Index” author Howard Wang introduced a new indicator that he called “the breakout relative strength index” or BRSI. The BRSI was intended to help traders to determine if a breakout was about to happen as well as the relative strength of the breakout.

█ CALCULATION

There are 2 major steps involved in calculting the BRSI. The first step is to construct the “breakout candlestick” using the original candlestick (i.e. high, low, open, close) data. And the second step is using the “breakout candlestick” along with volume data to calculate the BRSI.

  • Step 1: Construct “breakout candlestick”: use 2 days candlestick data (today’s and previous day’s data), the “breakout candlestick”’s high, low, open, close are the following
    • use High, Low, Open, Close to denote today’s prices
    • use Pre_High, Pre_Low, Pre_Open, Pre_Close to denote previous day’s prices
    • use break_High, break_Low, break_Open, break_Close to denote breakout prices
    • break_High: max(High, Pre_High), namely the higher of the “High” of the 2 days’ prices
    • break_Low: min(Low, Pre_Low),namely the lower of the “Low” of the 2 days data
    • break_Open and break_Close:
      • If there are two up days or two down days, then the open is the pre-day’s open, and the close is today’s close: If (Close>Open AND Pre_Close>Pre_Open) OR (Close<Open AND Pre_Close<Pre_Open) THEN break_Open=Pre_Open, break_Close=Close
      • If there is one up day and one down day, then the open is the pre-day’s close and the close is today’s close: If (Close>Open AND Pre_Close<Pre_Open) OR (Close< Open AND Pre_Close >Pre_Open) THEN break_Open=Pre_Close, break_Close=Close
  • Step 2: Calculate BRSI using breakout prices
    • High, Low, Open, Close to denote the breakout prices
    • Pre_Volume, Volume to denote previous day’s and today’s volume
    • breakout price = (High + Low + Open + Close)/4
    • breakout volume = (Pre_Volume + Volume)
    • breakout strength = (Close - Open)/(High - Low)
    • breakout power = breakout price * breakout strength * breakout volume
    • if today’s breakout power > previous day’s breakout power then it is Positive power: P = sum of positive power in 14 days
    • if today’s breakout power < previous day’s breakout power then it is Negative power: N = sum of absolute value of negative power in 14 days
    • breakout ratio = P/N
    • BRSI = 100 - (100/1 + breakout ratio)

█ EXPLANATION

BRSI ranges between 0 and 100:

  • If BRSI is above 80, it indicates the breakout is strong, the stock is overbought, and the price is likely to go down.
  • If BRSI is below 20, it indicates that breakout is weak, the stock is oversold, and the price is likely to go up.
  • If BRSI 50, it indicates hold the stock.

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)

1234

Download data 

#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)
#CBOE Volatility Index (^VIX) Chicago Options - Chicago Options Delayed Price. Currency in USD

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

benchmark_tickers = ['^GSPC', '^VIX']
tickers = benchmark_tickers + ['GSK', 'BST', 'PFE', 'AZN', 'BSX', 'NUVA', 'MDT']

#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(f"{datetime.now()}\t {ticker}\t {hist.shape}\t {hist.index.min()}\t {hist.index.max()}")
    dfs[ticker] = hist

2023-03-08 12:33:21.731271	 ^GSPC	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:22.023871	 ^VIX	 (5832, 7)	 2000-01-03 00:00:00-05:00	 2023-03-08 00:00:00-05:00
2023-03-08 12:33:22.416429	 GSK	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:22.690798	 BST	 (2102, 7)	 2014-10-29 00:00:00-04:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:23.130656	 PFE	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:23.519270	 AZN	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:23.820784	 BSX	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:24.115289	 NUVA	 (4736, 7)	 2004-05-13 00:00:00-04:00	 2023-03-07 00:00:00-05:00
2023-03-08 12:33:24.508971	 MDT	 (5831, 7)	 2000-01-03 00:00:00-05:00	 2023-03-07 00:00:00-05:00

ticker = '^GSPC'
dfs[ticker].tail(5)
Open High Low Close Volume Dividends Stock Splits
Date
2023-03-01 00:00:00-05:00 3963.340088 3971.729980 3939.050049 3951.389893 4249480000 0.0 0.0
2023-03-02 00:00:00-05:00 3938.679932 3990.840088 3928.159912 3981.350098 4244900000 0.0 0.0
2023-03-03 00:00:00-05:00 3998.020020 4048.290039 3995.169922 4045.639893 4084730000 0.0 0.0
2023-03-06 00:00:00-05:00 4055.149902 4078.489990 4044.610107 4048.419922 4000870000 0.0 0.0
2023-03-07 00:00:00-05:00 4048.260010 4050.000000 3980.310059 3986.370117 3922500000 0.0 0.0

Define Breakout Relative Strength Index (BRSI) calculation function 

import sys
sys.path.append(r"/kaggle/input/technical-indicators-core")

#from core.finta import TA
from finta import TA

df = dfs[ticker][['Open', 'High', 'Low', 'Close', 'Volume']]
df = df.round(2)

def cal_brsi(ohlcv: pd.DataFrame, 
                  period: int = 14, 
                 ) -> pd.Series:
    """
    // TASC SEP 2015: The Breakout Relative Strength Index by Howard Wang
    """
    ohlcv = ohlcv.copy(deep=True)
    ohlcv.columns = [c.lower() for c in ohlcv.columns]
    
    #step 1: get the breakout candlesticks
 
    break_high = ohlcv["high"].rolling(window=2).max()
    break_low = ohlcv["low"].rolling(window=2).min()
    two_signs = np.sign(ohlcv["close"]-ohlcv["open"]).rolling(window=2).apply(np.prod)
    break_open = ohlcv["close"].shift(1)
    break_open[two_signs>0] = (ohlcv["open"].shift(1))[two_signs>0]
    break_close = ohlcv["close"]

    break_vol = ohlcv["volume"].rolling(window=2).sum()
    df_break = pd.concat([break_open,break_high, break_low,  break_close], axis=1)
    df_break.columns = ['open', 'high', 'low', 'close']
    
    #step 2: calculate the BRSI

    break_price = df_break[['open', 'high', 'low', 'close']].mean(axis=1)
    break_strength = (df_break["close"] - df_break["open"])/(df_break["high"] - df_break["low"])
    break_power = break_price*break_strength*break_vol

    delta = break_power.diff()

    ## positive gains (up) and negative gains (down) Series
    up, down = delta.copy(), delta.copy()
    up[up < 0] = 0
    down[down > 0] = 0

    _gain = up.rolling(window=period).sum()
    _loss = down.abs().rolling(window=period).sum()

    RS = _gain / _loss 

    return pd.Series(100 - (100 / (1 + RS)), index=ohlcv.index, name=f"BRSI{period}")

Calculate Breakout Relative Strength Index (BRSI) 

#help(TA.RSI)

df['BRSI']=cal_brsi(df)
df['RSI']=TA.RSI(df, period = 14, column="close")
df['EMA50']=TA.EMA(df, period = 50, column="close")
df['EMA200']=TA.EMA(df, period = 200, column="close")

display(df.head(5))
display(df.tail(5))
Open High Low Close Volume BRSI RSI EMA50 EMA200
Date
2000-01-03 00:00:00-05:00 1469.25 1478.00 1438.36 1455.22 931800000 NaN NaN 1455.220000 1455.220000
2000-01-04 00:00:00-05:00 1455.22 1455.22 1397.43 1399.42 1009000000 NaN 0.000000 1426.762000 1427.180500
2000-01-05 00:00:00-05:00 1399.42 1413.27 1377.68 1402.11 1085500000 NaN 4.935392 1418.213826 1418.739960
2000-01-06 00:00:00-05:00 1402.11 1411.90 1392.10 1403.45 1092300000 NaN 7.387438 1414.298520 1414.859942
2000-01-07 00:00:00-05:00 1403.45 1441.47 1400.73 1441.47 1225200000 NaN 48.207241 1420.176074 1420.288924
Open High Low Close Volume BRSI RSI EMA50 EMA200
Date
2023-03-01 00:00:00-05:00 3963.34 3971.73 3939.05 3951.39 4249480000 50.335227 40.292240 4004.483349 4007.101141
2023-03-02 00:00:00-05:00 3938.68 3990.84 3928.16 3981.35 4244900000 57.286866 44.519159 4003.576159 4006.844911
2023-03-03 00:00:00-05:00 3998.02 4048.29 3995.17 4045.64 4084730000 54.623822 52.319603 4005.225721 4007.230932
2023-03-06 00:00:00-05:00 4055.15 4078.49 4044.61 4048.42 4000870000 45.664820 52.629750 4006.919615 4007.640773
2023-03-07 00:00:00-05:00 4048.26 4050.00 3980.31 3986.37 3922500000 43.431024 45.513517 4006.113747 4007.429124

Visualize Breakout Relative Strength Index (BRSI) 

#from core.visuals import *
from visuals import *

start = -100
end = df.shape[0]
df_sub = df.iloc[start:end]
# df_sub = df[(df.index<='2019-04-01') & (df.index>='2019-01-24')]
#names = {'main_title': f'{ticker}'}

names = {'main_title': f'{ticker} - Breakout Relative Strength Index (BRSI)'}

lines0,  ax_cfg0 = plot_overlay_lines(data = df_sub, overlay_columns = ['EMA50', 'EMA200'])

#lines1, ax_cfg1 = plot_macd(data = df_sub, macd= 'DI_PLUS', macd_signal = 'DI_MINUS', panel =1)

lines1, shadows1, ax_cfg1 = plot_add_lines(data = df_sub, line_columns=['RSI', 'BRSI'], 
                                           panel =1, bands = [20, 80])
#b_s_ = plot_buy_sell(data=df_sub, buy_column='DMI_BUY_Close', sell_column='DMI_SELL_Close')
lines_ = dict(**lines0, **lines1)
#lines_.update(lines2)
#lines_.update(b_s_)

shadows_ =    shadows1


fig_config_ = dict(figratio=(18,9), volume=True, volume_panel=2,panel_ratios=(4,2,2), tight_layout=True, returnfig=True,)

ax_cfg_ = ax_cfg0
ax_cfg_.update(ax_cfg1)
#ax_cfg_.update(ax_cfg2)


names = {'main_title': f'{ticker}'}

aa_, bb_ = make_panels(main_data = df_sub[['Open', 'High', 'Low', 'Close', 'Volume']], 
                       added_plots = lines_,
                       fill_betweens = shadows_, 
                       fig_config = fig_config_, 
                       axes_config = ax_cfg_,  
                       names = names)

png

Call the function from finta.py 

#help(TA.BRSI)

df_list = []
for ticker, df in dfs.items():
    df = df[['Open', 'High', 'Low', 'Close', 'Volume']].round(2)
    
    #df['BRSI']=cal_brsi(df)
    df['BRSI']=TA.BRSI(df, period = 14, )
    df['RSI']=TA.RSI(df, period = 14, column="close")
    df['EMA50']=TA.EMA(df, period = 50, column="close")
    df['EMA200']=TA.EMA(df, period = 200, column="close")
    df['ticker'] = ticker
    
    df_list.append(df)


df_all = pd.concat(df_list)
print(df_all.shape)
del df_list
gc.collect()

(47656, 10)





7073

dd = df_all.index
df_all.index = dd.date
df_all.index.name='Date'

df_all.tail(5)
Open High Low Close Volume BRSI RSI EMA50 EMA200 ticker
Date
2023-03-01 82.40 82.53 81.67 82.08 5249100 46.896985 44.022248 82.552691 86.762242 MDT
2023-03-02 81.49 82.56 81.20 82.26 4985100 56.823645 44.950352 82.541213 86.717444 MDT
2023-03-03 82.79 83.63 82.44 83.41 4771600 54.858382 50.587132 82.575283 86.684534 MDT
2023-03-06 83.49 83.83 81.45 81.93 6845500 42.926467 44.300301 82.549978 86.637225 MDT
2023-03-07 82.09 82.15 79.51 79.74 6876000 46.116515 36.977231 82.439782 86.568596 MDT 
df_all.to_csv('b_054_1_brsi_tasc201509.csv', index=True)