What is the Bollinger Bands?
A Bollinger Band® is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average (SMA) of a security’s price, but which can be adjusted to user preferences.https://www.investopedia.com/terms/b/bollingerbands.asp
The Bollinger Bands are used to discover if a stock is oversold or overbought. It is called a mean reversion indicator, which measures how far a price swing will stretch before a counter impulse triggers a retracement.
It is a lagging indicator, which is looking at historical background of the current price. Opposed to a leading indicator, which tries to where the price is heading.
Step 1: Get some time series data on a stock
In this tutorial we will use the Apple stock as example, which has ticker AAPL. You can change to any other stock of your interest by changing the ticker below. To find the ticker of your favorite company/stock you can use Yahoo! Finance ticker lookup.
To get some time series of stock data we will use the Pandas-datareader library to collect it from Yahoo! Finance.
import pandas_datareader as pdr import datetime as dt ticker = pdr.get_data_yahoo("AAPL", dt.datetime(2020, 1, 1), dt.datetime.now())[['Close', 'High', 'Low']] print(ticker)
We will use the Close, High and Low columns to do the further calculations.
Close High Low Date 2020-01-02 300.350006 300.600006 295.190002 2020-01-03 297.429993 300.579987 296.500000 2020-01-06 299.799988 299.959991 292.750000 2020-01-07 298.390015 300.899994 297.480011 2020-01-08 303.190002 304.440002 297.160004 ... ... ... ... 2020-08-06 455.609985 457.649994 439.190002 2020-08-07 444.450012 454.700012 441.170013 2020-08-10 450.910004 455.100006 440.000000 2020-08-11 437.500000 449.929993 436.429993 2020-08-12 452.040009 453.100006 441.190002
Step 2: How are the Bollinger Bands calculated
Luckily, we can refer to Investopedia.org to get the answer, which states that the Bollinger Bands are calculated as follows.
Where BOLU is the Upper Bollinger Band and BOLD is Lower Bollinger Band. The MA is the Moving Average. The TP and σ are calculated as follows.
TP (typical price)=(High+Low+Close)÷3
σ[TP,n] = Standard Deviation over last n periods of TP
Where n is the number of days in smoothing period (typically 20), and m is the number of standard deviations (typically 2).
Step 3: Calculate the Bollinger Bands
This is straight forward. We start by calculating the typical price TP and then the standard deviation over the last 20 days (the typical value). Then we calculate the simple moving average of rolling over the last 20 days (the typical value). Then we have the values to calculate the upper and lower values of the Bolling Bands (BOLU and BOLD).
ticker['TP'] = (ticker['Close'] + ticker['Low'] + ticker['High'])/3 ticker['std'] = ticker['TP'].rolling(20).std(ddof=0) ticker['MA-TP'] = ticker['TP'].rolling(20).mean() ticker['BOLU'] = ticker['MA-TP'] + 2*ticker['std'] ticker['BOLD'] = ticker['MA-TP'] - 2*ticker['std'] print(ticker)
Resulting in the following output.
Date Close High ... BOLU BOLD Date ... 2020-01-02 300.350006 300.600006 ... NaN NaN 2020-01-03 297.429993 300.579987 ... NaN NaN 2020-01-06 299.799988 299.959991 ... NaN NaN 2020-01-07 298.390015 300.899994 ... NaN NaN 2020-01-08 303.190002 304.440002 ... NaN NaN ... ... ... ... ... ... 2020-08-06 455.609985 457.649994 ... 445.784036 346.919631 2020-08-07 444.450012 454.700012 ... 453.154374 346.012626 2020-08-10 450.910004 455.100006 ... 459.958160 345.317173 2020-08-11 437.500000 449.929993 ... 464.516981 346.461685 2020-08-12 452.040009 453.100006 ... 469.891271 346.836730
Note, that if you compare you results with Yahoo! Finance for Apple, there will be some small difference. The reason is, that they by default use TP to be closing price and not the average of the Close, Low and High. If you change TP to equal Close only, you will get the same figures as they do.
Step 4: Plotting it on a graph
Plotting the three lines is straight forward by using plot() on the DataFrame. Making an filled area with color between BOLU and BOLD can be achieved by using fill_between().
This results in the full program to be.
import pandas_datareader as pdr import datetime as dt import matplotlib.pyplot as plt ticker = pdr.get_data_yahoo("AAPL", dt.datetime(2020, 1, 1), dt.datetime.now())[['Close', 'High', 'Low']] # Boillinger band calculations ticker['TP'] = (ticker['Close'] + ticker['Low'] + ticker['High'])/3 ticker['std'] = ticker['TP'].rolling(20).std(ddof=0) ticker['MA-TP'] = ticker['TP'].rolling(20).mean() ticker['BOLU'] = ticker['MA-TP'] + 2*ticker['std'] ticker['BOLD'] = ticker['MA-TP'] - 2*ticker['std'] ticker = ticker.dropna() print(ticker) # Plotting it all together ax = ticker[['Close', 'BOLU', 'BOLD']].plot(color=['blue', 'orange', 'yellow']) ax.fill_between(ticker.index, ticker['BOLD'], ticker['BOLU'], facecolor='orange', alpha=0.1) plt.show()
Giving the following graph.
Step 5: How to use the Bollinger Band Indicator?
If the stock price are continuously touching the upper Bollinger Band (BOLU) the market is thought to be overbought. While if the price continuously touches the lower Bollinger Band (BOLD) the market is thought to be oversold.
The more volatile the market is, the wider the upper and lower band will be. Hence, it also indicates how volatile the market is at a given period.
The volatility measured by the Bollinger Band is referred to as a squeeze when the upper and lower band are close. This is considered to be a sign that there will be more volatility in the coming future, which opens up for possible trading opportunities.
A common misconception of the bands are that when the price outbreaks the the bounds of the upper and lower band, it is a trading signal. This is not the case.
As with all trading indicators, it should not be used alone to make trading decisions.
12% Investment Solution
Would you like to get 12% in return of your investments?
D. A. Carter promises and shows how his simple investment strategy will deliver that in the book The 12% Solution. The book shows how to test this statement by using backtesting.
Did Carter find a strategy that will consistently beat the market?
Actually, it is not that hard to use Python to validate his calculations. But we can do better than that. If you want to work smarter than traditional investors then continue to read here.
Python for Finance: Unlock Financial Freedom and Build Your Dream Life
Discover the key to financial freedom and secure your dream life with Python for Finance!
Say goodbye to financial anxiety and embrace a future filled with confidence and success. If you’re tired of struggling to pay bills and longing for a life of leisure, it’s time to take action.
Imagine breaking free from that dead-end job and opening doors to endless opportunities. With Python for Finance, you can acquire the invaluable skill of financial analysis that will revolutionize your life.
Make informed investment decisions, unlock the secrets of business financial performance, and maximize your money like never before. Gain the knowledge sought after by companies worldwide and become an indispensable asset in today’s competitive market.
Don’t let your dreams slip away. Master Python for Finance and pave your way to a profitable and fulfilling career. Start building the future you deserve today!
Python for Finance a 21 hours course that teaches investing with Python.
Learn pandas, NumPy, Matplotlib for Financial Analysis & learn how to Automate Value Investing.
“Excellent course for anyone trying to learn coding and investing.” – Lorenzo B.