How To Calculate Bollinger Bands in Python?
How To Calculate Bollinger Bands in Python?
2 answers
by
shirley.reilly ,
3 months ago
@norberto_waelchi
To calculate Bollinger Bands in Python, you can use the following steps:
- Import the necessary libraries:
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import pandas as pd import numpy as np |
- Define a function to calculate the Bollinger Bands:
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def calculate_bollinger_bands(data, window_size, num_std):
# Calculate rolling mean and standard deviation
rolling_mean = data.rolling(window=window_size).mean()
rolling_std = data.rolling(window=window_size).std()
upper_band = rolling_mean + (rolling_std * num_std)
lower_band = rolling_mean - (rolling_std * num_std)
return upper_band, lower_band
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- Replace data with the time series data you want to calculate the Bollinger Bands for, window_size with the number of periods to use for the rolling mean and standard deviation calculations, and num_std with the number of standard deviations to use for the bands.
- Call the calculate_bollinger_bands function with your data:
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data = pd.Series([1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1]) window_size = 5 num_std = 2 upper_band, lower_band = calculate_bollinger_bands(data, window_size, num_std) |
- Plot the Bollinger Bands:
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import matplotlib.pyplot as plt plt.plot(data, label='Data') plt.plot(upper_band, label='Upper Band') plt.plot(lower_band, label='Lower Band') plt.legend() plt.show() |
This will plot the Bollinger Bands along with the data. You can customize the plot further with different colors, labels, etc.
by
ayana_reilly ,
14 days ago
@norberto_waelchi
Here is an example to calculate and visualize Bollinger Bands in Python:
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def calculate_bollinger_bands(data, window_size, num_std):
rolling_mean = data.rolling(window=window_size).mean()
rolling_std = data.rolling(window=window_size).std()
upper_band = rolling_mean + (rolling_std * num_std)
lower_band = rolling_mean - (rolling_std * num_std)
return upper_band, lower_band
# Sample data
data = pd.Series([23, 25, 27, 29, 31, 30, 29, 28, 27, 25, 24, 23])
window_size = 4
num_std = 2
upper_band, lower_band = calculate_bollinger_bands(data, window_size, num_std)
# Plotting
plt.figure(figsize=(12, 6))
plt.plot(data, label='Data')
plt.plot(upper_band, label='Upper Band', color='g')
plt.plot(lower_band, label='Lower Band', color='r')
plt.fill_between(data.index, lower_band, upper_band, color='gray', alpha=0.5)
plt.legend()
plt.title('Bollinger Bands')
plt.show()
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In this example, we have a sample data series, and we calculate the Bollinger Bands using the calculate_bollinger_bands function. The bands are then plotted along with the data using matplotlib. You can adjust the data, window_size, num_std, and customize the plot further as needed.
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