Calculate Bollinger Bands in Python

"""Bollinger Bands."""

import os
import pandas as pd
import matplotlib.pyplot as plt

def symbol_to_path(symbol, base_dir="data"):
"""Return CSV file path given ticker symbol."""
return os.path.join(base_dir, "{}.csv".format(str(symbol)))

def get_data(symbols, dates):
"""Read stock data (adjusted close) for given symbols from CSV files."""
df = pd.DataFrame(index=dates)
if 'NSE' not in symbols: # add SPY for reference, if absent
symbols.insert(0, 'NSE')

for symbol in symbols:
df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])
df_temp = df_temp.rename(columns={'Adj Close': symbol})
df = df.join(df_temp)
if symbol == 'NSE': # drop dates SPY did not trade
df = df.dropna(subset=["NSE"])

return df

def plot_data(df, title="Stock prices"):
"""Plot stock prices with a custom title and meaningful axis labels."""
ax = df.plot(title=title, fontsize=12)
ax.set_xlabel("Date")
ax.set_ylabel("Price")
plt.show()

def get_rolling_mean(values, window):
"""Return rolling mean of given values, using specified window size."""
return values.rolling(window).mean()

def get_rolling_std(values, window):
"""Return rolling standard deviation of given values, using specified window size."""
return values.rolling(window).std()

def get_bollinger_bands(rm, rstd):
"""Return upper and lower Bollinger Bands."""
lower_band = rm - rstd*2
upper_band = rm + rstd*2
return upper_band, lower_band

def test_run():
# Read data
dates = pd.date_range('2014-01-01', '2014-12-31')
symbols = ['NSE']
df = get_data(symbols, dates)

# Compute Bollinger Bands
# 1. Compute rolling mean
rm_SPY = get_rolling_mean(df['NSE'], window=20)

# 2. Compute rolling standard deviation
rstd_SPY = get_rolling_std(df['NSE'], window=20)

# 3. Compute upper and lower bands
upper_band, lower_band = get_bollinger_bands(rm_SPY, rstd_SPY)

# Plot raw SPY values, rolling mean and Bollinger Bands
ax = df['NSE'].plot(title="Bollinger Bands", label='NSE')
rm_SPY.plot(label='Rolling mean', ax=ax)
upper_band.plot(label='upper band', ax=ax)
lower_band.plot(label='lower band', ax=ax)

# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='upper left')
plt.show()

if __name__ == "__main__":
test_run()<

Bollinger band

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