Passive Monitoring System Design Theory

This is the notebook that is used in the section titled (WAIT FOR IT!)...Passive Monitoring System Design Theory.

import pandas as pd
import ta
import matplotlib.pyplot as plt
import numpy as np

ibm_prices = pd.read_csv('https://query.data.world/s/ovn37kwnxqy4txkj4f7s662wxz5ppu')
ibm_prices_subset = ibm_prices[0:300]
del ibm_prices

pd.set_option('display.float_format', lambda x: '%.2f' % x)
ibm_prices_subset.close.describe()

plt.plot(ibm_prices_subset.close)

indicator_bb = ta.volatility.BollingerBands(close=ibm_prices_subset.close, window=20, window_dev=3)
ibm_prices_subset['bb_bbm'] = indicator_bb.bollinger_mavg()
ibm_prices_subset['bb_bbh'] = indicator_bb.bollinger_hband()
ibm_prices_subset['bb_bbl'] = indicator_bb.bollinger_lband()

ma = ibm_prices_subset['bb_bbm']
upper_band = ibm_prices_subset['bb_bbh']
lower_band = ibm_prices_subset['bb_bbl']
price = ibm_prices_subset.close

plt.plot(price)
plt.plot(upper_band)
plt.plot(lower_band)
plt.legend()
plt.ylabel('value')
plt.xlabel('observation')

pd.set_option('display.float_format', lambda x: '%.5f' % x)
ibm_prices_subset[['close','close_first_difference']].describe()

plt.plot(ibm_prices_subset.close_first_difference)
plt.ylabel('value')
plt.xlabel('observation')

indicator_bb2 = ta.volatility.BollingerBands(close=ibm_prices_subset.close_first_difference, window=20, window_dev=3)
ibm_prices_subset['bb_bbm2'] = indicator_bb2.bollinger_mavg()
ibm_prices_subset['bb_bbh2'] = indicator_bb2.bollinger_hband()
ibm_prices_subset['bb_bbl2'] = indicator_bb2.bollinger_lband()

ma2 = ibm_prices_subset['bb_bbm2']
upper_band2 = ibm_prices_subset['bb_bbh2']
lower_band2 = ibm_prices_subset['bb_bbl2']
price2 = ibm_prices_subset.close_first_difference

plt.plot(price2)
plt.plot(upper_band2)
plt.plot(lower_band2)
plt.legend()
plt.ylabel('value')
plt.xlabel('observation')