import numpy as np
import pandas as pd
import pandas_datareader.data as web
# Get stock data
all_data = {ticker: web.DataReader(ticker,'stooq')
for ticker in ['AAPL', 'NVDA', 'MSFT', 'TSLA', 'AMZN', 'NFLX', 'QCOM', 'SBUX']}
# Extract the 'Adjusted Closing Price'
price = pd.DataFrame({ticker: data['Close']
for ticker, data in all_data.items() })
import matplotlib.pyplot as plt
colors = ['gold', 'red', 'blue', 'royalblue', 'black', 'purple', 'pink', 'green']
fig6 = plt.figure(figsize=(6, 6))
axes6 = fig6.add_subplot(1, 1, 1)
# Create the bar chart with custom colors
priceSTD = price.std()
priceSTD.plot(ax=axes6, kind="bar", rot=45, color=colors)
axes6.set_ylabel("STD", fontsize=12)
axes6.set_xlabel("Stocks", fontsize=12)
axes6.set_title("STD of all 8 stocks", fontsize=20)
plt.show()
#fig6.savefig('stocks4.png')
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We create and use a list called ‘colors’ which dictates the colors of each of the bars in the graph. Without the list all 8 graphs would be one color, but with it we get that APPL is gold, NVDA is red, and so on and so forth.
priceCORR = price.corr()
fig, ax = plt.subplots(figsize=(10, 6))
priceCORR.plot(kind='bar', stacked=True, ax=ax, color=colors)
ax.set_ylabel("Correlation", fontsize=12)
ax.set_xlabel("Stocks", fontsize=12)
ax.set_title("Correlation between Stocks", fontsize=16)
plt.legend(title='Stocks', loc='upper right')
plt.xticks(rotation=0) # Rotate x-axis labels if needed
plt.show()
#fig.savefig('stocks5.png')
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Since the list ‘color’ is already defined in the previous cell we don’t have to redefine it in order to reuse it.