Charting

import seaborn as sns
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

qb = QuantBook()
tickers = ["XLF",   # Financial Select Sector SPDR Fund
           "COF",   # Capital One Financial Corporation
           "GS",    # Goldman Sachs Group, Inc.
           "JPM",   # J P Morgan Chase & Co
           "WFC"]   # Wells Fargo & Company   
symbols = [qb.add_equity(ticker, Resolution.DAILY).symbol for ticker in tickers]
history = qb.history(symbols, datetime(2021, 1, 1), datetime(2022, 1, 1))

# Select a symbol to plot the candlestick plot.
symbol = symbols[0]

# Obtain the close price series of the symbol to plot.
data = history.loc[symbol]['close']

# Call the plot method with a title and figure size to plot the line chart.
plot = sns.lineplot(data=data,
                    x='time', 
                    y='close')
# In the same cell that you called the lineplot method, call the set method with the y-axis label and a title to update the plot settings.
plot.set(ylabel="price", title=f"{symbol} Price Over Time")

# Select 2 stocks to plot the correlation between their return series.
symbol1 = symbols[1]
symbol2 = symbols[2]
# Select the close column of the history DataFrame, call the unstack method, and then select the symbol1 and symbol2 columns.
close_prices = history['close'].unstack(0)[[symbol1, symbol2]]
# Obtain the daily return series of the 2 symbols to plot with.
daily_returns = close_prices.pct_change().dropna()

# Call the regplot method with the daily_returns DataFrame and the column names to plot the scatter plot.
plot = sns.regplot(data=daily_returns, 
                   x=daily_returns.columns[0], 
                   y=daily_returns.columns[1])
# In the same cell that you called the regplot method, call the set method with the axis labels and a title to update the layout.
plt.scatter(daily_returns1, daily_returns2)

# In the same cell, call the title, xlabel, and ylabel methods with a title and axis labels to decorate the plot.
plot.set(xlabel=f'{daily_returns.columns[0]} % Returns', 
         ylabel=f'{daily_returns.columns[1]} % Returns', 
         title=f'{symbol1} vs {symbol2} Daily % Returns')

# Obtain the daily return series for a single symbol.
symbol = symbols[0]
close_prices = history.loc[symbol]['close']
daily_returns = close_prices.pct_change().dropna()

# Call the DataFrame constructor with the daily_returns Series and then call the reset_index method.
daily_returns = pd.DataFrame(daily_returns).reset_index()

# Call the histplot method with the daily_returns, the close column name, and the number of bins to plot the histogram.
plot = sns.histplot(daily_returns, x='close', bins=20)
# In the same cell that you called the histplot method, call the set method with the axis labels and a title to update the layout.
plot.set(xlabel='Return', 
         ylabel='Frequency', 
         title=f'{symbol} Daily Return of Close Price Distribution')

# Obtain the returns of all stocks to compare their return.
close_prices = history['close'].unstack(level=0)
daily_returns = close_prices.pct_change() * 100

# Obtain the mean of the daily return.
avg_daily_returns = daily_returns.mean()

# Call the DataFrame constructor with the avg_daily_returns Series and then call the reset_index method.
avg_daily_returns = pd.DataFrame(avg_daily_returns, columns=["avg_daily_ret"]).reset_index()

# Call barplot method with the avg_daily_returns Series and the axes column names to plot the bar chart.
plot = sns.barplot(data=avg_daily_returns, x='symbol', y='avg_daily_ret')
# In the same cell that you called the barplot method, call the set method with the axis labels and a title to update layout.
plot.set(xlabel='Tickers', 
         ylabel='%', 
         title='Banking Stocks Average Daily % Returns')
# In the same cell that you called the set method, call the tick_params method to rotate the x-axis labels to rotate the labels of the x-axis for visibility.
plot.tick_params(axis='x', rotation=90)

# Obtain the returns of all stocks to compare their return.
close_prices = history['close'].unstack(level=0)
daily_returns = close_prices.pct_change()

# Call the corr method to create the correlation matrix to plot.
corr_matrix = daily_returns.corr()
                           
# Call the heatmap method with the corr_matrix and the annotation argument enabled to plot the heapmap.interpolation method to plot the heatmap.
plot = sns.heatmap(corr_matrix, annot=True)
# In the same cell that you called the heatmap method, call the set method with a title to update the layout.
plot.set(title='Bank Stocks and Bank Sector ETF Correlation Coefficients')

# Obtain the returns of all stocks to compare their return.
close_prices = history['close'].unstack(level=0)
daily_returns = close_prices.pct_change()

# Calculate the inverse of variances to plot with.
inverse_variance = 1 / daily_returns.var()

# Call the color_palette method with a palette name and then truncate the returned colors to so that you have one color for each security.
colors = sns.color_palette('pastel')[:len(inverse_variance.index)]

# Call the pie method with the security weights, labels, and colors to plot the pie chart.
plt.pie(inverse_variance, labels=inverse_variance.index, colors=colors, autopct='%1.1f%%')
# In the same cell that you called the pie method, call the title method with a title to update the layout.
plt.title(title='Banking Stocks and Bank Sector ETF Allocation')