Charting

# Import the matplotlib library for plots and settings.
import matplotlib.pyplot as plt
import mplfinance       # for candlestick
import numpy as np
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]

# Get the OHLCV data to plot.
data = history.loc[symbol]
data.columns = ['Close', 'High', 'Low', 'Open', 'Volume']

# Call the plot method with the data, chart type, style, title, y-axis label, and figure size to plot the candlesticks.
mplfinance.plot(data,
                type='candle',
                style='charles',
                title=f'{symbol.value} OHLC',
                ylabel='Price ($)',
                figratio=(15, 10))

# 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.
data.plot(title=f"{symbol} Close Price", figsize=(15, 10))

# Select 2 stocks to plot the correlation between their return series.
symbol1 = symbols[1]
symbol2 = symbols[2]
# Obtain the daily return series of the 2 stocks for plotting.
close_price1 = history.loc[symbol1]['close']
close_price2 = history.loc[symbol2]['close']
daily_returns1 = close_price1.pct_change().dropna()
daily_returns2 = close_price2.pct_change().dropna()

# Fit a linear regression model on the correlation.
m, b = np.polyfit(daily_returns1, daily_returns2, deg=1)
# This method call returns the slope and intercept of the ordinary least squares regression line.

# Generate a prediction line upon the linear regression model.
x = np.linspace(daily_returns1.min(), daily_returns1.max())
y = m*x + b

# Call the plot method with the coordinates and color of the regression line to plot the linear regression prediction line.
plt.plot(x, y, color='red')

# In the same cell, call the plot method, call the scatter method with the 2 daily return series to plot the scatter plot dots of the daily returns.
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.
plt.title(f'{symbol1} vs {symbol2} daily returns Scatter Plot')
plt.xlabel(symbol1.value)
plt.ylabel(symbol2.value)

# 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()

# Get the mean and standard deviation of the returns.
mean = daily_returns.mean()
std = daily_returns.std()
# Calculate the normal distribution PDF given the mean and SD.
x = np.linspace(-3*std, 3*std, 1000)
pdf = 1/(std * np.sqrt(2*np.pi)) * np.exp(-(x-mean)**2 / (2*std**2))

# Call the plot method with the data for the normal distribution curve.
plt.plot(x, pdf, label="Normal Distribution")
# In the same cell that you called the plot method, call the hist method with the daily return data and the number of bins to plot the histogram.
plt.hist(daily_returns, bins=20)
# In the same cell that you called the hist method, call the title, xlabel, and ylabel methods with a title and the axis labels to decorate the plot.
plt.title(f'{symbol} Return Distribution')
plt.xlabel('Daily Return')
plt.ylabel('Count')

# 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()

# Create the figure to plot on with the size set.
plt.figure(figsize=(15, 10))

# Call the bar method with the x-axis and y-axis values to plot the mean returns as bar chart.
plt.bar(avg_daily_returns.index, avg_daily_returns)
# In the same cell that you called the bar method, call the title, xlabel, and ylabel methods with a title and the axis labels to decorate the plot.
plt.title('Banking Stocks Average Daily % Returns')
plt.xlabel('Tickers')
plt.ylabel('%')

# 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 imshow method with the correlation matrix, a color map, and an interpolation method to plot the heatmap.
plt.imshow(corr_matrix, cmap='hot', interpolation='nearest')

# In the same cell that you called the imshow method, call the title, xticks, and yticks, methods with a title and the axis tick labels to decorate the plot.
plt.title('Banking Stocks and Bank Sector ETF Correlation Heat Map')
plt.xticks(np.arange(len(tickers)), labels=tickers)
plt.yticks(np.arange(len(tickers)), labels=tickers)

# Plot the color bar to the right of the heat map.
# In the same cell that you called the imshow method, call the colorbar method.
plt.colorbar()

# 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 pie method with the inverse_variance Series, the plot labels, and a display format to plot the pie chart.
plt.pie(inverse_variance, labels=inverse_variance.index, autopct='%1.1f%%')
# In the cell that you called the pie method, call the title method with a title.
plt.title('Banking Stocks and Bank Sector ETF Allocation')

# Select the asset to plot on each dimension.
x, y, z = symbols[:3]

# Select the close price series of each symbol to plot with.
x_hist = history.loc[x].close
y_hist = history.loc[y].close
z_hist = history.loc[z].close

# Construct the basic 3D plot layout.
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(projection='3d')

# Call the ax.scatter method with the 3 price series to plot the 3D graph.
ax.scatter(x_hist, y_hist, z_hist)

# Update the x, y, and z axis labels.
ax.set_xlabel(f"{x} Price")
ax.set_ylabel(f"{y} Price")
ax.set_zlabel(f"{z} Price")

# Display the 3D chart. Note that you need to zoom the chart to avoid z-axis cut off.
ax.set_box_aspect(None, zoom=0.85)
plt.show()