Charting

import plotly.express as px
import plotly.graph_objects as go

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 of the symbol to plot.
data = history.loc[symbol]

# Call the Candlestick constructor with the time and open, high, low, and close price series to create the candlestick plot.
candlestick = go.Candlestick(x=data.index,
                             open=data['open'],
                             high=data['high'],
                             low=data['low'],
                             close=data['close'])
                            
# Call the Layout constructor with a title and axes labels to set the plot layout.
layout = go.Layout(title=go.layout.Title(text=f'{symbol.value} OHLC'),
                   xaxis_title='Date',
                   yaxis_title='Price',
                   xaxis_rangeslider_visible=False)
                
# Call the Figure constructor with the candlestick and layout.
fig = go.Figure(data=[candlestick], layout=layout)

# Display the plot.
fig.show()

# Obtain the close price series for a single symbol.
symbol = symbols[0]
data = history.loc[symbol]['close']

# Call the DataFrame constructor with the data Series and then call the reset_index method to make the time column accessible to plotly.
data = pd.DataFrame(data).reset_index()

# Call the line method with data, the column names of the x- and y-axis in data, and the plot title to plot the close price series line chart.
fig = px.line(data, x='time', y='close', title=f'{symbol} Close price')
        
# Display the plot.
fig.show()

# 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()

# Call the scatter method with the 2 return Series, the trendline option, and axes labels to plot the correlation into a scatter plot.
fig = px.scatter(x=daily_return1, y=daily_return2, trendline='ols', 
                 labels={'x': symbol1.value, 'y': symbol2.value}) 

# Call the update_layout method with a title to update the plot title.
fig.update_layout(title=f'{symbol1.value} vs {symbol2.value} Daily % Returns')

# Display the plot.
fig.show()

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

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

# Call the histogram method with the daily_returns DataFrame, the x-axis label, a title, and the number of bins to plot the histogram.
fig = px.histogram(daily_returns, x='close', 
                   title=f'{symbol} Daily Return of Close Price Distribution', 
                   nbins=20)

# Display the plot.
fig.show()

# 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 the bar method with the avg_daily_returns and the axes column names to plot the bar chart.
fig = px.bar(avg_daily_returns, x='symbol', y='avg_daily_ret')
# Call the update_layout method with a title to decorate the plot.
fig.update_layout(title='Banking Stocks Average Daily % Returns')

# Display the plot.
fig.show()

# 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 corr_matrix and the axes labels to plot the heatmap.
fig = px.imshow(corr_matrix, x=tickers, y=tickers)

# ICall the update_layout method with a title to decorate the plot.
fig.update_layout(title='Banking Stocks and bank sector ETF Correlation Heat Map')

# Display the plot.
fig.show()

# 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 DataFrame constructor with the inverse_variance Series and then call the reset_index method.
inverse_variance = pd.DataFrame(inverse_variance, columns=["inverse variance"]).reset_index()

# Call the pie method with the inverse_variance DataFrame, the column name of the values, and the column name of the names to plot the pie chart.
fig = px.pie(inverse_variance, values='inverse variance', names='symbol')
# Call the update_layout method with a title to decorate the plot.
fig.update_layout(title='Asset Allocation of bank stocks and bank sector ETF')

# Display the plot.
fig.show()

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

# Call the Scatter3d constructor with the data for the x, y, and z axes to create the 3D scatter plot.
scatter = go.Scatter3d(
    x=history.loc[x].close, 
    y=history.loc[y].close, 
    z=history.loc[z].close,
    mode='markers',
    marker=dict(
        size=2,
        opacity=0.8
    )
)

# Call the Layout constructor with the axes titles and chart dimensions to create the layout of the plot.
layout = go.Layout(
    scene=dict(
        xaxis_title=f'{x.value} Price',
        yaxis_title=f'{y.value} Price',
        zaxis_title=f'{z.value} Price'
    ),
    width=700,
    height=700
)

# Call the Figure constructor with the scatter and layout variables to plot them.
fig = go.Figure(scatter, layout)

# Display the 3D chart.
fig.show()