Charting
Plotly
Preparation
Import Libraries
To research with the Plotly
library, import the libraries that you need.
import plotly.express as px import plotly.graph_objects as go
Get Historical Data
Get some historical market data to produce the plots. For example, to get data for a bank sector ETF and some banking companies over 2021, run:
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))
Create Candlestick Chart
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a candlestick chart that shows the open, high, low, and close prices of one of the banking securities. Follow these steps to create the candlestick chart:
# 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()
The Jupyter Notebook displays the candlestick chart.
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Create Line Chart
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a line chart that shows the closing price for one of the banking securities. Follow these steps to create the line chart:
# 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()
The Jupyter Notebook displays the line chart.
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Create Scatter Plot
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a scatter plot that shows the relationship between the daily returns of two banking securities. Follow these steps to create the scatter plot:
# 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()
The Jupyter Notebook displays the scatter plot.
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Create Histogram
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a histogram that shows the distribution of the daily percent returns of the bank sector ETF. Follow these steps to create the histogram:
# 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()
The Jupyter Notebook displays the histogram.
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Create Bar Chart
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a bar chart that shows the average daily percent return of the banking securities. Follow these steps to create the bar chart:
# 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()
The Jupyter Notebook displays the bar plot.
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Create Heat Map
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a heat map that shows the correlation between the daily returns of the banking securities. Follow these steps to create the heat map:
# 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()
The Jupyter Notebook displays the heat map.
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Create Pie Chart
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a pie chart that shows the weights of the banking securities in a portfolio if you allocate to them based on their inverse volatility. Follow these steps to create the pie chart:
# 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()
The Jupyter Notebook displays the pie chart.
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Create 3D Chart
You must import the plotting libraries and get some historical data to create candlestick charts.
In this example, you create a 3D chart that shows the price of an asset on each dimension. Follow these steps to create the 3D chart:
# 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()
The Jupyter Notebook displays the 3D chart.
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