Visualization¶

Backtrader provides multiple visualization backends for analyzing backtesting results, including Plotly for interactive charts, Bokeh for real-time updates, and Matplotlib for static publication-quality charts.

Quick Start¶

cerebro = bt.Cerebro()
# ... setup strategy and data ...
results = cerebro.run()

# Default matplotlib plot
cerebro.plot()

# Interactive Plotly chart
cerebro.plot(backend='plotly')

# Bokeh chart
cerebro.plot(backend='bokeh')

Plotly Backend (Recommended)¶

Plotly creates interactive HTML charts with zoom, pan, and hover capabilities.

Basic Usage¶

# Interactive plot
cerebro.plot(backend='plotly', style='candle')

# Customize appearance
cerebro.plot(
    backend='plotly',
    style='candle',           # 'candle', 'bar', 'line'
    barup='green',
    bardown='red',
    volup='lightgreen',
    voldown='lightcoral'
)

Save to HTML¶

from backtrader.plot import PlotlyPlot

# Create plotter
plotter = PlotlyPlot(style='candle')

# Generate figures
figs = plotter.plot(results[0])

# Save to HTML file
figs[0].write_html('backtest_chart.html')

# Save as static image (requires kaleido)
figs[0].write_image('backtest_chart.png', width=1920, height=1080)

Large Dataset Handling¶

Plotly efficiently handles large datasets with 100k+ data points:

cerebro.plot(
    backend='plotly',
    style='candle',
    numfigs=1,                # Single figure
    plotdist=0.1,             # Space between subplots
)

Bokeh Backend¶

Bokeh provides interactive charts with real-time update capabilities.

# Basic Bokeh plot
cerebro.plot(backend='bokeh')

# With output to file
from bokeh.io import output_file, save
output_file('backtest.html')
figs = cerebro.plot(backend='bokeh')

Matplotlib Backend¶

Matplotlib creates static, publication-quality charts.

Basic Plot¶

import matplotlib.pyplot as plt

# Default plot
cerebro.plot()

# Candlestick style
cerebro.plot(style='candle')

# Line chart
cerebro.plot(style='line')

# Bar chart (OHLC)
cerebro.plot(style='bar')

Customization¶

cerebro.plot(
    style='candle',
    barup='green',
    bardown='red',
    volup='lightgreen',
    voldown='lightcoral',
    fmt_x_data='%Y-%m-%d',
    fmt_x_ticks='%b %d',
    plotdist=0.5,
    numfigs=1,
    width=16,
    height=9,
    dpi=100,
    tight=True
)

Save Figure¶

import matplotlib.pyplot as plt

cerebro.plot(style='candle')
plt.savefig('backtest.png', dpi=300, bbox_inches='tight')
plt.savefig('backtest.pdf', bbox_inches='tight')

Indicator Visualization¶

Configure Indicator Display¶

class MyStrategy(bt.Strategy):
    def __init__(self):
        # Indicator in main plot
        self.sma = bt.indicators.SMA(period=20)
        self.sma.plotinfo.plotmaster = self.data

        # Indicator in subplot
        self.rsi = bt.indicators.RSI(period=14)
        self.rsi.plotinfo.subplot = True

        # Custom line styles
        self.bbands = bt.indicators.BollingerBands()
        self.bbands.plotlines.top._plotskip = False
        self.bbands.plotlines.mid.color = 'blue'
        self.bbands.plotlines.bot.linestyle = '--'

Custom Indicator Plot¶

class MyIndicator(bt.Indicator):
    lines = ('signal',)

    plotinfo = dict(
        plot=True,
        subplot=True,
        plotname='My Signal',
        plotabove=False,
        plotlinelabels=True
    )

    plotlines = dict(
        signal=dict(
            _name='Signal',
            color='blue',
            linewidth=1.5,
            linestyle='-'
        )
    )

Observer Visualization¶

Observers display trade information on charts:

cerebro = bt.Cerebro()

# Add built-in observers
cerebro.addobserver(bt.observers.BuySell)      # Buy/Sell markers
cerebro.addobserver(bt.observers.Trades)       # Trade P&L
cerebro.addobserver(bt.observers.Value)        # Portfolio value
cerebro.addobserver(bt.observers.DrawDown)     # Drawdown
cerebro.addobserver(bt.observers.Cash)         # Cash levels

# Customize observer appearance
cerebro.addobserver(
    bt.observers.BuySell,
    barplot=True,
    bardist=0.015
)

Multi-Data Visualization¶

cerebro = bt.Cerebro()
cerebro.adddata(data1, name='Stock1')
cerebro.adddata(data2, name='Stock2')

results = cerebro.run()

# Plot all data in separate figures
cerebro.plot(numfigs=2)

# Plot specific data
cerebro.plot(plotdata=[0])  # Only first data

Professional Reports¶

Generate comprehensive HTML reports:

# Add report analyzers
cerebro.add_report_analyzers(riskfree_rate=0.02)

# Run backtest
results = cerebro.run()

# Generate report
cerebro.generate_report(
    filename='report.html',
    user='Trader',
    memo='SMA Crossover Strategy Backtest',
    strategy_name='SMA Cross'
)

Report Contents¶

Summary Statistics: Total return, Sharpe ratio, max drawdown
Equity Curve: Portfolio value over time
Drawdown Chart: Drawdown percentage over time
Trade Analysis: Win rate, profit factor, average trade
Monthly Returns: Heatmap of monthly performance
Position Analysis: Trade entry/exit details

Plot Configuration Reference¶

plotinfo Options¶

Option	Default	Description
plot	True	Enable/disable plotting
subplot	False	Plot in separate subplot
plotmaster	None	Data to plot alongside
plotname	''	Custom plot name
plotabove	False	Plot above main chart
plotlinelabels	False	Show line labels

Style Options¶

Style	Description
candle	Japanese candlestick chart
bar	OHLC bar chart
line	Line chart (close prices)

Best Practices¶

Performance: Use Plotly for large datasets (>10k bars)
Publication: Use Matplotlib for papers and reports
Development: Use Bokeh for live/real-time development
Reports: Use generate_report() for comprehensive analysis