Performance Analysis¶

Deep dive into backtest performance metrics and visualizations.

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📋 Notebook Information

• RustyBT Version: 0.1.2+
• Last Validated: 2025-11-07
• API Compatibility: Verified ✅
• Documentation: API Reference

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In [1]:

from rustybt.analytics import setup_notebook

setup_notebook()

from rustybt.analytics import setup_notebook setup_notebook()

✅ Notebook environment configured successfully
   - Async/await support enabled
   - Pandas display options optimized
   - Progress bars configured

Visualizations¶

All visualizations support:

• Interactive hover tooltips
• Zoom and pan
• Light/dark themes
• Export to HTML/PNG

In [2]:

# Example: Visualize backtest results
# After running a backtest with run_algorithm(), you can visualize the results

from rustybt.analytics import (
    plot_equity_curve,
    plot_returns_distribution,
    plot_rolling_metrics,
    plot_drawdown,
)

# Assuming you have 'results' from a backtest:
# results = run_algorithm(...)

# Interactive equity curve with drawdown overlay
# fig = plot_equity_curve(results, show_drawdown=True, theme='dark')
# fig.show()

# Returns distribution with statistics
# fig = plot_returns_distribution(results)
# fig.show()

# Rolling Sharpe and volatility (60-day window)
# fig = plot_rolling_metrics(results, window=60)
# fig.show()

# Drawdown analysis
# fig = plot_drawdown(results)
# fig.show()

# Export visualizations
# fig.write_html('equity_curve.html')
# fig.write_image('equity_curve.png', width=1200, height=800)

# Example: Visualize backtest results # After running a backtest with run_algorithm(), you can visualize the results from rustybt.analytics import ( plot_equity_curve, plot_returns_distribution, plot_rolling_metrics, plot_drawdown, ) # Assuming you have 'results' from a backtest: # results = run_algorithm(...) # Interactive equity curve with drawdown overlay # fig = plot_equity_curve(results, show_drawdown=True, theme='dark') # fig.show() # Returns distribution with statistics # fig = plot_returns_distribution(results) # fig.show() # Rolling Sharpe and volatility (60-day window) # fig = plot_rolling_metrics(results, window=60) # fig.show() # Drawdown analysis # fig = plot_drawdown(results) # fig.show() # Export visualizations # fig.write_html('equity_curve.html') # fig.write_image('equity_curve.png', width=1200, height=800)