Report Generation Example¶

This notebook demonstrates how to generate professional backtest reports using RustyBT's ReportGenerator class.

Features¶

• Generate PDF and HTML reports
• Customize report sections
• Add custom charts
• Professional formatting

---

📋 Notebook Information

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

---

Setup¶

Import required libraries:

In [1]:

from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import HTML, display

from rustybt.analytics import setup_notebook
from rustybt.analytics.reports import ReportConfig, ReportGenerator

# Setup notebook environment
setup_notebook()

from pathlib import Path import matplotlib.pyplot as plt import numpy as np import pandas as pd from IPython.display import HTML, display from rustybt.analytics import setup_notebook from rustybt.analytics.reports import ReportConfig, ReportGenerator # Setup notebook environment setup_notebook()

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

Step 1: Create Sample Backtest Data¶

For this example, we'll create synthetic backtest results:

In [2]:

# Generate sample data
dates = pd.date_range(start="2024-01-01", end="2024-12-31", freq="D")
np.random.seed(42)

# Simulate returns
returns = np.random.normal(0.0008, 0.015, len(dates))

# Calculate portfolio values
starting_capital = 100000
portfolio_values = starting_capital * np.exp(np.cumsum(returns))

# Create DataFrame
backtest_data = pd.DataFrame(
    {
        "portfolio_value": portfolio_values,
        "returns": returns,
    },
    index=dates,
)

# Display summary

# Preview data
backtest_data.head()

# Generate sample data dates = pd.date_range(start="2024-01-01", end="2024-12-31", freq="D") np.random.seed(42) # Simulate returns returns = np.random.normal(0.0008, 0.015, len(dates)) # Calculate portfolio values starting_capital = 100000 portfolio_values = starting_capital * np.exp(np.cumsum(returns)) # Create DataFrame backtest_data = pd.DataFrame( { "portfolio_value": portfolio_values, "returns": returns, }, index=dates, ) # Display summary # Preview data backtest_data.head()

Out[2]:

	portfolio_value	returns
2024-01-01	100828.484323	0.008251
2024-01-02	100700.114198	-0.001274
2024-01-03	101764.595814	0.010515
2024-01-04	104199.539475	0.023645
2024-01-05	103917.301929	-0.002712

Step 2: Generate Basic Report¶

Create a report with default settings:

In [3]:

# Create report generator
generator = ReportGenerator(backtest_data)

# Generate HTML report
html_output = Path("basic_report.html")
generator.generate_report(html_output, format="html")

# Create report generator generator = ReportGenerator(backtest_data) # Generate HTML report html_output = Path("basic_report.html") generator.generate_report(html_output, format="html")

Display Report Inline¶

View the generated HTML report directly in the notebook:

In [4]:

# Display HTML report inline
with open(html_output) as f:
    html_content = f.read()

display(HTML(html_content))

# Display HTML report inline with open(html_output) as f: html_content = f.read() display(HTML(html_content))

Backtest Report

Generated on 2025-10-17 23:33:38

Performance Metrics

Metric	Value
Total Return	35.58%
Annual Return	23.31%
Sharpe Ratio	1.04
Sortino Ratio	1.58
Max Drawdown	-19.53%
Calmar Ratio	1.19
Volatility (Annual)	22.55%
Stability	0.775
Tail Ratio	1.15
Trading Days	366

Equity Curve

Drawdown Analysis

Returns Distribution

Position Distribution

Trade Statistics

Statistic	Value
Total Trades	366
Winning Trades	196
Losing Trades	170
Win Rate	53.55%
Average Win	1.138%
Average Loss	-1.112%
Profit Factor	1.18
Largest Win	5.859%
Largest Loss	-4.782%

Generated by RustyBT Report Generator

2025-10-17 23:33:38

Step 3: Generate PDF Report¶

Create a print-ready PDF version:

In [5]:

# Generate PDF report
pdf_output = Path("basic_report.pdf")
generator.generate_report(pdf_output, format="pdf")

# Generate PDF report pdf_output = Path("basic_report.pdf") generator.generate_report(pdf_output, format="pdf")

Step 4: Customized Report¶

Create a report with custom configuration:

In [6]:

# Define custom configuration
custom_config = ReportConfig(
    title="My Trading Strategy",
    subtitle="Momentum-Based Approach",
    include_equity_curve=True,
    include_drawdown=True,
    include_returns_distribution=True,
    include_metrics_table=True,
    include_trade_statistics=True,
    include_position_distribution=False,  # Disable this section
    dpi=200,  # Higher resolution
    figsize=(12, 7),  # Larger charts
)

# Create generator with custom config
custom_generator = ReportGenerator(backtest_data, custom_config)

# Generate customized report
custom_output = Path("custom_report.html")
custom_generator.generate_report(custom_output, format="html")

# Define custom configuration custom_config = ReportConfig( title="My Trading Strategy", subtitle="Momentum-Based Approach", include_equity_curve=True, include_drawdown=True, include_returns_distribution=True, include_metrics_table=True, include_trade_statistics=True, include_position_distribution=False, # Disable this section dpi=200, # Higher resolution figsize=(12, 7), # Larger charts ) # Create generator with custom config custom_generator = ReportGenerator(backtest_data, custom_config) # Generate customized report custom_output = Path("custom_report.html") custom_generator.generate_report(custom_output, format="html")

Step 5: Add Custom Charts¶

Extend reports with custom analytics:

In [7]:

def rolling_volatility_chart(data) -> str:
    """Custom chart: Rolling 30-day volatility."""
    import base64
    from io import BytesIO

    fig, ax = plt.subplots(figsize=(10, 6))

    returns = data["returns"]
    rolling_vol = returns.rolling(window=30).std() * np.sqrt(252) * 100

    ax.plot(data.index, rolling_vol, linewidth=2, color="#ff9800")
    ax.set_title("Rolling 30-Day Volatility", fontsize=14, fontweight="bold")
    ax.set_xlabel("Date")
    ax.set_ylabel("Annualized Volatility (%)")
    ax.grid(True, alpha=0.3)
    plt.xticks(rotation=45)
    plt.tight_layout()

    # Save as base64
    buf = BytesIO()
    fig.savefig(buf, format="png", dpi=150, bbox_inches="tight")
    buf.seek(0)
    img_base64 = base64.b64encode(buf.read()).decode("utf-8")
    plt.close(fig)

    return f"data:image/png;base64,{img_base64}"


# Create config with custom chart
advanced_config = ReportConfig(
    title="Advanced Strategy Report",
    subtitle="With Custom Analytics",
    custom_charts=[rolling_volatility_chart],
)

# Generate advanced report
advanced_generator = ReportGenerator(backtest_data, advanced_config)
advanced_output = Path("advanced_report.html")
advanced_generator.generate_report(advanced_output, format="html")

def rolling_volatility_chart(data) -> str: """Custom chart: Rolling 30-day volatility.""" import base64 from io import BytesIO fig, ax = plt.subplots(figsize=(10, 6)) returns = data["returns"] rolling_vol = returns.rolling(window=30).std() * np.sqrt(252) * 100 ax.plot(data.index, rolling_vol, linewidth=2, color="#ff9800") ax.set_title("Rolling 30-Day Volatility", fontsize=14, fontweight="bold") ax.set_xlabel("Date") ax.set_ylabel("Annualized Volatility (%)") ax.grid(True, alpha=0.3) plt.xticks(rotation=45) plt.tight_layout() # Save as base64 buf = BytesIO() fig.savefig(buf, format="png", dpi=150, bbox_inches="tight") buf.seek(0) img_base64 = base64.b64encode(buf.read()).decode("utf-8") plt.close(fig) return f"data:image/png;base64,{img_base64}" # Create config with custom chart advanced_config = ReportConfig( title="Advanced Strategy Report", subtitle="With Custom Analytics", custom_charts=[rolling_volatility_chart], ) # Generate advanced report advanced_generator = ReportGenerator(backtest_data, advanced_config) advanced_output = Path("advanced_report.html") advanced_generator.generate_report(advanced_output, format="html")

Step 6: Minimal Report¶

Create a focused executive summary:

In [8]:

# Minimal configuration
minimal_config = ReportConfig(
    title="Executive Summary",
    subtitle="Key Performance Metrics",
    include_equity_curve=True,
    include_drawdown=False,
    include_returns_distribution=False,
    include_metrics_table=True,
    include_trade_statistics=False,
    include_position_distribution=False,
)

minimal_generator = ReportGenerator(backtest_data, minimal_config)
minimal_output = Path("minimal_report.html")
minimal_generator.generate_report(minimal_output, format="html")

# Minimal configuration minimal_config = ReportConfig( title="Executive Summary", subtitle="Key Performance Metrics", include_equity_curve=True, include_drawdown=False, include_returns_distribution=False, include_metrics_table=True, include_trade_statistics=False, include_position_distribution=False, ) minimal_generator = ReportGenerator(backtest_data, minimal_config) minimal_output = Path("minimal_report.html") minimal_generator.generate_report(minimal_output, format="html")

Summary¶

You've learned how to:

1. ✅ Generate basic HTML and PDF reports
2. ✅ Customize report configuration
3. ✅ Add custom charts and analytics
4. ✅ Create focused executive summaries

Generated Files:¶

In [9]:

generated_files = [
    "basic_report.html",
    "basic_report.pdf",
    "custom_report.html",
    "advanced_report.html",
    "minimal_report.html",
]

print("\nGenerated Report Files:")
print("-" * 60)
for file in generated_files:
    if Path(file).exists():
        size = Path(file).stat().st_size
        print(f"✅ {file:<30} {size:>10,} bytes")
    else:
        print(f"❌ {file:<30} Not found")

generated_files = [ "basic_report.html", "basic_report.pdf", "custom_report.html", "advanced_report.html", "minimal_report.html", ] print("\nGenerated Report Files:") print("-" * 60) for file in generated_files: if Path(file).exists(): size = Path(file).stat().st_size print(f"✅ {file:<30} {size:>10,} bytes") else: print(f"❌ {file:<30} Not found")

Generated Report Files:
------------------------------------------------------------
✅ basic_report.html                 439,258 bytes
✅ basic_report.pdf                   64,848 bytes
✅ custom_report.html                634,530 bytes
✅ advanced_report.html              558,094 bytes
✅ minimal_report.html               127,822 bytes

Next Steps¶

• 🔧 Try different ReportConfig options
• 📊 Create custom chart functions for your specific metrics
• 📄 Generate reports for your actual backtest results
• 🎨 Customize the HTML template in rustybt/analytics/templates/report.html