Advanced Features

This tutorial covers advanced openseries features including custom analysis, integration with other libraries, and extending functionality.

Factor Analysis and Regression

Multi-Factor Model Analysis

import yfinance as yf
from openseries import OpenTimeSeries, OpenFrame

# Load factor data (Fama-French factors would be ideal, using proxies here)
factor_tickers = {
     "^GSPC": "Market",
     "^RUT": "Small Cap",  # Size factor proxy
     "EFA": "International",  # International factor
     "TLT": "Bonds"  # Interest rate factor
}

# Load factor data
factor_series = []
for ticker, name in factor_tickers.items():
     # This may fail if the ticker is invalid or data unavailable
     data = yf.Ticker(ticker).history(period="3y")
     series = OpenTimeSeries.from_df(dframe=data['Close'])
     series.set_new_label(lvl_zero=name)
     factor_series.append(series)

# Create factor frame
factors = OpenFrame(constituents=factor_series)

# Load individual stock for analysis
stock_data = yf.Ticker("AAPL").history(period="3y")
apple = OpenTimeSeries.from_df(dframe=stock_data['Close'])
apple.set_new_label(lvl_zero="Apple")

# Add stock to factor frame for regression
analysis_frame = OpenFrame(constituents=factor_series + [apple])

# Perform multi-factor regression
# This may fail with various exceptions
regression_results = analysis_frame.multi_factor_linear_regression(
     dependent_variable_idx=-1  # Apple is the last series (dependent variable)
)

print("\n=== MULTI-FACTOR REGRESSION RESULTS ===")
print("Regression Summary:")
print(regression_results['summary'])

print("\nFactor Loadings (Betas):")
for i, factor_name in enumerate([s.label for s in factor_series]):
     beta = regression_results['coefficients'][i+1]  # Skip intercept
     print(f"  {factor_name}: {beta:.4f}")

print(f"\nR-squared: {regression_results['r_squared']:.4f}")
print(f"Adjusted R-squared: {regression_results['adj_r_squared']:.4f}")

Rolling Factor Analysis

# Rolling beta analysis with market
market_series = factor_series[0]  # S&P 500
stock_vs_market = OpenFrame(constituents=[apple, market_series])

# Calculate rolling beta
rolling_beta = stock_vs_market.rolling_beta(observations=252)  # 1-year rolling

print(f"\n=== ROLLING BETA ANALYSIS ===")
print(f"Current Beta: {rolling_beta.iloc[-1, 0]:.3f}")
print(f"Average Beta: {rolling_beta.mean().iloc[0]:.3f}")
print(f"Beta Range: {rolling_beta.min().iloc[0]:.3f} to {rolling_beta.max().iloc[0]:.3f}")
print(f"Beta Volatility: {rolling_beta.std().iloc[0]:.3f}")

# Rolling correlation
rolling_corr = stock_vs_market.rolling_corr(observations=252)

print(f"\n=== ROLLING CORRELATION ANALYSIS ===")
print(f"Current Correlation: {rolling_corr.iloc[-1, 0]:.3f}")
print(f"Average Correlation: {rolling_corr.mean().iloc[0]:.3f}")
print(f"Correlation Range: {rolling_corr.min().iloc[0]:.3f} to {rolling_corr.max().iloc[0]:.3f}")

Exporting Custom Plotly Figures

The export_plotly_figure function allows you to export any Plotly figure to a mobile-responsive HTML file. This is useful when you create custom visualizations using Plotly’s graph objects that aren’t directly available through openseries plotting methods.

Creating Custom Plots

You can create any Plotly figure and export it using the same responsive HTML format that openseries uses internally:

import plotly.graph_objects as go
from plotly.subplots import make_subplots
from openseries import export_plotly_figure
from pathlib import Path

# Create a custom subplot figure
fig = make_subplots(
    rows=2, cols=2,
    subplot_titles=('Price Chart', 'Volume', 'Returns Distribution', 'Drawdown'),
    specs=[[{"secondary_y": True}, {"type": "bar"}],
          [{"type": "histogram"}, {"type": "scatter"}]]
)

# Add traces (example data)
fig.add_trace(
    go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13], name="Price"),
    row=1, col=1
)
fig.add_trace(
    go.Bar(x=[1, 2, 3, 4], y=[100, 200, 150, 300], name="Volume"),
    row=1, col=2
)
fig.add_trace(
    go.Histogram(x=[0.01, -0.02, 0.015, -0.01, 0.02], name="Returns"),
    row=2, col=1
)
fig.add_trace(
    go.Scatter(x=[1, 2, 3, 4], y=[0, -0.05, -0.03, -0.08], name="Drawdown"),
    row=2, col=2
)

# Update layout
fig.update_layout(height=800, title_text="Custom Multi-Panel Dashboard")

# Export to responsive HTML
output_path = export_plotly_figure(
    figure=fig,
    fig_config={"responsive": True},
    output_type="file",
    filename="custom_dashboard.html",
    include_plotlyjs="cdn",
    plotfile=Path("output/custom_dashboard.html"),
    title="Custom Financial Dashboard",
    auto_open=True,
)

print(f"Dashboard saved to: {output_path}")

Using with Plotly Express

You can also use export_plotly_figure with Plotly Express figures:

import plotly.express as px
import pandas as pd
from openseries import export_plotly_figure
from pathlib import Path

# Create sample data
df = pd.DataFrame({
    'Date': pd.date_range('2020-01-01', periods=100),
    'Asset_A': 100 + pd.Series(range(100)).cumsum() * 0.1,
    'Asset_B': 100 + pd.Series(range(100)).cumsum() * 0.15,
})

# Create a Plotly Express figure
fig = px.line(
    df, x='Date', y=['Asset_A', 'Asset_B'],
    title='Asset Comparison',
    labels={'value': 'Price', 'variable': 'Asset'}
)

# Export with responsive HTML
export_plotly_figure(
    figure=fig,
    fig_config={"responsive": True, "displayModeBar": True},
    output_type="file",
    filename="asset_comparison.html",
    include_plotlyjs="cdn",
    plotfile=Path("output/asset_comparison.html"),
    title="Asset Price Comparison",
    auto_open=False,
)

Inline HTML Output

For embedding in web applications or reports, you can generate inline HTML divs:

import plotly.graph_objects as go
from openseries import export_plotly_figure

# Create a simple figure
fig = go.Figure(data=go.Scatter(x=[1, 2, 3, 4], y=[10, 11, 12, 13]))

# Generate inline HTML div
html_div = export_plotly_figure(
    figure=fig,
    fig_config={},
    output_type="div",
    filename="my_plot.html",
    include_plotlyjs="cdn",
    plotfile=Path("dummy.html"),  # Ignored for div output
)

# html_div can now be embedded in HTML documents
print(html_div[:100])  # Preview the HTML

Benefits of export_plotly_figure

The export_plotly_figure function provides several advantages over Plotly’s default HTML export:

  • Mobile Responsive: Automatically adapts to different screen sizes and device orientations

  • Optimized Viewport: Proper viewport settings for mobile devices

  • Auto-Resize: JavaScript handles window resizing and orientation changes

  • Consistent Styling: Uses the same responsive CSS as openseries internal plots

  • Optional Title Container: Can include a title and logo in a responsive header

This makes it ideal for creating dashboards and reports that need to work well on both desktop and mobile devices.

This tutorial demonstrates how to extend openseries with advanced functionality for sophisticated financial analysis workflows.