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2D PDF visualization components.
Create single and comparison plots for probability density functions.
"""
import numpy as np
import plotly.graph_objects as go
from typing import Dict, List, Optional, Tuple
from src.visualization.themes import (
create_base_layout,
get_line_style,
format_hover_template,
DARK_THEME
)
def plot_pdf_2d(
strikes: np.ndarray,
pdf: np.ndarray,
spot_price: float,
title: str = "Option-Implied Probability Density",
show_spot: bool = True,
show_ci: bool = True,
ci_levels: Tuple[float, float] = (0.16, 0.84)
) -> go.Figure:
"""
Create 2D plot of probability density function.
Args:
strikes: Strike prices
pdf: PDF values
spot_price: Current spot price
title: Plot title
show_spot: Whether to show vertical line at spot price
show_ci: Whether to show confidence interval shading
ci_levels: Confidence interval levels (default: 68% CI)
Returns:
Plotly figure
"""
fig = go.Figure()
# Main PDF line
fig.add_trace(go.Scatter(
x=strikes,
y=pdf,
mode='lines',
name='PDF',
line=dict(
color=DARK_THEME['primary'],
width=3
),
fill='tozeroy',
fillcolor=f"rgba(0, 217, 255, 0.2)", # Semi-transparent cyan
hovertemplate=format_hover_template("Strike", "Probability Density")
))
# Add spot price indicator
if show_spot:
fig.add_vline(
x=spot_price,
line_dash="dash",
line_color=DARK_THEME['success'],
line_width=2,
annotation_text=f"Spot: ${spot_price:.2f}",
annotation_position="top"
)
# Add confidence interval shading
if show_ci and ci_levels:
try:
from scipy.integrate import cumulative_trapezoid
except ImportError:
from scipy.integrate import cumtrapz as cumulative_trapezoid
# Calculate CDF
cdf = cumulative_trapezoid(pdf, strikes, initial=0)
cdf = cdf / cdf[-1] # Normalize
# Find strikes at CI levels
lower_strike = np.interp(ci_levels[0], cdf, strikes)
upper_strike = np.interp(ci_levels[1], cdf, strikes)
# Add shaded region
ci_mask = (strikes >= lower_strike) & (strikes <= upper_strike)
fig.add_trace(go.Scatter(
x=strikes[ci_mask],
y=pdf[ci_mask],
mode='lines',
name=f'{int((ci_levels[1]-ci_levels[0])*100)}% CI',
line=dict(width=0),
fill='tozeroy',
fillcolor='rgba(0, 255, 136, 0.15)', # Semi-transparent green
showlegend=True,
hoverinfo='skip'
))
# Add vertical lines for CI bounds
fig.add_vline(
x=lower_strike,
line_dash="dot",
line_color=DARK_THEME['neutral'],
line_width=1,
annotation_text=f"${lower_strike:.2f}",
annotation_position="bottom"
)
fig.add_vline(
x=upper_strike,
line_dash="dot",
line_color=DARK_THEME['neutral'],
line_width=1,
annotation_text=f"${upper_strike:.2f}",
annotation_position="bottom"
)
# Layout
layout = create_base_layout(
title=title,
xaxis_title="Strike Price ($)",
yaxis_title="Probability Density",
showlegend=True,
legend=dict(
x=0.02,
y=0.98,
bgcolor='rgba(20,20,20,0.8)',
bordercolor=DARK_THEME['grid'],
borderwidth=1
)
)
fig.update_layout(**layout)
return fig
def plot_pdf_comparison(
pdf_data: Dict[str, Dict[str, np.ndarray]],
spot_price: float,
title: str = "PDF Comparison Across Expirations"
) -> go.Figure:
"""
Create comparison plot of multiple PDFs.
Args:
pdf_data: Dictionary with format:
{
'expiration_date': {
'strikes': np.ndarray,
'pdf': np.ndarray,
'days_to_expiry': int
}
}
spot_price: Current spot price
title: Plot title
Returns:
Plotly figure
"""
fig = go.Figure()
# Sort by days to expiry
sorted_data = sorted(
pdf_data.items(),
key=lambda x: x[1]['days_to_expiry']
)
# Add each PDF as a line
for idx, (exp_date, data) in enumerate(sorted_data):
strikes = data['strikes']
pdf = data['pdf']
days = data['days_to_expiry']
style = get_line_style(idx)
fig.add_trace(go.Scatter(
x=strikes,
y=pdf,
mode='lines',
name=f"{days}D ({exp_date})",
line=style,
hovertemplate=format_hover_template(
"Strike",
"Probability",
{'Expiration': exp_date, 'DTE': f'{days} days'}
)
))
# Add spot price indicator
fig.add_vline(
x=spot_price,
line_dash="dash",
line_color=DARK_THEME['success'],
line_width=2,
annotation_text=f"Spot: ${spot_price:.2f}",
annotation_position="top"
)
# Layout
layout = create_base_layout(
title=title,
xaxis_title="Strike Price ($)",
yaxis_title="Probability Density",
showlegend=True,
legend=dict(
x=0.02,
y=0.98,
bgcolor='rgba(20,20,20,0.8)',
bordercolor=DARK_THEME['grid'],
borderwidth=1
)
)
fig.update_layout(**layout)
return fig
def plot_cdf(
strikes: np.ndarray,
cdf: np.ndarray,
spot_price: float,
title: str = "Cumulative Distribution Function",
show_percentiles: bool = True
) -> go.Figure:
"""
Plot cumulative distribution function.
Args:
strikes: Strike prices
cdf: CDF values (0 to 1)
spot_price: Current spot price
title: Plot title
show_percentiles: Whether to show key percentile lines
Returns:
Plotly figure
"""
fig = go.Figure()
# Main CDF line
fig.add_trace(go.Scatter(
x=strikes,
y=cdf * 100, # Convert to percentage
mode='lines',
name='CDF',
line=dict(
color=DARK_THEME['secondary'],
width=3
),
hovertemplate=format_hover_template("Strike", "Cumulative Probability (%)")
))
# Add spot price indicator
fig.add_vline(
x=spot_price,
line_dash="dash",
line_color=DARK_THEME['success'],
line_width=2,
annotation_text=f"Spot: ${spot_price:.2f}",
annotation_position="top"
)
# Add percentile lines
if show_percentiles:
percentiles = [25, 50, 75]
for p in percentiles:
strike_at_p = np.interp(p / 100, cdf, strikes)
fig.add_hline(
y=p,
line_dash="dot",
line_color=DARK_THEME['neutral'],
line_width=1,
annotation_text=f"P{p}",
annotation_position="right"
)
fig.add_vline(
x=strike_at_p,
line_dash="dot",
line_color=DARK_THEME['neutral'],
line_width=1,
annotation_text=f"${strike_at_p:.0f}",
annotation_position="top"
)
# Layout
layout = create_base_layout(
title=title,
xaxis_title="Strike Price ($)",
yaxis_title="Cumulative Probability (%)",
showlegend=False
)
# Set y-axis range to 0-100%
layout['yaxis']['range'] = [0, 100]
fig.update_layout(**layout)
return fig
def plot_pdf_vs_normal(
strikes: np.ndarray,
pdf: np.ndarray,
mean: float,
std: float,
spot_price: float,
title: str = "PDF vs Normal Distribution"
) -> go.Figure:
"""
Compare PDF to normal distribution with same mean and std.
Args:
strikes: Strike prices
pdf: Actual PDF values
mean: PDF mean
std: PDF standard deviation
spot_price: Current spot price
title: Plot title
Returns:
Plotly figure
"""
from scipy.stats import norm
fig = go.Figure()
# Actual PDF
fig.add_trace(go.Scatter(
x=strikes,
y=pdf,
mode='lines',
name='Market PDF',
line=dict(
color=DARK_THEME['primary'],
width=3
),
hovertemplate=format_hover_template("Strike", "Probability Density")
))
# Normal distribution
normal_pdf = norm.pdf(strikes, loc=mean, scale=std)
fig.add_trace(go.Scatter(
x=strikes,
y=normal_pdf,
mode='lines',
name='Normal Distribution',
line=dict(
color=DARK_THEME['warning'],
width=2,
dash='dash'
),
hovertemplate=format_hover_template("Strike", "Normal PDF")
))
# Add spot price indicator
fig.add_vline(
x=spot_price,
line_dash="dot",
line_color=DARK_THEME['success'],
line_width=2,
annotation_text=f"Spot: ${spot_price:.2f}",
annotation_position="top"
)
# Layout
layout = create_base_layout(
title=title,
xaxis_title="Strike Price ($)",
yaxis_title="Probability Density",
showlegend=True,
legend=dict(
x=0.02,
y=0.98,
bgcolor='rgba(20,20,20,0.8)',
bordercolor=DARK_THEME['grid'],
borderwidth=1
)
)
fig.update_layout(**layout)
return fig
if __name__ == "__main__":
# Test 2D PDF plots
print("Testing 2D PDF plots...")
# Create synthetic PDF data
spot = 450.0
strikes = np.linspace(400, 500, 200)
mean = spot
std = 15
# Lognormal-like PDF
from scipy.stats import norm
pdf = norm.pdf(strikes, loc=mean, scale=std)
# Test single PDF plot
fig1 = plot_pdf_2d(strikes, pdf, spot)
fig1.write_html("test_pdf_2d.html")
print("✅ 2D PDF plot saved to test_pdf_2d.html")
# Test CDF plot
try:
from scipy.integrate import cumulative_trapezoid
except ImportError:
from scipy.integrate import cumtrapz as cumulative_trapezoid
cdf = cumulative_trapezoid(pdf, strikes, initial=0)
cdf = cdf / cdf[-1]
fig2 = plot_cdf(strikes, cdf, spot)
fig2.write_html("test_cdf.html")
print("✅ CDF plot saved to test_cdf.html")
# Test comparison plot
pdf_data = {
'2025-01-15': {
'strikes': strikes,
'pdf': norm.pdf(strikes, mean, std * 0.8),
'days_to_expiry': 15
},
'2025-02-01': {
'strikes': strikes,
'pdf': norm.pdf(strikes, mean, std),
'days_to_expiry': 30
},
'2025-03-01': {
'strikes': strikes,
'pdf': norm.pdf(strikes, mean, std * 1.2),
'days_to_expiry': 60
}
}
fig3 = plot_pdf_comparison(pdf_data, spot)
fig3.write_html("test_pdf_comparison.html")
print("✅ PDF comparison saved to test_pdf_comparison.html")
# Test PDF vs Normal
fig4 = plot_pdf_vs_normal(strikes, pdf, mean, std, spot)
fig4.write_html("test_pdf_vs_normal.html")
print("✅ PDF vs Normal saved to test_pdf_vs_normal.html")
print("\n✅ All 2D PDF visualization tests passed!")
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