Cracking the Volatility Code: How VIX Distributions Reveal the Best Times to Sell Options
When I started trading years ago, I heard an expression that stuck with me: “A low range begets a high range, and a high range begets a low range.” In other words, low volatility leads to high volatility, which then cycles back to low volatility—much like seasonal trends in the market.
Right now, the VIX is relatively low, hovering between 16 and 20 for months. We're in the low to mid-range of a moderate volatility environment, which is generally a great time for option sellers—premiums are reasonable relative to the risk taken.
But what if we could visualize a distribution of the VIX that breaks down the probability of low, moderate, and high volatility? You can do exactly that with Python—and generate this insightful graph.
So, what does it tell us?
🔹 94.27% of the time, the VIX remains in a low to moderate volatility stage.
🔹 54.01% of the time, it falls between 15.01 and 30—a goldmine for option sellers who know how to manage risk effectively.
What This Means for Option Sellers
✅ VIX is low (Green region) → Implied volatility (IV) is cheap, meaning option premiums are lower.
✅ VIX is moderate (Orange region) → There’s a balanced risk-reward setup, offering solid premium opportunities.
✅ VIX is high (Red region) → Options premiums are expensive, making selling more profitable but riskier.
Note: There’s a minor rounding error—percentages sum to 99.99% instead of exactly 100%.
Python Code
import yfinance as yf
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
save_path = "./results/vix-volatility-regimes-probability.png"
# Fetch VIX data for the last 10 years
vix = yf.Ticker("^VIX")
vix_data = vix.history(period="10y")
# Extract closing prices
vix_close = vix_data["Close"].dropna()
# Count total days
total_days = len(vix_close)
# Define VIX regimes
low_vol_days = (vix_close <= 15).sum()
moderate_vol_days = ((vix_close >= 15.01) & (vix_close <= 30)).sum()
high_vol_days = (vix_close >= 30.01).sum()
# Compute probabilities
low_vol_prob = (low_vol_days / total_days) * 100
moderate_vol_prob = (moderate_vol_days / total_days) * 100
high_vol_prob = (high_vol_days / total_days) * 100
# Print probabilities
print(f"Probability of Low Volatility (VIX ≤ 15): {low_vol_prob:.2f}%")
print(f"Probability of Moderate Volatility (15.01 ≤ VIX ≤ 30): {moderate_vol_prob:.2f}%")
print(f"Probability of High Volatility (VIX ≥ 30.01): {high_vol_prob:.2f}%")
# Plot distributions
plt.figure(figsize=(12, 6))
sns.set_theme(style="whitegrid")
sns.kdeplot(vix_close[vix_close <= 15], fill=True, color="green", alpha=0.6, label=f"Low Volatility ({low_vol_prob:.2f}%)")
sns.kdeplot(vix_close[(vix_close >= 15.01) & (vix_close <= 30)], fill=True, color="orange", alpha=0.6, label=f"Moderate Volatility ({moderate_vol_prob:.2f}%)")
sns.kdeplot(vix_close[vix_close >= 30.01], fill=True, color="red", alpha=0.6, label=f"High Volatility ({high_vol_prob:.2f}%)")
# Add title and labels
plt.title("VIX Closing Price Distributions & Probability by Regime (Last 10 Years)", fontsize=16)
plt.xlabel("VIX Value", fontsize=14)
plt.ylabel("Density", fontsize=14)
plt.legend()
# Add data source and copyright text
plt.figtext(0.15, 0.00, "Data Source: Yahoo Finance", fontsize=10, color='gray')
plt.figtext(0.75, 0.00, "(c) neuralmarkettrends.com", fontsize=10, color='gray')
# Show the plot
plt.savefig(save_path, dpi=300, bbox_inches="tight")
# Show the plot
plt.show()
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