Investigating Vix Levels And Returns¶

Python Imports¶

In [1]:
# Standard Library
import os
import sys
import datetime
import random
import warnings

# Data Handling
import pandas as pd
import numpy as np

# Data Visualization
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
import dataframe_image as dfi

from matplotlib.ticker import FuncFormatter, FormatStrFormatter, MultipleLocator

# Data Sources
import yfinance as yf

# Statistical Analysis
import statsmodels.api as sm
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# Suppress warnings if needed
# warnings.filterwarnings("ignore")

Set Base Directory¶

In [2]:
# Add the base directory to the system path
base_directory = "/home/jared/Cloud_Storage/Dropbox/Quant_Finance_Research"
sys.path.append(base_directory)

Import Functions¶

In [3]:
# TBD

Python Functions¶

In [4]:
# This function pulls data from Yahoo finance
def yf_data_updater(fund):
    
    # Download data from YF
    df_comp = yf.download(fund)

    # Drop the column level with the ticker symbol
    df_comp.columns = df_comp.columns.droplevel(1)

    # Reset index
    df_comp = df_comp.reset_index()

    # Remove the "Price" header from the index
    df_comp.columns.name = None

    # Reset date column
    df_comp['Date'] = df_comp['Date'].dt.tz_localize(None)

    # Set 'Date' column as index
    df_comp = df_comp.set_index('Date', drop=True)

    # Drop data from last day because it's not accrate until end of day
    df_comp = df_comp.drop(df_comp.index[-1])
    
    # Export data to excel
    file = fund + ".xlsx"
    df_comp.to_excel(file, sheet_name='data')

    print(f"The first and last date of data for {fund} is: ")
    print(df_comp[:1])
    print(df_comp[-1:])
    print(f"Data updater complete for {fund} data")
    
    return print(f"--------------------")
In [5]:
# Set number of decimal places in pandas
def dp(decimal_places):
    pd.set_option('display.float_format', lambda x: f'%.{decimal_places}f' % x)
In [6]:
def load_data(file):
    # Import CSV
    try:
        df = pd.read_csv(file)
    except:
        pass

    # Import excel
    try:
        df = pd.read_excel(file, sheet_name='data', engine='openpyxl')
    except:
        pass
        
    return df
In [7]:
# The `df_info` function returns some useful information about
# a dataframe, such as the columns, data types, and size.

def df_info(df):
    print('The columns, shape, and data types are:')
    print(df.info())
    print('The first 5 rows are:')
    display(df.head())
    print('The last 5 rows are:')
    display(df.tail())

Import Data¶

CBOE Volatility Index (VIX)¶

In [8]:
yf_data_updater('^VIX')

YF.download() has changed argument auto_adjust default to True

[*********************100%***********************]  1 of 1 completed

The first and last date of data for ^VIX is: 
            Close   High    Low   Open  Volume
Date                                          
1990-01-02  17.24  17.24  17.24  17.24       0
                Close   High        Low   Open  Volume
Date                                                  
2025-03-20  19.799999  21.17  19.299999  19.52       0
Data updater complete for ^VIX data
--------------------
In [9]:
dp(2)
In [10]:
# VIX
vix = load_data('^VIX.xlsx')

# Set 'Date' column as datetime
vix['Date'] = pd.to_datetime(vix['Date'])

# Drop 'Volume'
vix.drop(columns = {'Volume'}, inplace = True)

# Set Date as index
vix.set_index('Date', inplace = True)
In [11]:
# Check to see if there are any NaN values
vix[vix['High'].isna()]
Out[11]:
Close High Low Open
Date
In [12]:
# Forward fill to clean up missing data
vix['High'] = vix['High'].ffill()
In [13]:
df_info(vix)

The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8870 entries, 1990-01-02 to 2025-03-20
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   8870 non-null   float64
 1   High    8870 non-null   float64
 2   Low     8870 non-null   float64
 3   Open    8870 non-null   float64
dtypes: float64(4)
memory usage: 346.5 KB
None
The first 5 rows are:
Close High Low Open
Date
1990-01-02 17.24 17.24 17.24 17.24
1990-01-03 18.19 18.19 18.19 18.19
1990-01-04 19.22 19.22 19.22 19.22
1990-01-05 20.11 20.11 20.11 20.11
1990-01-08 20.26 20.26 20.26 20.26 
The last 5 rows are:
Close High Low Open
Date
2025-03-14 21.77 24.36 21.48 24.35
2025-03-17 20.51 22.95 20.32 22.89
2025-03-18 21.70 22.57 20.41 20.83
2025-03-19 19.90 22.10 19.42 21.84
2025-03-20 19.80 21.17 19.30 19.52
In [14]:
vix_stats = vix.describe()
vix_stats
Out[14]:
Close High Low Open
count 8870.00 8870.00 8870.00 8870.00
mean 19.46 20.37 18.80 19.56
std 7.82 8.36 7.39 7.90
min 9.14 9.31 8.56 9.01
25% 13.84 14.50 13.39 13.92
50% 17.62 18.32 17.03 17.66
75% 22.81 23.80 22.13 22.96
max 82.69 89.53 72.76 82.69
In [15]:
vix_stats.loc['mean + 1 std'] = {'Open': vix_stats.loc['mean']['Open'] + vix_stats.loc['std']['Open'],
                                 'High': vix_stats.loc['mean']['High'] + vix_stats.loc['std']['High'],
                                 'Low': vix_stats.loc['mean']['Low'] + vix_stats.loc['std']['Low'],
                                 'Close': vix_stats.loc['mean']['Close'] + vix_stats.loc['std']['Close']}
vix_stats.loc['mean + 2 std'] = {'Open': vix_stats.loc['mean']['Open'] + 2 * vix_stats.loc['std']['Open'],
                                 'High': vix_stats.loc['mean']['High'] + 2 * vix_stats.loc['std']['High'],
                                 'Low': vix_stats.loc['mean']['Low'] + 2 * vix_stats.loc['std']['Low'],
                                 'Close': vix_stats.loc['mean']['Close'] + 2 * vix_stats.loc['std']['Close']}
vix_stats.loc['mean - 1 std'] = {'Open': vix_stats.loc['mean']['Open'] - vix_stats.loc['std']['Open'],
                                 'High': vix_stats.loc['mean']['High'] - vix_stats.loc['std']['High'],
                                 'Low': vix_stats.loc['mean']['Low'] - vix_stats.loc['std']['Low'],
                                 'Close': vix_stats.loc['mean']['Close'] - vix_stats.loc['std']['Close']}
In [16]:
vix_stats
Out[16]:
Close High Low Open
count 8870.00 8870.00 8870.00 8870.00
mean 19.46 20.37 18.80 19.56
std 7.82 8.36 7.39 7.90
min 9.14 9.31 8.56 9.01
25% 13.84 14.50 13.39 13.92
50% 17.62 18.32 17.03 17.66
75% 22.81 23.80 22.13 22.96
max 82.69 89.53 72.76 82.69
mean + 1 std 27.29 28.73 26.19 27.45
mean + 2 std 35.11 37.09 33.58 35.35
mean - 1 std 11.64 12.00 11.40 11.66
In [17]:
deciles = vix.quantile(np.arange(0, 1.1, 0.1))
display(deciles)
Close High Low Open
0.00 9.14 9.31 8.56 9.01
0.10 12.11 12.62 11.72 12.13
0.20 13.24 13.86 12.84 13.29
0.30 14.57 15.27 14.06 14.64
0.40 16.07 16.74 15.53 16.11
0.50 17.62 18.32 17.03 17.66
0.60 19.53 20.36 18.98 19.65
0.70 21.59 22.60 20.96 21.74
0.80 24.28 25.29 23.45 24.37
0.90 28.67 29.95 27.74 28.82
1.00 82.69 89.53 72.76 82.69
In [18]:
# Plotting
plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

# Histogram
plt.hist([vix['High']], label=['High'], bins=200, edgecolor='black', color='steelblue')

# Set X axis
x_tick_spacing = 5  # Specify the interval for y-axis ticks
plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.xlabel("VIX", fontsize=10)
plt.xticks(rotation=0, fontsize=8)

# Set Y axis
y_tick_spacing = 25  # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel("# Of Datapoints", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("CBOE Volatility Index (VIX) Histogram (200 Bins)", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("04_Histogram.png", dpi=300, bbox_inches="tight")
plt.show()
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In [19]:
# Plotting
plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

# Histogram
plt.hist([vix['High']], label=['High'], bins=200, edgecolor='black', color='steelblue')

# Plot a vertical line at the mean, mean + 1 std, and mean + 2 std
plt.axvline(vix_stats.loc['mean']['High'], color='red', linestyle='dashed', linewidth=1, label=f'Mean: {vix_stats.loc['mean']['High']:.2f}')
plt.axvline(vix_stats.loc['mean + 1 std']['High'], color='green', linestyle='dashed', linewidth=1, label=f'Mean + 1 std: {vix_stats.loc['mean + 1 std']['High']:.2f}')
plt.axvline(vix_stats.loc['mean + 2 std']['High'], color='orange', linestyle='dashed', linewidth=1, label=f'Mean + 2 std: {vix_stats.loc['mean + 2 std']['High']:.2f}')
plt.axvline(vix_stats.loc['mean - 1 std']['High'], color='brown', linestyle='dashed', linewidth=1, label=f'Mean - 1 std: {vix_stats.loc['mean - 1 std']['High']:.2f}')

# Set X axis
x_tick_spacing = 5  # Specify the interval for y-axis ticks
plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.xlabel("VIX", fontsize=10)
plt.xticks(rotation=0, fontsize=8)

# Set Y axis
y_tick_spacing = 25  # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel("# Of Datapoints", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("CBOE Volatility Index (VIX) Histogram (200 Bins)", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("05_Histogram+Mean.png", dpi=300, bbox_inches="tight")
plt.show()
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Plots¶

CBOE Volatility Index (VIX)¶

1990 - 2009¶

In [20]:
# Plotting
plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

# Plot data
plt.plot(vix[vix.index <= '2009-12-31'].index, vix[vix.index <= '2009-12-31']['High'], label='High', linestyle='-', color='steelblue', linewidth=1.5)
# plt.plot(vix.index, vix['SMA_10'], label='10 Day SMA', linestyle='-', color='r', linewidth=1)
# plt.plot(vix.index, vix['SMA_20'], label='20 Day SMA', linestyle='-', color='orange', linewidth=1)
# plt.plot(vix.index, vix['SMA_50'], label='50 Day SMA', linestyle='-', color='g', linewidth=1)

# Set X axis
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel("Date", fontsize=10)
plt.xticks(rotation=45, fontsize=8)

# Set Y axis
y_tick_spacing = 5  # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel("VIX", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("CBOE Volatility Index (VIX), 1990 - 2009", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("06_Plot_1990-2009.png", dpi=300, bbox_inches="tight")
plt.show()
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In [21]:
# Plotting
plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

# Plot data
plt.plot(vix[vix.index > '2009-12-31'].index, vix[vix.index > '2009-12-31']['High'], label='High', linestyle='-', color='steelblue', linewidth=1.5)
# plt.plot(vix.index, vix['SMA_10'], label='10 Day SMA', linestyle='-', color='r', linewidth=1)
# plt.plot(vix.index, vix['SMA_20'], label='20 Day SMA', linestyle='-', color='orange', linewidth=1)
# plt.plot(vix.index, vix['SMA_50'], label='50 Day SMA', linestyle='-', color='g', linewidth=1)

# Set X axis
plt.gca().xaxis.set_major_locator(mdates.YearLocator())
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y'))
plt.xlabel("Date", fontsize=10)
plt.xticks(rotation=45, fontsize=8)

# Set Y axis
y_tick_spacing = 5  # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel("VIX", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("CBOE Volatility Index (VIX), 2010 - 2024", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("07_Plot_2010-2024.png", dpi=300, bbox_inches="tight")
plt.show()
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Determining A Spike Level¶

In [22]:
# Define the spike multiplier for detecting significant spikes
spike_level = 1.25

# =========================
# Simple Moving Averages (SMA)
# =========================

# Calculate 10-period SMA of 'High'
vix['High_SMA_10'] = vix['High'].rolling(window=10).mean()

# Shift the 10-period SMA by 1 to compare with current 'High'
vix['High_SMA_10_Shift'] = vix['High_SMA_10'].shift(1)

# Calculate the spike level based on shifted SMA and spike multiplier
vix['Spike_Level_SMA'] = vix['High_SMA_10_Shift'] * spike_level

# Calculate 20-period SMA of 'High'
vix['High_SMA_20'] = vix['High'].rolling(window=20).mean()

# Determine if 'High' exceeds the spike level (indicates a spike)
vix['Spike_SMA'] = vix['High'] >= vix['Spike_Level_SMA']

# Calculate 50-period SMA of 'High' for trend analysis
vix['High_SMA_50'] = vix['High'].rolling(window=50).mean()

# =========================
# Exponential Moving Averages (EMA)
# =========================

# Calculate 10-period EMA of 'High'
vix['High_EMA_10'] = vix['High'].ewm(span=10, adjust=False).mean()

# Shift the 10-period EMA by 1 to compare with current 'High'
vix['High_EMA_10_Shift'] = vix['High_EMA_10'].shift(1)

# Calculate the spike level based on shifted EMA and spike multiplier
vix['Spike_Level_EMA'] = vix['High_EMA_10_Shift'] * spike_level

# Calculate 20-period EMA of 'High'
vix['High_EMA_20'] = vix['High'].ewm(span=20, adjust=False).mean()

# Determine if 'High' exceeds the spike level (indicates a spike)
vix['Spike_EMA'] = vix['High'] >= vix['Spike_Level_EMA']

# Calculate 50-period EMA of 'High' for trend analysis
vix['High_EMA_50'] = vix['High'].ewm(span=50, adjust=False).mean()
In [23]:
vix.head()
Out[23]:
Close High Low Open High_SMA_10 High_SMA_10_Shift Spike_Level_SMA High_SMA_20 Spike_SMA High_SMA_50 High_EMA_10 High_EMA_10_Shift Spike_Level_EMA High_EMA_20 Spike_EMA High_EMA_50
Date
1990-01-02 17.24 17.24 17.24 17.24 NaN NaN NaN NaN False NaN 17.24 NaN NaN 17.24 False 17.24
1990-01-03 18.19 18.19 18.19 18.19 NaN NaN NaN NaN False NaN 17.41 17.24 21.55 17.33 False 17.28
1990-01-04 19.22 19.22 19.22 19.22 NaN NaN NaN NaN False NaN 17.74 17.41 21.77 17.51 False 17.35
1990-01-05 20.11 20.11 20.11 20.11 NaN NaN NaN NaN False NaN 18.17 17.74 22.18 17.76 False 17.46
1990-01-08 20.26 20.26 20.26 20.26 NaN NaN NaN NaN False NaN 18.55 18.17 22.71 18.00 False 17.57
In [24]:
vix.tail()
Out[24]:
Close High Low Open High_SMA_10 High_SMA_10_Shift Spike_Level_SMA High_SMA_20 Spike_SMA High_SMA_50 High_EMA_10 High_EMA_10_Shift Spike_Level_EMA High_EMA_20 Spike_EMA High_EMA_50
Date
2025-03-14 21.77 24.36 21.48 24.35 26.45 26.25 32.82 22.66 False 19.78 25.58 25.85 32.31 23.67 False 20.92
2025-03-17 20.51 22.95 20.32 22.89 26.31 26.45 33.06 23.04 False 19.88 25.10 25.58 31.97 23.60 False 21.00
2025-03-18 21.70 22.57 20.41 20.83 25.94 26.31 32.89 23.36 False 19.94 24.64 25.10 31.37 23.50 False 21.06
2025-03-19 19.90 22.10 19.42 21.84 25.66 25.94 32.42 23.67 False 20.03 24.18 24.64 30.80 23.37 False 21.11
2025-03-20 19.80 21.17 19.30 19.52 25.19 25.66 32.08 23.90 False 20.11 23.63 24.18 30.22 23.16 False 21.11
In [25]:
vix[vix['High'] >= 50]
Out[25]:
Close High Low Open High_SMA_10 High_SMA_10_Shift Spike_Level_SMA High_SMA_20 Spike_SMA High_SMA_50 High_EMA_10 High_EMA_10_Shift Spike_Level_EMA High_EMA_20 Spike_EMA High_EMA_50
Date
2008-10-06 52.05 58.24 45.12 45.12 42.92 40.52 50.65 37.24 True 28.17 44.33 41.24 51.55 38.82 True 31.65
2008-10-07 53.68 54.19 47.03 52.05 44.73 42.92 53.65 38.66 True 28.76 46.12 44.33 55.41 40.29 False 32.53
2008-10-08 57.53 59.06 51.90 53.68 46.97 44.73 55.91 40.34 True 29.46 48.47 46.12 57.65 42.07 True 33.57
2008-10-09 63.92 64.92 52.54 57.57 49.94 46.97 58.71 42.27 True 30.31 51.46 48.47 60.59 44.25 True 34.80
2008-10-10 69.95 76.94 65.63 65.85 53.99 49.94 62.42 44.79 True 31.39 56.10 51.46 64.33 47.36 True 36.46
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2020-03-31 53.54 58.75 50.88 56.69 70.93 73.54 91.93 66.93 False 39.38 67.38 69.30 86.62 63.00 False 46.75
2020-04-01 57.06 60.59 52.76 57.38 68.44 70.93 88.67 68.18 False 40.33 66.15 67.38 84.23 62.77 False 47.29
2020-04-02 50.91 57.24 50.45 54.46 65.74 68.44 85.56 68.90 False 41.21 64.53 66.15 82.68 62.24 False 47.68
2020-04-03 46.80 52.29 46.74 51.11 64.02 65.74 82.18 68.79 False 41.98 62.30 64.53 80.66 61.30 False 47.86
2024-08-05 38.57 65.73 23.39 23.39 23.84 18.95 23.69 19.11 True 15.66 28.04 19.66 24.58 22.15 True 17.62

93 rows × 16 columns

Spike Totals By Year¶

In [26]:
# Ensure the index is a DatetimeIndex
vix.index = pd.to_datetime(vix.index)

# Create a new column for the year extracted from the date index
vix['Year'] = vix.index.year

# Group by year and the "Spike_SMA" and "Spike_EMA" columns, then count occurrences
spike_count_SMA = vix.groupby(['Year', 'Spike_SMA']).size().unstack(fill_value=0)

spike_count_SMA
Out[26]:
Spike_SMA False True
Year
1990 248 5
1991 249 4
1992 250 4
1993 251 2
1994 243 9
1995 252 0
1996 248 6
1997 247 6
1998 243 9
1999 250 2
2000 248 4
2001 240 8
2002 248 4
2003 251 1
2004 250 2
2005 250 2
2006 242 9
2007 239 12
2008 238 15
2009 249 3
2010 239 13
2011 240 12
2012 248 2
2013 249 3
2014 235 17
2015 240 12
2016 234 18
2017 244 7
2018 228 23
2019 241 11
2020 224 29
2021 235 17
2022 239 12
2023 246 4
2024 237 15
2025 48 5
In [27]:
# Ensure the index is a DatetimeIndex
vix.index = pd.to_datetime(vix.index)

# Create a new column for the year extracted from the date index
vix['Year'] = vix.index.year

# Group by year and the "Spike_SMA" and "Spike_EMA" columns, then count occurrences
spike_count_EMA = vix.groupby(['Year', 'Spike_EMA']).size().unstack(fill_value=0)

spike_count_EMA
Out[27]:
Spike_EMA False True
Year
1990 247 6
1991 251 2
1992 253 1
1993 251 2
1994 247 5
1995 252 0
1996 252 2
1997 250 3
1998 246 6
1999 250 2
2000 250 2
2001 241 7
2002 250 2
2003 251 1
2004 251 1
2005 250 2
2006 248 3
2007 242 9
2008 240 13
2009 251 1
2010 243 9
2011 242 10
2012 250 0
2013 250 2
2014 236 16
2015 243 9
2016 238 14
2017 244 7
2018 230 21
2019 242 10
2020 228 25
2021 239 13
2022 244 7
2023 248 2
2024 244 8
2025 50 3
In [28]:
# Plotting
plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

# Bar positions
x = np.arange(len(spike_count_SMA[True].index))
width = 0.35

# Plot SMA bars
plt.bar(x - width / 2, spike_count_SMA[True].values, width, color="steelblue", label="Spike Counts Using SMA")

# Plot EMA bars
plt.bar(x + width / 2, spike_count_EMA[True].values, width, color="forestgreen", label="Spike Counts Using EMA")

# Set X axis
# x_tick_spacing = 5  # Specify the interval for y-axis ticks
# plt.gca().xaxis.set_major_locator(MultipleLocator(x_tick_spacing))
plt.xlabel("Year", fontsize=10)
plt.xticks(x, spike_count_SMA[True].index, rotation=45, fontsize=8)
plt.xlim(x[0] - 2 * width, x[-1] + 2 * width)

# # Set Y axis
y_tick_spacing = 2  # Specify the interval for y-axis ticks
plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
plt.ylabel("Count", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("Yearly Totals Of Spike Counts", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("08_Spike_Counts.png", dpi=300, bbox_inches="tight")
plt.show()
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In [29]:
def vix_plot(start_year, end_year):
    # Start and end dates
    start_date = start_year + '-01-01'
    end_date = end_year + '-12-31'

    # Create temporary dataframe for the specified date range
    vix_temp = vix[(vix.index >= start_date) & (vix.index <= end_date)]

    # Plotting
    plt.figure(figsize=(12, 6), facecolor="#F5F5F5")

    # Plot data
    plt.plot(vix_temp.index, vix_temp['High'], label='High', linestyle='-', color='steelblue', linewidth=1)
    plt.plot(vix_temp.index, vix_temp['Low'], label='Low', linestyle='-', color='brown', linewidth=1)
    plt.plot(vix_temp.index, vix_temp['High_SMA_10'], label='10 Day High SMA', linestyle='-', color='red', linewidth=1)
    plt.plot(vix_temp.index, vix_temp['High_SMA_20'], label='20 Day High SMA', linestyle='-', color='orange', linewidth=1)
    plt.plot(vix_temp.index, vix_temp['High_SMA_50'], label='50 Day High SMA', linestyle='-', color='green', linewidth=1)
    plt.scatter(vix_temp[vix_temp['Spike_SMA'] == True].index, vix_temp[vix_temp['Spike_SMA'] == True]['High'], label='Spike (High > 1.25 * 10 Day High SMA)', linestyle='-', color='black', s=20)

    # Set X axis
    plt.gca().xaxis.set_major_locator(mdates.MonthLocator())
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
    plt.xlabel("Date", fontsize=10)
    plt.xticks(rotation=45, fontsize=8)

    # Set Y axis
    y_tick_spacing = 5  # Specify the interval for y-axis ticks
    plt.gca().yaxis.set_major_locator(MultipleLocator(y_tick_spacing))
    plt.ylabel("VIX", fontsize=10)
    plt.yticks(fontsize=8)

    # Set title, layout, grid, and legend
    plt.title(f"CBOE Volatility Index (VIX), {start_year} - {end_year}", fontsize=12)
    plt.tight_layout()
    plt.grid(True, linestyle='--', alpha=0.7)
    plt.legend(fontsize=9)

    # Save figure and display plot
    plt.savefig(f"09_VIX_SMA_Spike_{start_year}_{end_year}.png", dpi=300, bbox_inches="tight")
    plt.show()

1990¶

In [30]:
vix_plot('1990', '1990')
No description has been provided for this image

1999¶

In [31]:
vix_plot('1999', '1999')
No description has been provided for this image

2011¶

In [32]:
vix_plot('2011', '2011')
No description has been provided for this image

2018¶

In [33]:
vix_plot('2018', '2018')
No description has been provided for this image

2019¶

In [34]:
vix_plot('2019', '2019')
No description has been provided for this image

2020¶

In [35]:
vix_plot('2020', '2020')
No description has been provided for this image

2021¶

In [36]:
vix_plot('2021', '2021')
No description has been provided for this image

2022¶

In [37]:
vix_plot('2022', '2022')
No description has been provided for this image

2023¶

In [38]:
vix_plot('2023', '2023')
No description has been provided for this image

2024¶

In [39]:
vix_plot('2024', '2024')
No description has been provided for this image

1990 - 1994¶

In [40]:
vix_plot('1990', '1994')
No description has been provided for this image

1995 - 1999¶

In [41]:
vix_plot('1995', '1999')
No description has been provided for this image

2000 - 2004¶

In [42]:
vix_plot('2000', '2004')
No description has been provided for this image

2005 - 2009¶

In [43]:
vix_plot('2005', '2009')
No description has been provided for this image

2010 - 2014¶

In [44]:
vix_plot('2010', '2014')
No description has been provided for this image

2015 - 2019¶

In [45]:
vix_plot('2015', '2019')
No description has been provided for this image

2020 - 2024¶

2025 - 2025¶

In [47]:
vix_plot('2025', '2025')
No description has been provided for this image
In [48]:
vix.tail(10)
Out[48]:
Close High Low Open High_SMA_10 High_SMA_10_Shift Spike_Level_SMA High_SMA_20 Spike_SMA High_SMA_50 High_EMA_10 High_EMA_10_Shift Spike_Level_EMA High_EMA_20 Spike_EMA High_EMA_50 Year
Date
2025-03-07 23.37 26.56 23.09 24.85 23.36 22.61 28.26 19.99 False 18.86 23.54 22.87 28.59 21.48 False 19.53 2025
2025-03-10 27.86 29.56 24.68 24.70 24.29 23.36 29.20 20.64 True 19.05 24.64 23.54 29.43 22.25 True 19.93 2025
2025-03-11 26.92 29.57 26.18 27.94 25.10 24.29 30.37 21.29 False 19.30 25.54 24.64 30.80 22.95 False 20.31 2025
2025-03-12 24.23 26.91 23.89 26.88 25.79 25.10 31.38 21.81 False 19.52 25.79 25.54 31.92 23.33 False 20.56 2025
2025-03-13 24.66 26.13 23.46 24.92 26.25 25.79 32.24 22.26 False 19.68 25.85 25.79 32.23 23.59 False 20.78 2025
2025-03-14 21.77 24.36 21.48 24.35 26.45 26.25 32.82 22.66 False 19.78 25.58 25.85 32.31 23.67 False 20.92 2025
2025-03-17 20.51 22.95 20.32 22.89 26.31 26.45 33.06 23.04 False 19.88 25.10 25.58 31.97 23.60 False 21.00 2025
2025-03-18 21.70 22.57 20.41 20.83 25.94 26.31 32.89 23.36 False 19.94 24.64 25.10 31.37 23.50 False 21.06 2025
2025-03-19 19.90 22.10 19.42 21.84 25.66 25.94 32.42 23.67 False 20.03 24.18 24.64 30.80 23.37 False 21.11 2025
2025-03-20 19.80 21.17 19.30 19.52 25.19 25.66 32.08 23.90 False 20.11 23.63 24.18 30.22 23.16 False 21.11 2025
In [ ]: