Investigating Vix Levels And Returns

Python Imports

# Standard Library
import datetime
import io
import os
import random
import sys
import warnings
from pathlib import Path

# Data Handling
import numpy as np
import pandas as pd

# Data Visualization
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import seaborn as sns
from matplotlib.ticker import FormatStrFormatter, FuncFormatter, MultipleLocator

# Data Sources
import yfinance as yf

# Statistical Analysis
import statsmodels.api as sm

# Machine Learning
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

# Suppress warnings
warnings.filterwarnings("ignore")

Add Directories To Path

# Add the source subdirectory to the system path to allow import config from settings.py
current_directory = Path(os.getcwd())
website_base_directory = current_directory.parent.parent.parent
src_directory = website_base_directory / "src"
sys.path.append(str(src_directory)) if str(src_directory) not in sys.path else None

# Import settings.py
from settings import config

# Add configured directories from config to path
SOURCE_DIR = config("SOURCE_DIR")
sys.path.append(str(Path(SOURCE_DIR))) if str(Path(SOURCE_DIR)) not in sys.path else None

QUANT_FINANCE_RESEARCH_BASE_DIR = config("QUANT_FINANCE_RESEARCH_BASE_DIR")
sys.path.append(str(Path(QUANT_FINANCE_RESEARCH_BASE_DIR))) if str(Path(QUANT_FINANCE_RESEARCH_BASE_DIR)) not in sys.path else None

QUANT_FINANCE_RESEARCH_SOURCE_DIR = config("QUANT_FINANCE_RESEARCH_SOURCE_DIR")
sys.path.append(str(Path(QUANT_FINANCE_RESEARCH_SOURCE_DIR))) if str(Path(QUANT_FINANCE_RESEARCH_SOURCE_DIR)) not in sys.path else None

# Add other configured directories
BASE_DIR = config("BASE_DIR")
CONTENT_DIR = config("CONTENT_DIR")
POSTS_DIR = config("POSTS_DIR")
PAGES_DIR = config("PAGES_DIR")
PUBLIC_DIR = config("PUBLIC_DIR")
SOURCE_DIR = config("SOURCE_DIR")
DATA_DIR = config("DATA_DIR")
DATA_MANUAL_DIR = config("DATA_MANUAL_DIR")

# Print system path
for i, path in enumerate(sys.path):
    print(f"{i}: {path}")

0: /usr/lib/python313.zip
1: /usr/lib/python3.13
2: /usr/lib/python3.13/lib-dynload
3: 
4: /home/jared/python-virtual-envs/general_313/lib/python3.13/site-packages
5: /home/jared/Cloud_Storage/Dropbox/Websites/jaredszajkowski.github.io/src
6: /home/jared/Cloud_Storage/Dropbox/Quant_Finance_Research
7: /home/jared/Cloud_Storage/Dropbox/Quant_Finance_Research/src

Track Index Dependencies

# Create file to track markdown dependencies
dep_file = Path("index_dep.txt")
dep_file.write_text("")

0

Python Functions

Typical Functions

# Import functions from source directories
from export_track_md_deps import export_track_md_deps
from df_info import df_info
from df_info_markdown import df_info_markdown
from pandas_set_decimal_places import pandas_set_decimal_places
from load_data import load_data

Project Specific Functions

# Import functions from source directories
from yf_pull_data import yf_pull_data

Data Overview

Acquire CBOE Volatility Index (VIX) Data

yf_pull_data(
    base_directory=DATA_DIR,
    ticker="^VIX",
    source="Yahoo_Finance", 
    asset_class="Indices", 
    excel_export=True,
    pickle_export=True,
)

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
2025-04-25	24.84	27.200001	24.84	26.219999	0

Yahoo Finance data complete for ^VIX
--------------------

	Close	High	Low	Open	Volume
Date
1990-01-02	17.240000	17.240000	17.240000	17.240000	0
1990-01-03	18.190001	18.190001	18.190001	18.190001	0
1990-01-04	19.219999	19.219999	19.219999	19.219999	0
1990-01-05	20.110001	20.110001	20.110001	20.110001	0
1990-01-08	20.260000	20.260000	20.260000	20.260000	0
...	...	...	...	...	...
2025-04-21	33.820000	35.750000	31.790001	32.750000	0
2025-04-22	30.570000	32.680000	30.080000	32.610001	0
2025-04-23	28.450001	30.290001	27.110001	28.750000	0
2025-04-24	26.469999	29.660000	26.360001	28.690001	0
2025-04-25	24.840000	27.200001	24.840000	26.219999	0

8895 rows × 5 columns

Set Decimal Places

pandas_set_decimal_places(2)

Load Data

# VIX
vix = load_data(
    base_directory=DATA_DIR,
    ticker="^VIX",
    source="Yahoo_Finance", 
    asset_class="Indices",
    timeframe="Daily",
)

# 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)

Check For Missing Values & Forward Fill Any Missing Values

# Check to see if there are any NaN values
vix[vix['High'].isna()]

	Close	High	Low	Open
Date

# Forward fill to clean up missing data
vix['High'] = vix['High'].ffill()

VIX DataFrame Info

df_info(vix)

The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8895 entries, 1990-01-02 to 2025-04-25
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   8895 non-null   float64
 1   High    8895 non-null   float64
 2   Low     8895 non-null   float64
 3   Open    8895 non-null   float64
dtypes: float64(4)
memory usage: 347.5 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
2025-04-21	33.82	35.75	31.79	32.75
2025-04-22	30.57	32.68	30.08	32.61
2025-04-23	28.45	30.29	27.11	28.75
2025-04-24	26.47	29.66	26.36	28.69
2025-04-25	24.84	27.20	24.84	26.22

# Copy this <!-- INSERT_01_DF_Info_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_DF_Info.md", content=df_info_markdown(vix))

✅ Exported and tracked: 01_DF_Info.md

Statistics

vix_stats = vix.describe()
num_std = [-1, 0, 1, 2, 3, 4, 5]
for num in num_std:
    vix_stats.loc[f"mean + {num} std"] = {
        'Open': vix_stats.loc['mean']['Open'] + num * vix_stats.loc['std']['Open'],
        'High': vix_stats.loc['mean']['High'] + num * vix_stats.loc['std']['High'],
        'Low': vix_stats.loc['mean']['Low'] + num * vix_stats.loc['std']['Low'],
        'Close': vix_stats.loc['mean']['Close'] + num * vix_stats.loc['std']['Close'],
    }

vix_stats

	Close	High	Low	Open
count	8895.00	8895.00	8895.00	8895.00
mean	19.49	20.40	18.82	19.58
std	7.85	8.41	7.40	7.92
min	9.14	9.31	8.56	9.01
25%	13.86	14.53	13.40	13.93
50%	17.64	18.35	17.06	17.68
75%	22.84	23.83	22.14	22.97
max	82.69	89.53	72.76	82.69
mean + -1 std	11.65	11.99	11.42	11.66
mean + 0 std	19.49	20.40	18.82	19.58
mean + 1 std	27.34	28.81	26.22	27.51
mean + 2 std	35.19	37.21	33.62	35.43
mean + 3 std	43.03	45.62	41.02	43.35
mean + 4 std	50.88	54.03	48.42	51.27
mean + 5 std	58.73	62.43	55.82	59.20

# Copy this <!-- INSERT_02_VIX_Stats_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VIX_Stats.md", content=vix_stats.to_markdown(floatfmt=".2f"))

✅ Exported and tracked: 02_VIX_Stats.md

Deciles

vix_deciles = vix.quantile(np.arange(0, 1.1, 0.1))
display(vix_deciles)

	Close	High	Low	Open
0.00	9.14	9.31	8.56	9.01
0.10	12.12	12.62	11.72	12.13
0.20	13.24	13.87	12.85	13.30
0.30	14.59	15.28	14.07	14.67
0.40	16.09	16.75	15.55	16.12
0.50	17.64	18.35	17.06	17.68
0.60	19.55	20.38	19.00	19.68
0.70	21.64	22.64	20.99	21.79
0.80	24.32	25.36	23.51	24.39
0.90	28.73	30.02	27.80	28.88
1.00	82.69	89.53	72.76	82.69

# Copy this <!-- INSERT_03_VIX_Deciles_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="03_VIX_Deciles.md", content=vix_deciles.to_markdown(floatfmt=".2f"))

✅ Exported and tracked: 03_VIX_Deciles.md

Histogram Distribution

# 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()

# 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 + -1 std']['High'], color='brown', linestyle='dashed', linewidth=1, label=f'Mean - 1 std: {vix_stats.loc['mean + -1 std']['High']:.2f}')
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 + 3 std']['High'], color='black', linestyle='dashed', linewidth=1, label=f'Mean + 3 std: {vix_stats.loc['mean + 3 std']['High']:.2f}')
plt.axvline(vix_stats.loc['mean + 4 std']['High'], color='yellow', linestyle='dashed', linewidth=1, label=f'Mean + 4 std: {vix_stats.loc['mean + 4 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()

Plots

Historical VIX Data

1990 - 2009

# 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()

2010 - Present

# 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 - Present", 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-Present.png", dpi=300, bbox_inches="tight")
plt.show()

Investigating A Signal

Determining A Spike Level

# 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()

vix.head()

	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

vix.tail()

	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
2025-04-21	33.82	35.75	31.79	32.75	44.65	45.63	57.04	34.58	False	26.76	37.87	38.34	47.93	35.70	False	29.40
2025-04-22	30.57	32.68	30.08	32.61	41.90	44.65	55.81	35.25	False	27.08	36.93	37.87	47.34	35.41	False	29.53
2025-04-23	28.45	30.29	27.11	28.75	39.18	41.90	52.38	35.88	False	27.35	35.72	36.93	46.16	34.92	False	29.56
2025-04-24	26.47	29.66	26.36	28.69	36.35	39.18	48.97	36.41	False	27.62	34.62	35.72	44.65	34.42	False	29.56
2025-04-25	24.84	27.20	24.84	26.22	33.58	36.35	45.44	36.80	False	27.82	33.27	34.62	43.27	33.73	False	29.47

vix[vix['High'] >= 50]

	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
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
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
2025-04-07	46.98	60.13	38.58	60.13	28.60	24.51	30.63	26.10	True	22.35	33.61	27.72	34.65	28.48	True	23.95
2025-04-08	52.33	57.52	36.48	44.04	32.58	28.60	35.76	27.50	True	23.05	37.96	33.61	42.01	31.25	True	25.27
2025-04-09	33.62	57.96	31.90	50.98	36.47	32.58	40.72	29.05	True	23.84	41.60	37.96	47.45	33.79	True	26.55
2025-04-10	40.72	54.87	34.44	34.44	40.03	36.47	45.59	30.49	True	24.58	44.01	41.60	51.99	35.80	True	27.66

97 rows × 16 columns

Spike Counts (Signals) By Year

# 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

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	67	11

# Copy this <!-- INSERT_08_Spike_Counts_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="08_Spike_Counts.md", content=spike_count_SMA.to_markdown())

✅ Exported and tracked: 08_Spike_Counts.md

# 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

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	69	9

# 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()

Spike Counts (Signals) Plots By Year

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()

Yearly Plots

for year in range(1990, 2026):
    vix_plot(str(year), str(year))

Spike Counts (Signals) Plots By Decade

1990 - 1994

vix_plot('1990', '1994')

1995 - 1999

vix_plot('1995', '1999')

2000 - 2004

vix_plot('2000', '2004')

2005 - 2009

vix_plot('2005', '2009')

2010 - 2014

vix_plot('2010', '2014')

2015 - 2019

vix_plot('2015', '2019')

2020 - 2024

vix_plot('2020', '2024')

2025 - Present

vix_plot('2025', '2029')

Trading History

Trades Executed

# Import CSV file of VIX transactions from IRA and Brokerage accounts
vix_transactions_IRA = load_data(
    base_directory=DATA_MANUAL_DIR,
    ticker="VIX_Transactions_IRA",
    source=None,
    asset_class=None,
    timeframe=None,
)

vix_transactions_Brokerage = load_data(
    base_directory=DATA_MANUAL_DIR,
    ticker="VIX_Transactions_Brokerage",
    source=None,
    asset_class=None,
    timeframe=None,
)

# Combine the two DataFrames
vix_transactions = pd.concat([vix_transactions_IRA, vix_transactions_Brokerage], ignore_index=True)

# Drop unnecessary columns
vix_transactions.drop(columns = {'Description'}, inplace=True)

# Convert Amount, Price, and Fees & Comm columns to numeric
vix_transactions['Amount'] = vix_transactions['Amount'].replace({'\$': '', ',': ''}, regex=True).astype(float)
vix_transactions['Price'] = vix_transactions['Price'].replace({'\$': '', ',': ''}, regex=True).astype(float)
vix_transactions['Fees & Comm'] = vix_transactions['Fees & Comm'].replace({'\$': '', ',': ''}, regex=True).astype(float)

# Convert Amount column to absolute values
vix_transactions['Amount'] = abs(vix_transactions['Amount'])

# Extract date for option expiration with regex (MM/DD/YYYY)
vix_transactions["Exp_Date"] = vix_transactions["Symbol"].str.extract(r'(\d{2}/\d{2}/\d{4})')

# Convert expiration date and trade date to datetime
vix_transactions["Exp_Date"] = pd.to_datetime(vix_transactions["Exp_Date"], format="%m/%d/%Y")
vix_transactions['Date'] = pd.to_datetime(vix_transactions['Date'])

# Rename date to trade date
vix_transactions.rename(columns={'Date': 'Trade_Date'}, inplace=True)

# Sort by Exp_Date
vix_transactions.sort_values(by=['Exp_Date', 'Trade_Date'], ascending=[True, True], inplace=True)

# Reset index
vix_transactions.reset_index(drop=True, inplace=True)
vix_transactions

	Trade_Date	Action	Symbol	Quantity	Price	Fees & Comm	Amount	Exp_Date
0	2024-08-05	Buy to Open	VIX 09/18/2024 34.00 P	1	10.95	1.08	1096.08	2024-09-18
1	2024-08-21	Sell to Close	VIX 09/18/2024 34.00 P	1	17.95	1.08	1793.92	2024-09-18
2	2024-08-05	Buy to Open	VIX 10/16/2024 40.00 P	1	16.35	1.08	1636.08	2024-10-16
3	2024-09-18	Sell to Close	VIX 10/16/2024 40.00 P	1	21.54	1.08	2152.92	2024-10-16
4	2024-08-07	Buy to Open	VIX 11/20/2024 25.00 P	2	5.90	2.16	1182.16	2024-11-20
5	2024-11-04	Sell to Close	VIX 11/20/2024 25.00 P	2	6.10	2.16	1217.84	2024-11-20
6	2024-08-06	Buy to Open	VIX 12/18/2024 30.00 P	1	10.25	1.08	1026.08	2024-12-18
7	2024-11-27	Sell to Close	VIX 12/18/2024 30.00 P	1	14.95	1.08	1493.92	2024-12-18
8	2025-03-04	Buy to Open	VIX 04/16/2025 25.00 P	5	5.65	5.40	2830.40	2025-04-16
9	2025-03-24	Sell to Close	VIX 04/16/2025 25.00 P	5	7.00	5.40	3494.60	2025-04-16
10	2025-03-10	Buy to Open	VIX 05/21/2025 26.00 P	5	7.10	5.40	3555.40	2025-05-21
11	2025-04-04	Buy to Open	VIX 05/21/2025 26.00 P	10	4.10	10.81	4110.81	2025-05-21
12	2025-04-04	Buy to Open	VIX 05/21/2025 37.00 P	3	13.20	3.24	3963.24	2025-05-21
13	2025-04-08	Buy to Open	VIX 05/21/2025 50.00 P	2	21.15	2.16	4232.16	2025-05-21
14	2025-04-24	Sell to Close	VIX 05/21/2025 50.00 P	1	25.30	1.08	2528.92	2025-05-21
15	2025-04-24	Sell to Close	VIX 05/21/2025 26.00 P	7	3.50	7.57	2442.43	2025-05-21
16	2025-04-25	Sell to Close	VIX 05/21/2025 50.00 P	1	25.65	1.08	2563.92	2025-05-21
17	2025-04-03	Buy to Open	VIX 06/18/2025 27.00 P	8	7.05	8.65	5648.65	2025-06-18
18	2025-04-04	Buy to Open	VIX 06/18/2025 36.00 P	3	13.40	3.24	4023.24	2025-06-18
19	2025-04-07	Buy to Open	VIX 06/18/2025 45.00 P	2	18.85	2.16	3772.16	2025-06-18
20	2025-04-08	Buy to Open	VIX 06/18/2025 27.00 P	4	4.55	4.32	1824.32	2025-06-18
21	2025-04-03	Buy to Open	VIX 07/16/2025 29.00 P	5	8.55	5.40	4280.40	2025-07-16
22	2025-04-04	Buy to Open	VIX 07/16/2025 36.00 P	3	13.80	3.24	4143.24	2025-07-16
23	2025-04-07	Buy to Open	VIX 07/16/2025 45.00 P	2	21.55	2.16	4312.16	2025-07-16
24	2025-04-07	Buy to Open	VIX 08/20/2025 45.00 P	2	21.75	2.16	4352.16	2025-08-20

vix_transactions_no_exp = vix_transactions.drop(columns=['Exp_Date'])

# Copy this <!-- INSERT_10_Trades_Executed_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="10_Trades_Executed.md", content=vix_transactions_no_exp.to_markdown(index=False, floatfmt=".2f"))

✅ Exported and tracked: 10_Trades_Executed.md

# Split buys and sells and sum the amounts
vix_transactions_sells = vix_transactions[vix_transactions['Action'] == 'Sell to Close']
vix_transactions_sells = vix_transactions_sells.groupby(['Symbol', 'Exp_Date'], as_index=False)[['Amount', 'Quantity']].sum()
vix_transactions_sells

	Symbol	Exp_Date	Amount	Quantity
0	VIX 04/16/2025 25.00 P	2025-04-16	3494.60	5
1	VIX 05/21/2025 26.00 P	2025-05-21	2442.43	7
2	VIX 05/21/2025 50.00 P	2025-05-21	5092.84	2
3	VIX 09/18/2024 34.00 P	2024-09-18	1793.92	1
4	VIX 10/16/2024 40.00 P	2024-10-16	2152.92	1
5	VIX 11/20/2024 25.00 P	2024-11-20	1217.84	2
6	VIX 12/18/2024 30.00 P	2024-12-18	1493.92	1

vix_transactions_sells.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7 entries, 0 to 6
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   Symbol    7 non-null      object        
 1   Exp_Date  7 non-null      datetime64[ns]
 2   Amount    7 non-null      float64       
 3   Quantity  7 non-null      int64         
dtypes: datetime64[ns](1), float64(1), int64(1), object(1)
memory usage: 356.0+ bytes

vix_transactions_buys = vix_transactions[vix_transactions['Action'] == 'Buy to Open']
vix_transactions_buys = vix_transactions_buys.groupby(['Symbol', 'Exp_Date'], as_index=False)[['Amount', 'Quantity']].sum()
vix_transactions_buys

	Symbol	Exp_Date	Amount	Quantity
0	VIX 04/16/2025 25.00 P	2025-04-16	2830.40	5
1	VIX 05/21/2025 26.00 P	2025-05-21	7666.21	15
2	VIX 05/21/2025 37.00 P	2025-05-21	3963.24	3
3	VIX 05/21/2025 50.00 P	2025-05-21	4232.16	2
4	VIX 06/18/2025 27.00 P	2025-06-18	7472.97	12
5	VIX 06/18/2025 36.00 P	2025-06-18	4023.24	3
6	VIX 06/18/2025 45.00 P	2025-06-18	3772.16	2
7	VIX 07/16/2025 29.00 P	2025-07-16	4280.40	5
8	VIX 07/16/2025 36.00 P	2025-07-16	4143.24	3
9	VIX 07/16/2025 45.00 P	2025-07-16	4312.16	2
10	VIX 08/20/2025 45.00 P	2025-08-20	4352.16	2
11	VIX 09/18/2024 34.00 P	2024-09-18	1096.08	1
12	VIX 10/16/2024 40.00 P	2024-10-16	1636.08	1
13	VIX 11/20/2024 25.00 P	2024-11-20	1182.16	2
14	VIX 12/18/2024 30.00 P	2024-12-18	1026.08	1

vix_transactions_buys.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15 entries, 0 to 14
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   Symbol    15 non-null     object        
 1   Exp_Date  15 non-null     datetime64[ns]
 2   Amount    15 non-null     float64       
 3   Quantity  15 non-null     int64         
dtypes: datetime64[ns](1), float64(1), int64(1), object(1)
memory usage: 612.0+ bytes

# Merge buys and sells dataframes back together
vix_transactions = pd.merge(vix_transactions_buys, vix_transactions_sells, on=['Symbol', 'Exp_Date'], how='outer', suffixes=('_Buy', '_Sell'))
vix_transactions = vix_transactions.sort_values(by=['Exp_Date'], ascending=[True])
vix_transactions = vix_transactions.reset_index(drop=True)
vix_transactions

	Symbol	Exp_Date	Amount_Buy	Quantity_Buy	Amount_Sell	Quantity_Sell
0	VIX 09/18/2024 34.00 P	2024-09-18	1096.08	1	1793.92	1.00
1	VIX 10/16/2024 40.00 P	2024-10-16	1636.08	1	2152.92	1.00
2	VIX 11/20/2024 25.00 P	2024-11-20	1182.16	2	1217.84	2.00
3	VIX 12/18/2024 30.00 P	2024-12-18	1026.08	1	1493.92	1.00
4	VIX 04/16/2025 25.00 P	2025-04-16	2830.40	5	3494.60	5.00
5	VIX 05/21/2025 26.00 P	2025-05-21	7666.21	15	2442.43	7.00
6	VIX 05/21/2025 37.00 P	2025-05-21	3963.24	3	NaN	NaN
7	VIX 05/21/2025 50.00 P	2025-05-21	4232.16	2	5092.84	2.00
8	VIX 06/18/2025 27.00 P	2025-06-18	7472.97	12	NaN	NaN
9	VIX 06/18/2025 36.00 P	2025-06-18	4023.24	3	NaN	NaN
10	VIX 06/18/2025 45.00 P	2025-06-18	3772.16	2	NaN	NaN
11	VIX 07/16/2025 29.00 P	2025-07-16	4280.40	5	NaN	NaN
12	VIX 07/16/2025 36.00 P	2025-07-16	4143.24	3	NaN	NaN
13	VIX 07/16/2025 45.00 P	2025-07-16	4312.16	2	NaN	NaN
14	VIX 08/20/2025 45.00 P	2025-08-20	4352.16	2	NaN	NaN

vix_transactions.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15 entries, 0 to 14
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype         
---  ------         --------------  -----         
 0   Symbol         15 non-null     object        
 1   Exp_Date       15 non-null     datetime64[ns]
 2   Amount_Buy     15 non-null     float64       
 3   Quantity_Buy   15 non-null     int64         
 4   Amount_Sell    7 non-null      float64       
 5   Quantity_Sell  7 non-null      float64       
dtypes: datetime64[ns](1), float64(3), int64(1), object(1)
memory usage: 852.0+ bytes

vix_transactions['Closed'] = (~vix_transactions['Amount_Sell'].isna()) & (~vix_transactions['Amount_Buy'].isna()) & (vix_transactions['Quantity_Buy'] == vix_transactions['Quantity_Sell'])
vix_transactions

	Symbol	Exp_Date	Amount_Buy	Quantity_Buy	Amount_Sell	Quantity_Sell	Closed
0	VIX 09/18/2024 34.00 P	2024-09-18	1096.08	1	1793.92	1.00	True
1	VIX 10/16/2024 40.00 P	2024-10-16	1636.08	1	2152.92	1.00	True
2	VIX 11/20/2024 25.00 P	2024-11-20	1182.16	2	1217.84	2.00	True
3	VIX 12/18/2024 30.00 P	2024-12-18	1026.08	1	1493.92	1.00	True
4	VIX 04/16/2025 25.00 P	2025-04-16	2830.40	5	3494.60	5.00	True
5	VIX 05/21/2025 26.00 P	2025-05-21	7666.21	15	2442.43	7.00	False
6	VIX 05/21/2025 37.00 P	2025-05-21	3963.24	3	NaN	NaN	False
7	VIX 05/21/2025 50.00 P	2025-05-21	4232.16	2	5092.84	2.00	True
8	VIX 06/18/2025 27.00 P	2025-06-18	7472.97	12	NaN	NaN	False
9	VIX 06/18/2025 36.00 P	2025-06-18	4023.24	3	NaN	NaN	False
10	VIX 06/18/2025 45.00 P	2025-06-18	3772.16	2	NaN	NaN	False
11	VIX 07/16/2025 29.00 P	2025-07-16	4280.40	5	NaN	NaN	False
12	VIX 07/16/2025 36.00 P	2025-07-16	4143.24	3	NaN	NaN	False
13	VIX 07/16/2025 45.00 P	2025-07-16	4312.16	2	NaN	NaN	False
14	VIX 08/20/2025 45.00 P	2025-08-20	4352.16	2	NaN	NaN	False

Closed Positions

closed_trades = vix_transactions[vix_transactions['Closed']]
closed_trades = closed_trades.reset_index(drop=True)
closed_trades['Realized_PnL'] = closed_trades['Amount_Sell'] - closed_trades['Amount_Buy']
closed_trades['Profit_Percent'] = closed_trades['Realized_PnL'] / closed_trades['Amount_Buy']
closed_trades.drop(columns={'Closed', 'Exp_Date'}, inplace=True)
closed_trades['Quantity_Sell'] = closed_trades['Quantity_Sell'].astype(int)
closed_trades

	Symbol	Amount_Buy	Quantity_Buy	Amount_Sell	Quantity_Sell	Realized_PnL	Profit_Percent
0	VIX 09/18/2024 34.00 P	1096.08	1	1793.92	1	697.84	0.64
1	VIX 10/16/2024 40.00 P	1636.08	1	2152.92	1	516.84	0.32
2	VIX 11/20/2024 25.00 P	1182.16	2	1217.84	2	35.68	0.03
3	VIX 12/18/2024 30.00 P	1026.08	1	1493.92	1	467.84	0.46
4	VIX 04/16/2025 25.00 P	2830.40	5	3494.60	5	664.20	0.23
5	VIX 05/21/2025 50.00 P	4232.16	2	5092.84	2	860.68	0.20

# Copy this <!-- INSERT_11_Closed_Positions_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="11_Closed_Positions.md", content=closed_trades.to_markdown(index=False, floatfmt=".2f"))

✅ Exported and tracked: 11_Closed_Positions.md

net_profit_percent = closed_trades['Realized_PnL'].sum() / closed_trades['Amount_Buy'].sum()
net_profit_percent_str = f"{round(net_profit_percent * 100, 2)}%"
net_profit_percent_str

'27.02%'

# Copy this <!-- INSERT_11_Net_Profit_Percent_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="11_Net_Profit_Percent.md", content=net_profit_percent_str)

✅ Exported and tracked: 11_Net_Profit_Percent.md

net_PnL = closed_trades['Realized_PnL'].sum()
net_PnL_str = f"${net_PnL:,.2f}"
net_PnL_str

'$3,243.08'

# Copy this <!-- INSERT_11_Net_PnL_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="11_Net_PnL.md", content=net_PnL_str)

✅ Exported and tracked: 11_Net_PnL.md

Open Positions

open_trades = vix_transactions[~vix_transactions['Closed']]
open_trades = open_trades.reset_index(drop=True)
open_trades.drop(columns={'Closed', 'Amount_Sell', 'Quantity_Sell', 'Exp_Date'}, inplace=True)
open_trades

	Symbol	Amount_Buy	Quantity_Buy
0	VIX 05/21/2025 26.00 P	7666.21	15
1	VIX 05/21/2025 37.00 P	3963.24	3
2	VIX 06/18/2025 27.00 P	7472.97	12
3	VIX 06/18/2025 36.00 P	4023.24	3
4	VIX 06/18/2025 45.00 P	3772.16	2
5	VIX 07/16/2025 29.00 P	4280.40	5
6	VIX 07/16/2025 36.00 P	4143.24	3
7	VIX 07/16/2025 45.00 P	4312.16	2
8	VIX 08/20/2025 45.00 P	4352.16	2

# Copy this <!-- INSERT_12_Open_Positions_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="12_Open_Positions.md", content=open_trades.to_markdown(index=False, floatfmt=".2f"))

✅ Exported and tracked: 12_Open_Positions.md