Investigating A VIX Trading Signal¶

Python Imports¶

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

from datetime import datetime, timedelta
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¶

In [2]:
# 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¶

In [3]:
# Create file to track markdown dependencies
dep_file = Path("index_dep.txt")
dep_file.write_text("")
Out[3]:
0

Python Functions¶

In [4]:
from calc_vix_trade_pnl import calc_vix_trade_pnl
from df_info import df_info
from df_info_markdown import df_info_markdown
from export_track_md_deps import export_track_md_deps
from load_data import load_data
from pandas_set_decimal_places import pandas_set_decimal_places
from plot_price import plot_price
from plot_stats import plot_stats
from plot_vix_with_trades import plot_vix_with_trades
from yf_pull_data import yf_pull_data

Data Overview (VIX)¶

Acquire CBOE Volatility Index (VIX) Data¶

In [5]:
yf_pull_data(
    base_directory=DATA_DIR,
    ticker="^VIX",
    source="Yahoo_Finance", 
    asset_class="Indices", 
    excel_export=True,
    pickle_export=True,
    output_confirmation=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











2025-06-09
17.16
17.719999
16.82
17.690001
0


















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









Out[5]:












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




...
...
...
...
...
...




2025-06-03
17.690001
19.209999
17.639999
18.830000
0




2025-06-04
17.610001
18.070000
17.410000
17.680000
0




2025-06-05
18.480000
18.799999
17.080000
17.680000
0




2025-06-06
16.770000
18.350000
16.650000
18.160000
0




2025-06-09
17.160000
17.719999
16.820000
17.690001
0






8925 rows × 5 columns


























Load Data - VIX¶


















In [6]:






# Set decimal places
pandas_set_decimal_places(2)

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

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

























DataFrame Info - VIX¶


















In [7]:






df_info(vix)

























The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 8925 entries, 1990-01-02 to 2025-06-09
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   8925 non-null   float64
 1   High    8925 non-null   float64
 2   Low     8925 non-null   float64
 3   Open    8925 non-null   float64
dtypes: float64(4)
memory usage: 348.6 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










2025-06-03
17.69
19.21
17.64
18.83




2025-06-04
17.61
18.07
17.41
17.68




2025-06-05
18.48
18.80
17.08
17.68




2025-06-06
16.77
18.35
16.65
18.16




2025-06-09
17.16
17.72
16.82
17.69


























In [8]:






# Copy this <!-- INSERT_01_VIX_DF_Info_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_DF_Info.md", content=df_info_markdown(vix))

























✅ Exported and tracked: 01_VIX_DF_Info.md

























Statistics - VIX¶


















In [9]:






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'],
    }
display(vix_stats)

































Close
High
Low
Open






count
8925.00
8925.00
8925.00
8925.00




mean
19.49
20.41
18.82
19.59




std
7.84
8.39
7.39
7.91




min
9.14
9.31
8.56
9.01




25%
13.88
14.53
13.41
13.93




50%
17.66
18.39
17.08
17.70




75%
22.84
23.84
22.15
22.98




max
82.69
89.53
72.76
82.69




mean + -1 std
11.66
12.01
11.43
11.68




mean + 0 std
19.49
20.41
18.82
19.59




mean + 1 std
27.33
28.80
26.21
27.50




mean + 2 std
35.16
37.19
33.60
35.41




mean + 3 std
43.00
45.59
40.99
43.32




mean + 4 std
50.84
53.98
48.38
51.23




mean + 5 std
58.67
62.38
55.77
59.14


























In [10]:






# Copy this <!-- INSERT_01_VIX_Stats_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Stats.md", content=vix_stats.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 01_VIX_Stats.md





















In [11]:






# Group by year and calculate mean and std for OHLC
vix_stats_by_year = vix.groupby(vix.index.year)[["Open", "High", "Low", "Close"]].agg(["mean", "std" ,"min", "max"])

# Flatten the column MultiIndex
vix_stats_by_year.columns = ['_'.join(col).strip() for col in vix_stats_by_year.columns.values]
vix_stats_by_year.index.name = "Year"

display(vix_stats_by_year)

































Open_mean
Open_std
Open_min
Open_max
High_mean
High_std
High_min
High_max
Low_mean
Low_std
Low_min
Low_max
Close_mean
Close_std
Close_min
Close_max




Year






















1990
23.06
4.74
14.72
36.47
23.06
4.74
14.72
36.47
23.06
4.74
14.72
36.47
23.06
4.74
14.72
36.47




1991
18.38
3.68
13.95
36.20
18.38
3.68
13.95
36.20
18.38
3.68
13.95
36.20
18.38
3.68
13.95
36.20




1992
15.23
2.26
10.29
20.67
16.03
2.19
11.90
25.13
14.85
2.14
10.29
19.67
15.45
2.12
11.51
21.02




1993
12.70
1.37
9.18
16.20
13.34
1.40
9.55
18.31
12.25
1.28
8.89
15.77
12.69
1.33
9.31
17.30




1994
13.79
2.06
9.86
23.61
14.58
2.28
10.31
28.30
13.38
1.99
9.59
23.61
13.93
2.07
9.94
23.87




1995
12.27
1.03
10.29
15.79
12.93
1.07
10.95
16.99
11.96
0.98
10.06
14.97
12.39
0.97
10.36
15.74




1996
16.31
1.92
11.24
23.90
16.99
2.12
12.29
27.05
15.94
1.82
11.11
21.43
16.44
1.94
12.00
21.99




1997
22.43
4.33
16.67
45.69
23.11
4.56
18.02
48.64
21.85
3.98
16.36
36.43
22.38
4.14
17.09
38.20




1998
25.68
6.96
16.42
47.95
26.61
7.36
16.50
49.53
24.89
6.58
16.10
45.58
25.60
6.86
16.23
45.74




1999
24.39
2.90
18.05
32.62
25.20
3.01
18.48
33.66
23.75
2.76
17.07
31.13
24.37
2.88
17.42
32.98




2000
23.41
3.43
16.81
33.70
24.10
3.66
17.06
34.31
22.75
3.19
16.28
30.56
23.32
3.41
16.53
33.49




2001
26.04
4.98
19.21
48.93
26.64
5.19
19.37
49.35
25.22
4.61
18.74
42.66
25.75
4.78
18.76
43.74




2002
27.53
7.03
17.23
48.17
28.28
7.25
17.51
48.46
26.60
6.64
17.02
42.05
27.29
6.91
17.40
45.08




2003
22.21
5.31
15.59
35.21
22.61
5.35
16.19
35.66
21.64
5.18
14.66
33.99
21.98
5.24
15.58
34.69




2004
15.59
1.93
11.41
21.06
16.05
2.02
11.64
22.67
15.05
1.79
11.14
20.61
15.48
1.92
11.23
21.58




2005
12.84
1.44
10.23
18.33
13.28
1.59
10.48
18.59
12.39
1.32
9.88
16.41
12.81
1.47
10.23
17.74




2006
12.90
2.18
9.68
23.45
13.33
2.46
10.06
23.81
12.38
1.96
9.39
21.45
12.81
2.25
9.90
23.81




2007
17.59
5.36
9.99
32.68
18.44
5.76
10.26
37.50
16.75
4.95
9.70
30.44
17.54
5.36
9.89
31.09




2008
32.83
16.41
16.30
80.74
34.57
17.83
17.84
89.53
30.96
14.96
15.82
72.76
32.69
16.38
16.30
80.86




2009
31.75
9.20
19.54
52.65
32.78
9.61
19.67
57.36
30.50
8.63
19.25
49.27
31.48
9.08
19.47
56.65




2010
22.73
5.29
15.44
47.66
23.69
5.82
16.00
48.20
21.69
4.61
15.23
40.30
22.55
5.27
15.45
45.79




2011
24.27
8.17
14.31
46.18
25.40
8.78
14.99
48.00
23.15
7.59
14.27
41.51
24.20
8.14
14.62
48.00




2012
17.93
2.60
13.68
26.35
18.59
2.72
14.08
27.73
17.21
2.37
13.30
25.72
17.80
2.54
13.45
26.66




2013
14.29
1.67
11.52
20.87
14.82
1.88
11.75
21.91
13.80
1.51
11.05
19.04
14.23
1.74
11.30
20.49




2014
14.23
2.65
10.40
29.26
14.95
3.02
10.76
31.06
13.61
2.21
10.28
24.64
14.17
2.62
10.32
25.27




2015
16.71
3.99
11.77
31.91
17.79
5.03
12.22
53.29
15.85
3.65
10.88
29.91
16.67
4.34
11.95
40.74




2016
16.01
4.05
11.32
29.01
16.85
4.40
11.49
32.09
15.16
3.66
10.93
26.67
15.83
3.97
11.27
28.14




2017
11.14
1.34
9.23
16.19
11.72
1.54
9.52
17.28
10.64
1.16
8.56
14.97
11.09
1.36
9.14
16.04




2018
16.63
5.01
9.01
37.32
18.03
6.12
9.31
50.30
15.53
4.25
8.92
29.66
16.64
5.09
9.15
37.32




2019
15.57
2.74
11.55
27.54
16.41
3.06
11.79
28.53
14.76
2.38
11.03
24.05
15.39
2.61
11.54
25.45




2020
29.54
12.45
12.20
82.69
31.46
13.89
12.42
85.47
27.51
10.85
11.75
70.37
29.25
12.34
12.10
82.69




2021
19.83
3.47
15.02
35.16
21.12
4.22
15.54
37.51
18.65
2.93
14.10
29.24
19.66
3.62
15.01
37.21




2022
25.98
4.30
16.57
37.50
27.25
4.59
17.81
38.94
24.69
3.91
16.34
33.11
25.62
4.22
16.60
36.45




2023
17.12
3.17
11.96
27.77
17.83
3.58
12.46
30.81
16.36
2.89
11.81
24.00
16.87
3.14
12.07
26.52




2024
15.69
3.14
11.53
33.71
16.65
4.73
12.23
65.73
14.92
2.58
10.62
24.02
15.61
3.36
11.86
38.57




2025
21.84
7.25
14.89
60.13
23.53
8.85
15.16
60.13
20.30
5.34
14.58
38.58
21.52
6.87
14.77
52.33


























In [12]:






# Copy this <!-- INSERT_01_VIX_Stats_By_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Stats_By_Year.md", content=vix_stats_by_year.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 01_VIX_Stats_By_Year.md





















In [13]:






# Group by month and calculate mean and std for OHLC
vix_stats_by_month = vix.groupby(vix.index.month)[["Open", "High", "Low", "Close"]].agg(["mean", "std", "min", "max"])

# Flatten the column MultiIndex
vix_stats_by_month.columns = ['_'.join(col).strip() for col in vix_stats_by_month.columns.values]
vix_stats_by_month.index.name = "Month"

display(vix_stats_by_month)

































Open_mean
Open_std
Open_min
Open_max
High_mean
High_std
High_min
High_max
Low_mean
Low_std
Low_min
Low_max
Close_mean
Close_std
Close_min
Close_max




Month






















1
19.34
7.21
9.01
51.52
20.13
7.58
9.31
57.36
18.60
6.87
8.92
49.27
19.22
7.17
9.15
56.65




2
19.67
7.22
10.19
52.50
20.51
7.65
10.26
53.16
18.90
6.81
9.70
48.97
19.58
7.13
10.02
52.62




3
20.47
9.63
10.59
82.69
21.39
10.49
11.24
85.47
19.54
8.65
10.53
70.37
20.35
9.56
10.74
82.69




4
19.43
7.48
10.39
60.13
20.24
7.93
10.89
60.59
18.65
6.88
10.22
52.76
19.29
7.28
10.36
57.06




5
18.60
6.04
9.75
47.66
19.40
6.43
10.14
48.20
17.89
5.63
9.56
40.30
18.51
5.96
9.77
45.79




6
18.45
5.80
9.79
44.09
19.15
6.07
10.28
44.44
17.73
5.44
9.37
34.97
18.34
5.72
9.75
40.79




7
17.87
5.75
9.18
48.17
18.58
5.98
9.52
48.46
17.24
5.48
8.84
42.05
17.80
5.67
9.36
44.92




8
19.17
6.74
10.04
45.34
20.12
7.45
10.32
65.73
18.44
6.38
9.52
41.77
19.18
6.87
9.93
48.00




9
20.51
8.32
9.59
48.93
21.35
8.64
9.83
49.35
19.74
7.90
9.36
43.74
20.43
8.20
9.51
46.72




10
21.83
10.28
9.23
79.13
22.83
11.10
9.62
89.53
20.93
9.51
9.11
67.80
21.75
10.24
9.19
80.06




11
20.34
9.65
9.31
80.74
21.04
10.03
9.74
81.48
19.55
9.02
8.56
72.76
20.16
9.52
9.14
80.86




12
19.34
8.26
9.36
66.68
20.09
8.53
9.55
68.60
18.63
7.88
8.89
62.31
19.29
8.16
9.31
68.51


























In [14]:






# Copy this <!-- INSERT_01_VIX_Stats_By_Month_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Stats_By_Month.md", content=vix_stats_by_month.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 01_VIX_Stats_By_Month.md

























Deciles - VIX¶


















In [15]:






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
12.63
11.72
12.13




0.20
13.26
13.88
12.85
13.31




0.30
14.61
15.29
14.08
14.68




0.40
16.10
16.76
15.56
16.13




0.50
17.66
18.39
17.08
17.70




0.60
19.55
20.39
19.00
19.69




0.70
21.64
22.65
20.99
21.79




0.80
24.31
25.36
23.50
24.38




0.90
28.70
30.00
27.78
28.86




1.00
82.69
89.53
72.76
82.69


























In [16]:






# Copy this <!-- INSERT_01_VIX_Deciles_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Deciles.md", content=vix_deciles.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 01_VIX_Deciles.md

























Plots - VIX¶






















Histogram Distribution - VIX¶


















In [17]:






# 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'High 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'High Mean: {vix_stats.loc['mean']['High']:.2f}')
plt.axvline(vix_stats.loc['mean + 1 std']['High'], color='green', linestyle='dashed', linewidth=1, label=f'High 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'High 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'High 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'High 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("01_Histogram+Mean+SD.png", dpi=300, bbox_inches="tight")
plt.show()
























No description has been provided for this image























Historical Data - VIX¶


















In [18]:






plot_price(
    price_df=vix,
    plot_start_date=None,
    plot_end_date="2009-12-31",
    plot_columns=["High", "Low"],
    title="CBOE Volatility Index (VIX), 1990 - 2009",
    x_label="Date",
    x_format="Year",
    y_label="VIX",
    y_format="Decimal",
    y_tick_spacing=5,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="01_VIX_Plot_1990-2009",
)
























No description has been provided for this image



















In [19]:






plot_price(
    price_df=vix,
    plot_start_date="2010-01-01",
    plot_end_date=None,
    plot_columns=["High", "Low"],
    title="CBOE Volatility Index (VIX), 2010 - Present",
    x_label="Date",
    x_format="Year",
    y_label="VIX",
    y_format="Decimal",
    y_tick_spacing=5,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="01_VIX_Plot_2010-Present",
)
























No description has been provided for this image























Stats By Year - VIX¶


















In [20]:






plot_stats(
    stats_df=vix_stats_by_year,
    plot_columns=["Open_mean", "High_mean", "Low_mean", "Close_mean"],
    title="VIX Mean OHLC By Year",
    x_label="Year",
    x_rotation=45,
    x_tick_spacing=1,
    y_label="Price",
    y_tick_spacing=1,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="01_VIX_Stats_By_Year"
)
























No description has been provided for this image























Stats By Month - VIX¶


















In [21]:






plot_stats(
    stats_df=vix_stats_by_month,
    plot_columns=["Open_mean", "High_mean", "Low_mean", "Close_mean"],
    title="VIX Mean OHLC By Month",
    x_label="Month",
    x_rotation=0,
    x_tick_spacing=1,
    y_label="Price",
    y_tick_spacing=1,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="01_VIX_Stats_By_Month"
)
























No description has been provided for this image























Data Overview (VVIX)¶






















Acquire CBOE VVIX Data¶


















In [22]:






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

























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
























The first and last date of data for ^VVIX is: 





















Close
High
Low
Open
Volume




Date











2007-01-03
87.63
87.63
87.63
87.63
0


























Close
High
Low
Open
Volume




Date











2025-06-09
88.97
91.84
88.59
91.58
0


















Yahoo Finance data complete for ^VVIX
--------------------









Out[22]:












Close
High
Low
Open
Volume




Date











2007-01-03
87.63
87.63
87.63
87.63
0




2007-01-04
88.19
88.19
88.19
88.19
0




2007-01-05
90.17
90.17
90.17
90.17
0




2007-01-08
92.04
92.04
92.04
92.04
0




2007-01-09
92.76
92.76
92.76
92.76
0




...
...
...
...
...
...




2025-06-03
90.89
94.38
90.06
91.58
0




2025-06-04
90.79
94.56
90.42
91.16
0




2025-06-05
93.20
94.53
88.92
90.26
0




2025-06-06
89.45
91.20
88.32
91.20
0




2025-06-09
88.97
91.84
88.59
91.58
0






4629 rows × 5 columns


























Load Data - VVIX¶


















In [23]:






# Set decimal places
pandas_set_decimal_places(2)

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

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

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

# Set Date as index
vvix.set_index('Date', inplace = True)

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

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

























DataFrame Info - VVIX¶


















In [24]:






df_info(vvix)

























The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4629 entries, 2007-01-03 to 2025-06-09
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   4629 non-null   float64
 1   High    4629 non-null   float64
 2   Low     4629 non-null   float64
 3   Open    4629 non-null   float64
dtypes: float64(4)
memory usage: 180.8 KB
None
The first 5 rows are:





















Close
High
Low
Open




Date










2007-01-03
87.63
87.63
87.63
87.63




2007-01-04
88.19
88.19
88.19
88.19




2007-01-05
90.17
90.17
90.17
90.17




2007-01-08
92.04
92.04
92.04
92.04




2007-01-09
92.76
92.76
92.76
92.76


















The last 5 rows are:





















Close
High
Low
Open




Date










2025-06-03
90.89
94.38
90.06
91.58




2025-06-04
90.79
94.56
90.42
91.16




2025-06-05
93.20
94.53
88.92
90.26




2025-06-06
89.45
91.20
88.32
91.20




2025-06-09
88.97
91.84
88.59
91.58


























In [25]:






# Copy this <!-- INSERT_02_VVIX_DF_Info_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VVIX_DF_Info.md", content=df_info_markdown(vix))

























✅ Exported and tracked: 02_VVIX_DF_Info.md

























Statistics - VVIX¶


















In [26]:






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

































Close
High
Low
Open






count
4629.00
4629.00
4629.00
4629.00




mean
93.50
95.55
91.94
93.75




std
16.44
18.04
15.09
16.49




min
59.74
59.74
59.31
59.31




25%
82.34
83.46
81.48
82.55




50%
90.54
92.26
89.37
90.86




75%
102.20
105.01
99.93
102.55




max
207.59
212.22
187.27
212.22




mean + -1 std
77.06
77.50
76.85
77.26




mean + 0 std
93.50
95.55
91.94
93.75




mean + 1 std
109.94
113.59
107.04
110.25




mean + 2 std
126.38
131.64
122.13
126.74




mean + 3 std
142.82
149.68
137.22
143.23




mean + 4 std
159.26
167.73
152.32
159.72




mean + 5 std
175.69
185.77
167.41
176.21


























In [27]:






# Copy this <!-- INSERT_02_VVIX_Stats_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VVIX_Stats.md", content=vvix_stats.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 02_VVIX_Stats.md





















In [28]:






# Group by year and calculate mean and std for OHLC
vvix_stats_by_year = vvix.groupby(vvix.index.year)[["Open", "High", "Low", "Close"]].agg(["mean", "std", "min", "max"])

# Flatten the column MultiIndex
vvix_stats_by_year.columns = ['_'.join(col).strip() for col in vvix_stats_by_year.columns.values]
vvix_stats_by_year.index.name = "Year"

display(vvix_stats_by_year)

































Open_mean
Open_std
Open_min
Open_max
High_mean
High_std
High_min
High_max
Low_mean
Low_std
Low_min
Low_max
Close_mean
Close_std
Close_min
Close_max




Year






















2007
87.68
13.31
63.52
142.99
87.68
13.31
63.52
142.99
87.68
13.31
63.52
142.99
87.68
13.31
63.52
142.99




2008
81.85
15.60
59.74
134.87
81.85
15.60
59.74
134.87
81.85
15.60
59.74
134.87
81.85
15.60
59.74
134.87




2009
79.78
8.63
64.95
104.02
79.78
8.63
64.95
104.02
79.78
8.63
64.95
104.02
79.78
8.63
64.95
104.02




2010
88.36
13.07
64.87
145.12
88.36
13.07
64.87
145.12
88.36
13.07
64.87
145.12
88.36
13.07
64.87
145.12




2011
92.94
10.21
75.94
134.63
92.94
10.21
75.94
134.63
92.94
10.21
75.94
134.63
92.94
10.21
75.94
134.63




2012
94.84
8.38
78.42
117.44
94.84
8.38
78.42
117.44
94.84
8.38
78.42
117.44
94.84
8.38
78.42
117.44




2013
80.52
8.97
62.71
111.43
80.52
8.97
62.71
111.43
80.52
8.97
62.71
111.43
80.52
8.97
62.71
111.43




2014
83.01
14.33
61.76
138.60
83.01
14.33
61.76
138.60
83.01
14.33
61.76
138.60
83.01
14.33
61.76
138.60




2015
95.44
15.59
73.07
212.22
98.47
16.39
76.41
212.22
92.15
13.35
72.20
148.68
94.82
14.75
73.18
168.75




2016
93.36
10.02
77.96
131.95
95.82
10.86
78.86
132.42
90.54
8.99
76.17
115.15
92.80
10.07
76.17
125.13




2017
90.50
8.65
75.09
134.98
92.94
9.64
77.34
135.32
87.85
7.78
71.75
117.29
90.01
8.80
75.64
135.32




2018
102.60
13.22
83.70
176.72
106.27
16.26
85.00
203.73
99.17
11.31
82.60
165.35
102.26
14.04
83.21
180.61




2019
91.28
8.43
75.58
112.75
93.61
8.98
75.95
117.63
88.90
7.86
74.36
111.48
91.03
8.36
74.98
114.40




2020
118.64
19.32
88.39
203.03
121.91
20.88
88.54
209.76
115.05
17.37
85.31
187.27
118.36
19.39
86.87
207.59




2021
115.51
9.37
96.09
151.35
119.29
11.70
98.36
168.78
111.99
8.14
95.92
144.19
115.32
10.20
97.09
157.69




2022
102.58
18.01
76.48
161.09
105.32
19.16
77.93
172.82
99.17
16.81
76.13
153.26
101.81
17.81
77.05
154.38




2023
90.95
8.64
74.43
127.73
93.72
9.98
75.31
137.65
88.01
7.37
72.27
119.64
90.34
8.38
73.88
124.75




2024
92.88
15.06
59.31
169.68
97.32
18.33
74.79
192.49
89.51
13.16
59.31
137.05
92.81
15.60
73.26
173.32




2025
106.30
15.89
83.19
186.33
111.30
18.68
85.82
189.03
101.64
12.37
81.73
146.51
105.26
15.14
81.89
170.92


























In [29]:






# Copy this <!-- INSERT_02_VVIX_Stats_By_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VVIX_Stats_By_Year.md", content=vvix_stats_by_year.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 02_VVIX_Stats_By_Year.md





















In [30]:






# Group by month and calculate mean and std for OHLC
vvix_stats_by_month = vvix.groupby(vvix.index.month)[["Open", "High", "Low", "Close"]].agg(["mean", "std", "min", "max"])

# Flatten the column MultiIndex
vvix_stats_by_month.columns = ['_'.join(col).strip() for col in vvix_stats_by_month.columns.values]
vvix_stats_by_month.index.name = "Year"

display(vvix_stats_by_month)

































Open_mean
Open_std
Open_min
Open_max
High_mean
High_std
High_min
High_max
Low_mean
Low_std
Low_min
Low_max
Close_mean
Close_std
Close_min
Close_max




Year






















1
92.46
15.63
64.87
161.09
94.37
17.63
64.87
172.82
90.69
14.23
64.87
153.26
92.23
15.78
64.87
157.69




2
93.49
18.24
65.47
176.72
95.39
20.70
65.47
203.73
91.39
16.43
65.47
165.35
93.13
18.58
65.47
180.61




3
95.30
21.66
66.97
203.03
97.38
23.56
66.97
209.76
92.94
19.51
66.97
187.27
94.89
21.59
66.97
207.59




4
92.18
19.03
59.74
186.33
94.01
20.57
59.74
189.03
90.30
17.21
59.74
152.01
91.88
18.60
59.74
170.92




5
92.25
16.93
61.76
145.18
93.95
17.99
61.76
151.50
90.54
16.14
61.76
145.12
91.79
16.79
61.76
146.28




6
92.91
14.96
63.52
155.48
94.44
16.21
63.52
172.21
91.30
13.92
63.52
140.15
92.72
14.93
63.52
151.60




7
89.97
13.16
67.21
138.42
91.46
14.23
67.21
149.60
88.48
12.26
67.21
133.82
89.84
13.12
67.21
139.54




8
96.83
16.94
68.05
212.22
98.89
18.72
68.05
212.22
94.68
14.86
68.05
148.68
96.61
16.63
68.05
173.32




9
94.71
14.03
67.94
135.17
96.50
15.52
67.94
146.31
92.86
12.50
67.94
128.46
94.40
13.78
67.94
138.93




10
97.74
14.01
64.97
149.60
99.43
15.11
64.97
154.99
96.14
13.35
64.97
144.55
97.52
14.15
64.97
152.01




11
93.53
14.17
63.77
142.68
95.07
15.36
63.77
161.76
91.98
13.39
63.77
140.44
93.28
14.24
63.77
149.74




12
93.35
15.03
59.31
151.35
95.33
16.63
62.71
168.37
91.78
13.70
59.31
144.19
93.46
15.07
62.71
156.10


























In [31]:






# Copy this <!-- INSERT_02_VVIX_Stats_By_Month_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VVIX_Stats_By_Month.md", content=vvix_stats_by_month.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 02_VVIX_Stats_By_Month.md

























Deciles - VVIX¶


















In [32]:






vvix_deciles = vvix.quantile(np.arange(0, 1.1, 0.1))
display(vvix_deciles)

































Close
High
Low
Open






0.00
59.74
59.74
59.31
59.31




0.10
75.83
76.02
75.44
75.80




0.20
80.58
81.43
79.82
80.75




0.30
83.90
85.22
83.01
84.17




0.40
87.08
88.57
85.98
87.46




0.50
90.54
92.26
89.37
90.86




0.60
94.21
96.14
93.03
94.51




0.70
99.11
101.53
97.43
99.40




0.80
106.06
109.40
103.90
106.48




0.90
115.27
118.80
112.48
115.53




1.00
207.59
212.22
187.27
212.22


























In [33]:






# Copy this <!-- INSERT_02_VVIX_Deciles_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="02_VVIX_Deciles.md", content=vvix_deciles.to_markdown(floatfmt=".2f"))

























✅ Exported and tracked: 02_VVIX_Deciles.md

























Plots - VVIX¶






















Histogram Distribution - VVIX¶


















In [34]:






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

# Histogram
plt.hist([vvix['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(vvix_stats.loc['mean + -1 std']['High'], color='brown', linestyle='dashed', linewidth=1, label=f'Mean - 1 std: {vvix_stats.loc['mean + -1 std']['High']:.2f}')
plt.axvline(vvix_stats.loc['mean']['High'], color='red', linestyle='dashed', linewidth=1, label=f'Mean: {vvix_stats.loc['mean']['High']:.2f}')
plt.axvline(vvix_stats.loc['mean + 1 std']['High'], color='green', linestyle='dashed', linewidth=1, label=f'Mean + 1 std: {vvix_stats.loc['mean + 1 std']['High']:.2f}')
plt.axvline(vvix_stats.loc['mean + 2 std']['High'], color='orange', linestyle='dashed', linewidth=1, label=f'Mean + 2 std: {vvix_stats.loc['mean + 2 std']['High']:.2f}')
plt.axvline(vvix_stats.loc['mean + 3 std']['High'], color='black', linestyle='dashed', linewidth=1, label=f'Mean + 3 std: {vvix_stats.loc['mean + 3 std']['High']:.2f}')
plt.axvline(vvix_stats.loc['mean + 4 std']['High'], color='yellow', linestyle='dashed', linewidth=1, label=f'Mean + 4 std: {vvix_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("VVIX", 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 VVIX 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("02_Histogram+Mean+SD.png", dpi=300, bbox_inches="tight")
plt.show()
























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Historical Data - VVIX¶


















In [35]:






plot_price(
    price_df=vvix,
    plot_start_date=None,
    plot_end_date="2016-12-31",
    plot_columns=["High", "Low"],
    title="CBOE VVIX, 2007 - 2016",
    x_label="Date",
    x_format="Year",
    y_label="VIX",
    y_format="Decimal",
    y_tick_spacing=15,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="02_VVIX_Plot_2007-2016",
)
























No description has been provided for this image



















In [36]:






plot_price(
    price_df=vvix,
    plot_start_date="2017-01-01",
    plot_end_date=None,
    plot_columns=["High", "Low"],
    title="CBOE VVIX, 2017 - Present",
    x_label="Date",
    x_format="Year",
    y_label="VIX",
    y_format="Decimal",
    y_tick_spacing=15,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="02_VVIX_Plot_2017-Present",
)
























No description has been provided for this image























Stats By Year - VVIX¶


















In [37]:






plot_stats(
    stats_df=vvix_stats_by_year,
    plot_columns=["Open_mean", "High_mean", "Low_mean", "Close_mean"],
    title="VVIX Mean OHLC By Year",
    x_label="Year",
    x_rotation=45,
    x_tick_spacing=1,
    y_label="Price",
    y_tick_spacing=5,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="02_VVIX_Stats_By_Year"
)
























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Stats By Month - VVIX¶


















In [38]:






plot_stats(
    stats_df=vvix_stats_by_month,
    plot_columns=["Open_mean", "High_mean", "Low_mean", "Close_mean"],
    title="VVIX Mean OHLC By Month",
    x_label="Month",
    x_rotation=0,
    x_tick_spacing=1,
    y_label="Price",
    y_tick_spacing=1,
    grid=True,
    legend=True,
    export_plot=True,
    plot_file_name="02_VVIX_Stats_By_Month"
)
























No description has been provided for this image



















In [39]:






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

# Plot data
plt.plot(vvix[vvix.index > '2023-12-31'].index, vvix[vvix.index > '2023-12-31']['High'], label='High', linestyle='-', color='steelblue', linewidth=1.5)
plt.plot(vvix[vvix.index > '2023-12-31'].index, vvix[vvix.index > '2023-12-31']['Low'], label='Low', linestyle='-', color='brown', linewidth=1.5)

# 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("VVIX", fontsize=10)
plt.yticks(fontsize=8)

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

# Save figure and display plot
plt.savefig("02_VVIX_Plot_2024-Present.png", dpi=300, bbox_inches="tight")
plt.show()
























No description has been provided for this image



















In [40]:






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

# Plot data
plt.plot(vvix[vvix.index > '2024-12-31'].index, vvix[vvix.index > '2024-12-31']['High'], label='High', linestyle='-', color='steelblue', linewidth=1.5)
plt.plot(vvix[vvix.index > '2024-12-31'].index, vvix[vvix.index > '2024-12-31']['Low'], label='Low', linestyle='-', color='brown', linewidth=1.5)

# 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("VVIX", fontsize=10)
plt.yticks(fontsize=8)

# Set title, layout, grid, and legend
plt.title("CBOE VVIX, 2025 - Present", fontsize=12)
plt.tight_layout()
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend(fontsize=9)

# Save figure and display plot
plt.savefig("02_VVIX_Plot_2025-Present.png", dpi=300, bbox_inches="tight")
plt.show()
























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Investigating A Signal¶






















Determining A Spike Level¶


















In [41]:






# 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 [42]:






display(vix)

































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




...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...




2025-06-03
17.69
19.21
17.64
18.83
20.82
20.89
26.11
21.04
False
27.75
20.58
20.88
26.10
21.93
False
24.37




2025-06-04
17.61
18.07
17.41
17.68
20.76
20.82
26.02
20.68
False
27.72
20.12
20.58
25.72
21.57
False
24.13




2025-06-05
18.48
18.80
17.08
17.68
20.53
20.76
25.95
20.34
False
27.74
19.88
20.12
25.15
21.30
False
23.92




2025-06-06
16.77
18.35
16.65
18.16
20.16
20.53
25.66
20.08
False
27.73
19.60
19.88
24.85
21.02
False
23.70




2025-06-09
17.16
17.72
16.82
17.69
19.38
20.16
25.20
19.82
False
27.70
19.26
19.60
24.50
20.71
False
23.46






8925 rows × 16 columns






















In [43]:






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





















Out[43]:












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¶


















In [44]:






# 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)

display(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
96
12


























In [45]:






# 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





















In [46]:






# 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)

display(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
99
9


























In [47]:






# 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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Spike Counts (Signals) Plots By Year¶


















In [48]:






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¶


















In [49]:






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
























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Spike Counts (Signals) Plots By Decade¶






















1990 - 1994¶


















In [50]:






vix_plot('1990', '1994')
























No description has been provided for this image























1995 - 1999¶


















In [51]:






vix_plot('1995', '1999')
























No description has been provided for this image























2000 - 2004¶


















In [52]:






vix_plot('2000', '2004')
























No description has been provided for this image























2005 - 2009¶


















In [53]:






vix_plot('2005', '2009')
























No description has been provided for this image























2010 - 2014¶


















In [54]:






vix_plot('2010', '2014')
























No description has been provided for this image























2015 - 2019¶


















In [55]:






vix_plot('2015', '2019')
























No description has been provided for this image























2020 - 2024¶


















In [56]:






vix_plot('2020', '2024')
























No description has been provided for this image























2025 - Present¶


















In [57]:






vix_plot('2025', '2029')
























No description has been provided for this image























Trading History¶






















Trades Executed¶


















In [58]:






# from schwab_order_history import schwab_order_history





















In [59]:






# from datetime import datetime
# import pandas as pd

# # Define your date ranges
# range_2024 = {
#     "from": "2024-01-01T00:00:00.000Z",
#     "to": "2024-12-31T23:59:59.000Z",
# }

# range_2025 = {
#     "from": "2025-01-01T00:00:00.000Z",
#     "to": datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S.000Z"),
# }

# # Pull both sets of orders
# df_2024 = schwab_order_history(
#     max_results=1000,  # or whatever large number you want
#     from_entered_time=range_2024["from"],
#     to_entered_time=range_2024["to"],
#     account_id=None,  # or pass your specific encrypted account ID
# )

# df_2025 = schwab_order_history(
#     max_results=1000,
#     from_entered_time=range_2025["from"],
#     to_entered_time=range_2025["to"],
#     account_id=None,
# )

# # Combine the two dataframes
# df_all = pd.concat([df_2024, df_2025], ignore_index=True)





















In [60]:






# df_2024





















In [61]:






# # Filter for symbols that start with "VIX"
# df_vix = df_all[df_all["symbol"].str.startswith("VIX")].copy()
# df_vix = df_vix.sort_values(by=['symbol', 'execution_time'], ascending=[True, True])





















In [62]:






# df_vix

























Trades Executed¶


















In [63]:






# Import CSV file of VIX transactions from IRA and Brokerage accounts
vix_transactions_IRA = pd.read_csv(DATA_MANUAL_DIR / "VIX_Transactions_IRA.csv")
vix_transactions_Brokerage = pd.read_excel(DATA_MANUAL_DIR / "VIX_Transactions_Brokerage.xlsx", sheet_name="VIX_Transactions_Brokerage")





















In [64]:






# 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})')

# Extract date for option strike price with regex and convert to float
vix_transactions["Strike_Price"] = vix_transactions["Symbol"].str.extract(r'(\d{2}\.\d{2})').astype(float)

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

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





















Out[64]:












Trade_Date
Action
Symbol
Quantity
Price
Fees & Comm
Amount
Approx_VIX_Level
Comments
Exp_Date
Strike_Price






0
2024-08-05
Buy to Open
VIX 09/18/2024 34.00 P
1
10.95
1.08
1096.08
34.33
NaN
2024-09-18
34.00




1
2024-08-21
Sell to Close
VIX 09/18/2024 34.00 P
1
17.95
1.08
1793.92
16.50
NaN
2024-09-18
34.00




2
2024-08-05
Buy to Open
VIX 10/16/2024 40.00 P
1
16.35
1.08
1636.08
42.71
NaN
2024-10-16
40.00




3
2024-09-18
Sell to Close
VIX 10/16/2024 40.00 P
1
21.54
1.08
2152.92
18.85
NaN
2024-10-16
40.00




4
2024-08-07
Buy to Open
VIX 11/20/2024 25.00 P
2
5.90
2.16
1182.16
27.11
NaN
2024-11-20
25.00




5
2024-11-04
Sell to Close
VIX 11/20/2024 25.00 P
2
6.10
2.16
1217.84
22.43
NaN
2024-11-20
25.00




6
2024-08-06
Buy to Open
VIX 12/18/2024 30.00 P
1
10.25
1.08
1026.08
32.27
NaN
2024-12-18
30.00




7
2024-11-27
Sell to Close
VIX 12/18/2024 30.00 P
1
14.95
1.08
1493.92
14.04
NaN
2024-12-18
30.00




8
2025-03-04
Buy to Open
VIX 04/16/2025 25.00 P
5
5.65
5.40
2830.40
25.75
NaN
2025-04-16
25.00




9
2025-03-24
Sell to Close
VIX 04/16/2025 25.00 P
5
7.00
5.40
3494.60
18.01
NaN
2025-04-16
25.00




10
2025-03-10
Buy to Open
VIX 05/21/2025 26.00 P
5
7.10
5.40
3555.40
27.54
Missed opportunity to close position for 20% p...
2025-05-21
26.00




11
2025-04-04
Buy to Open
VIX 05/21/2025 26.00 P
10
4.10
10.81
4110.81
38.88
Averaged down on existing position
2025-05-21
26.00




12
2025-04-24
Sell to Close
VIX 05/21/2025 26.00 P
7
3.50
7.57
2442.43
27.37
Sold half of position due to vol spike concern...
2025-05-21
26.00




13
2025-05-02
Sell to Close
VIX 05/21/2025 26.00 P
4
4.35
4.32
1735.68
22.73
Sold half of remaining position due to vol spi...
2025-05-21
26.00




14
2025-05-07
Sell to Close
VIX 05/21/2025 26.00 P
4
3.55
4.32
1415.68
24.49
Closed position ahead of Fed’s (Powell’s) comm...
2025-05-21
26.00




15
2025-04-04
Buy to Open
VIX 05/21/2025 37.00 P
3
13.20
3.24
3963.24
36.46
NaN
2025-05-21
37.00




16
2025-05-07
Sell to Close
VIX 05/21/2025 37.00 P
3
13.75
3.24
4121.76
24.51
Closed position ahead of Fed’s (Powell’s) comm...
2025-05-21
37.00




17
2025-04-08
Buy to Open
VIX 05/21/2025 50.00 P
2
21.15
2.16
4232.16
NaN
NaN
2025-05-21
50.00




18
2025-04-24
Sell to Close
VIX 05/21/2025 50.00 P
1
25.30
1.08
2528.92
NaN
NaN
2025-05-21
50.00




19
2025-04-25
Sell to Close
VIX 05/21/2025 50.00 P
1
25.65
1.08
2563.92
NaN
NaN
2025-05-21
50.00




20
2025-04-03
Buy to Open
VIX 06/18/2025 27.00 P
8
7.05
8.65
5648.65
27.62
NaN
2025-06-18
27.00




21
2025-04-08
Buy to Open
VIX 06/18/2025 27.00 P
4
4.55
4.32
1824.32
55.44
Averaged down on existing position
2025-06-18
27.00




22
2025-05-12
Sell to Close
VIX 06/18/2025 27.00 P
6
7.55
6.49
4523.51
19.05
Market up on positive news of lowering tariffs...
2025-06-18
27.00




23
2025-05-12
Sell to Close
VIX 06/18/2025 27.00 P
6
7.40
6.49
4433.51
19.47
Market up on positive news of lowering tariffs...
2025-06-18
27.00




24
2025-04-04
Buy to Open
VIX 06/18/2025 36.00 P
3
13.40
3.24
4023.24
36.61
NaN
2025-06-18
36.00




25
2025-05-12
Sell to Close
VIX 06/18/2025 36.00 P
3
16.00
3.24
4796.76
19.14
Market up on positive news of lowering tariffs...
2025-06-18
36.00




26
2025-04-07
Buy to Open
VIX 06/18/2025 45.00 P
2
18.85
2.16
3772.16
53.65
NaN
2025-06-18
45.00




27
2025-05-12
Sell to Close
VIX 06/18/2025 45.00 P
2
25.00
2.16
4997.84
19.24
Market up on positive news of lowering tariffs...
2025-06-18
45.00




28
2025-04-03
Buy to Open
VIX 07/16/2025 29.00 P
5
8.55
5.40
4280.40
29.03
NaN
2025-07-16
29.00




29
2025-05-13
Sell to Close
VIX 07/16/2025 29.00 P
3
10.40
3.24
3116.76
17.72
NaN
2025-07-16
29.00




30
2025-05-13
Sell to Close
VIX 07/16/2025 29.00 P
2
10.30
2.16
2057.84
17.68
NaN
2025-07-16
29.00




31
2025-04-04
Buy to Open
VIX 07/16/2025 36.00 P
3
13.80
3.24
4143.24
36.95
NaN
2025-07-16
36.00




32
2025-05-13
Sell to Close
VIX 07/16/2025 36.00 P
1
17.00
1.08
1698.92
17.79
NaN
2025-07-16
36.00




33
2025-05-13
Sell to Close
VIX 07/16/2025 36.00 P
2
16.90
2.16
3377.84
17.72
NaN
2025-07-16
36.00




34
2025-04-07
Buy to Open
VIX 07/16/2025 45.00 P
2
21.55
2.16
4312.16
46.17
NaN
2025-07-16
45.00




35
2025-05-13
Sell to Close
VIX 07/16/2025 45.00 P
2
25.65
2.16
5127.84
17.96
NaN
2025-07-16
45.00




36
2025-04-07
Buy to Open
VIX 08/20/2025 45.00 P
2
21.75
2.16
4352.16
49.07
NaN
2025-08-20
45.00




37
2025-05-13
Sell to Close
VIX 08/20/2025 45.00 P
2
25.40
2.16
5077.84
18.06
NaN
2025-08-20
45.00


























In [65]:






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





















Out[65]:












Trade_Date
Action
Symbol
Quantity
Price
Fees & Comm
Amount
Approx_VIX_Level
Comments






0
2024-08-05
Buy to Open
VIX 09/18/2024 34.00 P
1
10.95
1.08
1096.08
34.33
NaN




1
2024-08-21
Sell to Close
VIX 09/18/2024 34.00 P
1
17.95
1.08
1793.92
16.50
NaN




2
2024-08-05
Buy to Open
VIX 10/16/2024 40.00 P
1
16.35
1.08
1636.08
42.71
NaN




3
2024-09-18
Sell to Close
VIX 10/16/2024 40.00 P
1
21.54
1.08
2152.92
18.85
NaN




4
2024-08-07
Buy to Open
VIX 11/20/2024 25.00 P
2
5.90
2.16
1182.16
27.11
NaN




5
2024-11-04
Sell to Close
VIX 11/20/2024 25.00 P
2
6.10
2.16
1217.84
22.43
NaN




6
2024-08-06
Buy to Open
VIX 12/18/2024 30.00 P
1
10.25
1.08
1026.08
32.27
NaN




7
2024-11-27
Sell to Close
VIX 12/18/2024 30.00 P
1
14.95
1.08
1493.92
14.04
NaN




8
2025-03-04
Buy to Open
VIX 04/16/2025 25.00 P
5
5.65
5.40
2830.40
25.75
NaN




9
2025-03-24
Sell to Close
VIX 04/16/2025 25.00 P
5
7.00
5.40
3494.60
18.01
NaN




10
2025-03-10
Buy to Open
VIX 05/21/2025 26.00 P
5
7.10
5.40
3555.40
27.54
Missed opportunity to close position for 20% p...




11
2025-04-04
Buy to Open
VIX 05/21/2025 26.00 P
10
4.10
10.81
4110.81
38.88
Averaged down on existing position




12
2025-04-24
Sell to Close
VIX 05/21/2025 26.00 P
7
3.50
7.57
2442.43
27.37
Sold half of position due to vol spike concern...




13
2025-05-02
Sell to Close
VIX 05/21/2025 26.00 P
4
4.35
4.32
1735.68
22.73
Sold half of remaining position due to vol spi...




14
2025-05-07
Sell to Close
VIX 05/21/2025 26.00 P
4
3.55
4.32
1415.68
24.49
Closed position ahead of Fed’s (Powell’s) comm...




15
2025-04-04
Buy to Open
VIX 05/21/2025 37.00 P
3
13.20
3.24
3963.24
36.46
NaN




16
2025-05-07
Sell to Close
VIX 05/21/2025 37.00 P
3
13.75
3.24
4121.76
24.51
Closed position ahead of Fed’s (Powell’s) comm...




17
2025-04-08
Buy to Open
VIX 05/21/2025 50.00 P
2
21.15
2.16
4232.16
NaN
NaN




18
2025-04-24
Sell to Close
VIX 05/21/2025 50.00 P
1
25.30
1.08
2528.92
NaN
NaN




19
2025-04-25
Sell to Close
VIX 05/21/2025 50.00 P
1
25.65
1.08
2563.92
NaN
NaN




20
2025-04-03
Buy to Open
VIX 06/18/2025 27.00 P
8
7.05
8.65
5648.65
27.62
NaN




21
2025-04-08
Buy to Open
VIX 06/18/2025 27.00 P
4
4.55
4.32
1824.32
55.44
Averaged down on existing position




22
2025-05-12
Sell to Close
VIX 06/18/2025 27.00 P
6
7.55
6.49
4523.51
19.05
Market up on positive news of lowering tariffs...




23
2025-05-12
Sell to Close
VIX 06/18/2025 27.00 P
6
7.40
6.49
4433.51
19.47
Market up on positive news of lowering tariffs...




24
2025-04-04
Buy to Open
VIX 06/18/2025 36.00 P
3
13.40
3.24
4023.24
36.61
NaN




25
2025-05-12
Sell to Close
VIX 06/18/2025 36.00 P
3
16.00
3.24
4796.76
19.14
Market up on positive news of lowering tariffs...




26
2025-04-07
Buy to Open
VIX 06/18/2025 45.00 P
2
18.85
2.16
3772.16
53.65
NaN




27
2025-05-12
Sell to Close
VIX 06/18/2025 45.00 P
2
25.00
2.16
4997.84
19.24
Market up on positive news of lowering tariffs...




28
2025-04-03
Buy to Open
VIX 07/16/2025 29.00 P
5
8.55
5.40
4280.40
29.03
NaN




29
2025-05-13
Sell to Close
VIX 07/16/2025 29.00 P
3
10.40
3.24
3116.76
17.72
NaN




30
2025-05-13
Sell to Close
VIX 07/16/2025 29.00 P
2
10.30
2.16
2057.84
17.68
NaN




31
2025-04-04
Buy to Open
VIX 07/16/2025 36.00 P
3
13.80
3.24
4143.24
36.95
NaN




32
2025-05-13
Sell to Close
VIX 07/16/2025 36.00 P
1
17.00
1.08
1698.92
17.79
NaN




33
2025-05-13
Sell to Close
VIX 07/16/2025 36.00 P
2
16.90
2.16
3377.84
17.72
NaN




34
2025-04-07
Buy to Open
VIX 07/16/2025 45.00 P
2
21.55
2.16
4312.16
46.17
NaN




35
2025-05-13
Sell to Close
VIX 07/16/2025 45.00 P
2
25.65
2.16
5127.84
17.96
NaN




36
2025-04-07
Buy to Open
VIX 08/20/2025 45.00 P
2
21.75
2.16
4352.16
49.07
NaN




37
2025-05-13
Sell to Close
VIX 08/20/2025 45.00 P
2
25.40
2.16
5077.84
18.06
NaN


























In [66]:






# 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

























Volatility In August 2024¶


















In [67]:






esd = "2024-09-18"
eed = "2024-12-18"
tsd = "2024-08-05"
ted = "2024-11-27"

trades, closed_pos, open_pos, per_pnl, pnl = calc_vix_trade_pnl(
    transaction_df=vix_transactions,
    exp_start_date=esd,
    exp_end_date=eed,
    trade_start_date=tsd,
    trade_end_date=ted,
)

# Convert to datetime objects
tsd_dt = datetime.strptime(tsd, "%Y-%m-%d")
ted_dt = datetime.strptime(ted, "%Y-%m-%d")

# Adjust dates
plot_start = tsd_dt - timedelta(days=10)
plot_end = ted_dt + timedelta(days=10)

plot_vix_with_trades(
    vix_price_df=vix,
    trades_df=trades,
    plot_start_date=plot_start.strftime("%Y-%m-%d"),
    plot_end_date=plot_end.strftime("%Y-%m-%d"),
    x_tick_spacing=10,
    y_tick_spacing=5,
    index_number="11",
    export_plot=True,
)

print(f"<!-- INSERT_11_Closed_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_11_Open_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_11_Percent_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_11_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"11_Closed_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"11_Open_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"11_Percent_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=per_pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"11_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=pnl)
























No description has been provided for this image











<!-- INSERT_11_Closed_Positions_2024-09-18_2024-12-18_2024-08-05_2024-11-27_HERE -->
<!-- INSERT_11_Open_Positions_2024-09-18_2024-12-18_2024-08-05_2024-11-27_HERE -->
<!-- INSERT_11_Percent_PnL_2024-09-18_2024-12-18_2024-08-05_2024-11-27_HERE -->
<!-- INSERT_11_PnL_2024-09-18_2024-12-18_2024-08-05_2024-11-27_HERE -->
✅ Exported and tracked: 11_Closed_Positions_2024-09-18_2024-12-18_2024-08-05_2024-11-27.md
✅ Exported and tracked: 11_Open_Positions_2024-09-18_2024-12-18_2024-08-05_2024-11-27.md
✅ Exported and tracked: 11_Percent_PnL_2024-09-18_2024-12-18_2024-08-05_2024-11-27.md
✅ Exported and tracked: 11_PnL_2024-09-18_2024-12-18_2024-08-05_2024-11-27.md

























Volatility In March 2025¶


















In [68]:






esd = "2025-04-16"
eed = "2025-04-16"
tsd = "2025-03-04"
ted = "2025-03-24"

trades, closed_pos, open_pos, per_pnl, pnl = calc_vix_trade_pnl(
    transaction_df=vix_transactions,
    exp_start_date=esd,
    exp_end_date=eed,
    trade_start_date=tsd,
    trade_end_date=ted,
)

# Convert to datetime objects
tsd_dt = datetime.strptime(tsd, "%Y-%m-%d")
ted_dt = datetime.strptime(ted, "%Y-%m-%d")

# Adjust dates
plot_start = tsd_dt - timedelta(days=10)
plot_end = ted_dt + timedelta(days=10)

plot_vix_with_trades(
    vix_price_df=vix,
    trades_df=trades,
    plot_start_date=plot_start.strftime("%Y-%m-%d"),
    plot_end_date=plot_end.strftime("%Y-%m-%d"),
    x_tick_spacing=2,
    y_tick_spacing=2,
    index_number="12",
    export_plot=True,
)

print(f"<!-- INSERT_12_Closed_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_12_Open_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_12_Percent_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_12_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"12_Closed_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"12_Open_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"12_Percent_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=per_pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"12_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=pnl)
























No description has been provided for this image











<!-- INSERT_12_Closed_Positions_2025-04-16_2025-04-16_2025-03-04_2025-03-24_HERE -->
<!-- INSERT_12_Open_Positions_2025-04-16_2025-04-16_2025-03-04_2025-03-24_HERE -->
<!-- INSERT_12_Percent_PnL_2025-04-16_2025-04-16_2025-03-04_2025-03-24_HERE -->
<!-- INSERT_12_PnL_2025-04-16_2025-04-16_2025-03-04_2025-03-24_HERE -->
✅ Exported and tracked: 12_Closed_Positions_2025-04-16_2025-04-16_2025-03-04_2025-03-24.md
✅ Exported and tracked: 12_Open_Positions_2025-04-16_2025-04-16_2025-03-04_2025-03-24.md
✅ Exported and tracked: 12_Percent_PnL_2025-04-16_2025-04-16_2025-03-04_2025-03-24.md
✅ Exported and tracked: 12_PnL_2025-04-16_2025-04-16_2025-03-04_2025-03-24.md

























Volatility In April 2025¶


















In [69]:






esd = "2025-05-21"
eed = "2025-08-20"
tsd = "2025-03-10"
ted = "2025-05-13"

trades, closed_pos, open_pos, per_pnl, pnl = calc_vix_trade_pnl(
    transaction_df=vix_transactions,
    exp_start_date=esd,
    exp_end_date=eed,
    trade_start_date=tsd,
    trade_end_date=ted,
)

# Convert to datetime objects
tsd_dt = datetime.strptime(tsd, "%Y-%m-%d")
ted_dt = datetime.strptime(ted, "%Y-%m-%d")

# Adjust dates
plot_start = tsd_dt - timedelta(days=10)
plot_end = ted_dt + timedelta(days=10)

plot_vix_with_trades(
    vix_price_df=vix,
    trades_df=trades,
    plot_start_date=plot_start.strftime("%Y-%m-%d"),
    plot_end_date=plot_end.strftime("%Y-%m-%d"),
    x_tick_spacing=5,
    y_tick_spacing=5,
    index_number="13",
    export_plot=True,
)

print(f"<!-- INSERT_13_Closed_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_13_Open_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_13_Percent_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_13_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"13_Closed_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"13_Open_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"13_Percent_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=per_pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"13_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=pnl)
























No description has been provided for this image











<!-- INSERT_13_Closed_Positions_2025-05-21_2025-08-20_2025-03-10_2025-05-13_HERE -->
<!-- INSERT_13_Open_Positions_2025-05-21_2025-08-20_2025-03-10_2025-05-13_HERE -->
<!-- INSERT_13_Percent_PnL_2025-05-21_2025-08-20_2025-03-10_2025-05-13_HERE -->
<!-- INSERT_13_PnL_2025-05-21_2025-08-20_2025-03-10_2025-05-13_HERE -->
✅ Exported and tracked: 13_Closed_Positions_2025-05-21_2025-08-20_2025-03-10_2025-05-13.md
✅ Exported and tracked: 13_Open_Positions_2025-05-21_2025-08-20_2025-03-10_2025-05-13.md
✅ Exported and tracked: 13_Percent_PnL_2025-05-21_2025-08-20_2025-03-10_2025-05-13.md
✅ Exported and tracked: 13_PnL_2025-05-21_2025-08-20_2025-03-10_2025-05-13.md

























Complete Trade History¶


















In [70]:






esd = None
eed = None
tsd = None
ted = None

trades, closed_pos, open_pos, per_pnl, pnl = calc_vix_trade_pnl(
    transaction_df=vix_transactions,
    exp_start_date=esd,
    exp_end_date=eed,
    trade_start_date=tsd,
    trade_end_date=ted,
)

print(f"<!-- INSERT_14_Closed_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_14_Open_Positions_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_14_Percent_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
print(f"<!-- INSERT_14_PnL_{esd}_{eed}_{tsd}_{ted}_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"14_Closed_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"14_Open_Positions_{esd}_{eed}_{tsd}_{ted}.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"14_Percent_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=per_pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"14_PnL_{esd}_{eed}_{tsd}_{ted}.md", content=pnl)

























<!-- INSERT_14_Closed_Positions_None_None_None_None_HERE -->
<!-- INSERT_14_Open_Positions_None_None_None_None_HERE -->
<!-- INSERT_14_Percent_PnL_None_None_None_None_HERE -->
<!-- INSERT_14_PnL_None_None_None_None_HERE -->
✅ Exported and tracked: 14_Closed_Positions_None_None_None_None.md
✅ Exported and tracked: 14_Open_Positions_None_None_None_None.md
✅ Exported and tracked: 14_Percent_PnL_None_None_None_None.md
✅ Exported and tracked: 14_PnL_None_None_None_None.md