# 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 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

# 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

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

0

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

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:

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

# 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",
    file_format="excel",
)

# 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()

df_info(vix)

The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 9008 entries, 1990-01-02 to 2025-10-07
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   9008 non-null   float64
 1   High    9008 non-null   float64
 2   Low     9008 non-null   float64
 3   Open    9008 non-null   float64
dtypes: float64(4)
memory usage: 351.9 KB
None
The first 5 rows are:

The last 5 rows are:

# 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

vix_stats = vix.describe()
num_std = range(-1, 6)  # Adjusted to include -1 to 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)

# 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

# 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)

# 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

# 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)

# 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

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

# 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

# Group by year for deciles
vix_deciles_by_year = vix.groupby(vix.index.year)[["Open", "High", "Low", "Close"]].quantile(np.arange(0, 1.1, 0.1))

display(vix_deciles_by_year)

# Copy this <!-- INSERT_01_VIX_Deciles_By_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Deciles_By_Year.md", content=vix_deciles_by_year.to_markdown(floatfmt=".2f"))

✅ Exported and tracked: 01_VIX_Deciles_By_Year.md

current_year = datetime.now().year
last_year = current_year - 1

print(f"Last year: {last_year}")
vix_deciles_last_year = vix_deciles_by_year.loc[last_year]
display(vix_deciles_last_year)

print(f"Current year: {current_year}")
vix_deciles_current_year = vix_deciles_by_year.loc[current_year]
display(vix_deciles_current_year)

Last year: 2024

Current year: 2025

# Copy this <!-- INSERT_01_Last_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_Last_Year.md", content=f"{last_year}")

# Copy this <!-- INSERT_01_VIX_Deciles_Last_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Deciles_Last_Year.md", content=vix_deciles_last_year.to_markdown(floatfmt=".2f"))

✅ Exported and tracked: 01_Last_Year.md
✅ Exported and tracked: 01_VIX_Deciles_Last_Year.md

# Copy this <!-- INSERT_01_Current_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_Current_Year.md", content=f"{current_year}")

# Copy this <!-- INSERT_01_VIX_Deciles_Current_Year_HERE --> to index_temp.md
export_track_md_deps(dep_file=dep_file, md_filename="01_VIX_Deciles_Current_Year.md", content=vix_deciles_current_year.to_markdown(floatfmt=".2f"))

✅ Exported and tracked: 01_Current_Year.md
✅ Exported and tracked: 01_VIX_Deciles_Current_Year.md

import pandas as pd
import math
from typing import Literal

Op = Literal["==", ">=", ">", "<=", "<"]

def compare(value: float, threshold: float, op: Op) -> bool:
    if op == "==":
        return value == threshold
    if op == ">=":
        return value >= threshold
    if op == ">":
        return value > threshold
    if op == "<=":
        return value <= threshold
    if op == "<":
        return value < threshold
    raise ValueError(f"Unsupported op: {op}")

def compute_waits(
    df: pd.DataFrame,
    high_col: str = "High",
    trigger_a: float = 20.0,
    trigger_b: float = 20.0,
    op_a: Op = ">=",
    op_b: Op = ">=",
    strictly_after: bool = True,
) -> pd.DataFrame:
    """
    For each day i where df[high_col] op_a trigger_a, find the next day j
    (j > i if strictly_after else j >= i) where df[high_col] op_b trigger_b.
    """
    df = df.sort_index()
    idx = df.index
    highs = df[high_col].values
    n = len(df)

    rows = []
    for i in range(n):
        if compare(highs[i], trigger_a, op_a):
            start_j = i + 1 if strictly_after else i
            j_found = None
            for j in range(start_j, n):
                if compare(highs[j], trigger_b, op_b):
                    j_found = j
                    break

            if j_found is None:
                next_date = pd.NaT if isinstance(idx, pd.DatetimeIndex) else None
                next_high = math.nan
                wait_td = math.nan
                wait_cd = math.nan
            else:
                next_date = idx[j_found]
                next_high = float(highs[j_found])
                wait_td = j_found - i
                if isinstance(idx, pd.DatetimeIndex):
                    wait_cd = (idx[j_found].normalize() - idx[i].normalize()).days
                else:
                    wait_cd = math.nan

            rows.append(
                {
                    "date_a": idx[i],
                    "high_at_a": float(highs[i]),
                    "date_b": next_date,
                    "high_at_b": next_high,
                    "wait_trading_days": wait_td,
                    "wait_calendar_days": wait_cd,
                }
            )

    return pd.DataFrame(rows)

vix.tail(20)

# When daily high <= 15, how long until next daily high >= 20?
res_lt15_to_gt20 = compute_waits(
    df=vix,
    high_col="High", 
    trigger_a=15, 
    trigger_b=20, 
    op_a="<=", 
    op_b=">=",
    strictly_after=True,
)

res_lt15_to_gt20

# Plot histogram for wait times
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.hist(res_lt15_to_gt20['wait_trading_days'].dropna(), bins=200, alpha=0.5, label='LT 15 to GT 20')
plt.xlabel('Days')
plt.ylabel('Frequency')
plt.title('Wait Times for VIX Highs')
plt.legend()
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from typing import Optional, Tuple

def compute_wait_cdf(
    waits_df: pd.DataFrame,
    column: str = "wait_trading_days",
) -> pd.DataFrame:
    """
    Compute the empirical CDF for wait times.

    Returns a DataFrame with:
      - 'wait': unique wait values (sorted)
      - 'count': frequency for each wait
      - 'cdf': cumulative probability P(Wait <= x)
      - 'ccdf': complementary CDF = 1 - cdf (P(Wait > x))
    """
    # Drop NaNs; don't cast to int (keep original type)
    waits = waits_df[column].dropna().to_numpy()
    if waits.size == 0:
        return pd.DataFrame(columns=["wait", "count", "cdf", "ccdf"])

    # Unique values and counts
    vals, counts = np.unique(waits, return_counts=True)
    cum_counts = np.cumsum(counts)
    n = waits.size
    cdf = cum_counts / n
    ccdf = 1.0 - cdf

    out = pd.DataFrame(
        {"wait": vals, "count": counts, "cdf": cdf, "ccdf": ccdf}
    )
    return out

# def plot_wait_cdf(
#     cdf_df: pd.DataFrame,
#     title: str = "CDF of Wait Times",
#     show_ccdf: bool = False,
#     ax: Optional[plt.Axes] = None,
# ) -> Tuple[plt.Axes, Optional[plt.Axes]]:
#     """
#     Plot the empirical CDF (and optionally the complementary CDF).
#     Returns the axis (or axes).
#     """
#     if cdf_df.empty:
#         raise ValueError("CDF DataFrame is empty; nothing to plot.")

#     if ax is None:
#         fig, ax = plt.subplots(figsize=(7, 4))

#     # Right-continuous step (ECDF)
#     ax.step(cdf_df["wait"], cdf_df["cdf"], where="post")
#     ax.set_xlabel("Wait (days)")
#     ax.set_ylabel("Cumulative probability (CDF)")
#     ax.set_title(title)
#     ax.grid(True)

#     ax_ccdf = None
#     if show_ccdf:
#         fig2, ax_ccdf = plt.subplots(figsize=(7, 4))
#         ax_ccdf.step(cdf_df["wait"], cdf_df["ccdf"], where="post")
#         ax_ccdf.set_xlabel("Wait (days)")
#         ax_ccdf.set_ylabel("Tail probability (1 - CDF)")
#         ax_ccdf.set_title(f"Complementary CDF — {title}")
#         ax_ccdf.grid(True)

#     return ax, ax_ccdf

cdf_df = compute_wait_cdf(
    waits_df=res_lt15_to_gt20,
    column="wait_trading_days")

cdf_df

# Plot CDF
plt.figure(figsize=(12, 6))
plt.plot(cdf_df['wait'], cdf_df['cdf'])
plt.title('CDF of LT 15 to GT 20')
plt.xlabel('Wait')
plt.ylabel('CDF')
plt.grid()
plt.show()

import pandas as pd
import numpy as np

def empirical_hit_probabilities(
    df: pd.DataFrame,
    high_col: str,
    thresholds,
    horizons,
) -> pd.DataFrame:
    """
    Compute empirical probability of reaching a threshold high within given horizons.

    Parameters
    ----------
    df : pd.DataFrame
        Sorted daily data with a High column.
    high_col : str
        Name of the column with daily highs.
    thresholds : list of floats
        Target levels (e.g. [18,19,20,21,22]).
    horizons : list of ints
        Lookahead windows in trading days.

    Returns
    -------
    pd.DataFrame
        Rows = horizons, Cols = thresholds, entries = probability [0,1].
    """
    df = df.sort_index()
    highs = df[high_col].values
    n = len(highs)

    probs = pd.DataFrame(index=horizons, columns=thresholds, dtype=float)

    for h in horizons:
        valid_starts = n - h  # last h days have incomplete windows
        if valid_starts <= 0:
            continue
        window_max = np.array([highs[i+1:i+h+1].max() for i in range(valid_starts)])
        # note: exclude same day (i+1:...) so it's "future" only
        for thr in thresholds:
            hits = (window_max >= thr).sum()
            probs.loc[h, thr] = hits / valid_starts

    return probs

probs = empirical_hit_probabilities(
    df=vix, 
    high_col="High",
    thresholds=range(15, 30),
    horizons=range(5, 101,5),
)

display(probs)

import pandas as pd
import numpy as np

def conditional_hit_probabilities(
    df: pd.DataFrame,
    today_high: float,
    high_col: str,
    thresholds,
    horizons,
    tolerance: float,  # how close history must be to today's high
) -> pd.DataFrame:
    """
    Conditional probability of hitting thresholds within horizons,
    given today's high is near `today_high`.

    Parameters
    ----------
    df : pd.DataFrame
        Daily data with highs.
    today_high : float
        Today's observed high.
    high_col : str
        Column containing the daily highs.
    thresholds : list of floats
        Target levels to evaluate (e.g., [18,19,20,...]).
    horizons : list of ints
        Lookahead windows (in trading days).
    tolerance : float
        Acceptable deviation from today's high when finding historical analogues.

    Returns
    -------
    pd.DataFrame
        Probabilities indexed by horizon (rows) and thresholds (columns).
    """
    df = df.sort_index()
    highs = df[high_col].values
    n = len(highs)

    # Find indices where the high ~ today's high
    candidates = np.where((highs >= today_high - tolerance) & (highs <= today_high + tolerance))[0]
    if len(candidates) == 0:
        raise ValueError("No historical days found within tolerance of today's high")

    probs = pd.DataFrame(index=horizons, columns=thresholds, dtype=float)

    for h in horizons:
        valid_hits = 0
        total = 0
        for i in candidates:
            if i + h < n:  # need full window
                window_max = highs[i+1:i+h+1].max()  # strictly future days
                for thr in thresholds:
                    if np.isnan(probs.loc[h, thr]):
                        probs.loc[h, thr] = 0.0
                    if window_max >= thr:
                        probs.loc[h, thr] += 1
                total += 1
        if total > 0:
            probs.loc[h] /= total  # normalize to probability
        else:
            probs.loc[h] = np.nan  # no valid examples

    return probs

# Get yesterday's high as an example
yesterday = vix.iloc[-2]
yesterday_high = vix['High'].iloc[-2]

display(yesterday)
display(yesterday_high)

cond_probs = conditional_hit_probabilities(
    df=vix,
    today_high=yesterday_high,
    high_col="High",
    thresholds=range(15, 31),
    horizons=range(5, 71, 5),
    tolerance=0.25
)

display(cond_probs)

Close   16.37
High    17.09
Low     16.29
Open    16.74
Name: 2025-10-06 00:00:00, dtype: float64

np.float64(17.09000015258789)

# 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()

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",
)

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",
)

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"
)

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"
)

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:

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

# 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",
    file_format="excel",
)

# 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()

df_info(vvix)

The columns, shape, and data types are:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 4711 entries, 2007-01-03 to 2025-10-06
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   4711 non-null   float64
 1   High    4711 non-null   float64
 2   Low     4711 non-null   float64
 3   Open    4711 non-null   float64
dtypes: float64(4)
memory usage: 184.0 KB
None
The first 5 rows are:

The last 5 rows are:

# 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

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)

# 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

# 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)

# 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

# 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)

# 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

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

# 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

# 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()

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",
)

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",
)

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"
)

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"
)

# 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()

# 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()

# 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()

display(vix)

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

# 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)

# 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)

display(spike_count_EMA)

# 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()

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()

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

vix_plot('1990', '1994')

vix_plot('1995', '1999')

vix_plot('2000', '2004')

vix_plot('2005', '2009')

vix_plot('2010', '2014')

vix_plot('2015', '2019')

vix_plot('2020', '2024')

vix_plot('2025', '2029')

# from schwab_order_history import schwab_order_history

# 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)

# df_2024

# # 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])

# df_vix

# 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")

# 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

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

# 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

# Variables to be modifed
esd = "2024-09-18" # Expiration Start Date
eed = "2024-12-18" # Expiration End Date
tsd = "2024-08-05" # Trade Start Date
ted = "2024-11-27" # Trade End Date
index_number = "11"
x_tick_spacing = 10
y_tick_spacing = 5

############################################
## Do not modify the code below this line ##
############################################

trades, closed_pos, open_pos, per_pnl, pnl, tot_opened_pos_mkt_val, tot_closed_pos_mkt_val = 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 the plot start and end 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=x_tick_spacing,
    y_tick_spacing=y_tick_spacing,
    index_number=index_number,
    export_plot=True,
)

print(f"<!-- INSERT_{index_number}_Closed_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Open_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Opened_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Closed_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_PnL_HERE -->")
print(f"<!-- INSERT_{index_number}_Percent_PnL_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Closed_Positions.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Open_Positions.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Opened_Position_Market_Value.md", content=tot_opened_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Closed_Position_Market_Value.md", content=tot_closed_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_PnL.md", content=pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Percent_PnL.md", content=per_pnl)

<!-- INSERT_11_Closed_Positions_HERE -->
<!-- INSERT_11_Open_Positions_HERE -->
<!-- INSERT_11_Total_Opened_Position_Market_Value_HERE -->
<!-- INSERT_11_Total_Closed_Position_Market_Value_HERE -->
<!-- INSERT_11_PnL_HERE -->
<!-- INSERT_11_Percent_PnL_HERE -->
✅ Exported and tracked: 11_Closed_Positions.md
✅ Exported and tracked: 11_Open_Positions.md
✅ Exported and tracked: 11_Total_Opened_Position_Market_Value.md
✅ Exported and tracked: 11_Total_Closed_Position_Market_Value.md
✅ Exported and tracked: 11_PnL.md
✅ Exported and tracked: 11_Percent_PnL.md

# Variables to be modifed
esd = "2025-04-16" # Expiration Start Date
eed = "2025-04-16" # Expiration End Date
tsd = "2025-03-04" # Trade Start Date
ted = "2025-03-24" # Trade End Date
index_number = "12"
x_tick_spacing = 2
y_tick_spacing = 2

############################################
## Do not modify the code below this line ##
############################################

trades, closed_pos, open_pos, per_pnl, pnl, tot_opened_pos_mkt_val, tot_closed_pos_mkt_val = 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 the plot start and end 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=x_tick_spacing,
    y_tick_spacing=y_tick_spacing,
    index_number=index_number,
    export_plot=True,
)

print(f"<!-- INSERT_{index_number}_Closed_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Open_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Opened_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Closed_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_PnL_HERE -->")
print(f"<!-- INSERT_{index_number}_Percent_PnL_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Closed_Positions.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Open_Positions.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Opened_Position_Market_Value.md", content=tot_opened_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Closed_Position_Market_Value.md", content=tot_closed_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_PnL.md", content=pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Percent_PnL.md", content=per_pnl)

<!-- INSERT_12_Closed_Positions_HERE -->
<!-- INSERT_12_Open_Positions_HERE -->
<!-- INSERT_12_Total_Opened_Position_Market_Value_HERE -->
<!-- INSERT_12_Total_Closed_Position_Market_Value_HERE -->
<!-- INSERT_12_PnL_HERE -->
<!-- INSERT_12_Percent_PnL_HERE -->
✅ Exported and tracked: 12_Closed_Positions.md
✅ Exported and tracked: 12_Open_Positions.md
✅ Exported and tracked: 12_Total_Opened_Position_Market_Value.md
✅ Exported and tracked: 12_Total_Closed_Position_Market_Value.md
✅ Exported and tracked: 12_PnL.md
✅ Exported and tracked: 12_Percent_PnL.md

# Variables to be modifed
esd = "2025-05-21" # Expiration Start Date
eed = "2025-08-20" # Expiration End Date
tsd = "2025-03-10" # Trade Start Date
ted = "2025-05-13" # Trade End Date
index_number = "13"
x_tick_spacing = 5
y_tick_spacing = 5

############################################
## Do not modify the code below this line ##
############################################

trades, closed_pos, open_pos, per_pnl, pnl, tot_opened_pos_mkt_val, tot_closed_pos_mkt_val = 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 the plot start and end 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=x_tick_spacing,
    y_tick_spacing=y_tick_spacing,
    index_number=index_number,
    export_plot=True,
)

print(f"<!-- INSERT_{index_number}_Closed_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Open_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Opened_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Closed_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_PnL_HERE -->")
print(f"<!-- INSERT_{index_number}_Percent_PnL_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Closed_Positions.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Open_Positions.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Opened_Position_Market_Value.md", content=tot_opened_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Closed_Position_Market_Value.md", content=tot_closed_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_PnL.md", content=pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Percent_PnL.md", content=per_pnl)

<!-- INSERT_13_Closed_Positions_HERE -->
<!-- INSERT_13_Open_Positions_HERE -->
<!-- INSERT_13_Total_Opened_Position_Market_Value_HERE -->
<!-- INSERT_13_Total_Closed_Position_Market_Value_HERE -->
<!-- INSERT_13_PnL_HERE -->
<!-- INSERT_13_Percent_PnL_HERE -->
✅ Exported and tracked: 13_Closed_Positions.md
✅ Exported and tracked: 13_Open_Positions.md
✅ Exported and tracked: 13_Total_Opened_Position_Market_Value.md
✅ Exported and tracked: 13_Total_Closed_Position_Market_Value.md
✅ Exported and tracked: 13_PnL.md
✅ Exported and tracked: 13_Percent_PnL.md

# Variables to be modifed
esd = "2025-09-17" # Expiration Start Date
eed = "2025-12-31" # Expiration End Date
tsd = "2025-06-26" # Trade Start Date
ted = "2025-12-31"  # Trade End Date
index_number = "14"
x_tick_spacing = 5
y_tick_spacing = 1

############################################
## Do not modify the code below this line ##
############################################

trades, closed_pos, open_pos, per_pnl, pnl, tot_opened_pos_mkt_val, tot_closed_pos_mkt_val = 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 the plot start and end 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=x_tick_spacing,
    y_tick_spacing=y_tick_spacing,
    index_number=index_number,
    export_plot=True,
)

print(f"<!-- INSERT_{index_number}_Closed_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Open_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Opened_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Closed_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_PnL_HERE -->")
print(f"<!-- INSERT_{index_number}_Percent_PnL_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Closed_Positions.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Open_Positions.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Opened_Position_Market_Value.md", content=tot_opened_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Closed_Position_Market_Value.md", content=tot_closed_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_PnL.md", content=pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Percent_PnL.md", content=per_pnl)

<!-- INSERT_14_Closed_Positions_HERE -->
<!-- INSERT_14_Open_Positions_HERE -->
<!-- INSERT_14_Total_Opened_Position_Market_Value_HERE -->
<!-- INSERT_14_Total_Closed_Position_Market_Value_HERE -->
<!-- INSERT_14_PnL_HERE -->
<!-- INSERT_14_Percent_PnL_HERE -->
✅ Exported and tracked: 14_Closed_Positions.md
✅ Exported and tracked: 14_Open_Positions.md
✅ Exported and tracked: 14_Total_Opened_Position_Market_Value.md
✅ Exported and tracked: 14_Total_Closed_Position_Market_Value.md
✅ Exported and tracked: 14_PnL.md
✅ Exported and tracked: 14_Percent_PnL.md

# Variables to be modifed
esd = None
eed = None
tsd = None
ted = None
index_number = "99"

############################################
## Do not modify the code below this line ##
############################################

trades, closed_pos, open_pos, per_pnl, pnl, tot_opened_pos_mkt_val, tot_closed_pos_mkt_val = 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_{index_number}_Closed_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Open_Positions_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Opened_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_Total_Closed_Position_Market_Value_HERE -->")
print(f"<!-- INSERT_{index_number}_PnL_HERE -->")
print(f"<!-- INSERT_{index_number}_Percent_PnL_HERE -->")
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Closed_Positions.md", content=closed_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Open_Positions.md", content=open_pos.to_markdown(index=False, floatfmt=".2f"))
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Opened_Position_Market_Value.md", content=tot_opened_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Total_Closed_Position_Market_Value.md", content=tot_closed_pos_mkt_val)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_PnL.md", content=pnl)
export_track_md_deps(dep_file=dep_file, md_filename=f"{index_number}_Percent_PnL.md", content=per_pnl)

<!-- INSERT_99_Closed_Positions_HERE -->
<!-- INSERT_99_Open_Positions_HERE -->
<!-- INSERT_99_Total_Opened_Position_Market_Value_HERE -->
<!-- INSERT_99_Total_Closed_Position_Market_Value_HERE -->
<!-- INSERT_99_PnL_HERE -->
<!-- INSERT_99_Percent_PnL_HERE -->
✅ Exported and tracked: 99_Closed_Positions.md
✅ Exported and tracked: 99_Open_Positions.md
✅ Exported and tracked: 99_Total_Opened_Position_Market_Value.md
✅ Exported and tracked: 99_Total_Closed_Position_Market_Value.md
✅ Exported and tracked: 99_PnL.md
✅ Exported and tracked: 99_Percent_PnL.md

	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
...	...	...	...	...	...
2025-10-01	16.290001	17.280001	15.980000	17.280001	0
2025-10-02	16.629999	16.920000	15.930000	16.120001	0
2025-10-03	16.650000	17.059999	16.200001	16.350000	0
2025-10-06	16.370001	17.090000	16.290001	16.740000	0
2025-10-07	17.240000	17.549999	16.190001	16.580000	0

	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

	Close	High	Low	Open
Date
2025-10-01	16.29	17.28	15.98	17.28
2025-10-02	16.63	16.92	15.93	16.12
2025-10-03	16.65	17.06	16.20	16.35
2025-10-06	16.37	17.09	16.29	16.74
2025-10-07	17.24	17.55	16.19	16.58

	Close	High	Low	Open
count	9008.00	9008.00	9008.00	9008.00
mean	19.47	20.38	18.79	19.56
std	7.81	8.36	7.36	7.88
min	9.14	9.31	8.56	9.01
25%	13.91	14.57	13.43	13.96
50%	17.61	18.32	17.02	17.66
75%	22.78	23.77	22.11	22.93
max	82.69	89.53	72.76	82.69
mean + -1 std	11.66	12.01	11.43	11.68
mean + 0 std	19.47	20.38	18.79	19.56
mean + 1 std	27.27	28.74	26.16	27.44
mean + 2 std	35.08	37.10	33.52	35.32
mean + 3 std	42.88	45.46	40.88	43.20
mean + 4 std	50.69	53.83	48.24	51.08
mean + 5 std	58.50	62.19	55.60	58.96

	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	29.52	12.45	12.20	82.69	31.46	13.89	12.42	85.47	27.50	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	19.55	6.12	14.31	60.13	20.79	7.43	14.69	60.13	18.41	4.63	14.12	38.58	19.30	5.83	14.22	52.33

	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	18.46	5.75	9.79	44.09	19.15	6.02	10.28	44.44	17.73	5.40	9.37	34.97	18.34	5.68	9.75	40.79
7	17.83	5.67	9.18	48.17	18.53	5.90	9.52	48.46	17.21	5.41	8.84	42.05	17.76	5.60	9.36	44.92
8	19.09	6.67	10.04	45.34	20.03	7.38	10.32	65.73	18.35	6.32	9.52	41.77	19.09	6.80	9.93	48.00
9	20.37	8.23	9.59	48.93	21.21	8.55	9.83	49.35	19.62	7.82	9.36	43.74	20.29	8.12	9.51	46.72
10	21.80	10.25	9.23	79.13	22.79	11.08	9.62	89.53	20.90	9.49	9.11	67.80	21.71	10.22	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

	Close	High	Low	Open
0.00	9.14	9.31	8.56	9.01
0.10	12.13	12.65	11.73	12.15
0.20	13.29	13.89	12.88	13.34
0.30	14.64	15.33	14.14	14.72
0.40	16.10	16.76	15.56	16.13
0.50	17.61	18.32	17.02	17.66
0.60	19.49	20.34	18.95	19.62
0.70	21.57	22.57	20.92	21.70
0.80	24.26	25.25	23.42	24.34
0.90	28.66	29.95	27.73	28.81
1.00	82.69	89.53	72.76	82.69

	Close	High	Low	Open
Date
2025-09-10	15.35	15.63	14.63	14.98
2025-09-11	14.71	15.24	14.65	15.19
2025-09-12	14.76	14.97	14.41	14.62
2025-09-15	15.69	15.84	14.92	15.14
2025-09-16	16.36	16.38	15.44	15.60
2025-09-17	15.72	16.75	15.26	16.42
2025-09-18	15.70	15.80	14.33	14.93
2025-09-19	15.45	16.13	15.29	15.76
2025-09-22	16.10	16.35	15.78	16.14
2025-09-23	16.64	17.07	15.95	16.10
2025-09-24	16.18	17.21	16.18	16.57
2025-09-25	16.74	17.74	16.32	16.55
2025-09-26	15.29	17.05	15.29	16.89
2025-09-29	16.12	16.29	15.74	15.84
2025-09-30	16.28	16.70	16.02	16.49
2025-10-01	16.29	17.28	15.98	17.28
2025-10-02	16.63	16.92	15.93	16.12
2025-10-03	16.65	17.06	16.20	16.35
2025-10-06	16.37	17.09	16.29	16.74
2025-10-07	17.24	17.55	16.19	16.58

	date_a	high_at_a	date_b	high_at_b	wait_trading_days	wait_calendar_days
0	1990-06-21	14.72	1990-07-23	23.68	21.00	32.00
1	1991-03-14	14.94	1991-04-09	20.12	17.00	26.00
2	1991-03-15	14.90	1991-04-09	20.12	16.00	25.00
3	1991-08-13	14.73	1991-08-19	21.19	4.00	6.00
4	1991-08-22	14.59	1991-11-15	21.18	60.00	85.00
...	...	...	...	...	...	...
2526	2024-12-13	14.25	2024-12-18	28.32	3.00	5.00
2527	2024-12-16	14.69	2024-12-18	28.32	2.00	2.00
2528	2025-08-13	14.81	NaT	NaN	NaN	NaN
2529	2025-08-28	14.69	NaT	NaN	NaN	NaN
2530	2025-09-12	14.97	NaT	NaN	NaN	NaN

	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29
5	0.78	0.73	0.66	0.59	0.54	0.49	0.44	0.39	0.35	0.30	0.26	0.23	0.20	0.18	0.16
10	0.83	0.78	0.71	0.64	0.59	0.54	0.49	0.43	0.40	0.35	0.30	0.27	0.25	0.22	0.20
15	0.85	0.81	0.75	0.68	0.62	0.57	0.53	0.47	0.43	0.38	0.34	0.31	0.28	0.25	0.22
20	0.87	0.83	0.77	0.71	0.65	0.60	0.56	0.49	0.46	0.41	0.37	0.33	0.31	0.28	0.25
25	0.89	0.85	0.79	0.74	0.68	0.63	0.59	0.52	0.49	0.43	0.39	0.36	0.33	0.31	0.27
30	0.91	0.87	0.81	0.75	0.70	0.65	0.61	0.54	0.51	0.45	0.41	0.38	0.36	0.33	0.29
35	0.92	0.89	0.83	0.77	0.72	0.67	0.63	0.56	0.53	0.47	0.43	0.40	0.38	0.35	0.31
40	0.93	0.90	0.84	0.79	0.74	0.69	0.65	0.58	0.55	0.49	0.45	0.42	0.40	0.37	0.33
45	0.93	0.91	0.85	0.80	0.75	0.71	0.67	0.59	0.56	0.50	0.47	0.44	0.42	0.39	0.34
50	0.94	0.92	0.86	0.81	0.77	0.72	0.69	0.61	0.58	0.51	0.48	0.45	0.43	0.40	0.36
55	0.95	0.92	0.87	0.82	0.78	0.74	0.71	0.62	0.59	0.53	0.50	0.47	0.45	0.41	0.37
60	0.95	0.93	0.88	0.83	0.79	0.75	0.72	0.64	0.61	0.54	0.51	0.48	0.46	0.43	0.39
65	0.96	0.94	0.89	0.84	0.81	0.76	0.74	0.65	0.62	0.55	0.52	0.50	0.48	0.44	0.40
70	0.96	0.94	0.90	0.85	0.82	0.77	0.75	0.66	0.63	0.56	0.53	0.51	0.49	0.45	0.41
75	0.97	0.95	0.91	0.86	0.83	0.78	0.76	0.67	0.64	0.57	0.54	0.52	0.50	0.46	0.42
80	0.97	0.95	0.91	0.86	0.83	0.79	0.77	0.68	0.66	0.58	0.55	0.53	0.51	0.47	0.43
85	0.97	0.96	0.92	0.87	0.84	0.80	0.78	0.69	0.67	0.59	0.57	0.54	0.53	0.49	0.45
90	0.98	0.96	0.92	0.88	0.85	0.81	0.80	0.70	0.67	0.60	0.57	0.55	0.54	0.50	0.46
95	0.98	0.97	0.93	0.88	0.85	0.82	0.80	0.71	0.68	0.60	0.58	0.56	0.55	0.51	0.47
100	0.98	0.97	0.93	0.89	0.86	0.83	0.81	0.71	0.69	0.61	0.59	0.57	0.55	0.51	0.48

	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30
5	0.99	0.96	0.79	0.47	0.29	0.19	0.13	0.05	0.04	0.03	0.02	0.02	0.01	0.01	0.01	0.01
10	0.99	0.98	0.85	0.64	0.44	0.32	0.26	0.14	0.12	0.07	0.06	0.06	0.03	0.03	0.02	0.02
15	1.00	0.98	0.91	0.73	0.52	0.40	0.33	0.19	0.17	0.11	0.09	0.08	0.06	0.06	0.04	0.03
20	1.00	0.98	0.94	0.76	0.58	0.45	0.38	0.24	0.21	0.16	0.14	0.12	0.10	0.09	0.06	0.06
25	1.00	0.98	0.93	0.81	0.64	0.49	0.43	0.28	0.25	0.20	0.19	0.17	0.14	0.12	0.09	0.08
30	1.00	0.98	0.94	0.84	0.69	0.55	0.47	0.31	0.29	0.23	0.22	0.20	0.18	0.15	0.10	0.09
35	1.00	0.99	0.96	0.86	0.72	0.60	0.50	0.35	0.32	0.26	0.25	0.23	0.21	0.18	0.14	0.13
40	1.00	0.99	0.96	0.87	0.75	0.63	0.53	0.37	0.35	0.29	0.28	0.26	0.23	0.19	0.14	0.14
45	1.00	0.99	0.96	0.90	0.79	0.67	0.58	0.40	0.38	0.30	0.29	0.27	0.24	0.20	0.16	0.15
50	1.00	1.00	0.97	0.90	0.82	0.70	0.62	0.45	0.41	0.33	0.32	0.30	0.28	0.23	0.18	0.17
55	1.00	1.00	0.97	0.90	0.83	0.73	0.65	0.47	0.43	0.35	0.34	0.32	0.30	0.24	0.20	0.19
60	1.00	1.00	0.97	0.90	0.85	0.74	0.66	0.48	0.44	0.36	0.35	0.33	0.32	0.26	0.21	0.21
65	1.00	1.00	0.97	0.92	0.86	0.76	0.68	0.50	0.45	0.38	0.37	0.36	0.34	0.28	0.23	0.23
70	1.00	1.00	0.97	0.93	0.88	0.77	0.70	0.51	0.45	0.38	0.37	0.37	0.35	0.29	0.23	0.23

	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
...	...	...	...	...	...
2025-09-30	95.89	98.29	94.78	97.59	0
2025-10-01	93.22	96.60	92.36	96.60	0
2025-10-02	94.10	94.32	91.96	92.42	0
2025-10-03	94.79	95.86	93.45	93.82	0
2025-10-06	95.38	98.49	95.35	96.92	0

	Close	High	Low	Open
count	4711.00	4711.00	4711.00	4711.00
mean	93.55	95.62	91.98	93.80
std	16.31	17.93	14.98	16.37
min	59.74	59.74	59.31	59.31
25%	82.49	83.63	81.56	82.69
50%	90.71	92.47	89.58	91.03
75%	102.05	104.87	99.82	102.45
max	207.59	212.22	187.27	212.22
mean + -1 std	77.24	77.69	77.01	77.43
mean + 0 std	93.55	95.62	91.98	93.80
mean + 1 std	109.87	113.56	106.96	110.17
mean + 2 std	126.18	131.49	121.94	126.54
mean + 3 std	142.50	149.42	136.92	142.91
mean + 4 std	158.81	167.36	151.89	159.28
mean + 5 std	175.12	185.29	166.87	175.65

	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	102.07	13.46	83.19	186.33	106.39	16.21	85.82	189.03	98.45	10.50	81.73	146.51	101.39	12.76	81.89	170.92

	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	93.16	14.86	63.52	155.48	94.76	16.11	63.52	172.21	91.49	13.79	63.52	140.15	92.98	14.83	63.52	151.60
7	90.10	12.82	67.21	138.42	91.63	13.88	67.21	149.60	88.60	11.94	67.21	133.82	89.98	12.78	67.21	139.54
8	96.84	16.53	68.05	212.22	98.99	18.33	68.05	212.22	94.67	14.50	68.05	148.68	96.61	16.24	68.05	173.32
9	94.91	13.70	67.94	135.17	96.84	15.36	67.94	147.14	93.04	12.20	67.94	128.46	94.58	13.44	67.94	138.93
10	97.71	13.94	64.97	149.60	99.39	15.04	64.97	154.99	96.11	13.29	64.97	144.55	97.48	14.09	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

	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

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	178	13

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	181	10

	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	16.49	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	25.74	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
38	2025-06-26	Buy to Open	VIX 09/17/2025 20.00 C	10	3.00	10.81	3010.81	16.37	Opened long dated call position; VIX level at ...	2025-09-17	20.00
39	2025-08-01	Sell to Close	VIX 09/17/2025 20.00 C	5	3.05	5.40	1519.60	20.48	Sold half of position due to theta drag, held ...	2025-09-17	20.00

Investigating A VIX Trading Signal¶

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Trading History¶

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Volatility In August 2024¶

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Complete Trade History¶