IV_vs_Zscore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import numpy as np\n",
    "import json\n",
    "import re\n",
    "from gvol import GVol\n",
    "from plotly.subplots import make_subplots\n",
    "from datetime import datetime\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "GVOL_API_KEY = \"GVOL_API_KEY\"\n",
    "gvol_client = GVol(header='gvol-lite',gvol_api_key=GVOL_API_KEY)\n",
    "url = \"https://app.pinkswantrading.com/graphql\"\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>currency</th>\n",
       "      <th>ln</th>\n",
       "      <th>clse</th>\n",
       "      <th>zScore</th>\n",
       "      <th>dvol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-30</th>\n",
       "      <td>BTC</td>\n",
       "      <td>0.080</td>\n",
       "      <td>30470.45</td>\n",
       "      <td>0.03</td>\n",
       "      <td>43.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.221</td>\n",
       "      <td>30446.61</td>\n",
       "      <td>0.48</td>\n",
       "      <td>48.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-28</th>\n",
       "      <td>BTC</td>\n",
       "      <td>-2.032</td>\n",
       "      <td>30080.94</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>48.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-27</th>\n",
       "      <td>BTC</td>\n",
       "      <td>1.382</td>\n",
       "      <td>30692.14</td>\n",
       "      <td>0.53</td>\n",
       "      <td>49.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-26</th>\n",
       "      <td>BTC</td>\n",
       "      <td>-0.684</td>\n",
       "      <td>30269.26</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>49.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           currency     ln      clse  zScore   dvol\n",
       "date                                               \n",
       "2023-06-30      BTC  0.080  30470.45    0.03  43.52\n",
       "2023-06-29      BTC  1.221  30446.61    0.48  48.44\n",
       "2023-06-28      BTC -2.032  30080.94   -0.79  48.92\n",
       "2023-06-27      BTC  1.382  30692.14    0.53  49.51\n",
       "2023-06-26      BTC -0.684  30269.26   -0.26  49.51"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare the payload for the data request\n",
    "# This is a GraphQL query that fetches live fixed delta surface data from the Deribit exchange for BTC\n",
    "payload=\"{\\\"query\\\":\\\"query ZScoreDvol($symbol: SymbolEnumType, $exchange: ExchangeEnumType, $dateStart: String, $dateEnd: String) {ZScoreDvol(symbol: $symbol, exchange: $exchange dateStart: $dateStart dateEnd: $dateEnd) { date currency ln clse zScore dvol }   }\\\",\\\"variables\\\":{\\\"symbol\\\":\\\"BTC\\\",\\\"exchange\\\":\\\"deribit\\\",\\\"dateStart\\\":\\\"2020-01-01\\\",\\\"dateEnd\\\":\\\"2023-07-01\\\"}}\"\n",
    "\n",
    "# Set up the request headers\n",
    "# The x-oracle header is used for authentication\n",
    "headers = {\n",
    "  'x-oracle': GVOL_API_KEY,\n",
    "  'Content-Type': 'application/json',\n",
    "  'accept': '*/*',\n",
    "  'Accept-Language': 'en-US,en;q=0.9'\n",
    "}\n",
    "\n",
    "# Send the GET request to the specified URL\n",
    "# The response will contain the requested data\n",
    "response = requests.request(\"GET\", url, headers=headers, data=payload)\n",
    "\n",
    "# Parse the response text into JSON format\n",
    "data = json.loads(response.text)\n",
    "\n",
    "# Convert the JSON data into a pandas DataFrame\n",
    "# The json_normalize function is used to flatten the JSON into a table\n",
    "z_scores = pd.json_normalize(data['data']['ZScoreDvol'])\n",
    "\n",
    "# Convert 'date' column to datetime and set as index\n",
    "z_scores['date'] = pd.to_datetime(z_scores['date'], unit='ms')\n",
    "z_scores.set_index('date', inplace=True)\n",
    "z_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_close(df, lower_percentile, upper_percentile):\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=z_scores.index,\n",
    "        y=z_scores['dvol'],\n",
    "        mode='lines',\n",
    "        name='Closing Price',\n",
    "        line=dict(color='white')\n",
    "    ))\n",
    "\n",
    "    # Calculate the percentile values\n",
    "    upper_value = z_scores['dvol'].quantile(upper_percentile)\n",
    "    lower_value = z_scores['dvol'].quantile(lower_percentile)\n",
    "\n",
    "    # Add shaded region for values above upper percentile\n",
    "    fig.add_shape(\n",
    "        type='rect', \n",
    "        xref='x', yref='y', \n",
    "        x0=df.index.min(), y0=upper_value, \n",
    "        x1=df.index.max(), y1=z_scores['dvol'].max(), \n",
    "        fillcolor='Red', opacity=0.2, \n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Add shaded region for values below lower percentile\n",
    "    fig.add_shape(\n",
    "        type='rect', \n",
    "        xref='x', yref='y', \n",
    "        x0=df.index.min(), y0=lower_value, \n",
    "        x1=df.index.max(), y1=z_scores['dvol'].min(), \n",
    "        fillcolor='Green', opacity=0.2, \n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Add annotations for percentiles\n",
    "    fig.add_annotation(\n",
    "        x=.99, y=0.98, xref='paper', yref='paper',\n",
    "        text=f'Upper {upper_percentile*100} percentile',\n",
    "        showarrow=False, font=dict(color='Red')\n",
    "    )\n",
    "    fig.add_annotation(\n",
    "        x=0.99, y=0.925, xref='paper', yref='paper',\n",
    "        text=f'Lower {lower_percentile*100} percentile',\n",
    "        showarrow=False, font=dict(color='Green')\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=\"Bitcoin DVOL Closing Prices: High and Low Percentiles\",\n",
    "        yaxis_title=\"Closing Price ($USD)\",\n",
    "        template='plotly_dark',\n",
    "        yaxis=dict(range=[z_scores['dvol'].min(), z_scores['dvol'].max()]),  # Set y-axis range\n",
    "    )\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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         "text": "Bitcoin DVOL Closing Prices: High and Low Percentiles"
        },
        "yaxis": {
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         "title": {
          "text": "Closing Price ($USD)"
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   "source": [
    "plot_close(z_scores, 0.05, 0.95)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "AD_COLORS = ['#231342', '#882c91', '#e00d8e', '#4c61ab', '#87ddf3']\n",
    "AD_COLORS_FB = ['#4c61ab', '#e00d8e', '#89bed8', '#882c91', '#f16623']\n",
    "AD_COLORS_3 = ['#1c3664', '#89bed8', '#f16623']\n",
    "\n",
    "\n",
    "def plot_close(z_scores, lower_percentile, upper_percentile):\n",
    "    # Create subplots: use two rows and one column\n",
    "    fig = make_subplots(rows=2, cols=1)\n",
    "\n",
    "    # Line plot of z_scores['dvol']\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=z_scores.index,\n",
    "        y=z_scores['dvol'],\n",
    "        mode='lines',\n",
    "        name='DVOL',\n",
    "        line=dict(color='black')  # Set line color to black\n",
    "    ), row=1, col=1)\n",
    "\n",
    "    # Calculate the percentile values\n",
    "    upper_value = z_scores['dvol'].quantile(upper_percentile)\n",
    "    lower_value = z_scores['dvol'].quantile(lower_percentile)\n",
    "\n",
    "    # Calculate the y-axis range for the line plot\n",
    "    yaxis_range = [z_scores['dvol'].min() - 0.20 * z_scores['dvol'].min(), z_scores['dvol'].max()]\n",
    "\n",
    "    # Add shaded region for values above upper percentile\n",
    "    fig.add_shape(\n",
    "        type='rect',\n",
    "        xref='x1', yref='y1',\n",
    "        x0=z_scores.index.min(), y0=upper_value,\n",
    "        x1=z_scores.index.max(), y1=yaxis_range[1],\n",
    "        fillcolor=AD_COLORS_FB[1], opacity=0.2,  # Use Amberdata color for fill color and set opacity to 0.2\n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Add shaded region for values below lower percentile\n",
    "    fig.add_shape(\n",
    "        type='rect',\n",
    "        xref='x1', yref='y1',\n",
    "        x0=z_scores.index.min(), y0=yaxis_range[0],\n",
    "        x1=z_scores.index.max(), y1=lower_value,\n",
    "        fillcolor=AD_COLORS_FB[4], opacity=0.2,  # Use Amberdata color for fill color and set opacity to 0.2\n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Bar plot of z_scores['zScore']\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=z_scores.index,\n",
    "        y=z_scores['zScore'],\n",
    "        name='Z-Score',\n",
    "        marker_color='black',  # Set marker color to black\n",
    "        showlegend=False\n",
    "    ), row=2, col=1)\n",
    "\n",
    "    # Calculate the y-axis range for the bar chart\n",
    "    yaxis2_range = [z_scores['zScore'].min() - 0.20 * z_scores['zScore'].min(), z_scores['zScore'].max()]\n",
    "\n",
    "    # Add annotations for percentiles\n",
    "    fig.add_annotation(\n",
    "        x=0.99, y=0.98, xref='paper', yref='paper',\n",
    "        text=f'Upper {upper_percentile * 100} percentile',\n",
    "        showarrow=False, font=dict(color='black')\n",
    "    )\n",
    "    fig.add_annotation(\n",
    "        x=0.99, y=0.95, xref='paper', yref='paper',\n",
    "        text=f'Lower {lower_percentile * 100} percentile',\n",
    "        showarrow=False, font=dict(color='black')\n",
    "    )\n",
    "\n",
    "    # Update layout\n",
    "    fig.update_layout(\n",
    "        title=\"Bitcoin DVOL and Z-Scores: High and Low Percentiles\",\n",
    "        yaxis_title=\"DVOL\",\n",
    "        yaxis2_title=\"Z-Score\",\n",
    "        template='plotly_white',  # Use the 'plotly_white' template for a white background\n",
    "        yaxis=dict(range=yaxis_range),\n",
    "        yaxis2=dict(range=yaxis2_range),\n",
    "        height=700,\n",
    "        showlegend=False\n",
    "    )\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "plot_close(z_scores, 0.05, 0.95)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "AD_COLORS = ['#231342', '#882c91', '#e00d8e', '#4c61ab', '#87ddf3']\n",
    "AD_COLORS_FB = ['#4c61ab', '#e00d8e', '#89bed8', '#882c91', '#f16623']\n",
    "AD_COLORS_3 = ['#1c3664', '#89bed8', '#f16623']\n",
    "\n",
    "\n",
    "def plot_close(z_scores, lower_percentile, upper_percentile):\n",
    "    # Create subplots: use two rows and one column\n",
    "    fig = make_subplots(rows=2, cols=1)\n",
    "\n",
    "    # Line plot of z_scores['dvol']\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=z_scores.index,\n",
    "        y=z_scores['dvol'],\n",
    "        mode='lines',\n",
    "        name='DVOL',\n",
    "        line=dict(color='black')  # Set line color to black\n",
    "    ), row=1, col=1)\n",
    "\n",
    "    # Calculate the percentile values\n",
    "    upper_value = z_scores['dvol'].quantile(upper_percentile)\n",
    "    lower_value = z_scores['dvol'].quantile(lower_percentile)\n",
    "\n",
    "    # Calculate the y-axis range for the line plot\n",
    "    yaxis_range = [z_scores['dvol'].min() - 0.20 * z_scores['dvol'].min(), z_scores['dvol'].max()]\n",
    "\n",
    "    # Add shaded region for values above upper percentile\n",
    "    fig.add_shape(\n",
    "        type='rect',\n",
    "        xref='x1', yref='y1',\n",
    "        x0=z_scores.index.min(), y0=upper_value,\n",
    "        x1=z_scores.index.max(), y1=yaxis_range[1],\n",
    "        fillcolor=AD_COLORS_3[2], opacity=0.2,  # Use Amberdata color for fill color and set opacity to 0.2\n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Add shaded region for values below lower percentile\n",
    "    fig.add_shape(\n",
    "        type='rect',\n",
    "        xref='x1', yref='y1',\n",
    "        x0=z_scores.index.min(), y0=yaxis_range[0],\n",
    "        x1=z_scores.index.max(), y1=lower_value,\n",
    "        fillcolor=AD_COLORS_3[1], opacity=0.2,  # Use Amberdata color for fill color and set opacity to 0.2\n",
    "        line_width=0,\n",
    "    )\n",
    "\n",
    "    # Bar plot of z_scores['zScore']\n",
    "    fig.add_trace(go.Bar(\n",
    "        x=z_scores.index,\n",
    "        y=z_scores['zScore'],\n",
    "        name='Z-Score',\n",
    "        marker_color='black',  # Set marker color to black\n",
    "        showlegend=False\n",
    "    ), row=2, col=1)\n",
    "\n",
    "    # Calculate the y-axis range for the bar chart\n",
    "    yaxis2_range = [z_scores['zScore'].min() - 0.20 * z_scores['zScore'].min(), z_scores['zScore'].max()]\n",
    "\n",
    "    # Add annotations for percentiles\n",
    "    fig.add_annotation(\n",
    "        x=0.99, y=0.99, xref='paper', yref='paper',\n",
    "        text=f'Upper {upper_percentile * 100} percentile',\n",
    "        showarrow=False, font=dict(color='black')\n",
    "    )\n",
    "    fig.add_annotation(\n",
    "        x=0.99, y=0.95, xref='paper', yref='paper',\n",
    "        text=f'Lower {lower_percentile * 100} percentile',\n",
    "        showarrow=False, font=dict(color='black')\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=\"Bitcoin DVOL and Z-Scores: High and Low Percentiles\",\n",
    "        yaxis_title=\"DVOL\",\n",
    "        yaxis2_title=\"Z-Score\",\n",
    "        template='plotly_white',  # Use the 'plotly_white' template for a white background\n",
    "        yaxis=dict(range=yaxis_range),\n",
    "        yaxis2=dict(range=yaxis2_range),\n",
    "        height=700,\n",
    "        showlegend=False\n",
    "    )\n",
    "\n",
    "    fig.show()\n",
    "    fig.write_image('dvol_zscore_percentiles.svg', engine='kaleido', width=900, height=500)"
   ]
  },
  {
   "cell_type": "code",
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