gvol-starter-notebook.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "id": "9b8d8deb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gvol import GVol\n",
    "import pandas as pd\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "#pd.reset_option('display.float_format')\n",
    "\n",
    "import numpy as np\n",
    "import mibian\n",
    "\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "import datetime\n",
    "from datetime import date\n",
    "\n",
    "import scipy.interpolate as interpolate\n",
    "\n",
    "import ccxt\n",
    "deribit = ccxt.deribit()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.options.plotting.backend = \"plotly\"\n",
    "\n",
    "gvol_client = GVol(header='x-oracle',gvol_api_key=\"GVOL_API_KEY\")\n",
    "###LITE\n",
    "#gvol_client = GVol(header='gvol-lite',gvol_api_key=\"GVOL_API_KEY_LITE\")\n",
    "\n",
    "\n",
    "from colour import *\n",
    "\n",
    "colore = px.colors.sequential.thermal\n",
    "if len(colore[0])<17:\n",
    "    colore.reverse()\n",
    "\n",
    "###BSM formula for greeks calculation\n",
    "riskfree_rate = 0\n",
    "\n",
    "def BSM(x):\n",
    "    return (mibian.BS([x['indexPrice'],x['strike'],riskfree_rate,x['DTE']], volatility = x['markIv']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c063dcd8",
   "metadata": {},
   "source": [
    "# VOLATILITY CHARTS FOR SELECTED EXCHANGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "087b97e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##SELECT EXCHANGE: (Supported exchanges are |deribit|bitcom|okex|delta|)\n",
    "exchange = 'deribit'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28606a28",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data = gvol_client.options_orderbook_details(exchange=exchange)\n",
    "data = pd.json_normalize(data['UtilityRealtimeOptionbook'])\n",
    "data['date'] = pd.to_datetime(data['date'],unit='ms')\n",
    "data['expiration'] = pd.to_datetime(data['expiration'],unit='ms')\n",
    "\n",
    "data['DTE'] = pd.to_datetime(data['expiration'])- pd.to_datetime(data['date'],unit='ms')\n",
    "data['DTE'] = data['DTE'].apply(datetime.timedelta.total_seconds) / (60*60*24)\n",
    "\n",
    "data['BSM'] = data.apply(BSM,axis=1)\n",
    "data['delta_BSM'] = data.apply(lambda x: x['BSM'].callDelta if x['putCall']=='C' else x['BSM'].putDelta, axis=1)\n",
    "data.loc[data['delta_BSM']>0,'delta_grid'] = 1 - data.loc[data['delta_BSM']>0]['delta_BSM']\n",
    "data.loc[data['delta_BSM']<0,'delta_grid'] = -1 * data.loc[data['delta_BSM']<0]['delta_BSM']\n",
    "\n",
    "#BUILD MONEYNESS\n",
    "data.loc[(data['delta_grid']<0.50)&(data['putCall']=='P'),'moneyness'] = 'OTM'\n",
    "data.loc[(data['delta_grid']>=0.50)&(data['putCall']=='P'),'moneyness'] = 'ITM'\n",
    "data.loc[(data['delta_grid']<0.50)&(data['putCall']=='C'),'moneyness'] = 'ITM'\n",
    "data.loc[(data['delta_grid']>=0.50)&(data['putCall']=='C'),'moneyness'] = 'OTM'\n",
    "\n",
    "###FILTERING INSTRUMENTS FOR HAVING BETTER FITTING ON CURVE\n",
    "data = data[(data['delta_grid'].between(0.02,0.97,inclusive=True))]\n",
    "\n",
    "data = data[data['moneyness']=='OTM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b55f65c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BTC', 'ETH'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "currencies = data.currency.unique()\n",
    "currencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a956249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Select the pair of currencies to be compared in delta grid chart\n",
    "curr1 = currencies[0]\n",
    "curr2 = currencies[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98895ef5",
   "metadata": {},
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   "source": [
    "#DEFINE SUBPLOTS CHART\n",
    "fig = make_subplots(rows=1,cols=2,subplot_titles=(curr1, curr2,))\n",
    "\n",
    "##BTC DELTA GRID CHART\n",
    "orderbookskew = data[(data['currency']==curr1)&(data['moneyness']=='OTM')]\n",
    "i = 0\n",
    "for e in orderbookskew['expiration'].unique():\n",
    "    \n",
    "    orderbookskew_exp = orderbookskew[orderbookskew['expiration']==e]\n",
    "    orderbookskew_exp = orderbookskew_exp.sort_values('delta_grid')\n",
    "  \n",
    "    if len(orderbookskew_exp)<=3:\n",
    "        continue\n",
    "\n",
    "    delta_grid = orderbookskew_exp['delta_grid'].values\n",
    "    markIv = orderbookskew_exp['markIv'].values\n",
    "    \n",
    "    \n",
    "    volfit_param = interpolate.splrep(delta_grid, markIv, s=4)\n",
    "        \n",
    "    strikes_random = np.linspace(0.05,0.95,100)\n",
    "    \n",
    "    x = strikes_random\n",
    "    y = interpolate.splev(strikes_random, volfit_param,ext=3)\n",
    "      \n",
    "    \n",
    "    fig.add_trace(go.Scatter(x=x, y=y,\n",
    "                  name=curr1+\" \"+str(e)[2:10],\n",
    "                  line= dict(color=colore[i])),\n",
    "                  row=1, col=1\n",
    "                 )\n",
    "    \n",
    "    \n",
    "    i = i + 1\n",
    "\n",
    "\n",
    "##ETH DELTA GRID CHART\n",
    "orderbookskew = data[(data['currency']==curr2)&(data['moneyness']=='OTM')]\n",
    "i = 0\n",
    "for e in orderbookskew['expiration'].unique():\n",
    "    \n",
    "    orderbookskew_exp = orderbookskew[orderbookskew['expiration']==e]\n",
    "    orderbookskew_exp = orderbookskew_exp.sort_values('delta_grid')\n",
    "    \n",
    "    if len(orderbookskew_exp)<=3:\n",
    "        continue\n",
    "  \n",
    "    delta_grid = orderbookskew_exp['delta_grid'].values\n",
    "    markIv = orderbookskew_exp['markIv'].values\n",
    "    \n",
    "    \n",
    "    volfit_param = interpolate.splrep(delta_grid, markIv, s=4)\n",
    "        \n",
    "    strikes_random = np.linspace(0.05,0.95,100)\n",
    "    \n",
    "    x = strikes_random\n",
    "    y = interpolate.splev(strikes_random, volfit_param,ext=3)\n",
    "      \n",
    "    \n",
    "    fig.add_trace(go.Scatter(x=x, y=y,\n",
    "                  name=curr2+\" \"+str(e)[2:10],\n",
    "                  line= dict(color=colore[i])),\n",
    "                  row=1, col=2\n",
    "                 )\n",
    "    \n",
    "    \n",
    "    i = i + 1\n",
    "    \n",
    "#GLOBAL CHARTS PARAMETERS\n",
    "fig.update_layout(showlegend=True)\n",
    "fig.update_xaxes(range=[0.05,0.95])\n",
    "#base = max(50,min(y)-5)\n",
    "fig.update_yaxes(range=[50,120])\n",
    "\n",
    "fig.update_layout(title=exchange.upper()+\" VOL CURVE DELTA GRID\",title_x=0.50,\n",
    "                  yaxis_title=\"volatility\",\n",
    "                  legend=dict(\n",
    "                      orientation=\"h\",\n",
    "                      yanchor=\"bottom\",\n",
    "                      y=-0.25,\n",
    "                      xanchor=\"right\",\n",
    "                      x=1.01\n",
    "                      ),\n",
    "                   #['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
    "                   #'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
    "                   #'ygridoff', 'gridon', 'none']\n",
    "                  template = 'gridon')\n",
    "fig.show()"
   ]
  },
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   "execution_count": 7,
   "id": "85ac6539",
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       "                            \n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
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       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#nr_curr = len(data.currency.unique())\n",
    "#DEFINE SUBPLOTS CHART\n",
    "\n",
    "\n",
    "for curr in data.currency.unique():\n",
    "    \n",
    "    fig = make_subplots(rows=1,cols=1)\n",
    "\n",
    "    ##BTC DELTA GRID CHART\n",
    "    orderbookskew = data[data['currency']==curr]\n",
    "    i = 0\n",
    "    for e in orderbookskew['expiration'].unique():\n",
    "\n",
    "        orderbookskew_exp = orderbookskew[orderbookskew['expiration']==e]\n",
    "        orderbookskew_exp = orderbookskew_exp.sort_values('strike')\n",
    "        \n",
    "        if len(orderbookskew_exp)<=3:\n",
    "            continue\n",
    "            \n",
    "        strike = orderbookskew_exp['strike'].values\n",
    "        markIv = orderbookskew_exp['markIv'].values\n",
    "\n",
    "\n",
    "        volfit_param = interpolate.splrep(strike, markIv, s=4)\n",
    "\n",
    "        strikes_random = np.linspace(min(strike),max(strike),100)\n",
    "\n",
    "        x = strikes_random\n",
    "        y = interpolate.splev(strikes_random, volfit_param,ext=3)\n",
    "\n",
    "\n",
    "        fig.add_trace(go.Scatter(x=x, y=y,\n",
    "                      name='BTC '+str(e)[2:10],\n",
    "                      line= dict(color=colore[i])),\n",
    "                      row=1, col=1\n",
    "                     )\n",
    "\n",
    "\n",
    "        i = i + 1\n",
    "\n",
    "    #GLOBAL CHARTS PARAMETERS\n",
    "    fig.update_layout(showlegend=True)\n",
    "    #fig.update_xaxes(range=[20000,70000],row=1,col=1)\n",
    "    #base = max(50,min(y)-5)\n",
    "    #fig.update_yaxes(range=[50,120])\n",
    "    \n",
    "    fig.update_layout(title=curr+\" VOL CURVE STRIKE\",title_x=0.50,\n",
    "                      yaxis_title=\"volatility\",\n",
    "                      legend=dict(\n",
    "                          orientation=\"h\",\n",
    "                          yanchor=\"bottom\",\n",
    "                          y=-0.25,\n",
    "                          xanchor=\"center\",\n",
    "                          x=0.5\n",
    "                                  ),\n",
    "                   #['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
    "                   #'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
    "                   #'ygridoff', 'gridon', 'none']\n",
    "                  template = 'gridon')\n",
    "    fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390c9810",
   "metadata": {},
   "source": [
    "# COMPARISON BETWEEN EXCHANGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53cf5eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "exchange = ('deribit','bitcom','okex','delta')\n",
    "\n",
    "df = pd.DataFrame()\n",
    "data = pd.DataFrame()\n",
    "for e in exchange:\n",
    "    df = gvol_client.options_orderbook_details(exchange=e)\n",
    "    df = pd.json_normalize(df['UtilityRealtimeOptionbook'])\n",
    "    df['exchange'] = e\n",
    "    data = data.append(df)\n",
    "    \n",
    "data['date'] = pd.to_datetime(data['date'],unit='ms')\n",
    "data['expiration'] = pd.to_datetime(data['expiration'],unit='ms')\n",
    "data['exp_date'] = data['expiration'].dt.date.astype(str)\n",
    "\n",
    "data['DTE'] = pd.to_datetime(data['expiration'])- pd.to_datetime(data['date'],unit='ms')\n",
    "data['DTE'] = data['DTE'].apply(datetime.timedelta.total_seconds) / (60*60*24)\n",
    "\n",
    "\n",
    "data.loc[data['indexPrice'].isna(),'indexPrice'] = data.loc[data['indexPrice'].isna()]['underlyingPrice']\n",
    "\n",
    "data['BSM'] = data.apply(BSM,axis=1)\n",
    "data['delta_BSM'] = data.apply(lambda x: x['BSM'].callDelta if x['putCall']=='C' else x['BSM'].putDelta, axis=1)\n",
    "data.loc[data['delta_BSM']>0,'delta_grid'] = 1 - data.loc[data['delta_BSM']>0]['delta_BSM']\n",
    "data.loc[data['delta_BSM']<0,'delta_grid'] = -1 * data.loc[data['delta_BSM']<0]['delta_BSM']\n",
    "\n",
    "#BUILD MONEYNESS\n",
    "data.loc[(data['delta_grid']<0.50)&(data['putCall']=='P'),'moneyness'] = 'OTM'\n",
    "data.loc[(data['delta_grid']>=0.50)&(data['putCall']=='P'),'moneyness'] = 'ITM'\n",
    "data.loc[(data['delta_grid']<0.50)&(data['putCall']=='C'),'moneyness'] = 'ITM'\n",
    "data.loc[(data['delta_grid']>=0.50)&(data['putCall']=='C'),'moneyness'] = 'OTM'\n",
    "\n",
    "###FILTERING INSTRUMENTS FOR HAVING BETTER FITTING ON CURVE\n",
    "data = data[(data['delta_grid'].between(0.02,0.97,inclusive=True))]\n",
    "\n",
    "#data = data[data['moneyness']=='OTM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "722f9fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2022-03-24', '2022-03-25', '2022-04-01', '2022-04-08',\n",
       "       '2022-04-29', '2022-05-27', '2022-06-24', '2022-09-30',\n",
       "       '2022-12-30', '2022-03-23'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.exp_date.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8964c2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BTC', 'ETH', 'BCH', 'AVAX', 'BNB', 'LINK', 'MATIC', 'SOL', 'XRP'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.currency.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc78232e",
   "metadata": {
    "scrolled": false
   },
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   "source": [
    "##SELECT SINGLE EXPIRY/CURRENCY FROM ABOVE ONES\n",
    "exp_date = '2022-06-24'\n",
    "currency = 'BTC'\n",
    "\n",
    "data_exchange = data[(data['exp_date']==exp_date)&(data['currency']==currency)]\n",
    "\n",
    "\n",
    "#DEFINE SUBPLOTS CHART\n",
    "fig = go.Figure()\n",
    "\n",
    "for exch in data_exchange['exchange'].unique():\n",
    "    \n",
    "    orderbookskew_exp = data_exchange[data_exchange['exchange']==exch].sort_values('strike')\n",
    "    \n",
    "    ##CHART FIT CURVE WITH ONLY LIQUID PART OF THE CHAIN\n",
    "    orderbookskew_exp = orderbookskew_exp[orderbookskew_exp['moneyness']=='OTM']\n",
    "\n",
    "    if len(orderbookskew_exp)<=3:\n",
    "        continue\n",
    "\n",
    "    strike = orderbookskew_exp['strike'].values\n",
    "    markIv = orderbookskew_exp['markIv'].values\n",
    "\n",
    "\n",
    "    volfit_param = interpolate.splrep(strike, markIv, s=4)\n",
    "\n",
    "    strikes_random = np.linspace(min(strike),max(strike),100)\n",
    "\n",
    "    x = strikes_random\n",
    "    y = interpolate.splev(strikes_random, volfit_param,ext=3)\n",
    "\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=x, y=y,\n",
    "                  name=exch.upper(),showlegend=True))\n",
    "\n",
    "fig.update_layout(title='VOL FIT '+currency+\" \"+exp_date,title_x=0.50,\n",
    "                  yaxis_title=\"volatility\",\n",
    "                  legend=dict(\n",
    "                      orientation=\"h\",\n",
    "                      yanchor=\"bottom\",\n",
    "                      y=-0.25,\n",
    "                      xanchor=\"center\",\n",
    "                      x=0.5\n",
    "                              ),\n",
    "               #['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
    "               #'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
    "               #'ygridoff', 'gridon', 'none']\n",
    "              template = 'gridon')\n",
    "fig.show()\n",
    "\n",
    "\n",
    "#DEFINE SUBPLOTS CHART\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "for exch in data_exchange['exchange'].unique():\n",
    "    fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "    \n",
    "    orderbookskew_exp = data_exchange[data_exchange['exchange']==exch].sort_values('strike')\n",
    "    \n",
    "    ##CHART OPEN INTEREST PROFILE\n",
    "    ##Put\n",
    "    x = orderbookskew_exp[orderbookskew_exp['putCall']=='P']['strike']\n",
    "    y = orderbookskew_exp[orderbookskew_exp['putCall']=='P']['oi']\n",
    "    fig.add_trace(go.Bar(x=x,y=y,name='put',marker_color='red',marker=dict(opacity=0.7)),secondary_y=True)\n",
    "    ##Call\n",
    "    x = orderbookskew_exp[orderbookskew_exp['putCall']=='C']['strike']\n",
    "    y = orderbookskew_exp[orderbookskew_exp['putCall']=='C']['oi']\n",
    "    fig.add_trace(go.Bar(x=x,y=y,name='call',marker_color='green',marker=dict(opacity=0.7)),secondary_y=True)\n",
    "\n",
    "    ##CHART FIT CURVE WITH ONLY LIQUID PART OF THE CHAIN\n",
    "    orderbookskew_exp = orderbookskew_exp[orderbookskew_exp['moneyness']=='OTM']\n",
    "    \n",
    "    if len(orderbookskew_exp)<=3:\n",
    "        continue\n",
    "\n",
    "    strike = orderbookskew_exp['strike'].values\n",
    "    markIv = orderbookskew_exp['markIv'].values\n",
    "\n",
    "\n",
    "    volfit_param = interpolate.splrep(strike, markIv, s=4)\n",
    "\n",
    "    strikes_random = np.linspace(min(strike),max(strike),100)\n",
    "\n",
    "    x = strikes_random\n",
    "    y = interpolate.splev(strikes_random, volfit_param,ext=3)\n",
    "\n",
    "\n",
    "    fig.add_trace(go.Scatter(x=x, y=y,name='Vol fit',marker_color='grey',showlegend=False))\n",
    "    \n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "    fig.update_yaxes(title_text='open interest', secondary_y=True)\n",
    "    fig.update_layout(title=exch.upper()+\" \"+currency+\" \"+exp_date,title_x=0.50,\n",
    "                      yaxis_title=\"volatility\",\n",
    "                      legend=dict(\n",
    "                          orientation=\"h\",\n",
    "                          yanchor=\"bottom\",\n",
    "                          y=-0.25,\n",
    "                          xanchor=\"center\",\n",
    "                          x=0.5\n",
    "                                  ),\n",
    "                   #['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
    "                   #'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
    "                   #'ygridoff', 'gridon', 'none']\n",
    "                  template = 'gridon')\n",
    "    fig.show()"
   ]
  }
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