{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "baa49a5c-d599-439a-99eb-ef729adfab0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hestonpy.models.heston import Heston\n",
    "from hestonpy.models.calibration.volatilitySmile import VolatilitySmile\n",
    "from hestonpy.option.data import get_options_data, filter_data_for_maturity\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "\n",
    "fontdict_title = {\n",
    "    'fontsize': 20,\n",
    "    'fontweight': 'bold'\n",
    "    }\n",
    "from datetime import datetime\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ae987d-564a-42ac-abbd-38fde6f2101e",
   "metadata": {},
   "source": [
    "# Calibration of Heston models on market data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f15bea4-bee4-4544-add4-4e348def75f7",
   "metadata": {},
   "source": [
    "We will calibrate our models on S&P smiles. You can also try with Apple, but there is less liquidity on the market, so less available maturities. Some parameters,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "328842ef-cc4d-49da-b292-0b8d09d387ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = '^SPX'\n",
    "all_market_data, spot, maturities = get_options_data(symbol)\n",
    "if symbol == '^SPX':\n",
    "    considered_maturities = [maturities[7], maturities[14], maturities[28], maturities[38]]\n",
    "else:\n",
    "    considered_maturities = [maturities[1], maturities[3], maturities[8], maturities[14]]\n",
    "    \n",
    "r = 0.00\n",
    "params = {\n",
    "    \"vol_initial\": 0.06,\n",
    "    \"kappa\": 1.25,\n",
    "    \"theta\": 0.06,\n",
    "    \"drift_emm\": 0.00,\n",
    "    \"sigma\": 0.6,\n",
    "    \"rho\": -0.8,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42768608-2463-4681-8e33-06d50fa342cb",
   "metadata": {},
   "source": [
    "For each maturity/smile we filter the data (based on bid-ask spread, the moneyness etc.), then we calibrate with a local optimiser to initialise our global optimiser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05d0b385-0b4f-4de0-bd03-54a756bef07e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "2025-04-15\n",
      "================================================================================ \n",
      "\n",
      "Calibrated parameters: v0=0.031 | kappa=1.335 | theta=0.088 | sigma=0.395 | rho=1.000\n",
      "\n",
      "at minimum 0.183945 accepted 1\n",
      "Parameters: kappa=1.335 | theta=0.088 | sigma=0.395 | rho=1.000 \n",
      "\n",
      "at minimum 0.183928 accepted 1\n",
      "Parameters: kappa=1.165 | theta=0.096 | sigma=0.394 | rho=1.000 \n",
      "\n",
      "at minimum 0.183817 accepted 1\n",
      "Parameters: kappa=0.054 | theta=1.405 | sigma=0.391 | rho=1.000 \n",
      "\n",
      "at minimum 0.183817 accepted 1\n",
      "Parameters: kappa=0.057 | theta=1.337 | sigma=0.391 | rho=1.000 \n",
      "\n",
      "at minimum 0.183816 accepted 1\n",
      "Parameters: kappa=0.041 | theta=1.825 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "at minimum 0.183816 accepted 1\n",
      "Parameters: kappa=0.046 | theta=1.651 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "at minimum 0.183817 accepted 1\n",
      "Parameters: kappa=0.050 | theta=1.530 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "at minimum 0.187207 accepted 1\n",
      "Parameters: kappa=0.038 | theta=2.146 | sigma=0.429 | rho=0.888 \n",
      "\n",
      "at minimum 0.183816 accepted 1\n",
      "Parameters: kappa=0.043 | theta=1.773 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "at minimum 0.183815 accepted 1\n",
      "Parameters: kappa=0.037 | theta=2.064 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "at minimum 0.183815 accepted 1\n",
      "Parameters: kappa=0.033 | theta=2.282 | sigma=0.390 | rho=1.000 \n",
      "\n",
      "['requested number of basinhopping iterations completed successfully'] True\n",
      "Calibrated parameters: v0=0.031 | kappa=0.033 | theta=2.282 | sigma=0.390 | rho=1.000\n",
      "\n",
      "{'MSE': np.float64(0.184), 'RMSE': np.float64(0.429), 'MAE': np.float64(0.311), 'MSE_%': np.float64(4.567), 'RMSE_%': np.float64(2.137), 'MAE_%': np.float64(13.576)}\n",
      "{'MSE': np.float64(0.498), 'RMSE': np.float64(0.706), 'MAE': np.float64(0.471), 'MSE_%': np.float64(0.089), 'RMSE_%': np.float64(0.299), 'MAE_%': np.float64(2.142)}\n",
      "at minimum 0.110745 accepted 1\n",
      "Parameters: kappa=1.406 | theta=0.691 | sigma=0.001 | rho=-0.623 \n",
      "\n",
      "at minimum 0.110778 accepted 1\n",
      "Parameters: kappa=0.996 | theta=0.947 | sigma=0.001 | rho=-0.425 \n",
      "\n",
      "at minimum 0.014858 accepted 1\n",
      "Parameters: kappa=10.000 | theta=0.386 | sigma=5.081 | rho=-0.673 \n",
      "\n",
      "at minimum 0.014883 accepted 1\n",
      "Parameters: kappa=9.524 | theta=0.405 | sigma=5.085 | rho=-0.675 \n",
      "\n",
      "at minimum 0.014868 accepted 1\n",
      "Parameters: kappa=9.996 | theta=0.370 | sigma=4.912 | rho=-0.662 \n",
      "\n",
      "at minimum 0.994388 accepted 1\n",
      "Parameters: kappa=8.634 | theta=0.004 | sigma=5.284 | rho=-0.999 \n",
      "\n",
      "at minimum 0.014904 accepted 1\n",
      "Parameters: kappa=9.247 | theta=0.429 | sigma=5.204 | rho=-0.684 \n",
      "\n",
      "at minimum 0.014966 accepted 1\n",
      "Parameters: kappa=8.493 | theta=0.479 | sigma=5.323 | rho=-0.694 \n",
      "\n",
      "['success condition satisfied'] True\n",
      "Calibrated parameters: v0=0.031 | kappa=10.000 | theta=0.386 | sigma=5.081 | rho=-0.673\n",
      "\n",
      "{'MSE': np.float64(5.378), 'RMSE': np.float64(2.319), 'MAE': np.float64(0.761), 'MSE_%': np.float64(1.486), 'RMSE_%': np.float64(1.219), 'MAE_%': np.float64(9.319)}\n",
      "{'MSE': np.float64(0.561), 'RMSE': np.float64(0.749), 'MAE': np.float64(0.437), 'MSE_%': np.float64(0.165), 'RMSE_%': np.float64(0.406), 'MAE_%': np.float64(2.196)}\n",
      "================================================================================\n",
      "2025-04-25\n",
      "================================================================================ \n",
      "\n",
      "Calibrated parameters: v0=0.030 | kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000\n",
      "\n",
      "at minimum 3.690574 accepted 1\n",
      "Parameters: kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "at minimum 3.690574 accepted 1\n",
      "Parameters: kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "at minimum 3.690574 accepted 1\n",
      "Parameters: kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "at minimum 3.690574 accepted 1\n",
      "Parameters: kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "at minimum 3.690747 accepted 1\n",
      "Parameters: kappa=1.015 | theta=0.003 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "at minimum 3.690574 accepted 1\n",
      "Parameters: kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000 \n",
      "\n",
      "['success condition satisfied'] True\n",
      "Calibrated parameters: v0=0.030 | kappa=0.926 | theta=0.001 | sigma=0.152 | rho=1.000\n",
      "\n",
      "{'MSE': np.float64(3.691), 'RMSE': np.float64(1.921), 'MAE': np.float64(1.276), 'MSE_%': np.float64(3.77), 'RMSE_%': np.float64(1.942), 'MAE_%': np.float64(14.201)}\n",
      "{'MSE': np.float64(0.521), 'RMSE': np.float64(0.721), 'MAE': np.float64(0.582), 'MSE_%': np.float64(0.196), 'RMSE_%': np.float64(0.443), 'MAE_%': np.float64(3.37)}\n",
      "at minimum 0.010113 accepted 1\n",
      "Parameters: kappa=0.934 | theta=0.108 | sigma=0.001 | rho=-0.954 \n",
      "\n",
      "at minimum 0.010105 accepted 1\n",
      "Parameters: kappa=1.942 | theta=0.069 | sigma=0.001 | rho=-0.992 \n",
      "\n",
      "at minimum 0.010096 accepted 1\n",
      "Parameters: kappa=2.146 | theta=0.066 | sigma=0.001 | rho=-0.886 \n",
      "\n",
      "at minimum 0.010104 accepted 1\n",
      "Parameters: kappa=2.466 | theta=0.061 | sigma=0.001 | rho=-1.000 \n",
      "\n",
      "at minimum 0.003794 accepted 1\n",
      "Parameters: kappa=10.000 | theta=0.057 | sigma=1.016 | rho=-0.351 \n",
      "\n",
      "at minimum 0.003951 accepted 1\n",
      "Parameters: kappa=1.194 | theta=0.218 | sigma=0.822 | rho=-0.352 \n",
      "\n",
      "at minimum 0.003967 accepted 1\n",
      "Parameters: kappa=0.400 | theta=0.580 | sigma=0.804 | rho=-0.351 \n",
      "\n",
      "at minimum 0.003969 accepted 1\n",
      "Parameters: kappa=0.283 | theta=0.803 | sigma=0.801 | rho=-0.351 \n",
      "\n",
      "at minimum 0.003794 accepted 1\n",
      "Parameters: kappa=10.000 | theta=0.057 | sigma=1.016 | rho=-0.351 \n",
      "\n",
      "at minimum 0.003795 accepted 1\n",
      "Parameters: kappa=9.980 | theta=0.057 | sigma=1.016 | rho=-0.351 \n",
      "\n",
      "['requested number of basinhopping iterations completed successfully'] True\n",
      "Calibrated parameters: v0=0.030 | kappa=10.000 | theta=0.057 | sigma=1.016 | rho=-0.351\n",
      "\n",
      "{'MSE': np.float64(17.683), 'RMSE': np.float64(4.205), 'MAE': np.float64(1.635), 'MSE_%': np.float64(0.379), 'RMSE_%': np.float64(0.616), 'MAE_%': np.float64(4.876)}\n",
      "{'MSE': np.float64(0.951), 'RMSE': np.float64(0.975), 'MAE': np.float64(0.426), 'MSE_%': np.float64(0.455), 'RMSE_%': np.float64(0.674), 'MAE_%': np.float64(2.625)}\n",
      "================================================================================\n",
      "2025-07-18\n",
      "================================================================================ \n",
      "\n",
      "Calibrated parameters: v0=0.037 | kappa=0.004 | theta=3.000 | sigma=0.318 | rho=-0.730\n",
      "\n",
      "at minimum 0.975403 accepted 1\n",
      "Parameters: kappa=0.004 | theta=3.000 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "at minimum 0.975403 accepted 1\n",
      "Parameters: kappa=0.004 | theta=2.998 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "at minimum 0.975413 accepted 1\n",
      "Parameters: kappa=0.004 | theta=2.927 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "at minimum 0.975429 accepted 1\n",
      "Parameters: kappa=0.005 | theta=2.816 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "at minimum 0.975403 accepted 1\n",
      "Parameters: kappa=0.004 | theta=2.999 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "at minimum 0.975403 accepted 1\n",
      "Parameters: kappa=0.004 | theta=3.000 | sigma=0.318 | rho=-0.730 \n",
      "\n",
      "['success condition satisfied'] True\n",
      "Calibrated parameters: v0=0.037 | kappa=0.004 | theta=3.000 | sigma=0.318 | rho=-0.730\n",
      "\n",
      "{'MSE': np.float64(0.975), 'RMSE': np.float64(0.988), 'MAE': np.float64(0.82), 'MSE_%': np.float64(2.335), 'RMSE_%': np.float64(1.528), 'MAE_%': np.float64(7.485)}\n",
      "{'MSE': np.float64(0.125), 'RMSE': np.float64(0.354), 'MAE': np.float64(0.223), 'MSE_%': np.float64(0.051), 'RMSE_%': np.float64(0.225), 'MAE_%': np.float64(1.388)}\n",
      "at minimum 0.001370 accepted 1\n",
      "Parameters: kappa=0.022 | theta=3.000 | sigma=0.582 | rho=-0.722 \n",
      "\n",
      "at minimum 0.001370 accepted 1\n",
      "Parameters: kappa=0.024 | theta=2.808 | sigma=0.582 | rho=-0.722 \n",
      "\n",
      "at minimum 0.001370 accepted 1\n",
      "Parameters: kappa=0.022 | theta=2.999 | sigma=0.582 | rho=-0.722 \n",
      "\n",
      "['success condition satisfied'] True\n",
      "Calibrated parameters: v0=0.037 | kappa=0.024 | theta=2.808 | sigma=0.582 | rho=-0.722\n",
      "\n",
      "{'MSE': np.float64(20.174), 'RMSE': np.float64(4.492), 'MAE': np.float64(2.16), 'MSE_%': np.float64(0.137), 'RMSE_%': np.float64(0.37), 'MAE_%': np.float64(2.729)}\n",
      "{'MSE': np.float64(0.167), 'RMSE': np.float64(0.408), 'MAE': np.float64(0.227), 'MSE_%': np.float64(0.044), 'RMSE_%': np.float64(0.211), 'MAE_%': np.float64(1.245)}\n",
      "================================================================================\n",
      "2026-01-16\n",
      "================================================================================ \n",
      "\n",
      "Calibrated parameters: v0=0.040 | kappa=0.340 | theta=0.318 | sigma=0.655 | rho=-1.000\n",
      "\n",
      "at minimum 6049.358096 accepted 1\n",
      "Parameters: kappa=0.340 | theta=0.318 | sigma=0.655 | rho=-1.000 \n",
      "\n",
      "at minimum 6049.331692 accepted 1\n",
      "Parameters: kappa=0.344 | theta=0.314 | sigma=0.654 | rho=-1.000 \n",
      "\n",
      "['success condition satisfied'] True\n",
      "Calibrated parameters: v0=0.040 | kappa=0.344 | theta=0.314 | sigma=0.654 | rho=-1.000\n",
      "\n",
      "{'MSE': np.float64(6049.332), 'RMSE': np.float64(77.777), 'MAE': np.float64(31.917), 'MSE_%': np.float64(124.849), 'RMSE_%': np.float64(11.174), 'MAE_%': np.float64(53.89)}\n",
      "{'MSE': np.float64(1143.565), 'RMSE': np.float64(33.817), 'MAE': np.float64(14.747), 'MSE_%': np.float64(594.588), 'RMSE_%': np.float64(24.384), 'MAE_%': np.float64(101.997)}\n",
      "at minimum 0.008761 accepted 1\n",
      "Parameters: kappa=0.138 | theta=0.396 | sigma=0.518 | rho=-0.846 \n",
      "\n",
      "at minimum 0.008756 accepted 1\n",
      "Parameters: kappa=0.156 | theta=0.362 | sigma=0.526 | rho=-0.846 \n",
      "\n",
      "at minimum 0.008777 accepted 1\n",
      "Parameters: kappa=0.081 | theta=0.630 | sigma=0.503 | rho=-0.847 \n",
      "\n",
      "at minimum 0.008782 accepted 1\n",
      "Parameters: kappa=0.062 | theta=0.800 | sigma=0.497 | rho=-0.847 \n",
      "\n",
      "at minimum 0.008633 accepted 1\n",
      "Parameters: kappa=1.737 | theta=0.100 | sigma=1.039 | rho=-0.829 \n",
      "\n",
      "at minimum 0.008635 accepted 1\n",
      "Parameters: kappa=1.477 | theta=0.104 | sigma=0.948 | rho=-0.831 \n",
      "\n",
      "['requested number of basinhopping iterations completed successfully'] True\n",
      "Calibrated parameters: v0=0.040 | kappa=1.737 | theta=0.100 | sigma=1.039 | rho=-0.829\n",
      "\n",
      "{'MSE': np.float64(6572.069), 'RMSE': np.float64(81.068), 'MAE': np.float64(18.632), 'MSE_%': np.float64(0.863), 'RMSE_%': np.float64(0.929), 'MAE_%': np.float64(4.461)}\n",
      "{'MSE': np.float64(18.403), 'RMSE': np.float64(4.29), 'MAE': np.float64(1.019), 'MSE_%': np.float64(0.903), 'RMSE_%': np.float64(0.95), 'MAE_%': np.float64(3.123)}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, layout=\"constrained\")\n",
    "fig.suptitle(f'Market smiles: {symbol}', **fontdict_title)\n",
    "\n",
    "############################################################\n",
    "##### Absolute\n",
    "############################################################\n",
    "for maturity, ax in zip(considered_maturities, axs.flatten()):\n",
    "\n",
    "    print(\"=\"*80)\n",
    "    print(maturity)\n",
    "    print(\"=\"*80,\"\\n\")\n",
    "\n",
    "    ####################################\n",
    "    ### Getting and filtering data \n",
    "    ####################################\n",
    "\n",
    "    full_market_data = filter_data_for_maturity(all_market_data, maturity)\n",
    "    time_to_maturity = full_market_data['Time to Maturity'].iloc[0]\n",
    "    strikes = full_market_data['Strike'].values\n",
    "    bid_prices = full_market_data[\"Bid\"].values\n",
    "    ask_prices = full_market_data['Ask'].values\n",
    "    market_ivs = full_market_data['Implied Volatility'].values\n",
    "    market_prices = full_market_data['Call Price'].values\n",
    "\n",
    "    marketVolatilitySmile = VolatilitySmile(\n",
    "        strikes=strikes,\n",
    "        time_to_maturity=time_to_maturity,\n",
    "        atm=spot,\n",
    "        market_ivs=market_ivs,\n",
    "        r=r\n",
    "    )\n",
    "    market_data = marketVolatilitySmile.filters(full_market_data, select_mid_ivs=True)\n",
    "\n",
    "    ####################################\n",
    "    ### Calibration \n",
    "    ####################################\n",
    "    heston = Heston(spot=spot, r=r, **params)\n",
    "    initial_params = marketVolatilitySmile.calibration(\n",
    "        price_function=heston.call_price,\n",
    "        guess_correlation_sign='unknown',\n",
    "        initial_guess=[params['kappa'], params['theta'], params['sigma'], params['rho']],\n",
    "        speed='local',\n",
    "    )\n",
    "\n",
    "    # Absolute calibration\n",
    "    calibrated_params = marketVolatilitySmile.calibration(\n",
    "        relative_errors=False,\n",
    "        price_function=heston.call_price,\n",
    "        guess_correlation_sign='unknown',\n",
    "        initial_guess=[initial_params['kappa'], initial_params['theta'], initial_params['sigma'], initial_params['rho']],\n",
    "        speed='global',\n",
    "        power='mse'\n",
    "    )\n",
    "    calibrated_prices = heston.call_price(\n",
    "        strike=marketVolatilitySmile.strikes, time_to_maturity=time_to_maturity, **calibrated_params\n",
    "    )\n",
    "    print(marketVolatilitySmile.evaluate_calibration(calibrated_prices, 'price'))\n",
    "    calibrated_ivs = marketVolatilitySmile.compute_smile(prices=calibrated_prices)\n",
    "    print(marketVolatilitySmile.evaluate_calibration(calibrated_ivs, 'iv'))\n",
    "\n",
    "    # Relative calibration\n",
    "    calibrated_params_relative = marketVolatilitySmile.calibration(\n",
    "        relative_errors=True,\n",
    "        price_function=heston.call_price,\n",
    "        guess_correlation_sign='negative',\n",
    "        initial_guess=[initial_params['kappa'], initial_params['theta'], initial_params['sigma'], initial_params['rho']],\n",
    "        speed='global',\n",
    "        power='mse'\n",
    "    )\n",
    "    calibrated_prices_relative = heston.call_price(\n",
    "        strike=marketVolatilitySmile.strikes, time_to_maturity=time_to_maturity, **calibrated_params_relative\n",
    "    )\n",
    "    print(marketVolatilitySmile.evaluate_calibration(calibrated_prices_relative, 'price'))\n",
    "    calibrated_ivs_relative = marketVolatilitySmile.compute_smile(prices=calibrated_prices_relative)\n",
    "    print(marketVolatilitySmile.evaluate_calibration(calibrated_ivs_relative, 'iv'))\n",
    "\n",
    "    # Some plots\n",
    "    ask_ivs = market_data['Ask ivs'].values\n",
    "    bid_ivs = market_data['Bid ivs'].values\n",
    "    forward = marketVolatilitySmile.atm * np.exp(marketVolatilitySmile.r * marketVolatilitySmile.time_to_maturity)\n",
    "\n",
    "    if ax == axs.flatten()[-2]:\n",
    "        ax.axvline(1, linestyle=\"--\", color=\"gray\", label=\"ATM\")\n",
    "        ax.plot(marketVolatilitySmile.strikes / forward, calibrated_ivs, label=\"absolute calibration\", marker='+', color='blue', linestyle=\"dotted\", markersize=4)\n",
    "        ax.plot(marketVolatilitySmile.strikes / forward, calibrated_ivs_relative, label=\"relative calibration\", marker='+', color='green', linestyle=\"dotted\", markersize=4)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, marketVolatilitySmile.market_ivs, label=\"mid\", marker='o', color='red', s=20)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, bid_ivs, label=\"bid\", marker=6, color='black', s=20)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, ask_ivs, label=\"ask\", marker=7, color='gray', s=20)\n",
    "        ax.legend(loc='upper right', ncol=2, alignment='left')\n",
    "    else:\n",
    "        ax.axvline(1, linestyle=\"--\", color=\"gray\")\n",
    "        ax.plot(marketVolatilitySmile.strikes / forward, calibrated_ivs, marker='+', color='blue', linestyle=\"dotted\", markersize=4)\n",
    "        ax.plot(marketVolatilitySmile.strikes / forward, calibrated_ivs_relative, marker='+', color='green', linestyle=\"dotted\", markersize=4)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, marketVolatilitySmile.market_ivs, marker='o', color='red', s=20)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, bid_ivs, marker=6, color='black', s=20)\n",
    "        ax.scatter(marketVolatilitySmile.strikes / forward, ask_ivs, marker=7, color='gray', s=20)\n",
    "\n",
    "    ax.set_xlabel(\"Moneyness [%]\")\n",
    "    ax.set_ylabel(\"Implied Volatility [%]\")\n",
    "\n",
    "    date = datetime.strptime(maturity, '%Y-%m-%d').date().strftime(\"%d-%B-%y\")\n",
    "    title = f\"{date}: {marketVolatilitySmile.time_to_maturity * 252 / 5:.1f} semaines\"\n",
    "    ax.set_title(title)\n",
    "    ax.grid(visible=True, which=\"major\", linestyle=\"--\", dashes=(5, 10), color=\"gray\", linewidth=0.5, alpha=0.8)\n",
    "\n",
    "plt.show()"
   ]
  }
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