vol_term_skew.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Demystifying implied volatility: Understanding volatility term structure and skew\n",
    "\n",
    "The options implied volatility term structure is a way to understand how the market perceives the future volatility of a stock over time. It shows the relationship between the implied volatilities of options with different expiration dates on the same stock.\n",
    "\n",
    "And it matters.\n",
    "\n",
    "By analyzing this term structure, traders can gain insights into market expectations of future volatility. The term structure can take different shapes, such as upward-sloping, downward-sloping, or flat, which indicate the market’s expectations.\n",
    "\n",
    "There’s more.\n",
    "\n",
    "Implied volatility skew refers to the difference in implied volatilities between options with different strike prices but the same expiration date. It shows how the market prices options based on their proximity to the current stock price.\n",
    "\n",
    "These tools are essential for options traders and investors as they help in pricing options accurately, assessing risk, and constructing trading strategies."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[33mForecasting Toolkit is disabled. To use the Forecasting features please install the toolkit following the instructions here: https://docs.openbb.co/sdk/quickstart/installation/\u001B[0m\n\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000\">Forecasting Toolkit is disabled. To use the Forecasting features please install the toolkit following the instructions here: https://docs.openbb.co/sdk/quickstart/installation/</span>\n\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "\u001B[33mPortfolio Optimization Toolkit is disabled. To use the Optimization features please install the toolkit following the instructions here: https://docs.openbb.co/sdk/quickstart/installation/\u001B[0m\n\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000\">Portfolio Optimization Toolkit is disabled. To use the Optimization features please install the toolkit following the instructions here: https://docs.openbb.co/sdk/quickstart/installation/</span>\n\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "\u001B[?25l",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Output()",
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       "version_major": 2,
       "version_minor": 0,
       "model_id": "38e8797a50214b328ea7230c0719a834"
      }
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "\n\u001B[?25h",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imports and set up\n",
    "\n",
    "# OpenBB has a suite of methods to download options data. We’ll need it for today’s issue. You can also import Matplotlib to do some light formatting on the charts.\n",
    "\n",
    "from openbb_terminal.sdk import openbb\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc(\"font\", size=10)\n",
    "# Downloading the options chains is one line of code.\n",
    "\n",
    "chains = openbb.stocks.options.chains(\n",
    "    symbol=\"SPY\",\n",
    "    source=\"YahooFinance\"\n",
    ")\n",
    "\n",
    "expirations = openbb.stocks.options.expirations(\"SPY\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T12:57:29.575830Z",
     "start_time": "2023-07-26T12:57:07.433471Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "Loading Daily data for SPY with starting period 2020-07-21.\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for SPY with starting period 2020-07-21.\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the term structure\n",
    "\n",
    "# Now that you have the options chains, you need to do some pandas magic. But first, we need the last price of the underlying to find at the money options.\n",
    "\n",
    "last = (\n",
    "    openbb\n",
    "    .stocks\n",
    "    .load(\"SPY\")\n",
    "    .iloc[-1][\"Adj Close\"]\n",
    ")\n",
    "# Download the price data and just grab the last adjusted close price from the DataFrame.\n",
    "\n",
    "# Now, find the at the money (ATM) options.\n",
    "\n",
    "idx = (\n",
    "    (chains.strike - last)\n",
    "    .abs()\n",
    "    .sort_values()\n",
    "    .index[0]\n",
    ")\n",
    "atm_strike = (\n",
    "    chains\n",
    "    .iloc[idx]\n",
    "    .strike\n",
    ")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T12:57:29.831435Z",
     "start_time": "2023-07-26T12:57:29.576566Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'left':'IV term structure for ATM call options'}, xlabel='expiration'>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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OnZHo3MWn1r/0G+PXil/RsmVLuLV1q/Warz+7steV+OTDj/HNd9/g6XnP4JeKClzm44vF7yw66+XPt18qTt1n3du1x+HD/3vdUscOp5bw4npanL7MkT8epQSa5+9ac96Pfv/9dzz7/DzMf+kFDBs6DHPnPI5V3/6AwD49UVBQIJ6VSGfNvrh4eHhgy7pNWL9pQ72X27BxI37//XeMHjUaYVeF4YFZM896uRGjr0FuXv2PBJSWltb5Q6wysjJhZ2eHiy66COs31j+TETKzsjBj5v0I6t0HffrU/q6a0tJT3x4r+QFdxX98IWrXrp3ti9Kdt99R6zJZe/bgaPlR3DbuNnz3w/f1nq+0tBQdO3aEg4NDrUdMTjtSXGz71zBw6tveL73k0kbPnZmVicsvuxzRMdGN/tjGSkhKxK033YLW1tY4Wn4UwKkXUPcI7IE33n6zWa9rZMQIVFdXY/y/bqv1tNzwYcMx+Z5J6ODujsKz/Kv9XJq6tABA3B8/luC2ceNrLc5/dvq79O6adI9t7nZu7c562QshOTkJJ06cwHXXXofU9DTb22+9+Rbs+2kf9v1U9wvGx954IzIyM2p1bo6/a+fjflRTU4M1a9fg0KE8xOyIRkD3K7i40D9esy8ubm3bYvacR+H3x3PMfUP6wuLqihM1J3Dn7Xegq19X/PbbMTz06COIiYvF9KnT4OjoaPv5Lk2xbcd23D99Bp58fC6SU1Lg7t4emzZvRs6BHCQmJSIyaic+XPIB3l28COm702Fvbw9vL2+sWv0jspv5X+8NMXH8BFxyySU4cPAAjh37DT179EB3/+744OMPa12usrISCYkJmDVzFo4fP478ggJcftlleO2NBQ2+rpSUZFRVVeHRhx/BG2+/hTtvvwP9w2r/dNCamhq8uuA1PPvUM1j2+Ve212yEXRWGZ+fPq/Uv2m07tuH++2bgoyUfYsW3K+BykQsO/XwI23ZsBwDExMZg3M23Yv2G9ejUqRNefuFllB092uhGby18Gz988z1WffsDli3/GqVlZXBzc4OjY0u8v3RJo89Xn4XvvoOJ4ydg9Q+r8O7iRWhhZ4c5sx9Falpqk77Tpz4jhkcgOiYa6zasr/X2n/N/xtRJUzB86HB88vmnzXqd55KYlIj1G9dj3tPPwsvLCzGxMbBarejVsxdmzLwfAHDg4EE4ODjgqSeeRHJKMoYMGoJRf/kWXmdnZwy8eiAA2F4/NiJiBAAgNS3V9lTU6bf1vvLUt+MPHHA1ioqPYN+++heOPztcWIjPvvgcD82cBUdHRyQmJWLo4KG48fobcNu/a//U4MCAANx79z0oLS3F6FGjMXTwUNw6cXyty5zr75qHhwd6BPYAALi3d4erq8V2O7Zu24qqqqpmvR8teOU1ZGZlorCwEI6Ojpgw7jZUVFQgZVdKo85DZFbN+mrfxQsXqcqyijOOnD0/qYjhEQo49dN1d6ekqZkzHlCVZRV1/hTKrz77Uu1KSFbxUXHqycfn1vkdLxaLRb214E31U+ZedbSoVO3dvUf1DOxhe7+1lVW98drrtvfn/nRQbVy7wfZdHcCp7z5Y/uXXdd6uObMfs30nwenvDAD+990IjfnOiLvuuFOlJe1SRYcOq7LCEhUfFafuvfues172Mt/L1Ldfr1A/H8hTZYUlKiU+2fa+hn4nxLhbblWZqbvVkZ8L1cdLP1K9evQ863eq3HXHnSo+KlaVHi5Wh3Jy1ervV6k2rduccb4Z0+5TaUm7VFlhiTqUk6vu+L87bO/z9vJW635cq4rzi1R8VKy6uv8Atfr7VWd8V1FhXsE5O40cMVJt3bBZFecXqSM/F6q0pF3qycfnNul+ea7rDO0bqjau3aCK84tU3v6D6r13Ftu+U+v0If0JzRaLRZUeLlYP3j/zrO/fl5F9xneSnOu7is52nsZ+VxEAddFFF6n5zz6vstIy1NGiUrU/a596a8GbtS4z7+ln1U+Ze1XRocPqy0+/UFd0717rOwdP3x/Pdvx5nrouM2f2Y42auWXLlur5Z55T+zKyVenhYhUbGa1uHntTrcusWblaFRz8Wf18IE+VHi5WOzZvUyNHjGz037XT3513tuPPf/fPdT862+fzbH+Pv1m2XB3I3q/KCktUwcGf1bdfr1C9evZq8n2PBw8zHS3++D/N7vTP0LC0uRgAkPvTwVrPO7dt2xbde16Bu26/E/dNnV7rY2f/51F8+6fv1Lj44ovxyQcfY/PWLXhz4VvnY1wiMqE1K1ejsrISN/3xM1iISH8X7AfQ2dvbYcToEbZv2z3trXfervN59dMqKiqwYdNGdPb2Pp8jEhER0d/cBft26OiYGNw37T7bny+66KIGf6y1lRVjbxgreh0MERER6a/ZH3FxdHRE3k8HbT+ToDCvAN/98D1mPvwg3nrtDWSm7saxY78hLj4OU+6bWu+5tm/aitatW+PXX3/FmwvfwqYtm5t7XCIiItLIeXuNCxEREVFzM+Qn5xIRERE1BRcXIiIi0gYXFyIiItJGs7449/P/forc3LzmPKWN56WXIu+P3+ZLjcd+cmwow35ybCjDfnXz8vLEbf+eaPQYDdKsi0tubh4effyx5jyljbWVFeW/lJ+Xc/8TsJ8cG8qwnxwbyrBf3V6YN9/oERpMm6eKegQGGj2C1thPjg1l2E+ODWXYzxy0WVys1tZGj6A19pNjQxn2k2NDGfYzB20Wl/iEOKNH0Br7ybGhDPvJsaEM+5mDNouLlxd/T5EE+8mxoQz7ybGhDPuZgzaLi3t7d6NH0Br7ybGhDPvJsaEM+5mDNotLWnqa0SNojf3k2FCG/eTYUIb9zEGbxaW11Wr0CFpjPzk2lGE/OTaUYT9z0GZx8fT0MnoErbGfHBvKsJ8cG8qwnzlos7js3bfX6BG0xn5ybCjDfnJsKMN+5qDN4kJERESkzeLS5fIuRo+gNfaTY0MZ9pNjQxn2MwdtFpe8vFyjR9Aa+8mxoQz7ybGhDPuZgzaLy9Fy/mIsCfaTY0MZ9pNjQxn2MwdtFpfAAP5yLAn2k2NDGfaTY0MZ9jMHbRaXoiNFRo+gNfaTY0MZ9pNjQxn2MwdtFpfc3INGj6A19pNjQxn2k2NDGfYzB20Wl+CgEKNH0Br7ybGhDPvJsaEM+5mDNotLeflRo0fQGvvJsaEM+8mxoQz7mYM2i0tqGn85lgT7ybGhDPvJsaEM+5mDNotLeFi40SNojf3k2FCG/eTYUIb9zEGbxaW6usroEbTGfnJsKMN+cmwow37moM3iEh0bY/QIWmM/OTaUYT85NpRhP3PQZnHhQ3wy7CfHhjLsJ8eGMuxnDtosLg72DkaPoDX2k2NDGfaTY0MZ9jMHbRaXrdu3Gj2C1thPjg1l2E+ODWXYzxy0WVxCgoKNHkFr7CfHhjLsJ8eGMuxnDtosLi4uFqNH0Br7ybGhDPvJsaEM+5mDNotLDF8NLsJ+cmwow35ybCjDfuagzeLi5+dn9AhaYz85NpRhPzk2lGE/c9BmcXFr62b0CFpjPzk2lGE/OTaUYT9z0GZxSUpJMnoErbGfHBvKsJ8cG8qwnzlos7h0cO9g9AhaYz85NpRhPzk2lGE/c9BmcfHo5GH0CFpjPzk2lGE/OTaUYT9z0GZxyczKNHoErbGfHBvKsJ8cG8qwnzlos7g4OzsZPYLW2E+ODWXYT44NZdjPHLRZXHw6+xo9gtbYT44NZdhPjg1l2M8ctFlccg7sN3oErbGfHBvKsJ8cG8qwnzlos7hUVVUbPYLW2E+ODWXYT44NZdjPHLRZXPy7+Rs9gtbYT44NZdhPjg1l2M8ctFlc8gvyjR5Ba+wnx4Yy7CfHhjLsZw7aLC6FRYVGj6A19pNjQxn2k2NDGfYzB20Wl969ehs9gtbYT44NZdhPjg1l2M8ctFlcSkpLjB5Ba+wnx4Yy7CfHhjLsZw7aLC7Z2dlGj6A19pNjQxn2k2NDGfYzB20Wl9C+oUaPoDX2k2NDGfaTY0MZ9jMHbRaXY8cqjR5Ba+wnx4Yy7CfHhjLsJ+fp6YmPlnyAyrKKBl3+/ukzkJm6GynxyZhy7+Ra73NwcEBsZDSy07MaNYM2i0tcQrzRI2iN/eTYUIb95NhQhv1kPDw8sGXdJhzMzW3Q5YODgvGvCf9CSFgoIkZFYNYDDyIwIMD2/vunz8DeffsaPYc2i8vAAQONHkFr7CfHhjLsJ8eGMuwnk5+fjz6hwVj64QcNuvwtN92MDz76ABUVFXBxseDYb8cw+prRAABvL2+Mu2Uc5s2f1+g5tFlcak7UGD2C1thPjg1l2E+ODWXYr25WqxW+Pr7w9an/F1GW/1Le4HN29u6M/QdyMGrkKKz+YRV2pabC81JPAMCCV17FnCfm4Nix3xo9q0OjP6IeXl6esFgsCA8Lh4O9A7Zu34qQoGC4uFgQExsDPz8/uLV1Q1JKEjq4d4BHJw9kZmXC2dkJPp19kXNgP6qqquHfzR/5BfkoLCpE7169UVJagoyMDIwaOQrHjlUiLiEeAwcMRM2JGkTujES/vqFwcnJG5M5I9AgMhNXaGvEJcfDy8oZ7e3ekpaehtdUKT08v7N23FwDQ5fIuyMvLxdHycgQGBKLoSBFycw8iOCgE5eVHkZqWhvCwcFRXVyE6Nua83Kbs7GyE9g29ILep+ng1rK2sprpNF/rzdPLkCVgsFlPdpgv5eXK1WDB08BBT3aYL/XlytVgQMTzCVLfpQn6ekpOTEDE8wlS3qbk+T+eDY8uWmDZpCjp27IgxN1yHwYMGwb+bP8becCPKy8uxYdNGeHl6NencqrmOF+bNb7Zz/fUYOnjIeTv3P+FgPzY0+mA/NjT6YL+6j8Z8/fby9FKVZRXnvNzCN95Wq79fpSwWiwKgXnxuvpo54wG1dPH7au/uPSpjV7rKTs9S5UfKVMaudOXT2adB16/NU0VOTs5Gj6A19pNjQxn2k2NDGfY7f4L6BCE7PQv9w8Jtb1u15ke0adMGJ0+eRMcOHXD9dddj+bcrcNeke9Dliq7o3jMAQ0cOR2FhIbr3DEDOgZwGXVezPlV0PkXujDR6BK2xnxwbyrCfHBvKsJ+Mo6Mj8n46iBYtWgAACvMK8N0P32PStMlwdHSExdUCJycn2+XXrF2Dfn1DEbMjCg4ODpg951Hk5eU1yyyGPNTU2KN/WLjhD6XpfLAfGxp9sB8bGn2wX93H+fz63dyHNk8VWa2tjR5Ba+wnx4Yy7CfHhjLsZw7aLC7xCXFGj6A19pNjQxn2k2NDGfYzB20WFy8vb6NH0Br7ybGhDPvJsaEM+5mDNouLe3t3o0fQGvvJsaEM+8mxoQz7mYM2i0taeprRI2iN/eTYUIb95NhQhv3MQZvFpbXVavQIWmM/OTaUYT85NpRhP3PQZnHxbOKPBaZT2E+ODWXYT44NZdjPHLRZXE7/DgdqGvaTY0MZ9pNjQxn2MwdtFhciIiIibRaXLpd3MXoErbGfHBvKsJ8cG8qwnzlos7jk5eUaPYLW2E+ODWXYT44NZdjPHLRZXI6Wlxs9gtbYT44NZdhPjg1l2M8ctFlcAgMCjR5Ba+wnx4Yy7CfHhjLsZw7aLC5FR4qMHkFr7CfHhjLsJ8eGMuxnDtosLrm5B40eQWvsJ8eGMuwnx4Yy7GcO2iwuwUEhRo+gNfaTY0MZ9pNjQxn2MwdtFpfy8qNGj6A19pNjQxn2k2NDGfYzB20Wl9Q0/nIsCfaTY0MZ9pNjQxn2MwdtFpfwsHCjR9Aa+8mxoQz7ybGhDPuZgzaLS3V1ldEjaI395NhQhv3k2FCG/cxBm8UlOjbG6BG0xn5ybCjDfnJsKMN+5qDN4sKH+GTYT44NZdhPjg1l2M8ctFlcHOwdjB5Ba+wnx4Yy7CfHhjLsZw7aLC5bt281egStsZ8cG8qwnxwbyrCfOWizuIQEBRs9gtbYT44NZdhPjg1l2M8ctFlcXFwsRo+gNfaTY0MZ9pNjQxn2MwdtFpcYvhpchP3k2FCG/eTYUIb9zEGbxcXPz8/oEbTGfnJsKMN+cmwow37moM3i4tbWzegRtMZ+cmwow35ybCjDfuagzeKSlJJk9AhaYz85NpRhPzk2lGE/c9Bmceng3sHoEbTGfnJsKMN+cmwow37moM3i4tHJw+gRtMZ+cmwow35ybCjDfuagzeKSmZVp9AhaYz85NpRhPzk2lGE/c9BmcXF2djJ6BK2xnxwbyrCfHBvKsJ85aLO4+HT2NXoErbGfHBvKsJ8cG8qwnzlos7jkHNhv9AhaYz85NpRhPzk2lGE/c9BmcamqqjZ6BK2xnxwbyrCfHBvKsJ85aLO4+HfzN3oErbGfHBvKsJ8cG8qwnzlos7jkF+QbPYLW2E+ODWXYT44NZdjPHLRZXAqLCo0eQWvsJ8eGMuwnx4Yy7GcO2iwuvXv1NnoErbGfHBvKsJ8cG8qwnzlos7iUlJYYPYLW2E+ODWXYT44NZdjPHLRZXLKzs40eQWvsJ8eGMuwnx4Yy7GcO2iwuoX1DjR5Ba+wnx4Yy7CfHhjLsZw7aLC7HjlUaPYLW2E+ODWXYT44NZdjPHLRZXOIS4o0eQWvsJ8eGMuwnx4Yy7GcO2iwuAwcMNHoErbGfHBvKsJ8cG8qwnzlos7jUnKgxegStsZ8cG8qwnxwbyrCfOWizuETujDR6BK2xnxwbyrCfHBvKsJ85aLO49OOrwUXYT44NZdhPjg1l2M8ctFlcnJycjR5Ba+wnx4Yy7CfHhjLsZw7aLC58iE+G/eTYUIb95NhQhv3MQZvFpUdgoNEjaI395NhQhv3k2FCG/cxBm8XFam1t9AhaYz85NpRhPzk2lGE/c9BmcYlPiDN6BK2xnxwbyrCfHBvKsJ85aLO4eHl5Gz2C1thPjg1l2E+ODWXYzxy0WVzc27sbPYLW2E+ODWXYT44NZdjPHLRZXNLS04weQWvsJ8eGMuwnx4Yy7GcO2iwura1Wo0fQGvvJsaEM+8mxoQz7yXl6euKjJR+gsqyiQZe/f/oMZKbuRkp8MqbcO9n29nvvvgcZu9KRmbobT899qlEzaLO4eHp6GT2C1thPjg1l2E+ODWXYT8bDwwNb1m3CwdzcBl0+OCgY/5rwL4SEhSJiVARmPfAgAgMCAABKKfQN74crQ/pg1MhR6NO7T4Pn0GZx2btvr9EjaI395NhQhv3k2FCG/WTy8/PRJzQYSz/8oEGXv+Wmm/HBRx+goqICLi4WHPvtGEZfMxoA8P7SJaioqEBVVRXSd6fB18enwXNos7gQERHR+WG1WuHr4wtfH996L1f+S3mDz9nZuzP2H8jBqJGjsPqHVdiVmgrPSz1rXcbZ2Rl9Q/oiITGxwed1aPAlG8DLyxMWiwXhYeFwsHfA1u1bERIUDBcXC2JiY+Dn5we3tm5ISklCB/cO8OjkgcysTDg7O8Gnsy9yDuxHVVU1/Lv5I78gH4VFhejdqzdKSkvg7eWNLpd3wbFjlYhLiMfAAQNRc6IGkTsj0a9vKJycnBG5MxI9AgNhtbZGfEIcvLy84d7eHWnpaWhttcLT08u2cXe5vAvy8nJxtLwcgQGBKDpShNzcgwgOCkF5+VGkpqUhPCwc1dVViI6NOS+3KTs7G6F9Qy/IbfLv5o/3lrxnqtt0oT9PgQEBeOudt011my7k52n8reOwM2qnqW7Thf48jb91HGLiYk11my7k56lVKyt8fHxMdZua6/N0Pji2bIlpk6agY8eOGHPDdRg8aNAZ1/X6Kwvww8ofkHMgp1HnVs11vDBvfrOd669Hj4DA83buf8LBfmxo9MF+bGj0wX51H435+u3l6aUqyyrOebmFb7ytVn+/SlksFgVAvfjcfDVzxgO29z8x53H13dffKHt7+0bNqs1TRUfLG/7wFJ2J/eTYUIb95NhQhv3On6A+QchOz0L/sHDb21at+RFt2rTByZMn0bFDB1x/3fVY/u0KAMCUeydj0NWDcNv/TcSJEycadV3aLC6BAfzlWBLsJ8eGMuwnx4Yy7Cfj6OiIwrwCJESf+tUJhXkFWLxwke19FlcLnJycbJdfs3YNNmzaiJgdUdi0biNmz3kUeXl5cHV1xUvzX4SXpydidkQhNTEFn3z43wbP0ayvcTmfio4UGT2C1thPjg1l2E+ODWXYT+b48ePo4NnprO+Lio7CJZ09z3j73KefxNynn6z1tl9//RUXu1mbPIc2j7jk5h40egStsZ8cG8qwnxwbyrCfOWizuAQHhRg9gtbYT44NZdhPjg1l2M8ctFlcysuPGj2C1thPjg1l2E+ODWXYzxy0WVxS0/jLsSTYT44NZdhPjg1l2M8ctFlcwv/0LVbUeOwnx4Yy7CfHhjLsZw7aLC7V1VVGj6A19pNjQxn2k2NDGfYzB20Wl+jYGKNH0Br7ybGhDPvJsaEM+5mDNosLH+KTYT85NpRhPzk2lGE/c9BmcXGw1+Zn5f0tsZ8cG8qwnxwbyrCfOWizuGzdvtXoEbTGfnJsKMN+cmwow37moM3iEhIUbPQIWmM/OTaUYT85NpRhP3PQZnFxcbEYPYLW2E+ODWXYT44NZdjPHLRZXGL4anAR9pNjQxn2k2NDGfYzB20WFz8/P6NH0Br7ybGhDPvJsaEM+5mDNouLW1s3o0fQGvvJsaEM+8mxoQz7mYM2i0tSSpLRI2iN/eTYUIb95NhQhv3MQZvFpYN7B6NH0Br7ybGhDPvJsaEM+5mDNouLRycPo0fQGvvJsaEM+8mxoQz7mYM2i0tmVqbRI2iN/eTYUIb95NhQhv3MQZvFxdnZyegRtMZ+cmwow35ybCjDfuagzeLi09nX6BG0xn5ybCjDfnJsKMN+5qDN4pJzYL/RI2iN/eTYUIb95NhQhv3MQZvFpaqq2ugRtMZ+cmwow35ybCjDfuagzeLi383f6BG0xn5ybCjDfnJsKMN+5qDN4pJfkG/0CFpjPzk2lGE/OTaUYT9z0GZxKSwqNHoErbGfHBvKsJ8cG8qwnzlos7j07tXb6BG0xn5ybCjDfnJsKMN+5qDN4lJSWmL0CFpjPzk2lGE/OTaUYT9z0GZxyc7ONnoErbGfHBvKsJ8cG8qwnzlos7iE9g01egStsZ8cG8qwnxwbyrCfOWizuBw7Vmn0CFpjPzk2lGE/OTaUYT9z0GZxiUuIN3oErbGfHBvKsJ8cG8qwnzlos7gMHDDQ6BG0xn5ybCjDfnJsKMN+5qDN4lJzosboEbTGfnJsKMN+cmwow37moM3iErkz0ugRtMZ+cmwow35ybCjDfuagzeLSj68GF2E/OTaUYT85NpRhP3PQZnFxcnI2egStsZ8cG8qwnxwbyrCfOWizuPAhPhn2k2NDGfaTY0MZ9jMHbRaXHoGBRo+gNfaTY0MZ9pNjQxn2MwdtFhertbXRI2iN/eTYUIb95NhQhv3MQZvFJT4hzugRtMZ+cmwow35ybCjDfuagzeLi5eVt9AhaYz85NpRhPzk2lGE/c9BmcXFv7270CFpjPzk2lGE/OTaUYT9z0GZxSUtPM3oErbGfHBvKsJ8cG8qwnzlos7i0tlqNHkFr7CfHhjLsJ8eGMuxnDtosLp6eXkaPoDX2k2NDGfaTY0MZ9jMHbRaXvfv2Gj2C1thPjg1l2E+ODWXYzxy0WVyIiIiItFlculzexegRtMZ+cmwow35ybCjDfuagzeKSl5dr9AhaYz85NpRhPzk2lGE/c9BmcTlaXm70CFpjPzk2lGE/OTaUYT9z0GZxCQzgL8eSYD85NpRhPzk2lGE/c9BmcSk6UmT0CFpjPzk2lGE/OTaUYT9z0GZxyc09aPQIWmM/OTaUYT85NpRhP3PQZnEJDgoxegStsZ8cG8qwnxwbyrCfOWizuJSXHzV6BK2xnxwbyrCfHBvKsJ85aLO4pKbxl2NJsJ8cG8qwnxwbyrCfOWizuISHhRs9gtbYT44NZdhPjg1l2M8ctFlcqqurjB5Ba+wnx4Yy7CfHhjLsJ+fp6YmPlnyAyrKKBl3+/ukzkJm6GynxyZhy72Tb23sG9kD09p3YnZKGLz75HK6uro2aQzXX8cK8+c12rr8eFovlvJ37n3CwHxsafbAfGxp9sF/dR0O+fnt4eKh9Gdnq6blPqcqyinNePjgoWCVEx6uLL75YdXB3V/syslVgQIACoBKi49XggYMUAPXuW+80an9wgAZcXIAhg0OxcdMmo0fRFvvJsaEM+8mxoYxZ+h07Zsz15ufno09oMKxWKx6aOeucl7/lppvxwUcfoKKiAu3c2uHYb8cw+prRsLezx/Hj1di8dQvs7e1RU1ODMaNH49HHH2vQHH/7xcXFBajMAwD972zGYj85NpRhPzk2lDFHP4tn8y8vVqsVvj6+AID9OfvrvFz5L+WwWq0NOmdn787YtGUzRo0chVdefAkJiYnwvNQT3t7e2J+Tg44dO+KTD/+L7OxseF7q2eBZm3Vx8fLyhMViQXhYOBzsHbB1+1aEBAXDxcWCmNgY+Pn5wa2tG5JSktDBvQM8OnkgMysTzs5O8Onsi5wD+1FVVQ3/bv7IL8hHYVEh+vXtAWBtc45JRESkLevFrTDo6v6orq5CdGxMs3zNPR8cW7bEtElT0LFjR4y54ToMHjQI/t384ejoiMsvuwxb1m/C/JdexPJvluPmsTc1+LwtcOo5o2bxwrz5DX6opzFcXICrw/tjW+SOZj/3PwX7ybGhDPvJsaGMWfqdj6eKGvP128vTC5mpu2Fpc3G9l1v4xtvw6dwZN992KyorK/Hic/NxuLAQMXGx+H75txh57SgkJiUiMCAAH77/IYL6BTfo+v/2TxUBpz5JdvatDHtezwzYT44NZdhPjg1l2O/8CeoThM8//hR3TbobO3ZGAgBWrfkRc+c8jpMnT6Jjhw64/rrrMXTkcBQUFOCXX35By5YtAQDTp0zHsuXLGnxdWiwuABATG2P0CFpjPzk2lGE/OTaUYT8ZR0dH5P10EC1atAAAFOYV4LsfvsekaZPh6OgIi6sFTk5OtsuvWbsG/fqGImZHFBwcHDB7zqPIy8sDANxxz51Y8PKrcGvrho2bN+G1NxY0apYL+u1UTT36hfYz/NvFdD7Yjw2NPtiPDY0+2K/u43x+/W7uQ5sfQOfW1s3oEbTGfnJsKMN+cmwow37moM3ikpSSZPQIWmM/OTaUYT85NpRhP3PQZnHp4N7B6BG0xn5ybCjDfnJsKMN+5qDN4uLRycPoEbTGfnJsKMN+cmwow37moM3ikpmVafQIWmM/OTaUYT85NpRhP3PQZnFxdnY694WoTuwnx4Yy7CfHhjLsZw7aLC4+nX2NHkFr7CfHhjLsJ8eGMuxnDtosLjkH6v6lT3Ru7CfHhjLsJ8eGMuxnDtosLlVV1UaPoDX2k2NDGfaTY0MZ9jMHbRaX8/XbK/8p2E+ODWXYT44NZdjPHLRZXPIL8o0eQWvsJ8eGMuwnx4Yy7GcO2iwuhUWFRo+gNfaTY0MZ9pNjQxn2MwdtFpfevXobPYLW2E+ODWXYT44NZdjPHLRZXEpKS4weQWvsJ8eGMuwnx4Yy7GcO2iwu2dnZRo+gNfaTY0MZ9pNjQxn2MwdtFpfQvqFGj6A19pNjQxn2k2NDGfYzB20Wl2PHKo0eQWvsJ8eGMuwnx4Yy7GcO2iwucQnxRo+gNfaTY0MZ9pNjQxn2MwdtFpeBAwYaPYLW2E+ODWXYT44NZdjPHLRZXGpO1Bg9gtbYT44NZdhPjg1l2M8ctFlcIndGGj2C1thPjg1l2E+ODWXYzxy0WVz68dXgIuwnx4Yy7CfHhjLsZw7aLC5OTs5Gj6A19pNjQxn2k2NDGfYzB20WFz7EJ8N+cmwow35ybCjDfuagzeLSIzDQ6BG0xn5ybCjDfnJsKMN+5qDN4mK1tjZ6BK2xnxwbyrCfHBvKsJ85aLO4xCfEGT2C1thPjg1l2E+ODWXYzxy0WVy8vLyNHkFr7CfHhjLsJ8eGMuxnDtosLu7t3Y0eQWvsJ8eGMuwnx4Yy7GcO2iwuaelpRo+gNfaTY0MZ9pNjQxn2MwdtFpfWVqvRI2iN/eTYUIb95NhQhv3MQZvFxdPTy+gRtMZ+cmwow35ybCjDfuagzeKyd99eo0fQGvvJsaEM+8mxoQz7mYM2iwsRERGRNotLl8u7GD2C1thPjg1l2E+ODWXYzxy0WVzy8nKNHkFr7CfHhjLsJ8eGMuxnDtosLkfLy40eQWvsJ8eGMuwnx4Yy7GcO2iwugQH85VgS7CfHhjLsJ8eGMuxnDtosLkVHioweQWvsJ8eGMuwnx4Yy7GcO2iwuubkHjR5Ba+wnx4Yy7CfHhjLsZw7aLC7BQSFGj6A19pNjQxn2k2NDGfYzB20Wl/Lyo0aPoDX2k2NDGfaTY0MZ9jMHbRaX1DT+ciwJ9pNjQxn2k2NDGfYzB20Wl/CwcKNH0Br7ybGhDPvJsaEM+5mDNotLdXWV0SNojf3k2FCG/eTYUIb9zEGbxSU6NsboEbTGfnJsKMN+cmwow37moM3iwof4ZNhPjg1l2E+ODWXYzxy0WVwc7B2MHkFr7CfHhjLsJ8eGMuxnDtosLlu3bzV6BK2xnxwbyrCfHBvKsJ85aLO4hAQFGz2C1thPjg1l2E+ODWXYzxy0WVxcXCxGj6A19pNjQxn2k2NDGfYzB20Wlxi+GlyE/eTYUIb95NhQhv3MQZvFxc/Pz+gRtMZ+cmwow35ybCjDfuagzeLi1tbN6BG0xn5ybCjDfnJsKMN+5qDN4pKUkmT0CFpjPzk2lGE/OTaUYT9z0GZx6eDewegRtMZ+cmwow35ybCjDfuagzeLi0cnD6BG0xn5ybCjDfnJsKMN+5qDN4pKZlWn0CFpjPzk2lGE/OTaUYT9z0GZxcXZ2MnoErbGfHBvKsJ8cG8qwn8zggYOQHJeE9ORUvPPmQtjb29d7+afnPoWk2EQkxyXhztvvsL194ICrsXPrDuxKSMaOzdtwRffujZpDm8XFp7Ov0SNojf3k2FCG/eTYUIb9ms7Z2RnvvbMYE2+fiF7BvXHpJZfg3rvuqfPyN4+9CYEBAQgJ64uhI4Zh9kOPwKezDwDg/Xffw92T70HPoCvx6Ref4bFHHmvULNosLjkH9hs9gtbYT44NZdhPjg1l2K/phg8djvjEBOzOyICTkxOqjx/HmNFj6rx8aN9QrNuwHjU1NSgpLcH6DRswaOBAAMBJdRKXeFwCAGhtbY2ysrJGzaLN4lJVVW30CFpjPzk2lGE/OTaUYb+6Wa1W+Pr4wtfn7I9Kdfb2Rk5ODvy6+GHrhs0oLi6G56WX1nm+nJwc9A/rDzs7O1x00UVo3bo13NxO/RydSVMnY/HCRVi/eh2GDxuOJ595qlGzNuvv+Pby8oTFYkF4WDgc7B2wdftWhAQFw8XFgpjYGPj5+cGtrRuSUpLQwb0DPDp5IDMrE87OTvDp7IucA/tRVVUN/27+yC/IR2FRIXr36o2S0hJ4e3nDv5s/jh2rRFxCPAYOGIiaEzWI3BmJfn1D4eTkjMidkegRGAirtTXiE+Lg5eUN9/buSEtPQ2urFZ6eXti7by8AoMvlXZCXl4uj5eUIDAhE0ZEi5OYeRHBQCMrLjyI1LQ3hYeGorq5CdGzMeblN2dnZCO0bekFuk383f7y35D1T3aYL/XkKDAjAW++8barbdCE/T/+aMBE7o3aa6jZd6M/TvyZMRExcrKlu04X8PLVqZUWXLl1MdZua6/N0Lo6OjgjtG4qxN96IGQ8+8Mc8df/SyiUfLkXAFQFIiI7H0aNH4eBgj+iYaACAfzd/lJaVIj4hHv+a8C/0CAzE1u3bGrVvqOY6Xpg3v9nO9dfjyl5Xnrdz/xMO9mNDow/2Y0OjD/ar+zjX1+9xt9yq9mVkK18fXwVAjRo5Sn2zbHmDz7/ym+9Vv9B+qn27dqo4v0i1b9fOdt4t6zc3alZtnioqLCo0egStsZ8cG8qwnxwbyrBf023avAktHR0BAA4ODphy7yQsW/41ACCoTxCy07PQPyz8jI+zt7fHtMlTYbG4IjomGvb29rC3t7f9pm67FnaorPy1UbNos7j07tXb6BG0xn5ybCjDfnJsKMN+TXekuBgPPvwgvvrsS2SkpCMhKRFfLvsKwKmnkSyuFjg5/e/bzbv6dUVa0i6kJqbAz88PN94yFgBwuLAQj8yZjVXf/YDkuCRMnDARMx+e1ahZmvU1LudTSWmJ0SNojf3k2FCG/eTYUIb9ZFZ8+w1WfPvNGW+Pio7CJZ09a71tT/YeBPbuedbzvL90Cd5fuqTJc2jziEt2drbRI2iN/eTYUIb95NhQhv3MQZvFJbRvqNEjaI395NhQhv3k2FCG/cxBm8Xl2LFKo0fQGvvJsaEM+8mxoQz7mYM2i0tcQrzRI2iN/eTYUIb95NhQhv3MQZvFZeCAgUaPoDX2k2NDGfaTY0MZ9jMHbRaXmhM1Ro+gNfaTY0MZ9pNjQxn2MwdtFpfInZFGj6A19pNjQxn2k2NDGfYzB20Wl358NbgI+8mxoQz7ybGhDPuZgzaLi5OTs9EjaI395NhQhv3k2FCG/cxBm8WFD/HJsJ8cG8qwnxwbyrCfOWizuPQIDDR6BK2xnxwbyrCfHBvKsJ85aLO4WK2tjR5Ba+wnx4Yy7CfHhjLsZw7aLC7xCXFGj6A19pNjQxn2k2NDGfYzB20WFy8vb6NH0Br7ybGhDPvJsaEM+5mDNouLe3t3o0fQGvvJsaEM+8mxoQz7mYM2i0taeprRI2iN/eTYUIb95NhQhv3MQZvFpbXVavQIWmM/OTaUYT85NpRhP3PQZnHx9PQyegStsZ8cG8qwnxwbyrCfOWizuOzdt9foEbTGfnJsKMN+cmwow37moM3iQkRERKTN4tLl8i5Gj6A19pNjQxn2k2NDGfYzB20Wl7y8XKNH0Br7ybGhDPvJsaEM+5mDNovL0fJyo0fQGvvJsaEM+8mxoQz7mYM2i0tgAH85lgT7ybGhDPvJsaEM+5mDNotL0ZEio0fQGvvJsaEM+8mxoQz7mYM2i0tu7kGjR9Aa+8mxoQz7ybGhDPuZgzaLS3BQiNEjaI395NhQhv3k2FCG/cxBm8WlvPyo0SNojf3k2FCG/eTYUIb9zEGbxSU1jb8cS4L95NhQhv3k2FCG/cxBm8UlPCzc6BG0xn5ybCjDfnJsKMN+5qDN4lJdXWX0CFpjPzk2lGE/OTaUYT9z0GZxiY6NMXoErbGfHBvKsJ8cG8qwnzlos7jwIT4Z9pNjQxn2k2NDGfYzB20WFwd7B6NH0Br7ybGhDPvJsaEM+5mDNovL1u1bjR5Ba+wnx4Yy7CfHhjLsZw7aLC4hQcFGj6A19pNjQxn2k2NDGfYzB20WFxcXi9EjaI395NhQhv3k2FCG/cxBm8Ulhq8GF2E/OTaUYT85NpRhP3PQZnHx8/MzegStsZ8cG8qwnxwbyrCfOWizuLi1dTN6BK2xnxwbyrCfHBvKsJ85aLO4JKUkGT2C1thPjg1l2E+ODWXYzxy0WVw6uHcwegStsZ8cG8qwnxwbyrCfOWizuHh08jB6BK2xnxwbyrCfHBvKsJ85aLO4ZGZlGj2C1thPjg1l2E+ODWXYzxy0WVycnZ2MHkFr7CfHhjLsJ8eGMuxnDtosLj6dfY0eQWvsJ8eGMuwnx4Yy7GcO2iwuOQf2Gz2C1thPjg1l2E+ODWXYzxy0WVyqqqqNHkFr7CfHhjLsJ8eGMuxnDtosLv7d/I0eQWvsJ8eGMuwnx4Yy7GcO2iwu+QX5Ro+gNfaTY0MZ9pNjQxn2MwdtFpfCokKjR9Aa+8mxoQz7ybGhDPuZgzaLS+9evY0eQWvsJ8eGMuwnx4Yy7GcO2iwuJaUlRo+gNfaTY0MZ9pNjQxn2MwdtFpfs7GyjR9Aa+8mxoQz7ybGhDPuZgzaLS2jfUKNH0Br7ybGhDPvJsaEM+5mDNovLsWOVRo+gNfaTY0MZ9pNjQxn2MwdtFpe4hHijR9Aa+8mxoQz7ybGhDPuZgzaLy8ABA40eQWvsJ8eGMuwnx4Yy7GcO2iwuNSdqjB5Ba+wnx4Yy7CfHhjLsZw7aLC6ROyONHkFr7CfHhjLsJ8eGMuwnM3jgICTHJSE9ORXvvLkQ9vb29V7+6blPISk2EclxSbjz9jtqve/O2+9A9Pad2JWQjInjJzRqDm0Wl358NbgI+8mxoQz7ybGhDPs1nbOzM957ZzEm3j4RvYJ749JLLsG9d91T5+VvHnsTAgMCEBLWF0NHDMPshx6BT2cfAMBdd9yJa0eNwZgbrkPPoCvx6RefNWoWbRYXJydno0fQGvvJsaEM+8mxoQz71c1qtcLXxxe+Pr5nff/wocMRn5iA3RkZcHJyQvXx4xgzekyd5wvtG4p1G9ajpqYGJaUlWL9hAwYNHAgAuG/qfZh831QUlxQ3aVaHJn1UHby8PGGxWBAeFg4Hewds3b4VIUHBcHGxICY2Bn5+fnBr64aklCR0cO8Aj04eyMzKhLOzE3w6+yLnwH5UVVXDv5s/8gvyUVhUiN69eqOktAQZGbsxauQoHDtWibiEeAwcMBA1J2oQuTMS/fqGwsnJGZE7I9EjMBBWa2vEJ8TBy8sb7u3dkZaehtZWKzw9vbB3314AQJfLuyAvLxdHy8sRGBCIoiNFyM09iOCgEJSXH0VqWhrCw8JRXV2F6NiY83KbsrOzEdo39ILcppqaGlhbWU11my7056kFAIvFYqrbdCE/TxYXFwwdPMRUt+lCf54sLi6IGB5hqtt0IT9PSclJiBgeYarb1Fyfp3Pp7O2NnJwc+HXxw2cff4KExER069q1zsvn5OSgf1h/vL90CZycnNC6dWu4ubnB1dUVnTp2xKsvvgK/Ln44Wn4UDz/6CFJ2pTRq31DNdbwwb36zneuvR/+w8PN27n/CwX5saPTBfmxo9MF+dR/n+vr90MxZavO6TWpPeqaKGB6h/Lt1UwnR8XVe3tnZWS16+12VFJuoNq/bpLZv2qqmTpqiOnXqpH4+kKe6+nVVANSN19+gorfvbNSs2jxVZLW2NnoErbGfHBvKsJ8cG8qwX9Md+vkQvDw9MXLMKKxbvw6+PpchN+9gnZevqqrC5OlT0LtvHwyOGILy8nIk70pBSUkJHOwdkL331K9fWL9xQ51PT9VFm8UlPiHO6BG0xn5ybCjDfnJsKMN+Tbdp8ya0dHQEADg4OGDKvZOwbPnXAICgPkHITs9C/7DwMz7O3t4e0yZPhcXiiuiYaBw/fhzpGbtx4/U3AABGjbwGsXGxjZpFm8XFy8vb6BG0xn5ybCjDfnJsKMN+TXekuBgPPvwgvvrsS2SkpCMhKRFfLvsKAODo6AiLqwVOTk62y3f164q0pF1ITUyBn58fbrxlrO19M2bOwP3TZ9i+FXraA/c1apZmfXHu+eTe3t3oEbTGfnJsKMN+cmwow34yK779Biu+/eaMt0dFR+GSzp613rYnew8Ce/c863l2Z2RgwJCBTZ5Dm0dc0tLTjB5Ba+wnx4Yy7CfHhjLsZw7aLC6trVajR9Aa+8mxoQz7ybGhDPuZgzaLi6enl9EjaI395NhQhv3k2FCG/cxBm8Xl9A/moaZhPzk2lGE/OTaUYT9z0GZxISIiItJmcelyeRejR9Aa+8mxoQz7ybGhDPuZgzaLS15ertEjaI395NhQhv3k2FCG/cxBm8XlaHm50SNojf3k2FCG/eTYUIb9zEGbxSUwINDoEbTGfnJsKMN+cmwow37moM3iUnSkyOgRtMZ+cmwow35ybCjDfuagzeKSm1v3b6Gkc2M/OTaUYT85NpRhP3PQZnEJDgoxegStsZ8cG8qwnxwbyrCfOWizuJSXHzV6BK2xnxwbyrCfHBvKsJ85aLO4pKbxl2NJsJ8cG8qwnxwbyrCfOWizuISHhRs9gtbYT44NZdhPjg1l2M8ctFlcqqurjB5Ba+wnx4Yy7CfHhjLsZw7aLC7RsTFGj6A19pNjQxn2k2NDGfYzB20WFz7EJ8N+cmwow35ybCjDfuagzeLiYO9g9AhaYz85NpRhPzk2lGE/c9Bmcdm6favRI2iN/eTYUIb95NhQhv3MQZvFJSQo2OgRtMZ+cmwow35ybCjDfuagzeLi4mIxegStsZ8cG8qwnxwbyrCfOWizuMTw1eAi7CfHhjLsJ8eGMuxnDtosLn5+fkaPoDX2k2NDGfaTY0MZ9jMHbRYXt7ZuRo+gNfaTY0MZ9pNjQxn2MwdtFpeklCSjR9Aa+8mxoQz7ybGhDPuZgzaLSwf3DkaPoDX2k2NDGfaTY0MZ9jMHbRYXj04eRo+gNfaTY0MZ9pNjQxn2MwdtFpfMrEyjR9Aa+8mxoQz7ybGhDPuZgzaLi7Ozk9EjaI395NhQhv3k2FCG/cxBm8XFp7Ov0SNojf3k2FCG/eTYUIb9zEGbxSXnwH6jR9Aa+8mxoQz7ybGhDPuZgzaLS1VVtdEjaI395NhQhv3k2FCG/cxBm8XFv5u/0SNojf3k2FCG/eTYUIb9zEGbxSW/IN/oEbTGfnJsKMN+cmwow37moM3iUlhUaPQIWmM/OTaUYT85NpRhP3PQZnHp3au30SNojf3k2FCG/eTYUIb9zEGbxaWktMToEbTGfnJsKMN+cmwow37moM3ikp2dbfQIWmM/OTaUYT85NpRhP3PQZnEJ7Rtq9AhaYz85NpRhPzk2lGE/c9BmcTl2rNLoEbTGfnJsKMN+cmwow37moM3iEpcQb/QIWmM/OTaUYT85NpRhP3PQZnEZOGCg0SNojf3k2FCG/eTYUIb9zEGbxaXmRI3RI2iN/eTYUIb95NhQhv3MQZvFJXJnpNEjaI395NhQhv3k2FCG/czBoTlP5uXliRfmzW/OUwIArFYrAKC8vLzZz/1PwH5ybCjDfnJsKMN+9fPy8jR6hAZrAUAZPcS5+Pr4AgD25/BXkjcF+8mxoQz7ybGhDPuZhzZPFRERERFp8YgLEREREcBHXIiIiEgjXFyIiIhIG1xciIiISBtcXIiIiEgbjVpc7Ozs8J9H5yArLQOZqbsx6Z57AQBtWrfB8i+/xp70TGzdsBnd/f1tb/9o6YfITs9CYkwCrup3le1cSxe/j9TEFKQnpzb4Z7+Mu+VWpCenIjUxBU/PfQoAcOmllyI1McV2ZKVlID05tUm347T+YeFYs3I1diUk4/VXFzQ0zzn9HfsBQI+AQGxatxEp8cmIj4rFgPD+5zzX4IGDkByXhPTkVLzz5kLY29sDAMKvCkPczhhkpWVg2edfwWKxNDRPg5ilYX33wTmzH0POnp9s9+kp905uaJ5zMrpfd39/fL/8W2TsSq/1dovFgifmPI4jPxeif1h4g87l6emJj5Z8gMqyilq378//PUhL2oWKkub9uR3N2RAAHBwcEBsZjez0rAZdf133wWefesb2+Vi6+H04Ojo26XYAp9qu+vYHJMclISU+GTdef0ODZmuI5uy3ZuVqZOxKt32+g/oEnfP66+qn032QTn1XUYOOFi1aqCn3TlaOjo6qTes2KmfPT6pjx47qjddeV3P/84QCoCaOn6B2bt2hAKgO7u7qtlvHKwCqf1i4SoiOt53LycnJ9r9pSbtUr5696r1uDw8PtXf3HtWpUydlsVhU3M4Ydc3Ia8643JR7J6s3Xnu9SbcDgAoMCFCJMQkq4IorGtxF934b125QEcMjFAA1ImKE2rF5W73ncnZ2VvsystUV3bsrBwcH9cOK79SUeycrAGrMqDGqy+VdFAC16O131cwZD7BhI++Dc2Y/piaOn9Ds9z+j+/Xp3Udl7EpXL89/UWXsSq/1vqhtkerlF15SP2XuVf3Dws95Ozw8PNS+jGz19NynVGVZRZ2Xu2bkNeq7r7/52zYEoGY98KD69KNPVHZ6VoNud133wdOfDzs7O7Xux7XqujHXNvk++MF7S9S9d9+jAKju/v7q4L4Df8t+a1auVl6eXg2+7vr66XQf5CH44M3rNqmQ4BB1KCdXtba2VgDUhHG3qcqyCuXp6Vnrso6Ojqqk4MgZ5+jUqZNKS9ql3Nu3r/e67ps6Xb343HwFQLVv105t3bBZLXr73VqXsbOzU2lJu1RXv65Nuh0A1Nuvv6VuHnvTBYn/d+m3fvU6deftdygAavyt49QPK76r91zXjr5WffHJ5wqAslgs6usvlqnVP/x4xuXuvP0O9eaCN9iwkffB87m4GNnPwcFBubi4qP5h4WcsLtZWVgVAZexKb9AXjdMf4+XpVe8XjXU/rlXDhgz92zb09vJW8VFxqlvXrg1aXBry30FrK6uK2RHV6H98/fk+uGTRe+rpuU8pAOqqfleplPjkv2W/xi4u9fXT+T74Tzua/BoX9/bt4dO5MwoOF+DEiZM49tsxvPHa67jj9juQtWcPPC+t/eODhwweUutXiru1dUNyXBIyd+3G+o0bcKS4uN7r8/b2xv4DOegb0heb12/Cvp/2wfPSS2td5rox12L//v3Yk72n0bcjIzMDANCrZ0/ceP2NiN6+E3E7YzDullsbfK7G+Dv1m/XILDx4/0ysX70OD97/IGY8+EC95+rs7Y2cnBz4dfHD1g2bUVxcfMbnAgCGDBqCuLi4BhZpPJ0bnu12nL4P/vbbb3hs9qNIjkvC0sXvo327dg0+V2Nc6H41NTU4duzYWd9X/kvjH0o/18f0vrI32rm1w4ZNGxt97oaSNlzwyquY88QcHDv2W4Ou71z/HYzZEYWfsvYi58ABZO/d2+jbcfo++NS8ZzB86DBsWb8Z772zCLfffUeDz9UY0n6//PILVn77PeKj4vDk43NhZ1f/l7T6+ul6H/wnatLiYm9vjyWL3sfLr72C348fh729HTasXoeTJ09ixOiRqKz8FdXV1bbLd+rUCS/PfxH/mfu47W0lpSW4MqQ3vC/3QTe/rrj7zrts7/vz84M3XHc9AMDR0RHXjbkOSxe/jzvuuQtff7MC1cf/dx0AMGPaDCxc9E6tt61Zudp2rulTptV5O3799VcAwMUXX4wV367AVVeH49YJ4/HS/Jea/QvH363fZb6X4cSJE4iKjkL79u3RLzS03n6Ojo4I7RuKld9+j8efmos3F76J6urjtW7jhHG34dJLLsEXy75s1nan6d7wbLfj9H1wwZuv44pegQjqF4wDBw/inbdq36d17ddU9fWrz4zp9+Hd994VXXd9pA3H3nAjysvL6/yi1pT/Dob2vwqX+nrh2LFKzP3PE7a3N/Y+6OXpBVdXV2zbsQ2OLR0xdPCQZmlW1/U29T5464Rx6Bl0JQZHDMGVPXvVum1N/TpyNn/X++A/WaMfpnn/3cVq4RtvKwDK3t5elRQcsT0f2qJFC3Vgb47tYePTD1uOv3Vcnee79+571Gsvv1rvdT4y62EVHxVrO+/USVPUgldes72/X2g/lRSb2OTbcfrYvG6TCuoTZPtz5Jbt6speVzbrw1x/p34tWrRQPx/IUz0De9g67s/aV++5xt1yq9qXka18fXwVADVq5Cj1zbLltvcPGzJUpSam2J4vPx+H7g3ruw/++egREKhidkSZot/p42xPFZ0+GvMwPYA6H6b39PRUB/bmKBcXl7/tfXDp4vfV3t17VMaudJWdnqXKj5SpjF3pyqezT6Pvg3+9XMTwCLXiq+WNvh2nj9jIaDVm1Bhb45KCI8pisfyt+v31mDZ5qnp5/ov1XmdD+ul0H/ynHo1+xOWFefPh6nox7ps5AwBw4sQJrF2/Dq1aWQEAE8dPQHp6GoqOHIGzszOWf/U1Pv/yC3zx1dn/5e3q6orrRl+LjZs31Xu9q9euhrWVFSdOnISrqyvu+L/bsWz517b3z5jWuM32r7fjtO2R2zHxtgkATv0r2r29O7L3Zjf4vI29XqP7tWjRAg72DrC4ugI49Yr4Xyt/rfdcmzZvQss/vmPBwcEBU+6dZPtchASH4I3XXsfYW2/G4cOHGx6mEczQ8Gy346/s7Oxw+//djjXr1p7zXI1hVL8Lafrkqfj080/rfGpKqjka3jXpHnS5oiu69wzA0JHDUVhYiO49A5BzIKfO6z3XfwcBoGXLlrhl7E3YsHFDo2/H/87hCFfXU98RaGdnh+rj1fj9998bFqcBmvs+aG1lxdgbxmLt+nX1Xm9D+jWX830f/Kdr8Jbj362bqiyrUFlpGSo1MUWlJqaol55/QXl4eKjV369Su1PS1PrV61THDh0UcOpfYb8UH7VdNjUxxfbC1z3pmSolPlnt2LxN/d+//q9B1z910hSVHJekstIy1KR77rW93dfHV+X+dLDBm21dtwM4tdl/+ekXKi1pl4raFqmu7j+g2bbEv2u/66+9TiXFJqqU+GS1Zf1m2wv06jvG3nCjio+KU9npWeqpJ560vX37pq3qUE5urZlbtmzJho24D778wksqY1e6io+KU0898aRycHAwRb/LL7tcFeYVqOL8IlVRUq4K8wrUfx6dowCo1d+vUoV5BaqipFwV5xeptKRd9Z7L0dFRFeYVqKJDh1VlWYUqzCtQixcuUgBUq1at1KGc3DNe2Pl3bHj68PL0atCLc+u7D25et0mlJqao6O071UMzZ6kWLVo0+T4YflWYitkRpVLik1XUtkg1ImLE37Lf9k1bVWpiioraFqnG3XKrqJ9O98F/+sFfskhERETa4E/OJSIiIm1wcSEiIiJtcHEhIiIibXBxISIiIm1wcSEiIiJtcHEhIiIibXBxIfoHu2bkNchKy4DjHz9QUGLe08+ib0jfs75v64bNmHLvZPF1EBFxcSH6BysrK8O+fftw4sQJ8bn69O4Dl4suOuv79ufsR9GRIvF1EBFxcSHSzJBBgxG5ZTtSE1Pw3w8+RqtWrf74rdQzAQBBfYKwJz0Tbdu0xcTxE/DeO4vx9RfLkJ6cinffegctWrQAALz+6gIsXvguBg0cVOv8E8dPwKsvvYIli95Dxq50fPHJ57aPef/dxdiVkIz05FQsWfQe7O3tETE8AqmJKQjuE4T33lmM1MQUfLNsOQCgq19XpCamYNjQYbii+xW1rufG629AQnQ80pNT8fHSj2D948e9r1m5Gg/cdz/io+KQnZ6Fa0Zec157EpF+DP/xvTx48GjY0aZ1GxW5ZbuytrIqAOrF5+arxx55VHl5eqn9WftUt65dVcyOKDXo6oEKgJo4foIqOPizCgkOUXZ2dipyy3Y1ZNDgWuesLKtQ9vb2tj9PHD9BHfm5UI0cMVIBp36s+ulffXH6egGoH1Z8p0aNHGX785qVq23X+9djzuzH1Nz/PGH782W+l6nM1N2qU6dOCoB6+/W31IvPzbedZ+U336uLLrpIjYgYobZu2Gx4dx48ePx9Dj7iQqSR0NBQ+Pr6YsuGzUiKTcToUaPh5emF3LxczJn7H2xatxFbtm7Flm1bbR+zeu1qxMXH4eTJk9i+YwcCrgg45/X8uHY11qxdAwB46NFHsP+PX/x327jxiNkRhcSYBAQFBaFdu3ZNuh1DBg/G6rVrUFBQAAD44OMPER4Wbnv/a28swG+//Ybs7Gy0b9++SddBRObkYPQARNRw9nb2iI2LxQ03jz3jfc7OF8GuhR2UUnV+vPNFzqj4teKc13Oipsb2/+Pi4wAA3f398cCM+9GvfxhKy0qxeOGiWh9T3/X+VUuHlrUur5RCzZ+u889OP01FRATwNS5EWklMTkTfkL7o1aOn7W3Ozs7w79YNjz86BwOGDMQ1I6/BiIgRtvfbO5z694l7+/aIGDYc27Zvb9J1u7d3R3FxMUrLStHd3x9XDxhQ6/3FxcUIDAhs0LmiYqIxauQ1aOd26hGbu++4C+s2rG/SXET0z8JHXIg0UlBQgGkzpmPpe0vRsqUDfv+9Bo8/9QSemfsUnp73DPbu24sp903FF598jvBB/QEAo0Zcg4xd6aisrMTcp5/ET/t/AnDqRbCXeHgAAJLjErFt+3bcN3NGndcdGbUT+QUFyNiVjoO5uYiLj6/1/tfeWID3330P9959D3766Sdcd9MNuHb0tZj39DNo06YNAOCmG8di3MTxSE5JxutvvYHN6zcCALZt347X3ljQ7L2IyHxa4NSLXYjIhCaOn4BBAwfirkn3GD0KEVGz4FNFREREpA0+4kJERETa4CMuREREpA0uLkRERKQNLi5ERESkDS4uREREpA0uLkRERKQNLi5ERESkDS4uREREpA0uLkRERKQNLi5ERESkjf8Hh5CP087mHbQAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now it’s a simple filter on the DataFrame to get the ATM calls and puts.\n",
    "\n",
    "calls = (\n",
    "    chains[\n",
    "        (chains.strike == atm_strike)\n",
    "        & (chains.optionType == \"call\")\n",
    "    ]\n",
    ")\n",
    "# We can take a look at the term structure for call options.\n",
    "\n",
    "(\n",
    "    calls\n",
    "    .set_index(\"expiration\")\n",
    "    .impliedVolatility.plot(title=\"IV term structure for ATM call options\")\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T12:58:13.717868Z",
     "start_time": "2023-07-26T12:58:13.500233Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The term structure is normally upward sloping. There’s a few reasons for this:\n",
    "\n",
    "1. Higher Future Uncertainty: More volatility expected long-term than short-term. The more time there is until an option’s expiration, the more time there is for the underlying security’s price to change dramatically.\n",
    "\n",
    "2. Event Risk: Known potential events in the future (like earnings announcements, economic indicators release, etc.) that could cause the underlying asset to move significantly.\n",
    "\n",
    "3. Time Premium: Longer-dated options have more time value, increasing implied volatility. The more time an option has until expiration, the greater the chance it has of becoming profitable for the holder.\n",
    "\n",
    "There’s a spike in the curve since those options are expiring next week and traders are actively trading those contracts to reposition and speculate on future moves in the underlying.\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:title={'left':'IV term structure for call options expiring 2023-07-27'}, xlabel='strike'>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build the skew\n",
    "\n",
    "# To plot the implied volatility skew, select an expiration.\n",
    "\n",
    "calls = (\n",
    "    chains[\n",
    "        (chains.expiration == expirations[4])\n",
    "        & (chains.optionType == \"call\")\n",
    "    ]\n",
    ")\n",
    "# Instead of filtering the DataFrame by strike, we filter it by a single expiration.\n",
    "\n",
    "# Now plot the skew.\n",
    "\n",
    "(\n",
    "    calls\n",
    "    .set_index(\"strike\")\n",
    "    .impliedVolatility\n",
    "    .plot(title=f\"IV term structure for call options expiring {expirations[1]}\")\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T12:59:27.826911Z",
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    "We see somewhere between negative skew and a volatility “smile.” In the case of negative skew, the market may be preparing for a drop and traders are bidding up out of the money options for downside protection.\n",
    "\n",
    "A smile suggests the market might be expecting a big move in either direction. Smiles are often seen around news events or earnings announcements."
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