path: root/python/notebooks/Realized Vol.ipynb

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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import exploration.option_trades as rvol\n",
    "import datetime\n",
    "import pandas as pd\n",
    "\n",
    "from exploration.curve_trades import on_the_run\n",
    "from scipy.interpolate import interp1d\n",
    "from matplotlib import pyplot as plt\n",
    "from ipywidgets import widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "7d3eaa85fd6a4980bc652fd6a3614c9a"
      }
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = widgets.Dropdown(\n",
    "    options=['IG', 'HY'],\n",
    "    value='IG',\n",
    "    description='Index:',\n",
    "    disabled=False,\n",
    ")\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "index = w.value\n",
    "start_date=datetime.date(2014, 6, 11)\n",
    "onTR, model = rvol.realized_vol(index, on_the_run('IG'), \"5yr\", years=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#onTR.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Constant Mean - GARCH Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>spread_return</td>   <th>  R-squared:         </th>  <td>  -0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>   34.5073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>  -61.0146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>  -52.5061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>     <td>62</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Thu, Dec 21 2017</td>  <th>  Df Residuals:      </th>     <td>58</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>14:17:36</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Mean Model</caption>\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>mu</th> <td>   -0.0279</td> <td>1.758e-02</td> <td>   -1.587</td> <td>    0.113</td> <td>[-6.236e-02,6.557e-03]</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Volatility Model</caption>\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>      <th>95.0% Conf. Int.</th>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>omega</th>    <td>1.3703e-03</td> <td>1.681e-03</td> <td>    0.815</td> <td>    0.415</td> <td>[-1.924e-03,4.665e-03]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>alpha[1]</th> <td>9.1408e-14</td> <td>8.012e-03</td> <td>1.141e-11</td> <td>    1.000</td> <td>[-1.570e-02,1.570e-02]</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beta[1]</th>  <td>    0.9310</td> <td>9.562e-02</td> <td>    9.737</td> <td>2.098e-22</td>    <td>[  0.744,  1.118]</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                     Constant Mean - GARCH Model Results                      \n",
       "==============================================================================\n",
       "Dep. Variable:          spread_return   R-squared:                      -0.000\n",
       "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
       "Vol Model:                      GARCH   Log-Likelihood:                34.5073\n",
       "Distribution:                  Normal   AIC:                          -61.0146\n",
       "Method:            Maximum Likelihood   BIC:                          -52.5061\n",
       "                                        No. Observations:                   62\n",
       "Date:                Thu, Dec 21 2017   Df Residuals:                       58\n",
       "Time:                        14:17:36   Df Model:                            4\n",
       "                                  Mean Model                                 \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "mu            -0.0279  1.758e-02     -1.587      0.113 [-6.236e-02,6.557e-03]\n",
       "                               Volatility Model                              \n",
       "=============================================================================\n",
       "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
       "-----------------------------------------------------------------------------\n",
       "omega      1.3703e-03  1.681e-03      0.815      0.415 [-1.924e-03,4.665e-03]\n",
       "alpha[1]   9.1408e-14  8.012e-03  1.141e-11      1.000 [-1.570e-02,1.570e-02]\n",
       "beta[1]        0.9310  9.562e-02      9.737  2.098e-22      [  0.744,  1.118]\n",
       "=============================================================================\n",
       "\n",
       "Covariance estimator: robust\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/edwin/projects/code/python/yieldcurve.py:54: RuntimeWarning: cache miss for date: 2017-12-21\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1m</th>\n",
       "      <th>2m</th>\n",
       "      <th>3m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.025</th>\n",
       "      <td>-0.058402</td>\n",
       "      <td>-0.042662</td>\n",
       "      <td>-0.037079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.975</th>\n",
       "      <td>0.056015</td>\n",
       "      <td>0.042449</td>\n",
       "      <td>0.035047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1m        2m        3m\n",
       "0.025 -0.058402 -0.042662 -0.037079\n",
       "0.975  0.056015  0.042449  0.035047"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute lo and hi percentiles of atm volatility daily change (vol of vol)\n",
    "rvol.vol_var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = rvol.atm_vol(index, start_date, moneyness = .2)\n",
    "df['steepness'] = df.otm_vol - df.atm_vol\n",
    "df1 = df.reset_index()\n",
    "df1['date'] = df1.quotedate.dt.date\n",
    "df1 = df1.groupby(['date','expiry']).last()\n",
    "#Need to do: the vol looks jumpy, is it because of quote source issue? yes, need to first try to get the same quote source..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4594103780>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = []\n",
    "time = [1/12, 2/12, 3/12, 4/12, 5/12]\n",
    "for t in time:\n",
    "    for date, g in df1.groupby(level='date'):\n",
    "        f = interp1d(g['T'].values, g['steepness'].values, fill_value='extrapolate')\n",
    "        r.append((date, t, f(t)))\n",
    "steepness = pd.DataFrame(r, columns=['date', 'T', 'steepness'])\n",
    "steepness = steepness.set_index(['date','T']).unstack().astype('float')\n",
    "steepness.columns = steepness.columns.droplevel()\n",
    "steepness.ewm(span = 3).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "T\n",
       "0.083333    4.179405\n",
       "0.166667    3.624137\n",
       "0.250000    3.392283\n",
       "0.333333    2.487193\n",
       "0.416667    1.760266\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(steepness.iloc[-1] - steepness.mean()) / steepness.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Need to do: look at steepness not on moneyness but on delta range (60 delta vs 20 delta)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}