fitting_GBM_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation of parameters of Geometric Brownian Motion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The script below uses data available at Stooq to find parameters for the GBM process that can be used to model the evolution of asstets prices\n",
    "NOTE: obviously it does not mean that the asset priced actually follow the GBM, because they do not - \n",
    "cf. below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import get_data_from_stooq\n",
    "import numpy as np\n",
    "import scipy.stats as spst\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "import statsmodels.api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# prepare list of ticker symbols to analyze\n",
    "list_of_tickers = [\"tpe\", \"kgh\", \"peo\", \"lwb\", \"pkn\", \"lts\", \"opl\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ticker symbol in this iteration:  tpe\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=tpe&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  kgh\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=kgh&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  peo\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=peo&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  lwb\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=lwb&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  pkn\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=pkn&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  lts\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=lts&d1=20110101&d2=20161231&i=d\n",
      "Ticker symbol in this iteration:  opl\n",
      "Validation of start_date format result: positive...\n",
      "Validation of end_date format result: positive...\n",
      "Getting data from URL:  https://stooq.com/q/d/l/?s=opl&d1=20110101&d2=20161231&i=d\n",
      "Top 3 rows before adjustments...\n",
      "        Date     tpe     kgh     peo     lwb     pkn     lts     opl\n",
      "0 2011-01-03  5.3756  105.93  135.05  91.372  41.852  33.466  12.142\n",
      "1 2011-01-04  5.4079  106.55  135.49  93.841  40.542  33.296  11.137\n",
      "2 2011-01-05  5.3110  103.75  131.79  94.577  39.993  32.659  11.306\n",
      "Top 3 rows after adjustments...\n",
      "               tpe     kgh     peo     lwb     pkn     lts     opl\n",
      "Date                                                              \n",
      "2011-01-03  5.3756  105.93  135.05  91.372  41.852  33.466  12.142\n",
      "2011-01-04  5.4079  106.55  135.49  93.841  40.542  33.296  11.137\n",
      "2011-01-05  5.3110  103.75  131.79  94.577  39.993  32.659  11.306\n"
     ]
    }
   ],
   "source": [
    "# get data from stooq\n",
    "ts_start_date = \"2011-01-01\"\n",
    "ts_end_date = \"2016-12-31\"\n",
    "data_df = get_data_from_stooq.get_daily_data_from_stooq_for_tickers(list_of_tickers,\n",
    "                                                                   ts_start_date,\n",
    "                                                                   ts_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Head of normed data: \n",
      "                 tpe       kgh       peo       lwb       pkn       lts  \\\n",
      "Date                                                                     \n",
      "2011-01-03  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
      "2011-01-04  1.006009  1.005853  1.003258  1.027021  0.968699  0.994920   \n",
      "2011-01-05  0.987983  0.979420  0.975861  1.035076  0.955582  0.975886   \n",
      "\n",
      "                 opl  \n",
      "Date                  \n",
      "2011-01-03  1.000000  \n",
      "2011-01-04  0.917229  \n",
      "2011-01-05  0.931148  \n",
      "Tail of normed data: \n",
      "                 tpe       kgh       peo       lwb       pkn       lts  \\\n",
      "Date                                                                     \n",
      "2016-12-28  0.530173  0.868498  0.920770  0.791271  2.035745  1.148927   \n",
      "2016-12-29  0.532034  0.865666  0.938912  0.773760  2.026188  1.153111   \n",
      "2016-12-30  0.530173  0.873029  0.931507  0.760627  2.038134  1.142951   \n",
      "\n",
      "                 opl  \n",
      "Date                  \n",
      "2016-12-28  0.438972  \n",
      "2016-12-29  0.443914  \n",
      "2016-12-30  0.453797  \n"
     ]
    },
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       "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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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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\" width=\"639.8499802953507\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5428940438>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing a comparative plot of relative price changes\n",
    "data_df_normd = data_df/data_df.iloc[0,:]\n",
    "print(\"Head of normed data: \")\n",
    "print(data_df_normd.head(3))\n",
    "print(\"Tail of normed data: \")\n",
    "print(data_df_normd.tail(3))\n",
    "data_df_normd.plot()\n",
    "plt.grid(True)\n",
    "plt.title(\"Comparison of standardized performance over the analyzed period\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Standardized value/cumulative return\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [2011-01-03 00:00:00, 2011-01-04 00:00:00, 2011-01-05 00:00:00]\n"
     ]
    }
   ],
   "source": [
    "# calculating logarithmic returns for the data\n",
    "data_df_logrr = pd.DataFrame(index=data_df.index)\n",
    "\n",
    "\n",
    "\n",
    "# data_dict_in = {}\n",
    "\n",
    "# for colname in data_df_logrr.columns.values:\n",
    "    \n",
    "print(data_df_logrr.head(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# histograms for individual stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# descriptive statistics for individual stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# scatter plots matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# estimation of vector of average log RoRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# estimation of the correlation matrix for log RoRs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculation of a value trajectory for a fictional portfolio and some basic analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculation of historical 1% VaR for the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculation of analytical 1% VaR for the porfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculation of simulational 1% VaR for the portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Fitting ARIMA models for the analysed stocks"
   ]
  }
 ],
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