Project - 9 visualizations/5. Compare stock prices interactive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Compare stock prices interactively using Bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data from https://www.kaggle.com/datasets/dgawlik/nyse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bokeh\n",
      "  Downloading bokeh-3.2.1-py3-none-any.whl (7.8 MB)\n",
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      "Requirement already satisfied: six>=1.5 in c:\\users\\akirkup\\conda_trial_project\\env\\lib\\site-packages (from python-dateutil>=2.8.1->pandas>=1.2->bokeh) (1.16.0)\n",
      "Installing collected packages: xyzservices, PyYAML, bokeh\n",
      "Successfully installed PyYAML-6.0.1 bokeh-3.2.1 xyzservices-2023.7.0\n"
     ]
    }
   ],
   "source": [
    "!pip install bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd# make bokeh display figures inside the notebook\n",
    "from bokeh.io import output_notebook\n",
    "from bokeh.plotting import figure, show\n",
    "from ipywidgets import interact, widgets\n",
    "\n",
    "# set bokeh to display within notebook\n",
    "output_notebook()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"dc83ffb4-65a0-4660-989a-7ce3586593ce\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"dc83ffb4-65a0-4660-989a-7ce3586593ce\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.2.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"dc83ffb4-65a0-4660-989a-7ce3586593ce\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-05 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>123.430000</td>\n",
       "      <td>125.839996</td>\n",
       "      <td>122.309998</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>2163600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-06 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>125.239998</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>119.940002</td>\n",
       "      <td>125.540001</td>\n",
       "      <td>2386400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-07 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>116.379997</td>\n",
       "      <td>114.949997</td>\n",
       "      <td>114.930000</td>\n",
       "      <td>119.739998</td>\n",
       "      <td>2489500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-08 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>115.480003</td>\n",
       "      <td>116.620003</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>117.440002</td>\n",
       "      <td>2006300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>117.010002</td>\n",
       "      <td>114.970001</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>117.330002</td>\n",
       "      <td>1408600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date symbol        open       close         low        high  \\\n",
       "0  2016-01-05 00:00:00   WLTW  123.430000  125.839996  122.309998  126.250000   \n",
       "1  2016-01-06 00:00:00   WLTW  125.239998  119.980003  119.940002  125.540001   \n",
       "2  2016-01-07 00:00:00   WLTW  116.379997  114.949997  114.930000  119.739998   \n",
       "3  2016-01-08 00:00:00   WLTW  115.480003  116.620003  113.500000  117.440002   \n",
       "4  2016-01-11 00:00:00   WLTW  117.010002  114.970001  114.089996  117.330002   \n",
       "\n",
       "      volume  \n",
       "0  2163600.0  \n",
       "1  2386400.0  \n",
       "2  2489500.0  \n",
       "3  2006300.0  \n",
       "4  1408600.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/nyse kaggle/prices.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "      <th>short_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-05 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>123.430000</td>\n",
       "      <td>125.839996</td>\n",
       "      <td>122.309998</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>2163600.0</td>\n",
       "      <td>2016-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-06 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>125.239998</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>119.940002</td>\n",
       "      <td>125.540001</td>\n",
       "      <td>2386400.0</td>\n",
       "      <td>2016-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-07 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>116.379997</td>\n",
       "      <td>114.949997</td>\n",
       "      <td>114.930000</td>\n",
       "      <td>119.739998</td>\n",
       "      <td>2489500.0</td>\n",
       "      <td>2016-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-08 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>115.480003</td>\n",
       "      <td>116.620003</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>117.440002</td>\n",
       "      <td>2006300.0</td>\n",
       "      <td>2016-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>117.010002</td>\n",
       "      <td>114.970001</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>117.330002</td>\n",
       "      <td>1408600.0</td>\n",
       "      <td>2016-01-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date symbol        open       close         low        high  \\\n",
       "0  2016-01-05 00:00:00   WLTW  123.430000  125.839996  122.309998  126.250000   \n",
       "1  2016-01-06 00:00:00   WLTW  125.239998  119.980003  119.940002  125.540001   \n",
       "2  2016-01-07 00:00:00   WLTW  116.379997  114.949997  114.930000  119.739998   \n",
       "3  2016-01-08 00:00:00   WLTW  115.480003  116.620003  113.500000  117.440002   \n",
       "4  2016-01-11 00:00:00   WLTW  117.010002  114.970001  114.089996  117.330002   \n",
       "\n",
       "      volume  short_date  \n",
       "0  2163600.0  2016-01-05  \n",
       "1  2386400.0  2016-01-06  \n",
       "2  2489500.0  2016-01-07  \n",
       "3  2006300.0  2016-01-08  \n",
       "4  1408600.0  2016-01-11  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# map the date of each row to only the year-month-day format\n",
    "from datetime import datetime\n",
    "\n",
    "def shorten_time_stamp(timestamp):\n",
    "    shortened = timestamp[0]\n",
    "    \n",
    "    if len(shortened) > 10:\n",
    "        parsed_date=datetime.strptime(shortened, '%Y-%m-%d %H:%M:%S')\n",
    "        shortened=datetime.strftime(parsed_date, '%Y-%m-%d')\n",
    "    \n",
    "    return shortened\n",
    "\n",
    "df['short_date'] = df.apply(lambda x: shorten_time_stamp(x), axis=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_candle_plot(plot, stock_name, stock_range, color):\n",
    "    inc_1 = stock_range.close > stock_range.open\n",
    "    dec_1 = stock_range.open > stock_range.close\n",
    "    w = 0.5\n",
    "\n",
    "    plot.segment(stock_range['short_date'], stock_range['high'], \n",
    "                 stock_range['short_date'], stock_range['low'], \n",
    "                 color=\"grey\")\n",
    "\n",
    "    plot.vbar(stock_range['short_date'][inc_1], w, \n",
    "              stock_range['high'][inc_1], stock_range['close'][inc_1], \n",
    "              fill_color=\"green\", line_color=\"black\",\n",
    "              legend_label=('Mean price of ' + stock_name), muted_alpha=0.2)\n",
    "\n",
    "    plot.vbar(stock_range['short_date'][dec_1], w, \n",
    "              stock_range['high'][dec_1], stock_range['close'][dec_1], \n",
    "              fill_color=\"red\", line_color=\"black\",\n",
    "              legend_label=('Mean price of ' + stock_name), muted_alpha=0.2)\n",
    "\n",
    "    stock_mean_val=stock_range[['high', 'low']].mean(axis=1)\n",
    "    plot.line(stock_range['short_date'], stock_mean_val, \n",
    "              legend_label=('Mean price of ' + stock_name), muted_alpha=0.2,\n",
    "              line_color=color, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# method to build the plot\n",
    "def get_plot(stock_1, stock_2, date, value):    \n",
    "    stock_1 = df[df['symbol'] == stock_1]\n",
    "    stock_2 = df[df['symbol'] == stock_2]\n",
    "    \n",
    "    stock_1_name=stock_1['symbol'].unique()[0]\n",
    "    stock_1_range=stock_1[(stock_1['short_date'] >= date[0]) & (stock_1['short_date'] <= date[1])]\n",
    "    stock_2_name=stock_2['symbol'].unique()[0]\n",
    "    stock_2_range=stock_2[(stock_2['short_date'] >= date[0]) & (stock_2['short_date'] <= date[1])]\n",
    "\n",
    "    plot=figure(title='Stock prices', \n",
    "                     x_axis_label='Date', \n",
    "                     x_range=stock_1_range['short_date'], \n",
    "                     y_axis_label='Price in $USD',\n",
    "                     width=800, \n",
    "                     height=500)\n",
    "    \n",
    "    plot.xaxis.major_label_orientation = 1\n",
    "    plot.grid.grid_line_alpha=0.3\n",
    "    \n",
    "    if value == 'open-close':\n",
    "        add_candle_plot(plot, stock_1_name, stock_1_range, 'blue')\n",
    "        add_candle_plot(plot, stock_2_name, stock_2_range, 'orange')\n",
    "        \n",
    "    if value == 'volume':\n",
    "        plot.line(stock_1_range['short_date'], stock_1_range['volume'], \n",
    "                  legend_label=stock_1_name, muted_alpha=0.2)\n",
    "        plot.line(stock_2_range['short_date'], stock_2_range['volume'], \n",
    "                  legend_label=stock_2_name, muted_alpha=0.2,\n",
    "                  line_color='orange')\n",
    "    \n",
    "    plot.legend.click_policy=\"mute\"\n",
    "    \n",
    "    return plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extracing the necessary data\n",
    "stock_names=df['symbol'].unique()\n",
    "dates_2016=df[df['short_date'] >= '2016-01-01']['short_date']\n",
    "unique_dates_2016=sorted(dates_2016.unique())\n",
    "value_options=['open-close', 'volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting up the interaction elements\n",
    "drp_1=widgets.Dropdown(options=stock_names,\n",
    "                       value='AAPL',\n",
    "                       description='Compare:')\n",
    "\n",
    "drp_2=widgets.Dropdown(options=stock_names,\n",
    "                       value='AON',\n",
    "                       description='to:')\n",
    "\n",
    "range_slider=widgets.SelectionRangeSlider(options=unique_dates_2016, \n",
    "                                          index=(0,25), \n",
    "                                          continuous_update=False,\n",
    "                                          description='From-To',\n",
    "                                          layout={'width': '500px'})\n",
    "\n",
    "value_radio=widgets.RadioButtons(options=value_options,\n",
    "                                 value='open-close',\n",
    "                                 description='Metric')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c392718e16e44a5a24deaf621a1904b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Compare:', index=4, options=('WLTW', 'A', 'AAL', 'AAP', 'AAPL', 'A…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating the interact method \n",
    "@interact(stock_1=drp_1, stock_2=drp_2, date=range_slider, value=value_radio)\n",
    "def get_stock_for_2016(stock_1, stock_2, date, value):\n",
    "    show(get_plot(stock_1, stock_2, date, value))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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