plotly-charts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import datetime\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False\n",
    "\n",
    "import plotly.offline as py\n",
    "# That's line needed if you use jupyter notebook (.ipynb):\n",
    "py.init_notebook_mode(connected=True)\n",
    "\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def dataFrame(symbol, period, startdate, enddate):\n",
    "    \n",
    "    columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "    tickerData = yf.Ticker(symbol)\n",
    "    tickerDf = tickerData.history(period=period, start=startdate, end=enddate)\n",
    "    \n",
    "    return tickerDf[columns]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = datetime.date.today()\n",
    "start = datetime.datetime(today.year-5,today.month,today.day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(datetime.date(2020, 3, 27), datetime.datetime(2015, 3, 27, 0, 0))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today, start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickerSymbol = 'GOOG'"
   ]
  },
  {
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-03-27</th>\n",
       "      <td>551.49</td>\n",
       "      <td>553.76</td>\n",
       "      <td>546.63</td>\n",
       "      <td>546.84</td>\n",
       "      <td>1897400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-30</th>\n",
       "      <td>550.11</td>\n",
       "      <td>551.95</td>\n",
       "      <td>546.67</td>\n",
       "      <td>550.52</td>\n",
       "      <td>1287500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-31</th>\n",
       "      <td>548.49</td>\n",
       "      <td>553.19</td>\n",
       "      <td>545.22</td>\n",
       "      <td>546.50</td>\n",
       "      <td>1588000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>547.10</td>\n",
       "      <td>549.63</td>\n",
       "      <td>538.02</td>\n",
       "      <td>541.07</td>\n",
       "      <td>1963000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-02</th>\n",
       "      <td>539.37</td>\n",
       "      <td>539.37</td>\n",
       "      <td>532.39</td>\n",
       "      <td>534.06</td>\n",
       "      <td>1716300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close   Volume\n",
       "Date                                               \n",
       "2015-03-27  551.49  553.76  546.63  546.84  1897400\n",
       "2015-03-30  550.11  551.95  546.67  550.52  1287500\n",
       "2015-03-31  548.49  553.19  545.22  546.50  1588000\n",
       "2015-04-01  547.10  549.63  538.02  541.07  1963000\n",
       "2015-04-02  539.37  539.37  532.39  534.06  1716300"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dataFrame(tickerSymbol, '1d', start, today)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-20</th>\n",
       "      <td>1135.72</td>\n",
       "      <td>1143.99</td>\n",
       "      <td>1065.49</td>\n",
       "      <td>1072.32</td>\n",
       "      <td>3601800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-23</th>\n",
       "      <td>1061.32</td>\n",
       "      <td>1071.32</td>\n",
       "      <td>1013.54</td>\n",
       "      <td>1056.62</td>\n",
       "      <td>4044100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-24</th>\n",
       "      <td>1103.77</td>\n",
       "      <td>1135.00</td>\n",
       "      <td>1090.62</td>\n",
       "      <td>1134.46</td>\n",
       "      <td>3344500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-25</th>\n",
       "      <td>1126.47</td>\n",
       "      <td>1148.90</td>\n",
       "      <td>1086.01</td>\n",
       "      <td>1102.49</td>\n",
       "      <td>4081500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-26</th>\n",
       "      <td>1111.80</td>\n",
       "      <td>1169.97</td>\n",
       "      <td>1093.53</td>\n",
       "      <td>1161.75</td>\n",
       "      <td>3571700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close   Volume\n",
       "Date                                                   \n",
       "2020-03-20  1135.72  1143.99  1065.49  1072.32  3601800\n",
       "2020-03-23  1061.32  1071.32  1013.54  1056.62  4044100\n",
       "2020-03-24  1103.77  1135.00  1090.62  1134.46  3344500\n",
       "2020-03-25  1126.47  1148.90  1086.01  1102.49  4081500\n",
       "2020-03-26  1111.80  1169.97  1093.53  1161.75  3571700"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plotChart(df):\n",
    "    plt.figure(figsize=(15,6))\n",
    "    df['Close'].plot()\n",
    "    plt.title('Stock Price over Time (%s)'%tickerSymbol, fontsize=20)\n",
    "    plt.xlabel('Date', fontsize=16)\n",
    "    plt.ylabel('Price', fontsize=16)\n",
    "    \n",
    "    for year in range(2015,2021):\n",
    "        plt.axvline(pd.to_datetime(str(year)+'-01-01'), color='k', linestyle='--', alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotChart(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://towardsdatascience.com/python-stock-analysis-candlestick-chart-with-python-and-plotly-e619143642bb\n",
    "\n",
    "tickerSymbol = 'NFLX'\n",
    "today = datetime.date.today()\n",
    "start = datetime.datetime(today.year-5,today.month,today.day)\n",
    "\n",
    "\n",
    "def candleStick(symbol, startdate, enddate):\n",
    "    tickerData = yf.Ticker(symbol)\n",
    "    df = tickerData.history(period='1d', start=startdate, end=enddate)\n",
    "    \n",
    "    fig = go.Figure(data=[go.Candlestick(x=df.index,\n",
    "                    open=df['Open'],\n",
    "                    high=df['High'],\n",
    "                    low=df['Low'],\n",
    "                    close=df['Close'])])\n",
    "\n",
    "    fig.update_layout(\n",
    "        title= {\n",
    "            'text': symbol,\n",
    "          'y':0.9,\n",
    "            'x':0.5,\n",
    "            'xanchor': 'center',\n",
    "            'yanchor': 'top'},\n",
    "          font=dict(\n",
    "            family=\"Times New Roman\",\n",
    "            size=20,\n",
    "            color=\"#7f7f7f\"\n",
    "        )\n",
    "        )\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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          67.24,
          68.86,
          68.7,
          76,
          79.78,
          81.79,
          81.23,
          80.21,
          79.66,
          80.17,
          80.29,
          80.59,
          80.07,
          80.24,
          79.86,
          79.57,
          81.24,
          81.03,
          80.11,
          81.04,
          82.32,
          83.81,
          83.4,
          83.29,
          86.36,
          87.54,
          88.89,
          88.21,
          89.08,
          89.18,
          88.87,
          88.14,
          89.71,
          89.47,
          88.84,
          88.71,
          89.24,
          88.38,
          89.21,
          90.71,
          89.33,
          93.4,
          96.89,
          94.92,
          92.86,
          94.24,
          95.13,
          94.65,
          96.24,
          95.04,
          96.34,
          100.01,
          95.48,
          95.3,
          91.51,
          93.36,
          94.81,
          94,
          93.47,
          95.01,
          93.47,
          94.9,
          97.52,
          98.1,
          101.27,
          99.97,
          111.02,
          117.34,
          114.7,
          110.21,
          112.14,
          110.91,
          111.55,
          107.79,
          107.29,
          106.83,
          106.4,
          111.95,
          114.6,
          113.15,
          125.62,
          126.29,
          126.42,
          126.09,
          120.01,
          121.47,
          120.99,
          124.96,
          123.14,
          125.26,
          123.5,
          119.8,
          106.2,
          88.75,
          107.75,
          107.15,
          114.94,
          116.35,
          115.24,
          109.35,
          109.31,
          102.69,
          98.05,
          102.08,
          96.51,
          99.42,
          98.57,
          96.84,
          95.22,
          99.7,
          103.67,
          102,
          103.3,
          98.29,
          98.6,
          97.11,
          105.3,
          101.57,
          99.63,
          99.91,
          102.91,
          103.01,
          107.48,
          110.33,
          108.84,
          107.94,
          114.11,
          114.45,
          112.85,
          111.5,
          103.77,
          100.21,
          98.07,
          101.06,
          99.67,
          98.07,
          97.61,
          100.05,
          102.58,
          102.9,
          105,
          105.12,
          109.2,
          107.36,
          110.36,
          113.75,
          113.39,
          113.36,
          108.86,
          113.08,
          112.52,
          108.34,
          102.97,
          112.11,
          117.94,
          120.25,
          120.85,
          124.3,
          123.67,
          124.23,
          123.85,
          126.03,
          124.47,
          125.53,
          129.62,
          127,
          131.19,
          121.51,
          127.7,
          124.5,
          121.45,
          119.77,
          121.93,
          119.8,
          123.97,
          120.85,
          119.51,
          117.3,
          116.91,
          118.22,
          117.26,
          118.19,
          118.95,
          116.21,
          109,
          110.45,
          105.29,
          116.36,
          116.33,
          112.13,
          116.11,
          114.43,
          105.65,
          102.43,
          106.57,
          108.91,
          108.32,
          104.72,
          99.78,
          99.74,
          96.61,
          93.84,
          92,
          91.79,
          95.54,
          92.41,
          89.5,
          88.23,
          80.57,
          81.75,
          89,
          87.42,
          86.64,
          89,
          90.13,
          94.8,
          89.98,
          90.75,
          91.4,
          88.5,
          91.08,
          95.3,
          94.81,
          94.58,
          98.01,
          97.83,
          98.76,
          101,
          95,
          96.82,
          98.18,
          99.51,
          97.2,
          97.87,
          97.53,
          99.05,
          100.5,
          101.15,
          100.48,
          99.75,
          98.64,
          98.34,
          101.08,
          104.65,
          102.43,
          102.93,
          105.9,
          103.47,
          105.3,
          104.31,
          105.12,
          104.04,
          103.15,
          108,
          110.01,
          110.64,
          109.9,
          99.49,
          94.34,
          97.31,
          94.85,
          95.7,
          93.5,
          92.18,
          91.5,
          90.5,
          90.41,
          92.08,
          91,
          90.91,
          88.83,
          90.73,
          91.14,
          92.4,
          90.32,
          87.39,
          87.56,
          89,
          88.25,
          90.16,
          90.08,
          92.98,
          95.98,
          99,
          103.21,
          102.44,
          102.95,
          101.5,
          101.39,
          100.99,
          100.29,
          101.2,
          100.02,
          97.22,
          94.88,
          95.02,
          94.16,
          94.61,
          94.18,
          95.75,
          95.22,
          93.87,
          90.78,
          90.62,
          87.53,
          87.88,
          87.41,
          89.67,
          90.91,
          95,
          95.2,
          95.03,
          94.7,
          96,
          96.19,
          95.07,
          96.26,
          97,
          98.52,
          98.43,
          85.43,
          86.67,
          88.3,
          86.48,
          85.73,
          91.03,
          91.5,
          91.92,
          91.19,
          91.23,
          94,
          93.28,
          93.2,
          93.77,
          95.91,
          95.12,
          94.4,
          94.6,
          95.53,
          96.83,
          95.19,
          94.94,
          95.98,
          96.05,
          95.67,
          95.68,
          96.13,
          96.38,
          97.33,
          96.97,
          97.84,
          97.48,
          97.81,
          97.9,
          97.76,
          100.11,
          99.23,
          99.06,
          95.91,
          97.17,
          97.35,
          97.23,
          97.1,
          99.9,
          98.78,
          96.9,
          95.54,
          95.75,
          95.38,
          95.04,
          97.1,
          97.31,
          96.9,
          98,
          103.19,
          103.5,
          104.97,
          103.98,
          103.18,
          102.31,
          100.88,
          98.01,
          101.59,
          100.5,
          116.63,
          118.25,
          121.97,
          122.5,
          127.42,
          126.52,
          126.29,
          127.43,
          126.08,
          126.85,
          124.98,
          122.83,
          122.31,
          122,
          124.26,
          124.12,
          121.84,
          122.76,
          114.9,
          114.75,
          114.55,
          112.96,
          115.13,
          115.73,
          116.2,
          118.32,
          117.76,
          117.64,
          117.05,
          117.3,
          119,
          117.52,
          116.75,
          120.73,
          120.1,
          124.48,
          125.4,
          123.78,
          122.84,
          123.51,
          123.43,
          123.44,
          126.39,
          124.3,
          125.46,
          125.53,
          126.84,
          125.61,
          126.24,
          129.07,
          125.95,
          126.24,
          124.96,
          127.49,
          129.22,
          132.08,
          131.48,
          131.27,
          130.91,
          130.63,
          131.15,
          135.04,
          133.21,
          142.01,
          139.36,
          138.65,
          138.11,
          140.8,
          140.45,
          139.46,
          141.77,
          140.55,
          141.2,
          140.61,
          139.51,
          140,
          141.49,
          143.57,
          144.98,
          144.68,
          145.19,
          143.2,
          140.72,
          142.42,
          141.75,
          142.99,
          142.92,
          144.45,
          141.88,
          143.12,
          143.3,
          142.84,
          142.15,
          139.69,
          141,
          141.99,
          141.47,
          140.04,
          141.39,
          140.9,
          142.95,
          144.2,
          145.5,
          144.05,
          145.45,
          146.75,
          142.54,
          142.14,
          142.36,
          140.83,
          144.1,
          145.5,
          146.81,
          148,
          146.7,
          146.84,
          146.19,
          144,
          143.79,
          143.04,
          144.28,
          144.85,
          144.25,
          144.43,
          147.34,
          143.37,
          140.3,
          141.39,
          143.61,
          147.46,
          151.53,
          150.6,
          152.71,
          151.91,
          155.76,
          156.29,
          155.59,
          158.55,
          156.55,
          156.1,
          157.82,
          160.33,
          159.11,
          160.25,
          160.5,
          158.09,
          153.61,
          156.15,
          157.86,
          157.75,
          158.35,
          161,
          162.84,
          163.6,
          163.61,
          163.52,
          163.42,
          165.49,
          164.95,
          165.6,
          166.12,
          166.27,
          155.3,
          154.38,
          154.34,
          149.44,
          151.45,
          154.29,
          153.68,
          152.5,
          155.13,
          155.01,
          158.78,
          156.62,
          151.64,
          152.82,
          149.76,
          149.8,
          146.58,
          146.13,
          146.65,
          150.34,
          152.37,
          155.77,
          158.51,
          159.3,
          162.91,
          176.12,
          182.97,
          183.84,
          182.72,
          188.69,
          187.79,
          187.7,
          189.89,
          182,
          184.26,
          182.49,
          182.22,
          180.53,
          179.6,
          181,
          181.37,
          171.43,
          174.03,
          169.86,
          169.8,
          171.53,
          167.5,
          169.23,
          165.95,
          166.91,
          167.76,
          168.35,
          169.86,
          168.58,
          166.43,
          165,
          169.5,
          175.45,
          175.55,
          173.4,
          175.25,
          178.8,
          178.45,
          178.1,
          182.55,
          184.07,
          183.25,
          182.73,
          183.61,
          184.98,
          186.1,
          185.82,
          188.4,
          186.46,
          180.7,
          181.01,
          181.25,
          180.73,
          182.11,
          177.65,
          181.41,
          185.65,
          194.3,
          199.1,
          197.85,
          195.57,
          196.75,
          199.7,
          201.95,
          200.11,
          199.99,
          193.01,
          195.9,
          194.17,
          192.75,
          195.38,
          194.66,
          197.2,
          199.62,
          198.21,
          197.24,
          197.45,
          199.8,
          200,
          200,
          196,
          194.46,
          191.61,
          191.12,
          193.64,
          194.54,
          194.33,
          195.74,
          193.3,
          195.04,
          196.58,
          196.65,
          195.56,
          195.34,
          198.91,
          190.31,
          186.99,
          189.36,
          183.5,
          183.38,
          185.71,
          186.5,
          187.85,
          186.01,
          186.1,
          187.98,
          189.61,
          191.2,
          190.18,
          187.94,
          189.44,
          188.33,
          189.78,
          187.8,
          187.18,
          192.51,
          196.1,
          202.05,
          206.2,
          207.25,
          210.02,
          212.11,
          207.57,
          214.29,
          217.18,
          224.24,
          221,
          220.34,
          222.75,
          222,
          255.05,
          250.88,
          263,
          271.49,
          274.2,
          277,
          281.94,
          266.41,
          263,
          262,
          247.7,
          266.58,
          267.08,
          253.85,
          252.14,
          257.29,
          260.47,
          270.03,
          278.73,
          277.74,
          282.07,
          283.88,
          281,
          288.75,
          294.77,
          293.1,
          292.75,
          284.65,
          302.85,
          319.88,
          320,
          322.2,
          321.33,
          333.56,
          323.87,
          318.16,
          323.17,
          321.42,
          315.8,
          313.26,
          316.35,
          313.07,
          307.41,
          309.36,
          322.49,
          298.39,
          287,
          291.94,
          285.45,
          273.63,
          293.15,
          289.1,
          291.77,
          297.68,
          302.88,
          309.72,
          317.29,
          315.99,
          329.66,
          336.3,
          332.88,
          332.22,
          329.15,
          319.22,
          306.37,
          310,
          316.25,
          311.07,
          310.36,
          311.65,
          312.59,
          308.71,
          321.99,
          325.9,
          328.79,
          331.5,
          329.65,
          327.25,
          325.94,
          326.28,
          327.53,
          324.9,
          327.11,
          334.05,
          329.04,
          344.34,
          349.9,
          351.5,
          352.37,
          353.8,
          353.88,
          362.68,
          363.32,
          367.78,
          368.54,
          358.06,
          361.88,
          363.6,
          367.53,
          384.27,
          390.71,
          387.72,
          389.5,
          415.15,
          421.38,
          419.98,
          404.69,
          393.28,
          407.56,
          395,
          399.19,
          385.45,
          399.49,
          393.8,
          397.45,
          415.95,
          417.24,
          411.34,
          415.16,
          409.19,
          398.98,
          346.95,
          381.24,
          371.06,
          364.92,
          359.15,
          366.94,
          357.57,
          358.19,
          366.85,
          351.93,
          331.51,
          335.87,
          337.23,
          347.75,
          342.87,
          353.23,
          352.21,
          347.96,
          346.91,
          339.89,
          342.09,
          334.03,
          329.9,
          319.01,
          314.64,
          331,
          338.49,
          348.11,
          346,
          367.15,
          367.23,
          367.2,
          365,
          370.66,
          366.47,
          360,
          347.44,
          342.2,
          352.27,
          344.67,
          359.08,
          371.91,
          368.55,
          364.22,
          353.67,
          373.95,
          370.26,
          366.59,
          359,
          370.23,
          373.59,
          379.87,
          379.24,
          375.85,
          384.38,
          378.53,
          375.88,
          359.77,
          345.18,
          348.48,
          353.52,
          324.94,
          339.57,
          337.63,
          337.24,
          378.33,
          360.67,
          351,
          333.1,
          318,
          332.28,
          307.12,
          300.51,
          305.26,
          275.57,
          297.77,
          304.59,
          318,
          311.1,
          314.76,
          312.9,
          328,
          311.07,
          300,
          295,
          300.4,
          285.51,
          287.14,
          283.79,
          254.63,
          274.42,
          260.11,
          260.55,
          259.24,
          271.98,
          282.32,
          288,
          293.19,
          288.13,
          268.33,
          282.48,
          264.19,
          274.08,
          267.66,
          277.64,
          271.81,
          266.51,
          263.3,
          269.96,
          264.64,
          263.83,
          242,
          233.92,
          250.11,
          257.94,
          260.16,
          259.28,
          270.2,
          281.88,
          302.1,
          319.98,
          317.71,
          314.57,
          330.96,
          334.24,
          349.6,
          354,
          349.5,
          351.97,
          334.89,
          328.25,
          320.6,
          328.72,
          334.7,
          335.87,
          332.75,
          339.68,
          337.18,
          342.6,
          353.2,
          357,
          347.9,
          338,
          350,
          348.09,
          357.3,
          351.75,
          358.47,
          355.8,
          364.85,
          360.03,
          360.34,
          367.01,
          362.98,
          363.5,
          362.47,
          362.26,
          359.72,
          351.46,
          353.6,
          360.16,
          345.75,
          352,
          359.37,
          355.81,
          360.5,
          361.02,
          362.47,
          366.4,
          358.91,
          374,
          375.95,
          359,
          367.87,
          361,
          354.49,
          357.16,
          359,
          366.25,
          369.26,
          370.07,
          369,
          365.11,
          360.54,
          365.79,
          365,
          360.69,
          350.71,
          355,
          365.05,
          355,
          359.7,
          375.45,
          381.07,
          374.49,
          368.35,
          373.68,
          369.56,
          374,
          378,
          381.53,
          377.69,
          377,
          367.92,
          360.9,
          361.62,
          352.29,
          348.71,
          343.34,
          356.37,
          356.39,
          351.23,
          350.95,
          358.01,
          355.5,
          355.41,
          354.39,
          353.6,
          350.55,
          347.22,
          343.56,
          345,
          354.38,
          354.84,
          357.39,
          363.65,
          355,
          351.82,
          347.23,
          341.63,
          342.69,
          355.57,
          361.72,
          365.91,
          365,
          370.27,
          370.75,
          361.6,
          363.2,
          370.26,
          373.5,
          374.89,
          376.69,
          378.29,
          378.19,
          379.06,
          382.77,
          381.1,
          378.68,
          372.94,
          370.09,
          366.25,
          323.76,
          323.4,
          312,
          311.44,
          310.51,
          318.86,
          328.79,
          335.98,
          329.2,
          325.16,
          324.25,
          317.49,
          310.96,
          310.58,
          302.56,
          311.03,
          313.74,
          305.46,
          309.77,
          308.01,
          299.5,
          298.86,
          306.25,
          304.57,
          301.61,
          298.65,
          295,
          295.24,
          294.54,
          289.47,
          295,
          298.78,
          290.82,
          291.25,
          285.32,
          293.35,
          294.81,
          291.16,
          285.7,
          288.1,
          290.61,
          294.23,
          294.5,
          294.99,
          291.56,
          280.26,
          268.35,
          262.5,
          255.71,
          266.42,
          266.18,
          264,
          267.35,
          263.61,
          267.78,
          268.2,
          271.99,
          273.03,
          270.02,
          265.97,
          284.8,
          283.93,
          283.82,
          283.12,
          304.49,
          289.36,
          272.89,
          271.16,
          268.06,
          271.81,
          270.68,
          278.05,
          281.87,
          284.34,
          291,
          288.7,
          288,
          289.99,
          288.19,
          290.7,
          288.73,
          289.16,
          295.32,
          291.03,
          283.25,
          290.59,
          296,
          304.01,
          301.01,
          306,
          309.1,
          308.83,
          315,
          313.93,
          315.78,
          314.39,
          302.22,
          308.43,
          305.27,
          304.7,
          307.35,
          296.12,
          294.49,
          295.67,
          298.5,
          300.85,
          307.36,
          316.26,
          324.5,
          335,
          337.76,
          334.01,
          334.6,
          332.96,
          329.08,
          322,
          326.1,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('4963b6bb-4d23-4474-a7ec-384d67287bea');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "candleStick(tickerSymbol, start, today)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://plotly.com/~jackp/17421/plotly-candlestick-chart-in-python/#/\n",
    "# Useful for volume bar and moving average\n",
    "# Not for Plotly 4.x\n",
    "\n",
    "INCREASING_COLOR = '#17BECF'\n",
    "DECREASING_COLOR = '#7F7F7F'\n",
    "\n",
    "colors = []\n",
    "\n",
    "for i in range(len(df.Close)):\n",
    "    if i != 0:\n",
    "        if df.Close[i] > df.Close[i-1]:\n",
    "            colors.append(INCREASING_COLOR)\n",
    "        else:\n",
    "            colors.append(DECREASING_COLOR)\n",
    "    else:\n",
    "        colors.append(DECREASING_COLOR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_plot():\n",
    "\n",
    "\n",
    "    N = 40\n",
    "    x = np.linspace(0, 1, N)\n",
    "    y = np.random.randn(N)\n",
    "    df = pd.DataFrame({'x': x, 'y': y}) # creating a sample dataframe\n",
    "\n",
    "\n",
    "    data = [\n",
    "        go.Bar(\n",
    "            x=df['x'], # assign x as the dataframe column 'x'\n",
    "            y=df['y']\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder)\n",
    "\n",
    "    return graphJSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Bar({\n",
       "     'x': array([0.        , 0.02564103, 0.05128205, 0.07692308, 0.1025641 , 0.12820513,\n",
       "                 0.15384615, 0.17948718, 0.20512821, 0.23076923, 0.25641026, 0.28205128,\n",
       "                 0.30769231, 0.33333333, 0.35897436, 0.38461538, 0.41025641, 0.43589744,\n",
       "                 0.46153846, 0.48717949, 0.51282051, 0.53846154, 0.56410256, 0.58974359,\n",
       "                 0.61538462, 0.64102564, 0.66666667, 0.69230769, 0.71794872, 0.74358974,\n",
       "                 0.76923077, 0.79487179, 0.82051282, 0.84615385, 0.87179487, 0.8974359 ,\n",
       "                 0.92307692, 0.94871795, 0.97435897, 1.        ]),\n",
       "     'y': array([ 0.29903252,  0.30007739,  0.44539786, -1.50094151,  0.3247126 ,\n",
       "                 -1.45516459, -0.41155202,  0.12926944, -0.48312275, -1.49689779,\n",
       "                 -1.7594444 , -1.73094891,  0.92962325, -1.04949185,  0.8906089 ,\n",
       "                 -1.13709385, -0.08423517, -0.51649078,  0.87525095,  0.0542506 ,\n",
       "                  0.765405  ,  2.32791414,  0.90419352, -0.32683381, -1.73353563,\n",
       "                  2.1608645 , -0.56565339, -0.49393232, -2.03413146, -0.09206932,\n",
       "                  2.09733078, -0.10389283, -1.94496976, -0.28359898, -0.61140844,\n",
       "                  1.72884006,  0.05735496, -0.35933992, -0.57606647, -1.7068737 ])\n",
       " })]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = 40\n",
    "x = np.linspace(0, 1, N)\n",
    "y = np.random.randn(N)\n",
    "df = pd.DataFrame({'x': x, 'y': y}) # creating a sample dataframe\n",
    "\n",
    "\n",
    "data = [\n",
    "    go.Bar(\n",
    "        x=df['x'], # assign x as the dataframe column 'x'\n",
    "        y=df['y']\n",
    "    )\n",
    "]\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"x\": [0.0, 0.02564102564102564, 0.05128205128205128, 0.07692307692307693, 0.10256410256410256, 0.1282051282051282, 0.15384615384615385, 0.1794871794871795, 0.20512820512820512, 0.23076923076923075, 0.2564102564102564, 0.28205128205128205, 0.3076923076923077, 0.3333333333333333, 0.358974358974359, 0.3846153846153846, 0.41025641025641024, 0.4358974358974359, 0.4615384615384615, 0.48717948717948717, 0.5128205128205128, 0.5384615384615384, 0.5641025641025641, 0.5897435897435898, 0.6153846153846154, 0.641025641025641, 0.6666666666666666, 0.6923076923076923, 0.717948717948718, 0.7435897435897436, 0.7692307692307692, 0.7948717948717948, 0.8205128205128205, 0.8461538461538461, 0.8717948717948718, 0.8974358974358974, 0.923076923076923, 0.9487179487179487, 0.9743589743589743, 1.0], \"y\": [0.7203162866428584, 0.7356516119602248, 1.5852208198132405, -1.0240256314975615, -0.18191093040636203, 1.3123914280441014, -0.7362376113675277, -0.5080949266397642, -1.3413791710339413, 0.8746881779482129, -1.5196952654999583, -0.4970519302549483, 1.5798112273433051, 0.3779761594285029, -0.781932757531854, -1.5419585366793434, 0.8657467010695781, 0.6054925371633562, 1.9839628394270483, -1.140818356895113, -0.859651530729561, 1.014880854019069, 0.8258776102594128, -0.8298375390692595, -0.9936797397354954, -2.234433199302873, 2.297027081054263, -0.1287778723589725, -1.1091866428326889, 1.588382113837392, -0.5952750809084495, -1.7398793352922308, 0.9645423032917965, 0.8840558545103736, 0.20763468802930599, -2.6557826373330062, -1.1471719228526298, 0.28954137859928136, 0.08756331203398808, -0.22095858921025746], \"type\": \"bar\"}]'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "create_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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