bokeh_stock_plots.ipynb

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    {
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     "text": [
      "Creating C:\\Users\\Asus\\.bokeh directory\n",
      "Creating C:\\Users\\Asus\\.bokeh\\data directory\n",
      "Using data directory: C:\\Users\\Asus\\.bokeh\\data\n",
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     ]
    }
   ],
   "source": [
    "import bokeh.sampledata\n",
    "bokeh.sampledata.download()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT\n",
    "\n",
    "\n",
    "def datetime(x):\n",
    "    return np.array(x, dtype=np.datetime64)\n",
    "\n",
    "p1 = figure(x_axis_type=\"datetime\", title=\"Stock Closing Prices\")\n",
    "p1.grid.grid_line_alpha=0.3\n",
    "p1.xaxis.axis_label = 'Date'\n",
    "p1.yaxis.axis_label = 'Price'\n",
    "\n",
    "p1.line(datetime(AAPL['date']), AAPL['adj_close'], color='#A6CEE3', legend_label='AAPL')\n",
    "p1.line(datetime(GOOG['date']), GOOG['adj_close'], color='#B2DF8A', legend_label='GOOG')\n",
    "p1.line(datetime(IBM['date']), IBM['adj_close'], color='#33A02C', legend_label='IBM')\n",
    "p1.line(datetime(MSFT['date']), MSFT['adj_close'], color='#FB9A99', legend_label='MSFT')\n",
    "p1.legend.location = \"top_left\"\n",
    "\n",
    "aapl = np.array(AAPL['adj_close'])\n",
    "aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)\n",
    "\n",
    "window_size = 30\n",
    "window = np.ones(window_size)/float(window_size)\n",
    "aapl_avg = np.convolve(aapl, window, 'same')\n",
    "\n",
    "p2 = figure(x_axis_type=\"datetime\", title=\"AAPL One-Month Average\")\n",
    "p2.grid.grid_line_alpha = 0\n",
    "p2.xaxis.axis_label = 'Date'\n",
    "p2.yaxis.axis_label = 'Price'\n",
    "p2.ygrid.band_fill_color = \"olive\"\n",
    "p2.ygrid.band_fill_alpha = 0.1\n",
    "\n",
    "p2.scatter(aapl_dates, aapl, size=4, legend_label='close',\n",
    "           color='darkgrey', alpha=0.2)\n",
    "\n",
    "p2.line(aapl_dates, aapl_avg, legend_label='avg', color='navy')\n",
    "p2.legend.location = \"top_left\"\n",
    "\n",
    "show(gridplot([[p1,p2]], width=400, height=400))  # open a browser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi\n",
    "import pandas as pd\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.sampledata.stocks import MSFT\n",
    "\n",
    "df = pd.DataFrame(MSFT)[:50]\n",
    "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
    "\n",
    "inc = df.close > df.open\n",
    "dec = df.open > df.close\n",
    "w = 12*60*60*1000 # half day in ms\n",
    "\n",
    "TOOLS = \"pan,wheel_zoom,box_zoom,reset,save\"\n",
    "\n",
    "p = figure(x_axis_type=\"datetime\", tools=TOOLS, width=1000, title = \"MSFT Candlestick\")\n",
    "p.xaxis.major_label_orientation = pi/4\n",
    "p.grid.grid_line_alpha=0.3\n",
    "\n",
    "p.segment(df.date, df.high, df.date, df.low, color=\"black\")\n",
    "p.vbar(df.date[inc], w, df.open[inc], df.close[inc], fill_color=\"#D5E1DD\", line_color=\"black\")\n",
    "p.vbar(df.date[dec], w, df.open[dec], df.close[dec], fill_color=\"#F2583E\", line_color=\"black\")\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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