code that addresses issue24 user error

mpl_finance_issue24.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.dates  as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas.plotting  import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Time</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>736859.197917</td>\n",
       "      <td>1.18242</td>\n",
       "      <td>1.18245</td>\n",
       "      <td>1.18218</td>\n",
       "      <td>1.18221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>736859.201389</td>\n",
       "      <td>1.18221</td>\n",
       "      <td>1.18252</td>\n",
       "      <td>1.18199</td>\n",
       "      <td>1.18232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>736859.204861</td>\n",
       "      <td>1.18232</td>\n",
       "      <td>1.18275</td>\n",
       "      <td>1.18232</td>\n",
       "      <td>1.18253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>736859.208333</td>\n",
       "      <td>1.18254</td>\n",
       "      <td>1.18256</td>\n",
       "      <td>1.18172</td>\n",
       "      <td>1.18172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>736859.211806</td>\n",
       "      <td>1.18173</td>\n",
       "      <td>1.18211</td>\n",
       "      <td>1.18159</td>\n",
       "      <td>1.18187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Time     Open     High      Low    Close\n",
       "0  736859.197917  1.18242  1.18245  1.18218  1.18221\n",
       "1  736859.201389  1.18221  1.18252  1.18199  1.18232\n",
       "2  736859.204861  1.18232  1.18275  1.18232  1.18253\n",
       "3  736859.208333  1.18254  1.18256  1.18172  1.18172\n",
       "4  736859.211806  1.18173  1.18211  1.18159  1.18187"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#        Time     Open     High      Low    Close\n",
    "rows = []\n",
    "rows.append((736859.197917,  1.18242,  1.18245,  1.18218,  1.18221))\n",
    "rows.append((736859.201389,  1.18221,  1.18252,  1.18199,  1.18232))\n",
    "rows.append((736859.204861,  1.18232,  1.18275,  1.18232,  1.18253))\n",
    "rows.append((736859.208333,  1.18254,  1.18256,  1.18172,  1.18172))\n",
    "rows.append((736859.211806,  1.18173,  1.18211,  1.18159,  1.18187))\n",
    "df = pd.DataFrame(rows)\n",
    "df.columns = ('Time','Open','High','Low','Close')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib qt\n",
    "%matplotlib inline\n",
    "from mpl_finance import candlestick_ohlc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((10,8))\n",
    "_ = candlestick_ohlc(ax, df.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above plot looks strange because the data is *intraday* data (the x-axis points are *all for the same date*, approximately 5 minutes apart); but the default candle width assumes *daily* data (one ohlc data point per day).  So the candles are too wide and overlapping.\n",
    "----\n",
    "## Knowing that the ohlc data points are about 5 minutes apart, if we set the candle width to approximately 2 minutes the graph will look much nicer, as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((10,8))\n",
    "_ = candlestick_ohlc(ax, df.values, width=0.00125)  # .00125 of a day is 1.8 minutes\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Now all we have to do is tell matplotlib to interpret the x-axis as date2num's.  And since we know we have intraday data, we use DateFormatter to display the *time* as well as the date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_finance import candlestick_ohlc\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches((10,8))\n",
    "fig.autofmt_xdate()\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d, %H:%M'))\n",
    "ax.xaxis_date()\n",
    "_ = candlestick_ohlc(ax, df.values, width=0.00125)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}