candles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8a1600e-6938-4741-903f-e35fb02bdcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from binance.client import Client\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import mplfinance as fplt\n",
    "import matplotlib.dates as mpl_dates\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "077a1a3c-5bc4-4f09-8df5-37982f8503b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "candles = client.get_klines(\n",
    "    symbol='ETHUSDT', interval=Client.KLINE_INTERVAL_1MINUTE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a07331b6-1731-49af-a700-180996928658",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13b4e73f-5a34-46f5-8f7c-6648ad4726bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "candledf = pd.read_csv('./ETHUSDT_2_weeks_ago_UTC_1_hour_ago_UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abcad09d-7eef-4fd0-8b17-6ad02f447522",
   "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>Opentime</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Closetime</th>\n",
       "      <th>Quote asset volume</th>\n",
       "      <th>Number of trades</th>\n",
       "      <th>Taker by base</th>\n",
       "      <th>Taker buy quote</th>\n",
       "      <th>Ignore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-05-19 09:30:00</td>\n",
       "      <td>2955.72</td>\n",
       "      <td>2973.27</td>\n",
       "      <td>2945.27</td>\n",
       "      <td>2945.94</td>\n",
       "      <td>14053.00154</td>\n",
       "      <td>1621417499999</td>\n",
       "      <td>4.159221e+07</td>\n",
       "      <td>20493</td>\n",
       "      <td>6771.43203</td>\n",
       "      <td>2.004404e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-05-19 09:45:00</td>\n",
       "      <td>2946.13</td>\n",
       "      <td>2979.25</td>\n",
       "      <td>2933.00</td>\n",
       "      <td>2973.82</td>\n",
       "      <td>17338.57649</td>\n",
       "      <td>1621418399999</td>\n",
       "      <td>5.121055e+07</td>\n",
       "      <td>22237</td>\n",
       "      <td>8163.26296</td>\n",
       "      <td>2.411191e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-05-19 10:00:00</td>\n",
       "      <td>2973.82</td>\n",
       "      <td>2984.49</td>\n",
       "      <td>2911.80</td>\n",
       "      <td>2917.03</td>\n",
       "      <td>21660.63076</td>\n",
       "      <td>1621419299999</td>\n",
       "      <td>6.367338e+07</td>\n",
       "      <td>32809</td>\n",
       "      <td>10017.97884</td>\n",
       "      <td>2.944621e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-05-19 10:15:00</td>\n",
       "      <td>2917.04</td>\n",
       "      <td>2936.05</td>\n",
       "      <td>2901.21</td>\n",
       "      <td>2911.31</td>\n",
       "      <td>20353.97127</td>\n",
       "      <td>1621420199999</td>\n",
       "      <td>5.944318e+07</td>\n",
       "      <td>26160</td>\n",
       "      <td>10021.21543</td>\n",
       "      <td>2.926707e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-05-19 10:30:00</td>\n",
       "      <td>2911.32</td>\n",
       "      <td>2915.83</td>\n",
       "      <td>2842.58</td>\n",
       "      <td>2858.86</td>\n",
       "      <td>39045.95461</td>\n",
       "      <td>1621421099999</td>\n",
       "      <td>1.121856e+08</td>\n",
       "      <td>53635</td>\n",
       "      <td>16886.03571</td>\n",
       "      <td>4.852488e+07</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Opentime     Open     High      Low    Close       Volume  \\\n",
       "0  2021-05-19 09:30:00  2955.72  2973.27  2945.27  2945.94  14053.00154   \n",
       "1  2021-05-19 09:45:00  2946.13  2979.25  2933.00  2973.82  17338.57649   \n",
       "2  2021-05-19 10:00:00  2973.82  2984.49  2911.80  2917.03  21660.63076   \n",
       "3  2021-05-19 10:15:00  2917.04  2936.05  2901.21  2911.31  20353.97127   \n",
       "4  2021-05-19 10:30:00  2911.32  2915.83  2842.58  2858.86  39045.95461   \n",
       "\n",
       "       Closetime  Quote asset volume  Number of trades  Taker by base  \\\n",
       "0  1621417499999        4.159221e+07             20493     6771.43203   \n",
       "1  1621418399999        5.121055e+07             22237     8163.26296   \n",
       "2  1621419299999        6.367338e+07             32809    10017.97884   \n",
       "3  1621420199999        5.944318e+07             26160    10021.21543   \n",
       "4  1621421099999        1.121856e+08             53635    16886.03571   \n",
       "\n",
       "   Taker buy quote  Ignore  \n",
       "0     2.004404e+07       0  \n",
       "1     2.411191e+07       0  \n",
       "2     2.944621e+07       0  \n",
       "3     2.926707e+07       0  \n",
       "4     4.852488e+07       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "candledf.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "256276de-677c-47f6-8b04-1e5f1bc3ba8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "index  =  pd.to_datetime(candledf['Opentime'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c807be17-531d-43f1-a565-7d4919693dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "candledf = candledf.set_index(pd.DatetimeIndex(index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d06a8c4b-94f0-4294-bb3f-897e04a10928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-06-01 09:00:00', '2021-06-01 09:01:00',\n",
       "               '2021-06-01 09:02:00', '2021-06-01 09:03:00',\n",
       "               '2021-06-01 09:04:00', '2021-06-01 09:05:00',\n",
       "               '2021-06-01 09:06:00', '2021-06-01 09:07:00',\n",
       "               '2021-06-01 09:08:00', '2021-06-01 09:09:00',\n",
       "               ...\n",
       "               '2021-06-01 16:14:00', '2021-06-01 16:15:00',\n",
       "               '2021-06-01 16:16:00', '2021-06-01 16:17:00',\n",
       "               '2021-06-01 16:18:00', '2021-06-01 16:19:00',\n",
       "               '2021-06-01 16:20:00', '2021-06-01 16:21:00',\n",
       "               '2021-06-01 16:22:00', '2021-06-01 16:23:00'],\n",
       "              dtype='datetime64[ns]', name='time', length=444, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candledf.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ea56b1d-af84-4ee8-a194-2e6522809d12",
   "metadata": {},
   "outputs": [],
   "source": [
    "candledf = candledf.drop('Opentime', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27242ed8-77b3-43e7-b30b-beea7229dee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "candledf['Open'] = candledf['Open'].astype(float)\n",
    "candledf['Close'] = candledf['Close'].astype(float)\n",
    "candledf['High'] = candledf['High'].astype(float)\n",
    "candledf['Low'] = candledf['Low'].astype(float)\n",
    "candledf['Volume'] = candledf['Volume'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac02d5d4-b93f-46a5-aef6-cac0e6fbe6e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nithin/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/mplfinance/_arg_validators.py:35: UserWarning: \n",
      "\n",
      " ================================================================= \n",
      "\n",
      "   WARNING: YOU ARE PLOTTING SO MUCH DATA THAT IT MAY NOT BE\n",
      "            POSSIBLE TO SEE DETAILS (Candles, Ohlc-Bars, Etc.)\n",
      "   For more information see:\n",
      "   - https://github.com/matplotlib/mplfinance/wiki/Plotting-Too-Much-Data\n",
      "   \n",
      "   TO SILENCE THIS WARNING, set `type='line'` in `mpf.plot()`\n",
      "   OR set kwarg `warn_too_much_data=N` where N is an integer \n",
      "   LARGER than the number of data points you want to plot.\n",
      "\n",
      " ================================================================ \n",
      "  warnings.warn('\\n\\n ================================================================= '+\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fplt.plot(candledf, type='candle', style='charles',\n",
    "            title='ETHUSDT',\n",
    "            ylabel='Price ($)',\n",
    "            ylabel_lower='Shares \\nTraded',\n",
    "            volume=True, \n",
    "            mav=(3,10,100), \n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a056050-6355-4ccd-ae97-f5a579b54994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-06-01 09:00:00', '2021-06-01 09:01:00',\n",
       "               '2021-06-01 09:02:00', '2021-06-01 09:03:00',\n",
       "               '2021-06-01 09:04:00', '2021-06-01 09:05:00',\n",
       "               '2021-06-01 09:06:00', '2021-06-01 09:07:00',\n",
       "               '2021-06-01 09:08:00', '2021-06-01 09:09:00',\n",
       "               ...\n",
       "               '2021-06-01 16:14:00', '2021-06-01 16:15:00',\n",
       "               '2021-06-01 16:16:00', '2021-06-01 16:17:00',\n",
       "               '2021-06-01 16:18:00', '2021-06-01 16:19:00',\n",
       "               '2021-06-01 16:20:00', '2021-06-01 16:21:00',\n",
       "               '2021-06-01 16:22:00', '2021-06-01 16:23:00'],\n",
       "              dtype='datetime64[ns]', name='time', length=444, freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candledf.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a8df648-79cb-4548-b8a5-fded1896fc00",
   "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>time</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>2021-06-01 09:00:00</th>\n",
       "      <td>2570.50</td>\n",
       "      <td>2570.50</td>\n",
       "      <td>2570.50</td>\n",
       "      <td>2570.50</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:01:00</th>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>0.00486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:02:00</th>\n",
       "      <td>2570.50</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2448.77</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2.36829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:03:00</th>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>0.01000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:04:00</th>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>2579.91</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:19:00</th>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:20:00</th>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:21:00</th>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:22:00</th>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:23:00</th>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>2527.20</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        open     high      low    close   volume\n",
       "time                                                            \n",
       "2021-06-01 09:00:00  2570.50  2570.50  2570.50  2570.50  1.00000\n",
       "2021-06-01 09:01:00  2579.91  2579.91  2579.91  2579.91  0.00486\n",
       "2021-06-01 09:02:00  2570.50  2579.91  2448.77  2579.91  2.36829\n",
       "2021-06-01 09:03:00  2579.91  2579.91  2579.91  2579.91  0.01000\n",
       "2021-06-01 09:04:00  2579.91  2579.91  2579.91  2579.91  0.00000\n",
       "...                      ...      ...      ...      ...      ...\n",
       "2021-06-01 16:19:00  2527.20  2527.20  2527.20  2527.20  0.00000\n",
       "2021-06-01 16:20:00  2527.20  2527.20  2527.20  2527.20  0.00000\n",
       "2021-06-01 16:21:00  2527.20  2527.20  2527.20  2527.20  0.00000\n",
       "2021-06-01 16:22:00  2527.20  2527.20  2527.20  2527.20  0.00000\n",
       "2021-06-01 16:23:00  2527.20  2527.20  2527.20  2527.20  0.00000\n",
       "\n",
       "[444 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candledf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbc4c6b2-9b19-4cb9-b32f-c4f5595c4f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from finta import TA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f4aa82f9-44fb-4164-b9ff-b1ae5764734f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sma = pd.DataFrame(TA.SMA(candledf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "381da0fc-dd81-46d8-852d-a7cada8bccce",
   "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>41 period SMA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:00:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:01:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:02:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:03:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 09:04:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:19:00</th>\n",
       "      <td>2525.533171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:20:00</th>\n",
       "      <td>2525.845122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:21:00</th>\n",
       "      <td>2526.157073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:22:00</th>\n",
       "      <td>2526.469024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01 16:23:00</th>\n",
       "      <td>2526.780976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>444 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     41 period SMA\n",
       "time                              \n",
       "2021-06-01 09:00:00            NaN\n",
       "2021-06-01 09:01:00            NaN\n",
       "2021-06-01 09:02:00            NaN\n",
       "2021-06-01 09:03:00            NaN\n",
       "2021-06-01 09:04:00            NaN\n",
       "...                            ...\n",
       "2021-06-01 16:19:00    2525.533171\n",
       "2021-06-01 16:20:00    2525.845122\n",
       "2021-06-01 16:21:00    2526.157073\n",
       "2021-06-01 16:22:00    2526.469024\n",
       "2021-06-01 16:23:00    2526.780976\n",
       "\n",
       "[444 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "62faf75a-70ed-4a50-b819-12c321b6bff5",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Open'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3080\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Open'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-2fd9d4a20ada>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/mplfinance/plotting.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_valid_plot_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 298\u001b[0;31m     \u001b[0mdates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mopens\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhighs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcloses\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvolumes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_and_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    299\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'xlim'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_and_convert_xlim_configuration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/mplfinance/_arg_validators.py\u001b[0m in \u001b[0;36m_check_and_prepare_data\u001b[0;34m(data, config)\u001b[0m\n\u001b[1;32m     61\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Just in case someone was depending on this bug (Issue 236)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mdates\u001b[0m   \u001b[0;34m=\u001b[0m \u001b[0mmdates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_pydatetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m     \u001b[0mopens\u001b[0m   \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m     \u001b[0mhighs\u001b[0m   \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0mlows\u001b[0m    \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3022\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3023\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3024\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3025\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/trade-8o7ayx7U/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3081\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3084\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtolerance\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Open'"
     ]
    }
   ],
   "source": [
    "fplt.plot(sma, type='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75f4ad88-e911-4c23-8194-dad9caed10d8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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