Mastering DataFrame - how to aggregate OHLCV data in a different time period.ipynb

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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas_datareader/compat/__init__.py:7: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  from pandas.util.testing import assert_frame_equal\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/python\n",
    "import warnings\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as pdr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "warnings.simplefilter('ignore', FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fetch single asset data\n",
    "def getSingleAssetData(ticker, date_from, date_to):\n",
    "    def getData(ticker):\n",
    "        data = pdr.DataReader(ticker, 'yahoo', date_from, date_to)\n",
    "        return data\n",
    "    return getData(ticker)\n",
    "\n",
    "# fetch multiple asset data\n",
    "def getMultiAssetData(ticketList, date_from, date_to):\n",
    "    def getData(ticker):\n",
    "        data = pdr.DataReader(ticker, 'yahoo', date_from, date_to)\n",
    "        return data\n",
    "    datas = map(getData, tickerList)\n",
    "    return pd.concat(datas, keys=tickerList, names=['Ticker', 'Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n",
    "Y|AMZN|Amazon.com, Inc. - Common Stock|Q|Q|N|100|N|N||AMZN|N  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>2019-01-02</th>\n",
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       "                   High          Low         Open        Close   Volume  \\\n",
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       "\n",
       "              Adj Close  \n",
       "Date                     \n",
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "date_from = datetime.date(2019, 1, 1)\n",
    "date_to = datetime.date(2020, 8, 31)\n",
    "df = getSingleAssetData('AMZN', date_from, date_to) # read Amazon.com, Inc\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
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    "agg_dict = {'Open': 'first',\n",
    "          'High': 'max',\n",
    "          'Low': 'min',\n",
    "          'Close': 'last',\n",
    "          'Adj Close': 'last',\n",
    "          'Volume': 'mean'}"
   ]
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       "                   Open         High          Low        Close    Adj Close  \\\n",
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       "\n",
       "             Volume  \n",
       "Date                 \n",
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     "execution_count": 5,
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    "df.resample('W').agg(agg_dict).head()"
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       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-12-30  1465.199951  1594.000000  1460.930054  1575.390015  1575.390015   \n",
       "2019-01-06  1602.310059  1676.609985  1589.189941  1640.560059  1640.560059   \n",
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       "2019-01-27  1643.589966  1736.410034  1590.719971  1626.229980  1626.229980   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2018-12-30  8047100  \n",
       "2019-01-06  6883460  \n",
       "2019-01-13  5720140  \n",
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       "2019-01-27  7534160  "
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       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "2018-12-30  1465.199951  1594.000000  1460.930054  1575.390015  1575.390015   \n",
       "2019-01-06  1602.310059  1676.609985  1589.189941  1640.560059  1640.560059   \n",
       "2019-01-13  1615.000000  1716.199951  1595.150024  1696.199951  1696.199951   \n",
       "2019-01-20  1681.000000  1683.479980  1610.199951  1670.569946  1670.569946   \n",
       "2019-01-27  1643.589966  1736.410034  1590.719971  1626.229980  1626.229980   \n",
       "\n",
       "             Volume  \n",
       "Date                 \n",
       "2018-12-30  8047100  \n",
       "2019-01-06  6883460  \n",
       "2019-01-13  5720140  \n",
       "2019-01-20  5169450  \n",
       "2019-01-27  7534160  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.resample('W', label='left', closed='left').agg(agg_dict).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00    0\n",
       "2000-01-01 00:01:00    1\n",
       "2000-01-01 00:02:00    2\n",
       "2000-01-01 00:03:00    3\n",
       "2000-01-01 00:04:00    4\n",
       "2000-01-01 00:05:00    5\n",
       "2000-01-01 00:06:00    6\n",
       "2000-01-01 00:07:00    7\n",
       "2000-01-01 00:08:00    8\n",
       "Freq: T, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# official downsampling\n",
    "index = pd.date_range('1/1/2000', periods=9, freq='T')\n",
    "series = pd.Series(range(9), index=index)\n",
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     3\n",
       "2000-01-01 00:03:00    12\n",
       "2000-01-01 00:06:00    21\n",
       "Freq: 3T, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample('3T').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:03:00     3\n",
       "2000-01-01 00:06:00    12\n",
       "2000-01-01 00:09:00    21\n",
       "Freq: 3T, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample('3T', label='right').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000-01-01 00:00:00     0\n",
       "2000-01-01 00:03:00     6\n",
       "2000-01-01 00:06:00    15\n",
       "2000-01-01 00:09:00    15\n",
       "Freq: 3T, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.resample('3T', label='right', closed='right').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (Optional) How to gain OHLCV data from close prices and volume?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>1539.130005</td>\n",
       "      <td>7983100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>1500.280029</td>\n",
       "      <td>6975600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>1575.390015</td>\n",
       "      <td>9182600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>1629.510010</td>\n",
       "      <td>7993200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>1656.579956</td>\n",
       "      <td>8881400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Close   Volume\n",
       "Date                            \n",
       "2019-01-02  1539.130005  7983100\n",
       "2019-01-03  1500.280029  6975600\n",
       "2019-01-04  1575.390015  9182600\n",
       "2019-01-07  1629.510010  7993200\n",
       "2019-01-08  1656.579956  8881400"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ = df.loc[:, ['Close', 'Volume']]\n",
    "df_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>1539.130005</td>\n",
       "      <td>1575.390015</td>\n",
       "      <td>1500.280029</td>\n",
       "      <td>1575.390015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>1629.510010</td>\n",
       "      <td>1659.420044</td>\n",
       "      <td>1629.510010</td>\n",
       "      <td>1640.560059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>1617.209961</td>\n",
       "      <td>1696.199951</td>\n",
       "      <td>1617.209961</td>\n",
       "      <td>1696.199951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>1632.170044</td>\n",
       "      <td>1670.569946</td>\n",
       "      <td>1632.170044</td>\n",
       "      <td>1670.569946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>1637.890015</td>\n",
       "      <td>1718.729980</td>\n",
       "      <td>1593.880005</td>\n",
       "      <td>1626.229980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   open         high          low        close\n",
       "Date                                                          \n",
       "2019-01-06  1539.130005  1575.390015  1500.280029  1575.390015\n",
       "2019-01-13  1629.510010  1659.420044  1629.510010  1640.560059\n",
       "2019-01-20  1617.209961  1696.199951  1617.209961  1696.199951\n",
       "2019-01-27  1632.170044  1670.569946  1632.170044  1670.569946\n",
       "2019-02-03  1637.890015  1718.729980  1593.880005  1626.229980"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['Close'].resample('W').ohlc().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2019-01-06    8047100\n",
       "2019-01-13    6883460\n",
       "2019-01-20    5720140\n",
       "2019-01-27    5169450\n",
       "2019-02-03    7534160\n",
       "Freq: W-SUN, Name: Volume, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_['Volume'].resample('W').mean().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <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>2019-01-06</th>\n",
       "      <td>1539.130005</td>\n",
       "      <td>1575.390015</td>\n",
       "      <td>1500.280029</td>\n",
       "      <td>1575.390015</td>\n",
       "      <td>8047100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>1629.510010</td>\n",
       "      <td>1659.420044</td>\n",
       "      <td>1629.510010</td>\n",
       "      <td>1640.560059</td>\n",
       "      <td>6883460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>1617.209961</td>\n",
       "      <td>1696.199951</td>\n",
       "      <td>1617.209961</td>\n",
       "      <td>1696.199951</td>\n",
       "      <td>5720140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>1632.170044</td>\n",
       "      <td>1670.569946</td>\n",
       "      <td>1632.170044</td>\n",
       "      <td>1670.569946</td>\n",
       "      <td>5169450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>1637.890015</td>\n",
       "      <td>1718.729980</td>\n",
       "      <td>1593.880005</td>\n",
       "      <td>1626.229980</td>\n",
       "      <td>7534160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   open         high          low        close   Volume\n",
       "Date                                                                   \n",
       "2019-01-06  1539.130005  1575.390015  1500.280029  1575.390015  8047100\n",
       "2019-01-13  1629.510010  1659.420044  1629.510010  1640.560059  6883460\n",
       "2019-01-20  1617.209961  1696.199951  1617.209961  1696.199951  5720140\n",
       "2019-01-27  1632.170044  1670.569946  1632.170044  1670.569946  5169450\n",
       "2019-02-03  1637.890015  1718.729980  1593.880005  1626.229980  7534160"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last = pd.concat([df['Close'].resample('W').ohlc(), df['Volume'].resample('W').mean()], axis=1)\n",
    "last.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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