docs/tutorials/quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart tutorial "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sections\n",
    "* [Periods](#Periods)\n",
    "* [Anchor](#Anchor)\n",
    "* [Data availability](#Data-availability)\n",
    "* [Multiple exchanges](#Multiple-exchanges)\n",
    "* [.pt accessor](#.pt-accessor)\n",
    "* [Specific query methods](#Specific-query-methods)\n",
    "* [Further documentation](#Further-documentation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notes\n",
    "* The cell **outputs** shown in this tutorial are based on executing the cells at **2022-05-12 13:11 UTC** (before the NYSE open). Simply rerun the cells to bring any dynamic output up to date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to import tutorial dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from market_prices import PricesYahoo\n",
    "import pandas as pd\n",
    "from zoneinfo import ZoneInfo\n",
    "from market_prices.support import tutorial_helpers as th\n",
    "from market_prices import helpers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to define values used in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "_prices_mix = PricesYahoo(\"MSFT, 9988.HK\")\n",
    "xnys = _prices_mix.calendars[\"MSFT\"]\n",
    "xhkg = _prices_mix.calendars[\"9988.HK\"]\n",
    "_calendars = [xnys, xhkg]\n",
    "_session_length = [\n",
    "    pd.Timedelta(hours=6, minutes=30),\n",
    "    pd.Timedelta(hours=6, minutes=30),\n",
    "]\n",
    "# get sessions for which price data available at all base intervals\n",
    "_sessions_range = th.get_sessions_range_for_bi(\n",
    "    _prices_mix, _prices_mix.bis.T1\n",
    ")\n",
    "session = th.get_conforming_sessions(\n",
    "    _calendars, _session_length, *_sessions_range, 5\n",
    ")[-1]\n",
    "\n",
    "_prices_us = PricesYahoo(\"MSFT\")\n",
    "start_T1, end_T1 = th.get_sessions_range_for_bi(_prices_us, _prices_us.bis.T1)\n",
    "start_T5, end_T5 = th.get_sessions_range_for_bi(_prices_us, _prices_us.bis.T5)\n",
    "start_H1 = th.get_sessions_range_for_bi(_prices_us, _prices_us.bis.H1)[0]\n",
    "start_T5_oob = helpers.to_tz_naive(xnys.session_offset(start_T5, -2))\n",
    "start_H1_oob = helpers.to_tz_naive(xnys.session_offset(start_H1, -2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a prices object for one or more symbols..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = PricesYahoo(\"MSFT, GOOG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get some prices..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:20:00)  261.445007  261.950012   \n",
       "[2022-05-11 15:20:00, 2022-05-11 15:25:00)  261.519989  261.670013   \n",
       "[2022-05-11 15:25:00, 2022-05-11 15:30:00)  261.010010  261.859985   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:35:00)  261.209991  261.670013   \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:40:00)  261.079987  261.140015   \n",
       "[2022-05-11 15:40:00, 2022-05-11 15:45:00)  260.279999  260.575012   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:50:00)  260.144989  260.440002   \n",
       "[2022-05-11 15:50:00, 2022-05-11 15:55:00)  259.709991  260.320007   \n",
       "[2022-05-11 15:55:00, 2022-05-11 16:00:00)  260.100006  260.790009   \n",
       "\n",
       "symbol                                                                       \\\n",
       "                                                   low       close   volume   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:20:00)  260.750000  261.540009   513921   \n",
       "[2022-05-11 15:20:00, 2022-05-11 15:25:00)  260.859985  261.020111   465300   \n",
       "[2022-05-11 15:25:00, 2022-05-11 15:30:00)  260.920105  261.239990   432710   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:35:00)  260.910004  261.100006   621908   \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:40:00)  260.209991  260.304993   809542   \n",
       "[2022-05-11 15:40:00, 2022-05-11 15:45:00)  260.019989  260.154999   962406   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:50:00)  259.570007  259.700012  1259836   \n",
       "[2022-05-11 15:50:00, 2022-05-11 15:55:00)  259.299988  260.070007  1183760   \n",
       "[2022-05-11 15:55:00, 2022-05-11 16:00:00)  259.857697  260.660004  1666678   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:20:00)  2295.770020  2300.604980   \n",
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       "[2022-05-11 15:55:00, 2022-05-11 16:00:00)  2281.580078  2282.870117   \n",
       "\n",
       "symbol                                                                        \n",
       "                                                    low        close  volume  \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:20:00)  2291.817139  2298.800049   14237  \n",
       "[2022-05-11 15:20:00, 2022-05-11 15:25:00)  2292.320068  2293.300049   16273  \n",
       "[2022-05-11 15:25:00, 2022-05-11 15:30:00)  2293.159912  2295.351074   16135  \n",
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       "[2022-05-11 15:40:00, 2022-05-11 15:45:00)  2287.000000  2289.229980   22339  \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:50:00)  2283.709961  2287.199951   26622  \n",
       "[2022-05-11 15:50:00, 2022-05-11 15:55:00)  2277.475098  2281.439941   70314  \n",
       "[2022-05-11 15:55:00, 2022-05-11 16:00:00)  2274.189941  2279.219971  101702  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last 45 minutes of data at 5 minute intervals\n",
    "df_intraday = prices.get(interval=\"5min\", minutes=45)\n",
    "df_intraday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prices are returned as a pandas `DataFrame` with 'open', 'high', 'low', 'close', 'volume' columns for each symbol. For intraday data, rows are indexed with an `IntervalIndex` that describes the period over which the row's data corresponds. Intervals are closed on the 'left' such that the right side is NOT included in the period covered by a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>265.329987</td>\n",
       "      <td>262.269989</td>\n",
       "      <td>263.306000</td>\n",
       "      <td>4269418</td>\n",
       "      <td>2291.699951</td>\n",
       "      <td>2306.219971</td>\n",
       "      <td>2288.320068</td>\n",
       "      <td>2295.435059</td>\n",
       "      <td>214189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 13:30:00, 2022-05-11 14:30:00)</th>\n",
       "      <td>263.277191</td>\n",
       "      <td>263.839996</td>\n",
       "      <td>261.119995</td>\n",
       "      <td>262.579987</td>\n",
       "      <td>4538704</td>\n",
       "      <td>2294.669922</td>\n",
       "      <td>2317.060059</td>\n",
       "      <td>2286.219971</td>\n",
       "      <td>2307.704102</td>\n",
       "      <td>194410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 14:30:00, 2022-05-11 15:30:00)</th>\n",
       "      <td>262.579987</td>\n",
       "      <td>264.427887</td>\n",
       "      <td>260.750000</td>\n",
       "      <td>261.239990</td>\n",
       "      <td>5379009</td>\n",
       "      <td>2309.699951</td>\n",
       "      <td>2322.090088</td>\n",
       "      <td>2291.817139</td>\n",
       "      <td>2295.351074</td>\n",
       "      <td>166048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 15:30:00, 2022-05-11 16:30:00)</th>\n",
       "      <td>261.209991</td>\n",
       "      <td>261.670013</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.660004</td>\n",
       "      <td>6504130</td>\n",
       "      <td>2295.419922</td>\n",
       "      <td>2300.199951</td>\n",
       "      <td>2274.189941</td>\n",
       "      <td>2279.219971</td>\n",
       "      <td>267750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-04-28 09:30:00, 2022-04-28 10:30:00)  285.184998  287.609985   \n",
       "[2022-04-28 10:30:00, 2022-04-28 11:30:00)  283.329987  284.975006   \n",
       "[2022-04-28 11:30:00, 2022-04-28 12:30:00)  284.359985  286.029999   \n",
       "[2022-04-28 12:30:00, 2022-04-28 13:30:00)  286.000000  289.329987   \n",
       "[2022-04-28 13:30:00, 2022-04-28 14:30:00)  289.100006  289.619995   \n",
       "...                                                ...         ...   \n",
       "[2022-05-11 11:30:00, 2022-05-11 12:30:00)  266.140015  267.519989   \n",
       "[2022-05-11 12:30:00, 2022-05-11 13:30:00)  264.329987  265.329987   \n",
       "[2022-05-11 13:30:00, 2022-05-11 14:30:00)  263.277191  263.839996   \n",
       "[2022-05-11 14:30:00, 2022-05-11 15:30:00)  262.579987  264.427887   \n",
       "[2022-05-11 15:30:00, 2022-05-11 16:30:00)  261.209991  261.670013   \n",
       "\n",
       "symbol                                                                       \\\n",
       "                                                   low       close   volume   \n",
       "[2022-04-28 09:30:00, 2022-04-28 10:30:00)  281.456207  283.359985  8087499   \n",
       "[2022-04-28 10:30:00, 2022-04-28 11:30:00)  282.410004  284.290009  4022154   \n",
       "[2022-04-28 11:30:00, 2022-04-28 12:30:00)  282.920013  285.984985  2845340   \n",
       "[2022-04-28 12:30:00, 2022-04-28 13:30:00)  285.890015  289.079987  3322418   \n",
       "[2022-04-28 13:30:00, 2022-04-28 14:30:00)  288.429993  289.350006  2633731   \n",
       "...                                                ...         ...      ...   \n",
       "[2022-05-11 11:30:00, 2022-05-11 12:30:00)  264.130005  264.359985  2959808   \n",
       "[2022-05-11 12:30:00, 2022-05-11 13:30:00)  262.269989  263.306000  4269418   \n",
       "[2022-05-11 13:30:00, 2022-05-11 14:30:00)  261.119995  262.579987  4538704   \n",
       "[2022-05-11 14:30:00, 2022-05-11 15:30:00)  260.750000  261.239990  5379009   \n",
       "[2022-05-11 15:30:00, 2022-05-11 16:30:00)  259.299988  260.660004  6504130   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-04-28 09:30:00, 2022-04-28 10:30:00)  2342.300049  2349.489990   \n",
       "[2022-04-28 10:30:00, 2022-04-28 11:30:00)  2322.139893  2351.780029   \n",
       "[2022-04-28 11:30:00, 2022-04-28 12:30:00)  2345.576416  2358.500000   \n",
       "[2022-04-28 12:30:00, 2022-04-28 13:30:00)  2358.500000  2401.033447   \n",
       "[2022-04-28 13:30:00, 2022-04-28 14:30:00)  2388.949951  2396.703857   \n",
       "...                                                 ...          ...   \n",
       "[2022-05-11 11:30:00, 2022-05-11 12:30:00)  2310.979980  2324.000000   \n",
       "[2022-05-11 12:30:00, 2022-05-11 13:30:00)  2291.699951  2306.219971   \n",
       "[2022-05-11 13:30:00, 2022-05-11 14:30:00)  2294.669922  2317.060059   \n",
       "[2022-05-11 14:30:00, 2022-05-11 15:30:00)  2309.699951  2322.090088   \n",
       "[2022-05-11 15:30:00, 2022-05-11 16:30:00)  2295.419922  2300.199951   \n",
       "\n",
       "symbol                                                                        \n",
       "                                                    low        close  volume  \n",
       "[2022-04-28 09:30:00, 2022-04-28 10:30:00)  2302.877686  2323.570068  573390  \n",
       "[2022-04-28 10:30:00, 2022-04-28 11:30:00)  2319.720215  2344.899902  195341  \n",
       "[2022-04-28 11:30:00, 2022-04-28 12:30:00)  2330.020020  2357.570068  178195  \n",
       "[2022-04-28 12:30:00, 2022-04-28 13:30:00)  2356.193115  2390.770020  184522  \n",
       "[2022-04-28 13:30:00, 2022-04-28 14:30:00)  2384.360107  2395.435059  124659  \n",
       "...                                                 ...          ...     ...  \n",
       "[2022-05-11 11:30:00, 2022-05-11 12:30:00)  2292.879883  2293.239990  147100  \n",
       "[2022-05-11 12:30:00, 2022-05-11 13:30:00)  2288.320068  2295.435059  214189  \n",
       "[2022-05-11 13:30:00, 2022-05-11 14:30:00)  2286.219971  2307.704102  194410  \n",
       "[2022-05-11 14:30:00, 2022-05-11 15:30:00)  2291.817139  2295.351074  166048  \n",
       "[2022-05-11 15:30:00, 2022-05-11 16:30:00)  2274.189941  2279.219971  267750  \n",
       "\n",
       "[70 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last ten sessions at 1 hour intervals\n",
    "prices.get(\"1H\", days=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daily data is indexed with a `DatetimeIndex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-04-12</th>\n",
       "      <td>289.239990</td>\n",
       "      <td>290.739990</td>\n",
       "      <td>280.489990</td>\n",
       "      <td>282.059998</td>\n",
       "      <td>30966700</td>\n",
       "      <td>2648.469971</td>\n",
       "      <td>2648.469971</td>\n",
       "      <td>2551.520020</td>\n",
       "      <td>2567.489990</td>\n",
       "      <td>1150200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-13</th>\n",
       "      <td>282.730011</td>\n",
       "      <td>288.579987</td>\n",
       "      <td>281.299988</td>\n",
       "      <td>287.619995</td>\n",
       "      <td>21907200</td>\n",
       "      <td>2572.530029</td>\n",
       "      <td>2613.114990</td>\n",
       "      <td>2568.771973</td>\n",
       "      <td>2605.719971</td>\n",
       "      <td>977100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-14</th>\n",
       "      <td>288.089996</td>\n",
       "      <td>288.309998</td>\n",
       "      <td>279.320007</td>\n",
       "      <td>279.829987</td>\n",
       "      <td>28221600</td>\n",
       "      <td>2612.989990</td>\n",
       "      <td>2614.205078</td>\n",
       "      <td>2542.229980</td>\n",
       "      <td>2545.060059</td>\n",
       "      <td>1174200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-18</th>\n",
       "      <td>278.910004</td>\n",
       "      <td>282.459991</td>\n",
       "      <td>278.339996</td>\n",
       "      <td>280.519989</td>\n",
       "      <td>20778000</td>\n",
       "      <td>2548.199951</td>\n",
       "      <td>2574.239990</td>\n",
       "      <td>2531.569092</td>\n",
       "      <td>2559.219971</td>\n",
       "      <td>745900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-19</th>\n",
       "      <td>279.380005</td>\n",
       "      <td>286.170013</td>\n",
       "      <td>278.410004</td>\n",
       "      <td>285.299988</td>\n",
       "      <td>22297700</td>\n",
       "      <td>2561.540039</td>\n",
       "      <td>2618.074951</td>\n",
       "      <td>2549.030029</td>\n",
       "      <td>2610.620117</td>\n",
       "      <td>1136000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-20</th>\n",
       "      <td>289.399994</td>\n",
       "      <td>289.700012</td>\n",
       "      <td>285.369995</td>\n",
       "      <td>286.359985</td>\n",
       "      <td>22906700</td>\n",
       "      <td>2625.679932</td>\n",
       "      <td>2638.469971</td>\n",
       "      <td>2557.881104</td>\n",
       "      <td>2564.909912</td>\n",
       "      <td>1130500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-21</th>\n",
       "      <td>288.579987</td>\n",
       "      <td>293.299988</td>\n",
       "      <td>280.059998</td>\n",
       "      <td>280.809998</td>\n",
       "      <td>29454600</td>\n",
       "      <td>2587.000000</td>\n",
       "      <td>2606.149902</td>\n",
       "      <td>2493.000000</td>\n",
       "      <td>2498.750000</td>\n",
       "      <td>1507900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-22</th>\n",
       "      <td>281.679993</td>\n",
       "      <td>283.200012</td>\n",
       "      <td>273.380005</td>\n",
       "      <td>274.029999</td>\n",
       "      <td>29405800</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>2509.040039</td>\n",
       "      <td>2382.810059</td>\n",
       "      <td>2392.280029</td>\n",
       "      <td>2320500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-25</th>\n",
       "      <td>273.290009</td>\n",
       "      <td>281.109985</td>\n",
       "      <td>270.769989</td>\n",
       "      <td>280.720001</td>\n",
       "      <td>35678900</td>\n",
       "      <td>2388.590088</td>\n",
       "      <td>2465.560059</td>\n",
       "      <td>2375.385010</td>\n",
       "      <td>2465.000000</td>\n",
       "      <td>1726100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-26</th>\n",
       "      <td>277.500000</td>\n",
       "      <td>278.359985</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>270.220001</td>\n",
       "      <td>46518400</td>\n",
       "      <td>2455.000000</td>\n",
       "      <td>2455.000000</td>\n",
       "      <td>2383.237061</td>\n",
       "      <td>2390.120117</td>\n",
       "      <td>2469700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-27</th>\n",
       "      <td>282.100006</td>\n",
       "      <td>290.970001</td>\n",
       "      <td>279.160004</td>\n",
       "      <td>283.220001</td>\n",
       "      <td>63477700</td>\n",
       "      <td>2287.459961</td>\n",
       "      <td>2350.000000</td>\n",
       "      <td>2262.485107</td>\n",
       "      <td>2300.409912</td>\n",
       "      <td>3111900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-28</th>\n",
       "      <td>285.190002</td>\n",
       "      <td>290.980011</td>\n",
       "      <td>281.459991</td>\n",
       "      <td>289.630005</td>\n",
       "      <td>33646600</td>\n",
       "      <td>2342.300049</td>\n",
       "      <td>2408.770020</td>\n",
       "      <td>2302.877930</td>\n",
       "      <td>2388.229980</td>\n",
       "      <td>1839500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>288.609985</td>\n",
       "      <td>289.880005</td>\n",
       "      <td>276.500000</td>\n",
       "      <td>277.519989</td>\n",
       "      <td>37025000</td>\n",
       "      <td>2351.560059</td>\n",
       "      <td>2379.199951</td>\n",
       "      <td>2293.879883</td>\n",
       "      <td>2299.330078</td>\n",
       "      <td>1683500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-02</th>\n",
       "      <td>277.709991</td>\n",
       "      <td>284.940002</td>\n",
       "      <td>276.220001</td>\n",
       "      <td>284.470001</td>\n",
       "      <td>35151100</td>\n",
       "      <td>2278.129883</td>\n",
       "      <td>2346.790039</td>\n",
       "      <td>2267.989990</td>\n",
       "      <td>2343.139893</td>\n",
       "      <td>1514000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-03</th>\n",
       "      <td>283.959991</td>\n",
       "      <td>284.130005</td>\n",
       "      <td>280.149994</td>\n",
       "      <td>281.779999</td>\n",
       "      <td>25978600</td>\n",
       "      <td>2335.300049</td>\n",
       "      <td>2386.000000</td>\n",
       "      <td>2332.540039</td>\n",
       "      <td>2362.590088</td>\n",
       "      <td>1060800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-04</th>\n",
       "      <td>282.589996</td>\n",
       "      <td>290.880005</td>\n",
       "      <td>276.730011</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>33599300</td>\n",
       "      <td>2360.070068</td>\n",
       "      <td>2462.860107</td>\n",
       "      <td>2314.770020</td>\n",
       "      <td>2451.500000</td>\n",
       "      <td>1661600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>285.540009</td>\n",
       "      <td>286.350006</td>\n",
       "      <td>274.339996</td>\n",
       "      <td>277.350006</td>\n",
       "      <td>43260400</td>\n",
       "      <td>2404.409912</td>\n",
       "      <td>2424.665039</td>\n",
       "      <td>2303.649902</td>\n",
       "      <td>2334.929932</td>\n",
       "      <td>2154500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>274.809998</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>271.269989</td>\n",
       "      <td>274.730011</td>\n",
       "      <td>37748300</td>\n",
       "      <td>2310.379883</td>\n",
       "      <td>2349.969971</td>\n",
       "      <td>2282.860107</td>\n",
       "      <td>2313.199951</td>\n",
       "      <td>1764000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-09</th>\n",
       "      <td>270.059998</td>\n",
       "      <td>272.359985</td>\n",
       "      <td>263.320007</td>\n",
       "      <td>264.579987</td>\n",
       "      <td>47726000</td>\n",
       "      <td>2266.070068</td>\n",
       "      <td>2311.258057</td>\n",
       "      <td>2251.030029</td>\n",
       "      <td>2261.679932</td>\n",
       "      <td>1726000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-10</th>\n",
       "      <td>271.690002</td>\n",
       "      <td>273.750000</td>\n",
       "      <td>265.070007</td>\n",
       "      <td>269.500000</td>\n",
       "      <td>39336400</td>\n",
       "      <td>2320.810059</td>\n",
       "      <td>2333.820068</td>\n",
       "      <td>2267.666016</td>\n",
       "      <td>2291.689941</td>\n",
       "      <td>1557900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-11</th>\n",
       "      <td>265.679993</td>\n",
       "      <td>271.359985</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.549988</td>\n",
       "      <td>48894500</td>\n",
       "      <td>2274.209961</td>\n",
       "      <td>2333.419922</td>\n",
       "      <td>2273.000000</td>\n",
       "      <td>2279.219971</td>\n",
       "      <td>1822500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol            MSFT                                                \\\n",
       "                  open        high         low       close    volume   \n",
       "2022-04-12  289.239990  290.739990  280.489990  282.059998  30966700   \n",
       "2022-04-13  282.730011  288.579987  281.299988  287.619995  21907200   \n",
       "2022-04-14  288.089996  288.309998  279.320007  279.829987  28221600   \n",
       "2022-04-18  278.910004  282.459991  278.339996  280.519989  20778000   \n",
       "2022-04-19  279.380005  286.170013  278.410004  285.299988  22297700   \n",
       "2022-04-20  289.399994  289.700012  285.369995  286.359985  22906700   \n",
       "2022-04-21  288.579987  293.299988  280.059998  280.809998  29454600   \n",
       "2022-04-22  281.679993  283.200012  273.380005  274.029999  29405800   \n",
       "2022-04-25  273.290009  281.109985  270.769989  280.720001  35678900   \n",
       "2022-04-26  277.500000  278.359985  270.000000  270.220001  46518400   \n",
       "2022-04-27  282.100006  290.970001  279.160004  283.220001  63477700   \n",
       "2022-04-28  285.190002  290.980011  281.459991  289.630005  33646600   \n",
       "2022-04-29  288.609985  289.880005  276.500000  277.519989  37025000   \n",
       "2022-05-02  277.709991  284.940002  276.220001  284.470001  35151100   \n",
       "2022-05-03  283.959991  284.130005  280.149994  281.779999  25978600   \n",
       "2022-05-04  282.589996  290.880005  276.730011  289.980011  33599300   \n",
       "2022-05-05  285.540009  286.350006  274.339996  277.350006  43260400   \n",
       "2022-05-06  274.809998  279.250000  271.269989  274.730011  37748300   \n",
       "2022-05-09  270.059998  272.359985  263.320007  264.579987  47726000   \n",
       "2022-05-10  271.690002  273.750000  265.070007  269.500000  39336400   \n",
       "2022-05-11  265.679993  271.359985  259.299988  260.549988  48894500   \n",
       "\n",
       "symbol             GOOG                                                  \n",
       "                   open         high          low        close   volume  \n",
       "2022-04-12  2648.469971  2648.469971  2551.520020  2567.489990  1150200  \n",
       "2022-04-13  2572.530029  2613.114990  2568.771973  2605.719971   977100  \n",
       "2022-04-14  2612.989990  2614.205078  2542.229980  2545.060059  1174200  \n",
       "2022-04-18  2548.199951  2574.239990  2531.569092  2559.219971   745900  \n",
       "2022-04-19  2561.540039  2618.074951  2549.030029  2610.620117  1136000  \n",
       "2022-04-20  2625.679932  2638.469971  2557.881104  2564.909912  1130500  \n",
       "2022-04-21  2587.000000  2606.149902  2493.000000  2498.750000  1507900  \n",
       "2022-04-22  2500.000000  2509.040039  2382.810059  2392.280029  2320500  \n",
       "2022-04-25  2388.590088  2465.560059  2375.385010  2465.000000  1726100  \n",
       "2022-04-26  2455.000000  2455.000000  2383.237061  2390.120117  2469700  \n",
       "2022-04-27  2287.459961  2350.000000  2262.485107  2300.409912  3111900  \n",
       "2022-04-28  2342.300049  2408.770020  2302.877930  2388.229980  1839500  \n",
       "2022-04-29  2351.560059  2379.199951  2293.879883  2299.330078  1683500  \n",
       "2022-05-02  2278.129883  2346.790039  2267.989990  2343.139893  1514000  \n",
       "2022-05-03  2335.300049  2386.000000  2332.540039  2362.590088  1060800  \n",
       "2022-05-04  2360.070068  2462.860107  2314.770020  2451.500000  1661600  \n",
       "2022-05-05  2404.409912  2424.665039  2303.649902  2334.929932  2154500  \n",
       "2022-05-06  2310.379883  2349.969971  2282.860107  2313.199951  1764000  \n",
       "2022-05-09  2266.070068  2311.258057  2251.030029  2261.679932  1726000  \n",
       "2022-05-10  2320.810059  2333.820068  2267.666016  2291.689941  1557900  \n",
       "2022-05-11  2274.209961  2333.419922  2273.000000  2279.219971  1822500  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last month of daily prices\n",
    "df_daily = prices.get(\"1D\", months=1)\n",
    "df_daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[2020-10-01, 2021-03-01)</th>\n",
       "      <td>213.490005</td>\n",
       "      <td>246.130005</td>\n",
       "      <td>199.619995</td>\n",
       "      <td>232.380005</td>\n",
       "      <td>2.938861e+09</td>\n",
       "      <td>1484.270020</td>\n",
       "      <td>2152.679932</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>2036.859985</td>\n",
       "      <td>168267200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2021-03-01, 2021-08-01)</th>\n",
       "      <td>235.899994</td>\n",
       "      <td>290.149994</td>\n",
       "      <td>224.259995</td>\n",
       "      <td>284.910004</td>\n",
       "      <td>2.819937e+09</td>\n",
       "      <td>2056.520020</td>\n",
       "      <td>2800.219971</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2704.419922</td>\n",
       "      <td>141246200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2021-08-01, 2022-01-01)</th>\n",
       "      <td>286.359985</td>\n",
       "      <td>349.670013</td>\n",
       "      <td>280.250000</td>\n",
       "      <td>336.320007</td>\n",
       "      <td>2.596303e+09</td>\n",
       "      <td>2709.689941</td>\n",
       "      <td>3037.000000</td>\n",
       "      <td>2623.330078</td>\n",
       "      <td>2893.590088</td>\n",
       "      <td>114825700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-01-01, 2022-06-01)</th>\n",
       "      <td>335.350006</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.549988</td>\n",
       "      <td>3.317905e+09</td>\n",
       "      <td>2889.510010</td>\n",
       "      <td>3042.000000</td>\n",
       "      <td>2251.030029</td>\n",
       "      <td>2279.219971</td>\n",
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      ],
      "text/plain": [
       "symbol                          MSFT                                      \\\n",
       "                                open        high         low       close   \n",
       "[2020-10-01, 2021-03-01)  213.490005  246.130005  199.619995  232.380005   \n",
       "[2021-03-01, 2021-08-01)  235.899994  290.149994  224.259995  284.910004   \n",
       "[2021-08-01, 2022-01-01)  286.359985  349.670013  280.250000  336.320007   \n",
       "[2022-01-01, 2022-06-01)  335.350006  338.000000  259.299988  260.549988   \n",
       "\n",
       "symbol                                         GOOG                            \\\n",
       "                                volume         open         high          low   \n",
       "[2020-10-01, 2021-03-01)  2.938861e+09  1484.270020  2152.679932  1436.000000   \n",
       "[2021-03-01, 2021-08-01)  2.819937e+09  2056.520020  2800.219971  2010.000000   \n",
       "[2021-08-01, 2022-01-01)  2.596303e+09  2709.689941  3037.000000  2623.330078   \n",
       "[2022-01-01, 2022-06-01)  3.317905e+09  2889.510010  3042.000000  2251.030029   \n",
       "\n",
       "symbol                                              \n",
       "                                close       volume  \n",
       "[2020-10-01, 2021-03-01)  2036.859985  168267200.0  \n",
       "[2021-03-01, 2021-08-01)  2704.419922  141246200.0  \n",
       "[2021-08-01, 2022-01-01)  2893.590088  114825700.0  \n",
       "[2022-01-01, 2022-06-01)  2279.219971  140786100.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# last two years of prices at 5 month intervals\n",
    "prices.get(\"5M\", years=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or pass `start` and/or `end` to define the period from/to/between fixed minutes or dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2022-05-11 15:40:00-0400', tz='America/New_York'),\n",
       " Timestamp('2022-05-11 15:55:00-0400', tz='America/New_York'))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get some values for start and end\n",
    "start, end = df_intraday.index[-4].left, df_intraday.index[-2].right\n",
    "start, end  # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>[2022-05-11 15:45:00, 2022-05-11 15:48:00)</th>\n",
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       "      <th>[2022-05-11 15:48:00, 2022-05-11 15:51:00)</th>\n",
       "      <td>259.779999</td>\n",
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       "      <th>[2022-05-11 15:51:00, 2022-05-11 15:54:00)</th>\n",
       "      <td>259.690002</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:45:00)  260.309998  260.575012   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:48:00)  260.144989  260.190002   \n",
       "[2022-05-11 15:48:00, 2022-05-11 15:51:00)  259.779999  260.440002   \n",
       "[2022-05-11 15:51:00, 2022-05-11 15:54:00)  259.690002  260.269989   \n",
       "\n",
       "symbol                                                                        \\\n",
       "                                                   low       close    volume   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:45:00)  260.019989  260.154999  463166.0   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:48:00)  259.570007  259.779999  803120.0   \n",
       "[2022-05-11 15:48:00, 2022-05-11 15:51:00)  259.600006  259.700012  748407.0   \n",
       "[2022-05-11 15:51:00, 2022-05-11 15:54:00)  259.355011  259.519989  602194.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:45:00)  2290.199951  2291.419922   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:48:00)  2286.239990  2288.620117   \n",
       "[2022-05-11 15:48:00, 2022-05-11 15:51:00)  2287.000000  2290.520020   \n",
       "[2022-05-11 15:51:00, 2022-05-11 15:54:00)  2282.534912  2286.100098   \n",
       "\n",
       "symbol                                                                         \n",
       "                                                    low        close   volume  \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:45:00)  2287.149902  2289.229980  13354.0  \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:48:00)  2283.709961  2287.100098  14385.0  \n",
       "[2022-05-11 15:48:00, 2022-05-11 15:51:00)  2283.080078  2283.219971  22378.0  \n",
       "[2022-05-11 15:51:00, 2022-05-11 15:54:00)  2278.500000  2278.620117  41772.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.get(\"3T\", start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2022-04-19 00:00:00', freq='C'),\n",
       " Timestamp('2022-05-06 00:00:00', freq='C'))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get some alternative values that represent sessions for start and end\n",
    "start_daily, end_daily = df_daily.index[4], df_daily.index[-4]\n",
    "start_daily, end_daily  # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>[2022-04-21, 2022-04-26)</th>\n",
       "      <td>288.579987</td>\n",
       "      <td>293.299988</td>\n",
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       "      <th>[2022-04-26, 2022-04-29)</th>\n",
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       "      <th>[2022-04-29, 2022-05-04)</th>\n",
       "      <td>288.609985</td>\n",
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       "      <td>281.779999</td>\n",
       "      <td>98154700.0</td>\n",
       "      <td>2351.560059</td>\n",
       "      <td>2386.000000</td>\n",
       "      <td>2267.989990</td>\n",
       "      <td>2362.590088</td>\n",
       "      <td>4258300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-04, 2022-05-07)</th>\n",
       "      <td>282.589996</td>\n",
       "      <td>290.880005</td>\n",
       "      <td>271.269989</td>\n",
       "      <td>274.730011</td>\n",
       "      <td>114608000.0</td>\n",
       "      <td>2360.070068</td>\n",
       "      <td>2462.860107</td>\n",
       "      <td>2282.860107</td>\n",
       "      <td>2313.199951</td>\n",
       "      <td>5580100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                          MSFT                                      \\\n",
       "                                open        high         low       close   \n",
       "[2022-04-21, 2022-04-26)  288.579987  293.299988  270.769989  280.720001   \n",
       "[2022-04-26, 2022-04-29)  277.500000  290.980011  270.000000  289.630005   \n",
       "[2022-04-29, 2022-05-04)  288.609985  289.880005  276.220001  281.779999   \n",
       "[2022-05-04, 2022-05-07)  282.589996  290.880005  271.269989  274.730011   \n",
       "\n",
       "symbol                                        GOOG                            \\\n",
       "                               volume         open         high          low   \n",
       "[2022-04-21, 2022-04-26)   94539300.0  2587.000000  2606.149902  2375.385010   \n",
       "[2022-04-26, 2022-04-29)  143642700.0  2455.000000  2455.000000  2262.485107   \n",
       "[2022-04-29, 2022-05-04)   98154700.0  2351.560059  2386.000000  2267.989990   \n",
       "[2022-05-04, 2022-05-07)  114608000.0  2360.070068  2462.860107  2282.860107   \n",
       "\n",
       "symbol                                            \n",
       "                                close     volume  \n",
       "[2022-04-21, 2022-04-26)  2465.000000  5554500.0  \n",
       "[2022-04-26, 2022-04-29)  2388.229980  7421100.0  \n",
       "[2022-04-29, 2022-05-04)  2362.590088  4258300.0  \n",
       "[2022-05-04, 2022-05-07)  2313.199951  5580100.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interval as 3 sessions (not 3 calendar days!)\n",
    "prices.get(\"3D\", start_daily, end_daily)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above prices for the 'multiple sessions' interval are indexed with an `IntervalIndex`. Intervals are closed on the left such that rows' data does not include any session that is represented by the right side of the indice (any such session will be represented in the following row)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2022-05-06 00:00:00', freq='C'),\n",
       " Timestamp('2022-05-11 15:40:00-0400', tz='America/New_York'))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_time = start\n",
    "start_date = end_daily\n",
    "start_date, end_time # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
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       "      <th>...</th>\n",
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       "<p>1540 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-06 09:30:00, 2022-05-06 09:31:00)  274.804993  275.640015   \n",
       "[2022-05-06 09:31:00, 2022-05-06 09:32:00)  274.380005  276.100006   \n",
       "[2022-05-06 09:32:00, 2022-05-06 09:33:00)  276.029999  276.049988   \n",
       "[2022-05-06 09:33:00, 2022-05-06 09:34:00)  274.450012  274.649994   \n",
       "[2022-05-06 09:34:00, 2022-05-06 09:35:00)  274.059998  274.751709   \n",
       "...                                                ...         ...   \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:36:00)  261.079987  261.140015   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:37:00)  260.609985  260.989990   \n",
       "[2022-05-11 15:37:00, 2022-05-11 15:38:00)  260.790009  261.059998   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:39:00)  260.609985  260.769989   \n",
       "[2022-05-11 15:39:00, 2022-05-11 15:40:00)  260.750000  260.820007   \n",
       "\n",
       "symbol                                                                        \\\n",
       "                                                   low       close    volume   \n",
       "[2022-05-06 09:30:00, 2022-05-06 09:31:00)  274.200012  274.500000  950286.0   \n",
       "[2022-05-06 09:31:00, 2022-05-06 09:32:00)  274.380005  276.100006   91778.0   \n",
       "[2022-05-06 09:32:00, 2022-05-06 09:33:00)  274.399994  274.510010   96677.0   \n",
       "[2022-05-06 09:33:00, 2022-05-06 09:34:00)  273.660004  274.019989  136213.0   \n",
       "[2022-05-06 09:34:00, 2022-05-06 09:35:00)  273.880096  273.950012   83052.0   \n",
       "...                                                ...         ...       ...   \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:36:00)  260.410004  260.619995  228377.0   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:37:00)  260.510010  260.750000  116746.0   \n",
       "[2022-05-11 15:37:00, 2022-05-11 15:38:00)  260.529999  260.609985  134740.0   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:39:00)  260.309998  260.709991  199713.0   \n",
       "[2022-05-11 15:39:00, 2022-05-11 15:40:00)  260.209991  260.304993  129966.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-06 09:30:00, 2022-05-06 09:31:00)  2307.439941  2307.544922   \n",
       "[2022-05-06 09:31:00, 2022-05-06 09:32:00)  2307.199951  2314.266602   \n",
       "[2022-05-06 09:32:00, 2022-05-06 09:33:00)  2312.000000  2312.000000   \n",
       "[2022-05-06 09:33:00, 2022-05-06 09:34:00)  2295.419922  2300.000000   \n",
       "[2022-05-06 09:34:00, 2022-05-06 09:35:00)  2293.870117  2300.000000   \n",
       "...                                                 ...          ...   \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:36:00)  2295.280029  2295.280029   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:37:00)  2290.979980  2291.929932   \n",
       "[2022-05-11 15:37:00, 2022-05-11 15:38:00)  2290.250000  2292.215088   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:39:00)  2288.000000  2289.909912   \n",
       "[2022-05-11 15:39:00, 2022-05-11 15:40:00)  2289.969971  2289.969971   \n",
       "\n",
       "symbol                                                                         \n",
       "                                                    low        close   volume  \n",
       "[2022-05-06 09:30:00, 2022-05-06 09:31:00)  2307.199951  2307.199951  50651.0  \n",
       "[2022-05-06 09:31:00, 2022-05-06 09:32:00)  2305.425049  2311.860107   7028.0  \n",
       "[2022-05-06 09:32:00, 2022-05-06 09:33:00)  2295.449951  2295.449951  14278.0  \n",
       "[2022-05-06 09:33:00, 2022-05-06 09:34:00)  2293.409912  2295.340088   7782.0  \n",
       "[2022-05-06 09:34:00, 2022-05-06 09:35:00)  2293.870117  2293.995117   6934.0  \n",
       "...                                                 ...          ...      ...  \n",
       "[2022-05-11 15:35:00, 2022-05-11 15:36:00)  2291.239990  2291.239990   4004.0  \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:37:00)  2290.175049  2290.290039   7836.0  \n",
       "[2022-05-11 15:37:00, 2022-05-11 15:38:00)  2288.360107  2290.620117   8137.0  \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:39:00)  2287.649902  2289.665039   3776.0  \n",
       "[2022-05-11 15:39:00, 2022-05-11 15:40:00)  2286.000000  2286.000000   4683.0  \n",
       "\n",
       "[1540 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prices starting on a specific session ending on a specific minute\n",
    "prices.get(\"1T\", start_date, end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <td>278.269989</td>\n",
       "      <td>275.380005</td>\n",
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       "      <td>2346.410645</td>\n",
       "      <td>2318.110107</td>\n",
       "      <td>2341.689941</td>\n",
       "      <td>77140.0</td>\n",
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       "    <tr>\n",
       "      <th>[2022-05-06 12:35:00, 2022-05-06 13:12:00)</th>\n",
       "      <td>277.420013</td>\n",
       "      <td>278.790009</td>\n",
       "      <td>276.080109</td>\n",
       "      <td>277.940002</td>\n",
       "      <td>1928744.0</td>\n",
       "      <td>2342.409912</td>\n",
       "      <td>2348.580078</td>\n",
       "      <td>2333.830078</td>\n",
       "      <td>2340.409912</td>\n",
       "      <td>93734.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-06 09:30:00, 2022-05-06 10:07:00)  274.804993  276.100006   \n",
       "[2022-05-06 10:07:00, 2022-05-06 10:44:00)  274.029999  277.940002   \n",
       "[2022-05-06 10:44:00, 2022-05-06 11:21:00)  274.309998  278.739990   \n",
       "[2022-05-06 11:21:00, 2022-05-06 11:58:00)  278.130005  279.250000   \n",
       "[2022-05-06 11:58:00, 2022-05-06 12:35:00)  276.239990  278.269989   \n",
       "[2022-05-06 12:35:00, 2022-05-06 13:12:00)  277.420013  278.790009   \n",
       "\n",
       "symbol                                                                         \\\n",
       "                                                   low       close     volume   \n",
       "[2022-05-06 09:30:00, 2022-05-06 10:07:00)  271.360291  274.006897  4965855.0   \n",
       "[2022-05-06 10:07:00, 2022-05-06 10:44:00)  271.790009  274.279999  4077356.0   \n",
       "[2022-05-06 10:44:00, 2022-05-06 11:21:00)  273.920013  278.119995  3684361.0   \n",
       "[2022-05-06 11:21:00, 2022-05-06 11:58:00)  275.600006  276.269989  2576684.0   \n",
       "[2022-05-06 11:58:00, 2022-05-06 12:35:00)  275.380005  277.399994  1976187.0   \n",
       "[2022-05-06 12:35:00, 2022-05-06 13:12:00)  276.080109  277.940002  1928744.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-06 09:30:00, 2022-05-06 10:07:00)  2307.439941  2314.266602   \n",
       "[2022-05-06 10:07:00, 2022-05-06 10:44:00)  2305.439941  2340.000000   \n",
       "[2022-05-06 10:44:00, 2022-05-06 11:21:00)  2310.100098  2333.489990   \n",
       "[2022-05-06 11:21:00, 2022-05-06 11:58:00)  2332.405029  2349.969971   \n",
       "[2022-05-06 11:58:00, 2022-05-06 12:35:00)  2323.090088  2346.410645   \n",
       "[2022-05-06 12:35:00, 2022-05-06 13:12:00)  2342.409912  2348.580078   \n",
       "\n",
       "symbol                                                                          \n",
       "                                                    low        close    volume  \n",
       "[2022-05-06 09:30:00, 2022-05-06 10:07:00)  2282.860107  2305.439941  358940.0  \n",
       "[2022-05-06 10:07:00, 2022-05-06 10:44:00)  2288.889893  2314.000000  191993.0  \n",
       "[2022-05-06 10:44:00, 2022-05-06 11:21:00)  2304.770020  2328.477539  178208.0  \n",
       "[2022-05-06 11:21:00, 2022-05-06 11:58:00)  2315.947510  2322.600098  140844.0  \n",
       "[2022-05-06 11:58:00, 2022-05-06 12:35:00)  2318.110107  2341.689941   77140.0  \n",
       "[2022-05-06 12:35:00, 2022-05-06 13:12:00)  2333.830078  2340.409912   93734.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define period as a duration from a session open\n",
    "prices.get(\"37T\", start_date, hours=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end_time=Timestamp('2022-05-11 15:40:00-0400', tz='America/New_York')\n",
      "\n"
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      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-10 15:40:00, 2022-05-10 15:42:00)  269.559998  269.632202   \n",
       "[2022-05-10 15:42:00, 2022-05-10 15:44:00)  269.540009  269.690002   \n",
       "[2022-05-10 15:44:00, 2022-05-10 15:46:00)  269.589996  269.644989   \n",
       "[2022-05-10 15:46:00, 2022-05-10 15:48:00)  269.559998  270.220001   \n",
       "[2022-05-10 15:48:00, 2022-05-10 15:50:00)  270.140015  270.239990   \n",
       "...                                                ...         ...   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:32:00)  261.209991  261.429993   \n",
       "[2022-05-11 15:32:00, 2022-05-11 15:34:00)  261.160004  261.670013   \n",
       "[2022-05-11 15:34:00, 2022-05-11 15:36:00)  261.320007  261.359985   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:38:00)  260.609985  261.059998   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:40:00)  260.609985  260.820007   \n",
       "\n",
       "symbol                                                                        \\\n",
       "                                                   low       close    volume   \n",
       "[2022-05-10 15:40:00, 2022-05-10 15:42:00)  269.200012  269.519989  169139.0   \n",
       "[2022-05-10 15:42:00, 2022-05-10 15:44:00)  269.279999  269.584991  131122.0   \n",
       "[2022-05-10 15:44:00, 2022-05-10 15:46:00)  269.239990  269.549988  213282.0   \n",
       "[2022-05-10 15:46:00, 2022-05-10 15:48:00)  269.200012  270.110901  265996.0   \n",
       "[2022-05-10 15:48:00, 2022-05-10 15:50:00)  269.720001  269.729889  207759.0   \n",
       "...                                                ...         ...       ...   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:32:00)  260.910004  261.179993  326958.0   \n",
       "[2022-05-11 15:32:00, 2022-05-11 15:34:00)  261.089996  261.332092  183539.0   \n",
       "[2022-05-11 15:34:00, 2022-05-11 15:36:00)  260.410004  260.619995  339788.0   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:38:00)  260.510010  260.609985  251486.0   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:40:00)  260.209991  260.304993  329679.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-10 15:40:00, 2022-05-10 15:42:00)  2295.715088  2297.020020   \n",
       "[2022-05-10 15:42:00, 2022-05-10 15:44:00)  2295.669922  2296.689941   \n",
       "[2022-05-10 15:44:00, 2022-05-10 15:46:00)  2295.540039  2296.000000   \n",
       "[2022-05-10 15:46:00, 2022-05-10 15:48:00)  2294.020020  2299.620117   \n",
       "[2022-05-10 15:48:00, 2022-05-10 15:50:00)  2297.310059  2301.780029   \n",
       "...                                                 ...          ...   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:32:00)  2295.419922  2299.179932   \n",
       "[2022-05-11 15:32:00, 2022-05-11 15:34:00)  2298.760010  2300.199951   \n",
       "[2022-05-11 15:34:00, 2022-05-11 15:36:00)  2296.729980  2296.729980   \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:38:00)  2290.979980  2292.215088   \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:40:00)  2288.000000  2289.969971   \n",
       "\n",
       "symbol                                                                         \n",
       "                                                    low        close   volume  \n",
       "[2022-05-10 15:40:00, 2022-05-10 15:42:00)  2293.283936  2295.540039   8131.0  \n",
       "[2022-05-10 15:42:00, 2022-05-10 15:44:00)  2294.570068  2295.570068   6818.0  \n",
       "[2022-05-10 15:44:00, 2022-05-10 15:46:00)  2290.294922  2292.810059   7173.0  \n",
       "[2022-05-10 15:46:00, 2022-05-10 15:48:00)  2289.459961  2299.620117  11534.0  \n",
       "[2022-05-10 15:48:00, 2022-05-10 15:50:00)  2296.185059  2296.395020   9959.0  \n",
       "...                                                 ...          ...      ...  \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:32:00)  2295.199951  2297.000000   8075.0  \n",
       "[2022-05-11 15:32:00, 2022-05-11 15:34:00)  2297.139893  2297.139893   7367.0  \n",
       "[2022-05-11 15:34:00, 2022-05-11 15:36:00)  2291.239990  2291.239990   6899.0  \n",
       "[2022-05-11 15:36:00, 2022-05-11 15:38:00)  2288.360107  2290.620117  15973.0  \n",
       "[2022-05-11 15:38:00, 2022-05-11 15:40:00)  2286.000000  2286.000000   8459.0  \n",
       "\n",
       "[195 rows x 10 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define period as a duration to a minute\n",
    "print(f\"{end_time=}\\n\")  # for reference\n",
    "prices.get(\"2T\", end=end_time, days=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [periods](./periods.ipynb) tutorial for further examples and explanation of how the period over which prices are returned in evaluated.\n",
    "\n",
    "## Anchor\n",
    "\n",
    "By default indices are evaluated from ('anchored' on) each session's open. Alternatively they can be anchored on the period end and evaluted in terms of trading minutes. Compare the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2022-05-06 00:00:00', freq='C'),\n",
       " Timestamp('2022-05-11 15:38:00-0400', tz='America/New_York'))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end = end_time - pd.Timedelta(2, \"T\")\n",
    "start = start_date\n",
    "start, end  # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>274.804993</td>\n",
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       "      <td>271.360291</td>\n",
       "      <td>276.720001</td>\n",
       "      <td>20043126.0</td>\n",
       "      <td>2307.439941</td>\n",
       "      <td>2349.969971</td>\n",
       "      <td>2282.860107</td>\n",
       "      <td>2334.000000</td>\n",
       "      <td>1075458.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-06 13:30:00, 2022-05-06 17:30:00)</th>\n",
       "      <td>276.760010</td>\n",
       "      <td>277.299988</td>\n",
       "      <td>271.269989</td>\n",
       "      <td>274.730011</td>\n",
       "      <td>12733310.0</td>\n",
       "      <td>2336.830078</td>\n",
       "      <td>2340.725098</td>\n",
       "      <td>2293.340088</td>\n",
       "      <td>2311.689941</td>\n",
       "      <td>527669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-09 09:30:00, 2022-05-09 13:30:00)</th>\n",
       "      <td>271.000000</td>\n",
       "      <td>272.359985</td>\n",
       "      <td>264.549988</td>\n",
       "      <td>267.149994</td>\n",
       "      <td>24196295.0</td>\n",
       "      <td>2266.070068</td>\n",
       "      <td>2311.258057</td>\n",
       "      <td>2261.879883</td>\n",
       "      <td>2290.000000</td>\n",
       "      <td>911007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-09 13:30:00, 2022-05-09 17:30:00)</th>\n",
       "      <td>267.195007</td>\n",
       "      <td>267.500000</td>\n",
       "      <td>263.320007</td>\n",
       "      <td>264.589996</td>\n",
       "      <td>14108311.0</td>\n",
       "      <td>2290.810059</td>\n",
       "      <td>2293.219971</td>\n",
       "      <td>2251.030029</td>\n",
       "      <td>2261.659912</td>\n",
       "      <td>584935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-10 09:30:00, 2022-05-10 13:30:00)</th>\n",
       "      <td>272.750000</td>\n",
       "      <td>273.559998</td>\n",
       "      <td>265.070007</td>\n",
       "      <td>269.701294</td>\n",
       "      <td>22119339.0</td>\n",
       "      <td>2320.810059</td>\n",
       "      <td>2333.080078</td>\n",
       "      <td>2267.665771</td>\n",
       "      <td>2303.500000</td>\n",
       "      <td>835057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-10 13:30:00, 2022-05-10 17:30:00)</th>\n",
       "      <td>269.682587</td>\n",
       "      <td>273.750000</td>\n",
       "      <td>268.739990</td>\n",
       "      <td>269.450012</td>\n",
       "      <td>11793026.0</td>\n",
       "      <td>2303.250000</td>\n",
       "      <td>2333.820068</td>\n",
       "      <td>2287.139893</td>\n",
       "      <td>2291.070068</td>\n",
       "      <td>497866.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 09:30:00, 2022-05-11 13:30:00)</th>\n",
       "      <td>265.679993</td>\n",
       "      <td>271.070007</td>\n",
       "      <td>262.269989</td>\n",
       "      <td>263.306000</td>\n",
       "      <td>21432202.0</td>\n",
       "      <td>2274.209961</td>\n",
       "      <td>2333.419922</td>\n",
       "      <td>2273.000000</td>\n",
       "      <td>2295.435059</td>\n",
       "      <td>940311.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-06 09:30:00, 2022-05-06 13:30:00)  274.804993  279.250000   \n",
       "[2022-05-06 13:30:00, 2022-05-06 17:30:00)  276.760010  277.299988   \n",
       "[2022-05-09 09:30:00, 2022-05-09 13:30:00)  271.000000  272.359985   \n",
       "[2022-05-09 13:30:00, 2022-05-09 17:30:00)  267.195007  267.500000   \n",
       "[2022-05-10 09:30:00, 2022-05-10 13:30:00)  272.750000  273.559998   \n",
       "[2022-05-10 13:30:00, 2022-05-10 17:30:00)  269.682587  273.750000   \n",
       "[2022-05-11 09:30:00, 2022-05-11 13:30:00)  265.679993  271.070007   \n",
       "\n",
       "symbol                                                              \\\n",
       "                                                   low       close   \n",
       "[2022-05-06 09:30:00, 2022-05-06 13:30:00)  271.360291  276.720001   \n",
       "[2022-05-06 13:30:00, 2022-05-06 17:30:00)  271.269989  274.730011   \n",
       "[2022-05-09 09:30:00, 2022-05-09 13:30:00)  264.549988  267.149994   \n",
       "[2022-05-09 13:30:00, 2022-05-09 17:30:00)  263.320007  264.589996   \n",
       "[2022-05-10 09:30:00, 2022-05-10 13:30:00)  265.070007  269.701294   \n",
       "[2022-05-10 13:30:00, 2022-05-10 17:30:00)  268.739990  269.450012   \n",
       "[2022-05-11 09:30:00, 2022-05-11 13:30:00)  262.269989  263.306000   \n",
       "\n",
       "symbol                                                         GOOG  \\\n",
       "                                                volume         open   \n",
       "[2022-05-06 09:30:00, 2022-05-06 13:30:00)  20043126.0  2307.439941   \n",
       "[2022-05-06 13:30:00, 2022-05-06 17:30:00)  12733310.0  2336.830078   \n",
       "[2022-05-09 09:30:00, 2022-05-09 13:30:00)  24196295.0  2266.070068   \n",
       "[2022-05-09 13:30:00, 2022-05-09 17:30:00)  14108311.0  2290.810059   \n",
       "[2022-05-10 09:30:00, 2022-05-10 13:30:00)  22119339.0  2320.810059   \n",
       "[2022-05-10 13:30:00, 2022-05-10 17:30:00)  11793026.0  2303.250000   \n",
       "[2022-05-11 09:30:00, 2022-05-11 13:30:00)  21432202.0  2274.209961   \n",
       "\n",
       "symbol                                                                \\\n",
       "                                                   high          low   \n",
       "[2022-05-06 09:30:00, 2022-05-06 13:30:00)  2349.969971  2282.860107   \n",
       "[2022-05-06 13:30:00, 2022-05-06 17:30:00)  2340.725098  2293.340088   \n",
       "[2022-05-09 09:30:00, 2022-05-09 13:30:00)  2311.258057  2261.879883   \n",
       "[2022-05-09 13:30:00, 2022-05-09 17:30:00)  2293.219971  2251.030029   \n",
       "[2022-05-10 09:30:00, 2022-05-10 13:30:00)  2333.080078  2267.665771   \n",
       "[2022-05-10 13:30:00, 2022-05-10 17:30:00)  2333.820068  2287.139893   \n",
       "[2022-05-11 09:30:00, 2022-05-11 13:30:00)  2333.419922  2273.000000   \n",
       "\n",
       "symbol                                                              \n",
       "                                                  close     volume  \n",
       "[2022-05-06 09:30:00, 2022-05-06 13:30:00)  2334.000000  1075458.0  \n",
       "[2022-05-06 13:30:00, 2022-05-06 17:30:00)  2311.689941   527669.0  \n",
       "[2022-05-09 09:30:00, 2022-05-09 13:30:00)  2290.000000   911007.0  \n",
       "[2022-05-09 13:30:00, 2022-05-09 17:30:00)  2261.659912   584935.0  \n",
       "[2022-05-10 09:30:00, 2022-05-10 13:30:00)  2303.500000   835057.0  \n",
       "[2022-05-10 13:30:00, 2022-05-10 17:30:00)  2291.070068   497866.0  \n",
       "[2022-05-11 09:30:00, 2022-05-11 13:30:00)  2295.435059   940311.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default \"open\" anchor\n",
    "prices.get(\"4H\", start, end, anchor=\"open\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
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       "      <th>volume</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>[2022-05-09 12:38:00, 2022-05-10 10:08:00)</th>\n",
       "      <td>267.929993</td>\n",
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       "      <td>2251.030029</td>\n",
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       "    <tr>\n",
       "      <th>[2022-05-10 10:08:00, 2022-05-10 14:08:00)</th>\n",
       "      <td>273.079987</td>\n",
       "      <td>273.750000</td>\n",
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       "      <td>2324.209961</td>\n",
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       "    <tr>\n",
       "      <th>[2022-05-10 14:08:00, 2022-05-11 11:38:00)</th>\n",
       "      <td>272.635010</td>\n",
       "      <td>273.609985</td>\n",
       "      <td>263.790314</td>\n",
       "      <td>267.019989</td>\n",
       "      <td>23028039.0</td>\n",
       "      <td>2323.000000</td>\n",
       "      <td>2333.820068</td>\n",
       "      <td>2273.000000</td>\n",
       "      <td>2321.324951</td>\n",
       "      <td>999015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 11:38:00, 2022-05-11 15:38:00)</th>\n",
       "      <td>267.059998</td>\n",
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       "      <td>260.410004</td>\n",
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       "      <td>2324.000000</td>\n",
       "      <td>2324.000000</td>\n",
       "      <td>2286.219971</td>\n",
       "      <td>2290.620117</td>\n",
       "      <td>728309.0</td>\n",
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      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-06 11:08:00, 2022-05-06 15:08:00)  276.779999  279.250000   \n",
       "[2022-05-06 15:08:00, 2022-05-09 12:38:00)  272.570007  275.559998   \n",
       "[2022-05-09 12:38:00, 2022-05-10 10:08:00)  267.929993  273.559998   \n",
       "[2022-05-10 10:08:00, 2022-05-10 14:08:00)  273.079987  273.750000   \n",
       "[2022-05-10 14:08:00, 2022-05-11 11:38:00)  272.635010  273.609985   \n",
       "[2022-05-11 11:38:00, 2022-05-11 15:38:00)  267.059998  267.519989   \n",
       "\n",
       "symbol                                                              \\\n",
       "                                                   low       close   \n",
       "[2022-05-06 11:08:00, 2022-05-06 15:08:00)  272.540009  272.619995   \n",
       "[2022-05-06 15:08:00, 2022-05-09 12:38:00)  264.549988  267.959991   \n",
       "[2022-05-09 12:38:00, 2022-05-10 10:08:00)  263.320007  273.059998   \n",
       "[2022-05-10 10:08:00, 2022-05-10 14:08:00)  265.070007  272.649994   \n",
       "[2022-05-10 14:08:00, 2022-05-11 11:38:00)  263.790314  267.019989   \n",
       "[2022-05-11 11:38:00, 2022-05-11 15:38:00)  260.410004  260.609985   \n",
       "\n",
       "symbol                                                         GOOG  \\\n",
       "                                                volume         open   \n",
       "[2022-05-06 11:08:00, 2022-05-06 15:08:00)  14305807.0  2314.409912   \n",
       "[2022-05-06 15:08:00, 2022-05-09 12:38:00)  28531203.0  2301.284912   \n",
       "[2022-05-09 12:38:00, 2022-05-10 10:08:00)  24811954.0  2292.645020   \n",
       "[2022-05-10 10:08:00, 2022-05-10 14:08:00)  17339630.0  2327.520020   \n",
       "[2022-05-10 14:08:00, 2022-05-11 11:38:00)  23028039.0  2323.000000   \n",
       "[2022-05-11 11:38:00, 2022-05-11 15:38:00)  17796049.0  2324.000000   \n",
       "\n",
       "symbol                                                                \\\n",
       "                                                   high          low   \n",
       "[2022-05-06 11:08:00, 2022-05-06 15:08:00)  2349.969971  2300.580078   \n",
       "[2022-05-06 15:08:00, 2022-05-09 12:38:00)  2321.899902  2261.879883   \n",
       "[2022-05-09 12:38:00, 2022-05-10 10:08:00)  2333.080078  2251.030029   \n",
       "[2022-05-10 10:08:00, 2022-05-10 14:08:00)  2331.080078  2267.665771   \n",
       "[2022-05-10 14:08:00, 2022-05-11 11:38:00)  2333.820068  2273.000000   \n",
       "[2022-05-11 11:38:00, 2022-05-11 15:38:00)  2324.000000  2286.219971   \n",
       "\n",
       "symbol                                                              \n",
       "                                                  close     volume  \n",
       "[2022-05-06 11:08:00, 2022-05-06 15:08:00)  2301.580078   649269.0  \n",
       "[2022-05-06 15:08:00, 2022-05-09 12:38:00)  2297.452148  1087998.0  \n",
       "[2022-05-09 12:38:00, 2022-05-10 10:08:00)  2326.090088   950134.0  \n",
       "[2022-05-10 10:08:00, 2022-05-10 14:08:00)  2324.209961   684646.0  \n",
       "[2022-05-10 14:08:00, 2022-05-11 11:38:00)  2321.324951   999015.0  \n",
       "[2022-05-11 11:38:00, 2022-05-11 15:38:00)  2290.620117   728309.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.get(\"4H\", start, end, anchor=\"workback\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where sessions have a break, indices will respect that break where posssible. This can be seen in the following prices for Alibaba's Hong Kong listing where the exchange closes between 12:00 and 13:00. The prices cover a single session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_hk = PricesYahoo(\"9988.HK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "session=Timestamp('2022-04-25 00:00:00')\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">9988.HK</th>\n",
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       "      <th>[2022-04-25 09:30:00, 2022-04-25 10:10:00)</th>\n",
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       "      <td>3234464.0</td>\n",
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       "    <tr>\n",
       "      <th>[2022-04-25 11:30:00, 2022-04-25 12:10:00)</th>\n",
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       "      <td>83.550003</td>\n",
       "      <td>1437730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 13:00:00, 2022-04-25 13:40:00)</th>\n",
       "      <td>83.500000</td>\n",
       "      <td>83.599998</td>\n",
       "      <td>82.800003</td>\n",
       "      <td>82.949997</td>\n",
       "      <td>4775242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 13:40:00, 2022-04-25 14:20:00)</th>\n",
       "      <td>82.900002</td>\n",
       "      <td>83.099998</td>\n",
       "      <td>82.150002</td>\n",
       "      <td>82.699997</td>\n",
       "      <td>4282124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 14:20:00, 2022-04-25 15:00:00)</th>\n",
       "      <td>82.699997</td>\n",
       "      <td>82.849998</td>\n",
       "      <td>81.199997</td>\n",
       "      <td>81.800003</td>\n",
       "      <td>4624993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 15:00:00, 2022-04-25 15:40:00)</th>\n",
       "      <td>81.800003</td>\n",
       "      <td>82.550003</td>\n",
       "      <td>81.650002</td>\n",
       "      <td>81.800003</td>\n",
       "      <td>6633358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 15:40:00, 2022-04-25 16:20:00)</th>\n",
       "      <td>81.800003</td>\n",
       "      <td>82.099998</td>\n",
       "      <td>80.949997</td>\n",
       "      <td>81.599998</td>\n",
       "      <td>5671481.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                        9988.HK                        \\\n",
       "                                                 open       high        low   \n",
       "[2022-04-25 09:30:00, 2022-04-25 10:10:00)  84.800003  84.949997  83.000000   \n",
       "[2022-04-25 10:10:00, 2022-04-25 10:50:00)  83.650002  83.949997  83.099998   \n",
       "[2022-04-25 10:50:00, 2022-04-25 11:30:00)  83.699997  84.949997  83.500000   \n",
       "[2022-04-25 11:30:00, 2022-04-25 12:10:00)  83.550003  83.650002  83.349998   \n",
       "[2022-04-25 13:00:00, 2022-04-25 13:40:00)  83.500000  83.599998  82.800003   \n",
       "[2022-04-25 13:40:00, 2022-04-25 14:20:00)  82.900002  83.099998  82.150002   \n",
       "[2022-04-25 14:20:00, 2022-04-25 15:00:00)  82.699997  82.849998  81.199997   \n",
       "[2022-04-25 15:00:00, 2022-04-25 15:40:00)  81.800003  82.550003  81.650002   \n",
       "[2022-04-25 15:40:00, 2022-04-25 16:20:00)  81.800003  82.099998  80.949997   \n",
       "\n",
       "symbol                                                            \n",
       "                                                close     volume  \n",
       "[2022-04-25 09:30:00, 2022-04-25 10:10:00)  83.650002  3576365.0  \n",
       "[2022-04-25 10:10:00, 2022-04-25 10:50:00)  83.650002  2721809.0  \n",
       "[2022-04-25 10:50:00, 2022-04-25 11:30:00)  83.550003  3234464.0  \n",
       "[2022-04-25 11:30:00, 2022-04-25 12:10:00)  83.550003  1437730.0  \n",
       "[2022-04-25 13:00:00, 2022-04-25 13:40:00)  82.949997  4775242.0  \n",
       "[2022-04-25 13:40:00, 2022-04-25 14:20:00)  82.699997  4282124.0  \n",
       "[2022-04-25 14:20:00, 2022-04-25 15:00:00)  81.800003  4624993.0  \n",
       "[2022-04-25 15:00:00, 2022-04-25 15:40:00)  81.800003  6633358.0  \n",
       "[2022-04-25 15:40:00, 2022-04-25 16:20:00)  81.599998  5671481.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"{session=}\\n\")  # for reference\n",
    "prices_hk.get(\"40T\", session, session)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The period between the last indice of the morning session (ending 12.10) and the first indice of the afternoon session (starting 13.00) is not covered by the data.\n",
    "\n",
    "The `force` option can be used to force the final indices of each (sub)session to the (sub)session close..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">9988.HK</th>\n",
       "    </tr>\n",
       "    <tr>\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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 09:30:00, 2022-04-25 10:10:00)</th>\n",
       "      <td>84.800003</td>\n",
       "      <td>84.949997</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.650002</td>\n",
       "      <td>3576365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 10:10:00, 2022-04-25 10:50:00)</th>\n",
       "      <td>83.650002</td>\n",
       "      <td>83.949997</td>\n",
       "      <td>83.099998</td>\n",
       "      <td>83.650002</td>\n",
       "      <td>2721809.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 10:50:00, 2022-04-25 11:30:00)</th>\n",
       "      <td>83.699997</td>\n",
       "      <td>84.949997</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>83.550003</td>\n",
       "      <td>3234464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 11:30:00, 2022-04-25 12:00:00)</th>\n",
       "      <td>83.550003</td>\n",
       "      <td>83.650002</td>\n",
       "      <td>83.349998</td>\n",
       "      <td>83.550003</td>\n",
       "      <td>1437730.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 13:00:00, 2022-04-25 13:40:00)</th>\n",
       "      <td>83.500000</td>\n",
       "      <td>83.599998</td>\n",
       "      <td>82.800003</td>\n",
       "      <td>82.949997</td>\n",
       "      <td>4775242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 13:40:00, 2022-04-25 14:20:00)</th>\n",
       "      <td>82.900002</td>\n",
       "      <td>83.099998</td>\n",
       "      <td>82.150002</td>\n",
       "      <td>82.699997</td>\n",
       "      <td>4282124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 14:20:00, 2022-04-25 15:00:00)</th>\n",
       "      <td>82.699997</td>\n",
       "      <td>82.849998</td>\n",
       "      <td>81.199997</td>\n",
       "      <td>81.800003</td>\n",
       "      <td>4624993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 15:00:00, 2022-04-25 15:40:00)</th>\n",
       "      <td>81.800003</td>\n",
       "      <td>82.550003</td>\n",
       "      <td>81.650002</td>\n",
       "      <td>81.800003</td>\n",
       "      <td>6633358.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-25 15:40:00, 2022-04-25 16:00:00)</th>\n",
       "      <td>81.800003</td>\n",
       "      <td>82.099998</td>\n",
       "      <td>80.949997</td>\n",
       "      <td>81.599998</td>\n",
       "      <td>5671481.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                        9988.HK                        \\\n",
       "                                                 open       high        low   \n",
       "[2022-04-25 09:30:00, 2022-04-25 10:10:00)  84.800003  84.949997  83.000000   \n",
       "[2022-04-25 10:10:00, 2022-04-25 10:50:00)  83.650002  83.949997  83.099998   \n",
       "[2022-04-25 10:50:00, 2022-04-25 11:30:00)  83.699997  84.949997  83.500000   \n",
       "[2022-04-25 11:30:00, 2022-04-25 12:00:00)  83.550003  83.650002  83.349998   \n",
       "[2022-04-25 13:00:00, 2022-04-25 13:40:00)  83.500000  83.599998  82.800003   \n",
       "[2022-04-25 13:40:00, 2022-04-25 14:20:00)  82.900002  83.099998  82.150002   \n",
       "[2022-04-25 14:20:00, 2022-04-25 15:00:00)  82.699997  82.849998  81.199997   \n",
       "[2022-04-25 15:00:00, 2022-04-25 15:40:00)  81.800003  82.550003  81.650002   \n",
       "[2022-04-25 15:40:00, 2022-04-25 16:00:00)  81.800003  82.099998  80.949997   \n",
       "\n",
       "symbol                                                            \n",
       "                                                close     volume  \n",
       "[2022-04-25 09:30:00, 2022-04-25 10:10:00)  83.650002  3576365.0  \n",
       "[2022-04-25 10:10:00, 2022-04-25 10:50:00)  83.650002  2721809.0  \n",
       "[2022-04-25 10:50:00, 2022-04-25 11:30:00)  83.550003  3234464.0  \n",
       "[2022-04-25 11:30:00, 2022-04-25 12:00:00)  83.550003  1437730.0  \n",
       "[2022-04-25 13:00:00, 2022-04-25 13:40:00)  82.949997  4775242.0  \n",
       "[2022-04-25 13:40:00, 2022-04-25 14:20:00)  82.699997  4282124.0  \n",
       "[2022-04-25 14:20:00, 2022-04-25 15:00:00)  81.800003  4624993.0  \n",
       "[2022-04-25 15:00:00, 2022-04-25 15:40:00)  81.800003  6633358.0  \n",
       "[2022-04-25 15:40:00, 2022-04-25 16:00:00)  81.599998  5671481.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_hk.get(\"40T\", session, session, force=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that above the last indice of the morning subsession now ends at 12.00, the morning session close, and the last indice of the session now ends at 16.00, the session close.\n",
    "\n",
    "See the [anchors](./anchors.ipynb) tutorial for further examples and explanation of options that determine how indices are evaluted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data availability\n",
    "\n",
    "`market_prices` gets price data from a data provider. If the provider makes data available from which a request for prices at a specific interval over a specific period can be fulfilled, then `get` will fulfil it.\n",
    "\n",
    "However, if data is not available to fulfil a request then an error will be raised which advises why the request cannot be fulfilled and, if relevant, offers some options that will return prices given the data that is available.\n",
    "\n",
    "Consider the following request over a period for which data is not available at a sufficiently low interval to evaluate prices at the requested 4 minute interval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start=Timestamp('2022-03-14 00:00:00', freq='C')\n",
      "end=Timestamp('2022-05-11 00:00:00', freq='C')\n"
     ]
    }
   ],
   "source": [
    "start, end = start_T5, end_T1\n",
    "print(f\"{start=}\\n{end=}\")  # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.get(\"4T\", start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "---------------------------------------------------------------------------\n",
    "PricesIntradayUnavailableError            Traceback (most recent call last)\n",
    "<ipython-input-24-c48503aa405d> in <module>\n",
    "----> 1 prices.get(\"4T\", start, end)\n",
    "\n",
    "PricesIntradayUnavailableError: Data is unavailable at a sufficiently low base interval to evaluate prices at interval 0 days 00:04:00 anchored 'Anchor.OPEN'.\n",
    "Base intervals that are a factor of 0 days 00:04:00:\n",
    "\t[<BaseInterval.T1: Timedelta('0 days 00:01:00')>, <BaseInterval.T2: Timedelta('0 days 00:02:00')>].\n",
    "The earliest minute from which data is available at 0 days 00:02:00 is 2022-03-30 13:30:00+00:00, although at this base interval the requested period evaluates to (Timestamp('2022-03-14 13:30:00+0000', tz='UTC'), Timestamp('2022-05-11 20:02:00+0000', tz='UTC')).\n",
    "Period evaluated from parameters: {'minutes': 0, 'hours': 0, 'days': 0, 'weeks': 0, 'months': 0, 'years': 0, 'start': Timestamp('2022-03-14 00:00:00', freq='C'), 'end': Timestamp('2022-05-11 00:00:00', freq='C'), 'add_a_row': False}.\n",
    "Data is available from 2022-03-30 13:30:00+00:00 through to the end of the requested period. Consider passing `strict` as False to return prices for this part of the period.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pass `strict` as False to get prices over the period for which data is available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[2022-03-30 09:30:00, 2022-03-30 09:34:00)</th>\n",
       "      <td>313.760010</td>\n",
       "      <td>314.799988</td>\n",
       "      <td>313.410004</td>\n",
       "      <td>314.529999</td>\n",
       "      <td>953401.0</td>\n",
       "      <td>2857.399902</td>\n",
       "      <td>2862.679932</td>\n",
       "      <td>2854.449951</td>\n",
       "      <td>2855.850098</td>\n",
       "      <td>53371.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-03-30 09:34:00, 2022-03-30 09:38:00)</th>\n",
       "      <td>314.600006</td>\n",
       "      <td>314.959991</td>\n",
       "      <td>313.380005</td>\n",
       "      <td>313.529999</td>\n",
       "      <td>360997.0</td>\n",
       "      <td>2855.610107</td>\n",
       "      <td>2862.479980</td>\n",
       "      <td>2854.870117</td>\n",
       "      <td>2857.938721</td>\n",
       "      <td>17188.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-03-30 09:30:00, 2022-03-30 09:34:00)  313.760010  314.799988   \n",
       "[2022-03-30 09:34:00, 2022-03-30 09:38:00)  314.600006  314.959991   \n",
       "\n",
       "symbol                                                                        \\\n",
       "                                                   low       close    volume   \n",
       "[2022-03-30 09:30:00, 2022-03-30 09:34:00)  313.410004  314.529999  953401.0   \n",
       "[2022-03-30 09:34:00, 2022-03-30 09:38:00)  313.380005  313.529999  360997.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-03-30 09:30:00, 2022-03-30 09:34:00)  2857.399902  2862.679932   \n",
       "[2022-03-30 09:34:00, 2022-03-30 09:38:00)  2855.610107  2862.479980   \n",
       "\n",
       "symbol                                                                         \n",
       "                                                    low        close   volume  \n",
       "[2022-03-30 09:30:00, 2022-03-30 09:34:00)  2854.449951  2855.850098  53371.0  \n",
       "[2022-03-30 09:34:00, 2022-03-30 09:38:00)  2854.870117  2857.938721  17188.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = prices.get(\"4T\", start, end, strict=False)\n",
    "df[:2]  # only showing first two rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An error will also be raised if it's only possible to return prices EITHER over the full requested period OR that express the period end with the greatest possible accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start=Timestamp('2022-03-14 00:00:00', freq='C')\n",
      "end=Timestamp('2022-05-11 19:47:00+0000', tz='UTC')\n"
     ]
    }
   ],
   "source": [
    "end = xnys.session_close(end) - pd.Timedelta(13, \"T\")\n",
    "print(f\"{start=}\\n{end=}\")  # for reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.get(start=start, end=end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "---------------------------------------------------------------------------\n",
    "LastIndiceInaccurateError                 Traceback (most recent call last)\n",
    "<ipython-input-27-c5a0068ab121> in <module>\n",
    "----> 1 prices.get(start=start, end=end)\n",
    "\n",
    "LastIndiceInaccurateError: Full period available at the following intraday base intervals although these do not allow for representing the end indice with the greatest possible accuracy:\n",
    "\t[<BaseInterval.T5: Timedelta('0 days 00:05:00')>, <BaseInterval.H1: Timedelta('0 days 01:00:00')>].\n",
    "The following base intervals could represent the end indice with the greatest possible accuracy although have insufficient data available to cover the full period:\n",
    "\t[<BaseInterval.T1: Timedelta('0 days 00:01:00')>].\n",
    "The earliest minute from which data is available at 0 days 00:01:00 is 2022-04-12 13:30:00+00:00, although at this base interval the requested period evaluates to (Timestamp('2022-03-14 13:30:00+0000', tz='UTC'), Timestamp('2022-05-11 19:47:00+0000', tz='UTC')).\n",
    "Period evaluated from parameters: {'minutes': 0, 'hours': 0, 'days': 0, 'weeks': 0, 'months': 0, 'years': 0, 'start': Timestamp('2022-03-14 00:00:00', freq='C'), 'end': Timestamp('2022-05-11 19:47:00+0000', tz='UTC'), 'add_a_row': False}.\n",
    "Data that can express the period end with the greatest possible accuracy is available from 2022-04-12 13:30:00+00:00. Pass `strict` as False to return prices for this part of the period.\n",
    "Alternatively, consider creating a composite table (pass `composite` as True) or passing `priority` as 'period'.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One option is to create a composite table comprised of two different intervals. The earlier part of the table has a higher interval at which data is available over the full period whilst the later part has a lower interval able to express the period end with the greatest possible accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<p>3279 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-03-14 09:30:00, 2022-03-14 09:35:00)  280.250000  282.359985   \n",
       "[2022-03-14 09:35:00, 2022-03-14 09:40:00)  281.890015  282.850006   \n",
       "[2022-03-14 09:40:00, 2022-03-14 09:45:00)  281.829987  284.769989   \n",
       "[2022-03-14 09:45:00, 2022-03-14 09:50:00)  284.269989  284.329987   \n",
       "[2022-03-14 09:50:00, 2022-03-14 09:55:00)  283.019989  283.699005   \n",
       "...                                                ...         ...   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:43:00)  260.309998  260.575012   \n",
       "[2022-05-11 15:43:00, 2022-05-11 15:44:00)  260.190002  260.309998   \n",
       "[2022-05-11 15:44:00, 2022-05-11 15:45:00)  260.190002  260.269989   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:46:00)  260.144989  260.190002   \n",
       "[2022-05-11 15:46:00, 2022-05-11 15:47:00)  259.774994  260.019989   \n",
       "\n",
       "symbol                                                                         \\\n",
       "                                                   low       close     volume   \n",
       "[2022-03-14 09:30:00, 2022-03-14 09:35:00)  280.010010  281.869995  1721154.0   \n",
       "[2022-03-14 09:35:00, 2022-03-14 09:40:00)  281.040009  281.880005   720010.0   \n",
       "[2022-03-14 09:40:00, 2022-03-14 09:45:00)  281.369995  284.290009   808963.0   \n",
       "[2022-03-14 09:45:00, 2022-03-14 09:50:00)  282.480011  283.109985   593177.0   \n",
       "[2022-03-14 09:50:00, 2022-03-14 09:55:00)  282.470001  283.109985   367264.0   \n",
       "...                                                ...         ...        ...   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:43:00)  260.140015  260.184998   124295.0   \n",
       "[2022-05-11 15:43:00, 2022-05-11 15:44:00)  260.089996  260.212097   143383.0   \n",
       "[2022-05-11 15:44:00, 2022-05-11 15:45:00)  260.019989  260.154999   195488.0   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:46:00)  259.739990  259.789886   471234.0   \n",
       "[2022-05-11 15:46:00, 2022-05-11 15:47:00)  259.570007  259.790009   211710.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-03-14 09:30:00, 2022-03-14 09:35:00)  2611.459961  2620.520020   \n",
       "[2022-03-14 09:35:00, 2022-03-14 09:40:00)  2612.254883  2612.530029   \n",
       "[2022-03-14 09:40:00, 2022-03-14 09:45:00)  2596.054932  2610.689941   \n",
       "[2022-03-14 09:45:00, 2022-03-14 09:50:00)  2606.000000  2611.300049   \n",
       "[2022-03-14 09:50:00, 2022-03-14 09:55:00)  2606.540039  2609.879883   \n",
       "...                                                 ...          ...   \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:43:00)  2290.199951  2290.199951   \n",
       "[2022-05-11 15:43:00, 2022-05-11 15:44:00)  2288.985107  2291.419922   \n",
       "[2022-05-11 15:44:00, 2022-05-11 15:45:00)  2290.639893  2290.729980   \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:46:00)  2286.239990  2287.330078   \n",
       "[2022-05-11 15:46:00, 2022-05-11 15:47:00)  2286.090088  2288.620117   \n",
       "\n",
       "symbol                                                                         \n",
       "                                                    low        close   volume  \n",
       "[2022-03-14 09:30:00, 2022-03-14 09:35:00)  2610.110107  2611.310059  51968.0  \n",
       "[2022-03-14 09:35:00, 2022-03-14 09:40:00)  2592.870117  2598.800049  37872.0  \n",
       "[2022-03-14 09:40:00, 2022-03-14 09:45:00)  2593.939941  2608.560059  20875.0  \n",
       "[2022-03-14 09:45:00, 2022-03-14 09:50:00)  2603.959961  2603.959961  18132.0  \n",
       "[2022-03-14 09:50:00, 2022-03-14 09:55:00)  2600.342041  2601.419922  22693.0  \n",
       "...                                                 ...          ...      ...  \n",
       "[2022-05-11 15:42:00, 2022-05-11 15:43:00)  2289.929932  2290.080078   2213.0  \n",
       "[2022-05-11 15:43:00, 2022-05-11 15:44:00)  2288.985107  2290.520020   6573.0  \n",
       "[2022-05-11 15:44:00, 2022-05-11 15:45:00)  2287.149902  2289.229980   4568.0  \n",
       "[2022-05-11 15:45:00, 2022-05-11 15:46:00)  2283.709961  2284.699951   3533.0  \n",
       "[2022-05-11 15:46:00, 2022-05-11 15:47:00)  2284.929932  2288.620117   6985.0  \n",
       "\n",
       "[3279 rows x 10 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp = prices.get(start=start, end=end, composite=True)\n",
    "df_comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [data availability](./data_availability.ipynb) tutorial covers other options, further examples and further explanation including how `market_prices` defines base intervals from which all other price data is evaluted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple exchanges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`market_prices` really comes into its own when combining symbols that trade on different exchanges with different opening hours over different timezones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: this cell might take a little while to execute given the number of\n",
    "# different trading calendars that will be created.\n",
    "symbols = [\n",
    "    \"MSFT\",  # us stock\n",
    "    \"AZN.L\",  # uk stock\n",
    "    \"9988.HK\",  # hong kong stock\n",
    "    \"PETR3.SA\",  # brazilan stock\n",
    "    \"^FTSE\",  # equity index\n",
    "    \"ES=F\",  # futures\n",
    "    \"CL=F\",  # oil\n",
    "    \"GC=F\",  # gold\n",
    "    \"GBPEUR=X\",  # currency pair\n",
    "    \"BTC-USD\",  # crypto\n",
    "]\n",
    "prices_mult = PricesYahoo(symbols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `lead_symbol` determines the exchange against which the period should be evaluted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <td>7220.169922</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                        9988.HK                        \\\n",
       "                                                close       high        low   \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  80.000000  80.300003  79.900002   \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  80.300003  80.349998  79.949997   \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  79.949997  80.449997  79.800003   \n",
       "\n",
       "symbol                                                             AZN.L  \\\n",
       "                                                 open     volume   close   \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  80.199997  2499259.0  9801.0   \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  80.000000  2462802.0  9862.0   \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  80.349998  2616271.0  9824.0   \n",
       "\n",
       "symbol                                                                       \\\n",
       "                                              high     low    open   volume   \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  9821.0  9785.0  9799.0  55810.0   \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  9862.0  9794.0  9799.0  39472.0   \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  9866.0  9822.0  9863.0  39555.0   \n",
       "\n",
       "symbol                                      ... PETR3.SA                       \\\n",
       "                                            ...    close high low open volume   \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  ...      NaN  NaN NaN  NaN    NaN   \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  ...      NaN  NaN NaN  NaN    NaN   \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  ...      NaN  NaN NaN  NaN    NaN   \n",
       "\n",
       "symbol                                            ^FTSE               \\\n",
       "                                                  close         high   \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  7181.529785  7194.009766   \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  7220.209961  7220.770020   \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  7208.950195  7221.839844   \n",
       "\n",
       "symbol                                                                       \n",
       "                                                    low         open volume  \n",
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)  7171.970215  7191.529785    0.0  \n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)  7176.339844  7181.540039    0.0  \n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)  7204.810059  7220.169922    0.0  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 30 mins of data at 10min intervals ending on the most recent\n",
    "# minute that the Hong Kong equity market was open\n",
    "df_mult = prices_mult.get(\n",
    "    \"10min\", minutes=30, lead_symbol=\"9988.HK\", anchor=\"workback\"\n",
    ")\n",
    "df_mult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:\n",
    "* prices will show as missing for any instruments not trading over the evaluated period.\n",
    "* the default timezone for the index will be the timezone associated with `lead_symbol`, i.e. Hong Kong in the above example (pass `tzout` to change this).\n",
    "\n",
    "The [periods](./periods.ipynb) and [anchor](./anchor.ipynb) tutorials offer further examples and explanation of getting prices for symbols trading on multiple exchanges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## .pt accessor\n",
    "\n",
    "The .pt accessor provides access to a wealth of funcationality to interrogate and operate on the DataFrame returned by `get`.\n",
    "\n",
    "This section offers examples of only a few of the properties and methods available. See the [.pt accessor](./pt_accessor.ipynb) tutorial for a comprehensive overview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['9988.HK',\n",
       " 'AZN.L',\n",
       " 'BTC-USD',\n",
       " 'CL=F',\n",
       " 'ES=F',\n",
       " 'GBPEUR=X',\n",
       " 'GC=F',\n",
       " 'MSFT',\n",
       " 'PETR3.SA',\n",
       " '^FTSE']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-05-12 15:30:00+0800', tz='Asia/Hong_Kong')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.first_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.is_daily, df_mult.pt.is_intraday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TDInterval.T10: Timedelta('0 days 00:10:00')>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query how many trading minutes are in each indice, with trading minutes evaluated against a specfic calendar..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)    0\n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)    0\n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)    0\n",
       "Name: trading_mins, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.indices_trading_minutes(xnys)\n",
    "# New York is closed over this period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2022-05-12 15:30:00, 2022-05-12 15:40:00)    10\n",
       "[2022-05-12 15:40:00, 2022-05-12 15:50:00)    10\n",
       "[2022-05-12 15:50:00, 2022-05-12 16:00:00)    10\n",
       "Name: trading_mins, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mult.pt.indices_trading_minutes(xhkg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going back to the initial intraday dataframe..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TDInterval.T5: Timedelta('0 days 00:05:00')>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_intraday.pt.interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 15:15:00, 2022-05-11 15:30:00)</th>\n",
       "      <td>261.445007</td>\n",
       "      <td>261.950012</td>\n",
       "      <td>260.750000</td>\n",
       "      <td>261.239990</td>\n",
       "      <td>1411931.0</td>\n",
       "      <td>2295.770020</td>\n",
       "      <td>2300.604980</td>\n",
       "      <td>2291.817139</td>\n",
       "      <td>2295.351074</td>\n",
       "      <td>46645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 15:30:00, 2022-05-11 15:45:00)</th>\n",
       "      <td>261.209991</td>\n",
       "      <td>261.670013</td>\n",
       "      <td>260.019989</td>\n",
       "      <td>260.154999</td>\n",
       "      <td>2393856.0</td>\n",
       "      <td>2295.419922</td>\n",
       "      <td>2300.199951</td>\n",
       "      <td>2286.000000</td>\n",
       "      <td>2289.229980</td>\n",
       "      <td>69112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-11 15:45:00, 2022-05-11 16:00:00)</th>\n",
       "      <td>260.144989</td>\n",
       "      <td>260.790009</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.660004</td>\n",
       "      <td>4110274.0</td>\n",
       "      <td>2286.239990</td>\n",
       "      <td>2290.520020</td>\n",
       "      <td>2274.189941</td>\n",
       "      <td>2279.219971</td>\n",
       "      <td>198638.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                                            MSFT              \\\n",
       "                                                  open        high   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:30:00)  261.445007  261.950012   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:45:00)  261.209991  261.670013   \n",
       "[2022-05-11 15:45:00, 2022-05-11 16:00:00)  260.144989  260.790009   \n",
       "\n",
       "symbol                                                                         \\\n",
       "                                                   low       close     volume   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:30:00)  260.750000  261.239990  1411931.0   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:45:00)  260.019989  260.154999  2393856.0   \n",
       "[2022-05-11 15:45:00, 2022-05-11 16:00:00)  259.299988  260.660004  4110274.0   \n",
       "\n",
       "symbol                                             GOOG               \\\n",
       "                                                   open         high   \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:30:00)  2295.770020  2300.604980   \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:45:00)  2295.419922  2300.199951   \n",
       "[2022-05-11 15:45:00, 2022-05-11 16:00:00)  2286.239990  2290.520020   \n",
       "\n",
       "symbol                                                                          \n",
       "                                                    low        close    volume  \n",
       "[2022-05-11 15:15:00, 2022-05-11 15:30:00)  2291.817139  2295.351074   46645.0  \n",
       "[2022-05-11 15:30:00, 2022-05-11 15:45:00)  2286.000000  2289.229980   69112.0  \n",
       "[2022-05-11 15:45:00, 2022-05-11 16:00:00)  2274.189941  2279.219971  198638.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# downsample the 5 minute data to 15 minute intervals\n",
    "df_intraday.pt.downsample(\"15T\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[2022-04-13, 2022-04-20)</th>\n",
       "      <td>282.730011</td>\n",
       "      <td>288.579987</td>\n",
       "      <td>278.339996</td>\n",
       "      <td>285.299988</td>\n",
       "      <td>93204500.0</td>\n",
       "      <td>2572.530029</td>\n",
       "      <td>2618.074951</td>\n",
       "      <td>2531.569092</td>\n",
       "      <td>2610.620117</td>\n",
       "      <td>4033200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-20, 2022-04-26)</th>\n",
       "      <td>289.399994</td>\n",
       "      <td>293.299988</td>\n",
       "      <td>270.769989</td>\n",
       "      <td>280.720001</td>\n",
       "      <td>117446000.0</td>\n",
       "      <td>2625.679932</td>\n",
       "      <td>2638.469971</td>\n",
       "      <td>2375.385010</td>\n",
       "      <td>2465.000000</td>\n",
       "      <td>6685000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-04-26, 2022-05-02)</th>\n",
       "      <td>277.500000</td>\n",
       "      <td>290.980011</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>277.519989</td>\n",
       "      <td>180667700.0</td>\n",
       "      <td>2455.000000</td>\n",
       "      <td>2455.000000</td>\n",
       "      <td>2262.485107</td>\n",
       "      <td>2299.330078</td>\n",
       "      <td>9104600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-02, 2022-05-06)</th>\n",
       "      <td>277.709991</td>\n",
       "      <td>290.880005</td>\n",
       "      <td>274.339996</td>\n",
       "      <td>277.350006</td>\n",
       "      <td>137989400.0</td>\n",
       "      <td>2278.129883</td>\n",
       "      <td>2462.860107</td>\n",
       "      <td>2267.989990</td>\n",
       "      <td>2334.929932</td>\n",
       "      <td>6390900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[2022-05-06, 2022-05-12)</th>\n",
       "      <td>274.809998</td>\n",
       "      <td>279.250000</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.549988</td>\n",
       "      <td>173705200.0</td>\n",
       "      <td>2310.379883</td>\n",
       "      <td>2349.969971</td>\n",
       "      <td>2251.030029</td>\n",
       "      <td>2279.219971</td>\n",
       "      <td>6870400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                          MSFT                                      \\\n",
       "                                open        high         low       close   \n",
       "[2022-04-13, 2022-04-20)  282.730011  288.579987  278.339996  285.299988   \n",
       "[2022-04-20, 2022-04-26)  289.399994  293.299988  270.769989  280.720001   \n",
       "[2022-04-26, 2022-05-02)  277.500000  290.980011  270.000000  277.519989   \n",
       "[2022-05-02, 2022-05-06)  277.709991  290.880005  274.339996  277.350006   \n",
       "[2022-05-06, 2022-05-12)  274.809998  279.250000  259.299988  260.549988   \n",
       "\n",
       "symbol                                        GOOG                            \\\n",
       "                               volume         open         high          low   \n",
       "[2022-04-13, 2022-04-20)   93204500.0  2572.530029  2618.074951  2531.569092   \n",
       "[2022-04-20, 2022-04-26)  117446000.0  2625.679932  2638.469971  2375.385010   \n",
       "[2022-04-26, 2022-05-02)  180667700.0  2455.000000  2455.000000  2262.485107   \n",
       "[2022-05-02, 2022-05-06)  137989400.0  2278.129883  2462.860107  2267.989990   \n",
       "[2022-05-06, 2022-05-12)  173705200.0  2310.379883  2349.969971  2251.030029   \n",
       "\n",
       "symbol                                            \n",
       "                                close     volume  \n",
       "[2022-04-13, 2022-04-20)  2610.620117  4033200.0  \n",
       "[2022-04-20, 2022-04-26)  2465.000000  6685000.0  \n",
       "[2022-04-26, 2022-05-02)  2299.330078  9104600.0  \n",
       "[2022-05-02, 2022-05-06)  2334.929932  6390900.0  \n",
       "[2022-05-06, 2022-05-12)  2279.219971  6870400.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Downsample the daily table so that each indice contains 4 sessions.\n",
    "df_daily.pt.downsample(\"4D\", xnys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2022-05-11 15:15:00, 2022-05-11 15:20:00)   2022-05-11\n",
       "[2022-05-11 15:20:00, 2022-05-11 15:25:00)   2022-05-11\n",
       "[2022-05-11 15:25:00, 2022-05-11 15:30:00)   2022-05-11\n",
       "[2022-05-11 15:30:00, 2022-05-11 15:35:00)   2022-05-11\n",
       "[2022-05-11 15:35:00, 2022-05-11 15:40:00)   2022-05-11\n",
       "[2022-05-11 15:40:00, 2022-05-11 15:45:00)   2022-05-11\n",
       "[2022-05-11 15:45:00, 2022-05-11 15:50:00)   2022-05-11\n",
       "[2022-05-11 15:50:00, 2022-05-11 15:55:00)   2022-05-11\n",
       "[2022-05-11 15:55:00, 2022-05-11 16:00:00)   2022-05-11\n",
       "Name: session, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# session mapping\n",
    "df_intraday.pt.sessions(xnys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specific query methods\n",
    "\n",
    "Aside from `get`, the `Prices` class has a few other methods that return more specific price data.\n",
    "\n",
    "This section offers a quick example of each. Check out the [Specific query methods](./specific_query_methods.ipynb) tutorial for a comprehensive treatment.\n",
    "\n",
    "`session_prices` will return prices for a specific session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>symbol</th>\n",
       "      <th colspan=\"5\" halign=\"left\">MSFT</th>\n",
       "      <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-04-26</th>\n",
       "      <td>277.5</td>\n",
       "      <td>278.359985</td>\n",
       "      <td>270.0</td>\n",
       "      <td>270.220001</td>\n",
       "      <td>46518400</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>2455.0</td>\n",
       "      <td>2383.237061</td>\n",
       "      <td>2390.120117</td>\n",
       "      <td>2469700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol       MSFT                                             GOOG          \\\n",
       "             open        high    low       close    volume    open    high   \n",
       "2022-04-26  277.5  278.359985  270.0  270.220001  46518400  2455.0  2455.0   \n",
       "\n",
       "symbol                                         \n",
       "                    low        close   volume  \n",
       "2022-04-26  2383.237061  2390.120117  2469700  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.session_prices(\"2022-04-26\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`close_at` returns the most recent close prices as at a specific date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>symbol</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AZN.L</th>\n",
       "      <th>9988.HK</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>^FTSE</th>\n",
       "      <th>ES=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>GC=F</th>\n",
       "      <th>GBPEUR=X</th>\n",
       "      <th>BTC-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-25</th>\n",
       "      <td>334.690002</td>\n",
       "      <td>8611.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>30.440001</td>\n",
       "      <td>7372.100098</td>\n",
       "      <td>4715.75</td>\n",
       "      <td>73.790001</td>\n",
       "      <td>1811.199951</td>\n",
       "      <td>1.1834</td>\n",
       "      <td>50429.859375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol            MSFT   AZN.L  9988.HK   PETR3.SA        ^FTSE     ES=F  \\\n",
       "2021-12-25  334.690002  8611.0    113.0  30.440001  7372.100098  4715.75   \n",
       "\n",
       "symbol           CL=F         GC=F  GBPEUR=X       BTC-USD  \n",
       "2021-12-25  73.790001  1811.199951    1.1834  50429.859375  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_mult.close_at(\"2021-12-25\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`price_at` returns the most recent prices as at a specific minute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minute=Timestamp('2022-04-25 19:13:00+0000', tz='UTC')\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>symbol</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AZN.L</th>\n",
       "      <th>9988.HK</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>^FTSE</th>\n",
       "      <th>ES=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>GC=F</th>\n",
       "      <th>GBPEUR=X</th>\n",
       "      <th>BTC-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-04-25 19:13:00+00:00</th>\n",
       "      <td>276.769989</td>\n",
       "      <td>10244.0</td>\n",
       "      <td>81.599998</td>\n",
       "      <td>33.240002</td>\n",
       "      <td>7392.310059</td>\n",
       "      <td>4251.5</td>\n",
       "      <td>98.989998</td>\n",
       "      <td>1901.099976</td>\n",
       "      <td>1.18908</td>\n",
       "      <td>39835.207031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                           MSFT    AZN.L    9988.HK   PETR3.SA  \\\n",
       "2022-04-25 19:13:00+00:00  276.769989  10244.0  81.599998  33.240002   \n",
       "\n",
       "symbol                           ^FTSE    ES=F       CL=F         GC=F  \\\n",
       "2022-04-25 19:13:00+00:00  7392.310059  4251.5  98.989998  1901.099976   \n",
       "\n",
       "symbol                     GBPEUR=X       BTC-USD  \n",
       "2022-04-25 19:13:00+00:00   1.18908  39835.207031  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minute = xnys.session_close(session) - pd.Timedelta(47, \"T\")\n",
    "print(f\"{minute=}\\n\")  # for reference\n",
    "\n",
    "prices_mult.price_at(minute, tz=ZoneInfo(\"UTC\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or the most recent prices available as at 'now'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>symbol</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>AZN.L</th>\n",
       "      <th>9988.HK</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>^FTSE</th>\n",
       "      <th>ES=F</th>\n",
       "      <th>CL=F</th>\n",
       "      <th>GC=F</th>\n",
       "      <th>GBPEUR=X</th>\n",
       "      <th>BTC-USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-12 09:12:00-04:00</th>\n",
       "      <td>260.549988</td>\n",
       "      <td>9826.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>36.439999</td>\n",
       "      <td>7200.560059</td>\n",
       "      <td>3898.25</td>\n",
       "      <td>104.480003</td>\n",
       "      <td>1843.199951</td>\n",
       "      <td>1.17387</td>\n",
       "      <td>28412.521484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "symbol                           MSFT   AZN.L  9988.HK   PETR3.SA  \\\n",
       "2022-05-12 09:12:00-04:00  260.549988  9826.0     80.0  36.439999   \n",
       "\n",
       "symbol                           ^FTSE     ES=F        CL=F         GC=F  \\\n",
       "2022-05-12 09:12:00-04:00  7200.560059  3898.25  104.480003  1843.199951   \n",
       "\n",
       "symbol                     GBPEUR=X       BTC-USD  \n",
       "2022-05-12 09:12:00-04:00   1.17387  28412.521484  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_mult.price_at(tz=\"MSFT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`price_range` returns OHLCV data for a period evalauted from period parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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></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></th>\n",
       "      <th>symbol</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 rowspan=\"10\" valign=\"top\">(2022-05-05 14:00:00, 2022-05-12 14:00:00]</th>\n",
       "      <th>9988.HK</th>\n",
       "      <td>92.050003</td>\n",
       "      <td>92.849998</td>\n",
       "      <td>79.800003</td>\n",
       "      <td>79.949997</td>\n",
       "      <td>1.984346e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZN.L</th>\n",
       "      <td>10612.000000</td>\n",
       "      <td>10616.000000</td>\n",
       "      <td>9764.000000</td>\n",
       "      <td>9828.000000</td>\n",
       "      <td>7.804363e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC-USD</th>\n",
       "      <td>39519.300781</td>\n",
       "      <td>39519.667969</td>\n",
       "      <td>26350.490234</td>\n",
       "      <td>28411.187500</td>\n",
       "      <td>1.874105e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL=F</th>\n",
       "      <td>109.300003</td>\n",
       "      <td>111.370003</td>\n",
       "      <td>98.199997</td>\n",
       "      <td>104.059998</td>\n",
       "      <td>1.613489e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES=F</th>\n",
       "      <td>4264.000000</td>\n",
       "      <td>4264.500000</td>\n",
       "      <td>3884.750000</td>\n",
       "      <td>3899.500000</td>\n",
       "      <td>1.111346e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBPEUR=X</th>\n",
       "      <td>1.173830</td>\n",
       "      <td>1.176120</td>\n",
       "      <td>1.160000</td>\n",
       "      <td>1.173850</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GC=F</th>\n",
       "      <td>1907.599976</td>\n",
       "      <td>1909.199951</td>\n",
       "      <td>1830.599976</td>\n",
       "      <td>1844.599976</td>\n",
       "      <td>1.025028e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>285.540009</td>\n",
       "      <td>286.350006</td>\n",
       "      <td>259.299988</td>\n",
       "      <td>260.660004</td>\n",
       "      <td>1.789915e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PETR3.SA</th>\n",
       "      <td>34.500000</td>\n",
       "      <td>36.860001</td>\n",
       "      <td>33.349998</td>\n",
       "      <td>36.439999</td>\n",
       "      <td>9.984380e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>^FTSE</th>\n",
       "      <td>7603.399902</td>\n",
       "      <td>7603.799805</td>\n",
       "      <td>7158.529785</td>\n",
       "      <td>7196.100098</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                             open  \\\n",
       "                                           symbol                   \n",
       "(2022-05-05 14:00:00, 2022-05-12 14:00:00] 9988.HK      92.050003   \n",
       "                                           AZN.L     10612.000000   \n",
       "                                           BTC-USD   39519.300781   \n",
       "                                           CL=F        109.300003   \n",
       "                                           ES=F       4264.000000   \n",
       "                                           GBPEUR=X      1.173830   \n",
       "                                           GC=F       1907.599976   \n",
       "                                           MSFT        285.540009   \n",
       "                                           PETR3.SA     34.500000   \n",
       "                                           ^FTSE      7603.399902   \n",
       "\n",
       "                                                             high  \\\n",
       "                                           symbol                   \n",
       "(2022-05-05 14:00:00, 2022-05-12 14:00:00] 9988.HK      92.849998   \n",
       "                                           AZN.L     10616.000000   \n",
       "                                           BTC-USD   39519.667969   \n",
       "                                           CL=F        111.370003   \n",
       "                                           ES=F       4264.500000   \n",
       "                                           GBPEUR=X      1.176120   \n",
       "                                           GC=F       1909.199951   \n",
       "                                           MSFT        286.350006   \n",
       "                                           PETR3.SA     36.860001   \n",
       "                                           ^FTSE      7603.799805   \n",
       "\n",
       "                                                              low  \\\n",
       "                                           symbol                   \n",
       "(2022-05-05 14:00:00, 2022-05-12 14:00:00] 9988.HK      79.800003   \n",
       "                                           AZN.L      9764.000000   \n",
       "                                           BTC-USD   26350.490234   \n",
       "                                           CL=F         98.199997   \n",
       "                                           ES=F       3884.750000   \n",
       "                                           GBPEUR=X      1.160000   \n",
       "                                           GC=F       1830.599976   \n",
       "                                           MSFT        259.299988   \n",
       "                                           PETR3.SA     33.349998   \n",
       "                                           ^FTSE      7158.529785   \n",
       "\n",
       "                                                            close  \\\n",
       "                                           symbol                   \n",
       "(2022-05-05 14:00:00, 2022-05-12 14:00:00] 9988.HK      79.949997   \n",
       "                                           AZN.L      9828.000000   \n",
       "                                           BTC-USD   28411.187500   \n",
       "                                           CL=F        104.059998   \n",
       "                                           ES=F       3899.500000   \n",
       "                                           GBPEUR=X      1.173850   \n",
       "                                           GC=F       1844.599976   \n",
       "                                           MSFT        260.660004   \n",
       "                                           PETR3.SA     36.439999   \n",
       "                                           ^FTSE      7196.100098   \n",
       "\n",
       "                                                           volume  \n",
       "                                           symbol                  \n",
       "(2022-05-05 14:00:00, 2022-05-12 14:00:00] 9988.HK   1.984346e+08  \n",
       "                                           AZN.L     7.804363e+06  \n",
       "                                           BTC-USD   1.874105e+11  \n",
       "                                           CL=F      1.613489e+06  \n",
       "                                           ES=F      1.111346e+07  \n",
       "                                           GBPEUR=X  0.000000e+00  \n",
       "                                           GC=F      1.025028e+06  \n",
       "                                           MSFT      1.789915e+08  \n",
       "                                           PETR3.SA  9.984380e+07  \n",
       "                                           ^FTSE     0.000000e+00  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_mult.price_range(days=5, stack=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further documentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All documentation can be found [here](../tutorials_docs.md), including a wealth of tutorials that collectively cover all aspects of `get` and the specific query methods.\n",
    "\n",
    "Also, you should find each method's own documentation pretty comprehensive..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# or prices.get?\n",
    "help(prices.get)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "market_prices_ve_39",
   "language": "python",
   "name": "market_prices_ve_39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
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