{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a33fb14",
   "metadata": {},
   "source": [
    "# Time-Based Joins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eaac7ad-b507-48bf-aff2-7ff5d812e147",
   "metadata": {},
   "source": [
    "Time series analysis often involves looking up information from other time series for relevant time ranges. OneTick has built-in functions for this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f00e839-f381-49b2-9249-c1f36b36c12c",
   "metadata": {},
   "source": [
    "# `join_by_time`: Enhancing a time series with information from other time series at the time of each tick"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810b313d-8033-46ee-aac3-15c61faf6635",
   "metadata": {},
   "source": [
    "Below we'll enhance the trades with the prevailing quote (i.e., best bid and ask) at the time of each trade. We'll first look just at trades, then just at quotes, and finally we'll join the two."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bed10d4-dcd8-414d-b618-d600807f39af",
   "metadata": {},
   "source": [
    "Let's examine the trades first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7872865c-768c-4a7d-b30e-6887d0e7b1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Time</th>\n",
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      "text/plain": [
       "                              Time     PRICE  SIZE\n",
       "0    2023-03-29 10:00:00.005852928  399.8500   100\n",
       "1    2023-03-29 10:00:00.005852928  399.8500   100\n",
       "2    2023-03-29 10:00:00.005856000  399.8500   100\n",
       "3    2023-03-29 10:00:00.006580736  399.8500     1\n",
       "4    2023-03-29 10:00:00.012014592  399.8500     1\n",
       "...                            ...       ...   ...\n",
       "2136 2023-03-29 10:00:59.285156096  399.8800   100\n",
       "2137 2023-03-29 10:00:59.687216896  399.8900     1\n",
       "2138 2023-03-29 10:00:59.911493888  399.8899   100\n",
       "2139 2023-03-29 10:00:59.974551040  399.8701   100\n",
       "2140 2023-03-29 10:00:59.998262528  399.8900     1\n",
       "\n",
       "[2141 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import onetick.py as otp\n",
    "otp.config['tz'] = 'EST5EDT'\n",
    "from datetime import datetime\n",
    "otp.config['default_start_time'] = datetime(2003, 12, 1, 0, 0, 0)\n",
    "otp.config['default_end_time'] = datetime(2003, 12, 4, 0, 0, 0)\n",
    "otp.config['default_db'] = 'DEMO_L1'\n",
    "otp.config['default_symbol'] = 'AAPL'\n",
    "\n",
    "trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')\n",
    "trd = trd[['PRICE','SIZE']]\n",
    "otp.run(trd, start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd6139cf-af7b-4bc9-bcfe-08ef9271edd0",
   "metadata": {},
   "source": [
    "Now let's take a look at the quotes (or rather the 'national best bid/offer')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce9c9f4e-419b-4ebb-aba7-a40f8639ac77",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                               Time  BID_PRICE  ASK_PRICE\n",
       "0     2023-03-29 10:00:00.005847040     399.85     399.86\n",
       "1     2023-03-29 10:00:00.005907200     399.85     399.86\n",
       "2     2023-03-29 10:00:00.005957376     399.85     399.86\n",
       "3     2023-03-29 10:00:00.005992448     399.85     399.86\n",
       "4     2023-03-29 10:00:00.006007296     399.85     399.86\n",
       "...                             ...        ...        ...\n",
       "26409 2023-03-29 10:00:59.998479872     399.88     399.89\n",
       "26410 2023-03-29 10:00:59.998615296     399.88     399.89\n",
       "26411 2023-03-29 10:00:59.998635264     399.88     399.89\n",
       "26412 2023-03-29 10:00:59.999427072     399.88     399.89\n",
       "26413 2023-03-29 10:00:59.999838208     399.88     399.89\n",
       "\n",
       "[26414 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qte = otp.DataSource('TAQ_NBBO', tick_type='NBBO')\n",
    "qte = qte[['BID_PRICE','ASK_PRICE']]\n",
    "otp.run(qte, start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb18486d-ccb2-4add-b235-6803077465a0",
   "metadata": {},
   "source": [
    "We \"enhance\" the trades with the information from the quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5ca34e70-019c-45e0-923a-61765ed71e7a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2137</th>\n",
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       "    <tr>\n",
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       "      <td>2023-03-29 10:00:59.998262528</td>\n",
       "      <td>399.8900</td>\n",
       "      <td>1</td>\n",
       "      <td>399.88</td>\n",
       "      <td>399.89</td>\n",
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       "<p>2141 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                              Time     PRICE  SIZE  BID_PRICE  ASK_PRICE\n",
       "0    2023-03-29 10:00:00.005852928  399.8500   100     399.85     399.86\n",
       "1    2023-03-29 10:00:00.005852928  399.8500   100     399.85     399.86\n",
       "2    2023-03-29 10:00:00.005856000  399.8500   100     399.85     399.86\n",
       "3    2023-03-29 10:00:00.006580736  399.8500     1     399.84     399.85\n",
       "4    2023-03-29 10:00:00.012014592  399.8500     1     399.85     399.87\n",
       "...                            ...       ...   ...        ...        ...\n",
       "2136 2023-03-29 10:00:59.285156096  399.8800   100     399.88     399.89\n",
       "2137 2023-03-29 10:00:59.687216896  399.8900     1     399.87     399.89\n",
       "2138 2023-03-29 10:00:59.911493888  399.8899   100     399.87     399.89\n",
       "2139 2023-03-29 10:00:59.974551040  399.8701   100     399.87     399.89\n",
       "2140 2023-03-29 10:00:59.998262528  399.8900     1     399.88     399.89\n",
       "\n",
       "[2141 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enh_trd = otp.join_by_time([trd, qte])\n",
    "otp.run(enh_trd,  start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6478876f-e63d-4d04-81e5-5c314b9eaf07",
   "metadata": {},
   "source": [
    "In other words, each trade is joined with the quote that was active at the time the trade took place. We can examine the quote time to make sure it's before the trade time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3fbcedd7-9dba-4fa5-8dd8-f8f01c0226c0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>100</td>\n",
       "      <td>399.85</td>\n",
       "      <td>399.86</td>\n",
       "      <td>2023-03-29 10:00:00.005847040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-03-29 10:00:00.006580736</td>\n",
       "      <td>399.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>399.84</td>\n",
       "      <td>399.85</td>\n",
       "      <td>2023-03-29 10:00:00.006548736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-29 10:00:00.012014592</td>\n",
       "      <td>399.8500</td>\n",
       "      <td>1</td>\n",
       "      <td>399.85</td>\n",
       "      <td>399.87</td>\n",
       "      <td>2023-03-29 10:00:00.011275008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2136</th>\n",
       "      <td>2023-03-29 10:00:59.285156096</td>\n",
       "      <td>399.8800</td>\n",
       "      <td>100</td>\n",
       "      <td>399.88</td>\n",
       "      <td>399.89</td>\n",
       "      <td>2023-03-29 10:00:59.285148672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2137</th>\n",
       "      <td>2023-03-29 10:00:59.687216896</td>\n",
       "      <td>399.8900</td>\n",
       "      <td>1</td>\n",
       "      <td>399.87</td>\n",
       "      <td>399.89</td>\n",
       "      <td>2023-03-29 10:00:59.686418176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2138</th>\n",
       "      <td>2023-03-29 10:00:59.911493888</td>\n",
       "      <td>399.8899</td>\n",
       "      <td>100</td>\n",
       "      <td>399.87</td>\n",
       "      <td>399.89</td>\n",
       "      <td>2023-03-29 10:00:59.879587072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>2023-03-29 10:00:59.974551040</td>\n",
       "      <td>399.8701</td>\n",
       "      <td>100</td>\n",
       "      <td>399.87</td>\n",
       "      <td>399.89</td>\n",
       "      <td>2023-03-29 10:00:59.933540352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2140</th>\n",
       "      <td>2023-03-29 10:00:59.998262528</td>\n",
       "      <td>399.8900</td>\n",
       "      <td>1</td>\n",
       "      <td>399.88</td>\n",
       "      <td>399.89</td>\n",
       "      <td>2023-03-29 10:00:59.998239232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2141 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Time     PRICE  SIZE  BID_PRICE  ASK_PRICE                    quote_time\n",
       "0    2023-03-29 10:00:00.005852928  399.8500   100     399.85     399.86 2023-03-29 10:00:00.005847040\n",
       "1    2023-03-29 10:00:00.005852928  399.8500   100     399.85     399.86 2023-03-29 10:00:00.005847040\n",
       "2    2023-03-29 10:00:00.005856000  399.8500   100     399.85     399.86 2023-03-29 10:00:00.005847040\n",
       "3    2023-03-29 10:00:00.006580736  399.8500     1     399.84     399.85 2023-03-29 10:00:00.006548736\n",
       "4    2023-03-29 10:00:00.012014592  399.8500     1     399.85     399.87 2023-03-29 10:00:00.011275008\n",
       "...                            ...       ...   ...        ...        ...                           ...\n",
       "2136 2023-03-29 10:00:59.285156096  399.8800   100     399.88     399.89 2023-03-29 10:00:59.285148672\n",
       "2137 2023-03-29 10:00:59.687216896  399.8900     1     399.87     399.89 2023-03-29 10:00:59.686418176\n",
       "2138 2023-03-29 10:00:59.911493888  399.8899   100     399.87     399.89 2023-03-29 10:00:59.879587072\n",
       "2139 2023-03-29 10:00:59.974551040  399.8701   100     399.87     399.89 2023-03-29 10:00:59.933540352\n",
       "2140 2023-03-29 10:00:59.998262528  399.8900     1     399.88     399.89 2023-03-29 10:00:59.998239232\n",
       "\n",
       "[2141 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qte['quote_time'] = qte['Time']\n",
    "enh_trd = otp.join_by_time([trd, qte])\n",
    "otp.run(enh_trd,  start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['SPY'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c192bc-3273-44fe-a1e2-9a34dbbdd349",
   "metadata": {},
   "source": [
    "Let's try the same query for a less liquid symbol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1843eb21-3ed7-4aab-8b08-75b1bf9fd986",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>SIZE</th>\n",
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       "      <th>ASK_PRICE</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2023-03-29 10:00:00.734953984</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>7</td>\n",
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       "      <td>1969-12-31 19:00:00.000000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-03-29 10:00:01.492248576</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>100</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:01.158007296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-29 10:00:03.348103936</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>8</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.347166976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-03-29 10:00:03.348121856</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>391</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.348119296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-29 10:00:03.348169984</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>100</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.348169472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2023-03-29 10:00:53.957339392</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>61</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2023-03-29 10:00:53.957334528</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2023-03-29 10:00:55.377122560</td>\n",
       "      <td>41.0100</td>\n",
       "      <td>5</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.01</td>\n",
       "      <td>2023-03-29 10:00:54.791796736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2023-03-29 10:00:55.612973568</td>\n",
       "      <td>40.9916</td>\n",
       "      <td>1</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.01</td>\n",
       "      <td>2023-03-29 10:00:54.791796736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2023-03-29 10:00:57.381350656</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>10</td>\n",
       "      <td>40.99</td>\n",
       "      <td>41.03</td>\n",
       "      <td>2023-03-29 10:00:57.381330688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2023-03-29 10:00:57.382493184</td>\n",
       "      <td>41.0100</td>\n",
       "      <td>100</td>\n",
       "      <td>41.00</td>\n",
       "      <td>41.03</td>\n",
       "      <td>2023-03-29 10:00:57.381917184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Time    PRICE  SIZE  BID_PRICE  ASK_PRICE                    quote_time\n",
       "0   2023-03-29 10:00:00.734953984  40.9500     7        NaN        NaN 1969-12-31 19:00:00.000000000\n",
       "1   2023-03-29 10:00:01.492248576  40.9500   100      40.92      40.95 2023-03-29 10:00:01.158007296\n",
       "2   2023-03-29 10:00:03.348103936  40.9500     8      40.92      40.95 2023-03-29 10:00:03.347166976\n",
       "3   2023-03-29 10:00:03.348121856  40.9500   391      40.92      40.95 2023-03-29 10:00:03.348119296\n",
       "4   2023-03-29 10:00:03.348169984  40.9500   100      40.92      40.95 2023-03-29 10:00:03.348169472\n",
       "..                            ...      ...   ...        ...        ...                           ...\n",
       "177 2023-03-29 10:00:53.957339392  41.0000    61      40.98      41.00 2023-03-29 10:00:53.957334528\n",
       "178 2023-03-29 10:00:55.377122560  41.0100     5      40.98      41.01 2023-03-29 10:00:54.791796736\n",
       "179 2023-03-29 10:00:55.612973568  40.9916     1      40.98      41.01 2023-03-29 10:00:54.791796736\n",
       "180 2023-03-29 10:00:57.381350656  41.0000    10      40.99      41.03 2023-03-29 10:00:57.381330688\n",
       "181 2023-03-29 10:00:57.382493184  41.0100   100      41.00      41.03 2023-03-29 10:00:57.381917184\n",
       "\n",
       "[182 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otp.run(enh_trd,  start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['AA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1158bea9-f28f-4fa1-b655-a0224e76fece",
   "metadata": {},
   "source": [
    "We didn't find a matching quote for the first trade. This happens when the first event following the query start time is a trade rather than a quote. We can fix this by instructing OneTick to look for a quote back in time from the start time. A complete code snipped is below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "056d8e72-19ec-4fe8-97f4-3132652949d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>2023-03-29 10:00:00.734953984</td>\n",
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       "      <td>2023-03-29 10:00:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-03-29 10:00:01.492248576</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>100</td>\n",
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       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:01.158007296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-29 10:00:03.348103936</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>8</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.347166976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-03-29 10:00:03.348121856</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>391</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.348119296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-29 10:00:03.348169984</td>\n",
       "      <td>40.9500</td>\n",
       "      <td>100</td>\n",
       "      <td>40.92</td>\n",
       "      <td>40.95</td>\n",
       "      <td>2023-03-29 10:00:03.348169472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2023-03-29 10:00:53.957339392</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>61</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.00</td>\n",
       "      <td>2023-03-29 10:00:53.957334528</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2023-03-29 10:00:55.377122560</td>\n",
       "      <td>41.0100</td>\n",
       "      <td>5</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.01</td>\n",
       "      <td>2023-03-29 10:00:54.791796736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2023-03-29 10:00:55.612973568</td>\n",
       "      <td>40.9916</td>\n",
       "      <td>1</td>\n",
       "      <td>40.98</td>\n",
       "      <td>41.01</td>\n",
       "      <td>2023-03-29 10:00:54.791796736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2023-03-29 10:00:57.381350656</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>10</td>\n",
       "      <td>40.99</td>\n",
       "      <td>41.03</td>\n",
       "      <td>2023-03-29 10:00:57.381330688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2023-03-29 10:00:57.382493184</td>\n",
       "      <td>41.0100</td>\n",
       "      <td>100</td>\n",
       "      <td>41.00</td>\n",
       "      <td>41.03</td>\n",
       "      <td>2023-03-29 10:00:57.381917184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Time    PRICE  SIZE  BID_PRICE  ASK_PRICE                    quote_time\n",
       "0   2023-03-29 10:00:00.734953984  40.9500     7      40.92      40.95 2023-03-29 10:00:00.000000000\n",
       "1   2023-03-29 10:00:01.492248576  40.9500   100      40.92      40.95 2023-03-29 10:00:01.158007296\n",
       "2   2023-03-29 10:00:03.348103936  40.9500     8      40.92      40.95 2023-03-29 10:00:03.347166976\n",
       "3   2023-03-29 10:00:03.348121856  40.9500   391      40.92      40.95 2023-03-29 10:00:03.348119296\n",
       "4   2023-03-29 10:00:03.348169984  40.9500   100      40.92      40.95 2023-03-29 10:00:03.348169472\n",
       "..                            ...      ...   ...        ...        ...                           ...\n",
       "177 2023-03-29 10:00:53.957339392  41.0000    61      40.98      41.00 2023-03-29 10:00:53.957334528\n",
       "178 2023-03-29 10:00:55.377122560  41.0100     5      40.98      41.01 2023-03-29 10:00:54.791796736\n",
       "179 2023-03-29 10:00:55.612973568  40.9916     1      40.98      41.01 2023-03-29 10:00:54.791796736\n",
       "180 2023-03-29 10:00:57.381350656  41.0000    10      40.99      41.03 2023-03-29 10:00:57.381330688\n",
       "181 2023-03-29 10:00:57.382493184  41.0100   100      41.00      41.03 2023-03-29 10:00:57.381917184\n",
       "\n",
       "[182 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')\n",
    "trd = trd[['PRICE','SIZE']]\n",
    "\n",
    "qte = otp.DataSource('TAQ_NBBO', tick_type='NBBO', back_to_first_tick=60)\n",
    "qte = qte[['BID_PRICE','ASK_PRICE']]\n",
    "qte['quote_time'] = qte['Time']\n",
    "\n",
    "enh_trd = otp.join_by_time([trd, qte])\n",
    "otp.run(enh_trd,  start=otp.dt(2023,3,29,10), end=otp.dt(2023,3,29,10,1), symbols=['AA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f31c5b-05b3-4d7a-a208-33453fd99039",
   "metadata": {},
   "source": [
    "# Use case: Prevaling quote at the time of a trade\n",
    "The code snippet above implements this use case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e93226d-78dd-413d-ab56-5fbab57a604c",
   "metadata": {},
   "source": [
    "# Use case: Computing Markouts\n",
    "A common TCA and quant research use case is to see what happens to the quote at certain intervals (aka markouts) before/after the trade. We can compute markouts efficiently by shifting the time series for each markout and then doing `join_by_time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d154585-d546-4879-9337-913b04abf8b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>PRICE</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>ASK_PRICE_-1</th>\n",
       "      <th>BID_PRICE_-1</th>\n",
       "      <th>quote_time_-1</th>\n",
       "      <th>ASK_PRICE_1</th>\n",
       "      <th>BID_PRICE_1</th>\n",
       "      <th>quote_time_1</th>\n",
       "      <th>ASK_PRICE_5</th>\n",
       "      <th>BID_PRICE_5</th>\n",
       "      <th>quote_time_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-03-08 10:00:00.014762069</td>\n",
       "      <td>181.4250</td>\n",
       "      <td>300</td>\n",
       "      <td>181.47</td>\n",
       "      <td>181.41</td>\n",
       "      <td>2023-03-08 09:59:59.002564435</td>\n",
       "      <td>181.49</td>\n",
       "      <td>181.48</td>\n",
       "      <td>2023-03-08 10:00:01.000000000</td>\n",
       "      <td>181.15</td>\n",
       "      <td>181.04</td>\n",
       "      <td>2023-03-08 10:00:05.012557243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-03-08 10:00:00.014764168</td>\n",
       "      <td>181.4400</td>\n",
       "      <td>12</td>\n",
       "      <td>181.47</td>\n",
       "      <td>181.41</td>\n",
       "      <td>2023-03-08 09:59:59.002564435</td>\n",
       "      <td>181.49</td>\n",
       "      <td>181.48</td>\n",
       "      <td>2023-03-08 10:00:01.000000000</td>\n",
       "      <td>181.15</td>\n",
       "      <td>181.04</td>\n",
       "      <td>2023-03-08 10:00:05.012557243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-03-08 10:00:00.014767570</td>\n",
       "      <td>181.4400</td>\n",
       "      <td>200</td>\n",
       "      <td>181.47</td>\n",
       "      <td>181.41</td>\n",
       "      <td>2023-03-08 09:59:59.002564435</td>\n",
       "      <td>181.49</td>\n",
       "      <td>181.48</td>\n",
       "      <td>2023-03-08 10:00:01.000000000</td>\n",
       "      <td>181.15</td>\n",
       "      <td>181.04</td>\n",
       "      <td>2023-03-08 10:00:05.012557243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-03-08 10:00:00.014799259</td>\n",
       "      <td>181.4500</td>\n",
       "      <td>2</td>\n",
       "      <td>181.47</td>\n",
       "      <td>181.41</td>\n",
       "      <td>2023-03-08 09:59:59.002564435</td>\n",
       "      <td>181.49</td>\n",
       "      <td>181.48</td>\n",
       "      <td>2023-03-08 10:00:01.000000000</td>\n",
       "      <td>181.15</td>\n",
       "      <td>181.04</td>\n",
       "      <td>2023-03-08 10:00:05.012557243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-08 10:00:00.015374686</td>\n",
       "      <td>181.4400</td>\n",
       "      <td>300</td>\n",
       "      <td>181.47</td>\n",
       "      <td>181.41</td>\n",
       "      <td>2023-03-08 09:59:59.002564435</td>\n",
       "      <td>181.49</td>\n",
       "      <td>181.48</td>\n",
       "      <td>2023-03-08 10:00:01.000000000</td>\n",
       "      <td>181.15</td>\n",
       "      <td>181.04</td>\n",
       "      <td>2023-03-08 10:00:05.012557243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>2023-03-09 10:00:00.956041264</td>\n",
       "      <td>183.5899</td>\n",
       "      <td>500</td>\n",
       "      <td>183.46</td>\n",
       "      <td>183.43</td>\n",
       "      <td>2023-03-09 09:59:59.911620614</td>\n",
       "      <td>183.59</td>\n",
       "      <td>183.54</td>\n",
       "      <td>2023-03-09 10:00:01.945769144</td>\n",
       "      <td>183.65</td>\n",
       "      <td>183.57</td>\n",
       "      <td>2023-03-09 10:00:05.954673686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>706</th>\n",
       "      <td>2023-03-09 10:00:00.967881686</td>\n",
       "      <td>183.5800</td>\n",
       "      <td>25</td>\n",
       "      <td>183.46</td>\n",
       "      <td>183.43</td>\n",
       "      <td>2023-03-09 09:59:59.963372673</td>\n",
       "      <td>183.59</td>\n",
       "      <td>183.54</td>\n",
       "      <td>2023-03-09 10:00:01.963256371</td>\n",
       "      <td>183.66</td>\n",
       "      <td>183.57</td>\n",
       "      <td>2023-03-09 10:00:05.967652794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>2023-03-09 10:00:00.978963708</td>\n",
       "      <td>183.4718</td>\n",
       "      <td>136</td>\n",
       "      <td>183.46</td>\n",
       "      <td>183.42</td>\n",
       "      <td>2023-03-09 09:59:59.971349853</td>\n",
       "      <td>183.59</td>\n",
       "      <td>183.54</td>\n",
       "      <td>2023-03-09 10:00:01.963256371</td>\n",
       "      <td>183.66</td>\n",
       "      <td>183.57</td>\n",
       "      <td>2023-03-09 10:00:05.967652794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>2023-03-09 10:00:00.983358396</td>\n",
       "      <td>183.5500</td>\n",
       "      <td>20</td>\n",
       "      <td>183.46</td>\n",
       "      <td>183.41</td>\n",
       "      <td>2023-03-09 09:59:59.983288366</td>\n",
       "      <td>183.59</td>\n",
       "      <td>183.54</td>\n",
       "      <td>2023-03-09 10:00:01.963256371</td>\n",
       "      <td>183.66</td>\n",
       "      <td>183.57</td>\n",
       "      <td>2023-03-09 10:00:05.967652794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>2023-03-09 10:00:00.990945922</td>\n",
       "      <td>183.5650</td>\n",
       "      <td>200</td>\n",
       "      <td>183.44</td>\n",
       "      <td>183.41</td>\n",
       "      <td>2023-03-09 09:59:59.988781014</td>\n",
       "      <td>183.59</td>\n",
       "      <td>183.54</td>\n",
       "      <td>2023-03-09 10:00:01.963256371</td>\n",
       "      <td>183.66</td>\n",
       "      <td>183.57</td>\n",
       "      <td>2023-03-09 10:00:05.967652794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>710 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Time     PRICE  SIZE  ASK_PRICE_-1  BID_PRICE_-1                 quote_time_-1  ASK_PRICE_1  BID_PRICE_1                  quote_time_1  ASK_PRICE_5  BID_PRICE_5                  quote_time_5\n",
       "0   2023-03-08 10:00:00.014762069  181.4250   300        181.47        181.41 2023-03-08 09:59:59.002564435       181.49       181.48 2023-03-08 10:00:01.000000000       181.15       181.04 2023-03-08 10:00:05.012557243\n",
       "1   2023-03-08 10:00:00.014764168  181.4400    12        181.47        181.41 2023-03-08 09:59:59.002564435       181.49       181.48 2023-03-08 10:00:01.000000000       181.15       181.04 2023-03-08 10:00:05.012557243\n",
       "2   2023-03-08 10:00:00.014767570  181.4400   200        181.47        181.41 2023-03-08 09:59:59.002564435       181.49       181.48 2023-03-08 10:00:01.000000000       181.15       181.04 2023-03-08 10:00:05.012557243\n",
       "3   2023-03-08 10:00:00.014799259  181.4500     2        181.47        181.41 2023-03-08 09:59:59.002564435       181.49       181.48 2023-03-08 10:00:01.000000000       181.15       181.04 2023-03-08 10:00:05.012557243\n",
       "4   2023-03-08 10:00:00.015374686  181.4400   300        181.47        181.41 2023-03-08 09:59:59.002564435       181.49       181.48 2023-03-08 10:00:01.000000000       181.15       181.04 2023-03-08 10:00:05.012557243\n",
       "..                            ...       ...   ...           ...           ...                           ...          ...          ...                           ...          ...          ...                           ...\n",
       "705 2023-03-09 10:00:00.956041264  183.5899   500        183.46        183.43 2023-03-09 09:59:59.911620614       183.59       183.54 2023-03-09 10:00:01.945769144       183.65       183.57 2023-03-09 10:00:05.954673686\n",
       "706 2023-03-09 10:00:00.967881686  183.5800    25        183.46        183.43 2023-03-09 09:59:59.963372673       183.59       183.54 2023-03-09 10:00:01.963256371       183.66       183.57 2023-03-09 10:00:05.967652794\n",
       "707 2023-03-09 10:00:00.978963708  183.4718   136        183.46        183.42 2023-03-09 09:59:59.971349853       183.59       183.54 2023-03-09 10:00:01.963256371       183.66       183.57 2023-03-09 10:00:05.967652794\n",
       "708 2023-03-09 10:00:00.983358396  183.5500    20        183.46        183.41 2023-03-09 09:59:59.983288366       183.59       183.54 2023-03-09 10:00:01.963256371       183.66       183.57 2023-03-09 10:00:05.967652794\n",
       "709 2023-03-09 10:00:00.990945922  183.5650   200        183.44        183.41 2023-03-09 09:59:59.988781014       183.59       183.54 2023-03-09 10:00:01.963256371       183.66       183.57 2023-03-09 10:00:05.967652794\n",
       "\n",
       "[710 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = otp.dt(2023, 3, 8, 10)\n",
    "e = otp.dt(2023, 3, 9, 10, 0, 1)\n",
    "markouts = [-1, 1, 5] \n",
    "\n",
    "trd = otp.DataSource('NYSE_TAQ', tick_type='TRD')\n",
    "trd = trd[['PRICE', 'SIZE']]\n",
    "\n",
    "qte_by_markout = []\n",
    "for m in markouts:\n",
    "    qte = otp.DataSource('TAQ_NBBO', tick_type='NBBO', back_to_first_tick=86400)\n",
    "    qte = qte[['ASK_PRICE', 'BID_PRICE']]\n",
    "    qte = qte.rename({'ASK_PRICE': f'ASK_PRICE_{m}', \n",
    "                      'BID_PRICE': f'BID_PRICE_{m}'})\n",
    "    qte[f'quote_time_{m}'] = qte['Time']\n",
    "    \n",
    "    # shift the data by m seconds\n",
    "    qte = qte.time_interval_shift(m * 1000)\n",
    "    qte_by_markout.append(qte)\n",
    "\n",
    "trd = otp.join_by_time([trd] + qte_by_markout)\n",
    "otp.run(trd, start=s, end=e, symbols='TSLA', apply_times_daily=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f14ed4-8248-430b-b13a-74a4fe694223",
   "metadata": {},
   "source": [
    "# `join_with_query`: Executing a query on each tick"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12786f9-dbcd-4726-b667-6b47d71aff64",
   "metadata": {},
   "source": [
    "Sometimes we want to do a look up based on the information provided in a tick. For example, we may have a series of order ticks each containing an order arrival and exit times and we may want to find the market vwap during the interval. Let's take this one step at a time."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7db241f8-69e6-48dc-91a9-ebe496fb2a55",
   "metadata": {},
   "source": [
    "Let's find the market vwap for a given time range (i.e., for a given `start` and `end`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d3994b99-f9f7-4925-8352-ab3094c3d5ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>market_vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-04-10 10:27:00</td>\n",
       "      <td>160.477922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Time  market_vwap\n",
       "0 2023-04-10 10:27:00   160.477922"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q = otp.DataSource('NYSE_TAQ', tick_type='TRD')\n",
    "q = q.agg({'market_vwap': otp.agg.vwap('PRICE','SIZE')})\n",
    "otp.run(q, start=otp.dt(2023,4,10,10,25,59), end=otp.dt(2023,4,10,10,27), symbols='AAPL')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279ab9ef-844c-4a30-a526-52e09c1b65e2",
   "metadata": {},
   "source": [
    "Next let's create a couple of orders each with its own values for `start`/`end` specified in the `arrival`/`exit` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d38d98d1-e151-4f11-bc39-bf477aa692df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>arrival</th>\n",
       "      <th>exit</th>\n",
       "      <th>sym</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-12-01 00:00:00.000</td>\n",
       "      <td>2023-04-10 10:25:59</td>\n",
       "      <td>2023-04-10 10:26:07</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-12-01 00:00:00.001</td>\n",
       "      <td>2023-04-10 10:26:09</td>\n",
       "      <td>2023-04-10 10:27:48</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time             arrival                exit   sym\n",
       "0 2003-12-01 00:00:00.000 2023-04-10 10:25:59 2023-04-10 10:26:07  AAPL\n",
       "1 2003-12-01 00:00:00.001 2023-04-10 10:26:09 2023-04-10 10:27:48  MSFT"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = otp.Ticks(arrival=[otp.dt(2023,4,10,10,25,59), otp.dt(2023,4,10,10,26,9)], \n",
    "                      exit=[otp.dt(2023,4,10,10,26,7), otp.dt(2023,4,10,10,27,48)],\n",
    "                      sym=['AAPL', 'MSFT'])\n",
    "otp.run(orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844a4826-7771-41c7-8f5d-2a6ed65a83c2",
   "metadata": {},
   "source": [
    "We can wrap the code that finds vwap into a function and call it for each order while passing the relevant parameters for `start`, `end`, and `symbol`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb8b8e54-ac60-49d8-9927-9bddd873055b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>market_vwap</th>\n",
       "      <th>arrival</th>\n",
       "      <th>exit</th>\n",
       "      <th>sym</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-04-01 00:00:00.000</td>\n",
       "      <td>160.548890</td>\n",
       "      <td>2023-04-10 10:25:59</td>\n",
       "      <td>2023-04-10 10:26:07</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-04-01 00:00:00.001</td>\n",
       "      <td>286.136779</td>\n",
       "      <td>2023-04-10 10:26:09</td>\n",
       "      <td>2023-04-10 10:27:48</td>\n",
       "      <td>MSFT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time  market_vwap             arrival                exit   sym\n",
       "0 2022-04-01 00:00:00.000   160.548890 2023-04-10 10:25:59 2023-04-10 10:26:07  AAPL\n",
       "1 2022-04-01 00:00:00.001   286.136779 2023-04-10 10:26:09 2023-04-10 10:27:48  MSFT"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def vwap(symbol):\n",
    "    q = otp.DataSource('NYSE_TAQ', tick_type='TRD')\n",
    "    q = q.agg({'market_vwap': otp.agg.vwap('PRICE','SIZE')})\n",
    "    return q\n",
    "\n",
    "orders = otp.Ticks(arrival=[otp.dt(2023,4,10,10,25,59), otp.dt(2023,4,10,10,26,9)], \n",
    "                      exit=[otp.dt(2023,4,10,10,26,7), otp.dt(2023,4,10,10,27,48)],\n",
    "                      sym=['AAPL', 'MSFT'])\n",
    "orders = orders.join_with_query(vwap, start=orders['arrival'], end=orders['exit'], symbol=orders['sym'])\n",
    "otp.run(orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7624b26c-4e9d-4b74-8fc4-80bfa769162f",
   "metadata": {},
   "source": [
    "# Use case: Interval VWAP\n",
    "The code above provides an implementation for this use case. However, a more efficient implementation may be useful when the number of orders is large. We provide a more efficient implementation below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e5d69e8-571d-47be-8ce9-f07824c92d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders['_PARAM_START_TIME_NANOS'] = orders['arrival'] \n",
    "orders['_PARAM_END_TIME_NANOS'] = orders['exit'] \n",
    "orders['SYMBOL_NAME'] = orders['sym'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "618781ac-c3ff-4530-9a2b-1e5ad7ec5e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAPL':                  Time  market_vwap\n",
       " 0 2023-04-10 10:26:07    160.54889,\n",
       " 'MSFT':                  Time  market_vwap\n",
       " 0 2023-04-10 10:27:48   286.136779}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otp.run(vwap, symbols=orders, start=otp.dt(2022,12,1), end=otp.dt(2022,12,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6226756-6dfa-4177-a2a2-c0851f0f766b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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