examples/use-cases/instruments/Simple Equity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    <form action=\"javascript:code_toggle()\"><input type=\"submit\" id=\"toggleButton\" value=\"Toggle Docstring\"></form>\n",
       "    \n",
       "         <script>\n",
       "         function code_toggle() {\n",
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       "                 $('div.cell.code_cell.rendered.selected div.input').show();\n",
       "             }\n",
       "         }\n",
       "         </script>\n",
       "\n",
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "from lusidtools.jupyter_tools import toggle_code\n",
    "\n",
    "\"\"\"Equity - Computing P&L and Handling Dividends for Equities\n",
    "\n",
    "Attributes\n",
    "----------\n",
    "equity\n",
    "dividend\n",
    "recipes\n",
    "valuations\n",
    "P&L\n",
    "\"\"\"\n",
    "\n",
    "toggle_code(\"Toggle Docstring\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing P&L and Handling Dividends for Equities\n",
    "\n",
    "In this notebook, we demonstrate how P&L can be calculated for Equity instruments as well as how to book in dividends to our cash position. \n",
    "In this example, we will be using Microsoft shares.\n",
    "\n",
    "## Table of Contents:\n",
    "* [1. Create a portfolio](#1.-Create-Portfolio)\n",
    "* [2. Creating an equity instrument](#2.-Create-an-Equity-Instrument)\n",
    "* [3. Transactions](#3.-Transactions)\n",
    "* [4. Quotes](#4.-Quotes)\n",
    "* [5. Creating a corporate action](#5.-Create-a-Corporate-Action)\n",
    "* [6. Valuations](#6.-Valuations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LUSID Environment Initialised\n",
      "LUSID API Version : 0.6.11994.0\n"
     ]
    }
   ],
   "source": [
    "# Import generic non-LUSID packages\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import json\n",
    "import pytz\n",
    "import time\n",
    "from IPython.core.display import HTML\n",
    "\n",
    "# Import key modules from the LUSID package\n",
    "import lusid as lu\n",
    "import lusid.models as lm\n",
    "\n",
    "# Import key functions from Lusid-Python-Tools and other packages\n",
    "from lusidtools.pandas_utils.lusid_pandas import lusid_response_to_data_frame\n",
    "from lusidtools.lpt.lpt import to_date\n",
    "from lusidjam import RefreshingToken\n",
    "\n",
    "\n",
    "# Set DataFrame display formats\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "pd.options.display.float_format = \"{:,.2f}\".format\n",
    "from utilities.formatting_tools import cashladder_to_df\n",
    "\n",
    "# display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "\n",
    "# Set the secrets path\n",
    "secrets_path = os.getenv(\"FBN_SECRETS_PATH\")\n",
    "\n",
    "# For running the notebook locally\n",
    "if secrets_path is None:\n",
    "    secrets_path = os.path.join(os.path.dirname(os.getcwd()), \"secrets.json\")\n",
    "\n",
    "# Authenticate our user and create our API client\n",
    "api_factory = lu.utilities.ApiClientFactory(\n",
    "    token=RefreshingToken(), api_secrets_filename=secrets_path\n",
    ")\n",
    "\n",
    "print(\"LUSID Environment Initialised\")\n",
    "print(\n",
    "    \"LUSID API Version :\",\n",
    "    api_factory.build(lu.api.ApplicationMetadataApi).get_lusid_versions().build_version,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LUSID Variable Definitions\n",
    "portfolio_api = api_factory.build(lu.api.PortfoliosApi)\n",
    "transaction_portfolios_api = api_factory.build(lu.api.TransactionPortfoliosApi)\n",
    "instruments_api = api_factory.build(lu.api.InstrumentsApi)\n",
    "quotes_api = api_factory.build(lu.api.QuotesApi)\n",
    "configuration_recipe_api = api_factory.build(lu.api.ConfigurationRecipeApi)\n",
    "aggregration_api = api_factory.build(lu.api.AggregationApi)\n",
    "corporate_action_sources_api = api_factory.build(lu.api.CorporateActionSourcesApi)\n",
    "property_definitions_api = api_factory.build(lu.api.PropertyDefinitionsApi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define scopes\n",
    "scope = \"ibor\"\n",
    "quotes_scope = \"ibor\"\n",
    "ca_code = \"ibor_corp_act\"\n",
    "portfolio_code = \"EquityPortfolioForPnLCalc\"\n",
    "recipe_code = \"equityValuation\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Create a Portfolio\n",
    "\n",
    "We must first create a corporate action source to link to the portfolio we plan to keep our equity in. This will allow us to perform a valuation and inspect the cash flows at a later stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The portfolio already exists\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "\n",
    "    source_request = lm.CreateCorporateActionSourceRequest(\n",
    "        scope=scope,\n",
    "        code=ca_code,\n",
    "        display_name=\"Ibor Corporate Action Source\",\n",
    "        description=\"Corporate Actions source for sample notebook\",\n",
    "    )\n",
    "\n",
    "    source_result = api_factory.build(\n",
    "        lu.api.CorporateActionSourcesApi\n",
    "    ).create_corporate_action_source(\n",
    "        create_corporate_action_source_request=source_request\n",
    "    )\n",
    "\n",
    "except lu.ApiException as e:\n",
    "        detail = json.loads(e.body)\n",
    "        if detail['code'] ==173:\n",
    "            print('The portfolio already exists')\n",
    "        else:\n",
    "             raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not create a portfolio with id 'EquityPortfolioForPnLCalc' because it already exists in scope 'ibor'.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    transaction_portfolios_api.create_portfolio(\n",
    "        scope=scope,\n",
    "        create_transaction_portfolio_request=lm.CreateTransactionPortfolioRequest(\n",
    "            display_name=portfolio_code,\n",
    "            code=portfolio_code,\n",
    "            base_currency=\"USD\",\n",
    "            created=\"2010-01-01\",\n",
    "            sub_holding_keys=[],\n",
    "            corporate_action_source_id=lu.ResourceId(scope=scope, code=ca_code),\n",
    "        ),\n",
    "    )\n",
    "\n",
    "except lu.ApiException as e:\n",
    "    print(json.loads(e.body)[\"title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Creating an Equity Instrument\n",
    "\n",
    "We must first create a property definition for the dividend yield property we are planning on using later in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error creating Property Definition 'Instrument/ibor/dividend_yield' because it already exists.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    property_definition_request = lm.CreatePropertyDefinitionRequest(\n",
    "        domain=\"Instrument\",\n",
    "        scope=scope,\n",
    "        code=\"dividend_yield\",\n",
    "        display_name=\"Dividend Yield\",\n",
    "        data_type_id=lm.ResourceId(\n",
    "            scope=\"system\",\n",
    "            code=\"number\",\n",
    "        ),\n",
    "        life_time=\"Perpetual\",\n",
    "    )\n",
    "\n",
    "    property_definitions_api.create_property_definition(\n",
    "        create_property_definition_request=property_definition_request\n",
    "    )\n",
    "\n",
    "except lu.ApiException as e:\n",
    "    print(json.loads(e.body)[\"title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a function that takes in our equity variables and upserts an equity to LUSID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_equity(\n",
    "    name,\n",
    "    client_internal,\n",
    "    dom_ccy,\n",
    "    dividend_yield,\n",
    "):\n",
    "\n",
    "    equity = lm.SimpleInstrument(\n",
    "        instrument_type=\"SimpleInstrument\",\n",
    "        dom_ccy=dom_ccy,\n",
    "        asset_class=\"Equities\",\n",
    "        simple_instrument_type=\"Equity\",\n",
    "    )\n",
    "\n",
    "    # properties = lm.InstrumentProperties()\n",
    "    properties = lm.ModelProperty(\n",
    "        key=f\"Instrument/{scope}/dividend_yield\",\n",
    "        value=lm.PropertyValue(\n",
    "            metric_value=lm.MetricValue(\n",
    "                value=dividend_yield,\n",
    "                # unit=\"Decimal\",\n",
    "            )\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    equity_definition = lm.InstrumentDefinition(\n",
    "        name=name,\n",
    "        identifiers={\"ClientInternal\": lm.InstrumentIdValue(client_internal)},\n",
    "        definition=equity,\n",
    "        properties=[properties],\n",
    "    )\n",
    "\n",
    "    # upsert the instrument\n",
    "    upsert_request = {client_internal: equity_definition}\n",
    "    upsert_response = instruments_api.upsert_instruments(request_body=upsert_request)\n",
    "    equity_luid = upsert_response.values[client_internal].lusid_instrument_id\n",
    "    print(equity_luid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Upsert an Equity\n",
    "\n",
    "We can now set the variables of the equity and upsert it into LUSID using the function we built in the cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LUID_FP69LVAZ\n"
     ]
    }
   ],
   "source": [
    "name = \"Microsoft\"\n",
    "identifier = \"MSFT\"\n",
    "dom_ccy = \"USD\"\n",
    "dividend_yield = 0.88\n",
    "\n",
    "create_equity(name, identifier, dom_ccy, dividend_yield)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Transactions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Create Transaction Request\n",
    "\n",
    "Once the equity has been created, we can generate a transaction that adds it to our portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>txn_id</th>\n",
       "      <th>type</th>\n",
       "      <th>Isin</th>\n",
       "      <th>client_id</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>settlement_date</th>\n",
       "      <th>quantity</th>\n",
       "      <th>price</th>\n",
       "      <th>total_consideration</th>\n",
       "      <th>currency</th>\n",
       "      <th>portfolio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>txn001</td>\n",
       "      <td>StockIn</td>\n",
       "      <td>US5949181045</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-09-01T10:00:00Z</td>\n",
       "      <td>2021-09-02T10:00:00Z</td>\n",
       "      <td>1000</td>\n",
       "      <td>300</td>\n",
       "      <td>300000</td>\n",
       "      <td>USD</td>\n",
       "      <td>EquityPortfolioForPnLCalc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   txn_id     type          Isin client_id            trade_date  \\\n",
       "0  txn001  StockIn  US5949181045      MSFT  2021-09-01T10:00:00Z   \n",
       "\n",
       "        settlement_date  quantity  price  total_consideration currency  \\\n",
       "0  2021-09-02T10:00:00Z      1000    300               300000      USD   \n",
       "\n",
       "                   portfolio  \n",
       "0  EquityPortfolioForPnLCalc  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.read_csv(\"data/equity_transaction_data.csv\")\n",
    "transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transactions succesfully updated at time: 2021-11-25 17:54:21.763810+00:00\n"
     ]
    }
   ],
   "source": [
    "for portfolio_code, grouped_df in transactions.groupby(\"portfolio\"):\n",
    "\n",
    "    transaction_request = [\n",
    "        lm.TransactionRequest(\n",
    "            transaction_id=row[\"txn_id\"],\n",
    "            type=row[\"type\"],\n",
    "            instrument_identifiers={\n",
    "                \"Instrument/default/ClientInternal\": row[\"client_id\"],\n",
    "                \"Instrument/default/Isin\": row[\"Isin\"],\n",
    "            },\n",
    "            transaction_date=row[\"trade_date\"],\n",
    "            settlement_date=row[\"settlement_date\"],\n",
    "            units=row[\"quantity\"],\n",
    "            transaction_price=lm.TransactionPrice(price=row[\"price\"], type=\"Price\"),\n",
    "            total_consideration=lm.CurrencyAndAmount(\n",
    "                amount=row[\"total_consideration\"], currency=row[\"currency\"]\n",
    "            ),\n",
    "        )\n",
    "        for index, row in grouped_df.iterrows()\n",
    "    ]\n",
    "\n",
    "    response = transaction_portfolios_api.upsert_transactions(\n",
    "        scope=scope, code=portfolio_code, transaction_request=transaction_request\n",
    "    )\n",
    "\n",
    "    print(f\"Transactions succesfully updated at time: {response.version.as_at_date}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Quotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The equity is in our portfolio, but currently lacks pricing. We will thus upsert quotes for the equity that we retrieved from our market data system. We upload prices from when the equity was bought until today (at the time of writing, 12 November 2021). Below we can see the last 5 of the ~50 quotes that were uploaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ClientInternal</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-11-08</td>\n",
       "      <td>336.99</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-11-09</td>\n",
       "      <td>337.05</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-11-10</td>\n",
       "      <td>338.05</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-11-11</td>\n",
       "      <td>339.05</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2021-11-12</td>\n",
       "      <td>340.05</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClientInternal        date  price currency\n",
       "47           MSFT  2021-11-08 336.99      USD\n",
       "48           MSFT  2021-11-09 337.05      USD\n",
       "49           MSFT  2021-11-10 338.05      USD\n",
       "50           MSFT  2021-11-11 339.05      USD\n",
       "51           MSFT  2021-11-12 340.05      USD"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "equity_prices = pd.read_csv(\"data/equity_quotes_data.csv\")\n",
    "equity_prices.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quote successfully loaded into LUSID. 52 quotes loaded.\n"
     ]
    }
   ],
   "source": [
    "# Create quotes request\n",
    "instrument_quotes = {\n",
    "    index: lm.UpsertQuoteRequest(\n",
    "        quote_id=lm.QuoteId(\n",
    "            quote_series_id=lm.QuoteSeriesId(\n",
    "                provider=\"Lusid\",\n",
    "                instrument_id=row[\"ClientInternal\"],\n",
    "                instrument_id_type=\"ClientInternal\",\n",
    "                quote_type=\"Price\",\n",
    "                field=\"mid\",\n",
    "            ),\n",
    "            effective_at=to_date(row[\"date\"]).isoformat(),\n",
    "        ),\n",
    "        metric_value=lm.MetricValue(value=row[\"price\"], unit=row[\"currency\"]),\n",
    "    )\n",
    "    for index, row in equity_prices.iterrows()\n",
    "}\n",
    "\n",
    "# Upsert the quotes into LUSID\n",
    "response = quotes_api.upsert_quotes(scope=scope, request_body=instrument_quotes)\n",
    "\n",
    "if response.failed == {}:\n",
    "    print(\n",
    "        f\"Quote successfully loaded into LUSID. {len(response.values)} quotes loaded.\"\n",
    "    )\n",
    "\n",
    "else:\n",
    "    print(\n",
    "        f\"Some failures occurred during quotes upsertion, {len(response.failed)} did not get loaded into LUSID.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Creating a Corporate Action"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create our dividend corporate action, we define an input and an output transition. Our input transition is the number of shares in Microsoft, denoted by the Client Internal ID \"MSFT\". The output transition is the amount of USD per share we will receive. The result is that for every 1 share, we receive 0.56 USD. You can structure all sorts of corporate actions in this way. For our example, we structured a divident payment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'failed': {},\n",
       " 'href': None,\n",
       " 'links': [{'description': None,\n",
       "            'href': 'https://fbn-ci.lusid.com/api/api/schemas/entities/UpsertCorporateActionsResponse',\n",
       "            'method': 'GET',\n",
       "            'relation': 'EntitySchema'},\n",
       "           {'description': 'A link to the LUSID Insights website showing all '\n",
       "                           'logs related to this request',\n",
       "            'href': 'http://fbn-ci.lusid.com/app/insights/logs/0HMU3D0KFJM8O:00000249',\n",
       "            'method': 'GET',\n",
       "            'relation': 'RequestLogs'}],\n",
       " 'values': {'ibor_corp_act': {'announcement_date': datetime.datetime(2021, 9, 2, 0, 0, tzinfo=tzutc()),\n",
       "                              'corporate_action_code': 'ibor_corp_act',\n",
       "                              'description': None,\n",
       "                              'ex_date': datetime.datetime(2021, 9, 3, 0, 0, tzinfo=tzutc()),\n",
       "                              'payment_date': datetime.datetime(2021, 9, 9, 0, 0, tzinfo=tzutc()),\n",
       "                              'record_date': datetime.datetime(2021, 9, 4, 0, 0, tzinfo=tzutc()),\n",
       "                              'transitions': [{'input_transition': {'cost_factor': 1.0,\n",
       "                                                                    'instrument_identifiers': {'Instrument/default/ClientInternal': 'MSFT'},\n",
       "                                                                    'instrument_scope': 'default',\n",
       "                                                                    'instrument_uid': 'LUID_FP69LVAZ',\n",
       "                                                                    'units_factor': 1.0},\n",
       "                                               'output_transitions': [{'cost_factor': 1.0,\n",
       "                                                                       'instrument_identifiers': {'Instrument/default/Currency': 'USD'},\n",
       "                                                                       'instrument_scope': 'default',\n",
       "                                                                       'instrument_uid': 'CCY_USD',\n",
       "                                                                       'units_factor': 0.56}]}]}}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transitions = [\n",
    "    lm.CorporateActionTransition(\n",
    "        input_transition=lm.CorporateActionTransitionComponentRequest(\n",
    "            instrument_identifiers={\n",
    "                \"Instrument/default/ClientInternal\": \"MSFT\"\n",
    "            },\n",
    "            units_factor=1,\n",
    "            cost_factor=1,\n",
    "        ),\n",
    "        output_transitions=[\n",
    "            lm.CorporateActionTransitionComponentRequest(\n",
    "                instrument_identifiers={\"Instrument/default/Currency\": \"USD\"},\n",
    "                units_factor=0.56,\n",
    "                cost_factor=1,\n",
    "            )\n",
    "        ],\n",
    "    )\n",
    "]\n",
    "\n",
    "dividend = lm.UpsertCorporateActionRequest(\n",
    "    corporate_action_code=ca_code,\n",
    "    announcement_date=datetime(2021, 9, 2, 0, tzinfo=pytz.utc),\n",
    "    ex_date=datetime(2021, 9, 3, 0, tzinfo=pytz.utc),\n",
    "    record_date=datetime(2021, 9, 4, 0, tzinfo=pytz.utc),\n",
    "    payment_date=datetime(2021, 9, 9, 0, tzinfo=pytz.utc),\n",
    "    transitions=transitions,\n",
    ")\n",
    "\n",
    "corporate_action_sources_api.batch_upsert_corporate_actions(\n",
    "    scope=scope, code=ca_code, upsert_corporate_action_request=[dividend]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Valuations\n",
    "\n",
    "Once we have the equity booked into a portfolio, we can now value this portfolio. The recipe below describes how we will go about valuing the instruments in our portfolio (in this case just our equity)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Create valuation recipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a recipe to perform a valuation\n",
    "configuration_recipe = lm.ConfigurationRecipe(\n",
    "    scope=scope,\n",
    "    code=recipe_code,\n",
    "    market=lm.MarketContext(\n",
    "        market_rules=[\n",
    "            lm.MarketDataKeyRule(\n",
    "                key=\"Quote.ClientInternal.*\",\n",
    "                supplier=\"Lusid\",\n",
    "                data_scope=scope,\n",
    "                quote_type=\"Price\",\n",
    "                field=\"mid\",\n",
    "                quote_interval=\"5D.0D\",\n",
    "            )\n",
    "        ],\n",
    "        options=lm.MarketOptions(\n",
    "            default_supplier=\"Lusid\",\n",
    "            default_instrument_code_type=\"ClientInternal\",\n",
    "            default_scope=scope,\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "upsert_configuration_recipe_response = (\n",
    "    configuration_recipe_api.upsert_configuration_recipe(\n",
    "        upsert_recipe_request=lm.UpsertRecipeRequest(\n",
    "            configuration_recipe=configuration_recipe\n",
    "        )\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Create daily valuation function\n",
    "\n",
    "Once we have made a recipe, we can now create a function that outputs a dataframe with the valuation of our portfolio. We choose to display PV, PnL and some instrument identifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_val(date, portfolio_code):\n",
    "\n",
    "    valuation_request = lm.ValuationRequest(\n",
    "        recipe_id=lm.ResourceId(scope=scope, code=\"equityValuation\"),\n",
    "        metrics=[\n",
    "            lm.AggregateSpec(\"Instrument/default/Name\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Instrument/default/ClientInternal\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Quotes/Price\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Holding/default/Units\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Valuation/PV/Amount\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Valuation/PnL/Tm1\", \"Value\"),\n",
    "            lm.AggregateSpec(\"Instrument/ibor/dividend_yield\", \"Value\"),\n",
    "        ],\n",
    "        group_by=[\"Instrument/default/Name\"],\n",
    "        portfolio_entity_ids=[lm.PortfolioEntityId(scope=scope, code=portfolio_code)],\n",
    "        valuation_schedule=lm.ValuationSchedule(effective_at=date),\n",
    "    )\n",
    "\n",
    "    val_data = aggregration_api.get_valuation(valuation_request=valuation_request).data\n",
    "\n",
    "    vals_df = pd.DataFrame(val_data)\n",
    "\n",
    "    vals_df.rename(\n",
    "        columns={\n",
    "            \"Instrument/default/Name\": \"InstrumentName\",\n",
    "            \"Instrument/default/ClientInternal\": \"ClientInternal\",\n",
    "            \"Valuation/PV/Amount\": \"Present Value\",\n",
    "            \"Valuation/PnL/Tm1\": \"PnL (1-day)\",\n",
    "            \"Instrument/ibor/dividend_yield\": \"Dividend Yield\",\n",
    "        },\n",
    "        inplace=True,\n",
    "    )\n",
    "    try:\n",
    "        return vals_df.drop(\"Aggregation/Errors\", axis=1)\n",
    "    except:\n",
    "        return vals_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now display the first few days in the lifecycle of our equity. Recall that we have bought this equity on the 1st of September 2021."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day 1\n",
    "\n",
    "At 10AM on day 1, we value our equity position and find that it is worth 300,000 USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Present Value</th>\n",
       "      <th>InstrumentName</th>\n",
       "      <th>ClientInternal</th>\n",
       "      <th>Quotes/Price</th>\n",
       "      <th>Holding/default/Units</th>\n",
       "      <th>PnL (1-day)</th>\n",
       "      <th>Dividend Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300,000.00</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>300.00</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>None</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present Value InstrumentName ClientInternal  Quotes/Price  \\\n",
       "0     300,000.00      Microsoft           MSFT        300.00   \n",
       "\n",
       "   Holding/default/Units PnL (1-day)  Dividend Yield  \n",
       "0               1,000.00        None            0.88  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_val(\"2021-09-01T10:00:00Z\", portfolio_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day 2\n",
    "\n",
    "On day 2 the share price of MSFT moves to 301.15, giving us a 1 day PnL of 1,150 USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Present Value</th>\n",
       "      <th>InstrumentName</th>\n",
       "      <th>ClientInternal</th>\n",
       "      <th>Quotes/Price</th>\n",
       "      <th>Holding/default/Units</th>\n",
       "      <th>PnL (1-day)</th>\n",
       "      <th>Dividend Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301,150.00</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>301.15</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>1,150.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present Value InstrumentName ClientInternal  Quotes/Price  \\\n",
       "0     301,150.00      Microsoft           MSFT        301.15   \n",
       "\n",
       "   Holding/default/Units  PnL (1-day)  Dividend Yield  \n",
       "0               1,000.00     1,150.00            0.88  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_val(\"2021-09-02T10:00:00Z\", portfolio_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cash Ladder\n",
    "\n",
    "If we take a look at the cash ladder between our first day of owning this equity, 1 September 2021, and today (12 November 2021 at time of writing) we can see all cash flows associated with this position so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "If using all scalar values, you must pass an index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple Equity.ipynb Cell 37\u001b[0m line \u001b[0;36m1\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m cash_ladder \u001b[39m=\u001b[39m transaction_portfolios_api\u001b[39m.\u001b[39mget_portfolio_cash_ladder(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     scope\u001b[39m=\u001b[39mscope,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     code\u001b[39m=\u001b[39mportfolio_code,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     recipe_id_code\u001b[39m=\u001b[39mrecipe_code\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/adammassoud/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/Simple%20Equity.ipynb#X51sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m cashladder_to_df(cash_ladder)\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/utilities/formatting_tools.py:69\u001b[0m, in \u001b[0;36mcashladder_to_df\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m     66\u001b[0m shks \u001b[39m=\u001b[39m {shk: value\u001b[39m.\u001b[39mvalue\u001b[39m.\u001b[39mlabel_value \u001b[39mfor\u001b[39;00m shk, value \u001b[39min\u001b[39;00m element\u001b[39m.\u001b[39msub_holding_keys\u001b[39m.\u001b[39mitems()}\n\u001b[1;32m     68\u001b[0m \u001b[39mfor\u001b[39;00m record \u001b[39min\u001b[39;00m element\u001b[39m.\u001b[39mrecords:\n\u001b[0;32m---> 69\u001b[0m     working_df \u001b[39m=\u001b[39m add_open_row(element, record)\n\u001b[1;32m     71\u001b[0m     \u001b[39mfor\u001b[39;00m activity, value \u001b[39min\u001b[39;00m record\u001b[39m.\u001b[39mactivities\u001b[39m.\u001b[39mitems():\n\u001b[1;32m     72\u001b[0m         working_df \u001b[39m=\u001b[39m add_activity_row(element, record, working_df, activity, value)\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/examples/use-cases/instruments/utilities/formatting_tools.py:18\u001b[0m, in \u001b[0;36madd_open_row\u001b[0;34m(element, record)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39madd_open_row\u001b[39m(element, record):\n\u001b[1;32m     17\u001b[0m     df \u001b[39m=\u001b[39m new_data_frame()\n\u001b[0;32m---> 18\u001b[0m     df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mconcat([df, pd\u001b[39m.\u001b[39;49mDataFrame({\n\u001b[1;32m     19\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mCurrency\u001b[39;49m\u001b[39m\"\u001b[39;49m: element\u001b[39m.\u001b[39;49mcurrency,\n\u001b[1;32m     20\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mDate\u001b[39;49m\u001b[39m\"\u001b[39;49m: record\u001b[39m.\u001b[39;49meffective_date,\n\u001b[1;32m     21\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mActivity\u001b[39;49m\u001b[39m\"\u001b[39;49m: \u001b[39m\"\u001b[39;49m\u001b[39mOpen\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     22\u001b[0m         \u001b[39m\"\u001b[39;49m\u001b[39mValue\u001b[39;49m\u001b[39m\"\u001b[39;49m: record\u001b[39m.\u001b[39;49mopen\n\u001b[1;32m     23\u001b[0m     })])\u001b[39m.\u001b[39mreset_index(drop\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     25\u001b[0m     \u001b[39mreturn\u001b[39;00m df\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/.venv/lib/python3.11/site-packages/pandas/core/frame.py:709\u001b[0m, in \u001b[0;36mDataFrame.__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    703\u001b[0m     mgr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init_mgr(\n\u001b[1;32m    704\u001b[0m         data, axes\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mindex\u001b[39m\u001b[39m\"\u001b[39m: index, \u001b[39m\"\u001b[39m\u001b[39mcolumns\u001b[39m\u001b[39m\"\u001b[39m: columns}, dtype\u001b[39m=\u001b[39mdtype, copy\u001b[39m=\u001b[39mcopy\n\u001b[1;32m    705\u001b[0m     )\n\u001b[1;32m    707\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, \u001b[39mdict\u001b[39m):\n\u001b[1;32m    708\u001b[0m     \u001b[39m# GH#38939 de facto copy defaults to False only in non-dict cases\u001b[39;00m\n\u001b[0;32m--> 709\u001b[0m     mgr \u001b[39m=\u001b[39m dict_to_mgr(data, index, columns, dtype\u001b[39m=\u001b[39;49mdtype, copy\u001b[39m=\u001b[39;49mcopy, typ\u001b[39m=\u001b[39;49mmanager)\n\u001b[1;32m    710\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(data, ma\u001b[39m.\u001b[39mMaskedArray):\n\u001b[1;32m    711\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mnumpy\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mma\u001b[39;00m \u001b[39mimport\u001b[39;00m mrecords\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/.venv/lib/python3.11/site-packages/pandas/core/internals/construction.py:481\u001b[0m, in \u001b[0;36mdict_to_mgr\u001b[0;34m(data, index, columns, dtype, typ, copy)\u001b[0m\n\u001b[1;32m    477\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    478\u001b[0m         \u001b[39m# dtype check to exclude e.g. range objects, scalars\u001b[39;00m\n\u001b[1;32m    479\u001b[0m         arrays \u001b[39m=\u001b[39m [x\u001b[39m.\u001b[39mcopy() \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(x, \u001b[39m\"\u001b[39m\u001b[39mdtype\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39melse\u001b[39;00m x \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m arrays]\n\u001b[0;32m--> 481\u001b[0m \u001b[39mreturn\u001b[39;00m arrays_to_mgr(arrays, columns, index, dtype\u001b[39m=\u001b[39;49mdtype, typ\u001b[39m=\u001b[39;49mtyp, consolidate\u001b[39m=\u001b[39;49mcopy)\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/.venv/lib/python3.11/site-packages/pandas/core/internals/construction.py:115\u001b[0m, in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, columns, index, dtype, verify_integrity, typ, consolidate)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[39mif\u001b[39;00m verify_integrity:\n\u001b[1;32m    113\u001b[0m     \u001b[39m# figure out the index, if necessary\u001b[39;00m\n\u001b[1;32m    114\u001b[0m     \u001b[39mif\u001b[39;00m index \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 115\u001b[0m         index \u001b[39m=\u001b[39m _extract_index(arrays)\n\u001b[1;32m    116\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    117\u001b[0m         index \u001b[39m=\u001b[39m ensure_index(index)\n",
      "File \u001b[0;32m~/work/code/repos/sample-notebooks-1/.venv/lib/python3.11/site-packages/pandas/core/internals/construction.py:645\u001b[0m, in \u001b[0;36m_extract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mPer-column arrays must each be 1-dimensional\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    644\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m indexes \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m raw_lengths:\n\u001b[0;32m--> 645\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mIf using all scalar values, you must pass an index\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    647\u001b[0m \u001b[39mif\u001b[39;00m have_series:\n\u001b[1;32m    648\u001b[0m     index \u001b[39m=\u001b[39m union_indexes(indexes)\n",
      "\u001b[0;31mValueError\u001b[0m: If using all scalar values, you must pass an index"
     ]
    }
   ],
   "source": [
    "cash_ladder = transaction_portfolios_api.get_portfolio_cash_ladder(\n",
    "    scope=scope,\n",
    "    code=portfolio_code,\n",
    "    effective_at=\"2021-11-12T10:00:00Z\",\n",
    "    from_effective_at=\"2021-09-01T10:00:00Z\",\n",
    "    to_effective_at=\"2021-11-12T10:00:00Z\",\n",
    "    recipe_id_scope=scope,\n",
    "    recipe_id_code=recipe_code\n",
    ")\n",
    "\n",
    "cashladder_to_df(cash_ladder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividend Date\n",
    "In the cash ladder, we can see that our dividend comes in on the 9th of September 2021.\n",
    "\n",
    "We run our valuation again for the dividend date and find that we have received a dividend payment of 0.56 USD per share. This results in an cash position of 560 USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\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>Present Value</th>\n",
       "      <th>InstrumentName</th>\n",
       "      <th>ClientInternal</th>\n",
       "      <th>Quotes/Price</th>\n",
       "      <th>Holding/default/Units</th>\n",
       "      <th>PnL (1-day)</th>\n",
       "      <th>Dividend Yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>297,250.00</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>297.25</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>-2,960.00</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>560.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>None</td>\n",
       "      <td>nan</td>\n",
       "      <td>560.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>nan</td>\n",
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      ],
      "text/plain": [
       "   Present Value InstrumentName ClientInternal  Quotes/Price  \\\n",
       "0     297,250.00      Microsoft           MSFT        297.25   \n",
       "1         560.00            USD           None           nan   \n",
       "\n",
       "   Holding/default/Units  PnL (1-day)  Dividend Yield  \n",
       "0               1,000.00    -2,960.00            0.88  \n",
       "1                 560.00         0.00             nan  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_val(\"2021-09-09T10:00:00Z\", portfolio_code)"
   ]
  }
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