examples/use-cases/private-markets/Private Markets and Real Estate.ipynb

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       "\n",
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       "    \n",
       "         <script>\n",
       "         function code_toggle() {\n",
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   "source": [
    "from lusidtools.jupyter_tools import toggle_code\n",
    "\n",
    "\"\"\"Private Markets and Real Estate\n",
    "\n",
    "Attributes\n",
    "----------\n",
    "private markets\n",
    "real estate\n",
    "cash flows\n",
    "quotes\n",
    "transactions\n",
    "excel\n",
    "pdf\n",
    "lookthrough\n",
    "\"\"\"\n",
    "\n",
    "toggle_code(\"Toggle Docstring\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Private Markets and Real Estate\n",
    "\n",
    "Private markets and real estate notebook containing examples for modelling cash flows and portfolio securitization, among other things.\n",
    "\n",
    "The following list provides a step-by-step overview of the sections that will be covered. More detail on each section will be provided as we move through this notebook.\n",
    "\n",
    "Load data\n",
    "1. **[Setup LUSID](#1.-Setup-LUSID)**\n",
    "    - Packages\n",
    "2. **[Properties & Portfolios](#2.-Properties-&-Portfolios)**\n",
    "3. **[Securitise Porfolios](#3.-Securitise-portfolios)**\n",
    "4. **[Book in Cash](#4.-Book-in-Cash)**\n",
    "    - Capital Commitments\n",
    "    - Capital Calls\n",
    "    - Expenses\n",
    "5. **[Load Data into Portfolios](#5.-Load-Data-into-Portfolios)**\n",
    "    - Private Equity Fund Instruments\n",
    "    - Real Estate Fund Instruments\n",
    "    - Private Debt Fund Instruments\n",
    "    - Transactions\n",
    "    - Quotes\n",
    "    - Recipes\n",
    "8. **[Excel Report Writer](#6.-Excel-ILPA-Report-Writer-(for-General-Partnership))** (for General Partnership)\n",
    "    - Data Consolidation\n",
    "    - Excel Output\n",
    "6. **[Real Estate Cash Flows](#7.-Real-Estate-Cash-Flows)**\n",
    "    - Override Cash Flows\n",
    "    - Book Bond\n",
    "    - Data Calculations\n",
    "    - Data Visualisation\n",
    "7. **[PDF Report Writer](#8.-PDF-Report-Writer-(for-Real-Estate-Fund))** (for Real Estate Fund)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: fpdf in /usr/local/lib/python3.11/site-packages (1.7.2)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install fpdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Setup LUSID\n",
    "\n",
    "Import modules (both LUSID and non-LUSID) and define our API factories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LUSID Environment Initialised\n",
      "LUSID API Version : 0.6.12179.0\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, timedelta\n",
    "import json\n",
    "import pytz\n",
    "import time\n",
    "from IPython.core.display import HTML\n",
    "import openpyxl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import random\n",
    "from dateutil.relativedelta import relativedelta\n",
    "from fpdf import FPDF\n",
    "from IPython.display import IFrame\n",
    "\n",
    "# Import key modules from the LUSID package\n",
    "import lusid as lu\n",
    "import lusid.api as la\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.cocoon.cocoon import load_from_data_frame\n",
    "from lusidtools.cocoon.cocoon_printer import (\n",
    "    format_instruments_response,\n",
    "    format_holdings_response,\n",
    "    format_portfolios_response,\n",
    "    format_transactions_response,\n",
    "    format_quotes_response,)\n",
    "from lusidtools.cocoon.transaction_type_upload import upsert_transaction_type_alias\n",
    "from lusidtools.lpt.lpt import to_date\n",
    "from lusidjam import RefreshingToken\n",
    "import lusid_drive\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",
    "\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(),\n",
    "    api_secrets_filename=secrets_path\n",
    ")\n",
    "\n",
    "print(\"LUSID Environment Initialised\")\n",
    "print(\n",
    "    \"LUSID API Version :\",\n",
    "    api_factory.build(la.ApplicationMetadataApi).get_lusid_versions().build_version,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LUSID Variable Definitions\n",
    "transaction_portfolios_api = api_factory.build(la.TransactionPortfoliosApi)\n",
    "instruments_api = api_factory.build(la.InstrumentsApi)\n",
    "property_definitions_api = api_factory.build(la.PropertyDefinitionsApi)\n",
    "configuration_recipe_api = api_factory.build(la.ConfigurationRecipeApi)\n",
    "structured_result_data_api = api_factory.build(la.StructuredResultDataApi)\n",
    "aggregation_api = api_factory.build(la.AggregationApi)\n",
    "transaction_config_api = api_factory.build(la.TransactionConfigurationApi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scope = \"privateMarkets\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Properties & Portfolios\n",
    "\n",
    "First, we need to instantiate our properties and portfolios.\n",
    "\n",
    "The properties we create are:\n",
    "1. **Investor**: Investor who is responsible for a transaction\n",
    "2. **Holding Class**:  Class that the holding can be categorised into\n",
    "3. **Expense Type**: Category of an expense (only for expense transactions)\n",
    "\n",
    "We also create 4 portfolios:\n",
    "1. **General Partnership**\n",
    "2. **Real Estate Fund**\n",
    "3. **Private Equity Fund**\n",
    "4. **Private Debt Fund**\n",
    "\n",
    "The following diagram illustrates the structure of funds that we are building out, with each box representing a portfolio.\n",
    "\n",
    "We are able to associate the Real Estate, Private Equity and Private Debt fund portfolios with the General Partnership fund through a process called 'securitisation'. We will cover this in more detail during the next section.\n",
    "\n",
    "![Image showing fund structure of this notebook](./data/fund_structure.png \"Fund Structure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transaction/privateMarkets/expense_type property created\n",
      "Transaction/privateMarkets/investor property created\n",
      "Transaction/privateMarkets/holding_class property created\n"
     ]
    }
   ],
   "source": [
    "# Define custom property code and display names\n",
    "property_args = {\n",
    "    \"expense_type\": \"Expense Type\",\n",
    "    \"investor\": \"Investor\",\n",
    "    \"holding_class\": \"Holding Class\"\n",
    "}\n",
    "\n",
    "# Define function to upsert properties\n",
    "def transactionPropertyUpsert(k, v):\n",
    "    try:\n",
    "        resp = property_definitions_api.create_property_definition(\n",
    "            create_property_definition_request=lm.CreatePropertyDefinitionRequest(\n",
    "                domain=\"Transaction\",\n",
    "                scope=scope,\n",
    "                code=k,\n",
    "                value_required=None,\n",
    "                display_name=v,\n",
    "                data_type_id=lm.ResourceId(scope=\"system\", code=\"string\"),\n",
    "                life_time=None,\n",
    "            )\n",
    "        )\n",
    "        print(f\"{resp.key} property created\")\n",
    "\n",
    "    except lu.ApiException as e:\n",
    "        if json.loads(e.body)[\"code\"] == 124:  # PropertyAlreadyExists\n",
    "            print(json.loads(e.body)[\"title\"])\n",
    "        else:\n",
    "            raise e\n",
    "\n",
    "\n",
    "# Loop through dictionary and call function\n",
    "for k, v in property_args.items():\n",
    "    transactionPropertyUpsert(k, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generalPartnership portfolio created\n",
      "realEstateFund portfolio created\n",
      "privateEquityFund portfolio created\n",
      "privateDebtFund portfolio created\n"
     ]
    }
   ],
   "source": [
    "# Details of the portfolios we want to create, name and SHKs\n",
    "portfolio_details = {\n",
    "    \"generalPartnership\": [f\"Transaction/{scope}/investor\", f\"Transaction/{scope}/holding_class\"],\n",
    "    \"realEstateFund\": [f\"Transaction/{scope}/holding_class\"],\n",
    "    \"privateEquityFund\": [],\n",
    "    \"privateDebtFund\": []\n",
    "}\n",
    "\n",
    "\n",
    "def transactionPortfolioUpsert(portfolio_name, sub_holding_keys):\n",
    "    try:\n",
    "        resp = transaction_portfolios_api.create_portfolio(\n",
    "            scope=scope,\n",
    "            create_transaction_portfolio_request=lm.CreateTransactionPortfolioRequest(\n",
    "                display_name=portfolio_name,\n",
    "                code=portfolio_name,\n",
    "                base_currency=\"GBP\",\n",
    "                created=\"2021-01-01\",\n",
    "                sub_holding_keys=sub_holding_keys,\n",
    "            ),\n",
    "        )\n",
    "        print(f\"{resp.display_name} portfolio created\")\n",
    "\n",
    "    except lu.ApiException as e:\n",
    "        if json.loads(e.body)[\"code\"] == 112:  # PortfolioWithIdAlreadyExists\n",
    "            print(json.loads(e.body)[\"title\"])\n",
    "        else:\n",
    "            raise e\n",
    "        \n",
    "for portfolio_name, sub_holdings_keys in portfolio_details.items():\n",
    "    transactionPortfolioUpsert(portfolio_name, sub_holdings_keys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Securitise Portfolios\n",
    "\n",
    "The portfolios we have just created will now undergo a process known as 'securitisation'. By doing this, we encapsulate a portfolio into a security which then allows us to book it into a parent portfolio in the same way we would for any traditional security. In this case, we are securitising the child funds and then booking them into our General Partnership portfolio, creating a fund-of-funds architecture. \n",
    "\n",
    "The diagram below shows the fund structure.\n",
    "\n",
    "![Image showing portfolio securitisation](./data/lookthrough.png \"Securites Portfolios\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('realEstateFund', 'realEstateFundSecuritised'),\n",
       " ('privateEquityFund', 'privateEquityFundSecuritised'),\n",
       " ('privateDebtFund', 'privateDebtFundSecuritised')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of securitised portfolios and IDs, using our dictionary as basis\n",
    "portfolio_details.pop(\"generalPartnership\")\n",
    "securitised_portfolio_names = list(portfolio_details.keys())\n",
    "securitised_portfolio_ids = [portfolio_name + \"Securitised\" for portfolio_name in securitised_portfolio_names] \n",
    "securitised_portfolios = list(zip(securitised_portfolio_names, securitised_portfolio_ids))\n",
    "\n",
    "securitised_portfolios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5R created\n",
      "Instrument LUID_00003D5S created\n",
      "Instrument LUID_00003D5T created\n"
     ]
    }
   ],
   "source": [
    "# Insert the new portfolio instruments into LUSID\n",
    "for portfolio, instrument_id in securitised_portfolios:\n",
    "    try:\n",
    "        instr_result = instruments_api.upsert_instruments(\n",
    "            request_body={\n",
    "                \"look_through\": lm.InstrumentDefinition(\n",
    "                    name=portfolio,\n",
    "                    identifiers={\n",
    "                        \"ClientInternal\": lm.InstrumentIdValue(value=instrument_id)\n",
    "                    },\n",
    "                    look_through_portfolio_id=lm.ResourceId(scope=scope, code=portfolio),\n",
    "                )\n",
    "            }\n",
    "        )\n",
    "        print(f\"Instrument {instr_result.values['look_through'].lusid_instrument_id} created\")\n",
    "    except lu.ApiException as e:\n",
    "        raise e\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Book in Cash\n",
    "\n",
    "Next, let's populate our portfolios with some cash. The cash will take one of three forms:\n",
    "\n",
    "1. **Capital Commitments**\n",
    "2. **Capital Calls**\n",
    "3. **Expenses**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capital Commitments\n",
    "\n",
    "A capital commitment is some projected capital expendature that an entity commits to spending over a set period of time. In our example, we use GBP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>investor</th>\n",
       "      <th>txn_id</th>\n",
       "      <th>type</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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx001</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx002</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx003</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx004</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-01T08:00:00Z</td>\n",
       "      <td>2021-06-03T08:00:00Z</td>\n",
       "      <td>16,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>16,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx005</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-01T08:00:00Z</td>\n",
       "      <td>2021-06-03T08:00:00Z</td>\n",
       "      <td>12,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>12,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx006</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-06-01T08:00:00Z</td>\n",
       "      <td>2021-06-03T08:00:00Z</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx007</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-04T08:00:00Z</td>\n",
       "      <td>2021-01-06T08:00:00Z</td>\n",
       "      <td>14,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>14,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx008</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-04T08:00:00Z</td>\n",
       "      <td>2021-01-06T08:00:00Z</td>\n",
       "      <td>8,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>8,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx009</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-01-04T08:00:00Z</td>\n",
       "      <td>2021-01-06T08:00:00Z</td>\n",
       "      <td>8,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>8,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx010</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-06-01T08:00:00Z</td>\n",
       "      <td>2022-06-03T08:00:00Z</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx011</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-06-01T08:00:00Z</td>\n",
       "      <td>2022-06-03T08:00:00Z</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx012</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-06-01T08:00:00Z</td>\n",
       "      <td>2022-06-03T08:00:00Z</td>\n",
       "      <td>3,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>3,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Cash</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      investor txn_id               type  client_id            trade_date  \\\n",
       "0   Investor 1  tx001  CapitalCommitment        NaN  2022-01-01T08:00:00Z   \n",
       "1   Investor 2  tx002  CapitalCommitment        NaN  2022-01-01T08:00:00Z   \n",
       "2   Investor 3  tx003  CapitalCommitment        NaN  2022-01-01T08:00:00Z   \n",
       "3   Investor 1  tx004  CapitalCommitment        NaN  2021-06-01T08:00:00Z   \n",
       "4   Investor 2  tx005  CapitalCommitment        NaN  2021-06-01T08:00:00Z   \n",
       "5   Investor 3  tx006  CapitalCommitment        NaN  2021-06-01T08:00:00Z   \n",
       "6   Investor 1  tx007  CapitalCommitment        NaN  2021-01-04T08:00:00Z   \n",
       "7   Investor 2  tx008  CapitalCommitment        NaN  2021-01-04T08:00:00Z   \n",
       "8   Investor 3  tx009  CapitalCommitment        NaN  2021-01-04T08:00:00Z   \n",
       "9   Investor 1  tx010  CapitalCommitment        NaN  2022-06-01T08:00:00Z   \n",
       "10  Investor 2  tx011  CapitalCommitment        NaN  2022-06-01T08:00:00Z   \n",
       "11  Investor 3  tx012  CapitalCommitment        NaN  2022-06-01T08:00:00Z   \n",
       "\n",
       "         settlement_date       quantity  price total_consideration currency  \\\n",
       "0   2022-01-03T08:00:00Z  20,000,000.00      1       20,000,000.00      GBP   \n",
       "1   2022-01-03T08:00:00Z   5,000,000.00      1        5,000,000.00      GBP   \n",
       "2   2022-01-03T08:00:00Z   5,000,000.00      1        5,000,000.00      GBP   \n",
       "3   2021-06-03T08:00:00Z  16,000,000.00      1       16,000,000.00      GBP   \n",
       "4   2021-06-03T08:00:00Z  12,000,000.00      1       12,000,000.00      GBP   \n",
       "5   2021-06-03T08:00:00Z  10,000,000.00      1       10,000,000.00      GBP   \n",
       "6   2021-01-06T08:00:00Z  14,000,000.00      1       14,000,000.00      GBP   \n",
       "7   2021-01-06T08:00:00Z   8,000,000.00      1        8,000,000.00      GBP   \n",
       "8   2021-01-06T08:00:00Z   8,000,000.00      1        8,000,000.00      GBP   \n",
       "9   2022-06-03T08:00:00Z   2,000,000.00      1        2,000,000.00      GBP   \n",
       "10  2022-06-03T08:00:00Z   5,000,000.00      1        5,000,000.00      GBP   \n",
       "11  2022-06-03T08:00:00Z   3,000,000.00      1        3,000,000.00      GBP   \n",
       "\n",
       "             portfolio holding_class  \n",
       "0   generalPartnership          Cash  \n",
       "1   generalPartnership          Cash  \n",
       "2   generalPartnership          Cash  \n",
       "3   generalPartnership          Cash  \n",
       "4   generalPartnership          Cash  \n",
       "5   generalPartnership          Cash  \n",
       "6   generalPartnership          Cash  \n",
       "7   generalPartnership          Cash  \n",
       "8   generalPartnership          Cash  \n",
       "9   generalPartnership          Cash  \n",
       "10  generalPartnership          Cash  \n",
       "11  generalPartnership          Cash  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capital_commitments = pd.read_csv(\"./data/cash_commitments.csv\")\n",
    "capital_commitments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type 'CapitalCommitment' created\n"
     ]
    }
   ],
   "source": [
    "# Create CapitalCommitment type\n",
    "transaction_type_request = lu.models.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lu.models.TransactionTypeAlias(\n",
    "            type=\"CapitalCommitment\", # Label\n",
    "            description=\"Commitment of capital\",\n",
    "            transaction_class=\"CashTransfers\",\n",
    "            transaction_roles=\"Longer\",\n",
    "        )\n",
    "    ], \n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lu.models.TransactionTypeMovement(\n",
    "            movement_types=\"CashReceivable\", \n",
    "            side=\"Side1\", # Buy properties\n",
    "            direction=1, # Increase\n",
    "        ),\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\",\n",
    "        type=\"CapitalCommitment\",\n",
    "        transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "    \n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define our transaction mapping of fields that we'll use for upserts. This is for CapitalCommitments and we'll modify it for the other Transaction Types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "transaction_mapping = {\n",
    "    \"identifier_mapping\": {\"Currency\": \"currency\"},\n",
    "    \"required\": {\n",
    "        \"code\": \"portfolio\",\n",
    "        \"transaction_id\": \"txn_id\",\n",
    "        \"type\": \"type\",\n",
    "        \"transaction_price.price\": \"price\",\n",
    "        \"transaction_price.type\": \"$Price\",\n",
    "        \"total_consideration.amount\": \"total_consideration\",\n",
    "        \"units\": \"quantity\",\n",
    "        \"transaction_date\": \"trade_date\",\n",
    "        \"total_consideration.currency\": \"currency\",\n",
    "        \"settlement_date\": \"settlement_date\",\n",
    "    },\n",
    "    \"optional\": {},\n",
    "    \"properties\": [\"investor\", \"holding_class\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        1       0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=capital_commitments,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error      \n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "    \n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capital Calls\n",
    "\n",
    "Next, we can book capital calls for our general partnership. To do so, we are interacting directly with the securitised funds that we have just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>investor</th>\n",
       "      <th>txn_id</th>\n",
       "      <th>type</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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx013</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>privateEquityFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>200,000</td>\n",
       "      <td>100</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx014</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>realEstateFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>250,000</td>\n",
       "      <td>100</td>\n",
       "      <td>25,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx015</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>privateEquityFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>100,000</td>\n",
       "      <td>100</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx016</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>realEstateFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>100,000</td>\n",
       "      <td>100</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx017</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>privateDebtFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>40,000</td>\n",
       "      <td>100</td>\n",
       "      <td>4,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx018</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>privateEquityFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>100,000</td>\n",
       "      <td>100</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx019</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>realEstateFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>50,000</td>\n",
       "      <td>100</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx020</td>\n",
       "      <td>CapitalCall</td>\n",
       "      <td>privateDebtFundSecuritised</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>50,000</td>\n",
       "      <td>100</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     investor txn_id         type                     client_id  \\\n",
       "0  Investor 1  tx013  CapitalCall  privateEquityFundSecuritised   \n",
       "1  Investor 1  tx014  CapitalCall     realEstateFundSecuritised   \n",
       "2  Investor 2  tx015  CapitalCall  privateEquityFundSecuritised   \n",
       "3  Investor 2  tx016  CapitalCall     realEstateFundSecuritised   \n",
       "4  Investor 2  tx017  CapitalCall    privateDebtFundSecuritised   \n",
       "5  Investor 3  tx018  CapitalCall  privateEquityFundSecuritised   \n",
       "6  Investor 3  tx019  CapitalCall     realEstateFundSecuritised   \n",
       "7  Investor 3  tx020  CapitalCall    privateDebtFundSecuritised   \n",
       "\n",
       "             trade_date       settlement_date quantity  price  \\\n",
       "0  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z  200,000    100   \n",
       "1  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z  250,000    100   \n",
       "2  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z  100,000    100   \n",
       "3  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z  100,000    100   \n",
       "4  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z   40,000    100   \n",
       "5  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z  100,000    100   \n",
       "6  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z   50,000    100   \n",
       "7  2022-01-04T08:00:00Z  2022-01-04T08:00:00Z   50,000    100   \n",
       "\n",
       "  total_consideration currency           portfolio holding_class  \n",
       "0       20,000,000.00      GBP  generalPartnership          Fund  \n",
       "1       25,000,000.00      GBP  generalPartnership          Fund  \n",
       "2       10,000,000.00      GBP  generalPartnership          Fund  \n",
       "3       10,000,000.00      GBP  generalPartnership          Fund  \n",
       "4        4,000,000.00      GBP  generalPartnership          Fund  \n",
       "5       10,000,000.00      GBP  generalPartnership          Fund  \n",
       "6        5,000,000.00      GBP  generalPartnership          Fund  \n",
       "7        5,000,000.00      GBP  generalPartnership          Fund  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read our capital calls file into dataframe\n",
    "capital_calls = pd.read_csv(\"./data/capital_calls.csv\")\n",
    "\n",
    "gp_calls = capital_calls[capital_calls[\"portfolio\"] == \"generalPartnership\"]\n",
    "display(gp_calls)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type 'CapitalCall' created\n"
     ]
    }
   ],
   "source": [
    "# Create CapitalCall type\n",
    "transaction_type_request = lu.models.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lu.models.TransactionTypeAlias(\n",
    "            type=\"CapitalCall\", # Label\n",
    "            description=\"Capital call\",\n",
    "            transaction_class=\"Basic\",\n",
    "            transaction_roles=\"LongLonger\",\n",
    "        )\n",
    "    ], \n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lu.models.TransactionTypeMovement(\n",
    "            movement_types=\"StockMovement\", \n",
    "            side=\"Side1\", # Buy properties\n",
    "            direction=1, # Increase\n",
    "        ),\n",
    "        # Sell side movement\n",
    "        lu.models.TransactionTypeMovement(\n",
    "            movement_types=\"CashCommitment\",\n",
    "            side=\"Side2\", # Sell properties\n",
    "            direction=-1, # Decrease\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\",\n",
    "        type=\"CapitalCall\",\n",
    "        transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "    \n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the updated keys for Capital Call and update template dictionary\n",
    "transaction_mapping_capital_call = {\n",
    "    \"identifier_mapping\": {\"ClientInternal\": \"client_id\"},\n",
    "    \"properties\": [\"investor\", \"holding_class\"]\n",
    "}\n",
    "transaction_mapping.update(transaction_mapping_capital_call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        1       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=gp_calls,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error      \n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "    \n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>investor</th>\n",
       "      <th>txn_id</th>\n",
       "      <th>type</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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx021</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>20,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx022</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>realEstateFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx023</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>10,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx025</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>4,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>4,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx026</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>10,000,000</td>\n",
       "      <td>1</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>tx028</td>\n",
       "      <td>CapitalCommitment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>2022-01-04T08:00:00Z</td>\n",
       "      <td>5,500,000</td>\n",
       "      <td>1</td>\n",
       "      <td>5,500,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>Fund</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   investor txn_id               type client_id            trade_date  \\\n",
       "8       NaN  tx021  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "9       NaN  tx022  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "10      NaN  tx023  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "11      NaN  tx025  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "12      NaN  tx026  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "13      NaN  tx028  CapitalCommitment       NaN  2022-01-04T08:00:00Z   \n",
       "\n",
       "         settlement_date       quantity  price total_consideration currency  \\\n",
       "8   2022-01-04T08:00:00Z     20,000,000      1       20,000,000.00      GBP   \n",
       "9   2022-01-04T08:00:00Z  20,000,000.00      1       20,000,000.00      GBP   \n",
       "10  2022-01-04T08:00:00Z     10,000,000      1       10,000,000.00      GBP   \n",
       "11  2022-01-04T08:00:00Z      4,000,000      1        4,000,000.00      GBP   \n",
       "12  2022-01-04T08:00:00Z     10,000,000      1       10,000,000.00      GBP   \n",
       "13  2022-01-04T08:00:00Z      5,500,000      1        5,500,000.00      GBP   \n",
       "\n",
       "            portfolio holding_class  \n",
       "8   privateEquityFund          Fund  \n",
       "9      realEstateFund          Fund  \n",
       "10  privateEquityFund          Fund  \n",
       "11    privateDebtFund          Fund  \n",
       "12  privateEquityFund          Fund  \n",
       "13    privateDebtFund          Fund  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "underlying_calls = capital_calls[capital_calls[\"portfolio\"] != \"generalPartnership\"]\n",
    "display(underlying_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the updated keys for Underlying Calls and update template dictionary\n",
    "transaction_mapping_underlying_calls = {\n",
    "    \"identifier_mapping\": {\"Currency\": \"currency\"},\n",
    "     \"properties\": [\"holding_class\"]\n",
    "}\n",
    "transaction_mapping.update(transaction_mapping_underlying_calls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        3       0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=underlying_calls,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error      \n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "    \n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Expenses\n",
    "\n",
    "To match something typical of the real-world, we can book some expenses that have accrued throughout various trading activities. Each expense will have a type, and those types will fall into one of three distinct holding classes. The structure of the expense classes is as follows:\n",
    "\n",
    "1. **Management Fees**\n",
    "    - Rebate \n",
    "2. **Partnership Expenses**\n",
    "    - Accounting, Administration & IT\n",
    "    - Audit & Tax Prepatory\n",
    "    - Bank Fees\n",
    "    - Custody Fees\n",
    "    - Due Diligence\n",
    "    - Legal\n",
    "    - Organization Costs\n",
    "    - Other Travel & Entertainment\n",
    "    - Other\n",
    "3. **Offsets**\n",
    "    - Advisory Fee\n",
    "    - Broken Deal Fee\n",
    "    - Transaction & Deal Fee\n",
    "    - Directors Fee\n",
    "    - Monitoring Fee\n",
    "    - Capital Markets Fee\n",
    "    - Organization Costs\n",
    "    - Placement Fee\n",
    "    - Other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "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>investor</th>\n",
       "      <th>txn_id</th>\n",
       "      <th>type</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",
       "      <th>holding_class</th>\n",
       "      <th>expense_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx029</td>\n",
       "      <td>FundsIn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Management Fee</td>\n",
       "      <td>Rebate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx030</td>\n",
       "      <td>FundsIn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Management Fee</td>\n",
       "      <td>Rebate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Investor 3</td>\n",
       "      <td>tx031</td>\n",
       "      <td>FundsIn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Management Fee</td>\n",
       "      <td>Rebate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Investor 1</td>\n",
       "      <td>tx032</td>\n",
       "      <td>FundsOut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>1,250.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1,250.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Partnership Expense</td>\n",
       "      <td>Accounting, Administration &amp; IT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Investor 2</td>\n",
       "      <td>tx033</td>\n",
       "      <td>FundsOut</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-01T08:00:00Z</td>\n",
       "      <td>2022-01-03T08:00:00Z</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>generalPartnership</td>\n",
       "      <td>Partnership Expense</td>\n",
       "      <td>Accounting, Administration &amp; IT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     investor txn_id      type  client_id            trade_date  \\\n",
       "0  Investor 1  tx029   FundsIn        NaN  2022-01-01T08:00:00Z   \n",
       "1  Investor 2  tx030   FundsIn        NaN  2022-01-01T08:00:00Z   \n",
       "2  Investor 3  tx031   FundsIn        NaN  2022-01-01T08:00:00Z   \n",
       "3  Investor 1  tx032  FundsOut        NaN  2022-01-01T08:00:00Z   \n",
       "4  Investor 2  tx033  FundsOut        NaN  2022-01-01T08:00:00Z   \n",
       "\n",
       "        settlement_date  quantity  price total_consideration currency  \\\n",
       "0  2022-01-03T08:00:00Z        10      1                  10      GBP   \n",
       "1  2022-01-03T08:00:00Z        15      1                  15      GBP   \n",
       "2  2022-01-03T08:00:00Z        80      1                  80      GBP   \n",
       "3  2022-01-03T08:00:00Z  1,250.00      1            1,250.00      GBP   \n",
       "4  2022-01-03T08:00:00Z  1,000.00      1            1,000.00      GBP   \n",
       "\n",
       "            portfolio        holding_class                     expense_type  \n",
       "0  generalPartnership       Management Fee                           Rebate  \n",
       "1  generalPartnership       Management Fee                           Rebate  \n",
       "2  generalPartnership       Management Fee                           Rebate  \n",
       "3  generalPartnership  Partnership Expense  Accounting, Administration & IT  \n",
       "4  generalPartnership  Partnership Expense  Accounting, Administration & IT  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expenses = pd.read_csv(\"./data/expenses.csv\")\n",
    "expenses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type 'FundsIn' created\n",
      "Type 'FundsOut' created\n"
     ]
    }
   ],
   "source": [
    "# Create our FundsIn and FundsOut Transaction Types\n",
    "\n",
    "#FundsIn\n",
    "transaction_type_request = lm.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lm.TransactionTypeAlias(\n",
    "            type=\"FundsIn\", # Label\n",
    "            description=\"Deposit New Funds\",\n",
    "            transaction_class=\"CashTransfers\",\n",
    "            transaction_roles=\"Longer\",\n",
    "        )\n",
    "    ], \n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"CashReceivable\", \n",
    "            side=\"Side1\", # Buy properties\n",
    "            direction=1, # Increase\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\",\n",
    "        type=\"FundsIn\",\n",
    "        transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "    \n",
    "except lu.ApiException as e:\n",
    "    raise e\n",
    "\n",
    "#FundsOut\n",
    "transaction_type_request = lm.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lm.TransactionTypeAlias(\n",
    "            type=\"FundsOut\", # Label\n",
    "            description=\"Withdraw Funds\",\n",
    "            transaction_class=\"CashTransfers\",\n",
    "            transaction_roles=\"Shorter\",\n",
    "        )\n",
    "    ], \n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"CashReceivable\", \n",
    "            side=\"Side1\", # Buy properties\n",
    "            direction=-1, # Decrease\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\",\n",
    "        type=\"FundsOut\",\n",
    "        transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "    \n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the updated keys for Expenses and update template dictionary\n",
    "transaction_mapping_expenses = {\n",
    "    \"properties\": [\"investor\", \"holding_class\", \"expense_type\"]\n",
    "}\n",
    "transaction_mapping.update(transaction_mapping_expenses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        1       0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=expenses,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error      \n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "    \n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Load Data into Portfolios\n",
    "\n",
    "In this section, we begin to populate the portfolios with transactions, quotes and cash flows. We must also instantiate our instruments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Private Equity Fund\n",
    "\n",
    "In this section, we create the instruments and instrument properties for our Private Equity Fund.\n",
    "\n",
    "The instrument categories are as follows, with each of these being represented by an equity instrument in LUSID:\n",
    "1. **Preferred shares** (PFD)\n",
    "2. **Simple Agreement for Future Equity** (SAFE)\n",
    "3. **Equity**\n",
    "\n",
    "In addition to these instruments, we create a dividend yield property. This is to be used with the preferred shares later on in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument/privateMarkets/dividend_yield property created\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",
    "    resp = property_definitions_api.create_property_definition(\n",
    "        create_property_definition_request=property_definition_request\n",
    "    )\n",
    "    print(f\"{resp.key} property created\") \n",
    "\n",
    "except lu.ApiException as e:\n",
    "    if json.loads(e.body)[\"code\"] == 124: # PropertyAlreadyExists\n",
    "        print(json.loads(e.body)[\"title\"])\n",
    "    else:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_simple_instrument(\n",
    "    name,\n",
    "    type,\n",
    "    client_internal,\n",
    "    dom_ccy,\n",
    "    dividend_yield,\n",
    "):\n",
    "\n",
    "    simple_instrument = lm.SimpleInstrument(\n",
    "        instrument_type=\"SimpleInstrument\",\n",
    "        dom_ccy=dom_ccy,\n",
    "        asset_class=\"Unknown\",\n",
    "        simple_instrument_type=type,\n",
    "    )\n",
    "\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",
    "            )\n",
    "        ),\n",
    "    )\n",
    "\n",
    "    instrument_definition = lm.InstrumentDefinition(\n",
    "        name=name,\n",
    "        identifiers={\"ClientInternal\": lm.InstrumentIdValue(client_internal)},\n",
    "        definition=simple_instrument,\n",
    "        properties=[properties],\n",
    "    )\n",
    "\n",
    "    # upsert the instrument\n",
    "    upsert_request = {client_internal: instrument_definition}\n",
    "    try:\n",
    "        upsert_response = instruments_api.upsert_instruments(\n",
    "            request_body=upsert_request)\n",
    "        luid = upsert_response.values[client_internal].lusid_instrument_id\n",
    "        return luid\n",
    "    except lu.ApiException as e:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5U created\n"
     ]
    }
   ],
   "source": [
    "instrument_type = \"Equity\"\n",
    "\n",
    "name = \"AECI LTD 5.5% - PFD\"\n",
    "identifier = \"AECI_LTD_5.5\"\n",
    "dom_ccy = \"GBP\"\n",
    "dividend_yield = 5.5\n",
    "\n",
    "print(f\"Instrument {create_simple_instrument(name, instrument_type, identifier, dom_ccy, dividend_yield)} created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5V created\n"
     ]
    }
   ],
   "source": [
    "name = \"FINBOURNE Technology - SAFE\"\n",
    "identifier = \"Finbourne_SAFE\"\n",
    "dom_ccy = \"GBP\"\n",
    "dividend_yield = 0\n",
    "\n",
    "print(f\"Instrument {create_simple_instrument(name, instrument_type, identifier, dom_ccy, dividend_yield)} created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5W created\n"
     ]
    }
   ],
   "source": [
    "name = \"Morrison Supermarkets PLC\"\n",
    "identifier = \"Morrisons\"\n",
    "dom_ccy = \"GBP\"\n",
    "dividend_yield = 0\n",
    "\n",
    "print(f\"Instrument {create_simple_instrument(name, instrument_type, identifier, dom_ccy, dividend_yield)} created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real Estate Fund\n",
    "\n",
    "Next, let's create the instruments and instrument properties for our Real Estate Fund.\n",
    "\n",
    "The two real estate properties are:\n",
    "1. **Sydney Bondi Junction Westfield**, a shopping centre in Australia\n",
    "2. **One Carter Lane**, an office in London\n",
    "\n",
    "The five properties we create are:\n",
    "1. **Average Retail Lease**\n",
    "2. **Term**\n",
    "3. **Frequency**\n",
    "4. **Rating**\n",
    "5. **Rental Units**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmbs_properties = ['average_retail_lease', 'term', 'frequency', 'rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument/privateMarkets/rental_units property created\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    property_definition_request = lm.CreatePropertyDefinitionRequest(\n",
    "        domain=\"Instrument\",\n",
    "        scope=scope,\n",
    "        code=\"rental_units\",\n",
    "        display_name=\"rental_units\",\n",
    "        data_type_id=lm.ResourceId(\n",
    "            scope=\"system\",\n",
    "            code=\"number\",\n",
    "        ),\n",
    "        life_time=\"Perpetual\",\n",
    "    )\n",
    "\n",
    "    resp = property_definitions_api.create_property_definition(\n",
    "        create_property_definition_request=property_definition_request\n",
    "    )\n",
    "    print(f\"{resp.key} property created\") \n",
    "\n",
    "except lu.ApiException as e:\n",
    "    if json.loads(e.body)[\"code\"] == 124: # PropertyAlreadyExists\n",
    "        print(json.loads(e.body)[\"title\"])\n",
    "    else:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument/privateMarkets/average_retail_lease property created\n",
      "Instrument/privateMarkets/term property created\n",
      "Instrument/privateMarkets/frequency property created\n",
      "Instrument/privateMarkets/rating property created\n"
     ]
    }
   ],
   "source": [
    "for property in cmbs_properties:\n",
    "    try:\n",
    "        resp = property_definitions_api.create_property_definition(\n",
    "            create_property_definition_request=lm.CreatePropertyDefinitionRequest(\n",
    "                domain=\"Instrument\",\n",
    "                scope=scope,\n",
    "                code=property,\n",
    "                value_required=None,\n",
    "                display_name=property,\n",
    "                data_type_id=lm.ResourceId(scope=\"system\", code=\"string\"),\n",
    "                life_time=None,\n",
    "            )\n",
    "        )\n",
    "        print(f\"{resp.key} property created\") \n",
    "        \n",
    "    except lu.ApiException as e: \n",
    "        if json.loads(e.body)[\"code\"] == 124: # PropertyAlreadyExists\n",
    "            print(json.loads(e.body)[\"title\"])\n",
    "        else:\n",
    "            raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_cmbs_property(code, value):\n",
    "    property = lm.ModelProperty(\n",
    "                    key=f\"Instrument/{scope}/{code}\",\n",
    "                    value=lm.PropertyValue(\n",
    "                        label_value=value,\n",
    "                    ),\n",
    "                )\n",
    "    return property"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_CMBS(\n",
    "    name,\n",
    "    client_internal,\n",
    "    dom_ccy,\n",
    "    rental_units,\n",
    "    average_retail_lease,\n",
    "    term,\n",
    "    frequency,\n",
    "    rating\n",
    "):\n",
    "\n",
    "    cmbs = lm.SimpleInstrument(\n",
    "        instrument_type=\"SimpleInstrument\",\n",
    "        dom_ccy=dom_ccy,\n",
    "        asset_class=\"Unknown\",\n",
    "        simple_instrument_type=\"CMBS\"\n",
    "    )\n",
    "\n",
    "    # fill in dictionary of variables\n",
    "    properties = []\n",
    "    inputs = {\n",
    "        \"average_retail_lease\": average_retail_lease,\n",
    "        \"term\": term,\n",
    "        \"frequency\": frequency,\n",
    "        \"rating\": rating}\n",
    "\n",
    "    # properties = lm.InstrumentProperties()\n",
    "    properties.append(lm.ModelProperty(\n",
    "        key=f\"Instrument/{scope}/rental_units\",\n",
    "        value=lm.PropertyValue(\n",
    "            metric_value=lm.MetricValue(\n",
    "                value=rental_units,\n",
    "            )\n",
    "        ),\n",
    "    ))\n",
    "\n",
    "    for code in cmbs_properties:\n",
    "        properties.append(create_cmbs_property(code, inputs[code]))\n",
    "\n",
    "\n",
    "    cmbs_definition = lm.InstrumentDefinition(\n",
    "        name=name,\n",
    "        identifiers={\"ClientInternal\": lm.InstrumentIdValue(client_internal)},\n",
    "        definition=cmbs,\n",
    "        properties=properties,\n",
    "    )\n",
    "\n",
    "    # upsert the instrument\n",
    "    upsert_request = {client_internal: cmbs_definition}\n",
    "    try:\n",
    "        upsert_response = instruments_api.upsert_instruments(request_body=upsert_request)\n",
    "        cmbs_luid = upsert_response.values[client_internal].lusid_instrument_id\n",
    "        return cmbs_luid\n",
    "    except lu.ApiException as e:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5X created\n"
     ]
    }
   ],
   "source": [
    "name=\"Sydney Bondi Junction Westfield\"\n",
    "client_internal=\"SYD241232\"\n",
    "dom_ccy=\"GBP\"\n",
    "rental_units=76\n",
    "average_retail_lease=\"2.3Y\"\n",
    "term=\"5Y\"\n",
    "frequency=\"Q\"\n",
    "rating=\"B1\"\n",
    "\n",
    "sydney_luid = create_CMBS(name, client_internal, dom_ccy, rental_units, average_retail_lease, term, frequency, rating)\n",
    "print(f\"Instrument {sydney_luid} created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5Y created\n"
     ]
    }
   ],
   "source": [
    "name=\"One Carter Lane\"\n",
    "client_internal=\"LON343239\"\n",
    "dom_ccy=\"GBP\"\n",
    "rental_units=50\n",
    "average_retail_lease=\"1.8Y\"\n",
    "term=\"3Y\"\n",
    "frequency=\"SA\"\n",
    "rating=\"A1\"\n",
    "\n",
    "london_luid = create_CMBS(name, client_internal, dom_ccy, rental_units, average_retail_lease, term, frequency, rating)\n",
    "print(f\"Instrument {london_luid} created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Private Debt Fund\n",
    "\n",
    "For our Private Debt Fund, we will create three instruments. These instruments will be one of two types:\n",
    "1. **Bond**\n",
    "    - Morrisons 4 05/01/2025\n",
    "2. **Term Deposit**\n",
    "    - HSBC 0.10% 05/01/2023\n",
    "    - BNP 0.08% 05/07/2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_term_deposit(\n",
    "    name,\n",
    "    client_internal,\n",
    "    start_date,\n",
    "    maturity_date,\n",
    "    contract_size,\n",
    "    currency,\n",
    "    payment_frequency,\n",
    "    rate,\n",
    "):\n",
    "\n",
    "    term_deposit = lm.TermDeposit(\n",
    "        start_date=start_date,\n",
    "        maturity_date=maturity_date,\n",
    "        contract_size=contract_size,\n",
    "        flow_convention=lm.FlowConventions(\n",
    "            currency=currency,\n",
    "            payment_frequency=payment_frequency,\n",
    "            roll_convention=\"MF\",\n",
    "            day_count_convention=\"Act365\",\n",
    "            payment_calendars=[],\n",
    "            reset_calendars=[],\n",
    "            settle_days=1,\n",
    "            reset_days=0\n",
    "            ),\n",
    "        rate=rate,\n",
    "        instrument_type=\"TermDeposit\"\n",
    "    )\n",
    "\n",
    "    term_deposit_definition = lm.InstrumentDefinition(\n",
    "        name=name,\n",
    "        identifiers={\"ClientInternal\": lm.InstrumentIdValue(client_internal)},\n",
    "        definition=term_deposit,\n",
    "    )\n",
    "\n",
    "    # upsert the instrument\n",
    "    upsert_request = {client_internal: term_deposit_definition}\n",
    "    try:\n",
    "        upsert_response = instruments_api.upsert_instruments(request_body=upsert_request)\n",
    "        term_deposit_luid = upsert_response.values[client_internal].lusid_instrument_id\n",
    "        return term_deposit_luid\n",
    "    except lu.ApiException as e:\n",
    "        raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bond(\n",
    "    name,\n",
    "    client_internal,\n",
    "    start_date,\n",
    "    maturity_date,\n",
    "    dom_ccy,\n",
    "    coupon_rate,\n",
    "    payment_frequency,\n",
    "):\n",
    "\n",
    "    bond = lm.Bond(\n",
    "        start_date=start_date,\n",
    "        maturity_date=maturity_date,\n",
    "        dom_ccy=dom_ccy,\n",
    "        principal=1,\n",
    "        coupon_rate=coupon_rate,\n",
    "        flow_conventions=lm.FlowConventions(\n",
    "            scope=None,\n",
    "            code=None,\n",
    "            currency=\"GBP\",\n",
    "            payment_frequency=payment_frequency,\n",
    "            roll_convention=\"None\",\n",
    "            day_count_convention=\"Act365\",\n",
    "            payment_calendars=[],\n",
    "            reset_calendars=[],\n",
    "            settle_days=2,\n",
    "            reset_days=2\n",
    "            ),\n",
    "        identifiers={},\n",
    "        instrument_type=\"Bond\"\n",
    "    )\n",
    "\n",
    "    bond_definition = lm.InstrumentDefinition(\n",
    "        name=name,\n",
    "        identifiers={\"ClientInternal\": lm.InstrumentIdValue(client_internal)},\n",
    "        definition=bond,\n",
    "    )\n",
    "\n",
    "    # upsert the instrument\n",
    "    upsert_request = {client_internal: bond_definition}\n",
    "    try:\n",
    "        upsert_response = instruments_api.upsert_instruments(request_body=upsert_request)\n",
    "        bond_luid = upsert_response.values[client_internal].lusid_instrument_id\n",
    "        return bond_luid\n",
    "    except lu.ApiException as e:\n",
    "        raise e\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D5Z created\n"
     ]
    }
   ],
   "source": [
    "name=\"Morrisons 4 05/01/2025\"\n",
    "client_internal=\"Morrisons_4_05/01/2025_Private\"\n",
    "start_date=datetime(2022, 1, 5, 00, tzinfo=pytz.utc)\n",
    "maturity_date=datetime(2025, 1, 5, 00, tzinfo=pytz.utc)\n",
    "dom_ccy=\"GBP\"\n",
    "coupon_rate=0.04\n",
    "payment_frequency=\"6M\"\n",
    "\n",
    "morrisons_luid = create_bond(\n",
    "    name,\n",
    "    client_internal,\n",
    "    start_date,\n",
    "    maturity_date,\n",
    "    dom_ccy,\n",
    "    coupon_rate,\n",
    "    payment_frequency\n",
    ")\n",
    "\n",
    "print(f\"Instrument {morrisons_luid} created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D60 created\n"
     ]
    }
   ],
   "source": [
    "name = \"HSBC 0.10% 05/01/2023\"\n",
    "client_internal = \"HSBC_0.10_05/01/2023\"\n",
    "start_date = datetime(2022, 1, 5, 00, tzinfo=pytz.utc)\n",
    "maturity_date = datetime(2023, 1, 5, 00, tzinfo=pytz.utc)\n",
    "contract_size = 1\n",
    "currency = \"GBP\"\n",
    "payment_frequency = \"6M\"\n",
    "rate = 0.001\n",
    "\n",
    "hsbc_luid = create_term_deposit(\n",
    "    name,\n",
    "    client_internal,\n",
    "    start_date,\n",
    "    maturity_date,\n",
    "    contract_size,\n",
    "    currency,\n",
    "    payment_frequency,\n",
    "    rate,\n",
    ")\n",
    "\n",
    "print(f\"Instrument {hsbc_luid} created\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instrument LUID_00003D61 created\n"
     ]
    }
   ],
   "source": [
    "name = \"BNP 0.08% 05/07/2022\"\n",
    "client_internal = \"BNP_0.08_05/07/2022\"\n",
    "start_date = datetime(2022, 1, 5, 00, tzinfo=pytz.utc)\n",
    "maturity_date = datetime(2022, 7, 5, 00, tzinfo=pytz.utc)\n",
    "contract_size = 1\n",
    "currency = \"GBP\"\n",
    "payment_frequency = \"6M\"\n",
    "rate = 0.0008\n",
    "\n",
    "bnp_luid = create_term_deposit(\n",
    "    name,\n",
    "    client_internal,\n",
    "    start_date,\n",
    "    maturity_date,\n",
    "    contract_size,\n",
    "    currency,\n",
    "    payment_frequency,\n",
    "    rate,\n",
    ")\n",
    "\n",
    "print(f\"Instrument {bnp_luid} created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transactions\n",
    "\n",
    "Next, we can populate our funds with transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tx257</td>\n",
       "      <td>Buy</td>\n",
       "      <td>AECI_LTD_5.5</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>1,428,571</td>\n",
       "      <td>14.00</td>\n",
       "      <td>19,999,994.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tx258</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Finbourne_SAFE</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>100,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tx259</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Morrisons</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>3,484,000</td>\n",
       "      <td>2.87</td>\n",
       "      <td>9,999,080.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tx260</td>\n",
       "      <td>Buy</td>\n",
       "      <td>SYD241232</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>199,400</td>\n",
       "      <td>100.30</td>\n",
       "      <td>19,999,820.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>realEstateFund</td>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tx261</td>\n",
       "      <td>Buy</td>\n",
       "      <td>LON343239</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>197,823</td>\n",
       "      <td>101.10</td>\n",
       "      <td>19,999,905.30</td>\n",
       "      <td>GBP</td>\n",
       "      <td>realEstateFund</td>\n",
       "      <td>One Carter Lane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  txn_id type       client_id            trade_date       settlement_date  \\\n",
       "0  tx257  Buy    AECI_LTD_5.5  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z   \n",
       "1  tx258  Buy  Finbourne_SAFE  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z   \n",
       "2  tx259  Buy       Morrisons  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z   \n",
       "3  tx260  Buy       SYD241232  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z   \n",
       "4  tx261  Buy       LON343239  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z   \n",
       "\n",
       "    quantity  price total_consideration currency          portfolio  \\\n",
       "0  1,428,571  14.00       19,999,994.00      GBP  privateEquityFund   \n",
       "1    100,000 100.00       10,000,000.00      GBP  privateEquityFund   \n",
       "2  3,484,000   2.87        9,999,080.00      GBP  privateEquityFund   \n",
       "3    199,400 100.30       19,999,820.00      GBP     realEstateFund   \n",
       "4    197,823 101.10       19,999,905.30      GBP     realEstateFund   \n",
       "\n",
       "                     holding_class  \n",
       "0                              NaN  \n",
       "1                              NaN  \n",
       "2                              NaN  \n",
       "3  Sydney Bondi Junction Westfield  \n",
       "4                  One Carter Lane  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions = pd.read_csv(\"./data/portfolio_transactions.csv\")\n",
    "display(transactions.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll create the Transaction Types referenced in the file, which are Buy and StockOut."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type 'Buy' created\n",
      "Type 'Sell' created\n",
      "Type 'StockOut' created\n"
     ]
    }
   ],
   "source": [
    "# Buy\n",
    "transaction_type_request = lm.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lm.TransactionTypeAlias(\n",
    "            type=\"Buy\",  # Label\n",
    "            description=\"Purchase\",\n",
    "            transaction_class=\"Basic\",\n",
    "            transaction_roles=\"LongLonger\",\n",
    "        )\n",
    "    ],\n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"StockMovement\",\n",
    "            side=\"Side1\",  # Buy properties\n",
    "            direction=1,  # Increase\n",
    "        ),\n",
    "        # Sell side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"CashCommitment\",\n",
    "            side=\"Side2\",  # Sell properties\n",
    "            direction=-1,  # Decrease\n",
    "        ),\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\", type=\"Buy\", transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "except lu.ApiException as e:\n",
    "    raise e\n",
    "\n",
    "# StockOut\n",
    "transaction_type_request = lm.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lm.TransactionTypeAlias(\n",
    "            type=\"StockOut\",  # Label\n",
    "            description=\"Transfer Out\",\n",
    "            transaction_class=\"StockTransfers\",\n",
    "            transaction_roles=\"Shorter\",\n",
    "        )\n",
    "    ],\n",
    "    movements=[\n",
    "        # Buy side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"StockMovement\",\n",
    "            side=\"Side1\",  # Sell properties\n",
    "            direction=-1,  # Decrease\n",
    "        )\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\",\n",
    "        type=\"StockOut\",\n",
    "        transaction_type_request=transaction_type_request,\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll upload the Private Equity and Private Debt fund transactions that do not have a Subholding Key. We'll re-use and update the same transaction mapping template variable from before and then load them into LUSID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tx257</td>\n",
       "      <td>Buy</td>\n",
       "      <td>AECI_LTD_5.5</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>1,428,571</td>\n",
       "      <td>14.00</td>\n",
       "      <td>19,999,994.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tx258</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Finbourne_SAFE</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>100,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tx259</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Morrisons</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>3,484,000</td>\n",
       "      <td>2.87</td>\n",
       "      <td>9,999,080.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateEquityFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tx262</td>\n",
       "      <td>Buy</td>\n",
       "      <td>Morrisons_4_05/01/2025_Private</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>50,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tx263</td>\n",
       "      <td>Buy</td>\n",
       "      <td>HSBC_0.10_05/01/2023</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>20,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tx264</td>\n",
       "      <td>Buy</td>\n",
       "      <td>BNP_0.08_05/07/2022</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>20,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tx265</td>\n",
       "      <td>StockOut</td>\n",
       "      <td>Morrisons_4_05/01/2025_Private</td>\n",
       "      <td>2025-01-05T00:00:00Z</td>\n",
       "      <td>2025-01-08T00:00:00Z</td>\n",
       "      <td>50,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>5,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tx266</td>\n",
       "      <td>StockOut</td>\n",
       "      <td>HSBC_0.10_05/01/2023</td>\n",
       "      <td>2023-01-05T00:00:00Z</td>\n",
       "      <td>2023-01-10T00:00:00Z</td>\n",
       "      <td>20,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tx267</td>\n",
       "      <td>StockOut</td>\n",
       "      <td>BNP_0.08_05/07/2022</td>\n",
       "      <td>2022-07-05T01:00:00Z</td>\n",
       "      <td>2022-07-08T00:00:00Z</td>\n",
       "      <td>20,000</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>privateDebtFund</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   txn_id      type                       client_id            trade_date  \\\n",
       "0   tx257       Buy                    AECI_LTD_5.5  2022-01-05T08:00:00Z   \n",
       "1   tx258       Buy                  Finbourne_SAFE  2022-01-05T08:00:00Z   \n",
       "2   tx259       Buy                       Morrisons  2022-01-05T08:00:00Z   \n",
       "5   tx262       Buy  Morrisons_4_05/01/2025_Private  2022-01-05T08:00:00Z   \n",
       "6   tx263       Buy            HSBC_0.10_05/01/2023  2022-01-05T08:00:00Z   \n",
       "7   tx264       Buy             BNP_0.08_05/07/2022  2022-01-05T08:00:00Z   \n",
       "8   tx265  StockOut  Morrisons_4_05/01/2025_Private  2025-01-05T00:00:00Z   \n",
       "9   tx266  StockOut            HSBC_0.10_05/01/2023  2023-01-05T00:00:00Z   \n",
       "10  tx267  StockOut             BNP_0.08_05/07/2022  2022-07-05T01:00:00Z   \n",
       "\n",
       "         settlement_date   quantity  price total_consideration currency  \\\n",
       "0   2022-01-05T08:00:00Z  1,428,571  14.00       19,999,994.00      GBP   \n",
       "1   2022-01-05T08:00:00Z    100,000 100.00       10,000,000.00      GBP   \n",
       "2   2022-01-05T08:00:00Z  3,484,000   2.87        9,999,080.00      GBP   \n",
       "5   2022-01-05T08:00:00Z     50,000 100.00        5,000,000.00      GBP   \n",
       "6   2022-01-05T08:00:00Z     20,000 100.00        2,000,000.00      GBP   \n",
       "7   2022-01-05T08:00:00Z     20,000 100.00        2,000,000.00      GBP   \n",
       "8   2025-01-08T00:00:00Z     50,000 100.00        5,000,000.00      GBP   \n",
       "9   2023-01-10T00:00:00Z     20,000 100.00        2,000,000.00      GBP   \n",
       "10  2022-07-08T00:00:00Z     20,000 100.00        2,000,000.00      GBP   \n",
       "\n",
       "            portfolio holding_class  \n",
       "0   privateEquityFund           NaN  \n",
       "1   privateEquityFund           NaN  \n",
       "2   privateEquityFund           NaN  \n",
       "5     privateDebtFund           NaN  \n",
       "6     privateDebtFund           NaN  \n",
       "7     privateDebtFund           NaN  \n",
       "8     privateDebtFund           NaN  \n",
       "9     privateDebtFund           NaN  \n",
       "10    privateDebtFund           NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "private_debt_equity = transactions[(transactions[\"portfolio\"] == \"privateEquityFund\") | (transactions[\"portfolio\"] == \"privateDebtFund\")]\n",
    "display(private_debt_equity)\n",
    "\n",
    "# Define the updated keys for the real estate transactions\n",
    "transaction_mapping_private_equity = {\n",
    "    \"identifier_mapping\": {\"ClientInternal\": \"client_id\"},\n",
    "    \"properties\": []\n",
    "}\n",
    "transaction_mapping.update(transaction_mapping_private_equity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        3       0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=transactions,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error\n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "\n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll upsert Real Estate transactions with the Subholding Keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>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",
       "      <th>holding_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tx260</td>\n",
       "      <td>Buy</td>\n",
       "      <td>SYD241232</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>199,400</td>\n",
       "      <td>100.30</td>\n",
       "      <td>19,999,820.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>realEstateFund</td>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tx261</td>\n",
       "      <td>Buy</td>\n",
       "      <td>LON343239</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>2022-01-05T08:00:00Z</td>\n",
       "      <td>197,823</td>\n",
       "      <td>101.10</td>\n",
       "      <td>19,999,905.30</td>\n",
       "      <td>GBP</td>\n",
       "      <td>realEstateFund</td>\n",
       "      <td>One Carter Lane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  txn_id type  client_id            trade_date       settlement_date quantity  \\\n",
       "3  tx260  Buy  SYD241232  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z  199,400   \n",
       "4  tx261  Buy  LON343239  2022-01-05T08:00:00Z  2022-01-05T08:00:00Z  197,823   \n",
       "\n",
       "   price total_consideration currency       portfolio  \\\n",
       "3 100.30       19,999,820.00      GBP  realEstateFund   \n",
       "4 101.10       19,999,905.30      GBP  realEstateFund   \n",
       "\n",
       "                     holding_class  \n",
       "3  Sydney Bondi Junction Westfield  \n",
       "4                  One Carter Lane  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_estate = transactions[transactions[\"portfolio\"] == \"realEstateFund\"]\n",
    "display(real_estate)\n",
    "\n",
    "transaction_mapping_holding_class= {\n",
    "    \"identifier_mapping\": {\"ClientInternal\": \"client_id\"},\n",
    "    \"properties\": [\"holding_class\"]\n",
    "}\n",
    "transaction_mapping.update(transaction_mapping_holding_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed\n",
       "0        1       0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=real_estate,\n",
    "    mapping_required=transaction_mapping[\"required\"],\n",
    "    mapping_optional=transaction_mapping[\"optional\"],\n",
    "    file_type=\"transactions\",\n",
    "    identifier_mapping=transaction_mapping[\"identifier_mapping\"],\n",
    "    property_columns=transaction_mapping[\"properties\"],\n",
    ")\n",
    "\n",
    "if result['transactions']['errors']:\n",
    "    # Throw error      \n",
    "    raise result['transactions']['errors'][0]\n",
    "\n",
    "succ, failed = format_transactions_response(result)\n",
    "    \n",
    "pd.DataFrame(\n",
    "    data=[{\"success\": len(succ), \"failed\": len(failed)}]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quotes\n",
    "\n",
    "We can add quotes which represent the value of our entities at specific points in time. This allows them to be valued by the valuations engine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>client_id</th>\n",
       "      <th>date</th>\n",
       "      <th>quote</th>\n",
       "      <th>currency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AECI_LTD_5.5</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>14.10</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finbourne_SAFE</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>101.00</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Morrisons</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>2.87</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SYD241232</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>100.30</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LON343239</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>101.10</td>\n",
       "      <td>GBP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        client_id        date  quote currency\n",
       "0    AECI_LTD_5.5  10/01/2022  14.10      GBP\n",
       "1  Finbourne_SAFE  10/01/2022 101.00      GBP\n",
       "2       Morrisons  10/01/2022   2.87      GBP\n",
       "3       SYD241232  10/01/2022 100.30      GBP\n",
       "4       LON343239  10/01/2022 101.10      GBP"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quotes = pd.read_csv(\"./data/quotes.csv\")\n",
    "quotes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "quotes_mapping = {\n",
    "    \"quote_id.quote_series_id.instrument_id_type\": \"$ClientInternal\",\n",
    "    \"quote_id.effective_at\": \"date\",\n",
    "    \"quote_id.quote_series_id.provider\": \"$Lusid\",\n",
    "    \"quote_id.quote_series_id.quote_type\": \"$Price\",\n",
    "    \"quote_id.quote_series_id.instrument_id\": \"client_id\",\n",
    "    \"metric_value.unit\": \"currency\",\n",
    "    \"quote_id.quote_series_id.field\": \"$mid\",\n",
    "    \"metric_value.value\": \"quote\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>success</th>\n",
       "      <th>failed</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   success  failed  errors\n",
       "0       22       0       0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = load_from_data_frame(\n",
    "    api_factory=api_factory,\n",
    "    scope=scope,\n",
    "    data_frame=quotes,\n",
    "    mapping_required=quotes_mapping,\n",
    "    mapping_optional={},\n",
    "    file_type=\"quotes\",\n",
    ")\n",
    "\n",
    "if result[\"quotes\"][\"errors\"]:\n",
    "    # Throw error\n",
    "    raise result[\"quotes\"][\"errors\"][0]\n",
    "\n",
    "succ, failed, errors = format_quotes_response(result)\n",
    "\n",
    "display(\n",
    "    pd.DataFrame(\n",
    "        data=[{\"success\": len(succ), \"failed\": len(failed), \"errors\": len(errors)}]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recipes\n",
    "\n",
    "In order to create valuations we require a recipe. In this case, we are creating two. For an overview of recipe structure and creation see [here](https://support.lusid.com/knowledgebase/article/KA-01895), and for information regarding lookthrough recipes for use in fund of funds portfolios see [here](https://support.lusid.com/knowledgebase/article/KA-01854/en-us).\n",
    "\n",
    "The first recipe is a **lookthrough** recipe which means that it will fetch the valuations of the securitised funds during a valuation of the General Partnership (parent portfolio). \n",
    "\n",
    "The second recipe is a **non-lookthrough** recipe. This does not look into our securitised portfolios, and instead will value them by using quotes in our quotes store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful recipe creation at 2023-11-07 09:15:19.775580+00:00\n"
     ]
    }
   ],
   "source": [
    "# Create look-through-enabled recipe\n",
    "lookthrough_config_recipe = lm.ConfigurationRecipe(\n",
    "    scope=scope,\n",
    "    code=\"lookthrough\",\n",
    "    market=lm.MarketContext(\n",
    "        # The Market Rules tell the recipe how to look up to pricing data for different instrument types in order of priority\n",
    "        market_rules=[\n",
    "            # Look in the LUSID Quote Store for mid prices loaded using ClientInternal identifier, going back 10Y if needed\n",
    "            lm.MarketDataKeyRule(\n",
    "                key=\"Equity.ClientInternal.*\",\n",
    "                supplier=\"Lusid\",\n",
    "                data_scope=scope,\n",
    "                quote_type=\"Price\",\n",
    "                field=\"mid\",\n",
    "                quote_interval=\"10Y\"\n",
    "            ),\n",
    "            # Look in the LUSID Complex Market Data store for any Credit Curves, going back 10Y if needed\n",
    "            lm.MarketDataKeyRule(\n",
    "                key=\"Credit.*.*\",\n",
    "                supplier=\"Lusid\",\n",
    "                data_scope=scope,\n",
    "                quote_type=\"Price\",\n",
    "                quote_interval=\"10Y\"\n",
    "            ),\n",
    "        ],\n",
    "        # We can specify default suppliers based on asset class as a fallback\n",
    "        suppliers=lm.MarketContextSuppliers(\n",
    "            commodity=\"Lusid\",\n",
    "            credit=\"Lusid\",\n",
    "            equity=\"Lusid\",\n",
    "            fx=\"Lusid\",\n",
    "            rates=\"Lusid\"\n",
    "        ),\n",
    "        # Configure options on the recipe, such as the default supplier, default instrument identifier type and to infer missing FX if only inverse pair is available\n",
    "        options=lm.MarketOptions(\n",
    "            default_supplier=\"Lusid\",\n",
    "            default_instrument_code_type=\"ClientInternal\",\n",
    "            default_scope=scope,\n",
    "            attempt_to_infer_missing_fx=True\n",
    "        ),\n",
    "    ),\n",
    "    # We must define the pricing model to use when valuing our positions\n",
    "    pricing=lm.PricingContext(\n",
    "        model_rules=[\n",
    "            # Here we specify that for SimpleInstruments we want to value using built-in SimpleStatic model\n",
    "            lm.VendorModelRule(\n",
    "                supplier=\"Lusid\",\n",
    "                model_name=\"SimpleStatic\",\n",
    "                instrument_type=\"SimpleInstrument\",\n",
    "                # The IndexModelOptions allows us to 'look through' to value holdings in child portfolios\n",
    "                model_options=lm.IndexModelOptions(\n",
    "                    portfolio_scaling=\"Unity\",\n",
    "                    model_options_type=\"IndexModelOptions\"\n",
    "                ),\n",
    "            ),\n",
    "        ],\n",
    "        options=lm.PricingOptions(\n",
    "            window_valuation_on_instrument_start_end=False\n",
    "        ),\n",
    "        # This is how we access our external cashflows from the Structured Result Store (which we'll load later on in this notebook)\n",
    "        result_data_rules=[\n",
    "            lm.ResultDataKeyRule(\n",
    "                resource_key=\"UnitResult/*\",\n",
    "                supplier=\"Client\",\n",
    "                data_scope=scope,\n",
    "                document_code=\"realEstateCashFlows\",\n",
    "                quote_interval=\"10Y\",\n",
    "                document_result_type=\"UnitResult/Analytic\",\n",
    "                result_key_rule_type=\"ResultDataKeyRule\"\n",
    "            )\n",
    "        ],\n",
    "    ),\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = configuration_recipe_api.upsert_configuration_recipe(\n",
    "        upsert_recipe_request=lm.UpsertRecipeRequest(\n",
    "            configuration_recipe=lookthrough_config_recipe\n",
    "        )\n",
    "    )\n",
    "    print(f\"Successful recipe creation at {response.value}\")\n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successful recipe creation at 2023-11-07 09:15:21.230686+00:00\n"
     ]
    }
   ],
   "source": [
    "# Create a non-look-through recipe\n",
    "non_lookthrough_config_recipe = lm.ConfigurationRecipe(\n",
    "    scope=scope,\n",
    "    code=\"no-lookthrough\",\n",
    "    market=lm.MarketContext(\n",
    "        # The Market Rules tell the recipe how to look up to pricing data for different instrument types in order of priority\n",
    "        market_rules=[\n",
    "            # Look in the LUSID Quote Store for mid prices loaded using ClientInternal identifier\n",
    "            lm.MarketDataKeyRule(\n",
    "                key=\"Equity.ClientInternal.*\",\n",
    "                supplier=\"Lusid\",\n",
    "                data_scope=scope,\n",
    "                quote_type=\"Price\",\n",
    "                field=\"mid\",\n",
    "            ),\n",
    "            # Look into the LUSID Quote Store for any applicable FX currency pair rates\n",
    "            lm.MarketDataKeyRule(\n",
    "                key=\"FX.CurrencyPair.*\",\n",
    "                supplier=\"Lusid\",\n",
    "                data_scope=scope,\n",
    "                quote_type=\"Rate\",\n",
    "                field=\"mid\",\n",
    "            ),\n",
    "        ],\n",
    "        suppliers=lm.MarketContextSuppliers(\n",
    "            commodity=\"Lusid\",\n",
    "            credit=\"Lusid\",\n",
    "            equity=\"Lusid\",\n",
    "            fx=\"Lusid\",\n",
    "            rates=\"Lusid\"\n",
    "        ),\n",
    "        options=lm.MarketOptions(\n",
    "            default_supplier=\"Lusid\",\n",
    "            default_instrument_code_type=\"ClientInternal\",\n",
    "            default_scope=scope,\n",
    "            attempt_to_infer_missing_fx=True,\n",
    "        ),\n",
    "    ),\n",
    "    pricing=lm.PricingContext(\n",
    "        # Note how the model rule here does NOT specify an IndexModelOption to look through to child funds\n",
    "        model_rules=[\n",
    "            lm.VendorModelRule(\n",
    "                supplier=\"Lusid\",\n",
    "                model_name=\"IndexPrice\",\n",
    "                instrument_type=\"Index\",\n",
    "            )\n",
    "        ],\n",
    "        options=lm.PricingOptions(\n",
    "            window_valuation_on_instrument_start_end=False\n",
    "        ),\n",
    "        # Note how this recipe does not have a result_data_rules parameter, so no overrides will take place on valuations that use it\n",
    "    ),\n",
    ")\n",
    "\n",
    "configuration_recipe_api.upsert_configuration_recipe(\n",
    "    upsert_recipe_request=lm.UpsertRecipeRequest(\n",
    "        configuration_recipe=non_lookthrough_config_recipe\n",
    "    )\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = configuration_recipe_api.upsert_configuration_recipe(\n",
    "        upsert_recipe_request=lm.UpsertRecipeRequest(\n",
    "            configuration_recipe=lookthrough_config_recipe\n",
    "        )\n",
    "    )\n",
    "    print(f\"Successful recipe creation at {response.value}\")\n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Excel ILPA Report Writer (for General Partnership)\n",
    "\n",
    "This section will demonstrate how we are able to take data from LUSID, perform some data organisation, and produce an excel report. We will be following the ILPA template, a popular format for reporting in the private equity industry. See [here](https://ilpa.org/reporting-template/) for more info.\n",
    "\n",
    "The outputted template will show and aggregation of 3 investors and the expenses and cash accrued for each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ilpa_data(investors, from_date, to_date):\n",
    "    # get transactions for investors\n",
    "    transactions = transaction_portfolios_api.get_transactions(\n",
    "        scope=scope,\n",
    "        code=\"generalPartnership\",\n",
    "        from_transaction_date=from_date,\n",
    "        to_transaction_date=to_date,\n",
    "    ).values\n",
    "    \n",
    "    # parse response\n",
    "    transaction_df = pd.DataFrame(\n",
    "        data={\n",
    "            \"ID\": [i.transaction_id for i in transactions],\n",
    "            \"Units\": [i.units for i in transactions],\n",
    "            \"Type\": [i.type for i in transactions],\n",
    "            \"Holding Class\": [i.properties['Transaction/privateMarkets/holding_class'].value.label_value if 'Transaction/privateMarkets/holding_class' in i.properties else 'None' for i in transactions],\n",
    "            \"Investor\": [i.properties['Transaction/privateMarkets/investor'].value.label_value if 'Transaction/privateMarkets/investor' in i.properties else 'None' for i in transactions],\n",
    "            \"Expense Type\": [i.properties['Transaction/privateMarkets/expense_type'].value.label_value if 'Transaction/privateMarkets/expense_type' in i.properties else 'None' for i in transactions]\n",
    "        }\n",
    "    )\n",
    "\n",
    "    ilpa_temp = {\n",
    "        'Total Offsets to Fees & Expenses (applied during period)': 0,\n",
    "        'Management Fees – Gross of Offsets, Waivers & Rebates': 0,\n",
    "        'Contributions - Cash & Non-Cash': 0,\n",
    "        'Distributions - Cash & Non-Cash (input positive values)': 0\n",
    "    }\n",
    "    \n",
    "    for investor in investors:\n",
    "        for index, row in transaction_df.iterrows():\n",
    "            if row['Investor'] == investor:\n",
    "                # define our number of units\n",
    "                units = row['Units']\n",
    "                # negative movement\n",
    "                if row['Type'] == 'FundsOut':\n",
    "                    units = -units\n",
    "                    \n",
    "                # Partnership Expenses\n",
    "                if row['Holding Class'] == 'Partnership Expense':\n",
    "                    key = f\"Partnership Expenses - {row['Expense Type']}\"\n",
    "                # Management Fee    \n",
    "                elif row['Holding Class'] == 'Management Fee':\n",
    "                    key = f\"Management Fee {row['Expense Type']}\"\n",
    "                    # add to total\n",
    "                    ilpa_temp['Management Fees – Gross of Offsets, Waivers & Rebates'] += units\n",
    "                # Offsets\n",
    "                elif row['Holding Class'] == 'Offset':\n",
    "                    key = f\"{row['Expense Type']} Offset\"\n",
    "                    # add to total\n",
    "                    ilpa_temp['Total Offsets to Fees & Expenses (applied during period)'] += units\n",
    "                    ilpa_temp['Management Fees – Gross of Offsets, Waivers & Rebates'] += units\n",
    "                # Cash\n",
    "                elif row['Holding Class'] == 'Cash':\n",
    "                    key = \"Beginning NAV - Net of Incentive Allocation\"\n",
    "                elif row['Holding Class'] == 'Fund' and row['Type'] == 'Buy':\n",
    "                    # add to total\n",
    "                    ilpa_temp['Contributions - Cash & Non-Cash'] += units\n",
    "                elif row['Holding Class'] == 'Fund' and row['Type'] == 'Sell':\n",
    "                    # add to total\n",
    "                    ilpa_temp['Distributions - Cash & Non-Cash (input positive values)'] += units\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "                # add to our data template\n",
    "                if key not in ilpa_temp:\n",
    "                    ilpa_temp[key] = units\n",
    "                else:\n",
    "                    ilpa_temp[key] += units\n",
    "    \n",
    "    return ilpa_temp\n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the data into ILPA format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "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>LP QTD (April 01, 2022 - June 15, 2022)</th>\n",
       "      <th>LP YTD (January 01, 2022 - June 15, 2022)</th>\n",
       "      <th>LP Since Inception (January 01, 2021 - June 15, 2022)</th>\n",
       "      <th>Total Fund QTD (April 01, 2022 - June 15, 2022)</th>\n",
       "      <th>Total Fund YTD (January 01, 2022 - June 15, 2022)</th>\n",
       "      <th>Total Fund Since Inception (January 01, 2021 - June 15, 2022)</th>\n",
       "      <th>GP QTD (April 01, 2022 - June 15, 2022)</th>\n",
       "      <th>GP YTD (January 01, 2022 - June 15, 2022)</th>\n",
       "      <th>GP Since Inception (January 01, 2021 - June 15, 2022)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total Offsets to Fees &amp; Expenses (applied during period)</th>\n",
       "      <td>97,529.17</td>\n",
       "      <td>179,929.17</td>\n",
       "      <td>365,852.06</td>\n",
       "      <td>264,037.39</td>\n",
       "      <td>525,387.39</td>\n",
       "      <td>1,064,794.48</td>\n",
       "      <td>90,816.73</td>\n",
       "      <td>171,766.73</td>\n",
       "      <td>351,866.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Management Fees – Gross of Offsets, Waivers &amp; Rebates</th>\n",
       "      <td>98,857.98</td>\n",
       "      <td>181,267.98</td>\n",
       "      <td>369,519.68</td>\n",
       "      <td>267,679.76</td>\n",
       "      <td>529,134.76</td>\n",
       "      <td>1,075,184.22</td>\n",
       "      <td>91,169.87</td>\n",
       "      <td>172,199.87</td>\n",
       "      <td>352,853.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contributions - Cash &amp; Non-Cash</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distributions - Cash &amp; Non-Cash (input positive values)</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beginning NAV - Net of Incentive Allocation</th>\n",
       "      <td>2,000,000.00</td>\n",
       "      <td>22,000,000.00</td>\n",
       "      <td>52,000,000.00</td>\n",
       "      <td>10,000,000.00</td>\n",
       "      <td>40,000,000.00</td>\n",
       "      <td>108,000,000.00</td>\n",
       "      <td>3,000,000.00</td>\n",
       "      <td>8,000,000.00</td>\n",
       "      <td>26,000,000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    LP QTD (April 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                97,529.17   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                98,857.98   \n",
       "Contributions - Cash & Non-Cash                                                        0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                     0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                    2,000,000.00   \n",
       "\n",
       "                                                    LP YTD (January 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                 179,929.17   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                 181,267.98   \n",
       "Contributions - Cash & Non-Cash                                                          0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                       0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                     22,000,000.00   \n",
       "\n",
       "                                                    LP Since Inception (January 01, 2021 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                         365,852.06       \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                         369,519.68       \n",
       "Contributions - Cash & Non-Cash                                                                  0.00       \n",
       "Distributions - Cash & Non-Cash (input positive...                                               0.00       \n",
       "Beginning NAV - Net of Incentive Allocation                                             52,000,000.00       \n",
       "\n",
       "                                                    Total Fund QTD (April 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                       264,037.39   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                       267,679.76   \n",
       "Contributions - Cash & Non-Cash                                                                0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                             0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                           10,000,000.00   \n",
       "\n",
       "                                                    Total Fund YTD (January 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                         525,387.39   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                         529,134.76   \n",
       "Contributions - Cash & Non-Cash                                                                  0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                               0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                             40,000,000.00   \n",
       "\n",
       "                                                    Total Fund Since Inception (January 01, 2021 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                       1,064,794.48               \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                       1,075,184.22               \n",
       "Contributions - Cash & Non-Cash                                                                  0.00               \n",
       "Distributions - Cash & Non-Cash (input positive...                                               0.00               \n",
       "Beginning NAV - Net of Incentive Allocation                                            108,000,000.00               \n",
       "\n",
       "                                                    GP QTD (April 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                90,816.73   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                91,169.87   \n",
       "Contributions - Cash & Non-Cash                                                        0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                     0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                    3,000,000.00   \n",
       "\n",
       "                                                    GP YTD (January 01, 2022 - June 15, 2022)  \\\n",
       "Total Offsets to Fees & Expenses (applied durin...                                 171,766.73   \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                 172,199.87   \n",
       "Contributions - Cash & Non-Cash                                                          0.00   \n",
       "Distributions - Cash & Non-Cash (input positive...                                       0.00   \n",
       "Beginning NAV - Net of Incentive Allocation                                      8,000,000.00   \n",
       "\n",
       "                                                    GP Since Inception (January 01, 2021 - June 15, 2022)  \n",
       "Total Offsets to Fees & Expenses (applied durin...                                         351,866.98      \n",
       "Management Fees – Gross of Offsets, Waivers & R...                                         352,853.26      \n",
       "Contributions - Cash & Non-Cash                                                                  0.00      \n",
       "Distributions - Cash & Non-Cash (input positive...                                               0.00      \n",
       "Beginning NAV - Net of Incentive Allocation                                             26,000,000.00      "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_ilpa_template(lp, tf, gp, qtd, ytd, si, end):\n",
    "    ilpa_data = {}\n",
    "    \n",
    "    # Format Dates\n",
    "    qtd_str = datetime.fromisoformat(qtd).strftime(\"%B %d, %Y\")\n",
    "    ytd_str = datetime.fromisoformat(ytd).strftime(\"%B %d, %Y\")\n",
    "    si_str = datetime.fromisoformat(si).strftime(\"%B %d, %Y\")\n",
    "    end_str = datetime.fromisoformat(end).strftime(\"%B %d, %Y\")\n",
    "    \n",
    "    # Limited Partner\n",
    "    lp_qtd = get_ilpa_data(lp, qtd, end)\n",
    "    lp_ytd = get_ilpa_data(lp, ytd, end)\n",
    "    lp_si = get_ilpa_data(lp, si, end)\n",
    "    \n",
    "    # Total Fund\n",
    "    tf_qtd = get_ilpa_data(tf, qtd, end)\n",
    "    tf_ytd = get_ilpa_data(tf, ytd, end)\n",
    "    tf_si = get_ilpa_data(tf, si, end)\n",
    "    \n",
    "    # General Partner\n",
    "    gp_qtd = get_ilpa_data(gp, qtd, end)\n",
    "    gp_ytd = get_ilpa_data(gp, ytd, end)\n",
    "    gp_si = get_ilpa_data(gp, si, end)\n",
    "    \n",
    "    # Combine all data\n",
    "    for dictionary in [lp_qtd, lp_ytd, lp_si, tf_qtd, tf_ytd, tf_si, gp_qtd, gp_ytd, gp_si]:\n",
    "        for key in dictionary:\n",
    "            if key in ilpa_data:\n",
    "                ilpa_data[key].append(dictionary[key])\n",
    "            else:\n",
    "                ilpa_data[key] = [dictionary[key]]\n",
    "\n",
    "    ilpa_template = pd.DataFrame(\n",
    "        data=ilpa_data,\n",
    "        index=[f\"LP QTD ({qtd_str} - {end_str})\", \n",
    "               f\"LP YTD ({ytd_str} - {end_str})\", \n",
    "               f\"LP Since Inception ({si_str} - {end_str})\",\n",
    "               f\"Total Fund QTD ({qtd_str} - {end_str})\", \n",
    "               f\"Total Fund YTD ({ytd_str} - {end_str})\", \n",
    "               f\"Total Fund Since Inception ({si_str} - {end_str})\",\n",
    "               f\"GP QTD ({qtd_str} - {end_str})\", \n",
    "               f\"GP YTD ({ytd_str} - {end_str})\", \n",
    "               f\"GP Since Inception ({si_str} - {end_str})\"]\n",
    "    )\n",
    "    \n",
    "    ilpa_template = ilpa_template.swapaxes(\"index\", \"columns\")\n",
    "    return ilpa_template\n",
    "\n",
    "# Partners\n",
    "lp = [\"Investor 1\"]\n",
    "tf = [\"Investor 1\", \"Investor 2\", \"Investor 3\"]\n",
    "gp = [\"Investor 3\"]\n",
    "\n",
    "# Dates for search\n",
    "qtd = \"2022-04-01T00:00:00+00:00\"\n",
    "ytd = \"2022-01-01T00:00:00+00:00\"\n",
    "si = \"2021-01-01T00:00:00+00:00\"\n",
    "end = \"2022-06-15T00:00:00+00:00\" \n",
    "\n",
    "ilpa_template = build_ilpa_template(lp, tf, gp, qtd, ytd, si, end)\n",
    "ilpa_template.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the ILPA excel report. You can view this output within the `outputs` folder with filename `ilpa_report.xlsx`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File saved at ./outputs/ilpa_report.xlsx\n"
     ]
    }
   ],
   "source": [
    "# Load blank ILPA template\n",
    "workbook = openpyxl.load_workbook(\"./data/ILPA-template.xlsx\")\n",
    "worksheet = workbook.active\n",
    "\n",
    "# Load excel cell mapping from JSON\n",
    "with open(\"./data/ilpa_mapping.json\", \"r\") as fp:\n",
    "    excel_cells = json.load(fp)\n",
    "\n",
    "# Add values\n",
    "for index, row in ilpa_template.iterrows():\n",
    "    for i in range(len(row)):\n",
    "        cell = excel_cells[index][i]\n",
    "        worksheet[cell].value = row[i]\n",
    "\n",
    "# Add dates\n",
    "for index, date in enumerate([qtd, ytd, si, end]):\n",
    "    date_str = datetime.fromisoformat(date).strftime(\"%d/%m/%Y\")\n",
    "    cell = excel_cells[\"Dates\"][index]\n",
    "    worksheet[cell].value = date_str\n",
    "\n",
    "response = workbook.save(\"./outputs/ilpa_report.xlsx\")\n",
    "\n",
    "# Response is None if succesful\n",
    "if not response:\n",
    "    print(\"File saved at ./outputs/ilpa_report.xlsx\")\n",
    "else:\n",
    "    print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Real Estate Cash Flows\n",
    "\n",
    "In this section we will further adapt our real estate portfolio by including some realistic cash flows. The cash flows will represent rental income/outgoings and bond coupon payments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Override Cash Flows\n",
    "\n",
    "Our real estate assets are modelled as Simple Instruments, and as such, they **do not** natively have cash flows on their instruments. However, we are able to store cashflows from external sources, such as a third party, against any instrument by adding them into our [Structured Results Store (SRS)](https://support.lusid.com/knowledgebase/article/KA-01893/). During a valuation, by using a specific recipe configuration we are able to instruct the valuations engine to fetch the cash flows we have added."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "cash_flow_df = pd.read_csv(\"./data/cash_flow.csv\")\n",
    "\n",
    "cash_flow_df[\"settlement_date\"] = pd.to_datetime(cash_flow_df[\"settlement_date\"], format='%d/%m/%Y', utc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All cash flows uploaded successfully for LUID_00003D5Y\n",
      "All cash flows uploaded successfully for LUID_00003D5X\n"
     ]
    }
   ],
   "source": [
    "document_scope = \"privateMarkets\"\n",
    "document_code = \"realEstateCashFlows\"\n",
    "effective_at = datetime(2022, 1, 1, 0, 0, tzinfo=pytz.utc)\n",
    "\n",
    "\n",
    "def override_cashflow(luid, cash_flows, notional):\n",
    "    # Persist pay amounts - maintenance costs\n",
    "    cash_flow = [\n",
    "        lm.CashFlowValue(\n",
    "            result_value_type=\"CashFlowValue\",\n",
    "            payment_amount=row[\"total_consideration\"] / notional,\n",
    "            payment_date=row[\"settlement_date\"],\n",
    "            payment_ccy=row[\"currency\"],\n",
    "            cash_flow_lineage=lm.CashFlowLineage(\n",
    "                cash_flow_type=\"Cash\", pay_receive=row[\"pay_receive\"]\n",
    "            ),\n",
    "        )\n",
    "        for i, row in cash_flows.iterrows()\n",
    "    ]\n",
    "\n",
    "    # Create external cash flow set from pays and receives\n",
    "    external_cash_flow_set = lm.CashFlowValueSet(\n",
    "        cashflows=cash_flow, result_value_type=\"CashFlowValueSet\"\n",
    "    )\n",
    "\n",
    "    # Create a structured document ID\n",
    "    struct_result_data_id = lm.StructuredResultDataId(\n",
    "        source=\"Client\",\n",
    "        code=document_code,\n",
    "        effective_at=effective_at,\n",
    "        result_type=\"UnitResult/Analytic\",\n",
    "    )\n",
    "\n",
    "    upsert_result_values_data_request = lm.UpsertResultValuesDataRequest(\n",
    "        document_id=struct_result_data_id,\n",
    "        key={\n",
    "            # Instrument to which document resolves\n",
    "            f\"UnitResult/LusidInstrumentId\": f\"{luid}\"\n",
    "        },\n",
    "        data_address=\"UnitResult/Valuation/Cashflows\",  # Valuation result to override\n",
    "        result_value=external_cash_flow_set,\n",
    "    )\n",
    "\n",
    "    try:\n",
    "        response = structured_result_data_api.upsert_result_value(\n",
    "            scope=document_scope,\n",
    "            request_body={\"CashFlows\": upsert_result_values_data_request},\n",
    "        )\n",
    "        if not response.failed:\n",
    "            return f\"All cash flows uploaded successfully for {luid}\"\n",
    "    except lu.ApiException as e:\n",
    "        display(json.loads(e.body)[\"status\"], json.loads(e.body)[\"title\"])\n",
    "\n",
    "\n",
    "# Call function providing filtered cashflows for London\n",
    "print(\n",
    "    override_cashflow(\n",
    "        london_luid, cash_flow_df[cash_flow_df.client_id == \"LON343239\"], 197823\n",
    "    )\n",
    ")\n",
    "# Call function providing filtered cashflows for Sydney\n",
    "print(\n",
    "    override_cashflow(\n",
    "        sydney_luid, cash_flow_df[cash_flow_df.client_id == \"SYD241232\"], 199400\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Book Bond\n",
    "\n",
    "Unlike Simple Instruments, Bond instruments **do** support cashflows natively. By providing an economic definition for the bond, the LUSID valuations engine is able to project cash flows through to maturity.\n",
    "\n",
    "By booking a bond with an economic definition into this portfolio, we will be able to see how our valuations engine is able to dynamically integrate both the native instrument cash flows and the overriden SRS cash flows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bond LUID_00003D62 successfully created\n",
      "Type 'Sell' created\n",
      "Transaction successfully updated at time: 2023-11-07 09:12:34.883962+00:00\n"
     ]
    }
   ],
   "source": [
    "scope = \"privateMarkets\"\n",
    "portfolio_code = \"realEstateFund\"\n",
    "trade_date = datetime(2022, 1, 4, tzinfo=pytz.utc)\n",
    "start_date = datetime(2022, 1, 4, tzinfo=pytz.utc)\n",
    "maturity_date = datetime(2030, 1, 1, tzinfo=pytz.utc)\n",
    "bond_id = 300\n",
    "\n",
    "# Create Bond Instrument\n",
    "flow_conventions = lm.FlowConventions(\n",
    "    currency=\"GBP\",\n",
    "    payment_frequency=\"1M\",\n",
    "    roll_convention=\"ModifiedPrevious\",\n",
    "    day_count_convention=\"Actual365\",\n",
    "    payment_calendars=[],\n",
    "    reset_calendars=[],\n",
    "    settle_days=2,\n",
    "    reset_days=2,\n",
    ")\n",
    "\n",
    "bond = lm.Bond(\n",
    "    start_date=start_date,\n",
    "    maturity_date=maturity_date,\n",
    "    dom_ccy=\"GBP\",\n",
    "    principal=1,\n",
    "    coupon_rate=0.05,\n",
    "    flow_conventions=flow_conventions,\n",
    "    identifiers={},\n",
    "    instrument_type=\"Bond\",\n",
    "    calculation_type=\"Standard\",\n",
    ")\n",
    "\n",
    "# Define the instrument to be upserted\n",
    "bond_definition = lm.InstrumentDefinition(\n",
    "    name=\"Fixed Bond\",\n",
    "    identifiers={\"ClientInternal\": lm.InstrumentIdValue(\"Fixed Bond\")},\n",
    "    definition=bond,\n",
    "    properties=[],\n",
    ")\n",
    "\n",
    "# Upsert the instrument\n",
    "upsert_request = {\"Fixed Bond\": bond_definition}\n",
    "try:\n",
    "    upsert_response = instruments_api.upsert_instruments(\n",
    "        request_body=upsert_request, scope=\"default\"\n",
    "    )\n",
    "    luid = upsert_response.values[\"Fixed Bond\"].lusid_instrument_id\n",
    "    print(f\"Bond {luid} successfully created\")\n",
    "except lu.ApiException as e:\n",
    "    raise e\n",
    "\n",
    "# Create Transaction Type for sell\n",
    "transaction_type_request = lm.TransactionTypeRequest(\n",
    "    aliases=[\n",
    "        lm.TransactionTypeAlias(\n",
    "            type=\"Sell\",  # Label\n",
    "            description=\"Sale\",\n",
    "            transaction_class=\"Basic\",\n",
    "            transaction_roles=\"LongShorter\",\n",
    "        )\n",
    "    ],\n",
    "    movements=[\n",
    "        # Sell side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"StockMovement\",\n",
    "            side=\"Side1\",  # Sell properties\n",
    "            direction=-1,  # Decrease\n",
    "        ),\n",
    "        # Buy side movement\n",
    "        lm.TransactionTypeMovement(\n",
    "            movement_types=\"CashCommitment\",\n",
    "            side=\"Side2\",  # Buy properties\n",
    "            direction=1,  # Increase\n",
    "        ),\n",
    "    ],\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_config_api.set_transaction_type(\n",
    "        source=\"default\", type=\"Sell\", transaction_type_request=transaction_type_request\n",
    "    )\n",
    "    print(f\"Type '{response.aliases[0].type}' created\")\n",
    "except lu.ApiException as e:\n",
    "    raise e\n",
    "\n",
    "# Create Transaction\n",
    "trade_txn = lm.TransactionRequest(\n",
    "    transaction_id=f\"tx{bond_id}\",\n",
    "    type=\"Sell\",\n",
    "    instrument_identifiers={\"Instrument/default/LusidInstrumentId\": luid},\n",
    "    transaction_date=trade_date.isoformat(),\n",
    "    settlement_date=trade_date.isoformat(),\n",
    "    units=20000000,\n",
    "    transaction_price=lm.TransactionPrice(price=1, type=\"Price\"),\n",
    "    total_consideration=lm.CurrencyAndAmount(amount=1 * 20000000, currency=\"GBP\"),\n",
    "    exchange_rate=1,\n",
    "    source=scope,\n",
    "    transaction_currency=\"GBP\",\n",
    "    properties={\n",
    "        \"Transaction/privateMarkets/holding_class\": lm.PerpetualProperty(\n",
    "            key=\"Transaction/privateMarkets/holding_class\",\n",
    "            value=lm.PropertyValue(label_value=\"Bond\"),\n",
    "        )\n",
    "    },\n",
    ")\n",
    "\n",
    "try:\n",
    "    response = transaction_portfolios_api.upsert_transactions(\n",
    "        scope=scope, code=portfolio_code, transaction_request=[trade_txn]\n",
    "    )\n",
    "\n",
    "    print(f\"Transaction successfully updated at time: {response.version.as_at_date}\")\n",
    "except lu.ApiException as e:\n",
    "    raise e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Cash Ladder\n",
    "\n",
    "By fetching a cash ladder, we can see how the valuations engine is able to combine the two types of cash flows into one cohesive projected cash statement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Instrument</th>\n",
       "      <th>Activity</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-04 00:00:00+00:00</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Side2</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04 00:00:00+00:00</th>\n",
       "      <td>Fund</td>\n",
       "      <td>Side1</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20,000,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05 00:00:00+00:00</th>\n",
       "      <td>One Carter Lane</td>\n",
       "      <td>Side2</td>\n",
       "      <td>-19,999,905.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-19,999,905.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05 00:00:00+00:00</th>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "      <td>Side2</td>\n",
       "      <td>-19,999,820.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-19,999,820.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31 00:00:00+00:00</th>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "      <td>Cash</td>\n",
       "      <td>-18,570.00</td>\n",
       "      <td>-19,999,820.00</td>\n",
       "      <td>-20,018,390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31 00:00:00+00:00</th>\n",
       "      <td>One Carter Lane</td>\n",
       "      <td>Cash</td>\n",
       "      <td>-23,790.00</td>\n",
       "      <td>-19,999,905.30</td>\n",
       "      <td>-20,023,695.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01 00:00:00+00:00</th>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "      <td>Cash</td>\n",
       "      <td>199,950.00</td>\n",
       "      <td>-20,018,390.00</td>\n",
       "      <td>-19,818,440.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01 00:00:00+00:00</th>\n",
       "      <td>One Carter Lane</td>\n",
       "      <td>Cash</td>\n",
       "      <td>223,200.00</td>\n",
       "      <td>-20,023,695.30</td>\n",
       "      <td>-19,800,495.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01 00:00:00+00:00</th>\n",
       "      <td>Bond</td>\n",
       "      <td>Coupon</td>\n",
       "      <td>-83,333.33</td>\n",
       "      <td>20,000,000.00</td>\n",
       "      <td>19,916,666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28 00:00:00+00:00</th>\n",
       "      <td>Sydney Bondi Junction Westfield</td>\n",
       "      <td>Cash</td>\n",
       "      <td>-6,960.00</td>\n",
       "      <td>-19,818,440.00</td>\n",
       "      <td>-19,825,400.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Instrument Activity  \\\n",
       "Date                                                                  \n",
       "2022-01-04 00:00:00+00:00                             Bond    Side2   \n",
       "2022-01-04 00:00:00+00:00                             Fund    Side1   \n",
       "2022-01-05 00:00:00+00:00                  One Carter Lane    Side2   \n",
       "2022-01-05 00:00:00+00:00  Sydney Bondi Junction Westfield    Side2   \n",
       "2022-01-31 00:00:00+00:00  Sydney Bondi Junction Westfield     Cash   \n",
       "2022-01-31 00:00:00+00:00                  One Carter Lane     Cash   \n",
       "2022-02-01 00:00:00+00:00  Sydney Bondi Junction Westfield     Cash   \n",
       "2022-02-01 00:00:00+00:00                  One Carter Lane     Cash   \n",
       "2022-02-01 00:00:00+00:00                             Bond   Coupon   \n",
       "2022-02-28 00:00:00+00:00  Sydney Bondi Junction Westfield     Cash   \n",
       "\n",
       "                                  Amount           Open          Close  \n",
       "Date                                                                    \n",
       "2022-01-04 00:00:00+00:00  20,000,000.00           0.00  20,000,000.00  \n",
       "2022-01-04 00:00:00+00:00  20,000,000.00           0.00  20,000,000.00  \n",
       "2022-01-05 00:00:00+00:00 -19,999,905.30           0.00 -19,999,905.30  \n",
       "2022-01-05 00:00:00+00:00 -19,999,820.00           0.00 -19,999,820.00  \n",
       "2022-01-31 00:00:00+00:00     -18,570.00 -19,999,820.00 -20,018,390.00  \n",
       "2022-01-31 00:00:00+00:00     -23,790.00 -19,999,905.30 -20,023,695.30  \n",
       "2022-02-01 00:00:00+00:00     199,950.00 -20,018,390.00 -19,818,440.00  \n",
       "2022-02-01 00:00:00+00:00     223,200.00 -20,023,695.30 -19,800,495.30  \n",
       "2022-02-01 00:00:00+00:00     -83,333.33  20,000,000.00  19,916,666.67  \n",
       "2022-02-28 00:00:00+00:00      -6,960.00 -19,818,440.00 -19,825,400.00  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = transaction_portfolios_api.get_portfolio_cash_ladder(\n",
    "    scope=\"privateMarkets\",\n",
    "    code=\"realEstateFund\",\n",
    "    from_effective_at=\"2022-01-01T00:00:00+00:00\",\n",
    "    to_effective_at=\"2032-01-01T19:20:30+00:00\",\n",
    "    effective_at=\"2023-01-01T00:00:00+00:00\",\n",
    "    recipe_id_scope = \"privateMarkets\",\n",
    "    recipe_id_code = \"lookthrough\"\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "cl_data = {\n",
    "    \"Instrument\": [],\n",
    "    \"Date\": [],\n",
    "    \"Activity\": [],\n",
    "    \"Amount\": [],\n",
    "    \"Open\": [],\n",
    "    \"Close\":[]\n",
    "}\n",
    "\n",
    "# each value\n",
    "for val in response.values:\n",
    "    name = val.sub_holding_keys['Transaction/privateMarkets/holding_class'].value.label_value \n",
    "    # each record\n",
    "    for i in val.records:\n",
    "        # each activity in record\n",
    "        for key in i.activities:\n",
    "            cl_data[\"Instrument\"].append(name)\n",
    "            cl_data[\"Date\"].append(i.effective_date)\n",
    "            cl_data[\"Activity\"].append(key)\n",
    "            cl_data[\"Amount\"].append(i.activities[key])\n",
    "            cl_data[\"Open\"].append(i.open)\n",
    "            cl_data[\"Close\"].append(i.close)\n",
    "\n",
    "pd.DataFrame(data=cl_data).set_index(\"Date\").sort_index().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Calculations\n",
    "\n",
    "Using the cash flows, we will now perform some basic data processing and data calculations to help understand what our data means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Request Cash Flows\n",
    "\n",
    "First, request the real estate cash flows from LUSID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>One Carter Lane</th>\n",
       "      <th>Sydney Bondi Junction Westfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>-23,790.00</td>\n",
       "      <td>-18,570.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>223,200.00</td>\n",
       "      <td>199,950.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>-14,070.00</td>\n",
       "      <td>-6,960.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>201,600.00</td>\n",
       "      <td>180,600.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>-25,470.00</td>\n",
       "      <td>-26,520.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            One Carter Lane  Sydney Bondi Junction Westfield\n",
       "2022-01-31       -23,790.00                       -18,570.00\n",
       "2022-02-01       223,200.00                       199,950.00\n",
       "2022-02-28       -14,070.00                        -6,960.00\n",
       "2022-03-01       201,600.00                       180,600.00\n",
       "2022-03-31       -25,470.00                       -26,520.00"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_date = datetime(2032, 1, 1, 0, 0, tzinfo=pytz.utc)\n",
    "effective_at = datetime(2022, 1, 11, 0, 0, tzinfo=pytz.utc)\n",
    "\n",
    "# Request portfolio cash flows\n",
    "external_cash_flow_response = transaction_portfolios_api.get_portfolio_cash_flows(\n",
    "    scope=\"privateMarkets\",\n",
    "    code=\"realEstateFund\",\n",
    "    effective_at=effective_at,\n",
    "    window_start=effective_at,\n",
    "    window_end=end_date,\n",
    "    recipe_id_scope=\"privateMarkets\",\n",
    "    recipe_id_code=\"lookthrough\"\n",
    ")\n",
    "\n",
    "cf_data = {\n",
    "    'Payment Date':[],\n",
    "    'One Carter Lane':[],\n",
    "    'Sydney Bondi Junction Westfield':[]\n",
    "}\n",
    "\n",
    "for val in external_cash_flow_response.values:\n",
    "    # amount positive if recieve, negative if pay\n",
    "    amount = val.amount\n",
    "\n",
    "    if val.source_instrument_id == london_luid:\n",
    "        cf_data['One Carter Lane'].append(amount)\n",
    "        cf_data['Payment Date'].append(val.payment_date)\n",
    "\n",
    "    elif val.source_instrument_id == sydney_luid:\n",
    "        cf_data['Sydney Bondi Junction Westfield'].append(amount)\n",
    "\n",
    "cash_flow_df = pd.DataFrame(data=cf_data)\n",
    "cash_flow_df['Payment Date'] = pd.to_datetime(cash_flow_df['Payment Date'])\n",
    "cash_flow_df.set_index('Payment Date', inplace=True)\n",
    "\n",
    "# Plot net cash flows for year 1\n",
    "date_cash_flow_df = cash_flow_df\n",
    "date_cash_flow_df.index = date_cash_flow_df.index.date\n",
    "\n",
    "cash_flow_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by Yearly Cash Flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>One Carter Lane</th>\n",
       "      <th>Sydney Bondi Junction Westfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2,205,810.00</td>\n",
       "      <td>1,896,360.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2,366,280.00</td>\n",
       "      <td>2,111,850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2,629,530.00</td>\n",
       "      <td>2,351,100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2,410,800.00</td>\n",
       "      <td>2,135,070.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2,423,130.00</td>\n",
       "      <td>2,087,760.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2,178,240.00</td>\n",
       "      <td>1,959,030.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2,597,370.00</td>\n",
       "      <td>2,308,260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2,410,620.00</td>\n",
       "      <td>2,142,420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2,414,130.00</td>\n",
       "      <td>2,090,220.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2,395,230.00</td>\n",
       "      <td>2,142,420.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      One Carter Lane  Sydney Bondi Junction Westfield\n",
       "2022     2,205,810.00                     1,896,360.00\n",
       "2023     2,366,280.00                     2,111,850.00\n",
       "2024     2,629,530.00                     2,351,100.00\n",
       "2025     2,410,800.00                     2,135,070.00\n",
       "2026     2,423,130.00                     2,087,760.00\n",
       "2027     2,178,240.00                     1,959,030.00\n",
       "2028     2,597,370.00                     2,308,260.00\n",
       "2029     2,410,620.00                     2,142,420.00\n",
       "2030     2,414,130.00                     2,090,220.00\n",
       "2031     2,395,230.00                     2,142,420.00"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cash_flow_yearly = cash_flow_df.groupby(lambda x: x.year).sum()\n",
    "cash_flow_yearly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal Rate of Return (IRR)\n",
    "\n",
    "The annual rate of growth that an investment is expected to generate, over the 10 years we have cash flow data for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.47% - One Carter Lane internal rate of return (IRR)\n",
      "1.09% - Sydney Bondi Junction Westfield internal rate of return (IRR)\n"
     ]
    }
   ],
   "source": [
    "# Basic IRR algorithm\n",
    "def npv(rate, cashflows):\n",
    "    total = 0.0\n",
    "    for i, cashflow in enumerate(cashflows):\n",
    "        total += cashflow / (1 + rate)**i\n",
    "    return total\n",
    "\n",
    "def irr(cashflows, iterations=100):\n",
    "    if len(cashflows)==0:\n",
    "        raise 'Error - number of cash flows cannot be zero'\n",
    "    rate = 1.0\n",
    "    investment = cashflows[0]\n",
    "    for i in range(1, iterations+1):\n",
    "        rate *= (1 - npv(rate, cashflows) / investment)\n",
    "    # as 2dp percentage value\n",
    "    return '{:.2%}'.format(rate)\n",
    "\n",
    "# Get from Real Estate data and parse\n",
    "london_intial = float(real_estate[real_estate.client_id == 'LON343239']['total_consideration'].item().replace(',', ''))\n",
    "sydney_intial = float(real_estate[real_estate.client_id == 'SYD241232']['total_consideration'].item().replace(',', ''))\n",
    "\n",
    "london_irr = irr([-london_intial] + list(cash_flow_yearly['One Carter Lane']))\n",
    "sydney_irr = irr([-sydney_intial] + list(cash_flow_yearly['Sydney Bondi Junction Westfield']))\n",
    "\n",
    "print(f'{london_irr} - One Carter Lane internal rate of return (IRR)')\n",
    "print(f'{sydney_irr} - Sydney Bondi Junction Westfield internal rate of return (IRR)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Return on Investment (ROI)\n",
    "\n",
    "Next, we can calculate how long it will take until our intial investment is paid off."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>One Carter Lane</th>\n",
       "      <th>Sydney Bondi Junction Westfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>-23,790.00</td>\n",
       "      <td>-18,570.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-01</th>\n",
       "      <td>199,410.00</td>\n",
       "      <td>181,380.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>185,340.00</td>\n",
       "      <td>174,420.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-01</th>\n",
       "      <td>386,940.00</td>\n",
       "      <td>355,020.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>361,470.00</td>\n",
       "      <td>328,500.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            One Carter Lane  Sydney Bondi Junction Westfield\n",
       "2022-01-31       -23,790.00                       -18,570.00\n",
       "2022-02-01       199,410.00                       181,380.00\n",
       "2022-02-28       185,340.00                       174,420.00\n",
       "2022-03-01       386,940.00                       355,020.00\n",
       "2022-03-31       361,470.00                       328,500.00"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cumulative_cash_flows = cash_flow_df.cumsum()\n",
    "cumulative_cash_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2030-05-30 One Carter Lane ROI acheived\n",
      "2031-07-30 Sydney Bondi Junction Westfield ROI acheived\n"
     ]
    }
   ],
   "source": [
    "# Get earliest date where cash is > intial cost\n",
    "actual_london_roi_df = cumulative_cash_flows[cumulative_cash_flows['One Carter Lane'] > london_intial].idxmin()\n",
    "actual_london_roi = actual_london_roi_df['One Carter Lane']\n",
    "\n",
    "actual_sydney_roi_df = cumulative_cash_flows[cumulative_cash_flows['Sydney Bondi Junction Westfield'] > sydney_intial].idxmin()\n",
    "actual_sydney_roi = actual_sydney_roi_df['Sydney Bondi Junction Westfield']\n",
    "\n",
    "print(f\"{actual_london_roi} One Carter Lane ROI acheived\")\n",
    "print(f\"{actual_sydney_roi} Sydney Bondi Junction Westfield ROI acheived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 years, 4 months and 24 days to achieve ROI for One Carter Lane\n",
      "9 years, 6 months and 24 days to achieve ROI for Sydney Bondi Junction Westfield\n"
     ]
    }
   ],
   "source": [
    "def actual_roi(end_date, client_id):\n",
    "    \n",
    "    buy_date = real_estate[real_estate.client_id == client_id]['trade_date'].item()\n",
    "    buy_datetime = datetime.strptime(buy_date, \"%Y-%m-%dT%H:%M:%SZ\")\n",
    "\n",
    "    roi_delta = relativedelta(end_date, buy_datetime)\n",
    "    \n",
    "    return f\"{roi_delta.years} years, {roi_delta.months} months and {roi_delta.days} days\"\n",
    "\n",
    "london_actual_roi = actual_roi(actual_london_roi, 'LON343239')\n",
    "sydney_actual_roi = actual_roi(actual_sydney_roi, 'SYD241232')\n",
    "\n",
    "print(f\"{london_actual_roi} to achieve ROI for One Carter Lane\")\n",
    "print(f\"{sydney_actual_roi} to achieve ROI for Sydney Bondi Junction Westfield\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Type</th>\n",
       "      <th>One Carter Lane</th>\n",
       "      <th>Sydney Bondi Junction Westfield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IRR</td>\n",
       "      <td>3.47%</td>\n",
       "      <td>1.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Date of ROI</td>\n",
       "      <td>05/30/2030, 00:00:00</td>\n",
       "      <td>07/30/2031, 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Length of ROI</td>\n",
       "      <td>8 years, 4 months and 24 days</td>\n",
       "      <td>9 years, 6 months and 24 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type                One Carter Lane  \\\n",
       "0            IRR                          3.47%   \n",
       "1    Date of ROI           05/30/2030, 00:00:00   \n",
       "2  Length of ROI  8 years, 4 months and 24 days   \n",
       "\n",
       "  Sydney Bondi Junction Westfield  \n",
       "0                           1.09%  \n",
       "1            07/30/2031, 00:00:00  \n",
       "2   9 years, 6 months and 24 days  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf_data = pd.DataFrame(data={\n",
    "    \"Type\": [\n",
    "        \"IRR\",\n",
    "        \"Date of ROI\",\n",
    "        \"Length of ROI\"\n",
    "    ],\n",
    "    \"One Carter Lane\":[\n",
    "        london_irr, \n",
    "        actual_london_roi.strftime(\"%m/%d/%Y, %H:%M:%S\"),\n",
    "        london_actual_roi\n",
    "    ],\n",
    "    \"Sydney Bondi Junction Westfield\": [\n",
    "        sydney_irr, \n",
    "        actual_sydney_roi.strftime(\"%m/%d/%Y, %H:%M:%S\"), \n",
    "        sydney_actual_roi\n",
    "    ]\n",
    "})\n",
    "\n",
    "pdf_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation\n",
    "\n",
    "We can begin to visualise our cash flows on both a short term and long term basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = date_cash_flow_df.head(24).plot.bar(y=['One Carter Lane', 'Sydney Bondi Junction Westfield'], color=['#ff5200', '#0000ff'], figsize=[15, 10])\n",
    "\n",
    "plt.title('Cash Flow Chart - Year 1')\n",
    "plt.ylabel('Cash Amount (GBP)')\n",
    "plt.xlabel('Date')\n",
    "\n",
    "ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "\n",
    "# Save the graph\n",
    "plt.savefig('./outputs/cash_flow_y1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cumulative_cash_flows.plot.line(\n",
    "    y=['One Carter Lane', 'Sydney Bondi Junction Westfield'], \n",
    "    color=['#ff5200', '#0000ff'], \n",
    "    figsize=[9, 6]\n",
    ")\n",
    "\n",
    "plt.title('Cumulative Cash Flow')\n",
    "plt.ylabel('Cash Amount (USD)')\n",
    "plt.xlabel('Date')\n",
    "\n",
    "ax.yaxis.set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}'))\n",
    "ax.xaxis.set_major_formatter(mpl.dates.DateFormatter('%Y'))\n",
    "\n",
    "plt.axhline(london_intial, color='r')\n",
    "plt.axhline(sydney_intial, color='r')\n",
    "\n",
    "ax.annotate(\n",
    "    'Initial Cost of Properties',\n",
    "    xy=('2022-01-31 00:00:00+00:00', 19000000), \n",
    "    xytext=('2022-01-31 00:00:00+00:00', 19000000)\n",
    ")\n",
    "\n",
    "# Save the graph\n",
    "plt.savefig('./outputs/cumulative_cash_flow.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. PDF Report Writer (for Real Estate Fund)\n",
    "\n",
    "Using the calculations, cash flows and graphs from above, we are able to combine them to produce a concise and informative PDF report."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"1200\"\n",
       "            src=\"./outputs/real_estate_report.pdf\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x12f864e50>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf = FPDF()\n",
    "\n",
    "# Set cell height\n",
    "ch = 8\n",
    "\n",
    "# Title and Logo\n",
    "pdf.add_page()\n",
    "pdf.set_font(\"Arial\", style='B', size=14)\n",
    "pdf.text(txt='Real Estate Report', x=10, y=10)\n",
    "pdf.image(\"./data/finbourne.png\", x=150, y=3, w=50)\n",
    "pdf.ln(10)\n",
    "\n",
    "# Calculations Table header\n",
    "pdf.set_font(\"Arial\", style='B', size=10)\n",
    "pdf.set_fill_color(235, 235, 235)\n",
    "pdf.cell(w=30, h=ch, txt='Type', border=1, ln=0, align='C', fill=True)\n",
    "pdf.cell(w=80, h=ch, txt='One Carter Lane (OCL)', border=1, ln=0, align='C', fill=True)\n",
    "pdf.cell(w=80, h=ch, txt='Sydney Bondi Junction Westfield (SBJW)', border=1, ln=1, align='C', fill=True)\n",
    "\n",
    "# Calculations Table contents\n",
    "pdf.set_font('Arial', '', 10)\n",
    "for i in range(0, len(pdf_data)):\n",
    "    pdf.cell(w=30, h=ch, \n",
    "             txt=pdf_data['Type'].iloc[i], \n",
    "             border=1, ln=0, align='C')\n",
    "    pdf.cell(w=80, h=ch, \n",
    "             txt=pdf_data['One Carter Lane'].iloc[i], \n",
    "             border=1, ln=0, align='C')\n",
    "    pdf.cell(w=80, h=ch, \n",
    "             txt=pdf_data['Sydney Bondi Junction Westfield'].iloc[i], \n",
    "             border=1, ln=1, align='C')\n",
    "pdf.ln(ch)\n",
    "\n",
    "# Yearly Cashflow header\n",
    "pdf.set_font(\"Arial\", style='B', size=10)\n",
    "pdf.set_fill_color(235, 235, 235) # grey\n",
    "pdf.cell(w=20, h=ch, txt='Year', border=1, ln=0, align='C', fill=True)\n",
    "pdf.cell(w=20, h=ch, txt='OCL', border=1, ln=0, align='C', fill=True)\n",
    "pdf.cell(w=20, h=ch, txt='SBJW', border=1, ln=1, align='C', fill=True)\n",
    "\n",
    "# Yearly Cashflow contents\n",
    "pdf.set_font('Arial', '', 10)\n",
    "for i in range(0, len(cash_flow_yearly)):\n",
    "    pdf.cell(w=20, h=ch, \n",
    "             txt=str(cash_flow_yearly.index[i]),\n",
    "             border=1, ln=0, align='C')\n",
    "    pdf.cell(w=20, h=ch, \n",
    "             txt=str(round(cash_flow_yearly['One Carter Lane'].iloc[i])), \n",
    "             border=1, ln=0, align='C')\n",
    "    pdf.cell(w=20, h=ch, \n",
    "             txt=str(round(cash_flow_yearly['Sydney Bondi Junction Westfield'].iloc[i])), \n",
    "             border=1, ln=1, align='C')\n",
    "\n",
    "# Add Cumulative Cash Flow graph\n",
    "pdf.image(\"./outputs/cumulative_cash_flow.png\", x=75, y=58, w=138)\n",
    "\n",
    "# Add Yearly Cash Flow graph\n",
    "pdf.image(\"./outputs/cash_flow_y1.png\", y=140, w=222, x=-5)\n",
    "\n",
    "# Save PDF\n",
    "pdf.output(\"./outputs/real_estate_report.pdf\", \"F\")\n",
    "\n",
    "# Display PDF\n",
    "IFrame(\"./outputs/real_estate_report.pdf\", width=800, height=1200)"
   ]
  }
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