Investments_Tracker.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdd8t58F49nE"
      },
      "source": [
        "## MF Portfolio Visulizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BunEd9sT49nI"
      },
      "source": [
        "### TODO\n",
        " - [x] Connect ICRA APIs to pull portfolio holdings `class:mfAssetAllocations()`\n",
        " - [x] Visualise MF portfolio by individual assets and asset type\n",
        "  - [x] Add switch of visuals on MF portfolios and personal investments `weighted=True`\n",
        " - [x] Add visuals to see changes in current holdings on new fund addition\n",
        " - [ ] Connect APIs to track individual asset growth\n",
        " - [ ] Add historical NAVs and visuals for fund returns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "khO9HxVV49nJ"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import os.path\n",
        "import bs4\n",
        "import requests\n",
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup\n",
        "from functools import partial, reduce\n",
        "from collections import defaultdict\n",
        "\n",
        "# sys.path.insert(0, '/home/j5067250/credentials')\n",
        "# from credentials import HTTP_PROXY, HTTPS_PROXY\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "\n",
        "pd.options.mode.chained_assignment = None  # default='warn'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3SeAJL049nL",
        "outputId": "fafcc24e-c2bf-4172-d1c6-57a8b3cd1e19"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "requests  : 2.23.0\n",
            "bs4       : 0.0.1\n",
            "plotly    : 5.4.0\n",
            "IPython   : 5.5.0\n",
            "matplotlib: 3.2.2\n",
            "pandas    : 1.1.5\n",
            "sys       : 3.7.12 (default, Sep 10 2021, 00:21:48) \n",
            "[GCC 7.5.0]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%load_ext watermark\n",
        "%watermark --iversion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLP1MqH649nM",
        "outputId": "5f00ed03-fddc-432d-8ccd-db4b1372b94e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Templates configuration\n",
              "-----------------------\n",
              "    Default template: 'plotly'\n",
              "    Available templates:\n",
              "        ['ggplot2', 'seaborn', 'simple_white', 'plotly',\n",
              "         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',\n",
              "         'ygridoff', 'gridon', 'none']"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import plotly.io as pio\n",
        "pio.templates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ADn4ar2X49nM"
      },
      "outputs": [],
      "source": [
        "class mfAssetAllocations():\n",
        "    \"\"\"class `mfAssetAllocation`\n",
        "    \n",
        "    Reads asset allocation (portfolio) of a MF,\n",
        "    and returns a pandas dataframe\n",
        "    \"\"\"\n",
        "    def __init__(self, name, url, refresh=False, batch_size=3):\n",
        "        \"\"\"Initialising `class mfAssetAllocation`\n",
        "        \n",
        "        Inputs:\n",
        "            name(string): name of the mutual fund\n",
        "            url(string): MF Portfolio webpage on ICRA Website\n",
        "            refresh(bool): reload portfolio values from webpage (refresh=True) \n",
        "                or read from the pickle dataframe\n",
        "            batch_size(int): Number of columns expected from HTML table\n",
        "        \"\"\"\n",
        "        self.name = name\n",
        "        self.url = url\n",
        "        self.refresh = refresh\n",
        "        self.BATCH_SIZE = batch_size\n",
        "    \n",
        "    def __call__(self):\n",
        "        if self.refresh is True or not os.path.isfile('./Data/' + self.name + '.pkl') :\n",
        "            print(f'Downloading Portfolio for {self.name}')\n",
        "            return self.pipeline()\n",
        "        else:\n",
        "            print(\"Loading saved pickle file\")\n",
        "            return pd.read_pickle('./Data/' + self.name + '.pkl')\n",
        "    \n",
        "    def pipeline(self):\n",
        "        \"\"\"Pipeline to read webpage and convert to dataframe\n",
        "        \"\"\"\n",
        "        self.html_text = self.read_html()\n",
        "        self.beautiful_text = self.beautify_html()\n",
        "        self.extracted_data_list = self.extract_data()\n",
        "        self.processed_df = self.process_data()\n",
        "        self.processed_df.to_pickle('./Data/' + self.name + '.pkl')\n",
        "        return self.processed_df\n",
        "        \n",
        "    def read_html(self):\n",
        "        \"\"\"Load webpage using `requests` module\n",
        "        \"\"\"\n",
        "        self.response = requests.get(\n",
        "        url=self.url)\n",
        "        self.html_doc = self.response.text\n",
        "        \n",
        "    def beautify_html(self):\n",
        "        \"\"\"Refine HTML text from portfolio page\n",
        "        \"\"\"\n",
        "        self.soup = BeautifulSoup(self.html_doc, 'html.parser')\n",
        "        self.item = self.soup.find_all('div', {\"class\": \"accordion-body collapse in\"})[0]\n",
        "        self.html_text = self.item.text.strip()\n",
        "        return self.html_text\n",
        "    \n",
        "    def extract_data(self):\n",
        "        \"\"\"Extract portfolio company & asset details from text\n",
        "        \"\"\"\n",
        "        self.texts = [text for text in self.html_text.split('\\n') if text and text != 'Detailed Portfolio']\n",
        "        texts_subset = self.texts[self.BATCH_SIZE:]\n",
        "        self.data = defaultdict(list)\n",
        "        while texts_subset:\n",
        "            for i in range(self.BATCH_SIZE):\n",
        "                text = texts_subset.pop()\n",
        "                if i == 0:\n",
        "                    self.data['Percentage Allocation'].append(text)\n",
        "                elif i == 1:\n",
        "                    self.data['Asset Type'].append(text)\n",
        "                elif i == 2:\n",
        "                    self.data['Company Name'].append(text)\n",
        "        return self.data\n",
        "    \n",
        "    def process_data(self):\n",
        "        \"\"\"Convert extracted data to dataframe\n",
        "        \"\"\"\n",
        "        df = pd.DataFrame(self.extracted_data_list)\n",
        "        df['Asset Type'] = df['Asset Type'].str.strip()\n",
        "        df['Percentage Allocation'] = df['Percentage Allocation'].str.rstrip(' %')\n",
        "        df['Percentage Allocation'] = df['Percentage Allocation'].astype('float')\n",
        "        df = df.groupby(['Company Name', 'Asset Type'])['Percentage Allocation'].agg('sum').reset_index()\n",
        "        return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-1bM1OfE49nP"
      },
      "outputs": [],
      "source": [
        "ref = {\n",
        "    'Axis Focused 25 Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/16299',\n",
        "        'amount': 163735\n",
        "    },\n",
        "    'Parag Parikh Flexi Cap Fund' : {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/20004',\n",
        "        'amount': 53233\n",
        "    },\n",
        "    'Kotak Equity Hybrid Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/17288',\n",
        "        'amount': 102263\n",
        "    },\n",
        "    'IDFC Focused Equity Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/18094',\n",
        "        'amount': 59132\n",
        "    },\n",
        "    'Axis Long Term Equity Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/16271',\n",
        "        'amount': 149376\n",
        "    },\n",
        "    'DSP Tax Saver Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/16819',\n",
        "        'amount': 58677\n",
        "    },\n",
        "    'Invesco India Tax Plan Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/17484',\n",
        "        'amount': 57620\n",
        "    },\n",
        "    'Kotak Tax Saver Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/17355',\n",
        "        'amount': 57302\n",
        "    },\n",
        "    'ICICI Prudential Ultra Short Term Fund': {\n",
        "        'url': 'https://www.mutualfundindia.com/MF/Portfolio/Details/17114',\n",
        "        'amount': 419043\n",
        "    }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "32cgJHeU49nP"
      },
      "outputs": [],
      "source": [
        "def overall_portfolio(ref=ref, refresh=False):\n",
        "    \"\"\"Calculates investments in each asset (company) from reference investment amounts\n",
        "    \n",
        "    Input:\n",
        "        ref(dict): References data with investment amount and portfolio weblinks\n",
        "        \n",
        "    Return:\n",
        "        DataFrame: Dataframe with overall investments in each asset (company)\n",
        "    \"\"\"\n",
        "    portfolio_df = pd.DataFrame(columns=['Company Name', 'Asset Type', 'Weighted Amt'])\n",
        "    for mf in ref:\n",
        "        weights_df = mfAssetAllocations(name=mf, url=ref[mf]['url'], refresh=refresh)()\n",
        "        total_investment = ref[mf]['amount']\n",
        "        weights_df['Weighted Amt'] = weights_df['Percentage Allocation'] * total_investment / 100\n",
        "        weights_df = weights_df.drop(columns=['Percentage Allocation'], axis=1)\n",
        "        \n",
        "        portfolio_df = portfolio_df.merge(weights_df,\n",
        "                                          how='outer',\n",
        "                                          on=['Company Name', 'Asset Type'])\n",
        "        portfolio_df = portfolio_df.fillna(0)\n",
        "        portfolio_df['Weighted Amt'] = portfolio_df['Weighted Amt_x'] + portfolio_df['Weighted Amt_y']\n",
        "        portfolio_df = portfolio_df.drop(columns=['Weighted Amt_x', 'Weighted Amt_y'])\n",
        "        \n",
        "    return portfolio_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZI8LKGN49nQ",
        "outputId": "cdba6bf7-01e8-47be-ee32-6889db2e337e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading Portfolio for Axis Focused 25 Fund\n",
            "Downloading Portfolio for Parag Parikh Flexi Cap Fund\n",
            "Downloading Portfolio for Kotak Equity Hybrid Fund\n",
            "Downloading Portfolio for IDFC Focused Equity Fund\n",
            "Downloading Portfolio for Axis Long Term Equity Fund\n",
            "Downloading Portfolio for DSP Tax Saver Fund\n",
            "Downloading Portfolio for Invesco India Tax Plan Fund\n",
            "Downloading Portfolio for Kotak Tax Saver Fund\n",
            "Downloading Portfolio for ICICI Prudential Ultra Short Term Fund\n"
          ]
        }
      ],
      "source": [
        "my_investments = overall_portfolio(refresh=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "lPrM5JnF49nQ",
        "outputId": "41109db8-0bbe-4275-ed8f-e1ddecb55297"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Company Name</th>\n",
              "      <th>Asset Type</th>\n",
              "      <th>Weighted Amt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Currency Derivatives</td>\n",
              "      <td>Equity</td>\n",
              "      <td>-12493.7851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Net Receivables/(Payable)</td>\n",
              "      <td>Others</td>\n",
              "      <td>-10179.5938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>Nifty</td>\n",
              "      <td>Equity</td>\n",
              "      <td>-2035.0337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>264</th>\n",
              "      <td>Axis Bank Ltd.</td>\n",
              "      <td>Debt</td>\n",
              "      <td>-1759.9806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>8.19 Rajasthan UDAY Bond Jun 23 2026</td>\n",
              "      <td>Debt</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Tata Consultancy Services Ltd.</td>\n",
              "      <td>Equity</td>\n",
              "      <td>34038.5827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>ICICI Bank Ltd.</td>\n",
              "      <td>Equity</td>\n",
              "      <td>34183.3741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bajaj Finance Ltd.</td>\n",
              "      <td>Equity</td>\n",
              "      <td>35864.4812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>Treasury Bill (182D)</td>\n",
              "      <td>Others</td>\n",
              "      <td>45759.4956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Triparty Repo</td>\n",
              "      <td>Others</td>\n",
              "      <td>102113.7404</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>308 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              Company Name Asset Type  Weighted Amt\n",
              "41                    Currency Derivatives     Equity   -12493.7851\n",
              "19               Net Receivables/(Payable)     Others   -10179.5938\n",
              "107                                  Nifty     Equity    -2035.0337\n",
              "264                         Axis Bank Ltd.       Debt    -1759.9806\n",
              "75   8.19 Rajasthan UDAY Bond Jun 23 2026        Debt        0.0000\n",
              "..                                     ...        ...           ...\n",
              "27          Tata Consultancy Services Ltd.     Equity    34038.5827\n",
              "14                         ICICI Bank Ltd.     Equity    34183.3741\n",
              "5                       Bajaj Finance Ltd.     Equity    35864.4812\n",
              "306                   Treasury Bill (182D)     Others    45759.4956\n",
              "62                           Triparty Repo     Others   102113.7404\n",
              "\n",
              "[308 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "pd.set_option('display.max_rows', 10)\n",
        "my_investments.sort_values(by='Weighted Amt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haEqz1jM49nR",
        "outputId": "f816f3fc-0ba6-4c57-cfdb-737fbc522d88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1120571.638"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "my_investments['Weighted Amt'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlCpYX4049nR",
        "outputId": "4662a719-ef80-43f7-bfc0-65e765df2f17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Loading saved pickle file\n",
            "Tracked Amount:  1120571.638\n",
            "Investment Value:  1120381\n",
            "Difference:  190.6380000000354\n"
          ]
        }
      ],
      "source": [
        "tracked_amt = 0\n",
        "actual_amt = 0\n",
        "for mf in ref:\n",
        "    _df = mfAssetAllocations(name=mf, url=ref[mf]['url'])()\n",
        "    pc = _df['Percentage Allocation'].sum()\n",
        "    tracked_amt += ref[mf]['amount'] * pc / 100\n",
        "    actual_amt += ref[mf]['amount']\n",
        "\n",
        "print(\"Tracked Amount: \", tracked_amt)\n",
        "print(\"Investment Value: \", actual_amt)\n",
        "print(\"Difference: \", abs(tracked_amt - actual_amt))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "psQkkCyS49nR"
      },
      "outputs": [],
      "source": [
        "def fund_holdings(ref, weighted=False):\n",
        "    \"\"\"Proportion of investment of each fund in each asset.\n",
        "    If weighted is True, returns actual invesment amount for personal invesments from ref.\n",
        "    \n",
        "    Input:\n",
        "        ref(dict): References data with investment amount and portfolio weblinks\n",
        "        weighted(bool): If True, each fund column has actual investment proportion,\n",
        "            else a portfolio percentage allocations.\n",
        "    \"\"\"\n",
        "    dfs = [pd.read_pickle('./Data/' + mf + '.pkl').rename(columns={'Percentage Allocation': mf}) for mf in ref]\n",
        "    if weighted is True:\n",
        "        for (mf, df) in zip(ref, dfs):\n",
        "            df[mf] = df[mf] * ref[mf]['amount'] / 100\n",
        "    df = reduce(partial(pd.core.frame.DataFrame.merge, how='outer', on=['Company Name', 'Asset Type']), dfs).fillna(0)\n",
        "    df['Total Holdings'] = df[list(ref.keys())].sum(axis=1)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GwKuclKp49nS"
      },
      "outputs": [],
      "source": [
        "def plot_companies(df, ref, top_n=25, viz='plotly'):\n",
        "    \"\"\"Plot top N companies \n",
        "    \n",
        "    Input:\n",
        "        df(DataFrame): Holdings dataframe with a column for each fund holding value\n",
        "        ref(Dictionary): References\n",
        "        top_n(Integer): Number of assets in the plot with top holdings\n",
        "        viz(String): Visualisation library to use (plotly or matplotlib)\n",
        "    \"\"\"\n",
        "    \n",
        "    if top_n is None:\n",
        "        top_n = df.shape[0]\n",
        "    \n",
        "    funds = list(ref.keys())\n",
        "    title = 'Holdings distribution in each asset'\n",
        "    \n",
        "    index = df['Total Holdings'].sort_values(ascending=False).index\n",
        "    df = df.iloc[index[:top_n]]\n",
        "    \n",
        "    if viz == 'matplotlib':\n",
        "        df.plot.bar(\n",
        "            x='Company Name',\n",
        "            y=funds,\n",
        "            label=funds,\n",
        "            stacked=True,\n",
        "            title=title,\n",
        "            rot=90)\n",
        "    elif viz == 'plotly':\n",
        "        fig = px.bar(\n",
        "                df,\n",
        "                x='Company Name',\n",
        "                y=funds,\n",
        "                title=title,\n",
        "                template='plotly'\n",
        "                )\n",
        "        fig.update_layout(\n",
        "            font=dict(\n",
        "                size=10),\n",
        "            width=df.shape[0]*30,\n",
        "            height=600\n",
        "        )\n",
        "        fig.update_xaxes(title_font_family=\"Arial\")\n",
        "        fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
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        "id": "LWQeD_sg49nS",
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          "metadata": {}
        }
      ],
      "source": [
        "df_holdings = fund_holdings(ref, weighted=False)\n",
        "plot_companies(df_holdings, ref, top_n=30)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_holdings.head()"
      ],
      "metadata": {
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Company Name</th>\n",
              "      <th>Asset Type</th>\n",
              "      <th>Axis Focused 25 Fund</th>\n",
              "      <th>Parag Parikh Flexi Cap Fund</th>\n",
              "      <th>Kotak Equity Hybrid Fund</th>\n",
              "      <th>IDFC Focused Equity Fund</th>\n",
              "      <th>Total Holdings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Avenue Supermarts Ltd.</td>\n",
              "      <td>Equity</td>\n",
              "      <td>9.77</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Axis Bank Ltd. (368D)</td>\n",
              "      <td>Debt</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Axis Bank Ltd. (370D)</td>\n",
              "      <td>Debt</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Axis Bank Ltd. (372D)</td>\n",
              "      <td>Debt</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Axis Bank Ltd. (92D)</td>\n",
              "      <td>Debt</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Company Name Asset Type  ...  IDFC Focused Equity Fund  Total Holdings\n",
              "0  Avenue Supermarts Ltd.     Equity  ...                       0.0            9.77\n",
              "1   Axis Bank Ltd. (368D)       Debt  ...                       0.0            0.12\n",
              "2   Axis Bank Ltd. (370D)       Debt  ...                       0.0            0.37\n",
              "3   Axis Bank Ltd. (372D)       Debt  ...                       0.0            1.36\n",
              "4    Axis Bank Ltd. (92D)       Debt  ...                       0.0            0.12\n",
              "\n",
              "[5 rows x 7 columns]"
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          },
          "metadata": {},
          "execution_count": 16
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      "execution_count": null,
      "metadata": {
        "id": "CYwY92Te49nT"
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      "source": [
        "def plot_assets(df, ref, viz='plotly'):\n",
        "    \"\"\"Plot holdings by asset type\n",
        "    \"\"\"\n",
        "    funds = list(ref.keys())\n",
        "    title = \"Holdings Distribution by Asset Type\"\n",
        "    \n",
        "    df = df.groupby('Asset Type')['Total Holdings'].agg('sum').reset_index()\n",
        "    \n",
        "    total = df['Total Holdings'].sum()\n",
        "    labels = df['Asset Type'] + ', ' + (df['Total Holdings']/total*100).round(2).astype(str) + '%'\n",
        "    \n",
        "    if viz == 'matplotlib':\n",
        "        df.plot.pie(\n",
        "            y='Total Holdings',\n",
        "            labels=labels\n",
        "        )\n",
        "    elif viz == 'plotly':\n",
        "        fig = px.pie(\n",
        "                df,\n",
        "                names='Asset Type',\n",
        "                values='Total Holdings',\n",
        "                hole=0.5,\n",
        "                title=title,\n",
        "            )\n",
        "        fig.update_layout(\n",
        "            font=dict(\n",
        "                size=10),\n",
        "            width=400,\n",
        "            height=400\n",
        "        )\n",
        "        fig.update_xaxes(title_font_family=\"Arial\")\n",
        "        fig.show()"
      ]
    },
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_assets(df_holdings, ref)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZEU2Aw0s49nT"
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      "source": [
        "def plot_correlation(ref, fund1, fund2, top_n=None, sort_by='corr'):\n",
        "    \"\"\"Function to plot holdings of 2 funds showing the correlation in their portfolio assets\n",
        "        \n",
        "    Input:\n",
        "        top_n(Integer): Number of high value holdings to plot\n",
        "        sort_by(String): Sort final dataframe either by Correlation % or Absolute Holdings (hol)\n",
        "    \"\"\"\n",
        "    \n",
        "    title = f\"Fund Portfolio Correlation of {fund1} v/s {fund2}\"\n",
        "    \n",
        "    df = fund_holdings(ref, weighted=False)\n",
        "    df = df[['Company Name', 'Asset Type', fund1, fund2]]\n",
        "    df[fund1] = abs(df[fund1])\n",
        "    df[fund2] = abs(df[fund2])\n",
        "    df['Total Holdings'] = df[[fund1, fund2]].sum(axis=1)\n",
        "    df = df[df['Total Holdings'] > 0]\n",
        "    \n",
        "    df['Corr'] = df[[fund1, fund2]].min(axis=1)\n",
        "    \n",
        "    if sort_by == 'corr':\n",
        "        df = df.sort_values(by='Corr', ascending=False)\n",
        "    elif sort_by == 'hol':\n",
        "        df = df.sort_values(by='Total Holdings', ascending=False)\n",
        "    \n",
        "    if top_n is not None:\n",
        "        df = df.iloc[:top_n,:]\n",
        "\n",
        "    corr_trace = go.Bar(\n",
        "        name='Correlation',\n",
        "        x=df['Company Name'],\n",
        "        y=df['Corr'])\n",
        "    \n",
        "    fund1_trace = go.Bar(\n",
        "        name=fund1,\n",
        "        x=df['Company Name'],\n",
        "        y=df[fund1])\n",
        "    \n",
        "    fund2_trace = go.Bar(\n",
        "        name=fund2,\n",
        "        x=df['Company Name'],\n",
        "        y=df[fund2])\n",
        "    \n",
        "    fig = make_subplots(rows=2, shared_xaxes=True, horizontal_spacing=0.8, row_heights=[40, 10])\n",
        "    fig.add_trace(corr_trace, row=2, col=1)\n",
        "    \n",
        "    fig.add_trace(fund1_trace, row=1, col=1)\n",
        "    fig.add_trace(fund2_trace, row=1, col=1)\n",
        "    fig.update_layout(\n",
        "        barmode='stack',\n",
        "        title=title,\n",
        "        xaxis_title='Company Name',\n",
        "        font=dict(\n",
        "            size=10),\n",
        "        width=df.shape[0]*40,\n",
        "        height=600)\n",
        "    \n",
        "    print(f\"\\nIf invested equal amount in both \\n{fund1} and {fund2}, \\n{round(df['Corr'].sum(), 2)}% amount from each fund would be invested in same assets.\")\n",
        "    fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zolusaxO49nU",
        "outputId": "b420ef2a-21b1-4ecb-e7ce-a6ca8e28f3a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and Parag Parikh Flexi Cap Fund, \n",
            "6.92% amount from each fund would be invested in same assets.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and Kotak Equity Hybrid Fund, \n",
            "20.02% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and IDFC Focused Equity Fund, \n",
            "26.1% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and Axis Long Term Equity Fund, \n",
            "78.2% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and DSP Tax Saver Fund, \n",
            "12.37% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and Invesco India Tax Plan Fund, \n",
            "22.13% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and Kotak Tax Saver Fund, \n",
            "19.1% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Focused 25 Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "0.0% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and Kotak Equity Hybrid Fund, \n",
            "19.72% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and IDFC Focused Equity Fund, \n",
            "12.71% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and Axis Long Term Equity Fund, \n",
            "2.71% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and DSP Tax Saver Fund, \n",
            "20.85% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and Invesco India Tax Plan Fund, \n",
            "13.5% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and Kotak Tax Saver Fund, \n",
            "17.32% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Parag Parikh Flexi Cap Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "7.04% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and IDFC Focused Equity Fund, \n",
            "30.04% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and Axis Long Term Equity Fund, \n",
            "11.28% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and DSP Tax Saver Fund, \n",
            "36.8% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and Invesco India Tax Plan Fund, \n",
            "25.92% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and Kotak Tax Saver Fund, \n",
            "44.51% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Equity Hybrid Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "6.7% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "IDFC Focused Equity Fund and Axis Long Term Equity Fund, \n",
            "20.1% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "IDFC Focused Equity Fund and DSP Tax Saver Fund, \n",
            "35.75% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "IDFC Focused Equity Fund and Invesco India Tax Plan Fund, \n",
            "43.53% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "IDFC Focused Equity Fund and Kotak Tax Saver Fund, \n",
            "42.95% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "IDFC Focused Equity Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "1.06% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Long Term Equity Fund and DSP Tax Saver Fund, \n",
            "8.18% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Long Term Equity Fund and Invesco India Tax Plan Fund, \n",
            "16.92% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Long Term Equity Fund and Kotak Tax Saver Fund, \n",
            "11.74% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Axis Long Term Equity Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "0.0% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "DSP Tax Saver Fund and Invesco India Tax Plan Fund, \n",
            "39.93% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "DSP Tax Saver Fund and Kotak Tax Saver Fund, \n",
            "41.53% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "DSP Tax Saver Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "1.67% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Invesco India Tax Plan Fund and Kotak Tax Saver Fund, \n",
            "33.51% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Invesco India Tax Plan Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "1.78% amount from each fund would be invested in same assets.\n"
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            "\n",
            "If invested equal amount in both \n",
            "Kotak Tax Saver Fund and ICICI Prudential Ultra Short Term Fund, \n",
            "2.52% amount from each fund would be invested in same assets.\n"
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          "metadata": {}
        }
      ],
      "source": [
        "funds = list(ref.keys())\n",
        "\n",
        "for i in range(len(funds)):\n",
        "    for j in range(i+1, len(funds)):\n",
        "        plot_correlation(ref, funds[i], funds[j], top_n=25, sort_by='corr')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4XPKFwJ49nV"
      },
      "source": [
        "### Compare current folio funds to new fund"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9CcU_bDF49nV"
      },
      "outputs": [],
      "source": [
        "def plot_new_fund(ref, current_mfs, new_mf, sort_by='hol', top_n=None):\n",
        "    \n",
        "    if new_mf in current_mfs:\n",
        "        raise ValueError(\"The index of new fund must not be in current funds.\")\n",
        "    \n",
        "    current_mfs = [mf for (mf, idx) in zip(ref, range(len(ref))) if idx in current_mfs]\n",
        "    new_mf = [mf for (mf, idx) in zip(ref, range(len(ref))) if idx == new_mf][0]\n",
        "    \n",
        "    dfs = [pd.read_pickle('./Data/' + mf + '.pkl').rename(columns={'Percentage Allocation': mf}) for mf in current_mfs]\n",
        "    df_holdings = reduce(partial(pd.core.frame.DataFrame.merge, how='outer', on=['Company Name', 'Asset Type']), dfs).fillna(0)\n",
        "    df_holdings['Current Holdings'] = df_holdings[current_mfs].sum(axis=1)\n",
        "    df_holdings = df_holdings.drop(columns=current_mfs)\n",
        "    \n",
        "    df_new = pd.read_pickle('./Data/' + new_mf + '.pkl').rename(columns={'Percentage Allocation': new_mf})\n",
        "    df = df_new.merge(df_holdings, on=['Company Name', 'Asset Type'], how='inner').fillna(0)\n",
        "    \n",
        "    ## Visualisation \n",
        "    title = f\"Fund Portfolio Correlation of current investments v/s {new_mf}\"\n",
        "    \n",
        "    df[new_mf] = abs(df[new_mf])\n",
        "    df['Current Holdings'] = abs(df['Current Holdings'])\n",
        "    df['Total Holdings'] = df[['Current Holdings', new_mf]].sum(axis=1)\n",
        "    df = df[df['Total Holdings'] > 0]\n",
        "    \n",
        "    df['Corr'] = df[['Current Holdings', new_mf]].min(axis=1)\n",
        "    \n",
        "    if sort_by == 'corr':\n",
        "        df = df.sort_values(by='Corr', ascending=False)\n",
        "    elif sort_by == 'hol':\n",
        "        df = df.sort_values(by='Total Holdings', ascending=False)\n",
        "    \n",
        "    if top_n is not None:\n",
        "        df = df.iloc[:top_n,:]\n",
        "\n",
        "    corr_trace = go.Bar(\n",
        "        name='Correlation',\n",
        "        x=df['Company Name'],\n",
        "        y=df['Corr'],\n",
        "        marker_color='steelblue',\n",
        "        width=0.5)\n",
        "    \n",
        "    hol_trace = go.Bar(\n",
        "        name='Current Holdings',\n",
        "        x=df['Company Name'],\n",
        "        y=df['Current Holdings'],\n",
        "        marker_color='grey')\n",
        "    \n",
        "    fund2_trace = go.Bar(\n",
        "        name=new_mf,\n",
        "        x=df['Company Name'],\n",
        "        y=df[new_mf],\n",
        "        marker_color='firebrick')\n",
        "    \n",
        "    fig = make_subplots(rows=2, shared_xaxes=True, horizontal_spacing=0.8, row_heights=[40, 10])\n",
        "    fig.add_trace(corr_trace, row=2, col=1)\n",
        "    \n",
        "    fig.add_trace(hol_trace, row=1, col=1)\n",
        "    fig.add_trace(fund2_trace, row=1, col=1)\n",
        "    fig.update_layout(\n",
        "        barmode='stack',\n",
        "        title=title,\n",
        "        xaxis_title='Company Name',\n",
        "        font=dict(\n",
        "            size=10),\n",
        "        width=df.shape[0]*40,\n",
        "        height=600)\n",
        "    \n",
        "    print(f\"\\nYour current portfolio shares {round(df['Corr'].sum(), 2)}% holdings with {new_mf}.\")\n",
        "    fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lN-IvGj649nV",
        "outputId": "80d9e4a2-ef4d-4191-f358-f3226681501d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Your current portfolio shares 36.84% holdings with Axis Focused 25 Fund.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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      "source": [
        "for i in range(len(ref)):\n",
        "    new_fund = i\n",
        "    current_fund_indexes_in_ref = [idx for idx in range(len(ref)) if idx != new_fund]\n",
        "    plot_new_fund(ref, current_fund_indexes_in_ref, new_fund)"
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