{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Portfolios Example"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Packages to load"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "from ratingslib.datasets.filenames import FILENAME_PORTFOLIOS, dataset_path\n",
                "from ratingslib.ratings.aggregation import RatingAggregation\n",
                "from ratingslib.utils.enums import ratings\n",
                "from ratingslib.utils.methods import print_info"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Get filename path and set columns dict (item, ratings)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "filename = dataset_path(FILENAME_PORTFOLIOS)\n",
                "columns_dict = {'item': 'portfolio',\n",
                "                'ratings': ['R2', 'AvgReturn', 'maxDD']}"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Define votes or weight for each attribute"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": [
                "votes_or_weights = {\n",
                "    'ENGINEER [RISK-SEEKING]': {\n",
                "        'R2': 4.0,\n",
                "        'AvgReturn': 10.0,\n",
                "        'maxDD': 4.0},\n",
                "    'STARTUP [NEUTRAL]': {\n",
                "        'R2': 10.0,\n",
                "        'AvgReturn': 10.0,\n",
                "        'maxDD': 10.0},\n",
                "    'FUND [RISK-AVERSE]': {\n",
                "        'R2': 10.0,\n",
                "        'AvgReturn': 4.0,\n",
                "        'maxDD': 10.0}\n",
                "}"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Aggregation methods"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "versions = [ratings.AGGREGATIONMARKOV,\n",
                "            ratings.AGGREGATIONOD,\n",
                "            ratings.AGGREGATIONPERRON]"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Aggregation results"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "\n",
                        "\n",
                        "=====ENGINEER [RISK-SEEKING]=====\n",
                        "\n",
                        "\n",
                        "=====AggrMarkov=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.195942        2\n",
                        "1  portfolio2  0.182491        3\n",
                        "2  portfolio3  0.350205        1\n",
                        "3  portfolio4  0.181080        4\n",
                        "4  portfolio5  0.090283        5\n",
                        "\n",
                        "\n",
                        "=====AggrOD=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  2.227214        1\n",
                        "1  portfolio2  1.495427        3\n",
                        "2  portfolio3  1.542485        2\n",
                        "3  portfolio4  0.632074        4\n",
                        "4  portfolio5  0.220648        5\n",
                        "\n",
                        "\n",
                        "=====AggrPerron=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.218601        2\n",
                        "1  portfolio2  0.198084        3\n",
                        "2  portfolio3  0.316097        1\n",
                        "3  portfolio4  0.183130        4\n",
                        "4  portfolio5  0.084088        5\n",
                        "\n",
                        "\n",
                        "=====STARTUP [NEUTRAL]=====\n",
                        "\n",
                        "\n",
                        "=====AggrMarkov=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.205238        3\n",
                        "1  portfolio2  0.203258        4\n",
                        "2  portfolio3  0.293477        1\n",
                        "3  portfolio4  0.205287        2\n",
                        "4  portfolio5  0.092741        5\n",
                        "\n",
                        "\n",
                        "=====AggrOD=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  3.464231        1\n",
                        "1  portfolio2  1.557978        2\n",
                        "2  portfolio3  0.616994        4\n",
                        "3  portfolio4  1.508745        3\n",
                        "4  portfolio5  0.285026        5\n",
                        "\n",
                        "\n",
                        "=====AggrPerron=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.241543        1\n",
                        "1  portfolio2  0.212355        4\n",
                        "2  portfolio3  0.223183        3\n",
                        "3  portfolio4  0.230409        2\n",
                        "4  portfolio5  0.092510        5\n",
                        "\n",
                        "\n",
                        "=====FUND [RISK-AVERSE]=====\n",
                        "\n",
                        "\n",
                        "=====AggrMarkov=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.210850        4\n",
                        "1  portfolio2  0.237623        1\n",
                        "2  portfolio3  0.225970        3\n",
                        "3  portfolio4  0.231535        2\n",
                        "4  portfolio5  0.094023        5\n",
                        "\n",
                        "\n",
                        "=====AggrOD=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  5.098536        1\n",
                        "1  portfolio2  1.601722        3\n",
                        "2  portfolio3  0.246798        5\n",
                        "3  portfolio4  3.388840        2\n",
                        "4  portfolio5  0.336303        4\n",
                        "\n",
                        "\n",
                        "=====AggrPerron=====\n",
                        "         Item    rating  ranking\n",
                        "0  portfolio1  0.259312        2\n",
                        "1  portfolio2  0.225082        3\n",
                        "2  portfolio3  0.145131        4\n",
                        "3  portfolio4  0.273927        1\n",
                        "4  portfolio5  0.096548        5\n"
                    ]
                }
            ],
            "source": [
                "for key, vw in votes_or_weights.items():\n",
                "    print_info(key)\n",
                "    for version in versions:\n",
                "        print_info(version)\n",
                "        ra = RatingAggregation(version, votes_or_weights=vw)\n",
                "        ratings_df = ra.rate_from_file(\n",
                "            filename, pairwise=False, columns_dict=columns_dict)\n",
                "        print(ratings_df)"
            ]
        }
    ],
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