catorch · April 9, 2023 20:13
diff --git a/financial_projection.ipynb b/financial_projection.ipynb
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/a012154/1d4ea2443022d635b886d5025bb2c358/financial_projection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BK2jOzIGpkA5",
        "outputId": "81ad98b5-a098-494b-ba16-8b19dc0d80a4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\m1000\\AppData\\Local\\Temp\\ipykernel_46096\\3901370665.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  scenario_analysis_df = scenario_analysis_df.append(temp_df)\n",
            "C:\\Users\\m1000\\AppData\\Local\\Temp\\ipykernel_46096\\3901370665.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  scenario_analysis_df = scenario_analysis_df.append(temp_df)\n",
            "C:\\Users\\m1000\\AppData\\Local\\Temp\\ipykernel_46096\\3901370665.py:51: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  scenario_analysis_df = scenario_analysis_df.append(temp_df)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Financial Metrics:\n",
            "   Year      MRR       ARR     ARPU       CAC  Churn Rate      LTV      COGS  \\\n",
            "0     1  1.20000  1.150000  1.10000  1.050000         0.1  1.10000  1.050000   \n",
            "1     2  1.44000  1.322500  1.21000  1.102500         0.1  1.21000  1.102500   \n",
            "2     3  1.72800  1.520875  1.33100  1.157625         0.1  1.33100  1.157625   \n",
            "3     4  2.07360  1.749006  1.46410  1.215506         0.1  1.46410  1.215506   \n",
            "4     5  2.48832  2.011357  1.61051  1.276282         0.1  1.61051  1.276282   \n",
            "\n",
            "   Gross Margin  Break-Even Analysis  \n",
            "0     -0.050000            -1.110000  \n",
            "1     -0.102500            -2.360100  \n",
            "2     -0.157625            -3.790101  \n",
            "3     -0.215506            -5.452481  \n",
            "4     -0.276282            -7.416240  \n",
            "\n",
            "Scenario Analysis:\n",
            "  Year      Scenario   Revenue\n",
            "0    1  conservative  1.050000\n",
            "1    2  conservative  1.102500\n",
            "2    3  conservative  1.157625\n",
            "3    4  conservative  1.215506\n",
            "4    5  conservative  1.276282\n",
            "0    1      baseline  1.100000\n",
            "1    2      baseline  1.210000\n",
            "2    3      baseline  1.331000\n",
            "3    4      baseline  1.464100\n",
            "4    5      baseline  1.610510\n",
            "0    1    aggressive  1.150000\n",
            "1    2    aggressive  1.322500\n",
            "2    3    aggressive  1.520875\n",
            "3    4    aggressive  1.749006\n",
            "4    5    aggressive  2.011357\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assumptions and historical data\n",
        "years = np.arange(1, 6)\n",
        "mrr_growth_rate = 0.2\n",
        "arr_growth_rate = 0.15\n",
        "arpu_growth_rate = 0.1\n",
        "cac_growth_rate = 0.05\n",
        "churn_rate = 0.1\n",
        "ltv_growth_rate = 0.1\n",
        "cogs_growth_rate = 0.05\n",
        "\n",
        "# Revenue projections\n",
        "mrr = np.cumprod(np.ones(5) * (1 + mrr_growth_rate))\n",
        "arr = np.cumprod(np.ones(5) * (1 + arr_growth_rate))\n",
        "arpu = np.cumprod(np.ones(5) * (1 + arpu_growth_rate))\n",
        "\n",
        "# Costs projections\n",
        "cac = np.cumprod(np.ones(5) * (1 + cac_growth_rate))\n",
        "cogs = np.cumprod(np.ones(5) * (1 + cogs_growth_rate))\n",
        "ltv = np.cumprod(np.ones(5) * (1 + ltv_growth_rate))\n",
        "\n",
        "# Financial metrics\n",
        "gross_margin = 1 - cogs\n",
        "break_even_analysis = np.cumsum(mrr * gross_margin - cac)\n",
        "kpi_df = pd.DataFrame(\n",
        "    data={\n",
        "        'Year': years,\n",
        "        'MRR': mrr,\n",
        "        'ARR': arr,\n",
        "        'ARPU': arpu,\n",
        "        'CAC': cac,\n",
        "        'Churn Rate': churn_rate,\n",
        "        'LTV': ltv,\n",
        "        'COGS': cogs,\n",
        "        'Gross Margin': gross_margin,\n",
        "        'Break-Even Analysis': break_even_analysis,\n",
        "    }\n",
        ")\n",
        "\n",
        "# Scenario analysis\n",
        "scenarios = ['conservative', 'baseline', 'aggressive']\n",
        "growth_rates = [0.05, 0.1, 0.15]\n",
        "scenario_analysis_df = pd.DataFrame(columns=['Year', 'Scenario', 'Revenue'])\n",
        "\n",
        "for scenario, growth_rate in zip(scenarios, growth_rates):\n",
        "    revenue_scenario = np.cumprod(np.ones(5) * (1 + growth_rate))\n",
        "    temp_df = pd.DataFrame(data={'Year': years, 'Scenario': scenario, 'Revenue': revenue_scenario})\n",
        "    scenario_analysis_df = scenario_analysis_df.append(temp_df)\n",
        "\n",
        "# Visualization and report generation\n",
        "plt.figure(figsize=(10, 6))\n",
        "for scenario in scenarios:\n",
        "    plt.plot(\n",
        "        scenario_analysis_df[scenario_analysis_df['Scenario'] == scenario]['Year'],\n",
        "        scenario_analysis_df[scenario_analysis_df['Scenario'] == scenario]['Revenue'],\n",
        "        label=scenario,\n",
        "    )\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Revenue')\n",
        "plt.title('Scenario Analysis')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "print(\"Financial Metrics:\")\n",
        "print(kpi_df)\n",
        "\n",
        "print(\"\\nScenario Analysis:\")\n",
        "print(scenario_analysis_df)\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "gpu2",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }