finance.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3-final"
    },
    "orig_nbformat": 2,
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.7.3 64-bit",
      "metadata": {
        "interpreter": {
          "hash": "6e20e4cf813db25a064429209bc2aa0c85672235c9a4586176da9f8980112c00"
        }
      }
    },
    "colab": {
      "name": "finance.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJOwCSRb9rQk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78219e0b-8831-4a70-994e-3edd445536f4"
      },
      "source": [
        "!pip install investpy\n",
        "!pip install matplotlib\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting investpy\n",
            "  Downloading investpy-1.0.8.tar.gz (4.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.4 MB 26.4 MB/s \n",
            "\u001b[?25hCollecting Unidecode>=1.1.1\n",
            "  Downloading Unidecode-1.3.4-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 54.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools>=41.2.0 in /usr/local/lib/python3.7/dist-packages (from investpy) (57.4.0)\n",
            "Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from investpy) (1.21.6)\n",
            "Requirement already satisfied: pandas>=0.25.1 in /usr/local/lib/python3.7/dist-packages (from investpy) (1.3.5)\n",
            "Collecting lxml>=4.4.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 56.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.22.0 in /usr/local/lib/python3.7/dist-packages (from investpy) (2.23.0)\n",
            "Requirement already satisfied: pytz>=2019.3 in /usr/local/lib/python3.7/dist-packages (from investpy) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.1->investpy) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25.1->investpy) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (2022.5.18.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.22.0->investpy) (2.10)\n",
            "Building wheels for collected packages: investpy\n",
            "  Building wheel for investpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for investpy: filename=investpy-1.0.8-py3-none-any.whl size=4481592 sha256=8ead9f2d5c2fece9a83f5482c141f3a76d27957e377573f56e09ae45083e0879\n",
            "  Stored in directory: /root/.cache/pip/wheels/96/a8/a5/0d33c72eaf00b41df7b9dc1e15d2b7c7154b3f1379ed350211\n",
            "Successfully built investpy\n",
            "Installing collected packages: Unidecode, lxml, investpy\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed Unidecode-1.3.4 investpy-1.0.8 lxml-4.8.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.2)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ea8EixlNtPJs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f4da56e-2805-4fc8-c17c-c499b20de1cd"
      },
      "source": [
        "import datetime\n",
        "import investpy\n",
        "\n",
        "today = datetime.date.today()\n",
        "search_results = investpy.search_quotes(text='yar',\n",
        "                                        products=['stocks'],\n",
        "                                        countries=['norway'],                                        \n",
        "                                        n_results=10)\n",
        "a=search_results[0].retrieve_historical_data(from_date='01/01/2020', to_date=f'{today.day}/{today.month}/{today.year}')\n",
        "print()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHptn6Ad9rQn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "45d1c3d9-879e-4882-fa43-5ba1bb5aac31"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(a['High'])\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwgnk5bFESDr"
      },
      "source": [
        "ordrer = [('2/8/2021','Klp Aksjeglobal Mer Samfunnsansvar',140.256,250000,'sparing','nkr','sbanken'),\n",
        "          ('5/5/2021','Klp Aksjeglobal Mer Samfunnsansvar',124.77,200000,'sparing','nkr','sbanken'),\n",
        "          ('27/11/2019','Klp Aksjeglobal Mer Samfunnsansvar',171.1,220000,0,'barn','nkr','nordnet'),\n",
        "          ('8/4/2021','odin norge c',12,53190,'barn','nkr','sbanken'), # vist i nkr i sbanken\n",
        "          ('8/4/2021','odin norden c',9.43,37270,'barn','nkr','sbanken'),  # vist i nkr i sbanken\n",
        "          ('1/9/2010','Microsoft',97,97*27.15,'sparing','usd','stockplanconnet')\n",
        "\n",
        "]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfCj5ywT9rQo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "240fb605-8936-44e8-d5e2-da9b7f0ea307"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Andre kolonne er antall aksjer for (barn, Ivar, Ivars pensjon)\n",
        "# Siden det kommer mer til pensjon for hver måned, så har jeg foreløpig bare satt \n",
        "# pensjonen til å være 1 million, som den ca var i vår, så kan vi se utviklingen\n",
        "# Ellers er det litt vanskelig å se utviklingen for pensjon\n",
        "# Det betyr at pensjonen ikke er korrekt, fordi den ikke viser de månedlige tilleggene\n",
        "# Pensjonen begynte i Kron ca 1. april 2021\n",
        "p=[('Klp Aksjeglobal Mer Samfunnsansvar', (176, 125 + 141, 205),1,'funds','norway'), # Kjøpte 141 August 2021 250 kkr \n",
        "   ('Klp Aksjeglobal Indeks V', (0,0,142),1,'funds','norway'),\n",
        "   ('Klp Aksje Fremvoksende Markeder Indeks P', (0,0,112.5),1,'funds','norway'),\n",
        "   ('Storebrand Indeks - Nye Markeder A', (0,0,17.7),1,'funds','norway'),\n",
        "    ('Microsoft Corporation',(0,97.2,0),8.5,'stocks','united states'),\n",
        "    ('odin norge c',(12,0,0),1,'funds','norway'),\n",
        "    ('odin norden c',(9.43,0,0),8.5,'funds','norway'),    \n",
        "    ('Schlumberger NV',(0,0,0),8.5,'stocks','united states')\n",
        "\n",
        "]\n",
        "\n",
        "raw_values = []\n",
        "\n",
        "for i,t in enumerate(p):\n",
        "    print(t[0])\n",
        "    search_results = investpy.search_quotes(text=t[0],\n",
        "                                        products=[t[3]],\n",
        "                                        countries=[t[4]],                                        \n",
        "                                        n_results=10)\n",
        "    a=search_results[0].retrieve_historical_data(from_date='03/03/2020', to_date=f'{today.day}/{today.month}/{today.year}')    \n",
        "    raw_values.append(a)\n",
        "        \n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Klp Aksjeglobal Mer Samfunnsansvar\n",
            "Klp Aksjeglobal Indeks V\n",
            "Klp Aksje Fremvoksende Markeder Indeks P\n",
            "Storebrand Indeks - Nye Markeder A\n",
            "Microsoft Corporation\n",
            "odin norge c\n",
            "odin norden c\n",
            "Schlumberger NV\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4z-_o0tHF0h"
      },
      "source": [
        "# Get unique dates\n",
        "import pandas as pd\n",
        "\n",
        "portefolje = 1 # 0 er barna, 1 er egen sparing, 2 er pensjon\n",
        "\n",
        "sm = None\n",
        "highs = None\n",
        "cm = pd.DataFrame()\n",
        "\n",
        "a = raw_values[0]\n",
        "u = raw_values[0].index\n",
        "for a in raw_values[1:]:  \n",
        "    u.append(a.index)\n",
        "u = u.unique()\n",
        "cm = pd.DataFrame(u)\n",
        "col_names = ['Date']\n",
        "for a,t in zip(raw_values, p):  \n",
        "  antallAksjer = t[1][portefolje]\n",
        "  if portefolje == -1: # Spesiell, skal summere alle\n",
        "    antallAksjer = sum(t[1])\n",
        "  if antallAksjer == 0:\n",
        "    continue # hvis ingen investering\n",
        "  ser = a['High']*t[2]*antallAksjer\n",
        "  cm=cm.merge(ser, on='Date')\n",
        "  col_names.append(t[0])\n",
        "cm.columns =  col_names # ['a','b','c','d','e','f','g','h']#\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0dR3SPh9rQo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "outputId": "efff8168-8279-4934-d6c2-2fc48770e03f"
      },
      "source": [
        "start_date =  pd.to_datetime('2021-04-03')\n",
        "end_date =  pd.to_datetime('today')\n",
        "mask =  (cm['Date'] > start_date) & (cm['Date'] <= end_date)\n",
        "cm_sub = cm.loc[mask]\n",
        "# print(a.index)\n",
        "\n",
        "sb = cm_sub\n",
        "a = sb.sum(axis=1)\n",
        "sb.set_index(['Date'],inplace=True)\n",
        "sb.plot.area()\n",
        "plt.ylim([a.min(), a.max()+(a.max()-a.min())*0.05])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i61rNsiMJkxx"
      },
      "source": [
        ""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CozkB5_d-Et1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "18cc1e69-51a6-4bfa-8089-d2ddc7e3ca28"
      },
      "source": [
        "sm = cm_sub.sum(axis=1)\n",
        "sm = 100*sm/sm[0]\n",
        "plt.plot(sm)\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JItJNtX9eTz"
      },
      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
    }
  ]
}