numpy_financial.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "numpy_financial.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1K_6t0yZnOQgTCozV08_k6gsXg0DjQXwc",
      "authorship_tag": "ABX9TyM620shRvSrsZIjJIHmdy+j",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lismarcarolinacamacho/PYTHON/blob/main/numpy_financial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Python en finanzas"
      ],
      "metadata": {
        "id": "KzA-9lUXMi9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#variables numericas\n",
        "\n",
        "var1= 10\n",
        "var2=1000.5"
      ],
      "metadata": {
        "id": "YXpDQDJeMkfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#string\n",
        "\n",
        "str1= 'Balance'\n",
        "str2= 'Capital'\n",
        "\n"
      ],
      "metadata": {
        "id": "EeAV8RPjNLmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Diccionarios\n",
        "\n",
        "Dict1={}\n",
        "Dict1={'Juan':10, 'Maria':20, 'Matias':50}"
      ],
      "metadata": {
        "id": "eKqMt9zfNYCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Lista\n",
        "\n",
        "lista =['abc',123, 5.5,'Balance']"
      ],
      "metadata": {
        "id": "iIHvC-wmNzuR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculo iterativo\n",
        "\n",
        "tasa= 0.1\n",
        "CASH_FLOWS=[-200,50,50,50,50,50]\n",
        "\n",
        "VPN=0\n",
        "\n",
        "VPN+=(CASH_FLOWS[0]/((1+tasa)**0)) #momento 0\n",
        "VPN+=(CASH_FLOWS[1]/((1+tasa)**1)) #momento 1\n",
        "VPN+=(CASH_FLOWS[2]/((1+tasa)**2)) #momento 2\n",
        "VPN+=(CASH_FLOWS[3]/((1+tasa)**3)) #momento 3\n",
        "VPN+=(CASH_FLOWS[4]/((1+tasa)**4)) #momento 4\n",
        "VPN+=(CASH_FLOWS[5]/((1+tasa)**5)) #momento 5\n",
        "print(\"El valor Presente Neto es\",VPN)\n",
        "\n",
        "\n",
        "#BUCLE\n",
        "\n",
        "VPN_i=0\n",
        "for i in range(len(CASH_FLOWS)):\n",
        "  VPN_i += CASH_FLOWS[i]/((1+tasa)**i)\n",
        "print(\"El valor Presente Neto es\",VPN_i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8T56cTsXOGqn",
        "outputId": "d806f441-6543-4f8b-806d-f1fdde479a0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El valor Presente Neto es -10.460661529577635\n",
            "El valor Presente Neto es -10.460661529577635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min 27:05\n"
      ],
      "metadata": {
        "id": "9zTYp__DOGmJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install numpy_financial "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D5_QuFm1OJEh",
        "outputId": "ee288106-a97c-455a-fa49-46581482aa5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting numpy_financial\n",
            "  Downloading numpy_financial-1.0.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from numpy_financial) (1.21.5)\n",
            "Installing collected packages: numpy-financial\n",
            "Successfully installed numpy-financial-1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import numpy_financial as npf\n"
      ],
      "metadata": {
        "id": "NQHDQV45OJHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NVP_np=npf.npv(tasa,CASH_FLOWS)\n",
        "print(\"El valor Presente Neto es\", NVP_np)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "id4364R5OJK2",
        "outputId": "2ce76702-8855-4278-bef6-2b2c71199c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "El valor Presente Neto es -10.460661529577635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install yfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8FdXiGyOJOM",
        "outputId": "f6f7753a-0470-4b4f-d2fa-b5fb3b45f5fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 1.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.7.1-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 34.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (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>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.11)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\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",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.7.1 requests-2.27.1 yfinance-0.1.70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejemplo de correlación"
      ],
      "metadata": {
        "id": "aWouB9GIWBnQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import yfinance as yf # libreria de YaHoo Finance\n",
        "import pandas as pd\n",
        "tickers = ['SPY','GLD','QQQ','VWO','XLV','VNQ','XLE','XLP','XLU']"
      ],
      "metadata": {
        "id": "v4LGxX2eS2f_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "precios = {}\n",
        "i = 0\n",
        "\n",
        "for ticker in tickers:\n",
        "  print(f\"Descargando Datos de:{ticker}|Numero:{i}/{len(tickers)}\")\n",
        "  precios[ticker] =yf.download(ticker, period='10y')\n",
        "  i+=1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_0LczTPlS2if",
        "outputId": "f0240f7f-467e-43bd-ce41-8913cfa69a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descargando Datos de:SPY|Numero:0/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:GLD|Numero:1/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:QQQ|Numero:2/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:VWO|Numero:3/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:XLV|Numero:4/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:VNQ|Numero:5/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:XLE|Numero:6/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:XLP|Numero:7/9\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Descargando Datos de:XLU|Numero:8/9\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ejemplo de datos: apertura, maximo, minimo,cierre y volumen\n",
        "print(precios[tickers[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl_XluOmS2mu",
        "outputId": "242ed90a-bb96-4f27-e0d4-26fb8419f468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  Open        High  ...   Adj Close     Volume\n",
            "Date                                ...                       \n",
            "2012-02-16  134.570007  136.169998  ...  112.336594  186567800\n",
            "2012-02-17  136.520004  136.630005  ...  112.633842  129869400\n",
            "2012-02-21  136.729996  137.050003  ...  112.683418  134042300\n",
            "2012-02-22  136.259995  136.550003  ...  112.320068  124455300\n",
            "2012-02-23  135.960007  136.729996  ...  112.815506  137704300\n",
            "...                ...         ...  ...         ...        ...\n",
            "2022-02-10  451.339996  457.709991  ...  449.320007  140103700\n",
            "2022-02-11  449.410004  451.609985  ...  440.459991  153064100\n",
            "2022-02-14  439.920013  441.600006  ...  439.019989  122062800\n",
            "2022-02-15  443.730011  446.279999  ...  446.100006   88482700\n",
            "2022-02-16  443.929993  444.809998  ...  443.312805   29471923\n",
            "\n",
            "[2518 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min 30:49"
      ],
      "metadata": {
        "id": "DI1R1VfsOJRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# precios de cierre en un mismo dataframe\n",
        "\n",
        "fechas = precios[tickers[0]].index\n",
        "dataframe_cierre = pd.DataFrame(index=fechas)\n",
        "\n",
        "for ticker in tickers:\n",
        "  dataframe_cierre[ticker] = precios[ticker]['Close']\n",
        "\n",
        "#calculo de correlaciones\n",
        "\n",
        "correlations = dataframe_cierre.corr(method= 'pearson')\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(correlations)"
      ],
      "metadata": {
        "id": "u8EQ7wztOJYC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "604bcb09-9e8d-45f9-a6fc-a4b07247e4cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe_cierre.to_excel('dataframe_cierre.xlsx')\n",
        "# se muestran los datos ordenados no como en csv que es un desastre"
      ],
      "metadata": {
        "id": "7KT5NBGNw9eC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correlations.to_excel('correlacion.xlsx')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "N9kJe9ZTOJab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots()\n",
        "im = ax.imshow(correlations)\n",
        "# mostrar tickers\n",
        "ax.set_xticks(np.arange(len(tickers)))\n",
        "ax.set_yticks(np.arange(len(tickers)))\n",
        "\n",
        "#label\n",
        "ax.set_xticklabels(tickers)\n",
        "ax.set_yticklabels(tickers)\n",
        "\n",
        "# rotar tick label \n",
        "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "         rotation_mode=\"anchor\")\n",
        "\n",
        "#crear anotaciones de texto\n",
        "for i in range(len(tickers)):\n",
        "  for j in range(len(tickers)):\n",
        "    text= ax.text(j,i, round(correlations.loc[tickers[i]][tickers[j]],2),\n",
        "    ha=\"center\", va=\"center\", color=\"black\")\n",
        "ax.set_title(\"Mapa de Calor de Correlaciones\")\n",
        "fig.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "wrl7m7qGyRcY",
        "outputId": "95f10d0f-8fe3-469e-fe9f-0b0627f1ea1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlations.to_excel('correlacion2.xlsx')"
      ],
      "metadata": {
        "id": "BsZBPTKF4_BF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#este grafico se entiende mejor...\n",
        "from seaborn.matrix import heatmap\n",
        "import seaborn as sns\n",
        "mask =np.triu(np.ones_like(correlations, dtype=bool))\n",
        "heatmap=sns.heatmap(correlations,mask=mask,vmin=-1,vmax=1,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "pKDTAsuUCaEL",
        "outputId": "e7824887-d2f0-492c-a1b3-d1ccb99d6b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#min35:45\n"
      ],
      "metadata": {
        "id": "BsqLYAwBEwj_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install requests"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGFzAA-iMll1",
        "outputId": "e89ee9af-ee26-496e-eb3d-88ea37cc7aaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.27.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests) (2.0.11)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "URL_API ='https://test-algobalanz.herokuapp.com/api/v1/prices'\n",
        "URL_API_AND = 'https://test-algobalanz.herokuapp.com/api/v1/prices/security_id'\n",
        "\n",
        "req= requests.get(URL_API)\n",
        "req_json= req.json()\n"
      ],
      "metadata": {
        "id": "Q7b6nZTcEztg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "req_json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWsZagcOON1Z",
        "outputId": "044555fa-ea1f-4ea4-9d29-e9cd9b21123c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AL30-0001-C-CT-ARS': {'Bid': [5680.0, 5603.0, 5600.0, 5598.0, 5580.0],\n",
              "  'BidSize': [5102.0, 1233.0, 9976.0, 1440.0, 3049.0],\n",
              "  'Offer': [5750.0, 5799.0, 5822.0, 5900.0, 6090.0],\n",
              "  'OfferSize': [1000.0, 254.0, 21500.0, 16.0, 1000.0],\n",
              "  'currency': 'ARS',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262120',\n",
              "  'securityID': 'AL30-0001-C-CT-ARS',\n",
              "  'settlementType': 'CI',\n",
              "  'symbol': 'AL30',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'AL30-0002-C-CT-ARS': {'Bid': [5581.0, 5580.0, -1.0, -1.0, -1.0],\n",
              "  'BidSize': [55177.0, 100000.0, -1, -1, -1],\n",
              "  'Offer': [5600.0, 5998.0, -1.0, -1.0, -1.0],\n",
              "  'OfferSize': [150.0, 15000.0, -1, -1, -1],\n",
              "  'currency': 'ARS',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262341',\n",
              "  'securityID': 'AL30-0002-C-CT-ARS',\n",
              "  'settlementType': '24hs',\n",
              "  'symbol': 'AL30',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'AL30-0003-C-CT-ARS': {'Bid': [5680.0, 5650.0, 5630.0, 5615.0, 5613.0],\n",
              "  'BidSize': [21927.0, 28447.0, 4265.0, 2920.0, 268.0],\n",
              "  'Offer': [5550.0, 5580.0, 5590.0, 5603.0, 5784.0],\n",
              "  'OfferSize': [22360.0, 438.0, 2.0, 31.0, 20000.0],\n",
              "  'currency': 'ARS',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262382',\n",
              "  'securityID': 'AL30-0003-C-CT-ARS',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'AL30',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'AL30C-0003-C-CT-EXT': {'Bid': [33.1, -1.0, -1.0, -1.0, -1.0],\n",
              "  'BidSize': [200000.0, -1, -1, -1, -1],\n",
              "  'Offer': [33.66, 33.9, 34.0, 34.42, 34.66],\n",
              "  'OfferSize': [1900.0, 16700.0, 1765.0, 14600.0, 14600.0],\n",
              "  'currency': 'EXT',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262561',\n",
              "  'securityID': 'AL30C-0003-C-CT-EXT',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'AL30C',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'AL30D-0001-C-CT-USD': {'Bid': [33.59, 33.5, 33.25, 20.0, 10.0],\n",
              "  'BidSize': [55000.0, 1000.0, 2953.0, 917300.0, 900000.0],\n",
              "  'Offer': [33.6, 33.7, 33.72, 33.8, 33.81],\n",
              "  'OfferSize': [22045.0, 4408.0, 86000.0, 14377.0, 3214.0],\n",
              "  'currency': 'USD',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262179',\n",
              "  'securityID': 'AL30D-0001-C-CT-USD',\n",
              "  'settlementType': 'CI',\n",
              "  'symbol': 'AL30D',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'AL30D-0003-C-CT-USD': {'Bid': [34.0, 33.85, 33.8, 33.75, 33.61],\n",
              "  'BidSize': [25000.0, 13.0, 7396.0, 3000.0, 15000.0],\n",
              "  'Offer': [33.3, 33.4, 33.5, 33.55, 33.6],\n",
              "  'OfferSize': [2001.0, 1473.0, 8100.0, 500.0, 20508.0],\n",
              "  'currency': 'USD',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262613',\n",
              "  'securityID': 'AL30D-0003-C-CT-USD',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'AL30D',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30-0001-C-CT-ARS': {'Bid': [6295.0, 6210.0, 6180.0, 6160.0, -1.0],\n",
              "  'BidSize': [56.0, 23343.0, 25000.0, 3231.0, -1],\n",
              "  'Offer': [6315.0, 6320.0, 6350.0, -1.0, -1.0],\n",
              "  'OfferSize': [8500.0, 12000.0, 30000.0, -1, -1],\n",
              "  'currency': 'ARS',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262525',\n",
              "  'securityID': 'GD30-0001-C-CT-ARS',\n",
              "  'settlementType': 'CI',\n",
              "  'symbol': 'GD30',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30-0003-C-CT-ARS': {'Bid': [6245.0, 6230.0, 6200.0, 6195.0, 6185.0],\n",
              "  'BidSize': [7000.0, 150.0, 100.0, 100.0, 1.0],\n",
              "  'Offer': [6400.0, 6895.0, -1.0, -1.0, -1.0],\n",
              "  'OfferSize': [50000.0, 512.0, -1, -1, -1],\n",
              "  'currency': 'ARS',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262060',\n",
              "  'securityID': 'GD30-0003-C-CT-ARS',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'GD30',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30C-0001-C-CT-EXT': {'Bid': [34.01, -1.0, -1.0, -1.0, -1.0],\n",
              "  'BidSize': [7750.0, -1, -1, -1, -1],\n",
              "  'Offer': [-1.0, -1.0, -1.0, -1.0, -1.0],\n",
              "  'OfferSize': [-1, -1, -1, -1, -1],\n",
              "  'currency': 'EXT',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262582',\n",
              "  'securityID': 'GD30C-0001-C-CT-EXT',\n",
              "  'settlementType': 'CI',\n",
              "  'symbol': 'GD30C',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30C-0003-C-CT-EXT': {'Bid': [36.57, 36.0, -1.0, -1.0, -1.0],\n",
              "  'BidSize': [51.0, 3700.0, -1, -1, -1],\n",
              "  'Offer': [39.1, 39.3, 41.0, -1.0, -1.0],\n",
              "  'OfferSize': [638.0, 380.0, 14.0, -1, -1],\n",
              "  'currency': 'EXT',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262327',\n",
              "  'securityID': 'GD30C-0003-C-CT-EXT',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'GD30C',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30D-0001-C-CT-USD': {'Bid': [38.0, 37.0, -1.0, -1.0, -1.0],\n",
              "  'BidSize': [217.0, 50000.0, -1, -1, -1],\n",
              "  'Offer': [38.05, 38.3, 41.0, -1.0, -1.0],\n",
              "  'OfferSize': [24076.0, 2250.0, 5030.0, -1, -1],\n",
              "  'currency': 'USD',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-262457',\n",
              "  'securityID': 'GD30D-0001-C-CT-USD',\n",
              "  'settlementType': 'CI',\n",
              "  'symbol': 'GD30D',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None},\n",
              " 'GD30D-0003-C-CT-USD': {'Bid': [37.05, 37.0, 36.75, 36.5, 36.05],\n",
              "  'BidSize': [1000.0, 50000.0, 20.0, 362.0, 439.0],\n",
              "  'Offer': [37.05, 37.75, 42.0, 42.9, 43.5],\n",
              "  'OfferSize': [809.0, 5440.0, 20.0, 36.0, 2506.0],\n",
              "  'currency': 'USD',\n",
              "  'last': {'price': 0.0, 'size': 0, 'time': ''},\n",
              "  'mdReqID': 'MD-20210702-261920',\n",
              "  'securityID': 'GD30D-0003-C-CT-USD',\n",
              "  'settlementType': '48hs',\n",
              "  'symbol': 'GD30D',\n",
              "  'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''},\n",
              "  'underlying': None}}"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "                #ticker-plazo-moneda\n",
        "print(req_json['AL30-0002-C-CT-ARS'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zOgu2i3mEz0_",
        "outputId": "15bdf0a0-2bc9-4962-9e42-e53bbd623324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'securityID': 'AL30-0002-C-CT-ARS', 'mdReqID': 'MD-20210702-262341', 'currency': 'ARS', 'symbol': 'AL30', 'Bid': [5580.0, -1.0, -1.0, -1.0, -1.0], 'BidSize': [100000.0, -1, -1, -1, -1], 'Offer': [5680.0, 5998.0, -1.0, -1.0, -1.0], 'OfferSize': [2000.0, 15000.0, -1, -1, -1], 'last': {'price': 0.0, 'size': 0, 'time': ''}, 'underlying': None, 'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''}, 'settlementType': '24hs'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# IDENTIFICAR ESPECIES : FUNCIONES\n",
        "\n",
        "def ConstruirIdentificador(ticker,plazo,moneda):\n",
        "#plazo\n",
        "  if plazo == 'CI':\n",
        "    pl='0001'\n",
        "  elif plazo == '24hs':\n",
        "    pl='0002'\n",
        "  elif plazo == '48hs':\n",
        "    pl='0003'\n",
        "#moneda\n",
        "  if moneda == 'USD':\n",
        "    ticker=ticker+'D'\n",
        "  elif moneda == 'EXT':\n",
        "    ticker=ticker+'C'\n",
        "  string_id = ticker+'-'+pl+'-C-CT-'+moneda\n",
        "  return string_id\n"
      ],
      "metadata": {
        "id": "H7qS8UE4Ez61"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#construimos los strings\n",
        "AL30_T2 = ConstruirIdentificador('AL30','48hs','ARS')\n",
        "AL30D_T2 = ConstruirIdentificador('AL30','48hs','USD')\n",
        "AL30C_T2 = ConstruirIdentificador('AL30','48hs','EXT')"
      ],
      "metadata": {
        "id": "H5fTiRXOEz_G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(req_json[AL30D_T2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxeRmmYbTi1Q",
        "outputId": "42444709-5791-4995-c695-2311a62fddf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'securityID': 'AL30D-0003-C-CT-USD', 'mdReqID': 'MD-20210702-262613', 'currency': 'USD', 'symbol': 'AL30D', 'Bid': [34.0, 33.85, 33.8, 33.75, 33.61], 'BidSize': [25000.0, 13.0, 7396.0, 3000.0, 15000.0], 'Offer': [33.3, 33.4, 33.5, 33.55, 33.6], 'OfferSize': [2001.0, 1473.0, 8100.0, 500.0, 20508.0], 'last': {'price': 0.0, 'size': 0, 'time': ''}, 'underlying': None, 'tradeVolume': {'price': -1.0, 'size': -1, 'time': ''}, 'settlementType': '48hs'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ACCEDER AL PRECIO DE LAS PUNTAS\n",
        "ask_pesos = req_json[AL30_T2]['Bid'][0]\n",
        "bid_usd = req_json[AL30D_T2]['Offer'][0]\n",
        "bid_ccl = req_json[AL30C_T2]['Offer'][0]"
      ],
      "metadata": {
        "id": "yFo678m8VeBh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculo\n",
        "\n",
        "DOLAR_MEP = ask_pesos/bid_usd\n",
        "DOLAR_CCL = ask_pesos/bid_ccl\n",
        "print (f\" Dolar MEP: {round(DOLAR_MEP,2)} | Dolar CCL: {round(DOLAR_CCL,2)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpMj4GrPVeLR",
        "outputId": "73dc87db-100a-4def-db19-875dc7982693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Dolar MEP: 170.57 | Dolar CCL: 168.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install teepy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "XqPUmpH-VeRX",
        "outputId": "21edfa7a-afb9-40dd-bb33-601f2e21998c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting teepy\n",
            "  Downloading teepy-0.3.6.tar.gz (56 kB)\n",
            "\u001b[K     |████████████████████████████████| 56 kB 3.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from teepy) (4.6.3)\n",
            "Collecting cefpython3\n",
            "  Downloading cefpython3-66.0-py2.py3-none-manylinux1_x86_64.whl (79.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 79.6 MB 5.9 kB/s \n",
            "\u001b[?25hCollecting pint\n",
            "  Downloading Pint-0.18-py2.py3-none-any.whl (209 kB)\n",
            "\u001b[K     |████████████████████████████████| 209 kB 53.1 MB/s \n",
            "\u001b[?25hCollecting pyppeteer\n",
            "  Downloading pyppeteer-1.0.2-py3-none-any.whl (83 kB)\n",
            "\u001b[K     |████████████████████████████████| 83 kB 1.6 MB/s \n",
            "\u001b[?25hCollecting xlsxwriter\n",
            "  Downloading XlsxWriter-3.0.2-py3-none-any.whl (149 kB)\n",
            "\u001b[K     |████████████████████████████████| 149 kB 47.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from pint->teepy) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from pint->teepy) (4.11.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->pint->teepy) (3.7.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->pint->teepy) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->pint->teepy) (3.0.7)\n",
            "Collecting pyee<9.0.0,>=8.1.0\n",
            "  Downloading pyee-8.2.2-py2.py3-none-any.whl (12 kB)\n",
            "Collecting urllib3<2.0.0,>=1.25.8\n",
            "  Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)\n",
            "\u001b[K     |████████████████████████████████| 138 kB 53.6 MB/s \n",
            "\u001b[?25hCollecting websockets<11.0,>=10.0\n",
            "  Downloading websockets-10.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (111 kB)\n",
            "\u001b[K     |████████████████████████████████| 111 kB 52.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm<5.0.0,>=4.42.1 in /usr/local/lib/python3.7/dist-packages (from pyppeteer->teepy) (4.62.3)\n",
            "Requirement already satisfied: certifi>=2021 in /usr/local/lib/python3.7/dist-packages (from pyppeteer->teepy) (2021.10.8)\n",
            "Requirement already satisfied: appdirs<2.0.0,>=1.4.3 in /usr/local/lib/python3.7/dist-packages (from pyppeteer->teepy) (1.4.4)\n",
            "Building wheels for collected packages: teepy\n",
            "  Building wheel for teepy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for teepy: filename=teepy-0.3.6-py3-none-any.whl size=56743 sha256=87b0d6329daad1b7ad62f6ede0a8a446f615921130dd11b7eee267652dc98f2b\n",
            "  Stored in directory: /root/.cache/pip/wheels/47/47/99/d08c468bd4ce37c024b10313dd091564831a4f6fdfc0af229b\n",
            "Successfully built teepy\n",
            "Installing collected packages: websockets, urllib3, pyee, xlsxwriter, pyppeteer, pint, cefpython3, teepy\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed cefpython3-66.0 pint-0.18 pyee-8.2.2 pyppeteer-1.0.2 teepy-0.3.6 urllib3-1.26.8 websockets-10.1 xlsxwriter-3.0.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tweepy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0Nld0KQVeON",
        "outputId": "da3908a2-b9fb-4114-c862-9c673cea3407"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tweepy in /usr/local/lib/python3.7/dist-packages (3.10.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.3.1)\n",
            "Requirement already satisfied: requests[socks]>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from tweepy) (2.27.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tweepy) (1.15.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->tweepy) (3.2.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2021.10.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.26.8)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2.10)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (2.0.11)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]>=2.11.1->tweepy) (1.7.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install twitter_credentials"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmrW9wgJlwWr",
        "outputId": "b8d2950b-c9f9-4894-ca4b-8ed054ce080d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[31mERROR: Could not find a version that satisfies the requirement twitter_credentials (from versions: none)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for twitter_credentials\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BOT PARA TWITTER min 36:14\n",
        "import tweepy"
      ],
      "metadata": {
        "id": "CyPFmZ8lVeUf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "4gqJzwt0VeXy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}