Dual_Moving_Average.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Dual Moving Average.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPFmJmK0nVts4N6NDSGTPVd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Zen-trepreneur/Udacity--AI-for-Trading/blob/master/Dual_Moving_Average.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qEFY8DAAEm-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "9bd69088-6ee9-49e0-fcea-4182bf6d0fef"
      },
      "source": [
        "!pip install alpha_vantage"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting alpha_vantage\n",
            "  Downloading https://files.pythonhosted.org/packages/12/e3/72ec32d859a363c2eebaa42e0d4abb5ac7172a7a77a2307576ceb5fc1b42/alpha_vantage-2.2.0.tar.gz\n",
            "Collecting aiohttp\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7c/39/7eb5f98d24904e0f6d3edb505d4aa60e3ef83c0a58d6fe18244a51757247/aiohttp-3.6.2-cp36-cp36m-manylinux1_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 3.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from alpha_vantage) (2.23.0)\n",
            "Requirement already satisfied: chardet<4.0,>=2.0 in /usr/local/lib/python3.6/dist-packages (from aiohttp->alpha_vantage) (3.0.4)\n",
            "Collecting async-timeout<4.0,>=3.0\n",
            "  Downloading https://files.pythonhosted.org/packages/e1/1e/5a4441be21b0726c4464f3f23c8b19628372f606755a9d2e46c187e65ec4/async_timeout-3.0.1-py3-none-any.whl\n",
            "Collecting yarl<2.0,>=1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/77/9a/2365a077c21c3d711b2367199a81edbe3f362712a05f6437647ca770eab1/yarl-1.5.0-cp36-cp36m-manylinux1_x86_64.whl (257kB)\n",
            "\u001b[K     |████████████████████████████████| 266kB 16.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.5; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from aiohttp->alpha_vantage) (3.7.4.2)\n",
            "Collecting idna-ssl>=1.0; python_version < \"3.7\"\n",
            "  Downloading https://files.pythonhosted.org/packages/46/03/07c4894aae38b0de52b52586b24bf189bb83e4ddabfe2e2c8f2419eec6f4/idna-ssl-1.1.0.tar.gz\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.6/dist-packages (from aiohttp->alpha_vantage) (19.3.0)\n",
            "Collecting multidict<5.0,>=4.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/95/f50352b5366e7d579e8b99631680a9e32e1b22adfa1629a8f23b1d22d5e2/multidict-4.7.6-cp36-cp36m-manylinux1_x86_64.whl (148kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 17.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->alpha_vantage) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->alpha_vantage) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->alpha_vantage) (2.10)\n",
            "Building wheels for collected packages: alpha-vantage, idna-ssl\n",
            "  Building wheel for alpha-vantage (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for alpha-vantage: filename=alpha_vantage-2.2.0-cp36-none-any.whl size=25705 sha256=241f7a7de35bc2aae0c06990da5b4ba8d117179b134c57a93a54d3af89208e8a\n",
            "  Stored in directory: /root/.cache/pip/wheels/3a/fa/05/80b0afcbd2a4de8bd2a0c5cd77084e3d3a9cc12c48be9b7f84\n",
            "  Building wheel for idna-ssl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for idna-ssl: filename=idna_ssl-1.1.0-cp36-none-any.whl size=3162 sha256=9ce84a9ad4684ad2d0a1e0a4b878d4a58f6c157a953c1cd4c22070df2b5f29d4\n",
            "  Stored in directory: /root/.cache/pip/wheels/d3/00/b3/32d613e19e08a739751dd6bf998cfed277728f8b2127ad4eb7\n",
            "Successfully built alpha-vantage idna-ssl\n",
            "Installing collected packages: async-timeout, multidict, yarl, idna-ssl, aiohttp, alpha-vantage\n",
            "Successfully installed aiohttp-3.6.2 alpha-vantage-2.2.0 async-timeout-3.0.1 idna-ssl-1.1.0 multidict-4.7.6 yarl-1.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mQgcWFCAYOP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "094da315-1ab4-4853-f483-ea6a4b9beca5"
      },
      "source": [
        "import pandas as pd\n",
        "from alpha_vantage.timeseries import TimeSeries\n",
        "from alpha_vantage.techindicators import TechIndicators\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "api_key = 'RNZPXZ6Q9FEFMEHM'\n",
        "\n",
        "ts = TimeSeries(key=api_key, output_format='pandas')\n",
        "data_ts, meta_data_ts = ts.get_intraday(symbol='MSFT', interval='1min', outputsize='full')\n",
        "\n",
        "period = 60\n",
        "\n",
        "ti = TechIndicators(key=api_key, output_format='pandas')\n",
        "data_ti, meta_data_ti = ti.get_sma(symbol='MSFT', interval='1min',\n",
        "                                    time_period=period, series_type='close')\n",
        "\n",
        "df1 = data_ti\n",
        "df2 = data_ts['4. close'].iloc[period-1::]\n",
        "\n",
        "df2.index = df1.index\n",
        "\n",
        "total_df = pd.concat([df1, df2], axis=1)\n",
        "print(total_df)\n",
        "\n",
        "total_df.plot()\n",
        "plt.show()\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                          SMA  4. close\n",
            "date                                   \n",
            "2020-07-14 07:12:00  207.5440    204.28\n",
            "2020-07-14 07:13:00  207.5515    203.98\n",
            "2020-07-14 07:14:00  207.5485    204.01\n",
            "2020-07-14 07:15:00  207.5427    204.00\n",
            "2020-07-14 07:16:00  207.5403    203.99\n",
            "...                       ...       ...\n",
            "2020-07-27 19:54:00  204.1863    207.40\n",
            "2020-07-27 19:55:00  204.1947    207.40\n",
            "2020-07-27 19:56:00  204.2013    207.45\n",
            "2020-07-27 19:57:00  204.2078    207.25\n",
            "2020-07-27 19:59:00  204.2132    206.90\n",
            "\n",
            "[7461 rows x 2 columns]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3P--aW80RKkU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}