Math_Proj.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Math_Proj.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOsm45dfPjsAj65PYI/zX/y",
      "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/Harikrishna-AL/MathProject/blob/main/Math_Proj.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q_JPrTfTX2Z",
        "outputId": "450e5c88-3cbc-4ed6-eecb-6c170623824d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "www.nasdaq.com/market-activity/stocks/NRG\n",
            "           Date        Open        High  ...       Close   Adj Close     Volume\n",
            "0    2020-01-02   84.900002   86.139999  ...   86.052002   86.052002   47660500\n",
            "1    2020-01-03   88.099998   90.800003  ...   88.601997   88.601997   88892500\n",
            "2    2020-01-06   88.094002   90.311996  ...   90.307999   90.307999   50665000\n",
            "3    2020-01-07   92.279999   94.325996  ...   93.811996   93.811996   89410500\n",
            "4    2020-01-08   94.739998   99.697998  ...   98.428001   98.428001  155721500\n",
            "..          ...         ...         ...  ...         ...         ...        ...\n",
            "248  2020-12-24  642.989990  666.090027  ...  661.770020  661.770020   22865600\n",
            "249  2020-12-28  674.510010  681.400024  ...  663.690002  663.690002   32278600\n",
            "250  2020-12-29  661.000000  669.900024  ...  665.989990  665.989990   22910800\n",
            "251  2020-12-30  672.000000  696.599976  ...  694.780029  694.780029   42846000\n",
            "252  2020-12-31  699.989990  718.719971  ...  705.669983  705.669983   49649900\n",
            "\n",
            "[253 rows x 7 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import requests\n",
        "import csv\n",
        "url = 'http://api.marketstack.com/v1/tickers?access_key=dd3a2e191efbf0af92a61e70a1d180be'\n",
        "data = requests.get(url).json()\n",
        "\n",
        "sym = 'NRG'\n",
        "y = len(data['data'])\n",
        "for i in range(y):\n",
        "  if data['data'][i]['symbol'] == sym:\n",
        "    base_link = data['data'][i]['stock_exchange']['website']\n",
        "    link = base_link + '/market-activity/stocks/' + sym\n",
        "    print(link)\n",
        "    break\n",
        "  else:\n",
        "    link = 'www.nasdaq.com/market-activity/stocks/' + sym\n",
        "    print(link)\n",
        "    break\n",
        "df = pd.read_csv('TSLA.csv')\n",
        "print(df)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.set_index(pd.DatetimeIndex(df['Date']), inplace=True)\n",
        "df = df[['Adj Close']]\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W3eAhtLXVJXR",
        "outputId": "fca0e0ea-314b-4e5b-b39f-e5a980428059"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Adj Close\n",
            "Date                  \n",
            "2020-01-02   86.052002\n",
            "2020-01-03   88.601997\n",
            "2020-01-06   90.307999\n",
            "2020-01-07   93.811996\n",
            "2020-01-08   98.428001\n",
            "...                ...\n",
            "2020-12-24  661.770020\n",
            "2020-12-28  663.690002\n",
            "2020-12-29  665.989990\n",
            "2020-12-30  694.780029\n",
            "2020-12-31  705.669983\n",
            "\n",
            "[253 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df[['Adj Close']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "qrg6AtFZXONJ",
        "outputId": "427b1524-17fc-4d8a-a8ce-5f744dca202b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc2aca85590>]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "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": [
        "pip install pandas_ta\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dA59otjNZODu",
        "outputId": "6ccef121-aeeb-4cb8-d5b8-1a4bb6a2889a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pandas_ta\n",
            "  Downloading pandas_ta-0.3.14b.tar.gz (115 kB)\n",
            "\u001b[?25l\r\u001b[K     |██▉                             | 10 kB 25.3 MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 20 kB 10.4 MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 30 kB 6.8 MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 40 kB 6.3 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 51 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 61 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 71 kB 5.1 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 81 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▋      | 92 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 102 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 112 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 115 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandas_ta) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_ta) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_ta) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->pandas_ta) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pandas_ta) (1.15.0)\n",
            "Building wheels for collected packages: pandas-ta\n",
            "  Building wheel for pandas-ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pandas-ta: filename=pandas_ta-0.3.14b0-py3-none-any.whl size=218923 sha256=0c032dbb86e46020093902be8eb97da17e96781087085e49fd4d78bfbeb71de9\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/81/f0/cca85757840e4616a2c6b9fe12569d97d324c27cac60724c58\n",
            "Successfully built pandas-ta\n",
            "Installing collected packages: pandas-ta\n",
            "Successfully installed pandas-ta-0.3.14b0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas_ta\n",
        "df.ta.ema(close='Adj Close', length=10, append=True)\n",
        "df= df.iloc[10:]\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AV6g0BoXZ2Zz",
        "outputId": "e7ebc467-a19b-4cfa-fb3f-43f00f972acc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             Adj Close      EMA_10\n",
            "Date                              \n",
            "2020-01-16  102.697998   97.656035\n",
            "2020-01-17  102.099998   98.464028\n",
            "2020-01-21  109.440002  100.459660\n",
            "2020-01-22  113.912003  102.905540\n",
            "2020-01-23  114.440002  105.002715\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df[['EMA_10']])\n",
        "plt.plot(df[['Adj Close']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FlbXe1i9aqGq",
        "outputId": "f443a6ef-333d-46d2-bd2a-5c3565a96a95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc2abdfc090>]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "X_train, X_test, y_train, y_test = train_test_split(df[['Adj Close']], df[['EMA_10']], test_size=.2)"
      ],
      "metadata": {
        "id": "R2pF18G0Hruz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test.describe())\n",
        "print(X_train.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLpr9iMWLMOG",
        "outputId": "33b16659-5fa0-4bfb-b12d-a852c4fc5e50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Adj Close\n",
            "count   49.000000\n",
            "mean   324.078692\n",
            "std    157.384141\n",
            "min     72.244003\n",
            "25%    163.774002\n",
            "50%    299.821991\n",
            "75%    425.920013\n",
            "max    649.859985\n",
            "        Adj Close\n",
            "count  194.000000\n",
            "mean   291.361062\n",
            "std    169.974560\n",
            "min     85.505997\n",
            "25%    150.484497\n",
            "50%    210.486000\n",
            "75%    420.892510\n",
            "max    705.669983\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "#print(y_pred)\n",
        "plt.plot(X_test,y_pred)\n",
        "plt.scatter(X_test,y_test,color=\"orange\")"
      ],
      "metadata": {
        "id": "Im8jFf1LLef6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "416485b0-a1ea-44c5-9f9a-f9fcd2398796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fc2abb70ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "\n",
        "print(\"Model Coefficients:\", model.coef_)\n",
        "print(\"Mean Absolute Error:\", mean_absolute_error(y_test, y_pred))\n",
        "print(\"Coefficient of Determination:\", r2_score(y_test, y_pred))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GORyuMU5MAOD",
        "outputId": "8db40325-d3a2-4788-be39-3f1e135062ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Coefficients: [[0.94051982]]\n",
            "Mean Absolute Error: 15.465543262607364\n",
            "Coefficient of Determination: 0.9835690837021237\n"
          ]
        }
      ]
    }
  ]
}