yFinance_notebook.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buXPwtwiRTws",
        "outputId": "dc9fe1dc-3848-4714-ddaa-02dc98b09ddd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 631 kB/s \n",
            "\u001b[?25hRequirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.2.1)\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: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.0)\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",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
            "Installing collected packages: requests, 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",
            "Successfully installed requests-2.28.1 yfinance-0.1.74\n"
          ]
        }
      ],
      "source": [
        "pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "RilqYlb_Tsql"
      },
      "outputs": [],
      "source": [
        "import yfinance as yf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tju1nUt0T8HY"
      },
      "source": [
        "Microsoft"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "NdaG-vRgTv-7"
      },
      "outputs": [],
      "source": [
        "msft = yf.Ticker(\"MSFT\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6U2zueYAUW3f"
      },
      "source": [
        " get stock info and get historical data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clEsO3IzUWPN",
        "outputId": "77c84cd7-26eb-4e09-e88d-e8e5f6424c27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'zip': '98052-6399',\n",
              " 'sector': 'Technology',\n",
              " 'fullTimeEmployees': 221000,\n",
              " 'longBusinessSummary': 'Microsoft Corporation develops, licenses, and supports software, services, devices, and solutions worldwide. The company operates in three segments: Productivity and Business Processes, Intelligent Cloud, and More Personal Computing. The Productivity and Business Processes segment offers Office, Exchange, SharePoint, Microsoft Teams, Office 365 Security and Compliance, Microsoft Viva, and Skype for Business; Skype, Outlook.com, OneDrive, and LinkedIn; and Dynamics 365, a set of cloud-based and on-premises business solutions for organizations and enterprise divisions. The Intelligent Cloud segment licenses SQL, Windows Servers, Visual Studio, System Center, and related Client Access Licenses; GitHub that provides a collaboration platform and code hosting service for developers; Nuance provides healthcare and enterprise AI solutions; and Azure, a cloud platform. It also offers enterprise support, Microsoft consulting, and nuance professional services to assist customers in developing, deploying, and managing Microsoft server and desktop solutions; and training and certification on Microsoft products. The More Personal Computing segment provides Windows original equipment manufacturer (OEM) licensing and other non-volume licensing of the Windows operating system; Windows Commercial, such as volume licensing of the Windows operating system, Windows cloud services, and other Windows commercial offerings; patent licensing; and Windows Internet of Things. It also offers Surface, PC accessories, PCs, tablets, gaming and entertainment consoles, and other devices; Gaming, including Xbox hardware, and Xbox content and services; video games and third-party video game royalties; and Search, including Bing and Microsoft advertising. The company sells its products through OEMs, distributors, and resellers; and directly through digital marketplaces, online stores, and retail stores. Microsoft Corporation was founded in 1975 and is headquartered in Redmond, Washington.',\n",
              " 'city': 'Redmond',\n",
              " 'phone': '425 882 8080',\n",
              " 'state': 'WA',\n",
              " 'country': 'United States',\n",
              " 'companyOfficers': [],\n",
              " 'website': 'https://www.microsoft.com',\n",
              " 'maxAge': 1,\n",
              " 'address1': 'One Microsoft Way',\n",
              " 'fax': '425 706 7329',\n",
              " 'industry': 'Software—Infrastructure',\n",
              " 'ebitdaMargins': 0.49418998,\n",
              " 'profitMargins': 0.36686,\n",
              " 'grossMargins': 0.68402,\n",
              " 'operatingCashflow': 89034997760,\n",
              " 'revenueGrowth': 0.124,\n",
              " 'operatingMargins': 0.42055,\n",
              " 'ebitda': 97982996480,\n",
              " 'targetLowPrice': 250,\n",
              " 'recommendationKey': 'buy',\n",
              " 'grossProfits': 135620000000,\n",
              " 'freeCashflow': 49479000064,\n",
              " 'targetMedianPrice': 330,\n",
              " 'currentPrice': 276.44,\n",
              " 'earningsGrowth': 0.031,\n",
              " 'currentRatio': 1.785,\n",
              " 'returnOnAssets': 0.14919,\n",
              " 'numberOfAnalystOpinions': 47,\n",
              " 'targetMeanPrice': 333,\n",
              " 'debtToEquity': 47.075,\n",
              " 'returnOnEquity': 0.47151002,\n",
              " 'targetHighPrice': 411,\n",
              " 'totalCash': 104748998656,\n",
              " 'totalDebt': 78399995904,\n",
              " 'totalRevenue': 198269992960,\n",
              " 'totalCashPerShare': 14.045,\n",
              " 'financialCurrency': 'USD',\n",
              " 'revenuePerShare': 26.45,\n",
              " 'quickRatio': 1.567,\n",
              " 'recommendationMean': 1.7,\n",
              " 'exchange': 'NMS',\n",
              " 'shortName': 'Microsoft Corporation',\n",
              " 'longName': 'Microsoft Corporation',\n",
              " 'exchangeTimezoneName': 'America/New_York',\n",
              " 'exchangeTimezoneShortName': 'EDT',\n",
              " 'isEsgPopulated': False,\n",
              " 'gmtOffSetMilliseconds': '-14400000',\n",
              " 'quoteType': 'EQUITY',\n",
              " 'symbol': 'MSFT',\n",
              " 'messageBoardId': 'finmb_21835',\n",
              " 'market': 'us_market',\n",
              " 'annualHoldingsTurnover': None,\n",
              " 'enterpriseToRevenue': 10.315,\n",
              " 'beta3Year': None,\n",
              " 'enterpriseToEbitda': 20.872,\n",
              " '52WeekChange': -0.08466607,\n",
              " 'morningStarRiskRating': None,\n",
              " 'forwardEps': 11.93,\n",
              " 'revenueQuarterlyGrowth': None,\n",
              " 'sharesOutstanding': 7457889792,\n",
              " 'fundInceptionDate': None,\n",
              " 'annualReportExpenseRatio': None,\n",
              " 'totalAssets': None,\n",
              " 'bookValue': 22.313,\n",
              " 'sharesShort': 38192351,\n",
              " 'sharesPercentSharesOut': 0.0050999997,\n",
              " 'fundFamily': None,\n",
              " 'lastFiscalYearEnd': 1656547200,\n",
              " 'heldPercentInstitutions': 0.72033995,\n",
              " 'netIncomeToCommon': 72737996800,\n",
              " 'trailingEps': 9.65,\n",
              " 'lastDividendValue': 0.62,\n",
              " 'SandP52WeekChange': -0.08172697,\n",
              " 'priceToBook': 12.389191,\n",
              " 'heldPercentInsiders': 0.00074,\n",
              " 'nextFiscalYearEnd': 1719705600,\n",
              " 'yield': None,\n",
              " 'mostRecentQuarter': 1656547200,\n",
              " 'shortRatio': 1.43,\n",
              " 'sharesShortPreviousMonthDate': 1656547200,\n",
              " 'floatShares': 7450732296,\n",
              " 'beta': 0.927206,\n",
              " 'enterpriseValue': 2045080502272,\n",
              " 'priceHint': 2,\n",
              " 'threeYearAverageReturn': None,\n",
              " 'lastSplitDate': 1045526400,\n",
              " 'lastSplitFactor': '2:1',\n",
              " 'legalType': None,\n",
              " 'lastDividendDate': 1660694400,\n",
              " 'morningStarOverallRating': None,\n",
              " 'earningsQuarterlyGrowth': 0.017,\n",
              " 'priceToSalesTrailing12Months': 10.39824,\n",
              " 'dateShortInterest': 1659052800,\n",
              " 'pegRatio': 1.77,\n",
              " 'ytdReturn': None,\n",
              " 'forwardPE': 23.171835,\n",
              " 'lastCapGain': None,\n",
              " 'shortPercentOfFloat': 0.0050999997,\n",
              " 'sharesShortPriorMonth': 38896339,\n",
              " 'impliedSharesOutstanding': 0,\n",
              " 'category': None,\n",
              " 'fiveYearAverageReturn': None,\n",
              " 'previousClose': 277.75,\n",
              " 'regularMarketOpen': 276.44,\n",
              " 'twoHundredDayAverage': 293.03455,\n",
              " 'trailingAnnualDividendYield': 0.008928893,\n",
              " 'payoutRatio': 0.2508,\n",
              " 'volume24Hr': None,\n",
              " 'regularMarketDayHigh': 278.86,\n",
              " 'navPrice': None,\n",
              " 'averageDailyVolume10Day': 20211590,\n",
              " 'regularMarketPreviousClose': 277.75,\n",
              " 'fiftyDayAverage': 267.7574,\n",
              " 'trailingAnnualDividendRate': 2.48,\n",
              " 'open': 276.44,\n",
              " 'toCurrency': None,\n",
              " 'averageVolume10days': 20211590,\n",
              " 'expireDate': None,\n",
              " 'algorithm': None,\n",
              " 'dividendRate': 2.48,\n",
              " 'exDividendDate': 1660694400,\n",
              " 'circulatingSupply': None,\n",
              " 'startDate': None,\n",
              " 'regularMarketDayLow': 275.41,\n",
              " 'currency': 'USD',\n",
              " 'trailingPE': 28.646633,\n",
              " 'regularMarketVolume': 17527409,\n",
              " 'lastMarket': None,\n",
              " 'maxSupply': None,\n",
              " 'openInterest': None,\n",
              " 'marketCap': 2061659013120,\n",
              " 'volumeAllCurrencies': None,\n",
              " 'strikePrice': None,\n",
              " 'averageVolume': 26152722,\n",
              " 'dayLow': 275.41,\n",
              " 'ask': 276.39,\n",
              " 'askSize': 1100,\n",
              " 'volume': 17527409,\n",
              " 'fiftyTwoWeekHigh': 349.67,\n",
              " 'fromCurrency': None,\n",
              " 'fiveYearAvgDividendYield': 1.23,\n",
              " 'fiftyTwoWeekLow': 241.51,\n",
              " 'bid': 276.36,\n",
              " 'tradeable': False,\n",
              " 'dividendYield': 0.0089,\n",
              " 'bidSize': 800,\n",
              " 'dayHigh': 278.86,\n",
              " 'coinMarketCapLink': None,\n",
              " 'regularMarketPrice': 276.44,\n",
              " 'preMarketPrice': 276.15,\n",
              " 'logo_url': 'https://logo.clearbit.com/microsoft.com',\n",
              " 'trailingPegRatio': 2.0689}"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "msft.info\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "WqraG0w-UJxY",
        "outputId": "42000bfb-26dc-41fa-9533-7c3c3c3174b2"
      },
      "outputs": [
        {
          "ename": "",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31mFailed to start the Kernel. \n",
            "\u001b[1;31mUnable to start Kernel 'Python 3.10.6' due to connection timeout. \n",
            "View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
          ]
        }
      ],
      "source": [
        "hist = msft.history(period=\"3mo\",interval='1wk')\n",
        "hist.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY7zkw9xVOxg"
      },
      "source": [
        "Same can be done to get financials, balance_sheet, cashflow, recommendations,\n",
        "sustainability, dividends, major_holders and so on using\n",
        "  ticker_name.property"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW8SgdWHWRRo"
      },
      "source": [
        "Fetching data for ticker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "TBy3UXMxVGZw",
        "outputId": "c911c72a-d9b6-47b7-fa0b-7cc9b4b13c11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c39f717b-ee8b-4a29-952e-0865abf77d6c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-03</th>\n",
              "      <td>335.350006</td>\n",
              "      <td>338.000000</td>\n",
              "      <td>329.779999</td>\n",
              "      <td>334.750000</td>\n",
              "      <td>332.577087</td>\n",
              "      <td>28865100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-04</th>\n",
              "      <td>334.829987</td>\n",
              "      <td>335.200012</td>\n",
              "      <td>326.119995</td>\n",
              "      <td>329.010010</td>\n",
              "      <td>326.874359</td>\n",
              "      <td>32674300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05</th>\n",
              "      <td>325.859985</td>\n",
              "      <td>326.070007</td>\n",
              "      <td>315.980011</td>\n",
              "      <td>316.380005</td>\n",
              "      <td>314.326324</td>\n",
              "      <td>40054300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06</th>\n",
              "      <td>313.149994</td>\n",
              "      <td>318.700012</td>\n",
              "      <td>311.489990</td>\n",
              "      <td>313.880005</td>\n",
              "      <td>311.842560</td>\n",
              "      <td>39646100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07</th>\n",
              "      <td>314.149994</td>\n",
              "      <td>316.500000</td>\n",
              "      <td>310.089996</td>\n",
              "      <td>314.040009</td>\n",
              "      <td>312.001526</td>\n",
              "      <td>32720000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c39f717b-ee8b-4a29-952e-0865abf77d6c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c39f717b-ee8b-4a29-952e-0865abf77d6c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c39f717b-ee8b-4a29-952e-0865abf77d6c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  Open        High         Low       Close   Adj Close  \\\n",
              "Date                                                                     \n",
              "2022-01-03  335.350006  338.000000  329.779999  334.750000  332.577087   \n",
              "2022-01-04  334.829987  335.200012  326.119995  329.010010  326.874359   \n",
              "2022-01-05  325.859985  326.070007  315.980011  316.380005  314.326324   \n",
              "2022-01-06  313.149994  318.700012  311.489990  313.880005  311.842560   \n",
              "2022-01-07  314.149994  316.500000  310.089996  314.040009  312.001526   \n",
              "\n",
              "              Volume  \n",
              "Date                  \n",
              "2022-01-03  28865100  \n",
              "2022-01-04  32674300  \n",
              "2022-01-05  40054300  \n",
              "2022-01-06  39646100  \n",
              "2022-01-07  32720000  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = yf.download(\"MSFT\", start=\"2022-01-01\", end=\"2022-06-30\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufxCxVjaWIDI"
      },
      "source": [
        "To initialize multiple Ticker "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "IZfrA0xPW9SR"
      },
      "outputs": [],
      "source": [
        "tickers = yf.Tickers('MSFT GOOG AMZN AAPL')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oow6qGZX-TO"
      },
      "source": [
        "Fetching data for multiple tickers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "6ba8RCBlXFe7",
        "outputId": "4b291fdc-2745-4981-975f-d989cdc9f48d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  2 of 2 completed\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-50db9337-6781-41b0-a721-323e8936c52c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
              "      <th colspan=\"2\" halign=\"left\">High</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "      <th>AAPL</th>\n",
              "      <th>GOOG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-03</th>\n",
              "      <td>181.259933</td>\n",
              "      <td>145.074493</td>\n",
              "      <td>182.009995</td>\n",
              "      <td>145.074493</td>\n",
              "      <td>182.880005</td>\n",
              "      <td>145.550003</td>\n",
              "      <td>177.710007</td>\n",
              "      <td>143.502502</td>\n",
              "      <td>177.830002</td>\n",
              "      <td>144.475494</td>\n",
              "      <td>104487900</td>\n",
              "      <td>25214000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-04</th>\n",
              "      <td>178.959457</td>\n",
              "      <td>144.416504</td>\n",
              "      <td>179.699997</td>\n",
              "      <td>144.416504</td>\n",
              "      <td>182.940002</td>\n",
              "      <td>146.610001</td>\n",
              "      <td>179.119995</td>\n",
              "      <td>143.816147</td>\n",
              "      <td>182.630005</td>\n",
              "      <td>145.550507</td>\n",
              "      <td>99310400</td>\n",
              "      <td>22928000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05</th>\n",
              "      <td>174.199142</td>\n",
              "      <td>137.653503</td>\n",
              "      <td>174.919998</td>\n",
              "      <td>137.653503</td>\n",
              "      <td>180.169998</td>\n",
              "      <td>144.298004</td>\n",
              "      <td>174.639999</td>\n",
              "      <td>137.523499</td>\n",
              "      <td>179.610001</td>\n",
              "      <td>144.181000</td>\n",
              "      <td>94537600</td>\n",
              "      <td>49642000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06</th>\n",
              "      <td>171.291183</td>\n",
              "      <td>137.550995</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>137.550995</td>\n",
              "      <td>175.300003</td>\n",
              "      <td>139.686005</td>\n",
              "      <td>171.639999</td>\n",
              "      <td>136.763504</td>\n",
              "      <td>172.699997</td>\n",
              "      <td>137.497498</td>\n",
              "      <td>96904000</td>\n",
              "      <td>29050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07</th>\n",
              "      <td>171.460495</td>\n",
              "      <td>137.004501</td>\n",
              "      <td>172.169998</td>\n",
              "      <td>137.004501</td>\n",
              "      <td>174.139999</td>\n",
              "      <td>138.254745</td>\n",
              "      <td>171.029999</td>\n",
              "      <td>135.789001</td>\n",
              "      <td>172.889999</td>\n",
              "      <td>137.904999</td>\n",
              "      <td>86709100</td>\n",
              "      <td>19408000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50db9337-6781-41b0-a721-323e8936c52c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-50db9337-6781-41b0-a721-323e8936c52c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-50db9337-6781-41b0-a721-323e8936c52c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Adj Close                   Close                    High  \\\n",
              "                  AAPL        GOOG        AAPL        GOOG        AAPL   \n",
              "Date                                                                     \n",
              "2022-01-03  181.259933  145.074493  182.009995  145.074493  182.880005   \n",
              "2022-01-04  178.959457  144.416504  179.699997  144.416504  182.940002   \n",
              "2022-01-05  174.199142  137.653503  174.919998  137.653503  180.169998   \n",
              "2022-01-06  171.291183  137.550995  172.000000  137.550995  175.300003   \n",
              "2022-01-07  171.460495  137.004501  172.169998  137.004501  174.139999   \n",
              "\n",
              "                               Low                    Open              \\\n",
              "                  GOOG        AAPL        GOOG        AAPL        GOOG   \n",
              "Date                                                                     \n",
              "2022-01-03  145.550003  177.710007  143.502502  177.830002  144.475494   \n",
              "2022-01-04  146.610001  179.119995  143.816147  182.630005  145.550507   \n",
              "2022-01-05  144.298004  174.639999  137.523499  179.610001  144.181000   \n",
              "2022-01-06  139.686005  171.639999  136.763504  172.699997  137.497498   \n",
              "2022-01-07  138.254745  171.029999  135.789001  172.889999  137.904999   \n",
              "\n",
              "               Volume            \n",
              "                 AAPL      GOOG  \n",
              "Date                             \n",
              "2022-01-03  104487900  25214000  \n",
              "2022-01-04   99310400  22928000  \n",
              "2022-01-05   94537600  49642000  \n",
              "2022-01-06   96904000  29050000  \n",
              "2022-01-07   86709100  19408000  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt = yf.download(\"GOOG AAPL\", start=\"2022-01-01\", end=\"2022-06-30\")\n",
        "dt.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmkCQGoiaTjw",
        "outputId": "5d5ea75d-d480-461b-a5a0-98ef26eb441d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  Open        High         Low       Close   Adj Close  \\\n",
            "Date                                                                     \n",
            "2021-01-04  163.500000  163.600006  157.201004  159.331497  159.331497   \n",
            "2021-01-05  158.300507  161.169006  158.253006  160.925507  160.925507   \n",
            "2021-01-06  157.324005  159.875504  156.557999  156.919006  156.919006   \n",
            "2021-01-07  157.850006  160.427002  157.750000  158.108002  158.108002   \n",
            "2021-01-08  159.000000  159.531998  157.110001  159.134995  159.134995   \n",
            "\n",
            "              Volume  \n",
            "Date                  \n",
            "2021-01-04  88228000  \n",
            "2021-01-05  53110000  \n",
            "2021-01-06  87896000  \n",
            "2021-01-07  70290000  \n",
            "2021-01-08  70754000  \n"
          ]
        }
      ],
      "source": [
        "symbol = 'AMZN'\n",
        "start_date = '2021-01-01'\n",
        "end_date = '2021-01-31'\n",
        "period = \"1mo\"\n",
        "interval = \"1d\"\n",
        "\n",
        "data = yf.download(tickers=symbol, period=period, interval=interval,start = start_date, end = end_date)\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d71PyddEYpEy"
      },
      "source": [
        "Writing fetched data to a csv file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xPchtlCYcE8",
        "outputId": "4de0da8a-e558-44c2-9766-85a9c6338eb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        }
      ],
      "source": [
        "tesla = yf.download('TSLA', start='2010-06-29', end='2022-03-18', progress=True)\n",
        "tesla.to_csv('Tesla_Stock_Price.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzjVAUFCai90"
      },
      "source": [
        "Lowercase column name"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "31oMFPZ0aEsb",
        "outputId": "e3cf32b4-cc35-49a4-924c-77d181c8a70b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                  open        high         low       close   adj close  \\\n",
            "Date                                                                     \n",
            "2021-01-04  163.500000  163.600006  157.201004  159.331497  159.331497   \n",
            "2021-01-05  158.300507  161.169006  158.253006  160.925507  160.925507   \n",
            "2021-01-06  157.324005  159.875504  156.557999  156.919006  156.919006   \n",
            "2021-01-07  157.850006  160.427002  157.750000  158.108002  158.108002   \n",
            "2021-01-08  159.000000  159.531998  157.110001  159.134995  159.134995   \n",
            "\n",
            "              volume  \n",
            "Date                  \n",
            "2021-01-04  88228000  \n",
            "2021-01-05  53110000  \n",
            "2021-01-06  87896000  \n",
            "2021-01-07  70290000  \n",
            "2021-01-08  70754000  \n"
          ]
        }
      ],
      "source": [
        "data = data.rename(columns=str.lower)\n",
        "print(data.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "maUGCaWca8BD"
      },
      "source": [
        "Drop columns from data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "BgEMbGzda4Xy",
        "outputId": "aaf30734-44f1-490c-f20f-57b43318a7cd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e17acc13-3fda-427d-a3be-8d507e61d118\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-04</th>\n",
              "      <td>163.500000</td>\n",
              "      <td>163.600006</td>\n",
              "      <td>157.201004</td>\n",
              "      <td>159.331497</td>\n",
              "      <td>88228000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-05</th>\n",
              "      <td>158.300507</td>\n",
              "      <td>161.169006</td>\n",
              "      <td>158.253006</td>\n",
              "      <td>160.925507</td>\n",
              "      <td>53110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-06</th>\n",
              "      <td>157.324005</td>\n",
              "      <td>159.875504</td>\n",
              "      <td>156.557999</td>\n",
              "      <td>156.919006</td>\n",
              "      <td>87896000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-07</th>\n",
              "      <td>157.850006</td>\n",
              "      <td>160.427002</td>\n",
              "      <td>157.750000</td>\n",
              "      <td>158.108002</td>\n",
              "      <td>70290000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-08</th>\n",
              "      <td>159.000000</td>\n",
              "      <td>159.531998</td>\n",
              "      <td>157.110001</td>\n",
              "      <td>159.134995</td>\n",
              "      <td>70754000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e17acc13-3fda-427d-a3be-8d507e61d118')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e17acc13-3fda-427d-a3be-8d507e61d118 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e17acc13-3fda-427d-a3be-8d507e61d118');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  open        high         low       close    volume\n",
              "Date                                                                \n",
              "2021-01-04  163.500000  163.600006  157.201004  159.331497  88228000\n",
              "2021-01-05  158.300507  161.169006  158.253006  160.925507  53110000\n",
              "2021-01-06  157.324005  159.875504  156.557999  156.919006  87896000\n",
              "2021-01-07  157.850006  160.427002  157.750000  158.108002  70290000\n",
              "2021-01-08  159.000000  159.531998  157.110001  159.134995  70754000"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = data.drop(['adj close'], axis=1)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0RExEoskbPWH"
      },
      "source": [
        "Store data in csv uniquely"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "DLIOC1DkbFjL"
      },
      "outputs": [],
      "source": [
        "df.to_csv(f\"{symbol}_{start_date}_{end_date}.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0ocihFQcI3g"
      },
      "source": [
        "Ploting data for multiple ticker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "VjzPuVtvbwW0",
        "outputId": "7c65ebfd-92d9-4735-ad99-0ee1b17a8700"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc9eb782810>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgKOLSludRxI"
      },
      "source": [
        "example 1: Fetch data from yfinance library and make column names all lowercase, remove unnecessary columns like \"adj close\" and save this data in a specific folder with specific name with .csv format"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6_TACQ2Vd3g1",
        "outputId": "66f2c44d-02c9-4aa9-bdbc-5908571820d5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "                  open        high         low       close    volume\n",
            "Date                                                                \n",
            "2021-01-04  163.500000  163.600006  157.201004  159.331497  88228000\n",
            "2021-01-05  158.300507  161.169006  158.253006  160.925507  53110000\n",
            "2021-01-06  157.324005  159.875504  156.557999  156.919006  87896000\n",
            "2021-01-07  157.850006  160.427002  157.750000  158.108002  70290000\n",
            "2021-01-08  159.000000  159.531998  157.110001  159.134995  70754000\n"
          ]
        }
      ],
      "source": [
        "df1 = yf.download(\"AAPL\",start='2022-01-01', end='2022-06-30', interval= '1wk')\n",
        "df1 = data.drop(['adj close'], axis=1)\n",
        "print(df.head())\n",
        "df1.to_csv(f\"{symbol}_{interval}_{start_date}_{end_date}.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GjK00V-mgDkA"
      },
      "source": [
        "example 2: Read specific stock data from specific folder and add a new column in the data where if close > open then store green or if close < open then store red. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QS2_Q4_8fnxe",
        "outputId": "1d68bc38-0fec-45a8-9f26-63c7589a647f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "         Date        open        high         low       close    volume  color\n",
            "0  2021-01-04  163.500000  163.600006  157.201004  159.331497  88228000    red\n",
            "1  2021-01-05  158.300507  161.169006  158.253006  160.925507  53110000  green\n",
            "2  2021-01-06  157.324005  159.875504  156.557999  156.919006  87896000    red\n",
            "3  2021-01-07  157.850006  160.427002  157.750000  158.108002  70290000  green\n",
            "4  2021-01-08  159.000000  159.531998  157.110001  159.134995  70754000  green\n"
          ]
        }
      ],
      "source": [
        "df2 = pd.read_csv(f\"AMZN_2021-01-01_2021-01-31.csv\")\n",
        "df2['color'] = np.where(df2['close'] > df2['open'],'green','red')\n",
        "print(df2.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "722Vha2yiPBW"
      },
      "source": [
        "example 3: From the file named 'example3.csv'. There are 2 series s1 and s2. you have to \n",
        "- Count how many times s1 crossabove s2\n",
        "- Count how many times s1 crossbelow s2\n",
        "- Count how many times s1 cross s2\n",
        "and detect at which date and time this happen and return a list of datetime."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HOI3X40hkRZ",
        "outputId": "3e8bd059-e437-41d8-85f9-1bac621e8004"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                    Datetime  s1  s2\n",
            "0  2022-01-05 09:30:00-05:00 NaN NaN\n",
            "1  2022-01-05 09:31:00-05:00 NaN NaN\n",
            "2  2022-01-05 09:32:00-05:00 NaN NaN\n",
            "3  2022-01-05 09:33:00-05:00 NaN NaN\n",
            "4  2022-01-05 09:34:00-05:00 NaN NaN\n"
          ]
        }
      ],
      "source": [
        "df3 =pd.read_csv(f\"example3.csv\") \n",
        "df3.head()\n",
        "df3.rename(columns={'0': 's1','1': 's2'}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J5wr1SNRsmMA"
      },
      "source": [
        "Cross Above"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7t_VZfWhsper",
        "outputId": "b3e3b0ad-14c6-4b0b-bba8-d687bab857dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cross above count 240\n",
            "['2022-01-05 09:56:00-05:00', '2022-01-05 09:57:00-05:00', '2022-01-05 09:58:00-05:00', '2022-01-05 09:59:00-05:00', '2022-01-05 10:00:00-05:00', '2022-01-05 10:01:00-05:00', '2022-01-05 10:02:00-05:00', '2022-01-05 10:03:00-05:00', '2022-01-05 10:04:00-05:00', '2022-01-05 10:05:00-05:00', '2022-01-05 10:06:00-05:00', '2022-01-05 10:07:00-05:00', '2022-01-05 10:08:00-05:00', '2022-01-05 10:09:00-05:00', '2022-01-05 10:10:00-05:00', '2022-01-05 10:11:00-05:00', '2022-01-05 10:12:00-05:00', '2022-01-05 10:13:00-05:00', '2022-01-05 10:14:00-05:00', '2022-01-05 11:00:00-05:00', '2022-01-05 11:01:00-05:00', '2022-01-05 11:02:00-05:00', '2022-01-05 11:03:00-05:00', '2022-01-05 11:04:00-05:00', '2022-01-05 11:05:00-05:00', '2022-01-05 11:06:00-05:00', '2022-01-05 13:18:00-05:00', '2022-01-05 13:19:00-05:00', '2022-01-05 13:20:00-05:00', '2022-01-05 13:21:00-05:00', '2022-01-05 13:22:00-05:00', '2022-01-05 13:23:00-05:00', '2022-01-05 13:24:00-05:00', '2022-01-05 13:25:00-05:00', '2022-01-05 13:26:00-05:00', '2022-01-05 13:27:00-05:00', '2022-01-05 13:28:00-05:00', '2022-01-05 13:29:00-05:00', '2022-01-05 13:30:00-05:00', '2022-01-05 13:31:00-05:00', '2022-01-05 13:32:00-05:00', '2022-01-05 13:33:00-05:00', '2022-01-05 13:34:00-05:00', '2022-01-05 13:35:00-05:00', '2022-01-05 13:36:00-05:00', '2022-01-05 13:37:00-05:00', '2022-01-05 13:38:00-05:00', '2022-01-05 13:39:00-05:00', '2022-01-05 13:40:00-05:00', '2022-01-05 13:41:00-05:00', '2022-01-05 13:42:00-05:00', '2022-01-05 13:43:00-05:00', '2022-01-05 13:44:00-05:00', '2022-01-05 13:45:00-05:00', '2022-01-05 13:46:00-05:00', '2022-01-05 13:47:00-05:00', '2022-01-05 13:48:00-05:00', '2022-01-05 13:53:00-05:00', '2022-01-05 13:54:00-05:00', '2022-01-05 13:55:00-05:00', '2022-01-05 13:56:00-05:00', '2022-01-05 13:57:00-05:00', '2022-01-05 13:58:00-05:00', '2022-01-05 13:59:00-05:00', '2022-01-05 14:00:00-05:00', '2022-01-05 14:01:00-05:00', '2022-01-05 14:02:00-05:00', '2022-01-05 14:03:00-05:00', '2022-01-05 15:38:00-05:00', '2022-01-05 15:39:00-05:00', '2022-01-05 15:40:00-05:00', '2022-01-05 15:41:00-05:00', '2022-01-05 15:42:00-05:00', '2022-01-05 15:43:00-05:00', '2022-01-05 15:44:00-05:00', '2022-01-05 15:45:00-05:00', '2022-01-05 15:46:00-05:00', '2022-01-05 15:47:00-05:00', '2022-01-05 15:48:00-05:00', '2022-01-05 15:49:00-05:00', '2022-01-05 15:50:00-05:00', '2022-01-05 15:51:00-05:00', '2022-01-05 15:52:00-05:00', '2022-01-05 15:53:00-05:00', '2022-01-05 15:54:00-05:00', '2022-01-05 15:55:00-05:00', '2022-01-05 15:56:00-05:00', '2022-01-05 15:57:00-05:00', '2022-01-06 09:32:00-05:00', '2022-01-06 09:33:00-05:00', '2022-01-06 09:34:00-05:00', '2022-01-06 09:35:00-05:00', '2022-01-06 09:36:00-05:00', '2022-01-06 09:37:00-05:00', '2022-01-06 09:38:00-05:00', '2022-01-06 09:39:00-05:00', '2022-01-06 09:40:00-05:00', '2022-01-06 09:41:00-05:00', '2022-01-06 09:42:00-05:00', '2022-01-06 09:43:00-05:00', '2022-01-06 09:44:00-05:00', '2022-01-06 09:45:00-05:00', '2022-01-06 09:46:00-05:00', '2022-01-06 09:47:00-05:00', '2022-01-06 09:48:00-05:00', '2022-01-06 09:49:00-05:00', '2022-01-06 10:24:00-05:00', '2022-01-06 10:25:00-05:00', '2022-01-06 10:26:00-05:00', '2022-01-06 10:27:00-05:00', '2022-01-06 10:28:00-05:00', '2022-01-06 10:29:00-05:00', '2022-01-06 10:30:00-05:00', '2022-01-06 10:31:00-05:00', '2022-01-06 10:32:00-05:00', '2022-01-06 10:33:00-05:00', '2022-01-06 10:34:00-05:00', '2022-01-06 10:35:00-05:00', '2022-01-06 10:36:00-05:00', '2022-01-06 10:37:00-05:00', '2022-01-06 10:38:00-05:00', '2022-01-06 10:39:00-05:00', '2022-01-06 10:40:00-05:00', '2022-01-06 10:41:00-05:00', '2022-01-06 10:42:00-05:00', '2022-01-06 10:43:00-05:00', '2022-01-06 10:44:00-05:00', '2022-01-06 10:45:00-05:00', '2022-01-06 10:46:00-05:00', '2022-01-06 10:47:00-05:00', '2022-01-06 10:48:00-05:00', '2022-01-06 10:49:00-05:00', '2022-01-06 10:50:00-05:00', '2022-01-06 10:51:00-05:00', '2022-01-06 10:52:00-05:00', '2022-01-06 10:53:00-05:00', '2022-01-06 10:54:00-05:00', '2022-01-06 10:55:00-05:00', '2022-01-06 10:56:00-05:00', '2022-01-06 10:57:00-05:00', '2022-01-06 10:58:00-05:00', '2022-01-06 10:59:00-05:00', '2022-01-06 11:00:00-05:00', '2022-01-06 11:01:00-05:00', '2022-01-06 11:02:00-05:00', '2022-01-06 11:03:00-05:00', '2022-01-06 11:04:00-05:00', '2022-01-06 11:05:00-05:00', '2022-01-06 11:06:00-05:00', '2022-01-06 11:07:00-05:00', '2022-01-06 11:08:00-05:00', '2022-01-06 11:09:00-05:00', '2022-01-06 11:10:00-05:00', '2022-01-06 11:11:00-05:00', '2022-01-06 11:12:00-05:00', '2022-01-06 11:13:00-05:00', '2022-01-06 11:14:00-05:00', '2022-01-06 11:15:00-05:00', '2022-01-06 11:16:00-05:00', '2022-01-06 11:17:00-05:00', '2022-01-06 11:18:00-05:00', '2022-01-06 11:19:00-05:00', '2022-01-06 11:20:00-05:00', '2022-01-06 11:21:00-05:00', '2022-01-06 11:22:00-05:00', '2022-01-06 11:23:00-05:00', '2022-01-06 11:24:00-05:00', '2022-01-06 11:25:00-05:00', '2022-01-06 11:48:00-05:00', '2022-01-06 11:49:00-05:00', '2022-01-06 11:50:00-05:00', '2022-01-06 11:51:00-05:00', '2022-01-06 11:52:00-05:00', '2022-01-06 11:53:00-05:00', '2022-01-06 11:54:00-05:00', '2022-01-06 11:55:00-05:00', '2022-01-06 11:56:00-05:00', '2022-01-06 11:57:00-05:00', '2022-01-06 11:58:00-05:00', '2022-01-06 11:59:00-05:00', '2022-01-06 12:00:00-05:00', '2022-01-06 12:01:00-05:00', '2022-01-06 12:02:00-05:00', '2022-01-06 12:03:00-05:00', '2022-01-06 12:04:00-05:00', '2022-01-06 12:05:00-05:00', '2022-01-06 12:06:00-05:00', '2022-01-06 12:09:00-05:00', '2022-01-06 12:10:00-05:00', '2022-01-06 12:11:00-05:00', '2022-01-06 12:12:00-05:00', '2022-01-06 12:42:00-05:00', '2022-01-06 12:43:00-05:00', '2022-01-06 12:44:00-05:00', '2022-01-06 12:45:00-05:00', '2022-01-06 12:46:00-05:00', '2022-01-06 12:47:00-05:00', '2022-01-06 12:48:00-05:00', '2022-01-06 12:49:00-05:00', '2022-01-06 12:50:00-05:00', '2022-01-06 12:51:00-05:00', '2022-01-06 12:52:00-05:00', '2022-01-06 12:53:00-05:00', '2022-01-06 13:44:00-05:00', '2022-01-06 13:45:00-05:00', '2022-01-06 13:46:00-05:00', '2022-01-06 13:47:00-05:00', '2022-01-06 13:48:00-05:00', '2022-01-06 13:49:00-05:00', '2022-01-06 13:50:00-05:00', '2022-01-06 13:51:00-05:00', '2022-01-06 13:52:00-05:00', '2022-01-06 13:53:00-05:00', '2022-01-06 13:54:00-05:00', '2022-01-06 13:55:00-05:00', '2022-01-06 13:56:00-05:00', '2022-01-06 13:57:00-05:00', '2022-01-06 13:58:00-05:00', '2022-01-06 13:59:00-05:00', '2022-01-06 14:00:00-05:00', '2022-01-06 14:01:00-05:00', '2022-01-06 14:02:00-05:00', '2022-01-06 14:03:00-05:00', '2022-01-06 14:04:00-05:00', '2022-01-06 14:05:00-05:00', '2022-01-06 14:06:00-05:00', '2022-01-06 14:07:00-05:00', '2022-01-06 14:08:00-05:00', '2022-01-06 14:09:00-05:00', '2022-01-06 14:10:00-05:00', '2022-01-06 14:11:00-05:00', '2022-01-06 14:12:00-05:00', '2022-01-06 14:13:00-05:00', '2022-01-06 14:14:00-05:00', '2022-01-06 14:15:00-05:00', '2022-01-06 14:16:00-05:00', '2022-01-06 14:17:00-05:00', '2022-01-06 14:18:00-05:00', '2022-01-06 14:19:00-05:00', '2022-01-06 14:20:26-05:00']\n"
          ]
        }
      ],
      "source": [
        "cross_over = df3['s1'] >= df3['s2']\n",
        "cross_above_dates = df3['Datetime'].loc[cross_over]\n",
        "cross_above_count = cross_above_dates.count()\n",
        "cross_above_list = cross_above_dates.values.tolist()\n",
        "print(\"Cross_Above_Count:\",cross_above_count)\n",
        "print(cross_above_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YL4_g0XtqVY"
      },
      "source": [
        "Cross Below"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiLBYab9tsXJ",
        "outputId": "022536bb-21c2-4f02-a420-da55ddf34bd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cross below count 405\n",
            "['2022-01-05 10:16:00-05:00', '2022-01-05 10:17:00-05:00', '2022-01-05 10:18:00-05:00', '2022-01-05 10:19:00-05:00', '2022-01-05 10:20:00-05:00', '2022-01-05 10:21:00-05:00', '2022-01-05 10:22:00-05:00', '2022-01-05 10:23:00-05:00', '2022-01-05 10:24:00-05:00', '2022-01-05 10:25:00-05:00', '2022-01-05 10:26:00-05:00', '2022-01-05 10:27:00-05:00', '2022-01-05 10:28:00-05:00', '2022-01-05 10:29:00-05:00', '2022-01-05 10:30:00-05:00', '2022-01-05 10:31:00-05:00', '2022-01-05 10:32:00-05:00', '2022-01-05 10:33:00-05:00', '2022-01-05 10:34:00-05:00', '2022-01-05 10:35:00-05:00', '2022-01-05 10:36:00-05:00', '2022-01-05 10:37:00-05:00', '2022-01-05 10:38:00-05:00', '2022-01-05 10:39:00-05:00', '2022-01-05 10:40:00-05:00', '2022-01-05 10:41:00-05:00', '2022-01-05 10:42:00-05:00', '2022-01-05 10:43:00-05:00', '2022-01-05 10:44:00-05:00', '2022-01-05 10:45:00-05:00', '2022-01-05 10:46:00-05:00', '2022-01-05 10:47:00-05:00', '2022-01-05 10:48:00-05:00', '2022-01-05 10:49:00-05:00', '2022-01-05 10:50:00-05:00', '2022-01-05 10:51:00-05:00', '2022-01-05 10:52:00-05:00', '2022-01-05 10:53:00-05:00', '2022-01-05 10:54:00-05:00', '2022-01-05 10:55:00-05:00', '2022-01-05 10:56:00-05:00', '2022-01-05 10:57:00-05:00', '2022-01-05 10:58:00-05:00', '2022-01-05 10:59:00-05:00', '2022-01-05 11:08:00-05:00', '2022-01-05 11:09:00-05:00', '2022-01-05 11:10:00-05:00', '2022-01-05 11:11:00-05:00', '2022-01-05 11:12:00-05:00', '2022-01-05 11:13:00-05:00', '2022-01-05 11:14:00-05:00', '2022-01-05 11:15:00-05:00', '2022-01-05 11:16:00-05:00', '2022-01-05 11:17:00-05:00', '2022-01-05 11:18:00-05:00', '2022-01-05 11:19:00-05:00', '2022-01-05 11:20:00-05:00', '2022-01-05 11:21:00-05:00', '2022-01-05 11:22:00-05:00', '2022-01-05 11:23:00-05:00', '2022-01-05 11:24:00-05:00', '2022-01-05 11:25:00-05:00', '2022-01-05 11:26:00-05:00', '2022-01-05 11:27:00-05:00', '2022-01-05 11:28:00-05:00', '2022-01-05 11:29:00-05:00', '2022-01-05 11:30:00-05:00', '2022-01-05 11:31:00-05:00', '2022-01-05 11:32:00-05:00', '2022-01-05 11:33:00-05:00', '2022-01-05 11:34:00-05:00', '2022-01-05 11:35:00-05:00', '2022-01-05 11:36:00-05:00', '2022-01-05 11:37:00-05:00', '2022-01-05 11:38:00-05:00', '2022-01-05 11:39:00-05:00', '2022-01-05 11:40:00-05:00', '2022-01-05 11:41:00-05:00', '2022-01-05 11:42:00-05:00', '2022-01-05 11:43:00-05:00', '2022-01-05 11:44:00-05:00', '2022-01-05 11:45:00-05:00', '2022-01-05 11:46:00-05:00', '2022-01-05 11:47:00-05:00', '2022-01-05 11:48:00-05:00', '2022-01-05 11:49:00-05:00', '2022-01-05 11:50:00-05:00', '2022-01-05 11:51:00-05:00', '2022-01-05 11:52:00-05:00', '2022-01-05 11:53:00-05:00', '2022-01-05 11:54:00-05:00', '2022-01-05 11:55:00-05:00', '2022-01-05 11:56:00-05:00', '2022-01-05 11:57:00-05:00', '2022-01-05 11:58:00-05:00', '2022-01-05 11:59:00-05:00', '2022-01-05 12:00:00-05:00', '2022-01-05 12:01:00-05:00', '2022-01-05 12:02:00-05:00', '2022-01-05 12:03:00-05:00', '2022-01-05 12:04:00-05:00', '2022-01-05 12:05:00-05:00', '2022-01-05 12:06:00-05:00', '2022-01-05 12:07:00-05:00', '2022-01-05 12:08:00-05:00', '2022-01-05 12:09:00-05:00', '2022-01-05 12:10:00-05:00', '2022-01-05 12:11:00-05:00', '2022-01-05 12:12:00-05:00', '2022-01-05 12:13:00-05:00', '2022-01-05 12:14:00-05:00', '2022-01-05 12:15:00-05:00', '2022-01-05 12:16:00-05:00', '2022-01-05 12:17:00-05:00', '2022-01-05 12:18:00-05:00', '2022-01-05 12:19:00-05:00', '2022-01-05 12:20:00-05:00', '2022-01-05 12:21:00-05:00', '2022-01-05 12:22:00-05:00', '2022-01-05 12:23:00-05:00', '2022-01-05 12:24:00-05:00', '2022-01-05 12:25:00-05:00', '2022-01-05 12:26:00-05:00', '2022-01-05 12:27:00-05:00', '2022-01-05 12:28:00-05:00', '2022-01-05 12:29:00-05:00', '2022-01-05 12:30:00-05:00', '2022-01-05 12:31:00-05:00', '2022-01-05 12:32:00-05:00', '2022-01-05 12:33:00-05:00', '2022-01-05 12:34:00-05:00', '2022-01-05 12:35:00-05:00', '2022-01-05 12:36:00-05:00', '2022-01-05 12:37:00-05:00', '2022-01-05 12:38:00-05:00', '2022-01-05 12:39:00-05:00', '2022-01-05 12:40:00-05:00', '2022-01-05 12:41:00-05:00', '2022-01-05 12:42:00-05:00', '2022-01-05 12:43:00-05:00', '2022-01-05 12:44:00-05:00', '2022-01-05 12:45:00-05:00', '2022-01-05 12:46:00-05:00', '2022-01-05 12:47:00-05:00', '2022-01-05 12:48:00-05:00', '2022-01-05 12:49:00-05:00', '2022-01-05 12:50:00-05:00', '2022-01-05 12:51:00-05:00', '2022-01-05 12:52:00-05:00', '2022-01-05 12:53:00-05:00', '2022-01-05 12:54:00-05:00', '2022-01-05 12:55:00-05:00', '2022-01-05 12:56:00-05:00', '2022-01-05 12:57:00-05:00', '2022-01-05 12:58:00-05:00', '2022-01-05 12:59:00-05:00', '2022-01-05 13:00:00-05:00', '2022-01-05 13:01:00-05:00', '2022-01-05 13:02:00-05:00', '2022-01-05 13:03:00-05:00', '2022-01-05 13:04:00-05:00', '2022-01-05 13:05:00-05:00', '2022-01-05 13:06:00-05:00', '2022-01-05 13:07:00-05:00', '2022-01-05 13:08:00-05:00', '2022-01-05 13:09:00-05:00', '2022-01-05 13:10:00-05:00', '2022-01-05 13:11:00-05:00', '2022-01-05 13:12:00-05:00', '2022-01-05 13:13:00-05:00', '2022-01-05 13:14:00-05:00', '2022-01-05 13:15:00-05:00', '2022-01-05 13:16:00-05:00', '2022-01-05 13:17:00-05:00', '2022-01-05 13:50:00-05:00', '2022-01-05 13:51:00-05:00', '2022-01-05 13:52:00-05:00', '2022-01-05 14:05:00-05:00', '2022-01-05 14:06:00-05:00', '2022-01-05 14:07:00-05:00', '2022-01-05 14:08:00-05:00', '2022-01-05 14:09:00-05:00', '2022-01-05 14:10:00-05:00', '2022-01-05 14:11:00-05:00', '2022-01-05 14:12:00-05:00', '2022-01-05 14:13:00-05:00', '2022-01-05 14:14:00-05:00', '2022-01-05 14:15:00-05:00', '2022-01-05 14:16:00-05:00', '2022-01-05 14:17:00-05:00', '2022-01-05 14:18:00-05:00', '2022-01-05 14:19:00-05:00', '2022-01-05 14:20:00-05:00', '2022-01-05 14:21:00-05:00', '2022-01-05 14:22:00-05:00', '2022-01-05 14:23:00-05:00', '2022-01-05 14:24:00-05:00', '2022-01-05 14:25:00-05:00', '2022-01-05 14:26:00-05:00', '2022-01-05 14:27:00-05:00', '2022-01-05 14:28:00-05:00', '2022-01-05 14:29:00-05:00', '2022-01-05 14:30:00-05:00', '2022-01-05 14:31:00-05:00', '2022-01-05 14:32:00-05:00', '2022-01-05 14:33:00-05:00', '2022-01-05 14:34:00-05:00', '2022-01-05 14:35:00-05:00', '2022-01-05 14:36:00-05:00', '2022-01-05 14:37:00-05:00', '2022-01-05 14:38:00-05:00', '2022-01-05 14:39:00-05:00', '2022-01-05 14:40:00-05:00', '2022-01-05 14:41:00-05:00', '2022-01-05 14:42:00-05:00', '2022-01-05 14:43:00-05:00', '2022-01-05 14:44:00-05:00', '2022-01-05 14:45:00-05:00', '2022-01-05 14:46:00-05:00', '2022-01-05 14:47:00-05:00', '2022-01-05 14:48:00-05:00', '2022-01-05 14:49:00-05:00', '2022-01-05 14:50:00-05:00', '2022-01-05 14:51:00-05:00', '2022-01-05 14:52:00-05:00', '2022-01-05 14:53:00-05:00', '2022-01-05 14:54:00-05:00', '2022-01-05 14:55:00-05:00', '2022-01-05 14:56:00-05:00', '2022-01-05 14:57:00-05:00', '2022-01-05 14:58:00-05:00', '2022-01-05 14:59:00-05:00', '2022-01-05 15:00:00-05:00', '2022-01-05 15:01:00-05:00', '2022-01-05 15:02:00-05:00', '2022-01-05 15:03:00-05:00', '2022-01-05 15:04:00-05:00', '2022-01-05 15:05:00-05:00', '2022-01-05 15:06:00-05:00', '2022-01-05 15:07:00-05:00', '2022-01-05 15:08:00-05:00', '2022-01-05 15:09:00-05:00', '2022-01-05 15:10:00-05:00', '2022-01-05 15:11:00-05:00', '2022-01-05 15:12:00-05:00', '2022-01-05 15:13:00-05:00', '2022-01-05 15:14:00-05:00', '2022-01-05 15:15:00-05:00', '2022-01-05 15:16:00-05:00', '2022-01-05 15:17:00-05:00', '2022-01-05 15:18:00-05:00', '2022-01-05 15:19:00-05:00', '2022-01-05 15:20:00-05:00', '2022-01-05 15:21:00-05:00', '2022-01-05 15:22:00-05:00', '2022-01-05 15:23:00-05:00', '2022-01-05 15:24:00-05:00', '2022-01-05 15:25:00-05:00', '2022-01-05 15:26:00-05:00', '2022-01-05 15:27:00-05:00', '2022-01-05 15:28:00-05:00', '2022-01-05 15:29:00-05:00', '2022-01-05 15:30:00-05:00', '2022-01-05 15:31:00-05:00', '2022-01-05 15:32:00-05:00', '2022-01-05 15:33:00-05:00', '2022-01-05 15:34:00-05:00', '2022-01-05 15:35:00-05:00', '2022-01-05 15:36:00-05:00', '2022-01-05 15:37:00-05:00', '2022-01-05 15:59:00-05:00', '2022-01-06 09:30:00-05:00', '2022-01-06 09:31:00-05:00', '2022-01-06 09:51:00-05:00', '2022-01-06 09:52:00-05:00', '2022-01-06 09:53:00-05:00', '2022-01-06 09:54:00-05:00', '2022-01-06 09:55:00-05:00', '2022-01-06 09:56:00-05:00', '2022-01-06 09:57:00-05:00', '2022-01-06 09:58:00-05:00', '2022-01-06 09:59:00-05:00', '2022-01-06 10:00:00-05:00', '2022-01-06 10:01:00-05:00', '2022-01-06 10:02:00-05:00', '2022-01-06 10:03:00-05:00', '2022-01-06 10:04:00-05:00', '2022-01-06 10:05:00-05:00', '2022-01-06 10:06:00-05:00', '2022-01-06 10:07:00-05:00', '2022-01-06 10:08:00-05:00', '2022-01-06 10:09:00-05:00', '2022-01-06 10:10:00-05:00', '2022-01-06 10:11:00-05:00', '2022-01-06 10:12:00-05:00', '2022-01-06 10:13:00-05:00', '2022-01-06 10:14:00-05:00', '2022-01-06 10:15:00-05:00', '2022-01-06 10:16:00-05:00', '2022-01-06 10:17:00-05:00', '2022-01-06 10:18:00-05:00', '2022-01-06 10:19:00-05:00', '2022-01-06 10:20:00-05:00', '2022-01-06 10:21:00-05:00', '2022-01-06 10:22:00-05:00', '2022-01-06 10:23:00-05:00', '2022-01-06 11:27:00-05:00', '2022-01-06 11:28:00-05:00', '2022-01-06 11:29:00-05:00', '2022-01-06 11:30:00-05:00', '2022-01-06 11:31:00-05:00', '2022-01-06 11:32:00-05:00', '2022-01-06 11:33:00-05:00', '2022-01-06 11:34:00-05:00', '2022-01-06 11:35:00-05:00', '2022-01-06 11:36:00-05:00', '2022-01-06 11:37:00-05:00', '2022-01-06 11:38:00-05:00', '2022-01-06 11:39:00-05:00', '2022-01-06 11:40:00-05:00', '2022-01-06 11:41:00-05:00', '2022-01-06 11:42:00-05:00', '2022-01-06 11:43:00-05:00', '2022-01-06 11:44:00-05:00', '2022-01-06 11:45:00-05:00', '2022-01-06 11:46:00-05:00', '2022-01-06 11:47:00-05:00', '2022-01-06 12:08:00-05:00', '2022-01-06 12:14:00-05:00', '2022-01-06 12:15:00-05:00', '2022-01-06 12:16:00-05:00', '2022-01-06 12:17:00-05:00', '2022-01-06 12:18:00-05:00', '2022-01-06 12:19:00-05:00', '2022-01-06 12:20:00-05:00', '2022-01-06 12:21:00-05:00', '2022-01-06 12:22:00-05:00', '2022-01-06 12:23:00-05:00', '2022-01-06 12:24:00-05:00', '2022-01-06 12:25:00-05:00', '2022-01-06 12:26:00-05:00', '2022-01-06 12:27:00-05:00', '2022-01-06 12:28:00-05:00', '2022-01-06 12:29:00-05:00', '2022-01-06 12:30:00-05:00', '2022-01-06 12:31:00-05:00', '2022-01-06 12:32:00-05:00', '2022-01-06 12:33:00-05:00', '2022-01-06 12:34:00-05:00', '2022-01-06 12:35:00-05:00', '2022-01-06 12:36:00-05:00', '2022-01-06 12:37:00-05:00', '2022-01-06 12:38:00-05:00', '2022-01-06 12:39:00-05:00', '2022-01-06 12:40:00-05:00', '2022-01-06 12:41:00-05:00', '2022-01-06 12:55:00-05:00', '2022-01-06 12:56:00-05:00', '2022-01-06 12:57:00-05:00', '2022-01-06 12:58:00-05:00', '2022-01-06 12:59:00-05:00', '2022-01-06 13:00:00-05:00', '2022-01-06 13:01:00-05:00', '2022-01-06 13:02:00-05:00', '2022-01-06 13:03:00-05:00', '2022-01-06 13:04:00-05:00', '2022-01-06 13:05:00-05:00', '2022-01-06 13:06:00-05:00', '2022-01-06 13:07:00-05:00', '2022-01-06 13:08:00-05:00', '2022-01-06 13:09:00-05:00', '2022-01-06 13:10:00-05:00', '2022-01-06 13:11:00-05:00', '2022-01-06 13:12:00-05:00', '2022-01-06 13:13:00-05:00', '2022-01-06 13:14:00-05:00', '2022-01-06 13:15:00-05:00', '2022-01-06 13:16:00-05:00', '2022-01-06 13:17:00-05:00', '2022-01-06 13:18:00-05:00', '2022-01-06 13:19:00-05:00', '2022-01-06 13:20:00-05:00', '2022-01-06 13:21:00-05:00', '2022-01-06 13:22:00-05:00', '2022-01-06 13:23:00-05:00', '2022-01-06 13:24:00-05:00', '2022-01-06 13:25:00-05:00', '2022-01-06 13:26:00-05:00', '2022-01-06 13:27:00-05:00', '2022-01-06 13:28:00-05:00', '2022-01-06 13:29:00-05:00', '2022-01-06 13:30:00-05:00', '2022-01-06 13:31:00-05:00', '2022-01-06 13:32:00-05:00', '2022-01-06 13:33:00-05:00', '2022-01-06 13:34:00-05:00', '2022-01-06 13:35:00-05:00', '2022-01-06 13:36:00-05:00', '2022-01-06 13:37:00-05:00', '2022-01-06 13:38:00-05:00', '2022-01-06 13:39:00-05:00', '2022-01-06 13:40:00-05:00', '2022-01-06 13:41:00-05:00', '2022-01-06 13:42:00-05:00', '2022-01-06 13:43:00-05:00']\n"
          ]
        }
      ],
      "source": [
        "cross_down = (df3['s1'] <= df3['s2'])\n",
        "cross_below_dates = df3['Datetime'].loc[cross_down]\n",
        "print(\"cross below count\", cross_below_dates.count())\n",
        "cross_below_list = cross_below_dates.values.tolist()\n",
        "print(cross_below_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SWh4WN1uJHs"
      },
      "source": [
        "Cross"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3VDX1kauK4m",
        "outputId": "dfcc5a66-65ed-499e-8c26-c3a5a4195902"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Crossing Data count 20\n",
            "['2022-01-05 10:15:00-05:00', '2022-01-05 11:00:00-05:00', '2022-01-05 11:07:00-05:00', '2022-01-05 13:18:00-05:00', '2022-01-05 13:49:00-05:00', '2022-01-05 13:53:00-05:00', '2022-01-05 14:04:00-05:00', '2022-01-05 15:38:00-05:00', '2022-01-05 15:58:00-05:00', '2022-01-06 09:32:00-05:00', '2022-01-06 09:50:00-05:00', '2022-01-06 10:24:00-05:00', '2022-01-06 11:26:00-05:00', '2022-01-06 11:48:00-05:00', '2022-01-06 12:07:00-05:00', '2022-01-06 12:09:00-05:00', '2022-01-06 12:13:00-05:00', '2022-01-06 12:42:00-05:00', '2022-01-06 12:54:00-05:00', '2022-01-06 13:44:00-05:00']\n"
          ]
        }
      ],
      "source": [
        "crossing = ((df3['s1'] >= df3['s2']) | (df3['s1'] <= df3['s2']))\n",
        "cross_dates = df3['Datetime'].loc[crossing]\n",
        "print(\"Crossing Data count\",cross_dates.count())\n",
        "cross_dates_list = cross_dates.values.tolist()\n",
        "print(cross_dates_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QGiTgCk4vF0V",
        "outputId": "5421eeb0-4990-4cac-a41c-6c4427643cfb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc9e7e2d210>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df3.plot()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "yFinance_notebook.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.6 64-bit (windows store)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "9ff29ec90039eed23207186706e5f410a658cbd9bc5b0ff5fb0e10121cf7ac8f"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}