nasdaq-api-v3.0.2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "dBWzlt1BAkM4"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import re\n",
        "import requests\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import plotly.express as px\n",
        "from time import sleep\n",
        "from functools import wraps, partial\n",
        "from concurrent.futures import ProcessPoolExecutor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BASE_URL = f\"https://api.nasdaq.com/api\""
      ],
      "metadata": {
        "id": "Go9TmCu3OXrQ"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Utils"
      ],
      "metadata": {
        "id": "-gxnUk2LwhFl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def validator(function):\n",
        "        @wraps(function)\n",
        "        def wrapper(*args, **kwargs):\n",
        "            try:\n",
        "                return function(*args, **kwargs)\n",
        "            except (AttributeError, TypeError) as e:\n",
        "                pass\n",
        "        return wrapper\n"
      ],
      "metadata": {
        "id": "0B2n3MeOawcR"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_response(url):\n",
        "    headers = {\n",
        "        \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.2 Safari/605.1.15\" \n",
        "    }\n",
        "\n",
        "    session = requests.Session()\n",
        "    response = session.get(url=url, headers=headers)\n",
        "    return response.json()\n",
        "\n",
        "\n",
        "def get_dataframe(data):\n",
        "    cols, rows = data[\"headers\"], data[\"rows\"]\n",
        "\n",
        "    df = pd.DataFrame(data=rows, columns=list(cols.keys()))\n",
        "    df.columns = list(cols.values())\n",
        "    df.set_index(df.columns[0], inplace=True)\n",
        "    return df\n",
        "\n",
        "\n",
        "def snakecase_to_camelcase(s):\n",
        "    return re.sub(r'_([a-z])', lambda x: x.group(1).upper(), s)\n",
        "\n",
        "\n",
        "def string_to_float(s):\n",
        "    return float(re.sub(r\"[\\$\\,]+\", \"\", s))\n"
      ],
      "metadata": {
        "id": "crVI8b11A9lj"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Screener"
      ],
      "metadata": {
        "id": "Bci20M0OMVCX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "@validator\n",
        "def screener(limit=0, **kwargs):\n",
        "    \"\"\"\n",
        "    Inputs:\n",
        "        limit: 0 = all data, 1+ = number of rows (default=0)\n",
        "        exchange: nasdaq, nyse, amex (default=None)\n",
        "        marketcap: mega, large, mid, small, micro, nano (default=None)\n",
        "        recommendation: strong_buy, hold, buy, sell, strong_sell (default=None)\n",
        "        sector: technology, telecommunications, health_care, finance, real_estate,\n",
        "                consumer_discretionary, consumer_staples, industrials,\n",
        "                basic_materials, energy, utilities (default=None)\n",
        "        region: africa, asia, australia_and_south_pacific, caribbean, europe,\n",
        "                middle_east, north_america, south_america (default=None)\n",
        "        country: argentina, armenia, australia, austria, belgium, bermuda, brazil,\n",
        "                 canada, cayman_islands, chile, colombia, costa_rica, curacao,\n",
        "                 cyprus, denmark, finland, france, germany, greece, guernsey,\n",
        "                 hong_kong, india, indonesia, ireland, isle_of_man, israel, italy,\n",
        "                 japan, jersey, luxembourg, macau, mexico, monaco, netherlands,\n",
        "                 norway, panama, peru, philippines, puerto_rico, russia, singapore,\n",
        "                 south_africa, south_korea, spain, sweden, switzerland, taiwan,\n",
        "                 turkey, united_kingdom, united_states, usa (default=None)\n",
        "\n",
        "    Example: screener(exchange=\"nasdaq\", marketcap=[\"mega\", \"large\", \"mid\"], sector=[\"technology\", \"health_care\", \"finance\"])\n",
        "    \"\"\"\n",
        "    parameters = {\n",
        "        \"exchange\": [\"nasdaq\", \"nyse\", \"amex\"],\n",
        "        \"marketcap\": [\"mega\", \"large\", \"mid\", \"small\", \"micro\", \"nano\"],\n",
        "        \"recommendation\": [\"strong_buy\", \"hold\", \"buy\", \"sell\", \"strong_sell\"],\n",
        "        \"sector\": [\"technology\", \"telecommunications\", \"health_care\", \"finance\", \"real_estate\", \"consumer_discretionary\", \"consumer_staples\", \"industrials\", \"basic_materials\", \"energy\", \"utilities\"],\n",
        "        \"region\": [\"africa\", \"asia\", \"australia_and_south_pacific\", \"caribbean\", \"europe\", \"middle_east\", \"north_america\", \"south_america\"],\n",
        "        \"country\": [\"argentina\", \"armenia\", \"australia\", \"austria\", \"belgium\", \"bermuda\", \"brazil\", \"canada\", \"cayman_islands\", \"chile\", \"colombia\", \"costa_rica\", \"curacao\",\n",
        "                    \"cyprus\", \"denmark\", \"finland\", \"france\", \"germany\", \"greece\", \"guernsey\", \"hong_kong\", \"india\", \"indonesia\", \"ireland\", \"isle_of_man\", \"israel\", \"italy\",\n",
        "                    \"japan\", \"jersey\",\"luxembourg\", \"macau\", \"mexico\", \"monaco\", \"netherlands\", \"norway\", \"panama\", \"peru\", \"philippines\", \"puerto_rico\", \"russia\", \"singapore\",\n",
        "                    \"south_africa\", \"south_korea\",\"spain\", \"sweden\", \"switzerland\", \"taiwan\", \"turkey\", \"united_kingdom\", \"united_states\", \"usa\"]\n",
        "    }\n",
        "\n",
        "    url = f\"{BASE_URL}/screener/stocks?limit={limit}\"\n",
        "\n",
        "    for i, v in kwargs.items():\n",
        "        if isinstance(v, list):\n",
        "            v = \"|\".join(v) if all(item in parameters[i] for item in v) else \"\"\n",
        "        url += f\"&{i}={v}\"\n",
        "\n",
        "    response = get_response(url)\n",
        "    data = response[\"data\"][\"table\"]\n",
        "\n",
        "    df = get_dataframe(data)\n",
        "\n",
        "    if isinstance(df, pd.DataFrame):\n",
        "        df.index = [i.replace(\"/\", \".\") for i in df.index]\n",
        "\n",
        "    return df\n"
      ],
      "metadata": {
        "id": "R6E_OO178Rx4"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "screener(exchange=\"nasdaq\", marketcap=[\"mega\", \"large\", \"mid\"], sector=[\"technology\", \"health_care\", \"finance\"], country=\"usa\")"
      ],
      "metadata": {
        "id": "f04FPPjsvPD2"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SingleBase"
      ],
      "metadata": {
        "id": "003d58KgMu_l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class SingleBase:\n",
        "    def __init__(self, ticker):\n",
        "        self._years = 10\n",
        "        self._screener = screener()\n",
        "        self._ticker = ticker.upper()\n",
        "\n",
        "        self._base_url = BASE_URL\n",
        "        self._quote_url = f\"{self._base_url}/quote/{self._ticker}\"\n",
        "        self._company_url = f\"{self._base_url}/company/{self._ticker}\"\n",
        "        self._analyst_url = f\"{self._base_url}/analyst/{self._ticker}\"\n",
        "\n",
        "        self._total_days = 365 * self._years + 1\n",
        "        self._trading_days = 252 * self._years\n",
        "        self._start_date = (dt.date.today() - dt.timedelta(days=self._total_days)).strftime(\"%Y-%m-%d\")\n",
        "\n",
        "        if not self._ticker in self._screener.index:\n",
        "            raise Exception(f\"There is no company with a symbol {self._ticker}.\")\n",
        "\n",
        "\n",
        "    # Stock Info\n",
        "    @validator\n",
        "    def _get_info(self):\n",
        "        return self._screener.filter(items=[self._ticker], axis=0)\n",
        "\n",
        "\n",
        "    # Company Profile\n",
        "    @validator\n",
        "    def _get_profile(self):\n",
        "        url = f\"{self._company_url}/company-profile\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = list(response[\"data\"].values())\n",
        "\n",
        "        lbls = [i[\"label\"] for i in data]\n",
        "        vals = [j[\"value\"] for j in data]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=lbls, columns=[self._ticker])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Key Data\n",
        "    @validator\n",
        "    def _get_summary(self):\n",
        "        url = f\"{self._quote_url}/summary?assetclass=stocks\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = list(response[\"data\"][\"summaryData\"].values())\n",
        "\n",
        "        lbls = [i[\"label\"] for i in data]\n",
        "        vals = [j[\"value\"] for j in data]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=lbls, columns=[self._ticker])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Dividend History\n",
        "    @validator\n",
        "    def _get_dividends(self):\n",
        "        url = f\"{self._quote_url}/dividends?assetclass=stocks\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"dividends\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Historical Quotes (10-Years Maximum)\n",
        "    @validator\n",
        "    def _get_quotes_history(self):\n",
        "        url = f\"{self._quote_url}/historical?assetclass=stocks&fromdate={self._start_date}&limit={self._trading_days}\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"tradesTable\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Chart\n",
        "    @validator\n",
        "    def _get_chart(self, data):\n",
        "        data = data.applymap(lambda x: string_to_float(x))\n",
        "        data.index = pd.to_datetime(data.index)\n",
        "        data = data.sort_index()\n",
        "        chart = px.area(x=data.index, y=data[\"Close/Last\"])\n",
        "\n",
        "        axes_layout = {\n",
        "                        \"gridcolor\": \"rgb(240, 240, 240)\",\n",
        "                        \"showspikes\": True,\n",
        "                        \"spikecolor\": \"rgb(120, 120, 240)\",\n",
        "                        \"spikemode\": \"across\",\n",
        "                        \"spikesnap\": \"cursor\",\n",
        "                        \"spikedash\": \"dash\",\n",
        "                        \"spikethickness\": 0.5\n",
        "                        }\n",
        "\n",
        "        chart.update_xaxes(\n",
        "            axes_layout,\n",
        "            title_text=\"Date\",\n",
        "            rangeselector={\n",
        "                \"buttons\": [\n",
        "                    {\"count\": 1, \"label\": \"1M\", \"step\": \"month\", \"stepmode\": \"backward\"},\n",
        "                    {\"count\": 3, \"label\": \"3M\", \"step\": \"month\", \"stepmode\": \"backward\"},\n",
        "                    {\"count\": 6, \"label\": \"6M\", \"step\": \"month\", \"stepmode\": \"backward\"},\n",
        "                    {\"count\": 1, \"label\": \"YTD\", \"step\": \"year\", \"stepmode\": \"todate\"},\n",
        "                    {\"count\": 1, \"label\": \"1Y\", \"step\": \"year\", \"stepmode\": \"backward\"},\n",
        "                    {\"count\": 3, \"label\": \"3Y\", \"step\": \"year\", \"stepmode\": \"backward\"},\n",
        "                    {\"count\": 5, \"label\": \"5Y\", \"step\": \"year\", \"stepmode\": \"backward\"},\n",
        "                    {\"label\": \"MAX\", \"step\": \"all\"}]})\n",
        "\n",
        "        chart.update_yaxes(axes_layout, title_text=\"Price\", tickprefix=\"$\")\n",
        "\n",
        "        chart.update_layout(\n",
        "            plot_bgcolor=\"rgb(250, 250, 250)\",\n",
        "            hovermode=\"x\",\n",
        "            spikedistance=-1,\n",
        "            hoverdistance=-1,\n",
        "            showlegend=False,\n",
        "            title={\n",
        "                \"text\": f\"{self._ticker} Stock Price History\",\n",
        "                \"y\": 0.97,\n",
        "                \"x\": 0.5,\n",
        "                \"xanchor\": \"center\",\n",
        "                \"yanchor\": \"top\"})\n",
        "\n",
        "        chart.update_traces(hovertemplate=\"Date: %{x}<br>Price: %{y}\")\n",
        "        chart.show();\n",
        "\n",
        "\n",
        "    # Financials (Annual/Quarterly)\n",
        "    @validator\n",
        "    def _get_financials(self, table=None, frequency=None):\n",
        "        frequency = 2 if frequency == \"quarterly\" else 1\n",
        "\n",
        "        url = f\"{self._company_url}/financials?frequency={frequency}\"\n",
        "\n",
        "        response = get_response(url)\n",
        "\n",
        "        if table == \"balance_sheet\":\n",
        "            data = response[\"data\"][\"balanceSheetTable\"]\n",
        "        elif table == \"cash_flow\":\n",
        "            data = response[\"data\"][\"cashFlowTable\"]\n",
        "        elif table == \"financial_ratios\":\n",
        "            data = response[\"data\"][\"financialRatiosTable\"]\n",
        "        else:\n",
        "            data = response[\"data\"][\"incomeStatementTable\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Earnings Per Share (Estimated vs Reported)\n",
        "    @validator\n",
        "    def _get_eps(self):\n",
        "        url = f\"{self._quote_url}/eps\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"earningsPerShare\"]\n",
        "\n",
        "        lbls = [i[\"period\"] for i in data]\n",
        "        vals = [(j[\"consensus\"], j[\"earnings\"]) for j in data]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=lbls, columns=[\"Estimated EPS\", \"Reported EPS\"])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Earnings Forecast (Yearly/Quarterly)\n",
        "    @validator\n",
        "    def _get_earnings_forecast(self, frequency=None):\n",
        "        url = f\"{self._analyst_url}/earnings-forecast\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"quarterlyForecast\"] if frequency == \"quarterly\" else response[\"data\"][\"yearlyForecast\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Price/Earnings Ratio\n",
        "    @validator\n",
        "    def _get_pe_ratio(self):\n",
        "        url = f\"{self._analyst_url}/peg-ratio\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"per\"]\n",
        "\n",
        "        lbls = [i[\"x\"] for i in data[\"peRatioChart\"]]\n",
        "        vals = [j[\"y\"] for j in data[\"peRatioChart\"]]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=lbls, columns=[data[\"label\"]])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Forecast P/E Growth Rates\n",
        "    @validator\n",
        "    def _get_pe_growth(self):\n",
        "        url = f\"{self._analyst_url}/peg-ratio\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"gr\"]\n",
        "\n",
        "        lbls = [f\"{i['z']} {i['x']}\" for i in data[\"peGrowthChart\"]]\n",
        "        vals = [j[\"y\"] for j in data[\"peGrowthChart\"]]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=lbls, columns=[data[\"title\"]])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # PEG Ratio\n",
        "    @validator\n",
        "    def _get_peg_ratio(self):\n",
        "        url = f\"{self._analyst_url}/peg-ratio\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"pegr\"]\n",
        "\n",
        "        lbls = data[\"label\"]\n",
        "        vals = data[\"pegValue\"]\n",
        "\n",
        "        df = pd.DataFrame(data=vals, index=[lbls], columns=[\"PEG Ratio\"])\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Short Interest\n",
        "    @validator\n",
        "    def _get_short_interest(self):\n",
        "        url = f\"{self._quote_url}/short-interest?assetClass=stocks\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"shortInterestTable\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Institutional Holdings\n",
        "    @validator\n",
        "    def _get_institutional_holdings(self, limit=5000, **kwargs):\n",
        "        parameters = {\n",
        "            \"type_\": [\"total\", \"new\", \"increased\", \"decreased\", \"activity\", \"soldout\"],\n",
        "            \"sortColumn\": [\"ownerName\", \"date\", \"sharesHeld\", \"sharesChange\", \"sharesChangePCT\", \"marketValue\"],\n",
        "            \"sortOrder\": [\"asc\", \"desc\"]\n",
        "        }\n",
        "\n",
        "        url = f\"{self._company_url}/institutional-holdings?limit={limit}&tableonly=true\"\n",
        "\n",
        "        for i, v in kwargs.items():\n",
        "            i = snakecase_to_camelcase(i.strip('_'))\n",
        "            url += f\"&{i}={v}\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"holdingsTransactions\"][\"table\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Insider Activity\n",
        "    @validator\n",
        "    def _get_insider_activity(self, limit=5000, **kwargs):\n",
        "        parameters = {\n",
        "            \"type_\": [\"all\", \"buys\", \"sells\"],\n",
        "            \"sortColumn\": [\"insider\", \"relation\", \"lastDate\", \"transactionType\", \"ownType\", \"sharesTraded\", \"lastPrice\", \"sharesHeld\"],\n",
        "            \"sortOrder\": [\"asc\", \"desc\"]\n",
        "        }\n",
        "\n",
        "        url = f\"{self._company_url}/insider-trades?limit={limit}\"\n",
        "\n",
        "        for i, v in kwargs.items():\n",
        "            i = snakecase_to_camelcase(i.strip('_'))\n",
        "            url += f\"&{i}={v}\"\n",
        "\n",
        "        response = get_response(url)\n",
        "        data = response[\"data\"][\"transactionTable\"][\"table\"]\n",
        "\n",
        "        df = get_dataframe(data)\n",
        "        return df\n",
        "\n",
        "\n",
        "    # Revenue EPS\n",
        "    @validator\n",
        "    def _get_revenue_eps(self):\n",
        "        limit = 1\n",
        "        df = pd.DataFrame()\n",
        "\n",
        "        while True:\n",
        "            url = f\"{self._company_url}/revenue?limit={limit}\"\n",
        "\n",
        "            response = get_response(url)\n",
        "            data = response[\"data\"][\"revenueTable\"]\n",
        "\n",
        "            if not data:\n",
        "                break\n",
        "\n",
        "            tmp_df = get_dataframe(data)\n",
        "            df = pd.concat([df, tmp_df], axis=1)\n",
        "            limit += 1\n",
        "\n",
        "        return df\n",
        "\n",
        "    # Fair Value\n",
        "    @validator\n",
        "    def _get_fair_value(self, discount_rate):\n",
        "        eps = self._get_earnings_forecast()[\"Consensus EPS* Forecast\"]\n",
        "        eps.index = pd.to_datetime(eps.index).to_period('M').to_timestamp('M')\n",
        "\n",
        "        years_to_forecast = [dt.date.today().year + i for i in range(3)]\n",
        "\n",
        "        if len(eps) > 3:\n",
        "            eps = eps[eps.index.year.isin(years_to_forecast)]\n",
        "        elif len(eps) < 3:\n",
        "            eps_gr = eps.pct_change().mean()\n",
        "            eps_last = eps.iloc[-1] * (1 + eps_gr)\n",
        "            index_last = eps.index[-1] + dt.timedelta(days=365)\n",
        "            eps = eps.append(pd.Series(eps_last, index=[index_last]))\n",
        "\n",
        "        discount_periods = [round((eps.index[i].date() - dt.date.today()).days / 365, 2) for i in range(len(eps))]\n",
        "        discount_periods.append(discount_periods[-1])\n",
        "\n",
        "        pe_ratio = self._get_pe_ratio()\n",
        "        terminal_pe = pe_ratio.iloc[-1, 0]\n",
        "\n",
        "        terminal_value = terminal_pe * eps.values[-1]\n",
        "\n",
        "        eps_arr = np.append(eps.values, terminal_value)\n",
        "        rates_arr = pow(np.array([(1.0 + discount_rate)] * 4), discount_periods)\n",
        "\n",
        "        pv_arr = eps_arr / rates_arr\n",
        "\n",
        "        fair_value = round(sum(pv_arr), 2)\n",
        "\n",
        "        last_sale = string_to_float(self._get_info()[\"Last Sale\"][0])\n",
        "\n",
        "        profile = self._get_profile()\n",
        "        symbol = profile.loc[\"Symbol\"][0]\n",
        "        company_name = profile.loc[\"Company Name\"][0]\n",
        "        sector = profile.loc[\"Sector\"][0]\n",
        "        industry = profile.loc[\"Industry\"][0]\n",
        "\n",
        "        discount_rate = f\"{discount_rate * 100}%\"\n",
        "\n",
        "        data = {\n",
        "            \"Company Name\": company_name,\n",
        "            \"Sector\": sector,\n",
        "            \"Industry\": industry,\n",
        "            \"Discount Rate\": discount_rate,\n",
        "            \"Current Price\": last_sale,\n",
        "            \"Intrinsic Value\": fair_value\n",
        "        }\n",
        "\n",
        "        df = pd.DataFrame(data.values(), index=data.keys(), columns=[self._ticker])\n",
        "        return df\n"
      ],
      "metadata": {
        "id": "JLaJJmD0MuhY"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ticker"
      ],
      "metadata": {
        "id": "5z0Xf-aaidLw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Ticker(SingleBase):\n",
        "    def __repr__(self):\n",
        "        return \"nasdaq.Ticker object <%s>\" % self._ticker\n",
        "\n",
        "    @property\n",
        "    def info(self):\n",
        "        return self._get_info()\n",
        "\n",
        "    @property\n",
        "    def profile(self):\n",
        "        return self._get_profile()\n",
        "\n",
        "    @property\n",
        "    def summary(self):\n",
        "        return self._get_summary()\n",
        "\n",
        "    @property\n",
        "    def dividends(self):\n",
        "        return self._get_dividends()\n",
        "\n",
        "    @property\n",
        "    def quotes_history(self):\n",
        "        return self._get_quotes_history()\n",
        "\n",
        "    @property\n",
        "    def chart(self):\n",
        "        return self._get_chart(self._get_quotes_history())\n",
        "\n",
        "    @property\n",
        "    def income_statement_annual(self):\n",
        "        return self._get_financials()\n",
        "\n",
        "    @property\n",
        "    def balance_sheet_annual(self):\n",
        "        return self._get_financials(table=\"balance_sheet\")\n",
        "\n",
        "    @property\n",
        "    def cash_flow_annual(self):\n",
        "        return self._get_financials(table=\"cash_flow\")\n",
        "\n",
        "    @property\n",
        "    def financial_ratios_annual(self):\n",
        "        return self._get_financials(table=\"financial_ratios\")\n",
        "\n",
        "    @property\n",
        "    def income_statement_quarterly(self):\n",
        "        return self._get_financials(table=\"quarterly\")\n",
        "\n",
        "    @property\n",
        "    def balance_sheet_quarterly(self):\n",
        "        return self._get_financials(table=\"balance_sheet\", frequency=\"quarterly\")\n",
        "\n",
        "    @property\n",
        "    def cash_flow_quarterly(self):\n",
        "        return self._get_financials(table=\"cash_flow\", frequency=\"quarterly\")\n",
        "\n",
        "    @property\n",
        "    def financial_ratios_quarterly(self):\n",
        "        return self._get_financials(table=\"financial_ratios\", frequency=\"quarterly\")\n",
        "\n",
        "    @property\n",
        "    def eps(self):\n",
        "        return self._get_eps()\n",
        "\n",
        "    @property\n",
        "    def earnings_forecast_yearly(self):\n",
        "        return self._get_earnings_forecast()\n",
        "\n",
        "    @property\n",
        "    def earnings_forecast_quarterly(self):\n",
        "        return self._get_earnings_forecast(frequency=\"quarterly\")\n",
        "\n",
        "    @property\n",
        "    def pe_ratio(self):\n",
        "        return self._get_pe_ratio()\n",
        "\n",
        "    @property\n",
        "    def pe_growth(self):\n",
        "        return self._get_pe_growth()\n",
        "\n",
        "    @property\n",
        "    def peg_ratio(self):\n",
        "        return self._get_peg_ratio()\n",
        "\n",
        "    @property\n",
        "    def short_interest(self):\n",
        "        return self._get_short_interest()\n",
        "\n",
        "    def institutional_holdings(self, **kwargs):\n",
        "        \"\"\"\n",
        "        Inputs:\n",
        "            limit: 1+ (default=5000)\n",
        "            type_: total, new, increased, decreased, activity, soldout (default=total)\n",
        "            sort_column: ownerName, date, sharesHeld, sharesChange, sharesChangePCT, marketValue (default=marketValue)\n",
        "            sort_order: asc, desc (default=desc)\n",
        "        \n",
        "        Example: Ticker.institutional_holdings(limit=5000, type_=\"total\", sort_column=\"marketValue\", sort_order=\"desc\")\n",
        "        \"\"\"\n",
        "        return self._get_institutional_holdings(**kwargs)\n",
        "\n",
        "    def insider_activity(self, **kwargs):\n",
        "        \"\"\"\n",
        "        Inputs:\n",
        "            limit: 1+ (default=5000)\n",
        "            type_: all, buys, sells (default=all)\n",
        "            sort_column: insider, relation, lastDate, transactionType, ownType, sharesTraded, lastPrice, sharesHeld (default=lastDate)\n",
        "            sort_order: asc, desc (default=desc)\n",
        "        \n",
        "        Example: Ticker.insider_activity(limit=5000, type_=\"all\", sort_column=\"lastDate\", sort_order=\"desc\")\n",
        "        \"\"\"\n",
        "        return self._get_insider_activity(**kwargs)\n",
        "\n",
        "    @property\n",
        "    def revenue_eps(self):\n",
        "        return self._get_revenue_eps()\n",
        "\n",
        "    def fair_value(self, discount_rate=0.1):\n",
        "        return self._get_fair_value(discount_rate)\n"
      ],
      "metadata": {
        "id": "f9gCPVp7iYIL"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company = Ticker(\"aapl\")"
      ],
      "metadata": {
        "id": "0Lf_M_QirATN"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company.fair_value()"
      ],
      "metadata": {
        "id": "afIOxJokyy4t",
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          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "d6aac3fd-d2fa-4215-c984-48909bb309fd"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   AAPL\n",
              "Company Name                 Apple Inc.\n",
              "Sector                       Technology\n",
              "Industry         Computer Manufacturing\n",
              "Discount Rate                     10.0%\n",
              "Current Price                    141.86\n",
              "Intrinsic Value                  127.04"
            ],
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              "      <th>AAPL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Company Name</th>\n",
              "      <td>Apple Inc.</td>\n",
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              "    <tr>\n",
              "      <th>Sector</th>\n",
              "      <td>Technology</td>\n",
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              "      <th>Industry</th>\n",
              "      <td>Computer Manufacturing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate</th>\n",
              "      <td>10.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current Price</th>\n",
              "      <td>141.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Intrinsic Value</th>\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# S&P 500 Companies"
      ],
      "metadata": {
        "id": "rqa43u1990AJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sp500_companies():\n",
        "    url = \"https://en.wikipedia.org/wiki/List_of_S&P_500_companies\"\n",
        "    data = pd.read_html(url)[0]\n",
        "    return data"
      ],
      "metadata": {
        "id": "guSrRAdh91Cx"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MultiBase"
      ],
      "metadata": {
        "id": "HgKRnUgK-ENz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MultiBase:\n",
        "    def __init__(self, tickers):\n",
        "        tickers = tickers if isinstance(tickers, (list, set, tuple)) else tickers\n",
        "\n",
        "        self._tickers = [ticker.upper() for ticker in tickers]\n",
        "        self._workers = os.cpu_count() + 4\n",
        "\n",
        "\n",
        "    @validator\n",
        "    def _get_quotes_history(self, ticker):\n",
        "        data = Ticker(ticker)._get_quotes_history()\n",
        "        data = data.loc[:, \"Close/Last\"]\n",
        "        data.rename(ticker, inplace=True)\n",
        "        return data\n",
        "\n",
        "\n",
        "    def _get_fair_value(self, discount_rate, ticker):\n",
        "        sleep(0.2)\n",
        "        data = Ticker(ticker)._get_fair_value(discount_rate)\n",
        "        return data\n",
        "\n",
        "\n",
        "    def _multiprocessing(self, function, discount_rate):\n",
        "        with ProcessPoolExecutor(self._workers) as executor:\n",
        "            df = pd.concat(list(executor.map(partial(function, discount_rate), self._tickers)), axis=1)\n",
        "        return df\n"
      ],
      "metadata": {
        "id": "lePbcWzk-E74"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Tickers"
      ],
      "metadata": {
        "id": "q4mKii5o-bh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Tickers(MultiBase):\n",
        "    def __repr__(self):\n",
        "        return \"nasdaq.Tickers object <%s>\" % \", \".join(self._tickers)\n",
        "\n",
        "    @property\n",
        "    def quotes_history(self):\n",
        "        return self._multiprocessing(self._get_quotes_history)\n",
        "\n",
        "    def fair_value(self, discount_rate=0.1):\n",
        "        return self._multiprocessing(self._get_fair_value, discount_rate)\n"
      ],
      "metadata": {
        "id": "d-sL7JJ4-b52"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "symbols = sp500_companies()[\"Symbol\"].tolist()"
      ],
      "metadata": {
        "id": "FSL1GR6K-m8c"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "companies = Tickers(symbols)"
      ],
      "metadata": {
        "id": "rp4s41T1-xbI"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = companies.fair_value(discount_rate=0.12)\n",
        "df"
      ],
      "metadata": {
        "id": "SAmCBLwG-3b-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "outputId": "bce1e45b-d6ae-4454-a659-f46511891989"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             MMM  \\\n",
              "Company Name                          3M Company   \n",
              "Sector                               Industrials   \n",
              "Industry         Consumer Electronics/Appliances   \n",
              "Discount Rate                              12.0%   \n",
              "Current Price                             112.93   \n",
              "Intrinsic Value                           104.58   \n",
              "\n",
              "                                             AOS                         ABT  \\\n",
              "Company Name              A.O. Smith Corporation         Abbott Laboratories   \n",
              "Sector                               Industrials                 Health Care   \n",
              "Industry         Industrial Machinery/Components  Medical/Dental Instruments   \n",
              "Discount Rate                              12.0%                       12.0%   \n",
              "Current Price                              60.52                       111.0   \n",
              "Intrinsic Value                            56.74                       91.21   \n",
              "\n",
              "                                  ABBV                     ACN  \\\n",
              "Company Name               AbbVie Inc.           Accenture plc   \n",
              "Sector                     Health Care  Consumer Discretionary   \n",
              "Industry         Other Pharmaceuticals       Business Services   \n",
              "Discount Rate                    12.0%                   12.0%   \n",
              "Current Price                   147.79                  273.16   \n",
              "Intrinsic Value                 148.44                  234.84   \n",
              "\n",
              "                                             ATVI  \\\n",
              "Company Name             Activision Blizzard, Inc   \n",
              "Sector                     Consumer Discretionary   \n",
              "Industry         Recreational Games/Products/Toys   \n",
              "Discount Rate                               12.0%   \n",
              "Current Price                               74.64   \n",
              "Intrinsic Value                             56.63   \n",
              "\n",
              "                                            ADM  \\\n",
              "Company Name     Archer-Daniels-Midland Company   \n",
              "Sector                                     None   \n",
              "Industry                                   None   \n",
              "Discount Rate                             12.0%   \n",
              "Current Price                             85.57   \n",
              "Intrinsic Value                           86.95   \n",
              "\n",
              "                                                    ADBE  \\\n",
              "Company Name                                  Adobe Inc.   \n",
              "Sector                                        Technology   \n",
              "Industry         Computer Software: Prepackaged Software   \n",
              "Discount Rate                                      12.0%   \n",
              "Current Price                                     358.17   \n",
              "Intrinsic Value                                   292.67   \n",
              "\n",
              "                                             ADP                        AAP  \\\n",
              "Company Name     Automatic Data Processing, Inc.    Advance Auto Parts Inc.   \n",
              "Sector                    Consumer Discretionary     Consumer Discretionary   \n",
              "Industry                       Business Services  Auto & Home Supply Stores   \n",
              "Discount Rate                              12.0%                      12.0%   \n",
              "Current Price                             228.01                     149.11   \n",
              "Intrinsic Value                           196.21                     155.72   \n",
              "\n",
              "                 ...                                          WTW  \\\n",
              "Company Name     ...  Willis Towers Watson Public Limited Company   \n",
              "Sector           ...                                      Finance   \n",
              "Industry         ...                           Specialty Insurers   \n",
              "Discount Rate    ...                                        12.0%   \n",
              "Current Price    ...                                       253.57   \n",
              "Intrinsic Value  ...                                       240.25   \n",
              "\n",
              "                                          GWW                    WYNN  \\\n",
              "Company Name              W.W. Grainger, Inc.   Wynn Resorts, Limited   \n",
              "Sector                 Consumer Discretionary  Consumer Discretionary   \n",
              "Industry         Telecommunications Equipment          Hotels/Resorts   \n",
              "Discount Rate                           12.0%                   12.0%   \n",
              "Current Price                          559.85                   98.59   \n",
              "Intrinsic Value                        516.42                   76.17   \n",
              "\n",
              "                              XEL             XYL                     YUM  \\\n",
              "Company Name     Xcel Energy Inc.      Xylem Inc.       Yum! Brands, Inc.   \n",
              "Sector                  Utilities     Industrials  Consumer Discretionary   \n",
              "Industry         Power Generation  Fluid Controls             Restaurants   \n",
              "Discount Rate               12.0%           12.0%                   12.0%   \n",
              "Current Price               68.56          102.54                   129.2   \n",
              "Intrinsic Value             60.56           92.48                  117.65   \n",
              "\n",
              "                                           ZBRA                           ZBH  \\\n",
              "Company Name     Zebra Technologies Corporation  Zimmer Biomet Holdings, Inc.   \n",
              "Sector                               Technology                   Health Care   \n",
              "Industry          Computer peripheral equipment        Industrial Specialties   \n",
              "Discount Rate                             12.0%                         12.0%   \n",
              "Current Price                            310.57                         124.9   \n",
              "Intrinsic Value                          293.02                        114.69   \n",
              "\n",
              "                                      ZION  \\\n",
              "Company Name     Zions Bancorporation N.A.   \n",
              "Sector                             Finance   \n",
              "Industry                       Major Banks   \n",
              "Discount Rate                        12.0%   \n",
              "Current Price                        52.02   \n",
              "Intrinsic Value                      53.96   \n",
              "\n",
              "                                                        ZTS  \n",
              "Company Name                                    Zoetis Inc.  \n",
              "Sector                                          Health Care  \n",
              "Industry         Biotechnology: Pharmaceutical Preparations  \n",
              "Discount Rate                                         12.0%  \n",
              "Current Price                                        165.51  \n",
              "Intrinsic Value                                       137.4  \n",
              "\n",
              "[6 rows x 495 columns]"
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              "        vertical-align: top;\n",
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              "\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>MMM</th>\n",
              "      <th>AOS</th>\n",
              "      <th>ABT</th>\n",
              "      <th>ABBV</th>\n",
              "      <th>ACN</th>\n",
              "      <th>ATVI</th>\n",
              "      <th>ADM</th>\n",
              "      <th>ADBE</th>\n",
              "      <th>ADP</th>\n",
              "      <th>AAP</th>\n",
              "      <th>...</th>\n",
              "      <th>WTW</th>\n",
              "      <th>GWW</th>\n",
              "      <th>WYNN</th>\n",
              "      <th>XEL</th>\n",
              "      <th>XYL</th>\n",
              "      <th>YUM</th>\n",
              "      <th>ZBRA</th>\n",
              "      <th>ZBH</th>\n",
              "      <th>ZION</th>\n",
              "      <th>ZTS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Company Name</th>\n",
              "      <td>3M Company</td>\n",
              "      <td>A.O. Smith Corporation</td>\n",
              "      <td>Abbott Laboratories</td>\n",
              "      <td>AbbVie Inc.</td>\n",
              "      <td>Accenture plc</td>\n",
              "      <td>Activision Blizzard, Inc</td>\n",
              "      <td>Archer-Daniels-Midland Company</td>\n",
              "      <td>Adobe Inc.</td>\n",
              "      <td>Automatic Data Processing, Inc.</td>\n",
              "      <td>Advance Auto Parts Inc.</td>\n",
              "      <td>...</td>\n",
              "      <td>Willis Towers Watson Public Limited Company</td>\n",
              "      <td>W.W. Grainger, Inc.</td>\n",
              "      <td>Wynn Resorts, Limited</td>\n",
              "      <td>Xcel Energy Inc.</td>\n",
              "      <td>Xylem Inc.</td>\n",
              "      <td>Yum! Brands, Inc.</td>\n",
              "      <td>Zebra Technologies Corporation</td>\n",
              "      <td>Zimmer Biomet Holdings, Inc.</td>\n",
              "      <td>Zions Bancorporation N.A.</td>\n",
              "      <td>Zoetis Inc.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sector</th>\n",
              "      <td>Industrials</td>\n",
              "      <td>Industrials</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>None</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>...</td>\n",
              "      <td>Finance</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Utilities</td>\n",
              "      <td>Industrials</td>\n",
              "      <td>Consumer Discretionary</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Health Care</td>\n",
              "      <td>Finance</td>\n",
              "      <td>Health Care</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Industry</th>\n",
              "      <td>Consumer Electronics/Appliances</td>\n",
              "      <td>Industrial Machinery/Components</td>\n",
              "      <td>Medical/Dental Instruments</td>\n",
              "      <td>Other Pharmaceuticals</td>\n",
              "      <td>Business Services</td>\n",
              "      <td>Recreational Games/Products/Toys</td>\n",
              "      <td>None</td>\n",
              "      <td>Computer Software: Prepackaged Software</td>\n",
              "      <td>Business Services</td>\n",
              "      <td>Auto &amp; Home Supply Stores</td>\n",
              "      <td>...</td>\n",
              "      <td>Specialty Insurers</td>\n",
              "      <td>Telecommunications Equipment</td>\n",
              "      <td>Hotels/Resorts</td>\n",
              "      <td>Power Generation</td>\n",
              "      <td>Fluid Controls</td>\n",
              "      <td>Restaurants</td>\n",
              "      <td>Computer peripheral equipment</td>\n",
              "      <td>Industrial Specialties</td>\n",
              "      <td>Major Banks</td>\n",
              "      <td>Biotechnology: Pharmaceutical Preparations</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate</th>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>...</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "      <td>12.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Current Price</th>\n",
              "      <td>112.93</td>\n",
              "      <td>60.52</td>\n",
              "      <td>111.0</td>\n",
              "      <td>147.79</td>\n",
              "      <td>273.16</td>\n",
              "      <td>74.64</td>\n",
              "      <td>85.57</td>\n",
              "      <td>358.17</td>\n",
              "      <td>228.01</td>\n",
              "      <td>149.11</td>\n",
              "      <td>...</td>\n",
              "      <td>253.57</td>\n",
              "      <td>559.85</td>\n",
              "      <td>98.59</td>\n",
              "      <td>68.56</td>\n",
              "      <td>102.54</td>\n",
              "      <td>129.2</td>\n",
              "      <td>310.57</td>\n",
              "      <td>124.9</td>\n",
              "      <td>52.02</td>\n",
              "      <td>165.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Intrinsic Value</th>\n",
              "      <td>104.58</td>\n",
              "      <td>56.74</td>\n",
              "      <td>91.21</td>\n",
              "      <td>148.44</td>\n",
              "      <td>234.84</td>\n",
              "      <td>56.63</td>\n",
              "      <td>86.95</td>\n",
              "      <td>292.67</td>\n",
              "      <td>196.21</td>\n",
              "      <td>155.72</td>\n",
              "      <td>...</td>\n",
              "      <td>240.25</td>\n",
              "      <td>516.42</td>\n",
              "      <td>76.17</td>\n",
              "      <td>60.56</td>\n",
              "      <td>92.48</td>\n",
              "      <td>117.65</td>\n",
              "      <td>293.02</td>\n",
              "      <td>114.69</td>\n",
              "      <td>53.96</td>\n",
              "      <td>137.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6 rows × 495 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ea413ee-8621-4aea-b3ec-e55bbe1aca9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv(f\"stock-fair-values-drate12-{dt.date.today()}.csv\", encoding=\"utf-8-sig\")"
      ],
      "metadata": {
        "id": "J-55LfkZBcko"
      },
      "execution_count": 52,
      "outputs": []
    }
  ]
}