Ticker-v2.3.2.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Ticker-v2.3.2.ipynb","provenance":[{"file_id":"1oBc2n1M0eJ5eM0lwfAiRNkxuwKNyLSbT","timestamp":1655902497307},{"file_id":"1c92_qdvaeAOtCVV2YMkyqTeYe3mXCEpc","timestamp":1655733752984},{"file_id":"1l6QETrR3RzTQ9jk4V5cBvr-TbkwplyFg","timestamp":1655728519818},{"file_id":"10lD5Hdg_D_H4WQYnyISiCZLXNWW8m2Ch","timestamp":1655450322615},{"file_id":"1M7OZYQ9NSlhEwveTKPN2CiRaAQNXbNLZ","timestamp":1654168156548},{"file_id":"11JMf1HFIein_rbV8UG459nOawZNpYR-e","timestamp":1654164054039},{"file_id":"197qYXFW_JxoHtmlOmzV4e6nUo3yjXdQK","timestamp":1654155434202},{"file_id":"13hZwW7pHG-35Bsugyi6n3KDOC9xebZe1","timestamp":1654070601648},{"file_id":"17DLM2rFAxH_r_jOfnOQp55K0WKcSAlGc","timestamp":1653989430958},{"file_id":"1emJFn2O5zhQ9mtC32r2Nmm2hgd6ZPNhr","timestamp":1653897308316}],"collapsed_sections":[],"authorship_tag":"ABX9TyMLMhMzG+RGo0h2QCxFf8vK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"YAebsYawgpJP"},"outputs":[],"source":["import re\n","import requests\n","import numpy as np\n","import pandas as pd\n","import plotly.express as px\n","from bs4 import BeautifulSoup\n","from functools import wraps"]},{"cell_type":"code","source":["# Utils\n","\n","def get_response(url):\n","    user_agent_header = {\n","        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) \\\n","                       AppleWebKit/605.1.15 (KHTML, like Gecko) \\\n","                       Chrome/100.0.4896.127 Safari/605.1.15 Firefox/100.0\" \n","        }\n","\n","    session = requests.Session()\n","    response = session.get(url, headers=user_agent_header)\n","\n","    return response\n","\n","\n","def parse_quotes(data):\n","    return float(re.search(\"[0-9\\.\\-]+\", data.get_text()).group())"],"metadata":{"id":"1DQ8-vAn-MPT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class SingleBase:\n","    def __init__(self, ticker, region=\"usa\"):\n","        self._ticker = ticker.upper()\n","        self._region = region\n","        self._rowspan = 11\n","\n","\n","    def _get_short_name(self):\n","        url = f\"http://performance.morningstar.com/stock/performance-return.action?t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        response = get_response(url).text\n","        soup = BeautifulSoup(response)\n","\n","        short_name = soup.find(\"h1\").get_text()\n","\n","        return short_name\n","\n","\n","    def _get_quote_data(self):\n","        url = f\"http://performance.morningstar.com/perform/Performance/stock/quote-data-strip.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        response = get_response(url).text\n","        soup = BeautifulSoup(response)\n","\n","        data = soup.find_all(\"span\", attrs={\"class\": \"data_big\"})\n","        last_price = parse_quotes(data[0])\n","        daily_change = parse_quotes(data[1])\n","        daily_pct_change = parse_quotes(data[2])\n","\n","        quote_data = {\n","                    \"Last Price\": last_price,\n","                    \"Daily Change\": daily_change,\n","                    \"Daily Change (%)\": daily_pct_change\n","                    }\n","\n","        return quote_data\n","\n","\n","    def _get_performance(self, data):\n","        if data == \"history\":\n","            url = f\"http://performance.morningstar.com/perform/Performance/stock/performance-history.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","        elif data == \"returns\":\n","            url = f\"http://performance.morningstar.com/perform/Performance/stock/trailing-total-returns.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        df = pd.read_html(url, header=0, index_col=0)[0]\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","\n","        return df\n","\n","\n","    def _get_price_history(self, period, frequency):\n","        url = f\"http://performance.morningstar.com/perform/Performance/stock/price-history.action?&t={self._ticker}&region={self._region}&culture=en-US&pd={period}&freq={frequency}\"\n","\n","        df = pd.read_html(url, header=0, index_col=0, keep_default_na=False, parse_dates=True)[0]\n","        df[\"Volume\"] = df[\"Volume\"].apply(lambda x: pd.to_numeric(x.replace(\"Mil\", \"\"), errors=\"coerce\") * 10**6)\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","        df.rename_axis(index=None, inplace=True)\n","\n","        df.dropna(axis=0, how=\"all\", inplace=True)\n","\n","        return df\n","\n","\n","    def _visualize_price_history(self, data):\n","        chart = px.area(x=data.index, y=data[\"Close\"])\n","\n","        axes_layout = {\n","                       \"gridcolor\": \"rgb(240, 240, 240)\",\n","                       \"showspikes\": True,\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\": 5, \"label\": \"5D\", \"step\": \"day\", \"stepmode\": \"backward\"},\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","                    {\"count\": 10, \"label\": \"10Y\", \"step\": \"year\", \"stepmode\": \"backward\"},\n","                    {\"label\": \"MAX\", \"step\": \"all\"}]})\n","\n","        chart.update_yaxes(axes_layout, title_text=\"Price\", tickprefix=\"$\")\n","        \n","        chart.update_layout(\n","            plot_bgcolor=\"rgb(255, 255, 255)\",\n","            hovermode=\"x\",\n","            spikedistance=-1,\n","            hoverdistance=-1,\n","            showlegend=False,\n","            title={\n","                \"text\": f\"{self._ticker} Stock Price History\",\n","                \"y\": 0.97,\n","                \"x\": 0.5,\n","                \"xanchor\": \"center\",\n","                \"yanchor\": \"top\"})\n","\n","        chart.update_traces(hovertemplate=\"Date: %{x}<br>Price: %{y}\")\n","\n","        chart.show();\n","\n","\n","    def _get_annual_dividends(self):\n","        url = f\"http://performance.morningstar.com/perform/Performance/stock/annual-dividends.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        df = pd.read_html(url, header=0, index_col=0)[0]\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","        df = df.loc[[\"Dividend Amount\", \"Year-end Yield %\"]]\n","\n","        return df\n","\n","\n","    def _get_dividend_history(self):\n","        url = f\"http://performance.morningstar.com/perform/Performance/stock/dividend-history.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        response = get_response(url).text\n","        soup = BeautifulSoup(response)\n","\n","        th_str = soup.find_all(\"th\", attrs={\"class\": \"str\"})\n","        cols = [col.get_text() for col in th_str]\n","\n","        td = soup.find_all(\"td\")\n","        rows = [row.get_text() for row in td]\n","        rows = [rows[i: i+len(cols)] for i in range(0, len(rows), len(cols))]\n","\n","        df = pd.DataFrame(data=rows, columns=cols)\n","        cols_to_dt = [\"Ex-Dividend Date\", \"Declaration Date\", \"Record Date\", \"Payable Date\"]\n","        df[cols_to_dt] = df[cols_to_dt].apply(pd.to_datetime)\n","        df[\"Amount\"] = df[\"Amount\"].apply(lambda x: float(x.replace(\"$\", \"\")))\n","\n","        return df\n","\n","\n","    def _get_key_finanacial_ratios(self):\n","        url = f\"http://financials.morningstar.com/finan/financials/getFinancePart.html?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        response = get_response(url).json()\n","        soup = BeautifulSoup(response[\"componentData\"])\n","\n","        th_col = soup.find_all(\"th\", attrs={\"scope\": \"col\"})\n","        cols = [col.get_text() for col in th_col]\n","\n","        th_row = soup.find_all(\"th\", attrs={\"scope\": \"row\"})\n","        lbls = [lbl.get_text().replace(\" *\", \"\").replace(\"\\xa0\", \" \") for lbl in th_row][1:]\n","\n","        td = soup.find_all(\"td\")\n","        rows = [row.get_text().replace(\",\", \"\") for row in td if row.get_text()][:-1]\n","        rows = [rows[i: i+len(cols)] for i in range(0, len(rows), len(cols))]\n","\n","        df = pd.DataFrame(data=rows, columns=cols, index=lbls)\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","\n","        return df\n","\n","\n","    def _total_key_ratios_data(function):\n","\n","        @wraps(function)\n","        def wrapper(self, *args, **kwargs):\n","            url = f\"http://financials.morningstar.com/finan/financials/getKeyStatPart.html?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","            response = get_response(url).json()\n","            soup = BeautifulSoup(response[\"componentData\"])\n","\n","            th_col = soup.find_all(\"th\", attrs={\"scope\": \"col\"})\n","            cols = [col.get_text() for col in th_col]\n","\n","            th_row = soup.find_all(\"th\", attrs={\"scope\": \"row\"})\n","            lbls = [lbl.get_text().replace(\" *\", \"\").replace(\"\\xa0\", \" \") for lbl in th_row]\n","\n","            td = soup.find_all(\"td\")\n","            rows = [row.get_text().replace(\",\", \"\") for row in td if row.get_text()]\n","            rows = [rows[i: i+self._rowspan] for i in range(0, len(rows), self._rowspan)]\n","\n","            row_start, row_end, col_start, col_end, lbl_start, lbl_end, flag = function(self, *args, **kwargs)\n","\n","            if flag:\n","                [rows.insert(i, [\"—\"] * self._rowspan) for i in range(row_start, row_end, 5)]\n","                row_end += 4\n","                lbl_end += 4\n","\n","            df = pd.DataFrame(data=rows[row_start:row_end], columns=cols[col_start:col_end], index=lbls[lbl_start:lbl_end])\n","            df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","\n","            return df\n","\n","        return wrapper\n","\n","\n","    @_total_key_ratios_data\n","    def _get_margins(self):\n","        data = [0, 9, 1, 12, 0, 9, False]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_profitability(self):\n","        data = [9, 17, 13, 24, 9, 17, False]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_growth(self):\n","        data = [17, 33, 24, 35, 17, 33, True]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_cash_flow_ratios(self):\n","        data = [33, 38, 36, 47, 37, 42, False]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_balance_sheet_items(self):\n","        data = [38, 58, 48, 59, 42, 62, False]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_liquidity(self):\n","        data = [58, 62, 60, 71, 62, 66, False]\n","        return data\n","\n","\n","    @_total_key_ratios_data\n","    def _get_efficiency(self):\n","        data = [62, 70, 72, 83, 66, 74, False]\n","        return data\n","\n","\n","    def _get_financials(self, statement):\n","        url = f\"http://financials.morningstar.com/ajax/ReportProcess4HtmlAjax.html?&t={self._ticker}&region={self._region}&culture=en-US&reportType={statement}\"\n","\n","        response = get_response(url).json()\n","        soup = BeautifulSoup(response[\"result\"])\n","\n","        cols = soup.find_all(\"div\", attrs={\"class\": \"year\"})\n","        cols = [col.get_text() for col in cols]\n","\n","        lbls = soup.find_all(\"div\", attrs={\"class\": \"lbl\"})[1:]\n","        alts = soup.find_all(\"div\", title=True, attrs={\"class\": \"lbl\"})\n","\n","        lbls = [lbl.get_text() for lbl in lbls]\n","        lbls = list(filter(lambda x : x != \"\\xa0\", lbls))\n","\n","        for i, lbl in enumerate(lbls):\n","            for j, alt in enumerate(alts):\n","                if lbl == alt.get_text():\n","                    lbls[i] = alts[j][\"title\"]\n","\n","        rows = soup.find_all(\"div\", attrs={\"class\": \"pos\"})\n","        rows = [row.get_text().replace(\",\", \"\").replace(\"(\", \"-\").replace(\")\", \"\") for row in rows]\n","        rows = [rows[i: i+len(cols)] for i in range(0, len(rows), len(cols))]\n","\n","        df = pd.DataFrame(data=rows, columns=cols, index=lbls)\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","\n","        return df\n","\n","\n","    def _get_valuation(self, table):\n","        if table == \"current-valuation\":\n","            url = f\"http://financials.morningstar.com/valuate/current-valuation-list.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","        elif table == \"forward-valuation\":\n","            url = f\"http://financials.morningstar.com/valuate/forward-valuation-list.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","        elif table == \"forward-comparisons\":\n","            url = f\"http://financials.morningstar.com/valuate/forward-comparisons-list.action?&t={self._ticker}&region={self._region}&culture=en-US\"\n","\n","        df = pd.read_html(url, header=0, index_col=0)[0]\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","\n","        df.dropna(axis=0, how=\"all\", inplace=True)\n","        df.dropna(axis=1, how=\"all\", inplace=True)\n","\n","        return df\n","\n","\n","    def _get_valuation_history(self):\n","        url = f\"http://financials.morningstar.com/valuate/valuation-history.action?&t={self._ticker}&region={self._region}&culture=en-US&type=price-earnings\"\n","\n","        df = pd.read_html(url, index_col=0)[0]\n","        df = df.apply(lambda x: pd.to_numeric(x, errors=\"coerce\"))\n","        \n","        df.columns = self._get_key_finanacial_ratios().columns\n","        df.rename_axis(index=None, inplace=True)\n","\n","        return df\n","\n","\n","    def _get_eight_pillars(self):\n","        income_statement = self._get_financials(\"is\")\n","\n","        pe_ratio = self._get_valuation_history().loc[:\"Price/Book\"].loc[self._ticker][5:10]\n","        avg_pe_ratio = pe_ratio.mean()\n","\n","        roic = self._get_profitability().loc[\"Return on Invested Capital %\"][5:10]\n","        avg_roic = roic.mean()\n","\n","        revenue = income_statement.loc[\"Revenue\"][:-1]\n","        revenue_change = revenue[-1] - revenue[0]\n","        revenue_change = int(revenue_change) if not pd.isna(revenue_change) else revenue_change\n","\n","        net_income = income_statement.loc[\"Net income\"][:-1]\n","        net_income_change = net_income[-1] - net_income[0]\n","        net_income_change = int(net_income_change) if not pd.isna(net_income_change) else net_income_change\n","\n","        shares_outstanding = income_statement.loc[\"Weighted average shares outstanding\":].loc[\"Diluted\"][:-1]\n","        shares_outstanding_change = (shares_outstanding[-1] / shares_outstanding[0] - 1) * 100\n","\n","        free_cash_flow = self._get_financials(\"cf\").loc[\"Free cash flow\"][:-1]\n","        free_cash_flow_change = free_cash_flow[-1] - free_cash_flow[0]\n","        free_cash_flow_change = int(free_cash_flow_change) if not pd.isna(free_cash_flow_change) else free_cash_flow_change\n","        avg_free_cash_flow = free_cash_flow.mean()\n","\n","        long_term_liabilities = self._get_financials(\"bs\").loc[\"Total non-current liabilities\"][-1]\n","        ltl_to_fcf = long_term_liabilities / avg_free_cash_flow\n","\n","        current_price = self._get_quote_data()[\"Last Price\"]\n","        market_cap = shares_outstanding[-1] * current_price\n","        price_to_fcf = market_cap / avg_free_cash_flow\n","\n","        pillar_1 = \"✔️\" if avg_pe_ratio < 22.5 else \"❌\"\n","        pillar_2 = \"✔️\" if avg_roic > 9 else \"❌\"\n","        pillar_3 = \"✔️\" if revenue_change > 0 else \"❌\"\n","        pillar_4 = \"✔️\" if net_income_change > 0 else \"❌\"\n","        pillar_5 = \"✔️\" if shares_outstanding_change < 0 else \"❌\"\n","        pillar_6 = \"✔️\" if ltl_to_fcf < 5 else \"❌\"\n","        pillar_7 = \"✔️\" if free_cash_flow_change > 0 else \"❌\"\n","        pillar_8 = \"✔️\" if price_to_fcf < 22.5 else \"❌\"\n","\n","        print(f\"{pillar_1} Pillar #1: 5-Year P/E Ratio < 22.5            = {avg_pe_ratio:.2f}\")\n","        print(f\"{pillar_2} Pillar #2: 5-Year ROIC > 9%                   = {avg_roic:.2f}%\")\n","        print(f\"{pillar_3} Pillar #3: 5-Year Revenue Growth (Mil)        = {revenue_change}\")\n","        print(f\"{pillar_4} Pillar #4: 5-Year Net Income Growth (Mil)     = {net_income_change}\")\n","        print(f\"{pillar_5} Pillar #5: 5-Year Shares Outstanding          = {shares_outstanding_change:.2f}%\")\n","        print(f\"{pillar_6} Pillar #6: 5 Year LTL to FCF < 5              = {ltl_to_fcf:.2f}\")\n","        print(f\"{pillar_7} Pillar #7: 5-Year Free Cash Flow Growth (Mil) = {free_cash_flow_change}\")\n","        print(f\"{pillar_8} Pillar #8: 5-Year Price to FCF < 22.5         = {price_to_fcf:.2f}\")\n","\n","\n","    def _get_intrinsic_value(self, to_csv=False):\n","        company_name = self._get_short_name()\n","        industry = self._get_performance(\"history\").index[1]\n","        current_price = self._get_quote_data()[\"Last Price\"]\n","\n","        free_cash_flow = self._get_financials(\"cf\").loc[\"Free cash flow\"][-1]\n","\n","        try:\n","            growth_rates = {}\n","            forward_free_cash_flow = {}\n","            npv_free_cash_flow = {}\n","\n","            estimated_growth_rate = self._get_valuation(\"forward-comparisons\").loc[self._ticker][0] / 100\n","\n","            for i in range(1, 11):\n","                growth_rates[i] = growth_rates[i - 1] * (1.0 - GROWTH_DECLINE_RATE) \\\n","                                if i > 1 else estimated_growth_rate\n","\n","                forward_free_cash_flow[i] = forward_free_cash_flow[i - 1] * (1.0 + growth_rates[i]) \\\n","                                            if i > 1 else free_cash_flow * (1.0 + growth_rates[i])\n","\n","                npv_free_cash_flow[i] = forward_free_cash_flow[i] / (1.0 + DISCOUNT_RATE) ** i\n","\n","            shares_outstanding = self._get_key_finanacial_ratios().loc[\"Shares Mil\"][-1]\n","            market_cap = current_price * shares_outstanding\n","\n","            balance_sheet = self._get_financials(\"bs\")\n","\n","            short_term_debt = balance_sheet.loc[\"Short-term debt\"][-1]\n","            long_term_debt = balance_sheet.loc[\"Long-term debt\"][-1]\n","            cash_and_equivalents = balance_sheet.loc[\"Cash and cash equivalents\"][-1]\n","            total_debt = np.nansum([short_term_debt, long_term_debt])\n","            net_debt = total_debt - cash_and_equivalents\n","\n","            total_npv_free_cash_flow = sum(npv_free_cash_flow.values())\n","            terminal_multiple = market_cap / free_cash_flow\n","            terminal_value = npv_free_cash_flow[10] * terminal_multiple\n","            equity_value = total_npv_free_cash_flow + terminal_value - net_debt\n","\n","            intrinsic_value = equity_value / shares_outstanding\n","\n","            declined_growth_rate = growth_rates[10]\n","\n","            print(f\"Company Ticker                           =  {self._ticker}\")\n","            print(f\"Company Name                             =  {company_name}\")\n","            print(f\"Industry                                 =  {industry}\")\n","            print(\"-\" * 60)\n","            print(f\"Free Cash Flow (Mil)                     =  {int(free_cash_flow)}\")\n","            print(f\"Net Debt (Mil)                           =  {int(net_debt)}\")\n","            print(f\"Shares Outstanding (Mil)                 =  {int(shares_outstanding)}\")\n","            print(f\"Estimated Growth Rate (+5Y)              =  {round(estimated_growth_rate * 100, 2)}%\")\n","            print(f\"Growth Decline Rate                      =  {round(GROWTH_DECLINE_RATE * 100, 2)}%\")\n","            print(f\"Declined Estimated Growth Rate (10Y)     =  {round(declined_growth_rate * 100, 2)}%\")\n","            print(f\"Discount Rate                            =  {round(DISCOUNT_RATE * 100, 2)}%\")\n","            print(f\"Terminal Multiple                        =  {terminal_multiple:.2f}\")\n","            print(\"-\" * 60)\n","            print(f\"Total NPV FCF (Mil)                      =  {total_npv_free_cash_flow:.2f}\")\n","            print(f\"Terminal Value (Mil)                     =  {terminal_value:.2f}\")\n","            print(f\"Enterprise Value (Mil)                   =  {equity_value:.2f}\")\n","            print(\"-\" * 60)\n","            print(f\"Current Price                            =  {current_price}\")\n","            print(f\"Intrinsic Value                          =  {intrinsic_value:.2f}\")\n","        except:\n","            return None\n","\n","        if to_csv:\n","            results[self._ticker] = {\n","                                    \"Company Name\": company_name,\n","                                    \"Industry\": industry,\n","                                    \"Current Price\": current_price,\n","                                    \"Intrinsic Value\": round(intrinsic_value, 2)\n","                                }\n","\n","            return pd.DataFrame.from_dict(data=results).T.to_csv(\"dcf-model-results.csv\", index=self._ticker)"],"metadata":{"id":"vMSVTuGPhB1G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Ticker(SingleBase):\n","    def __repr__(self):\n","        return \"morningstar.Ticker object <%s>\" % self._ticker\n","\n","    @property\n","    def short_name(self):\n","        return self._get_short_name()\n","\n","    @property\n","    def quote_data(self):\n","        return self._get_quote_data()\n","\n","    @property\n","    def performance_history(self):\n","        return self._get_performance(\"history\")\n","\n","    @property\n","    def trailing_total_returns(self):\n","        return self._get_performance(\"returns\")\n","\n","    def price_history(self, period=\"5d\", frequency=\"d\"):\n","        \"\"\"\n","        :Parameters:\n","            period : str\n","                Valid periods: 5d, 1m, 3m, ytd, 1y, 3y, 5y, 10y, max\n","                Alternatively, you can use any decimal number with the following letters:\n","                \"d\", \"m\", \"y\" representing days, months and years to get the required period.\n","                E.g. 3d, 4m, 7y.\n","\n","            frequency : str\n","                Valid frequencies: d, w, m, q, a\n","        \"\"\"\n","        return self._get_price_history(period, frequency)\n","    \n","    @property\n","    def chart(self):\n","        return self._visualize_price_history(self._get_price_history(period=\"max\", frequency=\"d\"))\n","\n","    @property\n","    def annual_dividends(self):\n","        return self._get_annual_dividends()\n","\n","    @property\n","    def dividend_history(self):\n","        return self._get_dividend_history()\n","\n","    @property\n","    def key_finanacial_ratios(self):\n","        return self._get_key_finanacial_ratios()\n","\n","    @property\n","    def margins(self):\n","        return self._get_margins()\n","\n","    @property\n","    def profitability(self):\n","        return self._get_profitability()\n","\n","    @property\n","    def growth(self):\n","        return self._get_growth()\n","\n","    @property\n","    def cash_flow_ratios(self):\n","        return self._get_cash_flow_ratios()\n","\n","    @property\n","    def balance_sheet_items(self):\n","        return self._get_balance_sheet_items()\n","\n","    @property\n","    def liquidity(self):\n","        return self._get_liquidity()\n","\n","    @property\n","    def efficiency(self):\n","        return self._get_efficiency()\n","\n","    @property\n","    def income_statement(self):\n","        return self._get_financials(\"is\")\n","\n","    @property\n","    def balance_sheet(self):\n","        return self._get_financials(\"bs\")\n","\n","    @property\n","    def cash_flow(self):\n","        return self._get_financials(\"cf\")\n","\n","    @property\n","    def current_valuation(self):\n","        return self._get_valuation(\"current-valuation\")\n","\n","    @property\n","    def forward_valuation(self):\n","        return self._get_valuation(\"forward-valuation\")\n","\n","    @property\n","    def forward_comparisons(self):\n","        return self._get_valuation(\"forward-comparisons\")\n","\n","    @property\n","    def valuation_history(self):\n","        return self._get_valuation_history()\n","\n","    @property\n","    def eight_pillars(self):\n","        return self._get_eight_pillars()\n","    \n","    @property\n","    def intrinsic_value(self):\n","        return self._get_intrinsic_value(to_csv=False)\n","\n","    @property\n","    def intrinsic_value_to_csv(self):\n","        return self._get_intrinsic_value(to_csv=True)"],"metadata":{"id":"lpFWp7QmhXRh"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["GROWTH_DECLINE_RATE = 0.1\n","DISCOUNT_RATE = 0.125"],"metadata":{"id":"NJg85GgWM1NW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["company = Ticker(\"aapl\")\n","company.eight_pillars"],"metadata":{"id":"XlJ6SmCfmSPt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# !pip install aiohttp\n","\n","import asyncio\n","import aiohttp\n","\n","import nest_asyncio\n","nest_asyncio.apply()"],"metadata":{"id":"pUuxM4bebM8G"},"execution_count":null,"outputs":[]},{"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","\n","\n","    def _get_price_history(self, ticker, data):\n","        df = pd.read_html(data, header=0, index_col=0, parse_dates=True)[0][\"Close\"]\n","        df.dropna(inplace=True)\n","        df.rename(ticker, inplace=True)\n","\n","        return df\n","\n","\n","    async def _get_response(self, ticker, session, period, frequency):\n","        url = f\"http://performance.morningstar.com/perform/Performance/stock/price-history.action?&t={ticker}&pd={period}&freq={frequency}\"\n","\n","        async with session.get(url) as response:\n","            data = await response.text()\n","\n","            return self._get_price_history(ticker, data)\n","\n","\n","    async def _gather_tasks(self, period, frequency):\n","        async with aiohttp.ClientSession() as session:\n","            return pd.concat(\n","                await asyncio.gather(*[\n","                                    asyncio.create_task(self._get_response(ticker, session, period, frequency))\n","                                    for ticker in self._tickers\n","                                    ]),\n","                axis=1\n","                ).sort_index(ascending=False)"],"metadata":{"id":"x1ZPU-82Sxs1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class Tickers(MultiBase):\n","    def __repr__(self):\n","        return \"morningstar.Tickers object <%s>\" % \", \".join(self._tickers)\n","\n","    def price_history(self, period=\"5d\", frequency=\"d\"):\n","        \"\"\"\n","        :Parameters:\n","            period : str\n","                Valid periods: 5d, 1m, 3m, ytd, 1y, 3y, 5y, 10y, max\n","                Alternatively, you can use any decimal number with the following letters:\n","                \"d\", \"m\", \"y\" representing days, months and years to get the required period.\n","                E.g. 3d, 4m, 7y.\n","\n","            frequency : str\n","                Valid frequencies: d, w, m, q, a\n","        \"\"\"\n","        return asyncio.run(self._gather_tasks(period, frequency))"],"metadata":{"id":"DTuyaEnDjIIa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tickers = [\n","           \"AAPL\", \"MSFT\", \"AMZN\", \"GOOGL\", \"FB\", \"INTC\", \"AMD\", \"MU\", \"IBM\", \"HPQ\",\n","           \"COST\", \"LOW\", \"TGT\", \"WMT\", \"HD\", \"BBY\", \"HON\", \"KO\", \"PEP\", \"MCD\",\n","           \"XOM\", \"CVX\", \"MA\", \"V\", \"PYPL\", \"CAT\", \"DE\", \"MMM\", \"TJX\", \"WM\",\n","           \"UNH\", \"ABBV\", \"ABT\", \"AMGN\", \"MRK\", \"PFE\", \"LLY\", \"TMO\", \"BMY\", \"PG\",\n","           \"JNJ\", \"CL\", \"NKE\"\n","           ]\n","\n","companies = Tickers(tickers)\n","companies.price_history(\"max\")"],"metadata":{"id":"C919-f9lVYRi","colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"status":"ok","timestamp":1654177356680,"user_tz":-120,"elapsed":34234,"user":{"displayName":"Evgeny Zorin","userId":"06960824921814317938"}},"outputId":"4067efff-1bde-448c-a450-2e7fc1b4860d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              AAPL    MSFT     AMZN    GOOGL      FB   INTC     AMD     MU  \\\n","Date                                                                         \n","2022-06-01  148.71  272.42  2433.68  2277.84  188.64  44.11  101.22  73.55   \n","2022-05-31  148.84  271.87  2404.19  2275.24  193.64  44.42  101.86  73.84   \n","2022-05-30  149.64  273.24  2302.93  2246.33  195.13  44.55  102.26  73.32   \n","2022-05-27  149.64  273.24  2302.93  2246.33  195.13  44.55  102.26  73.32   \n","2022-05-26  143.78  265.90  2221.55  2155.85  191.63  43.48   98.75  70.60   \n","...            ...     ...      ...      ...     ...    ...     ...    ...   \n","2006-11-15    3.00   29.13    42.60   246.20     NaN  22.32   21.71  14.83   \n","2006-11-14    3.04   29.42    41.51   245.21     NaN  21.88   21.34  14.95   \n","2006-11-13    3.01   29.35    39.99   240.75     NaN  21.02   21.20  14.21   \n","2006-11-10    2.97   29.24    39.26   237.00     NaN  20.58   21.07  14.01   \n","2006-11-09    2.98   29.26    38.84   236.54     NaN  20.42   20.89  13.57   \n","\n","               IBM    HPQ  ...    AMGN    MRK    PFE     LLY     TMO    BMY  \\\n","Date                       ...                                                \n","2022-06-01  139.43  40.34  ...  253.42  91.14  52.37  311.08  556.22  74.96   \n","2022-05-31  138.84  38.84  ...  256.74  92.03  53.04  313.44  567.57  75.45   \n","2022-05-30  139.27  38.75  ...  255.26  93.08  53.91  323.48  572.35  76.14   \n","2022-05-27  139.27  38.75  ...  255.26  93.08  53.91  323.48  572.35  76.14   \n","2022-05-26  136.89  36.77  ...  253.05  92.31  53.99  313.46  540.11  77.59   \n","...            ...    ...  ...     ...    ...    ...     ...     ...    ...   \n","2006-11-15   88.94  18.06  ...   73.61  42.10  25.13   54.37   43.94  24.50   \n","2006-11-14   89.11  18.46  ...   73.14  41.85  24.87   53.85   43.71  24.44   \n","2006-11-13   87.94  18.26  ...   72.70  41.44  24.53   54.06   43.62  24.41   \n","2006-11-10   87.65  18.16  ...   74.60  41.12  24.36   54.14   45.03  24.42   \n","2006-11-09   88.28  17.96  ...   73.53  40.89  24.48   54.87   43.37  24.58   \n","\n","                PG     JNJ     CL     NKE  \n","Date                                       \n","2022-06-01  145.64  177.71  78.20  118.68  \n","2022-05-31  147.88  179.53  78.81  118.85  \n","2022-05-30  148.72  181.09  78.86  115.99  \n","2022-05-27  148.72  181.09  78.86  115.99  \n","2022-05-26  146.48  179.46  78.16  112.94  \n","...            ...     ...    ...     ...  \n","2006-11-15   63.12   66.54  32.16   11.94  \n","2006-11-14   63.20   66.56  32.37   11.84  \n","2006-11-13   63.25   65.94  32.44   11.77  \n","2006-11-10   63.70   66.29  32.62   11.76  \n","2006-11-09   63.80   66.15  32.53   11.60  \n","\n","[4015 rows x 43 columns]"],"text/html":["\n","  <div id=\"df-4055bdd0-6f29-4c14-9af7-e9451cbf549e\">\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>AAPL</th>\n","      <th>MSFT</th>\n","      <th>AMZN</th>\n","      <th>GOOGL</th>\n","      <th>FB</th>\n","      <th>INTC</th>\n","      <th>AMD</th>\n","      <th>MU</th>\n","      <th>IBM</th>\n","      <th>HPQ</th>\n","      <th>...</th>\n","      <th>AMGN</th>\n","      <th>MRK</th>\n","      <th>PFE</th>\n","      <th>LLY</th>\n","      <th>TMO</th>\n","      <th>BMY</th>\n","      <th>PG</th>\n","      <th>JNJ</th>\n","      <th>CL</th>\n","      <th>NKE</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","      <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>2022-06-01</th>\n","      <td>148.71</td>\n","      <td>272.42</td>\n","      <td>2433.68</td>\n","      <td>2277.84</td>\n","      <td>188.64</td>\n","      <td>44.11</td>\n","      <td>101.22</td>\n","      <td>73.55</td>\n","      <td>139.43</td>\n","      <td>40.34</td>\n","      <td>...</td>\n","      <td>253.42</td>\n","      <td>91.14</td>\n","      <td>52.37</td>\n","      <td>311.08</td>\n","      <td>556.22</td>\n","      <td>74.96</td>\n","      <td>145.64</td>\n","      <td>177.71</td>\n","      <td>78.20</td>\n","      <td>118.68</td>\n","    </tr>\n","    <tr>\n","      <th>2022-05-31</th>\n","      <td>148.84</td>\n","      <td>271.87</td>\n","      <td>2404.19</td>\n","      <td>2275.24</td>\n","      <td>193.64</td>\n","      <td>44.42</td>\n","      <td>101.86</td>\n","      <td>73.84</td>\n","      <td>138.84</td>\n","      <td>38.84</td>\n","      <td>...</td>\n","      <td>256.74</td>\n","      <td>92.03</td>\n","      <td>53.04</td>\n","      <td>313.44</td>\n","      <td>567.57</td>\n","      <td>75.45</td>\n","      <td>147.88</td>\n","      <td>179.53</td>\n","      <td>78.81</td>\n","      <td>118.85</td>\n","    </tr>\n","    <tr>\n","      <th>2022-05-30</th>\n","      <td>149.64</td>\n","      <td>273.24</td>\n","      <td>2302.93</td>\n","      <td>2246.33</td>\n","      <td>195.13</td>\n","      <td>44.55</td>\n","      <td>102.26</td>\n","      <td>73.32</td>\n","      <td>139.27</td>\n","      <td>38.75</td>\n","      <td>...</td>\n","      <td>255.26</td>\n","      <td>93.08</td>\n","      <td>53.91</td>\n","      <td>323.48</td>\n","      <td>572.35</td>\n","      <td>76.14</td>\n","      <td>148.72</td>\n","      <td>181.09</td>\n","      <td>78.86</td>\n","      <td>115.99</td>\n","    </tr>\n","    <tr>\n","      <th>2022-05-27</th>\n","      <td>149.64</td>\n","      <td>273.24</td>\n","      <td>2302.93</td>\n","      <td>2246.33</td>\n","      <td>195.13</td>\n","      <td>44.55</td>\n","      <td>102.26</td>\n","      <td>73.32</td>\n","      <td>139.27</td>\n","      <td>38.75</td>\n","      <td>...</td>\n","      <td>255.26</td>\n","      <td>93.08</td>\n","      <td>53.91</td>\n","      <td>323.48</td>\n","      <td>572.35</td>\n","      <td>76.14</td>\n","      <td>148.72</td>\n","      <td>181.09</td>\n","      <td>78.86</td>\n","      <td>115.99</td>\n","    </tr>\n","    <tr>\n","      <th>2022-05-26</th>\n","      <td>143.78</td>\n","      <td>265.90</td>\n","      <td>2221.55</td>\n","      <td>2155.85</td>\n","      <td>191.63</td>\n","      <td>43.48</td>\n","      <td>98.75</td>\n","      <td>70.60</td>\n","      <td>136.89</td>\n","      <td>36.77</td>\n","      <td>...</td>\n","      <td>253.05</td>\n","      <td>92.31</td>\n","      <td>53.99</td>\n","      <td>313.46</td>\n","      <td>540.11</td>\n","      <td>77.59</td>\n","      <td>146.48</td>\n","      <td>179.46</td>\n","      <td>78.16</td>\n","      <td>112.94</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2006-11-15</th>\n","      <td>3.00</td>\n","      <td>29.13</td>\n","      <td>42.60</td>\n","      <td>246.20</td>\n","      <td>NaN</td>\n","      <td>22.32</td>\n","      <td>21.71</td>\n","      <td>14.83</td>\n","      <td>88.94</td>\n","      <td>18.06</td>\n","      <td>...</td>\n","      <td>73.61</td>\n","      <td>42.10</td>\n","      <td>25.13</td>\n","      <td>54.37</td>\n","      <td>43.94</td>\n","      <td>24.50</td>\n","      <td>63.12</td>\n","      <td>66.54</td>\n","      <td>32.16</td>\n","      <td>11.94</td>\n","    </tr>\n","    <tr>\n","      <th>2006-11-14</th>\n","      <td>3.04</td>\n","      <td>29.42</td>\n","      <td>41.51</td>\n","      <td>245.21</td>\n","      <td>NaN</td>\n","      <td>21.88</td>\n","      <td>21.34</td>\n","      <td>14.95</td>\n","      <td>89.11</td>\n","      <td>18.46</td>\n","      <td>...</td>\n","      <td>73.14</td>\n","      <td>41.85</td>\n","      <td>24.87</td>\n","      <td>53.85</td>\n","      <td>43.71</td>\n","      <td>24.44</td>\n","      <td>63.20</td>\n","      <td>66.56</td>\n","      <td>32.37</td>\n","      <td>11.84</td>\n","    </tr>\n","    <tr>\n","      <th>2006-11-13</th>\n","      <td>3.01</td>\n","      <td>29.35</td>\n","      <td>39.99</td>\n","      <td>240.75</td>\n","      <td>NaN</td>\n","      <td>21.02</td>\n","      <td>21.20</td>\n","      <td>14.21</td>\n","      <td>87.94</td>\n","      <td>18.26</td>\n","      <td>...</td>\n","      <td>72.70</td>\n","      <td>41.44</td>\n","      <td>24.53</td>\n","      <td>54.06</td>\n","      <td>43.62</td>\n","      <td>24.41</td>\n","      <td>63.25</td>\n","      <td>65.94</td>\n","      <td>32.44</td>\n","      <td>11.77</td>\n","    </tr>\n","    <tr>\n","      <th>2006-11-10</th>\n","      <td>2.97</td>\n","      <td>29.24</td>\n","      <td>39.26</td>\n","      <td>237.00</td>\n","      <td>NaN</td>\n","      <td>20.58</td>\n","      <td>21.07</td>\n","      <td>14.01</td>\n","      <td>87.65</td>\n","      <td>18.16</td>\n","      <td>...</td>\n","      <td>74.60</td>\n","      <td>41.12</td>\n","      <td>24.36</td>\n","      <td>54.14</td>\n","      <td>45.03</td>\n","      <td>24.42</td>\n","      <td>63.70</td>\n","      <td>66.29</td>\n","      <td>32.62</td>\n","      <td>11.76</td>\n","    </tr>\n","    <tr>\n","      <th>2006-11-09</th>\n","      <td>2.98</td>\n","      <td>29.26</td>\n","      <td>38.84</td>\n","      <td>236.54</td>\n","      <td>NaN</td>\n","      <td>20.42</td>\n","      <td>20.89</td>\n","      <td>13.57</td>\n","      <td>88.28</td>\n","      <td>17.96</td>\n","      <td>...</td>\n","      <td>73.53</td>\n","      <td>40.89</td>\n","      <td>24.48</td>\n","      <td>54.87</td>\n","      <td>43.37</td>\n","      <td>24.58</td>\n","      <td>63.80</td>\n","      <td>66.15</td>\n","      <td>32.53</td>\n","      <td>11.60</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4015 rows × 43 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4055bdd0-6f29-4c14-9af7-e9451cbf549e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              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