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stephenmm/stockfundamentalanalysis.ipynb

Created March 5, 2022 18:27
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stockfundamentalanalysis.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "StockFundamentalAnalysis.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyOKJeLmeE68SF4M3UrDIeQr",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/stephenmm/8e7249b8ce580956c43e895f4b255db8/stockfundamentalanalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"Just a playground to help me understand and analyze stocks."
],
"metadata": {
"id": "1cen60EkScD1"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mE5-9bmhSaNj",
"outputId": "4b27894c-9892-4c91-8fb5-780b991a25db"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" 10.000e-006\n",
" -10.000e-006\n",
" 123.456e+003\n",
"-100.000e-003\n"
]
}
],
"source": [
"# Display numbers in engineering notation\n",
"from math import log10\n",
"def eng_str(x, significance=8, precision=3):\n",
" y = abs(x)\n",
" exponent = int(log10(y))\n",
" engr_exponent = exponent - exponent%3\n",
" z = y/10**engr_exponent\n",
" if x < 0:\n",
" z=z*-1\n",
" if engr_exponent > 0:\n",
" exp_str=f\"+{engr_exponent:03}\"\n",
" else:\n",
" exp_str=f\"{engr_exponent:04}\"\n",
" return f\"{z:{significance}.{precision}f}\"+f\"e{exp_str}\"\n",
"\n",
"print(eng_str(.00001))\n",
"print(eng_str(-.00001))\n",
"print(eng_str(123456.2342))\n",
"print(eng_str(-.1))"
]
},
{
"cell_type": "code",
"source": [
"pip install yfinance"
],
"metadata": {
"id": "nm_dWNWYTWvH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import yfinance as yf\n",
"data = yf.download(\"SPY AAPL\", start=\"2017-01-01\", end=\"2017-04-30\", group_by=\"ticker\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vdk0L94TTJKd",
"outputId": "5c96bc27-09ce-4c37-faf9-618b6e826393"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[*********************100%***********************] 2 of 2 completed\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"microsoft = yf.Ticker('MSFT')\n",
"dict = microsoft.info\n",
"df = pd.DataFrame.from_dict(dict,orient='index')\n",
"df = df.reset_index()\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "mrsU-yuNTocT",
"outputId": "bd8250ad-394a-4804-c842-a1aa380f87ef"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"\n",
" <div id=\"df-154ab8d5-f4c3-4bfc-8b58-02bd1423070d\">\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>index</th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>zip</td>\n",
" <td>98052-6399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>sector</td>\n",
" <td>Technology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>fullTimeEmployees</td>\n",
" <td>181000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>longBusinessSummary</td>\n",
" <td>Microsoft Corporation develops, licenses, and ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>city</td>\n",
" <td>Redmond</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>dayHigh</td>\n",
" <td>295.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>regularMarketPrice</td>\n",
" <td>289.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>preMarketPrice</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>logo_url</td>\n",
" <td>https://logo.clearbit.com/microsoft.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>trailingPegRatio</td>\n",
" <td>2.4159</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>153 rows × 2 columns</p>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-154ab8d5-f4c3-4bfc-8b58-02bd1423070d')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
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" </svg>\n",
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" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
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" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
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"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-154ab8d5-f4c3-4bfc-8b58-02bd1423070d button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-154ab8d5-f4c3-4bfc-8b58-02bd1423070d');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
" </div>\n",
" "
],
"text/plain": [
" index 0\n",
"0 zip 98052-6399\n",
"1 sector Technology\n",
"2 fullTimeEmployees 181000\n",
"3 longBusinessSummary Microsoft Corporation develops, licenses, and ...\n",
"4 city Redmond\n",
".. ... ...\n",
"148 dayHigh 295.64\n",
"149 regularMarketPrice 289.86\n",
"150 preMarketPrice None\n",
"151 logo_url https://logo.clearbit.com/microsoft.com\n",
"152 trailingPegRatio 2.4159\n",
"\n",
"[153 rows x 2 columns]"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"dict['zip']\n",
"list(dict.keys())\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aP9u3mOZUHPT",
"outputId": "96242c1c-74dd-4e5a-d033-692c44e12a3d"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['zip',\n",
" 'sector',\n",
" 'fullTimeEmployees',\n",
" 'longBusinessSummary',\n",
" 'city',\n",
" 'phone',\n",
" 'state',\n",
" 'country',\n",
" 'companyOfficers',\n",
" 'website',\n",
" 'maxAge',\n",
" 'address1',\n",
" 'industry',\n",
" 'ebitdaMargins',\n",
" 'profitMargins',\n",
" 'grossMargins',\n",
" 'operatingCashflow',\n",
" 'revenueGrowth',\n",
" 'operatingMargins',\n",
" 'ebitda',\n",
" 'targetLowPrice',\n",
" 'recommendationKey',\n",
" 'grossProfits',\n",
" 'freeCashflow',\n",
" 'targetMedianPrice',\n",
" 'currentPrice',\n",
" 'earningsGrowth',\n",
" 'currentRatio',\n",
" 'returnOnAssets',\n",
" 'numberOfAnalystOpinions',\n",
" 'targetMeanPrice',\n",
" 'debtToEquity',\n",
" 'returnOnEquity',\n",
" 'targetHighPrice',\n",
" 'totalCash',\n",
" 'totalDebt',\n",
" 'totalRevenue',\n",
" 'totalCashPerShare',\n",
" 'financialCurrency',\n",
" 'revenuePerShare',\n",
" 'quickRatio',\n",
" 'recommendationMean',\n",
" 'exchange',\n",
" 'shortName',\n",
" 'longName',\n",
" 'exchangeTimezoneName',\n",
" 'exchangeTimezoneShortName',\n",
" 'isEsgPopulated',\n",
" 'gmtOffSetMilliseconds',\n",
" 'quoteType',\n",
" 'symbol',\n",
" 'messageBoardId',\n",
" 'market',\n",
" 'annualHoldingsTurnover',\n",
" 'enterpriseToRevenue',\n",
" 'beta3Year',\n",
" 'enterpriseToEbitda',\n",
" '52WeekChange',\n",
" 'morningStarRiskRating',\n",
" 'forwardEps',\n",
" 'revenueQuarterlyGrowth',\n",
" 'sharesOutstanding',\n",
" 'fundInceptionDate',\n",
" 'annualReportExpenseRatio',\n",
" 'totalAssets',\n",
" 'bookValue',\n",
" 'sharesShort',\n",
" 'sharesPercentSharesOut',\n",
" 'fundFamily',\n",
" 'lastFiscalYearEnd',\n",
" 'heldPercentInstitutions',\n",
" 'netIncomeToCommon',\n",
" 'trailingEps',\n",
" 'lastDividendValue',\n",
" 'SandP52WeekChange',\n",
" 'priceToBook',\n",
" 'heldPercentInsiders',\n",
" 'nextFiscalYearEnd',\n",
" 'yield',\n",
" 'mostRecentQuarter',\n",
" 'shortRatio',\n",
" 'sharesShortPreviousMonthDate',\n",
" 'floatShares',\n",
" 'beta',\n",
" 'enterpriseValue',\n",
" 'priceHint',\n",
" 'threeYearAverageReturn',\n",
" 'lastSplitDate',\n",
" 'lastSplitFactor',\n",
" 'legalType',\n",
" 'lastDividendDate',\n",
" 'morningStarOverallRating',\n",
" 'earningsQuarterlyGrowth',\n",
" 'priceToSalesTrailing12Months',\n",
" 'dateShortInterest',\n",
" 'pegRatio',\n",
" 'ytdReturn',\n",
" 'forwardPE',\n",
" 'lastCapGain',\n",
" 'shortPercentOfFloat',\n",
" 'sharesShortPriorMonth',\n",
" 'impliedSharesOutstanding',\n",
" 'category',\n",
" 'fiveYearAverageReturn',\n",
" 'previousClose',\n",
" 'regularMarketOpen',\n",
" 'twoHundredDayAverage',\n",
" 'trailingAnnualDividendYield',\n",
" 'payoutRatio',\n",
" 'volume24Hr',\n",
" 'regularMarketDayHigh',\n",
" 'navPrice',\n",
" 'averageDailyVolume10Day',\n",
" 'regularMarketPreviousClose',\n",
" 'fiftyDayAverage',\n",
" 'trailingAnnualDividendRate',\n",
" 'open',\n",
" 'toCurrency',\n",
" 'averageVolume10days',\n",
" 'expireDate',\n",
" 'algorithm',\n",
" 'dividendRate',\n",
" 'exDividendDate',\n",
" 'circulatingSupply',\n",
" 'startDate',\n",
" 'regularMarketDayLow',\n",
" 'currency',\n",
" 'trailingPE',\n",
" 'regularMarketVolume',\n",
" 'lastMarket',\n",
" 'maxSupply',\n",
" 'openInterest',\n",
" 'marketCap',\n",
" 'volumeAllCurrencies',\n",
" 'strikePrice',\n",
" 'averageVolume',\n",
" 'dayLow',\n",
" 'ask',\n",
" 'askSize',\n",
" 'volume',\n",
" 'fiftyTwoWeekHigh',\n",
" 'fromCurrency',\n",
" 'fiveYearAvgDividendYield',\n",
" 'fiftyTwoWeekLow',\n",
" 'bid',\n",
" 'tradeable',\n",
" 'dividendYield',\n",
" 'bidSize',\n",
" 'dayHigh',\n",
" 'regularMarketPrice',\n",
" 'preMarketPrice',\n",
" 'logo_url',\n",
" 'trailingPegRatio']"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"stks = ['crus','aapl']\n",
"prms = ['totalRevenue', 'trailingPE', 'sharesOutstanding']\n",
"for stk in stks:\n",
" info = yf.Ticker(stk).info\n",
" print(\"\\n%s rev/shr: %f \" % (stk, info['totalRevenue']/info['sharesOutstanding']) )\n",
" for prm in prms:\n",
" print(\" %s: %s |\" % (prm, eng_str(info[prm])), end='')\n",
" "
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WYBWua9uUNil",
"outputId": "ed1a6103-fd73-4b75-9ead-64bc0392ab9d"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"crus rev/shr: 27.670270 \n",
" totalRevenue: 1.585e+009 | trailingPE: 19.797e0000 | sharesOutstanding: 57.283e+006 |\n",
"aapl rev/shr: 23.182409 \n",
" totalRevenue: 378.323e+009 | trailingPE: 27.127e0000 | sharesOutstanding: 16.319e+009 |"
]
}
]
}
]
}
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