value_screen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Value Candidates with FMP Stock Screen \n",
    "#### Source Article: [Building an Investment Model using Financial Ratios in Python](https://codingandfun.com/building-an-investing-model-using-financial-ratios-and-python/#comments) \n",
    "---\n",
    "## Basic Criteria:\n",
    "\n",
    "* D/E <= 0.50 low debt \n",
    "* Current Ratio > 1.5 more than enough cash to cover current liabilities\n",
    "* ROE >= 8.00% stable growth for the past 10-years\n",
    "* FCF stable growth last 10 years (cash-rich)\n",
    "* Operating Margin - stable operating margins over the last 10 years (strong competitive position)\n",
    "* Companies that grow without issuing additional shares"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as datetime\n",
    "from datetime import datetime\n",
    "import requests\n",
    "import os\n",
    "import json\n",
    "from dotenv import load_dotenv\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "load_dotenv()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# Read in API key from env file\n",
    "FMP_API_KEY = os.getenv('FMP_API_KEY')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create Basic Stock Screen"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "companies = []\n",
    "\n",
    "market_cap = '1000000000' \n",
    "volume = '10000'\n",
    "beta = 1\n",
    "dividend = 1\n",
    "limit = 1000\n",
    "exchange = 'NYSE,NASDAQ'\n",
    "\n",
    "url = (f'https://financialmodelingprep.com/api/v3/stock-screener?marketCapMoreThan={market_cap}&betaMoreThan={beta}&volumeMoreThan={volume}&dividendMoreThan={dividend}&exchange={exchange}&limit={limit}&apikey={FMP_API_KEY}')\n",
    "\n",
    "# Get companies based on criteria defined about\n",
    "screen = requests.get(url).json()\n",
    "#screen"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Pull Financial Ratios"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Add selected companies to a list\n",
    "for item in screen:\n",
    "\tcompanies.append(item['symbol'])\n",
    "\t\n",
    "# Print(companies)\n",
    "value_ratios = {}\n",
    "\n",
    "# Get the financial ratios\n",
    "count = 0\n",
    "\n",
    "for company in companies:\n",
    "\n",
    "\ttry:\n",
    "\t\tif count <100:\n",
    "\t\t\tcount = count + 1\n",
    "\t\n",
    "\t\t\tfin_ratios = requests.get(f'https://financialmodelingprep.com/api/v3/ratios/{company}?apikey={FMP_API_KEY}').json()\n",
    "\n",
    "\t\t\tvalue_ratios[company] = {}\n",
    "\n",
    "\t\t\tvalue_ratios[company]['ROE'] = fin_ratios[0]['returnOnEquity']\n",
    "\t\t\tvalue_ratios[company]['ROA'] = fin_ratios[0]['returnOnAssets']\n",
    "\t\t\tvalue_ratios[company]['Debt_Eq_Ratio'] = fin_ratios[0]['debtEquityRatio']\n",
    "\t\t\tvalue_ratios[company]['Current_Ratio'] = fin_ratios[0]['currentRatio']\n",
    "\t\t\tvalue_ratios[company]['PB'] = fin_ratios[0]['priceToBookRatio']\n",
    "\t\t\tvalue_ratios[company]['PS'] = fin_ratios[0]['priceToSalesRatio']\n",
    "\t\t\tvalue_ratios[company]['PE'] = fin_ratios[0]['priceEarningsRatio']\n",
    "\t\t\tvalue_ratios[company]['Dividend_Yield'] = fin_ratios[0]['dividendYield']\n",
    "\t\t\tvalue_ratios[company]['Operating_Margin'] = fin_ratios[0]['operatingProfitMargin']\n",
    "\t\t\t\n",
    "\t\t\t# More financials on growth\n",
    "\t\t\tgrowth_ratios = requests.get(f'https://financialmodelingprep.com/api/v3/financial-growth/{company}?apikey={FMP_API_KEY}').json()\n",
    "\t\t\t\n",
    "\t\t\tvalue_ratios[company]['Revenue_Growth'] = growth_ratios[0]['revenueGrowth']\n",
    "\t\t\tvalue_ratios[company]['FCF_Growth'] = growth_ratios[0]['freeCashFlowGrowth']\n",
    "\t\t\tvalue_ratios[company]['Net_Income_Growth'] = growth_ratios[0]['netIncomeGrowth']\n",
    "\t\t\tvalue_ratios[company]['EPS_Growth'] = growth_ratios[0]['epsgrowth']\n",
    "\t\t\tvalue_ratios[company]['Avg_Shares_Dil_Growth'] = growth_ratios[0]['weightedAverageSharesDilutedGrowth']\n",
    "\t\t\n",
    "\t\t\t\t\t\t\t\t\n",
    "\texcept:\n",
    "\t\tpass\n",
    "\t\n",
    "#print(value_ratios)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Create dataframe of filtered companies with financial stats\n",
    "df = pd.DataFrame.from_dict(value_ratios, orient='index')\n",
    "df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROE</th>\n",
       "      <th>ROA</th>\n",
       "      <th>Debt_Eq_Ratio</th>\n",
       "      <th>Current_Ratio</th>\n",
       "      <th>PB</th>\n",
       "      <th>PS</th>\n",
       "      <th>PE</th>\n",
       "      <th>Dividend_Yield</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Revenue_Growth</th>\n",
       "      <th>FCF_Growth</th>\n",
       "      <th>Net_Income_Growth</th>\n",
       "      <th>EPS_Growth</th>\n",
       "      <th>Avg_Shares_Dil_Growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TSM</th>\n",
       "      <td>0.279990</td>\n",
       "      <td>0.187591</td>\n",
       "      <td>0.492031</td>\n",
       "      <td>1.769721</td>\n",
       "      <td>9.751356</td>\n",
       "      <td>13.467853</td>\n",
       "      <td>34.827524</td>\n",
       "      <td>0.014376</td>\n",
       "      <td>0.423213</td>\n",
       "      <td>0.251638</td>\n",
       "      <td>1.103943</td>\n",
       "      <td>0.463168</td>\n",
       "      <td>0.499411</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPM</th>\n",
       "      <td>0.104280</td>\n",
       "      <td>0.008603</td>\n",
       "      <td>11.121076</td>\n",
       "      <td>11.898671</td>\n",
       "      <td>1.418505</td>\n",
       "      <td>3.314832</td>\n",
       "      <td>13.602863</td>\n",
       "      <td>0.032024</td>\n",
       "      <td>0.979288</td>\n",
       "      <td>0.033868</td>\n",
       "      <td>-14.217003</td>\n",
       "      <td>-0.200379</td>\n",
       "      <td>-0.158820</td>\n",
       "      <td>-0.045939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HD</th>\n",
       "      <td>3.899970</td>\n",
       "      <td>0.182287</td>\n",
       "      <td>20.394665</td>\n",
       "      <td>1.229258</td>\n",
       "      <td>84.738245</td>\n",
       "      <td>2.116051</td>\n",
       "      <td>21.727924</td>\n",
       "      <td>0.023076</td>\n",
       "      <td>0.138354</td>\n",
       "      <td>0.198548</td>\n",
       "      <td>0.482662</td>\n",
       "      <td>0.144458</td>\n",
       "      <td>0.169014</td>\n",
       "      <td>0.000929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA</th>\n",
       "      <td>1.003129</td>\n",
       "      <td>0.190894</td>\n",
       "      <td>4.239712</td>\n",
       "      <td>1.613320</td>\n",
       "      <td>49.539399</td>\n",
       "      <td>20.691870</td>\n",
       "      <td>49.384854</td>\n",
       "      <td>0.005069</td>\n",
       "      <td>0.533495</td>\n",
       "      <td>-0.093704</td>\n",
       "      <td>-0.125956</td>\n",
       "      <td>-0.210273</td>\n",
       "      <td>-0.210046</td>\n",
       "      <td>-0.020548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>-0.136721</td>\n",
       "      <td>-0.067438</td>\n",
       "      <td>1.027356</td>\n",
       "      <td>0.796498</td>\n",
       "      <td>1.166614</td>\n",
       "      <td>1.072252</td>\n",
       "      <td>-8.532814</td>\n",
       "      <td>0.077634</td>\n",
       "      <td>-0.164906</td>\n",
       "      <td>-0.301307</td>\n",
       "      <td>-1.488142</td>\n",
       "      <td>-2.564854</td>\n",
       "      <td>-2.564854</td>\n",
       "      <td>0.000468</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWKS</th>\n",
       "      <td>0.195668</td>\n",
       "      <td>0.159555</td>\n",
       "      <td>0.226334</td>\n",
       "      <td>5.168599</td>\n",
       "      <td>5.717152</td>\n",
       "      <td>7.094604</td>\n",
       "      <td>29.218660</td>\n",
       "      <td>0.012895</td>\n",
       "      <td>0.269869</td>\n",
       "      <td>-0.006249</td>\n",
       "      <td>-0.146186</td>\n",
       "      <td>-0.045455</td>\n",
       "      <td>-0.010567</td>\n",
       "      <td>-0.026361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPLX</th>\n",
       "      <td>-0.055312</td>\n",
       "      <td>-0.019773</td>\n",
       "      <td>1.797419</td>\n",
       "      <td>0.726270</td>\n",
       "      <td>1.865915</td>\n",
       "      <td>2.945502</td>\n",
       "      <td>-33.734182</td>\n",
       "      <td>0.123762</td>\n",
       "      <td>0.406136</td>\n",
       "      <td>-0.043942</td>\n",
       "      <td>0.994026</td>\n",
       "      <td>-1.696999</td>\n",
       "      <td>-1.701646</td>\n",
       "      <td>0.158765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSX</th>\n",
       "      <td>-0.209387</td>\n",
       "      <td>-0.072641</td>\n",
       "      <td>1.748736</td>\n",
       "      <td>1.394831</td>\n",
       "      <td>1.568268</td>\n",
       "      <td>0.464252</td>\n",
       "      <td>-7.489811</td>\n",
       "      <td>0.052902</td>\n",
       "      <td>-0.023266</td>\n",
       "      <td>-0.402300</td>\n",
       "      <td>-1.865241</td>\n",
       "      <td>-2.292263</td>\n",
       "      <td>-2.332353</td>\n",
       "      <td>-0.032548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FITB</th>\n",
       "      <td>0.061745</td>\n",
       "      <td>0.006972</td>\n",
       "      <td>7.856389</td>\n",
       "      <td>0.000839</td>\n",
       "      <td>0.894688</td>\n",
       "      <td>2.858722</td>\n",
       "      <td>14.489936</td>\n",
       "      <td>0.041495</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002757</td>\n",
       "      <td>-0.991299</td>\n",
       "      <td>-0.431927</td>\n",
       "      <td>-0.455621</td>\n",
       "      <td>-0.000458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VFC</th>\n",
       "      <td>0.133458</td>\n",
       "      <td>0.029655</td>\n",
       "      <td>3.500422</td>\n",
       "      <td>2.165085</td>\n",
       "      <td>11.453152</td>\n",
       "      <td>3.788652</td>\n",
       "      <td>85.818509</td>\n",
       "      <td>0.021621</td>\n",
       "      <td>0.067973</td>\n",
       "      <td>-0.119151</td>\n",
       "      <td>0.921661</td>\n",
       "      <td>-0.399706</td>\n",
       "      <td>-0.381949</td>\n",
       "      <td>-0.019541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ROE       ROA  Debt_Eq_Ratio  Current_Ratio         PB         PS  \\\n",
       "TSM   0.279990  0.187591       0.492031       1.769721   9.751356  13.467853   \n",
       "JPM   0.104280  0.008603      11.121076      11.898671   1.418505   3.314832   \n",
       "HD    3.899970  0.182287      20.394665       1.229258  84.738245   2.116051   \n",
       "MA    1.003129  0.190894       4.239712       1.613320  49.539399  20.691870   \n",
       "XOM  -0.136721 -0.067438       1.027356       0.796498   1.166614   1.072252   \n",
       "...        ...       ...            ...            ...        ...        ...   \n",
       "SWKS  0.195668  0.159555       0.226334       5.168599   5.717152   7.094604   \n",
       "MPLX -0.055312 -0.019773       1.797419       0.726270   1.865915   2.945502   \n",
       "PSX  -0.209387 -0.072641       1.748736       1.394831   1.568268   0.464252   \n",
       "FITB  0.061745  0.006972       7.856389       0.000839   0.894688   2.858722   \n",
       "VFC   0.133458  0.029655       3.500422       2.165085  11.453152   3.788652   \n",
       "\n",
       "             PE  Dividend_Yield  Operating_Margin  Revenue_Growth  FCF_Growth  \\\n",
       "TSM   34.827524        0.014376          0.423213        0.251638    1.103943   \n",
       "JPM   13.602863        0.032024          0.979288        0.033868  -14.217003   \n",
       "HD    21.727924        0.023076          0.138354        0.198548    0.482662   \n",
       "MA    49.384854        0.005069          0.533495       -0.093704   -0.125956   \n",
       "XOM   -8.532814        0.077634         -0.164906       -0.301307   -1.488142   \n",
       "...         ...             ...               ...             ...         ...   \n",
       "SWKS  29.218660        0.012895          0.269869       -0.006249   -0.146186   \n",
       "MPLX -33.734182        0.123762          0.406136       -0.043942    0.994026   \n",
       "PSX   -7.489811        0.052902         -0.023266       -0.402300   -1.865241   \n",
       "FITB  14.489936        0.041495          0.000000       -0.002757   -0.991299   \n",
       "VFC   85.818509        0.021621          0.067973       -0.119151    0.921661   \n",
       "\n",
       "      Net_Income_Growth  EPS_Growth  Avg_Shares_Dil_Growth  \n",
       "TSM            0.463168    0.499411               0.000000  \n",
       "JPM           -0.200379   -0.158820              -0.045939  \n",
       "HD             0.144458    0.169014               0.000929  \n",
       "MA            -0.210273   -0.210046              -0.020548  \n",
       "XOM           -2.564854   -2.564854               0.000468  \n",
       "...                 ...         ...                    ...  \n",
       "SWKS          -0.045455   -0.010567              -0.026361  \n",
       "MPLX          -1.696999   -1.701646               0.158765  \n",
       "PSX           -2.292263   -2.332353              -0.032548  \n",
       "FITB          -0.431927   -0.455621              -0.000458  \n",
       "VFC           -0.399706   -0.381949              -0.019541  \n",
       "\n",
       "[97 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create Value Filter"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Filter value_candidates based on the specific financial criteria\n",
    "value_candidates = df[(df['ROE'] >= 0.08) & (df['Debt_Eq_Ratio'] <= 0.50) & (df['Current_Ratio'] >= 1.50) & (df['FCF_Growth'] > 0) & (df['Net_Income_Growth'] > 0) & (df['Operating_Margin'] > 0) & (df['Avg_Shares_Dil_Growth'] <= 0)]\n",
    "value_candidates.index"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['TSM', 'TROW', 'FAST'], dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Filtered Value Company Financial Stats "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# List of candidate companies for further research\n",
    "research_candidates = df.loc[['TSM','TROW','FAST']]\n",
    "research_candidates"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROE</th>\n",
       "      <th>ROA</th>\n",
       "      <th>Debt_Eq_Ratio</th>\n",
       "      <th>Current_Ratio</th>\n",
       "      <th>PB</th>\n",
       "      <th>PS</th>\n",
       "      <th>PE</th>\n",
       "      <th>Dividend_Yield</th>\n",
       "      <th>Operating_Margin</th>\n",
       "      <th>Revenue_Growth</th>\n",
       "      <th>FCF_Growth</th>\n",
       "      <th>Net_Income_Growth</th>\n",
       "      <th>EPS_Growth</th>\n",
       "      <th>Avg_Shares_Dil_Growth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TSM</th>\n",
       "      <td>0.279990</td>\n",
       "      <td>0.187591</td>\n",
       "      <td>0.492031</td>\n",
       "      <td>1.769721</td>\n",
       "      <td>9.751356</td>\n",
       "      <td>13.467853</td>\n",
       "      <td>34.827524</td>\n",
       "      <td>0.014376</td>\n",
       "      <td>0.423213</td>\n",
       "      <td>0.251638</td>\n",
       "      <td>1.103943</td>\n",
       "      <td>0.463168</td>\n",
       "      <td>0.499411</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TROW</th>\n",
       "      <td>0.307863</td>\n",
       "      <td>0.222601</td>\n",
       "      <td>0.383028</td>\n",
       "      <td>11.055372</td>\n",
       "      <td>4.645468</td>\n",
       "      <td>5.768383</td>\n",
       "      <td>15.089402</td>\n",
       "      <td>0.023624</td>\n",
       "      <td>0.442377</td>\n",
       "      <td>0.104808</td>\n",
       "      <td>0.292997</td>\n",
       "      <td>0.113264</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>-0.031014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAST</th>\n",
       "      <td>0.314320</td>\n",
       "      <td>0.216687</td>\n",
       "      <td>0.450571</td>\n",
       "      <td>4.079647</td>\n",
       "      <td>9.557687</td>\n",
       "      <td>4.625763</td>\n",
       "      <td>30.407485</td>\n",
       "      <td>0.030754</td>\n",
       "      <td>0.201937</td>\n",
       "      <td>0.058796</td>\n",
       "      <td>0.565823</td>\n",
       "      <td>0.086231</td>\n",
       "      <td>0.087116</td>\n",
       "      <td>-0.002437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ROE       ROA  Debt_Eq_Ratio  Current_Ratio        PB         PS  \\\n",
       "TSM   0.279990  0.187591       0.492031       1.769721  9.751356  13.467853   \n",
       "TROW  0.307863  0.222601       0.383028      11.055372  4.645468   5.768383   \n",
       "FAST  0.314320  0.216687       0.450571       4.079647  9.557687   4.625763   \n",
       "\n",
       "             PE  Dividend_Yield  Operating_Margin  Revenue_Growth  FCF_Growth  \\\n",
       "TSM   34.827524        0.014376          0.423213        0.251638    1.103943   \n",
       "TROW  15.089402        0.023624          0.442377        0.104808    0.292997   \n",
       "FAST  30.407485        0.030754          0.201937        0.058796    0.565823   \n",
       "\n",
       "      Net_Income_Growth  EPS_Growth  Avg_Shares_Dil_Growth  \n",
       "TSM            0.463168    0.499411               0.000000  \n",
       "TROW           0.113264    0.142857              -0.031014  \n",
       "FAST           0.086231    0.087116              -0.002437  "
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "# Print research candidates\n",
    "research_candidates.to_csv('resources/research_candidate.csv')"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
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