First Screener India.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "89ff72aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as web\n",
    "import pandas as pd\n",
    "from yahoo_fin import stock_info as si\n",
    "import datetime as dt\n",
    "import yfinance as yf\n",
    "import ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d2859b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = si.tickers_nifty50()\n",
    "# tickers = tickers[250:300]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "498435f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = dt.datetime.now() - dt.timedelta(days=365)\n",
    "end = dt.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "73542e98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-02</th>\n",
       "      <td>17706.199219</td>\n",
       "      <td>17794.599609</td>\n",
       "      <td>17674.800781</td>\n",
       "      <td>17780.000000</td>\n",
       "      <td>17780.000000</td>\n",
       "      <td>271200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-03</th>\n",
       "      <td>17767.750000</td>\n",
       "      <td>17781.150391</td>\n",
       "      <td>17511.150391</td>\n",
       "      <td>17560.199219</td>\n",
       "      <td>17560.199219</td>\n",
       "      <td>226600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-04</th>\n",
       "      <td>17590.199219</td>\n",
       "      <td>17617.800781</td>\n",
       "      <td>17462.550781</td>\n",
       "      <td>17516.300781</td>\n",
       "      <td>17516.300781</td>\n",
       "      <td>261400</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-07</th>\n",
       "      <td>17456.300781</td>\n",
       "      <td>17536.750000</td>\n",
       "      <td>17119.400391</td>\n",
       "      <td>17213.599609</td>\n",
       "      <td>17213.599609</td>\n",
       "      <td>265000</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-08</th>\n",
       "      <td>17279.849609</td>\n",
       "      <td>17306.449219</td>\n",
       "      <td>17043.650391</td>\n",
       "      <td>17266.750000</td>\n",
       "      <td>17266.750000</td>\n",
       "      <td>268500</td>\n",
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       "    <tr>\n",
       "      <th>2023-01-25</th>\n",
       "      <td>18093.349609</td>\n",
       "      <td>18100.599609</td>\n",
       "      <td>17846.150391</td>\n",
       "      <td>17891.949219</td>\n",
       "      <td>17891.949219</td>\n",
       "      <td>257200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-27</th>\n",
       "      <td>17877.199219</td>\n",
       "      <td>17884.750000</td>\n",
       "      <td>17493.550781</td>\n",
       "      <td>17604.349609</td>\n",
       "      <td>17604.349609</td>\n",
       "      <td>476300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-30</th>\n",
       "      <td>17541.949219</td>\n",
       "      <td>17709.150391</td>\n",
       "      <td>17405.550781</td>\n",
       "      <td>17648.949219</td>\n",
       "      <td>17648.949219</td>\n",
       "      <td>432400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-31</th>\n",
       "      <td>17731.449219</td>\n",
       "      <td>17735.699219</td>\n",
       "      <td>17537.550781</td>\n",
       "      <td>17662.150391</td>\n",
       "      <td>17662.150391</td>\n",
       "      <td>398300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01</th>\n",
       "      <td>17811.599609</td>\n",
       "      <td>17972.199219</td>\n",
       "      <td>17353.400391</td>\n",
       "      <td>17616.300781</td>\n",
       "      <td>17616.300781</td>\n",
       "      <td>512900</td>\n",
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       "<p>249 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2022-02-02  17706.199219  17794.599609  17674.800781  17780.000000   \n",
       "2022-02-03  17767.750000  17781.150391  17511.150391  17560.199219   \n",
       "2022-02-04  17590.199219  17617.800781  17462.550781  17516.300781   \n",
       "2022-02-07  17456.300781  17536.750000  17119.400391  17213.599609   \n",
       "2022-02-08  17279.849609  17306.449219  17043.650391  17266.750000   \n",
       "...                  ...           ...           ...           ...   \n",
       "2023-01-25  18093.349609  18100.599609  17846.150391  17891.949219   \n",
       "2023-01-27  17877.199219  17884.750000  17493.550781  17604.349609   \n",
       "2023-01-30  17541.949219  17709.150391  17405.550781  17648.949219   \n",
       "2023-01-31  17731.449219  17735.699219  17537.550781  17662.150391   \n",
       "2023-02-01  17811.599609  17972.199219  17353.400391  17616.300781   \n",
       "\n",
       "               Adj Close  Volume  \n",
       "Date                              \n",
       "2022-02-02  17780.000000  271200  \n",
       "2022-02-03  17560.199219  226600  \n",
       "2022-02-04  17516.300781  261400  \n",
       "2022-02-07  17213.599609  265000  \n",
       "2022-02-08  17266.750000  268500  \n",
       "...                  ...     ...  \n",
       "2023-01-25  17891.949219  257200  \n",
       "2023-01-27  17604.349609  476300  \n",
       "2023-01-30  17648.949219  432400  \n",
       "2023-01-31  17662.150391  398300  \n",
       "2023-02-01  17616.300781  512900  \n",
       "\n",
       "[249 rows x 6 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_df = yf.download('^NSEI',start,end)\n",
    "sp500_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1e07ae07",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Pct Change</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2022-02-02</th>\n",
       "      <td>17706.199219</td>\n",
       "      <td>17794.599609</td>\n",
       "      <td>17674.800781</td>\n",
       "      <td>17780.000000</td>\n",
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       "      <td>271200</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2022-02-03</th>\n",
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       "      <td>-0.012362</td>\n",
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       "      <th>2022-02-04</th>\n",
       "      <td>17590.199219</td>\n",
       "      <td>17617.800781</td>\n",
       "      <td>17462.550781</td>\n",
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       "      <td>261400</td>\n",
       "      <td>-0.002500</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-07</th>\n",
       "      <td>17456.300781</td>\n",
       "      <td>17536.750000</td>\n",
       "      <td>17119.400391</td>\n",
       "      <td>17213.599609</td>\n",
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       "      <td>265000</td>\n",
       "      <td>-0.017281</td>\n",
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       "    <tr>\n",
       "      <th>2022-02-08</th>\n",
       "      <td>17279.849609</td>\n",
       "      <td>17306.449219</td>\n",
       "      <td>17043.650391</td>\n",
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       "      <th>2023-01-25</th>\n",
       "      <td>18093.349609</td>\n",
       "      <td>18100.599609</td>\n",
       "      <td>17846.150391</td>\n",
       "      <td>17891.949219</td>\n",
       "      <td>17891.949219</td>\n",
       "      <td>257200</td>\n",
       "      <td>-0.012493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-27</th>\n",
       "      <td>17877.199219</td>\n",
       "      <td>17884.750000</td>\n",
       "      <td>17493.550781</td>\n",
       "      <td>17604.349609</td>\n",
       "      <td>17604.349609</td>\n",
       "      <td>476300</td>\n",
       "      <td>-0.016074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-30</th>\n",
       "      <td>17541.949219</td>\n",
       "      <td>17709.150391</td>\n",
       "      <td>17405.550781</td>\n",
       "      <td>17648.949219</td>\n",
       "      <td>17648.949219</td>\n",
       "      <td>432400</td>\n",
       "      <td>0.002533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-31</th>\n",
       "      <td>17731.449219</td>\n",
       "      <td>17735.699219</td>\n",
       "      <td>17537.550781</td>\n",
       "      <td>17662.150391</td>\n",
       "      <td>17662.150391</td>\n",
       "      <td>398300</td>\n",
       "      <td>0.000748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-01</th>\n",
       "      <td>17811.599609</td>\n",
       "      <td>17972.199219</td>\n",
       "      <td>17353.400391</td>\n",
       "      <td>17616.300781</td>\n",
       "      <td>17616.300781</td>\n",
       "      <td>512900</td>\n",
       "      <td>-0.002596</td>\n",
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       "</table>\n",
       "<p>249 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2022-02-02  17706.199219  17794.599609  17674.800781  17780.000000   \n",
       "2022-02-03  17767.750000  17781.150391  17511.150391  17560.199219   \n",
       "2022-02-04  17590.199219  17617.800781  17462.550781  17516.300781   \n",
       "2022-02-07  17456.300781  17536.750000  17119.400391  17213.599609   \n",
       "2022-02-08  17279.849609  17306.449219  17043.650391  17266.750000   \n",
       "...                  ...           ...           ...           ...   \n",
       "2023-01-25  18093.349609  18100.599609  17846.150391  17891.949219   \n",
       "2023-01-27  17877.199219  17884.750000  17493.550781  17604.349609   \n",
       "2023-01-30  17541.949219  17709.150391  17405.550781  17648.949219   \n",
       "2023-01-31  17731.449219  17735.699219  17537.550781  17662.150391   \n",
       "2023-02-01  17811.599609  17972.199219  17353.400391  17616.300781   \n",
       "\n",
       "               Adj Close  Volume  Pct Change  \n",
       "Date                                          \n",
       "2022-02-02  17780.000000  271200         NaN  \n",
       "2022-02-03  17560.199219  226600   -0.012362  \n",
       "2022-02-04  17516.300781  261400   -0.002500  \n",
       "2022-02-07  17213.599609  265000   -0.017281  \n",
       "2022-02-08  17266.750000  268500    0.003088  \n",
       "...                  ...     ...         ...  \n",
       "2023-01-25  17891.949219  257200   -0.012493  \n",
       "2023-01-27  17604.349609  476300   -0.016074  \n",
       "2023-01-30  17648.949219  432400    0.002533  \n",
       "2023-01-31  17662.150391  398300    0.000748  \n",
       "2023-02-01  17616.300781  512900   -0.002596  \n",
       "\n",
       "[249 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_df['Pct Change'] = sp500_df['Adj Close'].pct_change()\n",
    "sp500_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "077e1338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.990793069811586"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp500_return = (sp500_df['Pct Change'] + 1).cumprod()[-1]\n",
    "sp500_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3693ab4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "return_list = []\n",
    "final_df = pd.DataFrame(columns = ['Ticker','Price','Score','PE Ratio','PEG Ratio','SMA 100','SMA 200','52 Weeks Low','52 Weeks High'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fc44edf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def file_exists(path):\n",
    "    \"\"\"Test whether a path exists.  Returns False for broken symbolic links\"\"\"\n",
    "    import os\n",
    "    try:\n",
    "        st = os.stat(path)\n",
    "    except os.error:\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "492a4f1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index -1 is out of bounds for axis 0 with size 0\n"
     ]
    }
   ],
   "source": [
    "for ticker in tickers:\n",
    "    if(file_exists(f'stock_data/{ticker}.NS.csv') == False):\n",
    "        try:\n",
    "            df = yf.download(ticker,start,end)\n",
    "            df.to_csv(f'stock_data/{ticker}.NS.csv')\n",
    "            df['Pct Change'] = df['Adj Close'].pct_change()\n",
    "            stock_return = (df['Pct Change'] + 1).cumprod()[-1]\n",
    "            return_compared = round((stock_return/sp500_return),2)\n",
    "            return_list.append(return_compared)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "        \n",
    "    else:\n",
    "        try:\n",
    "            df = pd.read_csv(f'stock_data/{ticker}.NS.csv',index_col = 0)\n",
    "            df['Pct Change'] = df['Adj Close'].pct_change()\n",
    "            stock_return = (df['Pct Change'] + 1).cumprod()[-1]\n",
    "            return_compared = round((stock_return/sp500_return),2)\n",
    "            return_list.append(return_compared)\n",
    "        except Exception as e:\n",
    "            print(e)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9e986277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "return_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f22de372",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "04ea544a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Ticker</th>\n",
       "      <th>Returns Compared</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ADANIENT.NS</td>\n",
       "      <td>1.21</td>\n",
       "      <td>82.758621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BAJAJ-AUTO.NS</td>\n",
       "      <td>1.13</td>\n",
       "      <td>79.310345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BRITANNIA.NS</td>\n",
       "      <td>1.26</td>\n",
       "      <td>86.206897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CIPLA.NS</td>\n",
       "      <td>1.10</td>\n",
       "      <td>72.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COALINDIA.NS</td>\n",
       "      <td>1.51</td>\n",
       "      <td>93.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ITC.NS</td>\n",
       "      <td>1.65</td>\n",
       "      <td>96.551724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LT.NS</td>\n",
       "      <td>1.11</td>\n",
       "      <td>75.862069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NESTLEIND.NS</td>\n",
       "      <td>1.28</td>\n",
       "      <td>89.655172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TATACONSUM.NS</td>\n",
       "      <td>2.37</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ticker  Returns Compared       Score\n",
       "0     ADANIENT.NS              1.21   82.758621\n",
       "2   BAJAJ-AUTO.NS              1.13   79.310345\n",
       "6    BRITANNIA.NS              1.26   86.206897\n",
       "7        CIPLA.NS              1.10   72.413793\n",
       "8    COALINDIA.NS              1.51   93.103448\n",
       "14         ITC.NS              1.65   96.551724\n",
       "16          LT.NS              1.11   75.862069\n",
       "19   NESTLEIND.NS              1.28   89.655172\n",
       "23  TATACONSUM.NS              2.37  100.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_performers = pd.DataFrame(list(zip(tickers,return_list)), columns = ['Ticker','Returns Compared'])\n",
    "best_performers['Score'] = best_performers['Returns Compared'].rank(pct = True) * 100\n",
    "best_performers = best_performers[best_performers['Score']>=best_performers['Score'].quantile(0.7)]\n",
    "best_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "58f359c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:295: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = tables[0].append(tables[1])\n",
      "C:\\Users\\bucky\\anaconda3\\lib\\site-packages\\yahoo_fin\\stock_info.py:302: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  data = data.append(quote_price)\n"
     ]
    }
   ],
   "source": [
    "final_df = pd.DataFrame(columns = ['Ticker','Price','Score','PE Ratio','PEG Ratio','SMA 100','SMA 200','52 Weeks Low','52 Weeks High'])   \n",
    "\n",
    "for ticker in best_performers['Ticker']:\n",
    "    try:\n",
    "        df = pd.read_csv(f'stock_data/{ticker}.NS.csv',index_col = 0)\n",
    "        df['SMA 100'] = ta.trend.sma_indicator(df['Adj Close'], window=100, fillna=False)\n",
    "        df['SMA 200'] = ta.trend.sma_indicator(df['Adj Close'], window=200, fillna=False)\n",
    "        close = df['Adj Close'][-1]\n",
    "        pe_ratio = float(si.get_quote_table(ticker)['PE Ratio (TTM)'])\n",
    "        peg_ratio = float(si.get_stats_valuation(ticker)[1][4])\n",
    "        sma_100 = df['SMA 100'][-1]\n",
    "        sma_200 = df['SMA 200'][-1]\n",
    "        low_52_weeks = round(min(df['Low'][-(52*5):]),2)\n",
    "        high_52_weeks = round(max(df['High'][-(52*5):]),2)\n",
    "        score = round(best_performers[best_performers['Ticker']==ticker]['Score'].tolist()[0],2)\n",
    "        \n",
    "        condition_1 = close > sma_100 > sma_200\n",
    "        condition_2 = close > (1.3 * low_52_weeks)\n",
    "        condition_3 = close > (0.75 * high_52_weeks)\n",
    "        condition_4 = pe_ratio < 40\n",
    "        condition_5 = peg_ratio < 2\n",
    "        \n",
    "        if(condition_1 and condition_2 and condition_3 and condition_4 and condition_5 ):\n",
    "            print(f'{ticker} is eligible')\n",
    "            final_df = final_df.append({\n",
    "                'Ticker' : ticker,\n",
    "                'Price' : close,\n",
    "                'Score' : score,\n",
    "                'PE Ratio' : pe_ratio,\n",
    "                'PEG Ratio' : peg_ratio,\n",
    "                'SMA 100': sma_100,\n",
    "                'SMA 200' : sma_200,\n",
    "                '52 Weeks Low' : low_52_weeks,\n",
    "                '52 Weeks High' : high_52_weeks,\n",
    "            },ignore_index=True)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f'{e} for {ticker}')\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eada3471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Price</th>\n",
       "      <th>Score</th>\n",
       "      <th>PE Ratio</th>\n",
       "      <th>PEG Ratio</th>\n",
       "      <th>SMA 100</th>\n",
       "      <th>SMA 200</th>\n",
       "      <th>52 Weeks Low</th>\n",
       "      <th>52 Weeks High</th>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Ticker, Price, Score, PE Ratio, PEG Ratio, SMA 100, SMA 200, 52 Weeks Low, 52 Weeks High]\n",
       "Index: []"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "23f195b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>PE Ratio</th>\n",
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       "      <th>SMA 100</th>\n",
       "      <th>SMA 200</th>\n",
       "      <th>52 Weeks Low</th>\n",
       "      <th>52 Weeks High</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Ticker, Price, Score, PE Ratio, PEG Ratio, SMA 100, SMA 200, 52 Weeks Low, 52 Weeks High]\n",
       "Index: []"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = final_df.sort_values(by='Score',ascending = False)\n",
    "pd.set_option('display.max_columns',20)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c1e95a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv(\"final_df_screened_india.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ffaf3b6",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d735b47",
   "metadata": {},
   "outputs": [],
   "source": []
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