Trade scanner - Github.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trade scanner for VIX and consecutive weeks SPY closed lower"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This program can be executed daily (after market open hours) as an easy way to determine if trades should be placed. It checks if the VIX value from the day before is below the 21-day exponential moving average of VIX, and also if VIX is increasing. If both conditions are met, the prompt prints a message indicating to the user to open trade.\n",
    "- OpentradeTT - a more stringent rule which requires that both conditions are True on the same day.\n",
    "- OpentradeFTT - a less stringent rule which requires that VIX is increasing, but if VIX is less than VIX-21D EMA the day before, open trade == True.\n",
    "\n",
    "The program also checks the number of consecutive wks SPY has closed lower, for an overall indicator of the health of the stock market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd            \n",
    "import pandas_datareader.data as web\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader.data as web\n",
    "\n",
    "all_data = {ticker: web.get_data_yahoo(ticker)       #dictionary comprehension\n",
    "           for ticker in [\"^VIX\",\"SPY\"]}\n",
    "# putting [\"^VIX\",\"SPY\"] results in error during 315am to 915am ET due to uneven length of columns\n",
    "# print(all_data)\n",
    "\n",
    "try:\n",
    "    vix_df0 = pd.DataFrame({ticker: data ['Adj Close']\n",
    "               for ticker, data in all_data.items()})\n",
    "except:\n",
    "    print(\"Error encountered during copy from Yahoo Finance, should self resolve during market hours. putting [^VIX,SPY] results in error during 315am to 915am ET due to uneven length of columns\")\n",
    "\n",
    "vix_df = vix_df0.fillna(method=\"ffill\")                 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vix_df = vix_df.loc[vix_df.index > '2021-12-31']\n",
    "vix_df0 = vix_df0.loc[vix_df0.index > '2021-12-31']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^VIX</th>\n",
       "      <th>SPY</th>\n",
       "      <th>VIX_21d</th>\n",
       "      <th>^VIX-1d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-01-03</th>\n",
       "      <td>16.600000</td>\n",
       "      <td>476.230530</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>16.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-04</th>\n",
       "      <td>16.910000</td>\n",
       "      <td>476.071014</td>\n",
       "      <td>16.628182</td>\n",
       "      <td>19.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-05</th>\n",
       "      <td>19.730000</td>\n",
       "      <td>466.929443</td>\n",
       "      <td>16.910166</td>\n",
       "      <td>19.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-06</th>\n",
       "      <td>19.610001</td>\n",
       "      <td>466.490784</td>\n",
       "      <td>17.155605</td>\n",
       "      <td>18.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-07</th>\n",
       "      <td>18.760000</td>\n",
       "      <td>464.646515</td>\n",
       "      <td>17.301459</td>\n",
       "      <td>19.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>26.190001</td>\n",
       "      <td>412.929993</td>\n",
       "      <td>28.331234</td>\n",
       "      <td>25.690001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>25.690001</td>\n",
       "      <td>409.589996</td>\n",
       "      <td>28.091122</td>\n",
       "      <td>24.719999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-02</th>\n",
       "      <td>24.719999</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.784657</td>\n",
       "      <td>25.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-03</th>\n",
       "      <td>25.450001</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.572415</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ^VIX         SPY    VIX_21d    ^VIX-1d\n",
       "2022-01-03  16.600000  476.230530  16.600000  16.910000\n",
       "2022-01-04  16.910000  476.071014  16.628182  19.730000\n",
       "2022-01-05  19.730000  466.929443  16.910166  19.610001\n",
       "2022-01-06  19.610001  466.490784  17.155605  18.760000\n",
       "2022-01-07  18.760000  464.646515  17.301459  19.400000\n",
       "...               ...         ...        ...        ...\n",
       "2022-05-30   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-31  26.190001  412.929993  28.331234  25.690001\n",
       "2022-06-01  25.690001  409.589996  28.091122  24.719999\n",
       "2022-06-02  24.719999  417.390015  27.784657  25.450001\n",
       "2022-06-03  25.450001  417.390015  27.572415        NaN\n",
       "\n",
       "[152 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vix_df.index = pd.to_datetime(vix_df.index)\n",
    "\n",
    "\n",
    "vix_df['VIX_21d'] = vix_df['^VIX'].ewm(alpha=2/22, adjust=False).mean()        #calculates 21 day EMA from VIX value\n",
    "vix_df = vix_df.fillna(method='bfill')                 \n",
    "vix_df['^VIX-1d'] = vix_df['^VIX']\n",
    "vix_df['^VIX-1d'] = vix_df['^VIX-1d'].shift(-1)                  #delete 1 cell and shift up once to compare against itself\n",
    "\n",
    "\n",
    "\n",
    "date_index = pd.date_range(start=min(vix_df.index), end=max(vix_df.index), freq='D')\n",
    "vix_df=vix_df.reindex(date_index, fill_value=0)\n",
    "\n",
    "display(vix_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^VIX</th>\n",
       "      <th>SPY</th>\n",
       "      <th>VIX_21d</th>\n",
       "      <th>^VIX-1d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-20</th>\n",
       "      <td>29.430000</td>\n",
       "      <td>389.630005</td>\n",
       "      <td>29.026399</td>\n",
       "      <td>28.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-21</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-23</th>\n",
       "      <td>28.480000</td>\n",
       "      <td>396.920013</td>\n",
       "      <td>28.976726</td>\n",
       "      <td>29.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-24</th>\n",
       "      <td>29.450001</td>\n",
       "      <td>393.890015</td>\n",
       "      <td>29.019751</td>\n",
       "      <td>28.370001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-25</th>\n",
       "      <td>28.370001</td>\n",
       "      <td>397.369995</td>\n",
       "      <td>28.960683</td>\n",
       "      <td>27.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-26</th>\n",
       "      <td>27.500000</td>\n",
       "      <td>405.309998</td>\n",
       "      <td>28.827894</td>\n",
       "      <td>25.719999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-27</th>\n",
       "      <td>25.719999</td>\n",
       "      <td>415.260010</td>\n",
       "      <td>28.545358</td>\n",
       "      <td>26.190001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-29</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>26.190001</td>\n",
       "      <td>412.929993</td>\n",
       "      <td>28.331234</td>\n",
       "      <td>25.690001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>25.690001</td>\n",
       "      <td>409.589996</td>\n",
       "      <td>28.091122</td>\n",
       "      <td>24.719999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-02</th>\n",
       "      <td>24.719999</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.784657</td>\n",
       "      <td>25.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-03</th>\n",
       "      <td>25.450001</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.572415</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                 ^VIX         SPY    VIX_21d    ^VIX-1d\n",
       "2022-05-20  29.430000  389.630005  29.026399  28.480000\n",
       "2022-05-21   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-22   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-23  28.480000  396.920013  28.976726  29.450001\n",
       "2022-05-24  29.450001  393.890015  29.019751  28.370001\n",
       "2022-05-25  28.370001  397.369995  28.960683  27.500000\n",
       "2022-05-26  27.500000  405.309998  28.827894  25.719999\n",
       "2022-05-27  25.719999  415.260010  28.545358  26.190001\n",
       "2022-05-28   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-29   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-30   0.000000    0.000000   0.000000   0.000000\n",
       "2022-05-31  26.190001  412.929993  28.331234  25.690001\n",
       "2022-06-01  25.690001  409.589996  28.091122  24.719999\n",
       "2022-06-02  24.719999  417.390015  27.784657  25.450001\n",
       "2022-06-03  25.450001  417.390015  27.572415        NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vix_df.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns today's VIX values\n",
    "tday = pd.Timestamp('today').normalize()\n",
    "tday = str(tday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Instances where open trade = True\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^VIX</th>\n",
       "      <th>SPY</th>\n",
       "      <th>VIX_21d</th>\n",
       "      <th>^VIX-1d</th>\n",
       "      <th>lt_21d</th>\n",
       "      <th>vix_inc</th>\n",
       "      <th>open_tradeTT</th>\n",
       "      <th>open_tradeFTT</th>\n",
       "      <th>open_trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-02-02</th>\n",
       "      <td>22.090000</td>\n",
       "      <td>455.933594</td>\n",
       "      <td>23.761084</td>\n",
       "      <td>24.350000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-03</th>\n",
       "      <td>24.350000</td>\n",
       "      <td>445.216888</td>\n",
       "      <td>23.814621</td>\n",
       "      <td>23.219999</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-10</th>\n",
       "      <td>23.910000</td>\n",
       "      <td>447.928467</td>\n",
       "      <td>23.224196</td>\n",
       "      <td>27.360001</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-23</th>\n",
       "      <td>23.570000</td>\n",
       "      <td>443.799988</td>\n",
       "      <td>27.499687</td>\n",
       "      <td>21.670000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-30</th>\n",
       "      <td>19.330000</td>\n",
       "      <td>458.700012</td>\n",
       "      <td>24.636117</td>\n",
       "      <td>20.559999</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-31</th>\n",
       "      <td>20.559999</td>\n",
       "      <td>451.640015</td>\n",
       "      <td>24.265560</td>\n",
       "      <td>19.629999</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-05</th>\n",
       "      <td>21.030001</td>\n",
       "      <td>451.029999</td>\n",
       "      <td>23.152435</td>\n",
       "      <td>22.100000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-06</th>\n",
       "      <td>22.100000</td>\n",
       "      <td>446.519989</td>\n",
       "      <td>23.056759</td>\n",
       "      <td>21.549999</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-14</th>\n",
       "      <td>22.700001</td>\n",
       "      <td>437.790009</td>\n",
       "      <td>22.899386</td>\n",
       "      <td>22.170000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-21</th>\n",
       "      <td>22.680000</td>\n",
       "      <td>438.059998</td>\n",
       "      <td>22.501546</td>\n",
       "      <td>28.209999</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-05</th>\n",
       "      <td>31.200001</td>\n",
       "      <td>413.809998</td>\n",
       "      <td>27.204548</td>\n",
       "      <td>30.190001</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-18</th>\n",
       "      <td>30.959999</td>\n",
       "      <td>391.859985</td>\n",
       "      <td>28.949643</td>\n",
       "      <td>29.350000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-24</th>\n",
       "      <td>29.450001</td>\n",
       "      <td>393.890015</td>\n",
       "      <td>29.019751</td>\n",
       "      <td>28.370001</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-28</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-03</th>\n",
       "      <td>25.450001</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.572415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ^VIX         SPY    VIX_21d    ^VIX-1d  lt_21d vix_inc  \\\n",
       "2022-02-02  22.090000  455.933594  23.761084  24.350000    True    True   \n",
       "2022-02-03  24.350000  445.216888  23.814621  23.219999   False    True   \n",
       "2022-02-10  23.910000  447.928467  23.224196  27.360001   False    True   \n",
       "2022-03-23  23.570000  443.799988  27.499687  21.670000    True    True   \n",
       "2022-03-30  19.330000  458.700012  24.636117  20.559999    True    True   \n",
       "2022-03-31  20.559999  451.640015  24.265560  19.629999    True    True   \n",
       "2022-04-05  21.030001  451.029999  23.152435  22.100000    True    True   \n",
       "2022-04-06  22.100000  446.519989  23.056759  21.549999    True    True   \n",
       "2022-04-09   0.000000    0.000000   0.000000   0.000000   False    True   \n",
       "2022-04-14  22.700001  437.790009  22.899386  22.170000    True    True   \n",
       "2022-04-21  22.680000  438.059998  22.501546  28.209999   False    True   \n",
       "2022-05-05  31.200001  413.809998  27.204548  30.190001   False    True   \n",
       "2022-05-18  30.959999  391.859985  28.949643  29.350000   False    True   \n",
       "2022-05-24  29.450001  393.890015  29.019751  28.370001   False    True   \n",
       "2022-05-28   0.000000    0.000000   0.000000   0.000000   False    True   \n",
       "2022-06-03  25.450001  417.390015  27.572415        NaN    True    True   \n",
       "\n",
       "            open_tradeTT  open_tradeFTT  open_trade  \n",
       "2022-02-02          True          False        True  \n",
       "2022-02-03         False           True        True  \n",
       "2022-02-10         False           True        True  \n",
       "2022-03-23          True          False        True  \n",
       "2022-03-30          True          False        True  \n",
       "2022-03-31          True          False        True  \n",
       "2022-04-05          True          False        True  \n",
       "2022-04-06          True          False        True  \n",
       "2022-04-09         False           True        True  \n",
       "2022-04-14          True          False        True  \n",
       "2022-04-21         False           True        True  \n",
       "2022-05-05         False           True        True  \n",
       "2022-05-18         False           True        True  \n",
       "2022-05-24         False           True        True  \n",
       "2022-05-28         False           True        True  \n",
       "2022-06-03          True          False        True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vix_df['lt_21d'] = vix_df['VIX_21d'] > vix_df['^VIX']           #lt_21d checks if the current VIX is below the 21d EMA\n",
    "\n",
    "vix_df['vix_inc'] = vix_df['^VIX-1d'] > vix_df['^VIX']         #vix_inc checks if vix is increasing, when compared to the VIX value the day before\n",
    "vix_df['vix_inc'] = vix_df['vix_inc'].shift(1)\n",
    "\n",
    "#open trade if both lt21d and vix_inc are True\n",
    "vix_df['open_tradeTT'] = (vix_df['lt_21d'] & vix_df['vix_inc'])   \n",
    "\n",
    "#open trade if lt21d is F and vix_inc is T and lt21d(previous day) is T --> for cases where VIX spikes suddenly making open_tradeTT false\n",
    "vix_df['open_tradeFTT'] = (vix_df['lt_21d'] == False) & (vix_df['vix_inc'] == True) & (vix_df['lt_21d'].shift(1) == True)\n",
    "\n",
    "# combine these 2 conditions\n",
    "vix_df['open_trade'] = vix_df['open_tradeTT'] | vix_df['open_tradeFTT']\n",
    "\n",
    "print('\\nInstances where open trade = True')\n",
    "display(vix_df.loc[vix_df['open_trade']==True])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Consecutive weeks that SPY dropped scanner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = web.DataReader('SPY', 'yahoo', start='2022-01-01')\n",
    "df['SPY'] = df['Close']\n",
    "df = df.drop(['High','Low','Open','Volume','Adj Close', 'Close'], axis=1)\n",
    "\n",
    "date_index = pd.date_range(start=min(df.index), end=max(df.index), freq='D')\n",
    "df = df.reindex(date_index, method='nearest')\n",
    "df['date1'] = df.index\n",
    "df['day'] = df.date1.dt.dayofweek\n",
    "\n",
    "df = df.loc[df.day == 4]               # take day = 0 or Monday as the start of the week\n",
    "df['SPY-1wk'] = df['SPY'].shift(1)     #'SPY-1wk' or SPY minus 1 wk is the price of SPY 1 wk earlier\n",
    "df['SPY-2wk'] = df['SPY'].shift(2)\n",
    "df['SPY-3wk'] = df['SPY'].shift(3)\n",
    "df['SPY-4wk'] = df['SPY'].shift(4)\n",
    "df['SPY-5wk'] = df['SPY'].shift(5)\n",
    "df['SPY+1wk'] = df['SPY'].shift(-1)\n",
    "\n",
    "#checks for consecutive SPY declines\n",
    "\n",
    "df['0<-1'] = df['SPY'] < df['SPY-1wk']\n",
    "df['-1<-2'] = df['SPY-1wk'] < df['SPY-2wk']\n",
    "df['-2<-3'] = df['SPY-2wk'] < df['SPY-3wk']\n",
    "df['-3<-4'] = df['SPY-3wk'] < df['SPY-4wk']\n",
    "df['-4<-5'] = df['SPY-4wk'] < df['SPY-5wk']\n",
    "df['2consec_drops'] = (df['0<-1']) & (df['-1<-2'])\n",
    "df['3consec_drops'] = (df['0<-1']) & (df['-1<-2']) & (df['-2<-3'])\n",
    "df['4consec_drops'] = (df['0<-1']) & (df['-1<-2']) & (df['-2<-3']) & (df['-3<-4'])\n",
    "df['5consec_drops'] = (df['0<-1']) & (df['-1<-2']) & (df['-2<-3']) & (df['-3<-4']) & (df['-4<-5'])\n",
    "\n",
    "# display(df.tail(15))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consecutive wk drops in SPY, calculated for this week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results for this week\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2consec_drops</th>\n",
       "      <th>3consec_drops</th>\n",
       "      <th>4consec_drops</th>\n",
       "      <th>5consec_drops</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-30</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            2consec_drops  3consec_drops  4consec_drops  5consec_drops\n",
       "2022-05-30          False          False          False          False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['2consec_drops','3consec_drops','4consec_drops','5consec_drops']]\n",
    "print('\\nResults for this week')\n",
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe VIX since 1 Jan 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    106.000000\n",
       "mean      25.988774\n",
       "std        4.794323\n",
       "min       16.600000\n",
       "25%       21.992499\n",
       "50%       25.710000\n",
       "75%       30.110000\n",
       "max       36.450001\n",
       "Name: ^VIX, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vix_df0['^VIX'].describe())\n",
    "# display(vix_df0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Describe VIX since given date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24.000000\n",
       "mean     28.925834\n",
       "std       2.782365\n",
       "min      24.719999\n",
       "25%      26.167500\n",
       "50%      29.060000\n",
       "75%      31.020000\n",
       "max      34.750000\n",
       "Name: ^VIX, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = '2022-05-01'\n",
    "vix_df1 = vix_df0.loc[vix_df0.index > date]\n",
    "display(vix_df1['^VIX'].describe())\n",
    "# display(vix_df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Should I open a put trade cos VIX is below 21d EMA & VIX is inc?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<<<< Yes, open Trade today! >>>>\n",
      "\n",
      "Today's VIX data\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>^VIX</th>\n",
       "      <th>SPY</th>\n",
       "      <th>VIX_21d</th>\n",
       "      <th>^VIX-1d</th>\n",
       "      <th>lt_21d</th>\n",
       "      <th>vix_inc</th>\n",
       "      <th>open_tradeTT</th>\n",
       "      <th>open_tradeFTT</th>\n",
       "      <th>open_trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-05-31</th>\n",
       "      <td>26.190001</td>\n",
       "      <td>412.929993</td>\n",
       "      <td>28.331234</td>\n",
       "      <td>25.690001</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-01</th>\n",
       "      <td>25.690001</td>\n",
       "      <td>409.589996</td>\n",
       "      <td>28.091122</td>\n",
       "      <td>24.719999</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-02</th>\n",
       "      <td>24.719999</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.784657</td>\n",
       "      <td>25.450001</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-03</th>\n",
       "      <td>25.450001</td>\n",
       "      <td>417.390015</td>\n",
       "      <td>27.572415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ^VIX         SPY    VIX_21d    ^VIX-1d  lt_21d vix_inc  \\\n",
       "2022-05-31  26.190001  412.929993  28.331234  25.690001    True   False   \n",
       "2022-06-01  25.690001  409.589996  28.091122  24.719999    True   False   \n",
       "2022-06-02  24.719999  417.390015  27.784657  25.450001    True   False   \n",
       "2022-06-03  25.450001  417.390015  27.572415        NaN    True    True   \n",
       "\n",
       "            open_tradeTT  open_tradeFTT  open_trade  \n",
       "2022-05-31         False          False       False  \n",
       "2022-06-01         False          False       False  \n",
       "2022-06-02         False          False       False  \n",
       "2022-06-03          True          False        True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    if vix_df.loc[tday,'open_trade'] == True:\n",
    "        print(\"<<<< Yes, open Trade today! >>>>\")\n",
    "    else:\n",
    "        print(\"<<<< No, keep on waiting >>>> \")\n",
    "        \n",
    "except:\n",
    "    print(\"KEY ERROR because today's date,\",tday,\"is not found in database yet!\")\n",
    "    \n",
    "print(\"\\nToday's VIX data\")\n",
    "display(vix_df.tail(4))\n",
    "\n"
   ]
  }
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