SPX-NDX streaks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e0cb3f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas.tseries.frequencies import to_offset\n",
    "import yfinance as yf\n",
    "from datetime import datetime\n",
    "\n",
    "today = datetime.today().strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f83f14c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  3 of 3 completed\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>^DJI</th>\n",
       "      <th>^GSPC</th>\n",
       "      <th>^NDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-03</th>\n",
       "      <td>3201.500000</td>\n",
       "      <td>419.339996</td>\n",
       "      <td>334.825012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-10</th>\n",
       "      <td>3199.500000</td>\n",
       "      <td>415.100006</td>\n",
       "      <td>347.109985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-17</th>\n",
       "      <td>3265.000000</td>\n",
       "      <td>418.859985</td>\n",
       "      <td>349.730011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-24</th>\n",
       "      <td>3232.800049</td>\n",
       "      <td>415.480011</td>\n",
       "      <td>345.454987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>3223.399902</td>\n",
       "      <td>408.779999</td>\n",
       "      <td>338.315002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-07</th>\n",
       "      <td>3225.399902</td>\n",
       "      <td>411.089996</td>\n",
       "      <td>346.804993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-14</th>\n",
       "      <td>3246.000000</td>\n",
       "      <td>412.480011</td>\n",
       "      <td>346.695007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-21</th>\n",
       "      <td>3280.199951</td>\n",
       "      <td>411.429993</td>\n",
       "      <td>342.390015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-28</th>\n",
       "      <td>3267.699951</td>\n",
       "      <td>412.700012</td>\n",
       "      <td>345.880005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-03-06</th>\n",
       "      <td>3221.600098</td>\n",
       "      <td>404.440002</td>\n",
       "      <td>333.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ^DJI       ^GSPC        ^NDX\n",
       "Date                                           \n",
       "1992-01-03  3201.500000  419.339996  334.825012\n",
       "1992-01-10  3199.500000  415.100006  347.109985\n",
       "1992-01-17  3265.000000  418.859985  349.730011\n",
       "1992-01-24  3232.800049  415.480011  345.454987\n",
       "1992-01-31  3223.399902  408.779999  338.315002\n",
       "1992-02-07  3225.399902  411.089996  346.804993\n",
       "1992-02-14  3246.000000  412.480011  346.695007\n",
       "1992-02-21  3280.199951  411.429993  342.390015\n",
       "1992-02-28  3267.699951  412.700012  345.880005\n",
       "1992-03-06  3221.600098  404.440002  333.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices = yf.download(\"^GSPC ^NDX ^DJI\", start=\"1985-10-01\", end=today)['Adj Close']\n",
    "prices = prices.resample(\"W\").last()\n",
    "prices.index = prices.index + to_offset(\"-2d\")\n",
    "prices.dropna(inplace=True)\n",
    "prices[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a99fa77a",
   "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>^DJI</th>\n",
       "      <th>^GSPC</th>\n",
       "      <th>^NDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-10</th>\n",
       "      <td>-0.000625</td>\n",
       "      <td>-0.010111</td>\n",
       "      <td>0.036691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-17</th>\n",
       "      <td>0.020472</td>\n",
       "      <td>0.009058</td>\n",
       "      <td>0.007548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-24</th>\n",
       "      <td>-0.009862</td>\n",
       "      <td>-0.008069</td>\n",
       "      <td>-0.012224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>-0.002908</td>\n",
       "      <td>-0.016126</td>\n",
       "      <td>-0.020668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-07</th>\n",
       "      <td>0.000620</td>\n",
       "      <td>0.005651</td>\n",
       "      <td>0.025095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-22</th>\n",
       "      <td>-0.018572</td>\n",
       "      <td>-0.027503</td>\n",
       "      <td>-0.038604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>-0.024672</td>\n",
       "      <td>-0.032738</td>\n",
       "      <td>-0.037589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>-0.002360</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>-0.012545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>-0.021359</td>\n",
       "      <td>-0.024119</td>\n",
       "      <td>-0.024117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-20</th>\n",
       "      <td>-0.029305</td>\n",
       "      <td>-0.030592</td>\n",
       "      <td>-0.041314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1585 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ^DJI     ^GSPC      ^NDX\n",
       "Date                                    \n",
       "1992-01-10 -0.000625 -0.010111  0.036691\n",
       "1992-01-17  0.020472  0.009058  0.007548\n",
       "1992-01-24 -0.009862 -0.008069 -0.012224\n",
       "1992-01-31 -0.002908 -0.016126 -0.020668\n",
       "1992-02-07  0.000620  0.005651  0.025095\n",
       "...              ...       ...       ...\n",
       "2022-04-22 -0.018572 -0.027503 -0.038604\n",
       "2022-04-29 -0.024672 -0.032738 -0.037589\n",
       "2022-05-06 -0.002360 -0.002079 -0.012545\n",
       "2022-05-13 -0.021359 -0.024119 -0.024117\n",
       "2022-05-20 -0.029305 -0.030592 -0.041314\n",
       "\n",
       "[1585 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns = prices.pct_change()\n",
    "returns.dropna(inplace=True)\n",
    "returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae1f80e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = 0\n",
    "def downWeeks(x):\n",
    "    global counter\n",
    "    \n",
    "    if x >= 0:\n",
    "        counter = 0\n",
    "        return counter\n",
    "    else:\n",
    "        counter += 1\n",
    "        return counter\n",
    "streaks = pd.Dataframe()\n",
    "streaks.loc[returns.index.min(),'SPX down weeks']\n",
    "for i in range(0, len(returns):\n",
    "               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "775ca1f4",
   "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>SPX down weeks</th>\n",
       "      <th>NDX down weeks</th>\n",
       "      <th>DJIA down weeks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992-01-10</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-02-07</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-22</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-29</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-06</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-13</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-20</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1585 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            SPX down weeks  NDX down weeks  DJIA down weeks\n",
       "Date                                                       \n",
       "1992-01-10               9               0                8\n",
       "1992-01-17               0               0                0\n",
       "1992-01-24               1               1                1\n",
       "1992-01-31               2               2                2\n",
       "1992-02-07               0               0                0\n",
       "...                    ...             ...              ...\n",
       "2022-04-22               3               3                4\n",
       "2022-04-29               4               4                5\n",
       "2022-05-06               5               5                6\n",
       "2022-05-13               6               6                7\n",
       "2022-05-20               7               7                8\n",
       "\n",
       "[1585 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "streaks = pd.DataFrame()\n",
    "firstDate = streaks.index.min()\n",
    "streaks['SPX down weeks'] = returns['^GSPC'].apply(downWeeks, axis=1, minDate)\n",
    "streaks['NDX down weeks'] = returns['^NDX'].apply(ownWeeks, axis=1, minDate)\n",
    "streaks['DJIA down weeks'] = returns['^DJI'].apply(ownWeeks, axis=1, minDate)\n",
    "streaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "63575c75",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "Input \u001b[0;32mIn [33]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mwkly_returns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstreaks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSPX down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\n\u001b[1;32m      2\u001b[0m \u001b[43m                 \u001b[49m\u001b[43m(\u001b[49m\u001b[43mstreaks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNDX down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                 \u001b[49m\u001b[43m(\u001b[49m\u001b[43mstreaks\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDJIA down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSPX down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNDX down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDJIA down weeks\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:961\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    959\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m    960\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m--> 961\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    963\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m    964\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:1149\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1146\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take_opportunity(tup):\n\u001b[1;32m   1147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_multi_take(tup)\n\u001b[0;32m-> 1149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_tuple_same_dim\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtup\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:827\u001b[0m, in \u001b[0;36m_LocationIndexer._getitem_tuple_same_dim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    824\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_null_slice(key):\n\u001b[1;32m    825\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m--> 827\u001b[0m retval \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mretval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    828\u001b[0m \u001b[38;5;66;03m# We should never have retval.ndim < self.ndim, as that should\u001b[39;00m\n\u001b[1;32m    829\u001b[0m \u001b[38;5;66;03m#  be handled by the _getitem_lowerdim call above.\u001b[39;00m\n\u001b[1;32m    830\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m retval\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:1182\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1180\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_slice_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   1181\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m-> 1182\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getbool_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1183\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like_indexer(key):\n\u001b[1;32m   1184\u001b[0m \n\u001b[1;32m   1185\u001b[0m     \u001b[38;5;66;03m# an iterable multi-selection\u001b[39;00m\n\u001b[1;32m   1186\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(labels, MultiIndex)):\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:984\u001b[0m, in \u001b[0;36m_LocationIndexer._getbool_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m    981\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getbool_axis\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, axis: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;66;03m# caller is responsible for ensuring non-None axis\u001b[39;00m\n\u001b[1;32m    983\u001b[0m     labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[0;32m--> 984\u001b[0m     key \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_bool_indexer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    985\u001b[0m     inds \u001b[38;5;241m=\u001b[39m key\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    986\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(inds, axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "File \u001b[0;32m~/anaconda3/envs/p123/lib/python3.9/site-packages/pandas/core/indexing.py:2383\u001b[0m, in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(index, key)\u001b[0m\n\u001b[1;32m   2381\u001b[0m     mask \u001b[38;5;241m=\u001b[39m isna(result\u001b[38;5;241m.\u001b[39m_values)\n\u001b[1;32m   2382\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m-> 2383\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\n\u001b[1;32m   2384\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnalignable boolean Series provided as \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2385\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindexer (index of the boolean Series and of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2386\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe indexed object do not match).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2387\u001b[0m         )\n\u001b[1;32m   2388\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mbool\u001b[39m)\u001b[38;5;241m.\u001b[39m_values\n\u001b[1;32m   2389\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_object_dtype(key):\n\u001b[1;32m   2390\u001b[0m     \u001b[38;5;66;03m# key might be object-dtype bool, check_array_indexer needs bool array\u001b[39;00m\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    }
   ],
   "source": [
    "wkly_returns.loc[(streaks['SPX down weeks'] >=6) &\n",
    "                 (streaks['NDX down weeks'] >= 6) &\n",
    "                 (streaks['DJIA down weeks'] >= 6),['SPX down weeks', 'NDX down weeks','DJIA down weeks']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc9ffdb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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