S&P Returns.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.0'"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_datareader.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = '1900/01/01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = '2022/02/25' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.DataReader(['^GSPC'], 'yahoo',start=start, end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = df['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f8d926cd0>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.plot(figsize=(20,10), kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f8d78da10>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.pct_change().plot(figsize=(20,10), kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_index = (1 + prices.pct_change()).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret_index[0:1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f8d61ebd0>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ret_index.plot(figsize=(20,10), kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbols\n",
       "^GSPC    247.281424\n",
       "Name: 2022-02-25 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_index.iloc[-1]-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f8d612d50>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.pct_change().plot(figsize=(20,10), kind='hist', bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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>Symbols</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.011962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.204669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.004526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.005432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.166096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         ^GSPC\n",
       "count    23651.000000\n",
       "mean         0.000305\n",
       "std          0.011962\n",
       "min         -0.204669\n",
       "25%         -0.004526\n",
       "50%          0.000493\n",
       "75%          0.005432\n",
       "max          0.166096"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.pct_change().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7f8c02d950>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.resample('M').last().pct_change().hist(figsize=(20,10), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>Symbols</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.006335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.053647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.299423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.019061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.009146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.035776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.391376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols        ^GSPC\n",
       "count    1130.000000\n",
       "mean        0.006335\n",
       "std         0.053647\n",
       "min        -0.299423\n",
       "25%        -0.019061\n",
       "50%         0.009146\n",
       "75%         0.035776\n",
       "max         0.391376"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.resample('M').last().pct_change().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.9821238839077677"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(-.10 - .006335) / .053647"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023851764341508513"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.norm.cdf(-1.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>Symbols</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1927-12-31</th>\n",
       "      <td>17.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-01-31</th>\n",
       "      <td>17.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-02-29</th>\n",
       "      <td>17.260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-03-31</th>\n",
       "      <td>19.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928-04-30</th>\n",
       "      <td>19.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-31</th>\n",
       "      <td>4605.379883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>4567.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>4766.180176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-31</th>\n",
       "      <td>4515.549805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-02-28</th>\n",
       "      <td>4384.649902</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1131 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols           ^GSPC\n",
       "Date                   \n",
       "1927-12-31    17.660000\n",
       "1928-01-31    17.570000\n",
       "1928-02-29    17.260000\n",
       "1928-03-31    19.280001\n",
       "1928-04-30    19.750000\n",
       "...                 ...\n",
       "2021-10-31  4605.379883\n",
       "2021-11-30  4567.000000\n",
       "2021-12-31  4766.180176\n",
       "2022-01-31  4515.549805\n",
       "2022-02-28  4384.649902\n",
       "\n",
       "[1131 rows x 1 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.resample('M').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7f87b83390>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A0JTwAwAAANCU8AMAAADQlPADAAAA0NT/A4lZWM23gV4lAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices.resample('Y').last().pct_change().hist(figsize=(20,10), bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "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>Symbols</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.078003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.189621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.470665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.057062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.107868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.209716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.450222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols      ^GSPC\n",
       "count    95.000000\n",
       "mean      0.078003\n",
       "std       0.189621\n",
       "min      -0.470665\n",
       "25%      -0.057062\n",
       "50%       0.107868\n",
       "75%       0.209716\n",
       "max       0.450222"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.resample('Y').last().pct_change().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "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>Symbols</th>\n",
       "      <th>^GSPC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1929-10-28</th>\n",
       "      <td>-0.129403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929-10-29</th>\n",
       "      <td>-0.101583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-10-19</th>\n",
       "      <td>-0.204669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-16</th>\n",
       "      <td>-0.119841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols        ^GSPC\n",
       "Date                \n",
       "1929-10-28 -0.129403\n",
       "1929-10-29 -0.101583\n",
       "1987-10-19 -0.204669\n",
       "2020-03-16 -0.119841"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.pct_change()[prices.pct_change() < -.10].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2020-03-12</th>\n",
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       "      <th>2020-03-13</th>\n",
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       "Symbols           ^GSPC\n",
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    "prices.loc['2020-03-12':'2020-03-17']"
   ]
  },
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