04_returns.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Data and returns"]},{"cell_type":"code","execution_count":1,"metadata":{"trusted":true},"outputs":[],"source":["# Install and import pandas_datareader\n","import pandas as pd\n","import numpy as np\n","# !pip install --upgrade pandas-datareader\n","from pandas_datareader import DataReader as pdr\n","# !pip install --upgrade yfinance\n","import yfinance as yf\n","pd.options.display.float_format = '{:.2f}'.format"]},{"cell_type":"markdown","metadata":{},"source":["## Stock prices"]},{"cell_type":"code","execution_count":2,"metadata":{"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["d:\\bbcx-investments-quarto\\quarto\\venv\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py:1162: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n","  warnings.warn(\n"]},{"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>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","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01-03</th>\n","      <td>0.94</td>\n","      <td>1.00</td>\n","      <td>0.91</td>\n","      <td>1.00</td>\n","      <td>0.85</td>\n","      <td>535796800</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-04</th>\n","      <td>0.97</td>\n","      <td>0.99</td>\n","      <td>0.90</td>\n","      <td>0.92</td>\n","      <td>0.78</td>\n","      <td>512377600</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-05</th>\n","      <td>0.93</td>\n","      <td>0.99</td>\n","      <td>0.92</td>\n","      <td>0.93</td>\n","      <td>0.79</td>\n","      <td>778321600</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-06</th>\n","      <td>0.95</td>\n","      <td>0.96</td>\n","      <td>0.85</td>\n","      <td>0.85</td>\n","      <td>0.72</td>\n","      <td>767972800</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-07</th>\n","      <td>0.86</td>\n","      <td>0.90</td>\n","      <td>0.85</td>\n","      <td>0.89</td>\n","      <td>0.76</td>\n","      <td>460734400</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-23</th>\n","      <td>132.16</td>\n","      <td>132.43</td>\n","      <td>130.78</td>\n","      <td>130.96</td>\n","      <td>129.41</td>\n","      <td>88223700</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-24</th>\n","      <td>131.32</td>\n","      <td>133.46</td>\n","      <td>131.10</td>\n","      <td>131.97</td>\n","      <td>130.40</td>\n","      <td>54930100</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-28</th>\n","      <td>133.99</td>\n","      <td>137.34</td>\n","      <td>133.51</td>\n","      <td>136.69</td>\n","      <td>135.07</td>\n","      <td>124486200</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-29</th>\n","      <td>138.05</td>\n","      <td>138.79</td>\n","      <td>134.34</td>\n","      <td>134.87</td>\n","      <td>133.27</td>\n","      <td>121047300</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-30</th>\n","      <td>135.58</td>\n","      <td>135.99</td>\n","      <td>133.40</td>\n","      <td>133.72</td>\n","      <td>132.13</td>\n","      <td>96452100</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5283 rows × 6 columns</p>\n","</div>"],"text/plain":["             Open   High    Low  Close  Adj Close     Volume\n","Date                                                        \n","2000-01-03   0.94   1.00   0.91   1.00       0.85  535796800\n","2000-01-04   0.97   0.99   0.90   0.92       0.78  512377600\n","2000-01-05   0.93   0.99   0.92   0.93       0.79  778321600\n","2000-01-06   0.95   0.96   0.85   0.85       0.72  767972800\n","2000-01-07   0.86   0.90   0.85   0.89       0.76  460734400\n","...           ...    ...    ...    ...        ...        ...\n","2020-12-23 132.16 132.43 130.78 130.96     129.41   88223700\n","2020-12-24 131.32 133.46 131.10 131.97     130.40   54930100\n","2020-12-28 133.99 137.34 133.51 136.69     135.07  124486200\n","2020-12-29 138.05 138.79 134.34 134.87     133.27  121047300\n","2020-12-30 135.58 135.99 133.40 133.72     132.13   96452100\n","\n","[5283 rows x 6 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["# Pull stock information\n","ticker = 'AAPL'\n","df = yf.download('AAPL', start='2000-01-01', end='2020-12-31', progress=False)\n","df.index = df.index.to_period('D')\n","df"]},{"cell_type":"markdown","metadata":{},"source":["### Daily returns"]},{"cell_type":"code","execution_count":3,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Date\n","2000-01-03       NaN\n","2000-01-04   -0.0843\n","2000-01-05    0.0146\n","2000-01-06   -0.0865\n","2000-01-07    0.0474\n","               ...  \n","2020-12-23   -0.0070\n","2020-12-24    0.0077\n","2020-12-28    0.0358\n","2020-12-29   -0.0133\n","2020-12-30   -0.0085\n","Freq: D, Name: Adj Close, Length: 5283, dtype: float64"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["pd.options.display.float_format = '{:.4f}'.format\n","ret = df['Adj Close'].pct_change()\n","ret"]},{"cell_type":"markdown","metadata":{},"source":["### Monthly returns"]},{"cell_type":"code","execution_count":4,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Date\n","2000-01     0.7896\n","2000-02     0.8724\n","2000-03     1.0337\n","2000-04     0.9442\n","2000-05     0.6393\n","            ...   \n","2020-08   127.2897\n","2020-09   114.2392\n","2020-10   107.3834\n","2020-11   117.6378\n","2020-12   132.1338\n","Freq: M, Name: Adj Close, Length: 252, dtype: float64"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# Keep just the last data adjusted close data point each month\n","df_monthly = df[\"Adj Close\"].resample(\"M\").last()\n","df_monthly"]},{"cell_type":"code","execution_count":5,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Date\n","2000-01       NaN\n","2000-02    0.1048\n","2000-03    0.1848\n","2000-04   -0.0865\n","2000-05   -0.3229\n","            ...  \n","2020-08    0.2166\n","2020-09   -0.1025\n","2020-10   -0.0600\n","2020-11    0.0955\n","2020-12    0.1232\n","Freq: M, Name: Adj Close, Length: 252, dtype: float64"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Calculate a return\n","ret_monthly = df_monthly.pct_change()\n","ret_monthly"]},{"cell_type":"code","execution_count":6,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["PeriodIndex(['2000-01', '2000-02', '2000-03', '2000-04', '2000-05', '2000-06',\n","             '2000-07', '2000-08', '2000-09', '2000-10',\n","             ...\n","             '2020-03', '2020-04', '2020-05', '2020-06', '2020-07', '2020-08',\n","             '2020-09', '2020-10', '2020-11', '2020-12'],\n","            dtype='period[M]', name='Date', length=252)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["ret_monthly.index"]},{"cell_type":"code","execution_count":7,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["'Adj Close'"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["ret_monthly.name"]},{"cell_type":"code","execution_count":8,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["Date\n","2000-01       NaN\n","2000-02    0.1048\n","2000-03    0.1848\n","2000-04   -0.0865\n","2000-05   -0.3229\n","            ...  \n","2020-08    0.2166\n","2020-09   -0.1025\n","2020-10   -0.0600\n","2020-11    0.0955\n","2020-12    0.1232\n","Freq: M, Name: ret, Length: 252, dtype: float64"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Add a name to the series\n","ret_monthly.name = \"ret\"\n","ret_monthly"]},{"cell_type":"code","execution_count":9,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Avg monthly return:\t  0.0279\n","SD(monthly returns):\t  0.1165\n","\n","Annualized stats:\n","Avg return:\t  0.3343\n","SD(return):\t  0.4035\n"]}],"source":["# Summary statistics\n","print(f'Avg monthly return:\\t {ret_monthly.mean(): ,.4f}')\n","print(f'SD(monthly returns):\\t {ret_monthly.std(): ,.4f}')\n","\n","# annualized summary stats\n","print('\\nAnnualized stats:')\n","print(f'Avg return:\\t {ret_monthly.mean()*12: ,.4f}')\n","print(f'SD(return):\\t {ret_monthly.std()*np.sqrt(12): ,.4f}')"]},{"cell_type":"code","execution_count":10,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<AxesSubplot:xlabel='Date'>"]},"execution_count":10,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAEKCAYAAADpfBXhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsTAAALEwEAmpwYAAAq7klEQVR4nO3deXyc1X3v8c9vRvtua/Eu78aYzQaz7xBSEggkTUqghBBK6qQJbdKmS9KkTXpzc0tyszRpElITSOCWkA0oNGUJYV8NNthgY+NN3mRbq7VrpFl+948ZCdlIsqwZbaPv+/Wal57nPNuZB/Hz0e85zznm7oiISHoJjHUFREQk9RTcRUTSkIK7iEgaUnAXEUlDCu4iImlIwV1EJA0dNbib2Z1mVmtmG/uU/crM1ic+u8xsfaJ8npl19tn2kxGsu4iIDCBjCPv8HPghcHdPgbt/tGfZzL4DNPfZf4e7L09R/UREZBiOGtzd/Vkzm9ffNjMz4BrgkhTXS0REkjCUlvtgzgdq3H1bn7L5ZvY60AJ8xd2fO9pJysrKfN68eUlWRURkclm3bl29u5f3ty3Z4H4dcG+f9QNApbs3mNlpwH+Z2Qnu3nLkgWa2ClgFUFlZydq1a5OsiojI5GJmuwfaNuzeMmaWAfwx8KueMnfvcveGxPI6YAewpL/j3X21u69095Xl5f3+wyMiIsOUTFfI9wBb3H1fT4GZlZtZMLG8AFgM7EyuiiIicqyG0hXyXuAl4Dgz22dmNyc2XcvhKRmAC4A3El0jfwt82t0bU1hfEREZgqH0lrlugPJP9FN2H3Bf8tUSEZFk6A1VEZE0pOAuIpKGFNxFRNKQgruIyAT06MYDg25XcBcRmYAe2Xhw0O0K7iIiE1BLZ3jQ7QruIiITUEsoMuh2BXcRkQmoNaSWu4hI2mnpVMtdRCTttKjlLiIysdW2hAiFo73rkWiMju7oIEcouIuIjHtX/Pvz3P7sOwPsth7lYSoouIuIjGuxmFPX2sW+Q529ZUdLyYCCu4jIuBaKxNMvTZ3dvWVHe5gKCu4iIuNaZyK33tznpaWjdYMEBXcRkXGtM9wT3N9prSstIyIywfX0kmnuUFpGRCRtdHbHgMPTMmq5i4hMcD1pmfbuKOFoPNAfbVwZUHAXERnXOvu8vNTTem/pDFOYM/gU2AruIiLjWGefN1GbOhLBPRSmKCdz0OOOGtzN7E4zqzWzjX3KvmZm1Wa2PvF5f59tXzKz7Wb2tpn90TC+i4iIJIT6abm3hiIpabn/HLi8n/LvufvyxOdhADNbBlwLnJA45sdmFhzCNUREpB+Hp2XiPWZaOsMU5SbZcnf3Z4HGIdbjauCX7t7l7lXAduCMIR4rIiJH6JuW6c25hyLJp2UGcYuZvZFI20xJlM0C9vbZZ1+iTEREhqFvy70n594aClM0Qg9UbwMWAsuBA8B3jvUEZrbKzNaa2dq6urphVkNEJL2FwlHM4st9e8sknZbpj7vXuHvU3WPA7byTeqkG5vTZdXairL9zrHb3le6+sry8fDjVEBFJe53dUfIygxRmZ9DcGSYcjdESilA8EsHdzGb0Wf0Q0NOT5iHgWjPLNrP5wGLgleFcQ0REoCMcJTcrSFFuJs0dYQ61xx+qlhVmD3rc4EkbwMzuBS4CysxsH/BV4CIzWw44sAv4FIC7bzKzXwNvARHgs+4++HQhIiIyoFB3lJzMIMW5mTR3hqlviwf38oKsQY87anB39+v6Kb5jkP2/AXzjaOcVEZGj6wxHyU0E96bOMA3tXQCUFgzectcbqiIi41hnIi1TnJtJU0c39W2J4J4/eMtdwV1EZBzrTKRlygqyqW3toiGRllHLXURkAgsl0jKVU/NoDUXYUddOVjAwYv3cRURkFPTk3OdMzQVg/d4mSguysJ7O7wNQcBcRGcc6w1HysoLMnpIHwNsHWyg9Sk8ZUHAXERnXOrtj5GQFqSyNB/eYQ2n+4Pl2UHAXERnXenLuRTmZvW+llh3lYSoouIuIjFvu3ptzB3rz7mVKy4iITFzhqBONOblZ8eBeOTWemlHOXURkAusZ7jenp+WeeKiqnLuIyATWM8VeT1pmtlruIiITX88sTLlZ8VC9Yk4JuZlBFlUUHPXYow4cJiIiY6PziJb7ibOK2fz1/qa0fje13EVExqkjc+7HQsFdRGScCnUf3nI/FgruIiLjVEciuOdlHXsGXcFdRGScau2KT4hdeJQRIPuj4C4iMk61dEYABXcRkbTSGuppuWce87EK7iIi41RLKEJOZoCsjGMP1Uc9wszuNLNaM9vYp+z/mtkWM3vDzB4ws5JE+Twz6zSz9YnPT465RiIiAsRb7sNptcPQWu4/B47sNf84cKK7nwxsBb7UZ9sOd1+e+Hx6WLUSERFaQpFh5dthCMHd3Z8FGo8o+727RxKrLwOzh3V1EREZUEtnmKIRbLkfzZ8Bj/RZn29mr5vZM2Z2fgrOLyIyKbUm0XJPamwZM/syEAHuSRQdACrdvcHMTgP+y8xOcPeWfo5dBawCqKysTKYaIiJpqTUUZlZJ7rCOHXbL3cw+AVwJXO/uDuDuXe7ekFheB+wAlvR3vLuvdveV7r6yvLx8uNUQEUlbLaEIRbkjlHPvj5ldDvw9cJW7d/QpLzezYGJ5AbAY2DmsmomITHLJ9JY56j8JZnYvcBFQZmb7gK8S7x2TDTxuZgAvJ3rGXAD8LzMLAzHg0+7e2O+JRURkQN2RGKFwjMLsEcq5u/t1/RTfMcC+9wH3DasmIiLSq+ft1KLcsestIyIiKdYaGv64MqDgLiIyLr0T3NVyFxFJGy09aRm13EVE0kcyI0KCgruIyLjUopy7iEj6aelUbxkRkbTT80C1YJj93BXcRUTGoZZQmILsDIIBG9bxCu4iIuNQMiNCgoK7iMi41NTRzZS8rGEfr+AuIjIONbZ3MyV/eA9TQcFdRGRcauoIq+UuIpJuGju6mZqv4C4ikjYi0RjNnWq5i4iklebOMO4wJU85dxGRtHGoI/526hSlZURE0sehjm4A5dxFRNJJY3s8uCvnLiKSRg71BHe13EVE0kdjT1pGLXcRkfTR1BEmJzNAblZw2OcYUnA3szvNrNbMNvYpm2pmj5vZtsTPKYlyM7MfmNl2M3vDzE4ddu1ERCahxvbupFrtMPSW+8+By48o+yLwhLsvBp5IrAO8D1ic+KwCbkuqhiIik8yh9m5KRiO4u/uzQOMRxVcDdyWW7wI+2Kf8bo97GSgxsxlJ1VJEZBJJdugBSC7nPs3dDySWDwLTEsuzgL199tuXKBMRkSFo6ggn1VMGUvRA1d0d8GM5xsxWmdlaM1tbV1eXimqIiKSFeM59+EMPQHLBvaYn3ZL4WZsorwbm9NlvdqLsMO6+2t1XuvvK8vLyJKohIpI+WkNhmjvDTCvOSeo8yQT3h4AbE8s3Ag/2Kf94otfMWUBzn/SNiIgMYndDBwDzS/OTOs+QJugzs3uBi4AyM9sHfBW4Ffi1md0M7AauSez+MPB+YDvQAdyUVA1FRNJYJBojI/hOO7uqvh2AeWWjENzd/boBNl3az74OfDaZSomITAbrdh/iutUv88QXLmTO1DwAdvUE9yRb7npDVURkjDy1pZbuaIxtta29ZVUN7cwozknq7VRQcBcRGTNrd8dfH6pp6eot21XfnnSrHRTcRUTGRDgaY8PeZgBqWkK95VX17Unn20HBXURkTGw+0EJnOAq803Jv7ghzqCPM/LK8pM+v4C4iMgbW7joEQFlBNrWJlntVQ2oepsIQe8uIiEhqvbGviRnFOSydXsjBRHCvPtQJ0NtzJhlquYuIjIGqhg4WlhcwvTinNy1T3xb/WVGYnfT5FdxFRMbAnoZ2KkvzqCjMoaG9i3A0Rl1rF8GAJTV3ag+lZURERllzZ/zB6dypeRTmZOIeb7XXtXZRmp9FIGBJX0PBXURklO1JjB8ztzSPzMTQAzUtXdS3dVFWkHxKBhTcRURG3e7GeK+YuaX5RGPx0dJrWkLUtXVRnoJ8OyjnLiIy6npGfqycmse0ovjQvjUtIepb1XIXEZmw9jR0UFaQTX52BrmZQTICxv4mtdxFRCa0XQ3tzC2N92UPBIyF5QW8uquRcNQpK0i+pwwouIuIjLo9jR29wR3ghJlFvL4n/saqWu4iIhNQayjMgeYQC/oMDrZsZhGJ56qUpyjnruAuIjKKthyMj92+bGZRb1nfZbXcRUQmoE3V8WF+l80o7i07oc9yqnrLKLiLiIyitw60UJqfxbSid4J4cV4ms0pyyQwaxbmZKbmOgruIyCh660ALy2YWYXb4EAMnzSqmojAnJUMPgPq5i4iMmnA0xtaDbdx07rx3bfvyFcf3jgqZCsMO7mZ2HPCrPkULgH8GSoA/B+oS5f/o7g8P9zoiIulie20b3dHYYQ9Qe8yZmpeScdx7DDu4u/vbwHIAMwsC1cADwE3A99z926mooIhIuthaE+8ps3T6u4N7qqUq534psMPdd6fofCIiaWdnXTtmHPYC00hJVXC/Fri3z/otZvaGmd1pZlP6O8DMVpnZWjNbW1dX198uIiJpZVdDO7NKcsnJDI74tZIO7maWBVwF/CZRdBuwkHjK5gDwnf6Oc/fV7r7S3VeWl5cnWw0RkXGvqr6d+WXJT349FKloub8PeM3dawDcvcbdo+4eA24HzkjBNUREJjR3p6puYgX36+iTkjGzGX22fQjYmIJriIhMaA3t3bR2RUYtuCfVz93M8oHLgE/1Kf6WmS0HHNh1xDYRkUmpqj4++9K8iRDc3b0dKD2i7IakaiQikoaq6uLBfcEESsuIiKS1Rzce4KUdDUmdo6qhnYyAMaskN0W1GpyGHxARGUQkGuOzv3idaMy56pSZfP/a5e8aF2YoNh9oYX5ZPhnB0WlTq+UuIjKI+rZuojHnuGmFPLRhP89sPfb3cqIxZ92uQ6ycN3UEatg/BXcRkUHUtoYA+Nx7FlM5NY9bH9lCrGfapCHacrCF1q4IZ8zv953OEaHgLiIyiNqW+EiNM0tyueXiRWw52Mqm/S1DPr6tK8KrVY0AnDG/9Ch7p45y7iIig6htjQf3isJsejLtNS0hTqJ44IMSXtxRz/U/XUNFYTazSnJH7WEqqOUuIjKonrRMWUF27/ymdUMcd/2pLbW4Q01LF6fPG72UDKjlLiIyqNrWLqbmZ5GVEaC0IAuAutahBfdXqho5be4U3n/SDC5cMrpjaKnlLiIyiNqWLioSLfbsjCAleZlDCu5tXRE27m/hnIWl3HzefBZVFIx0VQ+j4C4iMoi61lBvOgagvCB7wOAejTnPb6vH3Xlt9yGiMeeM+aPX/bEvBXcRkUHUtnZRUZjTu15emD1gzv236/bysTvWsGFfM69UNRIMGKdWjm6uvYeCu4jIAGIxp661i4qiPi33wuwBJ7J+aMN+ID5X6pvVzSyZVkh+9tg82lRwFxEZwKGObiIx7825Q7zXTH9pmbrWrt7xZ6rq29he28biUc6z96XgLiIygJ70y5FpmY7uKO1dkcP2fWTjAWIO+VlB3trfQnVTJwvLxy64qyukiMgAdjd0AByelilI9HVv7Tos5fLUlloWlOczrzSfF7bXA4x6D5m+1HIXETnCPWt2c/9r+/jXhzczsziHE2YW9W7r70Umd+f1vU2cPncq80rz6YrEgLEN7mq5i4j00dYV4csPxGcHDRj8ctXZ5GW9Eyp7g3ufvPuuhg6aOsKsqCwhnBhULGAwryxvFGt+OAV3EZE+ttW0AnDzefM5Y/7Ud/VT7y+4v77nEAArKqf09qSZW5pPdkZwNKrcLwV3EZn0altClOTFhxjYVtMGwA1nze13vtMpeVkEA8bBllBv2et7mijIzmBRRQEFOfGwOpYPU0E5dxGZ5Nq7Ilz63Wf4zu/fBmBrTSvZGQHmTO0/pRIMGPNK89he29Zb9vreQ5wyp5hgwJhRlEN5YTYrKktGo/oDSjq4m9kuM3vTzNab2dpE2VQze9zMtiV+js0rWiIiR/HU27W0hiLc91o1kWiMbbVtLCwvIBgYeCq9pTOKePtgPH1TVd/OW/tbOD0xy1IgYDz5hQv51AULRqX+A0lVy/1id1/u7isT618EnnD3xcATiXURkTEXf2D6Jr9Zuxd355E3D2IG9W1dPL+9nm01rSyZNnhKZem0QvY0dtDWFeFHT20nMxjgT8+s7N1emJM5anOlDmSkcu5XAxcllu8Cngb+YYSuJSIyJK2hMB/7aXzsl3vW7OF/3jzAmp2NfOTU2fz+rRp+9sIu9jeHWDytcNDzHDc9vv2JzTU88Ho1N54977AXncaDVAR3B35vZg78h7uvBqa5+4HE9oPAtBRcR0QkKQ+u38+Gfc3cdv2pVDd18u9PbqczHOVDp86itCCbnzyzA4AlRwnux8+I93v/xv9sxoBPXTi2KZj+pCK4n+fu1WZWATxuZlv6bnR3TwT+w5jZKmAVQGVl5ZGbRURS5tZHtnDp8RW8vLOB6UU5XH7idMyMa8+oZFN1M2cuKOXsBaVUTs3jwfXVrJw7+GPCWSW55GcFqW3t4qpTZjKtaHy12iEFwd3dqxM/a83sAeAMoMbMZrj7ATObAdT2c9xqYDXAypUrj20qcRGRIapu6uQnz+zgtT2H2FnXznmLSjGLPywtyM7gzAXxSavNjD89s/Kw3PlAAgFjyfRCXt/TxI3nzB3R+g9XUhl/M8s3s8KeZeC9wEbgIeDGxG43Ag8mcx0RkeFau6sRiE95V9/WxVmJYJ6sy0+YzmXLpo3ZeO1Hk2zLfRrwQOJfwQzgF+7+qJm9CvzazG4GdgPXJHkdEZFheaWqkZzMAN2RGDGHsxemJrh/6sKFfColZxoZSQV3d98JnNJPeQNwaTLnFhEZrgfXV7NizhQqS/N4dVcjZ8wvJSNgbKttpXKAl5PSjYYfEJG0srOujc/9cj2XLq3gO9ecwtaaNq46ZSY3nDWP9u5Ib7493Sm4i0ha+fXafQA8+XYt339iGwCnz5tKcV4mxXmZY1m1UaWxZUQkLbg7tS0hfrtuHyvnTiEjYPzshV1cuKS8d2iAyUQtdxFJC1/4zQbuf60agFv/+CSe21bHmqpGfnDtCgKDjBOTrhTcRWTCe2lHA/e/Vs2HT53NBUvKuPT4Ci49vgJ3JmVgBwV3EZng3J1vPPwWs0py+caHTiQn850JMibJs9N+KecuIhPavkOdbKxu4ebz5h8W2Cc7BXcRmdDW7o6/gZqqN0/ThYK7iExor+46RGF2Ru8wvBKn4C4iKdUVifI3v1rPY5sOjsr11u5q5NS5UwadOWkyUnAXkaR0R2Jc+e/PcesjW3B3vvv7rdz/ejV/de/rvLmveUSv3dTRzdaatqMO0TsZqbeMiCTlhe31bKxuYWN1S3x5fzNXL5/J2l2HuPmuV/nNp89mbmn+iFy756+DlZPwJaWjUctdRJLyuzcOUJiTwacvXEjA4ONnzeX/fOgkfnbT6XRHY1z/0zU0d4QBiMac57fVs6u+HffkpnGoburkf//PZk6tLOGM+QruR1LLXUSO2e3P7uSCJeXMK8vj928d5I9OmM4X37f0sH2WTCvkjhtP58O3vcidL1Tx15ct4Z41u/nnBzcB8OX3H8+fX3Bs09OFozFC4Sh5WRn8za/WE4s53/vocuXb+6HgLiLH5O2DrXzj4c08saWGj501l9ZQhCtOmtHvvqfNncJ7l03jZy9UcfP58/n5i7s4YWYR0Zjzuzf2H1Nw/+7jW/nRU9uJuXP63Km8squRb33k5BFL+Ux0SsuIyDF5aEN8/JaXdzbyzw9uYsm0As5fXDbg/n95yWJaQhGuW/0yO+vaufm8+Vx58gw27GumtjXUu18s5kRj/adqtte28qOntnPB4jKuPb2SV3c3cvkJ0/mT02an9sulEQV3ERkyd+e/NxzgtLlTKMrJoLG9m6994AQyggOHkpNmF/O1DyxjR10bZQXZXHHyDC49fhoAT2+p693vnx7cyEf/46V3Hd8difH1320mLzPIt//kFP71j0/i2b+7mB9ct2LSjM0+HErLiMiQvbaniT2NHdxyySIM2NPYwTmLBm619/jEufO5/MQZdEdiZGcEWTq9kJnFOTy+uYZrTp9DdyTGQxv20xqKUN/WRVlBNgAHm0N8/M41bK1p45+uXEZponzOJJlNKRkK7iIyZP/2h60U52byvhOnU5hzbBNfTC/O6V02M95/0gx+/uIu9jZ2UFXfTmsoAsDLOxu4+LgK8rKC/Pjp7eyq7+DOT6zkkqXTUvpd0p2Cu4gMqrM7ysfuWENOZoAXtjfwlSuOP+bA3p9Pnr+Au1/azW3P7MCAvKwgBjy4fj9ffmAjp8+bwgvbG7h6+UwF9mFQcBeRQf3sxSrW7Y6P3zK/LJ8bzp6bkvNOL87hmtNn84s1e8gIBLj0+ApC4SiPv1VDwOAPm2sBuPn8+Sm53mQz7OBuZnOAu4FpgAOr3f37ZvY14M+Bnicl/+juDydbUREZfbUtIW57egeXLq3gR9efSjTmZGekbljdL1x2HFnBIE2d3dx0znxe2lnPU2/XseqChUzJy6SxvZul04tSdr3JJJmWewT4gru/ZmaFwDozezyx7Xvu/u3kqyciEO+lEgrHyMoIjMoLO+7Og+v38y//vYmuSIy/v3zpiIyVPiU/i3/+wLLe9WnF2Rxs7uKWSxZRkK3EQjKGfffc/QBwILHcamabgVmpqpiIxLV1RfjQj15gW20bp1aWcN9fnDOiXQCjMecz96zjsU01rKgs4ZsfPpkl00ZnON2KwpzDgr0MX0r6uZvZPGAFsCZRdIuZvWFmd5qZhmsTScLPnq9iW20b7102jdf2NLFhhEda/MWa3Ty2qYa/+6Pj+O2nzxm1wC6plXRwN7MC4D7g8+7eAtwGLASWE2/Zf2eA41aZ2VozW1tXV9ffLiKTXnNnmNuf28l7jp/Gdz+6nPysIPe8vHvErlff1sW3Hnub8xaV8ZmLFmrMlgksqeBuZpnEA/s97n4/gLvXuHvU3WPA7cAZ/R3r7qvdfaW7rywvL0+mGiJp4band/DJu15l3e5DxGJOfVsXN/3sFdq6Ivz1ZYspyM7g6hWzeGjDfv71kc3UtXalvA4/eGIbHd1RvnbVCXr7c4JLpreMAXcAm939u33KZyTy8QAfAjYmV0WR9Ffd1Mn3Ht9KOBbjD5trKcjOoL07QmYgwI+vP5UTZhYD8JmLFrKzro07nqtiW00bd37i9GO+Vlckypfuf5MXttezbEYRd37idMyMvY0d3PvKHq5ZOYdFFQWp/ooyypJ5HH0ucAPwppmtT5T9I3CdmS0n3j1yF/CpJK4hMin88MltADz2+QtYv6eJN6ubKS/M5rJl0zh+xjtdAWdPyeOXq87mtqd38M1Ht/DSjgbOXnhsE0P/y3+/xf2vVbNy7hSeeruOl3c2sqOujTueryJgxucuXZzS7yZjw5IdMD8VVq5c6WvXrh3raoiMiYa2Ls78P09w3RmVfP2DJw7pmFA4ysXffpqZJbnc9xfnDPlaD795gM/c8xqfvnAhn3/PYs775pNEYk5TR5hTZhdzyyWLuWyZ3gadKMxsnbuv7G+bRoUUGWOPbjpIJOZce8acIR+Tkxnk5vPms273IbbVtB51/1d3NbKxupmvPbSJE2cV8bfvXUJOZpCbzp1PU0eYG86ay3999lwF9jSitwRExtjvNhxgQVk+y2Yc25uYH1wxi1sf2cJv1+3jS+8/fsD9ntxSw5/9PP6XccDgpzeu7B2i91MXLOC0uVM4Y95UPUBNMwruImOotjXEmqoGbrl40TEH17KCbC46roL7X6/mxnPmMbMkN37OlhBPv13HBUvKcZy/+fUGjp9RxNXLZzI1P4uTZ5f0niMjGOCsBceWs5eJQcFdZAzd/eJuYg4fOGXmsI6/6dx53HDHGs795pNkBQOEozF6JjO66LhyKgqz6eiO8uPrT2V+maajm0wU3EXGSHVTJ7c/t5OrTpnJ4mG+BXruojKe+buLeXB9Na1dEbKCAQpzMqhr7eL256owgxvPnqfAPgkpuIuMoHA0xos7GphZnENlaR53PF/F/3tpN3/9niX8Zt1eAP7hfUuTusacqXnccsnh3RdD4Si/e+MAhzq6+czFC5M6v0xMCu4iI+jHT+3ge3/YCoAZuMdz5X9/3xtkZwT41kdOZlYiV55KOZlBfvKx02jqDFNRmHP0AyTtKLiLJGl3Qzu/eGUPWcEAxbmZdEdjlORmsWxmEbc/t5OLjivng8tnsb22jcqpeVy9Yib/+fIezl5QyrKZIzdW+SlzSkbs3DL+KbiLDFNta4h/+8M2fvnKHoIBI+bx4XL7Chh85Ypl73qd/+bzNLuQjCwFd5FjEInGeGxTDfe9to9nttZhwMfPnsdnLlpIcV4mnd1RsjOC1LSEeHD9fqYWZGmcFhkTCu4iQ7R+bxP/8Ns3eLumlelFOay6YAHXrJxzWE+Unino5pXl87n3aIwWGTsK7iJ9tITCPLm5lmlFOSyeFm9x17d1MSUvi5t+9gq5mUFuu/5U3nvCdI11LuOagrtIwoHmTm688xW21rS9a1tBdgaRWIzffPpsFlVoZiIZ/xTcZdLqjsQIWPwV/IPNIa75j5c41B5m9Q2nkZeVwdaaVmLuZGUE+PXavdx83nwFdpkwFNxlVLk7+5tDTC/K4ZGNByjIzuCi4yretV9bV4T8rGDKB7PqikTZfKCV367by4Pr95OdEeD6M+dy32v7aOoI85+fPJPliS6E5y0u6z3u42fPS2k9REaagruMqm899ja3Pb2D3MwgneEoBdkZvPDFSyjOzQSgqaObbz66hd+s3cc5i8r49kdOprwwm+21bcwryyczeOyjVIejMd7Y18y3Ht3CmqpGALIzArzvxOlU1bfz/Se2sXR6Id+/dkVvYBeZ6BTcZVTEYs69r+7htqd38N5l05ian8WcqXn838fe5j9f3s1nL15ESyjMDXe8wpaDLVx+4nQef6uGs299krKCLGpauvjkefP5ypXLhnzNSDTGD57Yxk+e3Ul3JEZpfhZ/eckiFpYXcMnxFRTlZBKNOfubOpk9JVdD3kpaUXCXERGOxujoirJhXxNPbK7h+e317Khr58z5U/nhn55KVka8Bb6mqpE7n6+iKDeTu1/cxa6GdlbfsJKLl1aws66NB16vZnttG00dYe5+eTd/dt783qFt+xOJxnh000EeefMga6oaqG/r5gOnzOSSpeVcsnRa718IPYIBY87UvBG9FyJjQdPsSco9tGE/X3ngTVpCEQByM4OsqCzhT1bO5qpTZh3WhXBjdTOfvGstB1tCzCrJ5dYPn8T5i8vfdc59hzq45NvPcMqcYj586myuXj6L3KzgYfvsrGvjb369gfV7mygvzOb8RWVcfuJ03nvC9JH9wiJjZLBp9hTcZcjcnbrWLl7f28Tjb9VQVpDN/LI8zIxozNmwt4kXdtSzt7GTFZUlXHnyTCqn5nH+4jJyMoMDnjcaczYfaGFBeT55WQP/MXnn81X86KntNLR3U1aQzZUnz2BKXhb7mzpp747w6MaD5GUF+foHT+TKk2eqH7qkvTEJ7mZ2OfB9IAj81N1vHWhfBffREwpHeWNfM49uPMj2ujb2NnbQ0hlm2cwiVswpIRJz9jR2cMnSCo6fUcTWmlZe3tlIKBzlpR0NHGwJAVCUk0FHd5RIn7FUCnMyOGtBKRcuKeejp88Z1sPPo3F31u4+xG1P7+DFHfV0RWKUFWRjwHuWTePzly6mokijIMrkMOrB3cyCwFbgMmAf8Cpwnbu/1d/+PcE9Eo3hMCJBId2EozECZtS1drF+7yE27W+hKCeTBeX5lBdmU9faRV5WBu1dEd460MKm/c1s2t/CvkOdQLy3yHHTC5kzJY/87CAbq1vYcrCFgBkleVnUt3X1Xqs4N5O8rCAnzy7mnIVlLK4o4PT5U4nGnPq2Ltzjw9nOKM4d1dZydyRGNObvSs+ITBaDBfeReqB6BrDd3XcmKvBL4Gqg3+Aejsa44gfPsa22je5IjJnFOVy8tILO7igtoTBdkRjVhzq5/MTpfPL8BUzJyzysZ4O7Y2a4O53hKF3hGMW5mcTciTlkBm3QnhDuTiTmh01RFrB4rjhVPShiMSfq3jtqYMCMYCBe565IjO5IjO5o/Gff9a5wtLe8uTPM2zWtvFLVyJv7mg9rNQcMYoP8Oz2/LJ9T5pRwzco5LKoo4IIl5RRkH/6fv6M7niPPyQjyZnUzew91UFaQzRnzphLoJ2hnBmH2lLF7GNnzUFZE3m2kgvssYG+f9X3AmQNWIhCgrCCb8xaVkZsV5K39Lfxm3T6m5GVSmp9NMGDMLMnlx0/v4MdP7yAjEG9d5mYFaO+K0tTRTW5mkFCiJQeHB7tgwMjNDJKXFSQzGKA7GiMcjRGOxAhHne5orN96BSzeai3IySCQCPI9Ic6OWO9ZMOITMoTCUToTn1C4//MPR2bQOHl2CTefN5+czCDFuZmsqCxh2cwiOrqi7Kxvp661i4qibDq7o2RlBDh+RtG7Anl/+ua7T5lTovHARSawMesKaWargFUAlZWV3PVnZxy2vac13tf6vU2s3dVIY3s3hzq6CYVj5GQGmZqfSWd3jNysAIU5mWQGAzR1dJMZDBAw6AxH6eiO0t4VSbTkA2QFjYxgoHc5MxggMyNA0Awn3uJvC0Vo7gzTGgrH69RbN45Y98PWjXirPzcrSG5mkOyMAIGAETTrbQG7O9FYPJ2RnREgq+cTjP/sLQsGe9eLcjOZUZwz4MPJ7Iwgp+VnDfu/iYikj5EK7tXAnD7rsxNlvdx9NbAa4jn3I0/QXzpk+ZwSvUEoIjIEI5W0fBVYbGbzzSwLuBZ4aISuJSIiRxiRlru7R8zsFuAx4l0h73T3TSNxLRERebcRy7m7+8PAwyN1fhERGZj6komIpCEFdxGRNKTgLiKShhTcRUTSkIK7iEgaGhdD/ppZM7AtiVMUA80T+PgyoH4Mrz/Rj9f90/2brPdvrru/ewIEiL8GP9YfYPUkP37tBK//WB+v+6f7N2nv30Cf8ZKW+e9Jfnyyxrr+Y318ssa6/mN9fLLGuv5jfXyyRuT64yItM9mZ2VofYExmOTrdv+To/iVnvN6/8dJyn+xWj3UFJjjdv+To/iVnXN4/tdxFRNKQWu4iImlIwX2EmNkcM3vKzN4ys01m9rlE+VQze9zMtiV+TkmUm5n9wMy2m9kbZnbqEecrMrN9ZvbDsfg+oy2V98/MvmlmGxOfj47VdxpNw7h/S83sJTPrMrO/7ed8QTN73cx+N9rfZSyk8v6Z2ecSv3ubzOzzo/UdFNxHTgT4grsvA84CPmtmy4AvAk+4+2LgicQ6wPuAxYnPKuC2I873deDZ0aj4OJGS+2dmVwCnAsuJT/X4t2ZWNIrfY6wc6/1rBP4K+PYA5/scsHlkqzyupOT+mdmJwJ8Tn1f6FOBKM1s0Gl9AwX2EuPsBd38tsdxK/H+MWcQnCr8rsdtdwAcTy1cDd3vcy0CJmc0AMLPTgGnA70fvG4ytFN6/ZcCz7h5x93bgDeDy0fsmY+NY75+717r7q0D4yHOZ2WzgCuCnI1/z8SGF9+94YI27d7h7BHgG+OOR/wYK7qPCzOYBK4A1wDR3P5DYdJB40Ib+JxWfZWYB4DvAu/5UniySuX/ABuByM8szszLgYg6fAjLtDfH+DebfgL8HUjfT+wSS5P3bCJxvZqVmlge8n1H6/RuzCbInCzMrAO4DPu/uLX3nhnV3N7OjdVf6DPCwu+/rb17ZdJfs/XP335vZ6cCLQB3wEhAdwSqPK8nePzO7Eqh193VmdtFI1nU8SsHv32Yz+ybxv7rbgfWM0u+fWu4jyMwyif9i3OPu9yeKa/qkW2YAtYnygSYVPxu4xcx2Ec/nfdzMbh2F6o+5FN0/3P0b7r7c3S8DDNg6GvUfa8d4/wZyLnBV4vfvl8AlZvafI1TlcSVF9w93v8PdT3P3C4BDjNLvn4L7CLH4P/F3AJvd/bt9Nj0E3JhYvhF4sE/5xxO9Ps4CmhN5v+vdvdLd5xFPzdzt7l8kzaXq/iV6eZQmznkycDKT4NnFMO5fv9z9S+4+O/H7dy3wpLt/bASqPK6k6v4lzlWR+FlJPN/+i9TWdgAjMWCNPg5wHuDEH+CtT3zeD5QSf8q+DfgDMDWxvwE/AnYAbwIr+znnJ4AfjvV3m0j3D8gB3kp8XgaWj/V3G6f3bzrx5xQtQFNiueiIc14E/G6sv9tEu3/Ac4nfvw3ApaP1HfSGqohIGlJaRkQkDSm4i4ikIQV3EZE0pOAuIpKGFNxFRNKQgrtMSmYWNbP1iZH6NpjZFxJDPQx2zDwz+9PRqqNIMhTcZbLq9PhbqycAlxEfVfKrRzlmHqDgLhOC+rnLpGRmbe5e0Gd9AfAqUAbMBf4fkJ/YfIu7v2hmLxMf5a+K+IiAPwBuJf5yTzbwI3f/j1H7EiKDUHCXSenI4J4oawKOA1qBmLuHzGwxcK+7r0wMnPW37n5lYv9VQIW7/28zywZeAP7E3atG8auI9EujQoq8WybwQzNbTnwEvyUD7Pde4GQz+0hivZj4ZCEK7jLmFNxF6E3LRImP8vdVoIb4zDkBIDTQYcBfuvtjo1JJkWOgB6oy6ZlZOfAT4oOyOfEW+AF3jwE3AMHErq1AYZ9DHwP+IjE0LGa2xMzyERkH1HKXySrXzNYTT8FEiD9A7Rna9cfAfWb2ceBR4pMsQHyEwKiZbQB+DnyfeA+a1xJDxNbxzrR/ImNKD1RFRNKQ0jIiI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A prettier picture with hoverdata\n","import plotly.graph_objects as go\n","trace1= go.Scatter(x=ret_monthly.index.to_timestamp(\"M\"), y=(1+ret_monthly).cumprod(), mode=\"lines\")\n","fig = go.Figure()\n","fig.add_trace(trace1)\n","fig.show()"]},{"cell_type":"code","execution_count":12,"metadata":{"trusted":true},"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>Open</th>\n","      <th>High</th>\n","      <th>Low</th>\n","      <th>Close</th>\n","      <th>Adj Close</th>\n","      <th>Volume</th>\n","      <th>ret</th>\n","      <th>year</th>\n","      <th>month</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01-03</th>\n","      <td>0.9364</td>\n","      <td>1.0045</td>\n","      <td>0.9079</td>\n","      <td>0.9994</td>\n","      <td>0.8519</td>\n","      <td>535796800</td>\n","      <td>NaN</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-04</th>\n","      <td>0.9665</td>\n","      <td>0.9877</td>\n","      <td>0.9035</td>\n","      <td>0.9152</td>\n","      <td>0.7801</td>\n","      <td>512377600</td>\n","      <td>0.9157</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-05</th>\n","      <td>0.9263</td>\n","      <td>0.9872</td>\n","      <td>0.9196</td>\n","      <td>0.9286</td>\n","      <td>0.7915</td>\n","      <td>778321600</td>\n","      <td>1.0146</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-06</th>\n","      <td>0.9475</td>\n","      <td>0.9554</td>\n","      <td>0.8482</td>\n","      <td>0.8482</td>\n","      <td>0.7230</td>\n","      <td>767972800</td>\n","      <td>0.9135</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2000-01-07</th>\n","      <td>0.8616</td>\n","      <td>0.9018</td>\n","      <td>0.8527</td>\n","      <td>0.8884</td>\n","      <td>0.7573</td>\n","      <td>460734400</td>\n","      <td>1.0474</td>\n","      <td>2000</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-23</th>\n","      <td>132.1600</td>\n","      <td>132.4300</td>\n","      <td>130.7800</td>\n","      <td>130.9600</td>\n","      <td>129.4066</td>\n","      <td>88223700</td>\n","      <td>0.9930</td>\n","      <td>2020</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-24</th>\n","      <td>131.3200</td>\n","      <td>133.4600</td>\n","      <td>131.1000</td>\n","      <td>131.9700</td>\n","      <td>130.4046</td>\n","      <td>54930100</td>\n","      <td>1.0077</td>\n","      <td>2020</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-28</th>\n","      <td>133.9900</td>\n","      <td>137.3400</td>\n","      <td>133.5100</td>\n","      <td>136.6900</td>\n","      <td>135.0686</td>\n","      <td>124486200</td>\n","      <td>1.0358</td>\n","      <td>2020</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-29</th>\n","      <td>138.0500</td>\n","      <td>138.7900</td>\n","      <td>134.3400</td>\n","      <td>134.8700</td>\n","      <td>133.2702</td>\n","      <td>121047300</td>\n","      <td>0.9867</td>\n","      <td>2020</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12-30</th>\n","      <td>135.5800</td>\n","      <td>135.9900</td>\n","      <td>133.4000</td>\n","      <td>133.7200</td>\n","      <td>132.1338</td>\n","      <td>96452100</td>\n","      <td>0.9915</td>\n","      <td>2020</td>\n","      <td>12</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5283 rows × 9 columns</p>\n","</div>"],"text/plain":["               Open     High      Low    Close  Adj Close     Volume    ret  \\\n","Date                                                                          \n","2000-01-03   0.9364   1.0045   0.9079   0.9994     0.8519  535796800    NaN   \n","2000-01-04   0.9665   0.9877   0.9035   0.9152     0.7801  512377600 0.9157   \n","2000-01-05   0.9263   0.9872   0.9196   0.9286     0.7915  778321600 1.0146   \n","2000-01-06   0.9475   0.9554   0.8482   0.8482     0.7230  767972800 0.9135   \n","2000-01-07   0.8616   0.9018   0.8527   0.8884     0.7573  460734400 1.0474   \n","...             ...      ...      ...      ...        ...        ...    ...   \n","2020-12-23 132.1600 132.4300 130.7800 130.9600   129.4066   88223700 0.9930   \n","2020-12-24 131.3200 133.4600 131.1000 131.9700   130.4046   54930100 1.0077   \n","2020-12-28 133.9900 137.3400 133.5100 136.6900   135.0686  124486200 1.0358   \n","2020-12-29 138.0500 138.7900 134.3400 134.8700   133.2702  121047300 0.9867   \n","2020-12-30 135.5800 135.9900 133.4000 133.7200   132.1338   96452100 0.9915   \n","\n","            year  month  \n","Date                     \n","2000-01-03  2000      1  \n","2000-01-04  2000      1  \n","2000-01-05  2000      1  \n","2000-01-06  2000      1  \n","2000-01-07  2000      1  \n","...          ...    ...  \n","2020-12-23  2020     12  \n","2020-12-24  2020     12  \n","2020-12-28  2020     12  \n","2020-12-29  2020     12  \n","2020-12-30  2020     12  \n","\n","[5283 rows x 9 columns]"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# Alternatively, monthly returns can be calculated by compounding daily rets\n","df['ret']= df['Adj Close'].pct_change()+1\n","df['year']=df.index.year\n","df['month']=df.index.month\n","df\n"]},{"cell_type":"code","execution_count":13,"metadata":{"trusted":true},"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>year</th>\n","      <th>month</th>\n","      <th>ret</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2000</td>\n","      <td>1</td>\n","      <td>-0.0731</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2000</td>\n","      <td>2</td>\n","      <td>0.1048</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2000</td>\n","      <td>3</td>\n","      <td>0.1848</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2000</td>\n","      <td>4</td>\n","      <td>-0.0865</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2000</td>\n","      <td>5</td>\n","      <td>-0.3229</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>247</th>\n","      <td>2020</td>\n","      <td>8</td>\n","      <td>0.2166</td>\n","    </tr>\n","    <tr>\n","      <th>248</th>\n","      <td>2020</td>\n","      <td>9</td>\n","      <td>-0.1025</td>\n","    </tr>\n","    <tr>\n","      <th>249</th>\n","      <td>2020</td>\n","      <td>10</td>\n","      <td>-0.0600</td>\n","    </tr>\n","    <tr>\n","      <th>250</th>\n","      <td>2020</td>\n","      <td>11</td>\n","      <td>0.0955</td>\n","    </tr>\n","    <tr>\n","      <th>251</th>\n","      <td>2020</td>\n","      <td>12</td>\n","      <td>0.1232</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>252 rows × 3 columns</p>\n","</div>"],"text/plain":["     year  month     ret\n","0    2000      1 -0.0731\n","1    2000      2  0.1048\n","2    2000      3  0.1848\n","3    2000      4 -0.0865\n","4    2000      5 -0.3229\n","..    ...    ...     ...\n","247  2020      8  0.2166\n","248  2020      9 -0.1025\n","249  2020     10 -0.0600\n","250  2020     11  0.0955\n","251  2020     12  0.1232\n","\n","[252 rows x 3 columns]"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["mrets = df.groupby(['year','month'])['ret'].prod()-1\n","mrets = mrets.reset_index()\n","mrets"]},{"cell_type":"code","execution_count":14,"metadata":{"trusted":true},"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>year</th>\n","      <th>month</th>\n","      <th>ret</th>\n","      <th>date</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2000-01</th>\n","      <td>2000</td>\n","      <td>1</td>\n","      <td>-0.0731</td>\n","      <td>2000-01</td>\n","    </tr>\n","    <tr>\n","      <th>2000-02</th>\n","      <td>2000</td>\n","      <td>2</td>\n","      <td>0.1048</td>\n","      <td>2000-02</td>\n","    </tr>\n","    <tr>\n","      <th>2000-03</th>\n","      <td>2000</td>\n","      <td>3</td>\n","      <td>0.1848</td>\n","      <td>2000-03</td>\n","    </tr>\n","    <tr>\n","      <th>2000-04</th>\n","      <td>2000</td>\n","      <td>4</td>\n","      <td>-0.0865</td>\n","      <td>2000-04</td>\n","    </tr>\n","    <tr>\n","      <th>2000-05</th>\n","      <td>2000</td>\n","      <td>5</td>\n","      <td>-0.3229</td>\n","      <td>2000-05</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>2020-08</th>\n","      <td>2020</td>\n","      <td>8</td>\n","      <td>0.2166</td>\n","      <td>2020-08</td>\n","    </tr>\n","    <tr>\n","      <th>2020-09</th>\n","      <td>2020</td>\n","      <td>9</td>\n","      <td>-0.1025</td>\n","      <td>2020-09</td>\n","    </tr>\n","    <tr>\n","      <th>2020-10</th>\n","      <td>2020</td>\n","      <td>10</td>\n","      <td>-0.0600</td>\n","      <td>2020-10</td>\n","    </tr>\n","    <tr>\n","      <th>2020-11</th>\n","      <td>2020</td>\n","      <td>11</td>\n","      <td>0.0955</td>\n","      <td>2020-11</td>\n","    </tr>\n","    <tr>\n","      <th>2020-12</th>\n","      <td>2020</td>\n","      <td>12</td>\n","      <td>0.1232</td>\n","      <td>2020-12</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>252 rows × 4 columns</p>\n","</div>"],"text/plain":["         year  month     ret     date\n","date                                 \n","2000-01  2000      1 -0.0731  2000-01\n","2000-02  2000      2  0.1048  2000-02\n","2000-03  2000      3  0.1848  2000-03\n","2000-04  2000      4 -0.0865  2000-04\n","2000-05  2000      5 -0.3229  2000-05\n","...       ...    ...     ...      ...\n","2020-08  2020      8  0.2166  2020-08\n","2020-09  2020      9 -0.1025  2020-09\n","2020-10  2020     10 -0.0600  2020-10\n","2020-11  2020     11  0.0955  2020-11\n","2020-12  2020     12  0.1232  2020-12\n","\n","[252 rows x 4 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Convert date to PeriodIndex\n","mrets['date'] = pd.PeriodIndex(pd.to_datetime(mrets[['year','month']].assign(day=1)), freq='M')\n","mrets.index = mrets.date\n","mrets"]},{"cell_type":"code","execution_count":15,"metadata":{"trusted":true},"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>ret_x</th>\n","      <th>ret_y</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>ret_x</th>\n","      <td>1.0000</td>\n","      <td>1.0000</td>\n","    </tr>\n","    <tr>\n","      <th>ret_y</th>\n","      <td>1.0000</td>\n","      <td>1.0000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       ret_x  ret_y\n","ret_x 1.0000 1.0000\n","ret_y 1.0000 1.0000"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# Combine two methods of monthly returns and check correlation\n","df_joined = pd.merge(mrets['ret'],ret_monthly,left_index=True,right_index=True)\n","df_joined.corr()\n","# df_joined"]},{"cell_type":"markdown","metadata":{},"source":["## Economic data from FRED"]},{"cell_type":"code","execution_count":16,"metadata":{"trusted":true},"outputs":[],"source":["# 10-year Treasury Yield from FRED\n","y10 = pdr(\"DGS10\", \"fred\", start=\"1929-12-01\")"]},{"cell_type":"code","execution_count":17,"metadata":{"trusted":true},"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>DGS10</th>\n","    </tr>\n","    <tr>\n","      <th>DATE</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1962-01-02</th>\n","      <td>4.0600</td>\n","    </tr>\n","    <tr>\n","      <th>1962-01-03</th>\n","      <td>4.0300</td>\n","    </tr>\n","    <tr>\n","      <th>1962-01-04</th>\n","      <td>3.9900</td>\n","    </tr>\n","    <tr>\n","      <th>1962-01-05</th>\n","      <td>4.0200</td>\n","    </tr>\n","    <tr>\n","      <th>1962-01-08</th>\n","      <td>4.0300</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2023-01-13</th>\n","      <td>3.4900</td>\n","    </tr>\n","    <tr>\n","      <th>2023-01-16</th>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2023-01-17</th>\n","      <td>3.5300</td>\n","    </tr>\n","    <tr>\n","      <th>2023-01-18</th>\n","      <td>3.3700</td>\n","    </tr>\n","    <tr>\n","      <th>2023-01-19</th>\n","      <td>3.3900</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>15928 rows × 1 columns</p>\n","</div>"],"text/plain":["            DGS10\n","DATE             \n","1962-01-02 4.0600\n","1962-01-03 4.0300\n","1962-01-04 3.9900\n","1962-01-05 4.0200\n","1962-01-08 4.0300\n","...           ...\n","2023-01-13 3.4900\n","2023-01-16    NaN\n","2023-01-17 3.5300\n","2023-01-18 3.3700\n","2023-01-19 3.3900\n","\n","[15928 rows x 1 columns]"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["y10"]},{"cell_type":"code","execution_count":18,"metadata":{"trusted":true},"outputs":[{"data":{"text/plain":["<AxesSubplot:xlabel='DATE'>"]},"execution_count":18,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# A quick and dirty plot\n","y10.plot()"]},{"cell_type":"code","execution_count":19,"metadata":{"trusted":true},"outputs":[{"data":{"application/vnd.plotly.v1+json":{"config":{"plotlyServerURL":"https://plot.ly"},"data":[{"mode":"lines","type":"scatter","x":["1962-01-02T00:00:00","1962-01-03T00:00:00","1962-01-04T00:00:00","1962-01-05T00:00:00","1962-01-08T00:00:00","1962-01-09T00:00:00","1962-01-10T00:00:00","1962-01-11T00:00:00","1962-01-12T00:00:00","1962-01-15T00:00:00","1962-01-16T00:00:00","1962-01-17T00:00:00","1962-01-18T00:00:00","1962-01-19T00:00:00","1962-01-22T00:00:00","1962-01-23T00:00:00","1962-01-24T00:00:00","1962-01-25T00:00:00","1962-01-26T00:00:00","1962-01-29T00:00:00","1962-01-30T00:00:00","1962-01-31T00:00:00","1962-02-01T00:00:00","1962-02-02T00:00:00","1962-02-05T00:00:00","1962-02-06T00:00:00","1962-02-07T00:00:00","1962-02-08T00:00:00","1962-02-09T00:00:00","1962-02-12T00:00:00","1962-02-13T00:00:00","1962-02-14T00:00:00","1962-02-15T00:00:00","1962-02-16T00:00:00","1962-02-19T00:00:00","1962-02-20T00:00:00","1962-02-21T00:00:00","1962-02-22T00:00:00","1962-02-23T00:00:00","1962-02-26T00:00:00","1962-02-27T00:00:00","1962-02-28T00:00:00","1962-03-01T00:00:00","1962-03-02T00:00:00","1962-03-05T00:00:00","1962-03-06T00:00:00","1962-03-07T00:00:00","1962-03-08T00:00:00","1962-03-09T00:00:00","1962-03-12T00:00:00","1962-03-13T00:00:00","1962-03-14T00:00:00","1962-03-15T00:00:00","1962-03-16T00:00:00","1962-03-19T00:00:00","1962-03-20T00:00:00","1962-03-21T00:00:00","1962-03-22T00:00:00","1962-03-23T00:00:00","1962-03-26T00:00:00","1962-03-27T00:00:00","1962-03-28T00:00:00","1962-03-29T00:00:00","1962-03-30T00:00:00","1962-04-02T00:00:00","1962-04-03T00:00:00","1962-04-04T00:00:00","1962-04-05T00:00:00","1962-04-06T00:00:00","1962-04-09T00:00:00","1962-04-10T00:00:00","1962-04-11T00:00:00","1962-04-12T00:00:00","1962-04-13T00:00:00","1962-04-16T00:00:00","1962-04-17T00:00:00","1962-04-18T00:00:00","1962-04-19T00:00:00","1962-04-20T00:00:00","1962-04-23T00:00:00","1962-04-24T00:00:00","1962-04-25T00:00:00","1962-04-26T00:00:00","1962-04-27T00:00:00","1962-04-30T00:00:00","1962-05-01T00:00:00","1962-05-02T00:00:00","1962-05-03T00:00:00","1962-05-04T00:00:00","1962-05-07T00:00:00","1962-05-08T00:00:00","1962-05-09T00:00:00","1962-05-10T00:00:00","1962-05-11T00:00:00","1962-05-14T00:00:00","1962-05-15T00:00:00","1962-05-16T00:00:00","1962-05-17T00:00:00","1962-05-18T00:00:00","1962-05-21T00:00:00","1962-05-22T00:00:00","1962-05-23T00:00:00","1962-05-24T00:00:00","1962-05-25T00:00:00","1962-05-28T00:00:00","1962-05-29T00:00:00","1962-05-30T00:00:00","1962-05-31T00:00:00","1962-06-01T00:00:00","1962-06-04T00:00:00","1962-06-05T00:00:00","1962-06-06T00:00:00","1962-06-07T00:00:00","1962-06-08T00:00:00","1962-06-11T00:00:00","1962-06-12T00:00:00","1962-06-13T00:00:00","1962-06-14T00:00:00","1962-06-15T00:00:00","1962-06-18T00:00:00","1962-06-19T00:00:00","1962-06-20T00:00:00","1962-06-21T00:00:00","1962-06-22T00:00:00","1962-06-25T00:00:00","1962-06-26T00:00:00","1962-06-27T00:00:00","1962-06-28T00:00:00","1962-06-29T00:00:00","1962-07-02T00:00:00","1962-07-03T00:00:00","1962-07-04T00:00:00","1962-07-05T00:00:00","1962-07-06T00:00:00","1962-07-09T00:00:00","1962-07-10T00:00:00","1962-07-11T00:00:00","1962-07-12T00:00:00","1962-07-13T00:00:00","1962-07-16T00:00:00","1962-07-17T00:00:00","1962-07-18T00:00:00","1962-07-19T00:00:00","1962-07-20T00:00:00","1962-07-23T00:00:00","1962-07-24T00:00:00","1962-07-25T00:00:00","1962-07-26T00:00:00","1962-07-27T00:00:00","1962-07-30T00:00:00","1962-07-31T00:00:00","1962-08-01T00:00:00","1962-08-02T00:00:00","1962-08-03T00:00:00","1962-08-06T00:00:00","1962-08-07T00:00:00","1962-08-08T00:00:00","1962-08-09T00:00:00","1962-08-10T00:00:00","1962-08-13T00:00:00","1962-08-14T00:00:00","1962-08-15T00:00:00","1962-08-16T00:00:00","1962-08-17T00:00:00","1962-08-20T00:00:00","1962-08-21T00:00:00","1962-08-22T00:00:00","1962-08-23T00:00:00","1962-08-24T00:00:00","1962-08-27T00:00:00","1962-08-28T00:00:00","1962-08-29T00:00:00","1962-08-30T00:00:00","1962-08-31T00:00:00","1962-09-03T00:00:00","1962-09-04T00:00:00","1962-09-05T00:00:00","1962-09-06T00:00:00","1962-09-07T00:00:00","1962-09-10T00:00:00","1962-09-11T00:00:00","1962-09-12T00:00:00","1962-09-13T00:00:00","1962-09-14T00:00:00","1962-09-17T00:00:00","1962-09-18T00:00:00","1962-09-19T00:00:00","1962-09-20T00:00:00","1962-09-21T00:00:00","1962-09-24T00:00:00","1962-09-25T00:00:00","1962-09-26T00:00:00","1962-09-27T00:00:00","1962-09-28T00:00:00","1962-10-01T00:00:00","1962-10-02T00:00:00","1962-10-03T00:00:00","1962-10-04T00:00:00","1962-10-05T00:00:00","1962-10-08T00:00:00","1962-10-09T00:00:00","1962-10-10T00:00:00","1962-10-11T00:00:00","1962-10-12T00:00:00","1962-10-15T00:00:00","1962-10-16T00:00:00","1962-10-17T00:00:00","1962-10-18T00:00:00","1962-10-19T00:00:00","1962-10-22T00:00:00","1962-10-23T00:00:00","1962-10-24T00:00:00","1962-10-25T00:00:00","1962-10-26T00:00:00","1962-10-29T00:00:00","1962-10-30T00:00:00","1962-10-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A prettier picture with hoverdata\n","import plotly.graph_objects as go\n","trace1= go.Scatter(x=y10.index, y=y10.DGS10, mode=\"lines\")\n","fig = go.Figure()\n","fig.add_trace(trace1)\n","fig.show()"]},{"cell_type":"markdown","metadata":{},"source":["#### Data on equity portfolios from Ken French Data Library\n","https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html"]},{"cell_type":"code","execution_count":20,"metadata":{"trusted":true},"outputs":[],"source":["# Some factors\n","ff3 = pdr('F-F_Research_Data_Factors','famafrench', start=1900)[0]/100\n","# Clean-up missings\n","for c in ff3.columns:\n","    ff3[c] = np.where(ff3[c]==-99.99, np.nan, ff3[c])\n","\n","# Some industry portfolios\n","ff48 = pdr(\"48_Industry_Portfolios\", \"famafrench\", start=1900)[0]\n","# Clean-up missings\n","for c in ff48.columns:\n","    ff48[c] = np.where(ff48[c]==-99.99, np.nan, ff48[c])\n","ff48 = ff48/100"]},{"cell_type":"code","execution_count":21,"metadata":{"trusted":true},"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>Mkt-RF</th>\n","      <th>SMB</th>\n","      <th>HML</th>\n","      <th>RF</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1926-07</th>\n","      <td>0.0296</td>\n","      <td>-0.0256</td>\n","      <td>-0.0243</td>\n","      <td>0.0022</td>\n","    </tr>\n","    <tr>\n","      <th>1926-08</th>\n","      <td>0.0264</td>\n","      <td>-0.0117</td>\n","      <td>0.0382</td>\n","      <td>0.0025</td>\n","    </tr>\n","    <tr>\n","      <th>1926-09</th>\n","      <td>0.0036</td>\n","      <td>-0.0140</td>\n","      <td>0.0013</td>\n","      <td>0.0023</td>\n","    </tr>\n","    <tr>\n","      <th>1926-10</th>\n","      <td>-0.0324</td>\n","      <td>-0.0009</td>\n","      <td>0.0070</td>\n","      <td>0.0032</td>\n","    </tr>\n","    <tr>\n","      <th>1926-11</th>\n","      <td>0.0253</td>\n","      <td>-0.0010</td>\n","      <td>-0.0051</td>\n","      <td>0.0031</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>2022-07</th>\n","      <td>0.0957</td>\n","      <td>0.0281</td>\n","      <td>-0.0410</td>\n","      <td>0.0008</td>\n","    </tr>\n","    <tr>\n","      <th>2022-08</th>\n","      <td>-0.0377</td>\n","      <td>0.0139</td>\n","      <td>0.0031</td>\n","      <td>0.0019</td>\n","    </tr>\n","    <tr>\n","      <th>2022-09</th>\n","      <td>-0.0935</td>\n","      <td>-0.0082</td>\n","      <td>0.0003</td>\n","      <td>0.0019</td>\n","    </tr>\n","    <tr>\n","      <th>2022-10</th>\n","      <td>0.0783</td>\n","      <td>0.0010</td>\n","      <td>0.0805</td>\n","      <td>0.0023</td>\n","    </tr>\n","    <tr>\n","      <th>2022-11</th>\n","      <td>0.0460</td>\n","      <td>-0.0340</td>\n","      <td>0.0139</td>\n","      <td>0.0029</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1157 rows × 4 columns</p>\n","</div>"],"text/plain":["         Mkt-RF     SMB     HML     RF\n","Date                                  \n","1926-07  0.0296 -0.0256 -0.0243 0.0022\n","1926-08  0.0264 -0.0117  0.0382 0.0025\n","1926-09  0.0036 -0.0140  0.0013 0.0023\n","1926-10 -0.0324 -0.0009  0.0070 0.0032\n","1926-11  0.0253 -0.0010 -0.0051 0.0031\n","...         ...     ...     ...    ...\n","2022-07  0.0957  0.0281 -0.0410 0.0008\n","2022-08 -0.0377  0.0139  0.0031 0.0019\n","2022-09 -0.0935 -0.0082  0.0003 0.0019\n","2022-10  0.0783  0.0010  0.0805 0.0023\n","2022-11  0.0460 -0.0340  0.0139 0.0029\n","\n","[1157 rows x 4 columns]"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["ff3"]},{"cell_type":"code","execution_count":22,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The arithmetic average market risk premium (annualized): 8.09%\n"]}],"source":["# What does the following represent?\n","mrp = ff3['Mkt-RF'].mean()*12\n","print(f'The arithmetic average market risk premium (annualized): {mrp:.2%}')"]},{"cell_type":"code","execution_count":23,"metadata":{"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["The geometric average market risk premium (annualized): 6.37%\n"]}],"source":["from scipy.stats import gmean\n","\n","geomean = (gmean(1+ff3['Mkt-RF'])-1)*12\n","print(f'The geometric average market risk premium (annualized): {geomean:.2%}')"]},{"cell_type":"code","execution_count":24,"metadata":{"trusted":true},"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>Agric</th>\n","      <th>Food</th>\n","      <th>Soda</th>\n","      <th>Beer</th>\n","      <th>Smoke</th>\n","      <th>Toys</th>\n","      <th>Fun</th>\n","      <th>Books</th>\n","      <th>Hshld</th>\n","      <th>Clths</th>\n","      <th>...</th>\n","      <th>Boxes</th>\n","      <th>Trans</th>\n","      <th>Whlsl</th>\n","      <th>Rtail</th>\n","      <th>Meals</th>\n","      <th>Banks</th>\n","      <th>Insur</th>\n","      <th>RlEst</th>\n","      <th>Fin</th>\n","      <th>Other</th>\n","    </tr>\n","    <tr>\n","      <th>Date</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1926-07</th>\n","      <td>0.0237</td>\n","      <td>0.0012</td>\n","      <td>NaN</td>\n","      <td>-0.0519</td>\n","      <td>0.0129</td>\n","      <td>0.0865</td>\n","      <td>0.0250</td>\n","      <td>0.5021</td>\n","      <td>-0.0048</td>\n","      <td>0.0808</td>\n","      <td>...</td>\n","      <td>0.0770</td>\n","      <td>0.0192</td>\n","      <td>-0.2379</td>\n","      <td>0.0007</td>\n","      <td>0.0187</td>\n","      <td>0.0461</td>\n","      <td>-0.0054</td>\n","      <td>0.0289</td>\n","      <td>-0.0577</td>\n","      <td>0.0520</td>\n","    </tr>\n","    <tr>\n","      <th>1926-08</th>\n","      <td>0.0223</td>\n","      <td>0.0268</td>\n","      <td>NaN</td>\n","      <td>0.2703</td>\n","      <td>0.0650</td>\n","      <td>0.1681</td>\n","      <td>-0.0076</td>\n","      <td>0.4298</td>\n","      <td>-0.0358</td>\n","      <td>-0.0251</td>\n","      <td>...</td>\n","      <td>-0.0238</td>\n","      <td>0.0485</td>\n","      <td>0.0539</td>\n","      <td>-0.0075</td>\n","      <td>-0.0013</td>\n","      <td>0.1183</td>\n","      <td>0.0257</td>\n","      <td>0.0530</td>\n","      <td>0.0032</td>\n","      <td>0.0676</td>\n","    </tr>\n","    <tr>\n","      <th>1926-09</th>\n","      <td>-0.0057</td>\n","      <td>0.0158</td>\n","      <td>NaN</td>\n","      <td>0.0402</td>\n","      <td>0.0126</td>\n","      <td>0.0833</td>\n","      <td>0.0642</td>\n","      <td>-0.0491</td>\n","      <td>0.0073</td>\n","      <td>-0.0051</td>\n","      <td>...</td>\n","      <td>-0.0554</td>\n","      <td>0.0008</td>\n","      <td>-0.0787</td>\n","      <td>0.0025</td>\n","      <td>-0.0056</td>\n","      <td>-0.0175</td>\n","      <td>0.0072</td>\n","      <td>-0.0306</td>\n","      <td>-0.0481</td>\n","      <td>-0.0386</td>\n","    </tr>\n","    <tr>\n","      <th>1926-10</th>\n","      <td>-0.0046</td>\n","      <td>-0.0368</td>\n","      <td>NaN</td>\n","      <td>-0.0331</td>\n","      <td>0.0106</td>\n","      <td>-0.0140</td>\n","      <td>-0.0509</td>\n","      <td>0.0537</td>\n","      <td>-0.0468</td>\n","      <td>0.0012</td>\n","      <td>...</td>\n","      <td>-0.0508</td>\n","      <td>-0.0262</td>\n","      <td>-0.1538</td>\n","      <td>-0.0220</td>\n","      <td>-0.0411</td>\n","      <td>-0.1182</td>\n","      <td>-0.0428</td>\n","      <td>-0.0574</td>\n","      <td>-0.0094</td>\n","      <td>-0.0849</td>\n","    </tr>\n","    <tr>\n","      <th>1926-11</th>\n","      <td>0.0675</td>\n","      <td>0.0626</td>\n","      <td>NaN</td>\n","      <td>0.0729</td>\n","      <td>0.0455</td>\n","      <td>0.0000</td>\n","      <td>0.0182</td>\n","      <td>-0.0640</td>\n","      <td>-0.0054</td>\n","      <td>0.0187</td>\n","      <td>...</td>\n","      <td>0.0384</td>\n","      <td>0.0161</td>\n","      <td>0.0467</td>\n","      <td>0.0652</td>\n","      <td>0.0433</td>\n","      <td>-0.0297</td>\n","      <td>0.0358</td>\n","      <td>0.0221</td>\n","      <td>0.0513</td>\n","      <td>0.0400</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2022-07</th>\n","      <td>0.0638</td>\n","      <td>0.0368</td>\n","      <td>0.0328</td>\n","      <td>0.0549</td>\n","      <td>0.0056</td>\n","      <td>0.0563</td>\n","      <td>0.1704</td>\n","      <td>0.1208</td>\n","      <td>0.0076</td>\n","      <td>0.1186</td>\n","      <td>...</td>\n","      <td>0.0709</td>\n","      <td>0.0933</td>\n","      <td>0.0908</td>\n","      <td>0.1633</td>\n","      <td>0.1189</td>\n","      <td>0.0854</td>\n","      <td>0.0315</td>\n","      <td>0.1410</td>\n","      <td>0.1044</td>\n","      <td>0.1001</td>\n","    </tr>\n","    <tr>\n","      <th>2022-08</th>\n","      <td>0.0523</td>\n","      <td>-0.0046</td>\n","      <td>-0.0440</td>\n","      <td>-0.0187</td>\n","      <td>-0.0012</td>\n","      <td>-0.0577</td>\n","      <td>-0.0226</td>\n","      <td>-0.0500</td>\n","      <td>-0.0216</td>\n","      <td>-0.0601</td>\n","      <td>...</td>\n","      <td>-0.0911</td>\n","      <td>-0.0146</td>\n","      <td>-0.0160</td>\n","      <td>-0.0346</td>\n","      <td>-0.0147</td>\n","      <td>-0.0341</td>\n","      <td>-0.0084</td>\n","      <td>-0.0612</td>\n","      <td>-0.0102</td>\n","      <td>-0.0403</td>\n","    </tr>\n","    <tr>\n","      <th>2022-09</th>\n","      <td>-0.0703</td>\n","      <td>-0.0765</td>\n","      <td>-0.0814</td>\n","      <td>-0.0521</td>\n","      <td>-0.1055</td>\n","      <td>-0.1543</td>\n","      <td>-0.0410</td>\n","      <td>-0.1338</td>\n","      <td>-0.1060</td>\n","      <td>-0.1736</td>\n","      <td>...</td>\n","      <td>-0.1385</td>\n","      <td>-0.1424</td>\n","      <td>-0.0946</td>\n","      <td>-0.0767</td>\n","      <td>-0.0626</td>\n","      <td>-0.0921</td>\n","      <td>-0.0445</td>\n","      <td>-0.1289</td>\n","      <td>-0.0847</td>\n","      <td>-0.0596</td>\n","    </tr>\n","    <tr>\n","      <th>2022-10</th>\n","      <td>0.1379</td>\n","      <td>0.1112</td>\n","      <td>0.0713</td>\n","      <td>0.0949</td>\n","      <td>0.1208</td>\n","      <td>0.0548</td>\n","      <td>0.1498</td>\n","      <td>0.0882</td>\n","      <td>0.0568</td>\n","      <td>0.1072</td>\n","      <td>...</td>\n","      <td>0.0560</td>\n","      <td>0.0668</td>\n","      <td>0.1365</td>\n","      <td>0.0194</td>\n","      <td>0.1026</td>\n","      <td>0.1354</td>\n","      <td>0.1283</td>\n","      <td>0.0682</td>\n","      <td>0.1134</td>\n","      <td>0.1048</td>\n","    </tr>\n","    <tr>\n","      <th>2022-11</th>\n","      <td>0.0253</td>\n","      <td>0.0284</td>\n","      <td>0.0769</td>\n","      <td>0.0323</td>\n","      <td>0.0557</td>\n","      <td>0.0979</td>\n","      <td>0.0724</td>\n","      <td>0.1570</td>\n","      <td>0.0944</td>\n","      <td>0.1734</td>\n","      <td>...</td>\n","      <td>0.1058</td>\n","      <td>0.1032</td>\n","      <td>0.0505</td>\n","      <td>0.0295</td>\n","      <td>0.0565</td>\n","      <td>0.0519</td>\n","      <td>0.0141</td>\n","      <td>0.1088</td>\n","      <td>0.0830</td>\n","      <td>0.0711</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1157 rows × 48 columns</p>\n","</div>"],"text/plain":["          Agric   Food    Soda    Beer    Smoke   Toys    Fun     Books  \\\n","Date                                                                      \n","1926-07  0.0237  0.0012     NaN -0.0519  0.0129  0.0865  0.0250  0.5021   \n","1926-08  0.0223  0.0268     NaN  0.2703  0.0650  0.1681 -0.0076  0.4298   \n","1926-09 -0.0057  0.0158     NaN  0.0402  0.0126  0.0833  0.0642 -0.0491   \n","1926-10 -0.0046 -0.0368     NaN -0.0331  0.0106 -0.0140 -0.0509  0.0537   \n","1926-11  0.0675  0.0626     NaN  0.0729  0.0455  0.0000  0.0182 -0.0640   \n","...         ...     ...     ...     ...     ...     ...     ...     ...   \n","2022-07  0.0638  0.0368  0.0328  0.0549  0.0056  0.0563  0.1704  0.1208   \n","2022-08  0.0523 -0.0046 -0.0440 -0.0187 -0.0012 -0.0577 -0.0226 -0.0500   \n","2022-09 -0.0703 -0.0765 -0.0814 -0.0521 -0.1055 -0.1543 -0.0410 -0.1338   \n","2022-10  0.1379  0.1112  0.0713  0.0949  0.1208  0.0548  0.1498  0.0882   \n","2022-11  0.0253  0.0284  0.0769  0.0323  0.0557  0.0979  0.0724  0.1570   \n","\n","          Hshld   Clths  ...   Boxes   Trans   Whlsl   Rtail   Meals   Banks  \\\n","Date                     ...                                                   \n","1926-07 -0.0048  0.0808  ...  0.0770  0.0192 -0.2379  0.0007  0.0187  0.0461   \n","1926-08 -0.0358 -0.0251  ... -0.0238  0.0485  0.0539 -0.0075 -0.0013  0.1183   \n","1926-09  0.0073 -0.0051  ... -0.0554  0.0008 -0.0787  0.0025 -0.0056 -0.0175   \n","1926-10 -0.0468  0.0012  ... -0.0508 -0.0262 -0.1538 -0.0220 -0.0411 -0.1182   \n","1926-11 -0.0054  0.0187  ...  0.0384  0.0161  0.0467  0.0652  0.0433 -0.0297   \n","...         ...     ...  ...     ...     ...     ...     ...     ...     ...   \n","2022-07  0.0076  0.1186  ...  0.0709  0.0933  0.0908  0.1633  0.1189  0.0854   \n","2022-08 -0.0216 -0.0601  ... -0.0911 -0.0146 -0.0160 -0.0346 -0.0147 -0.0341   \n","2022-09 -0.1060 -0.1736  ... -0.1385 -0.1424 -0.0946 -0.0767 -0.0626 -0.0921   \n","2022-10  0.0568  0.1072  ...  0.0560  0.0668  0.1365  0.0194  0.1026  0.1354   \n","2022-11  0.0944  0.1734  ...  0.1058  0.1032  0.0505  0.0295  0.0565  0.0519   \n","\n","          Insur   RlEst   Fin     Other  \n","Date                                     \n","1926-07 -0.0054  0.0289 -0.0577  0.0520  \n","1926-08  0.0257  0.0530  0.0032  0.0676  \n","1926-09  0.0072 -0.0306 -0.0481 -0.0386  \n","1926-10 -0.0428 -0.0574 -0.0094 -0.0849  \n","1926-11  0.0358  0.0221  0.0513  0.0400  \n","...         ...     ...     ...     ...  \n","2022-07  0.0315  0.1410  0.1044  0.1001  \n","2022-08 -0.0084 -0.0612 -0.0102 -0.0403  \n","2022-09 -0.0445 -0.1289 -0.0847 -0.0596  \n","2022-10  0.1283  0.0682  0.1134  0.1048  \n","2022-11  0.0141  0.1088  0.0830  0.0711  \n","\n","[1157 rows x 48 columns]"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["ff48"]},{"cell_type":"markdown","metadata":{},"source":["### Our own function to pull monthly returns data"]},{"cell_type":"code","execution_count":25,"metadata":{"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["d:\\bbcx-investments-quarto\\quarto\\venv\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py:1162: UserWarning:\n","\n","Converting to PeriodArray/Index representation will drop timezone information.\n","\n","d:\\bbcx-investments-quarto\\quarto\\venv\\lib\\site-packages\\pandas\\core\\arrays\\datetimes.py:1162: UserWarning:\n","\n","Converting to PeriodArray/Index representation will drop timezone information.\n","\n"]}],"source":["# Function to fetch a yahoo time-series\n","def returns(ticker):\n","    ret = yf.download(ticker, start='2000-01-01', end='2020-12-31', progress=False)\n","    ret.index = ret.index.to_period('D')\n","    ret = ret[\"Adj Close\"].resample(\"M\").last()\n","    ret = ret.pct_change()\n","    # ret.index = ret.index.to_period(\"M\")\n","    ret.name = ticker\n","    return ret\n","aapl = returns('AAPL')\n","ibm = returns('IBM')"]}],"metadata":{"kernelspec":{"display_name":"Python 3.10.6 ('venv': venv)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.6"},"vscode":{"interpreter":{"hash":"cc788b38d8cb434c82f45ef9d481d0310e17b79321e4c8cbfbfe90f656a3f023"}}},"nbformat":4,"nbformat_minor":4}