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crusaderky/1 Markowitz Bullet.ipynb

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1 Markowitz Bullet.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from datetime import datetime\n",
"\n",
"import dask\n",
"import dask.array as da\n",
"import numpy\n",
"import yfinance\n",
"import numpy as np\n",
"import pandas as pd\n",
"import xarray\n",
"\n",
"import markowitz"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"symbols = dask.config.get(\"markowitz.symbols\")\n",
"tickers = yfinance.Tickers(\" \".join(symbols))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[*********************100%***********************] 80 of 80 completed\n"
]
},
{
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"10\" halign=\"left\">Close</th>\n",
" <th>...</th>\n",
" <th colspan=\"10\" halign=\"left\">Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>AAPL</th>\n",
" <th>ADBE</th>\n",
" <th>ALL</th>\n",
" <th>AMZN</th>\n",
" <th>BA</th>\n",
" <th>BAC</th>\n",
" <th>BIO</th>\n",
" <th>BIP</th>\n",
" <th>BYND</th>\n",
" <th>CE</th>\n",
" <th>...</th>\n",
" <th>VXUS</th>\n",
" <th>VZ</th>\n",
" <th>W</th>\n",
" <th>WFC</th>\n",
" <th>WMT</th>\n",
" <th>WTM</th>\n",
" <th>XOM</th>\n",
" <th>ZG</th>\n",
" <th>ZM</th>\n",
" <th>ZS</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",
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" <th></th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-05-03</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-04</th>\n",
" <td>17.286030</td>\n",
" <td>32.610001</td>\n",
" <td>27.829645</td>\n",
" <td>223.990005</td>\n",
" <td>62.421898</td>\n",
" <td>6.673128</td>\n",
" <td>103.750000</td>\n",
" <td>12.067763</td>\n",
" <td>NaN</td>\n",
" <td>38.826321</td>\n",
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" <td>55000.0</td>\n",
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" <td>27154400.0</td>\n",
" <td>6944500.0</td>\n",
" <td>9500.0</td>\n",
" <td>15055500.0</td>\n",
" <td>2069156.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-07</th>\n",
" <td>17.415396</td>\n",
" <td>32.520000</td>\n",
" <td>27.878458</td>\n",
" <td>225.160004</td>\n",
" <td>62.520679</td>\n",
" <td>6.862804</td>\n",
" <td>103.550003</td>\n",
" <td>12.110381</td>\n",
" <td>NaN</td>\n",
" <td>38.382442</td>\n",
" <td>...</td>\n",
" <td>81000.0</td>\n",
" <td>12062700.0</td>\n",
" <td>NaN</td>\n",
" <td>28211800.0</td>\n",
" <td>8658200.0</td>\n",
" <td>12000.0</td>\n",
" <td>12018100.0</td>\n",
" <td>2313851.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-08</th>\n",
" <td>17.375639</td>\n",
" <td>32.669998</td>\n",
" <td>28.228365</td>\n",
" <td>223.899994</td>\n",
" <td>62.059765</td>\n",
" <td>6.716237</td>\n",
" <td>102.750000</td>\n",
" <td>11.928297</td>\n",
" <td>NaN</td>\n",
" <td>37.787819</td>\n",
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" <td>98800.0</td>\n",
" <td>11314400.0</td>\n",
" <td>NaN</td>\n",
" <td>27069000.0</td>\n",
" <td>10273300.0</td>\n",
" <td>21700.0</td>\n",
" <td>19602400.0</td>\n",
" <td>3258312.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-09</th>\n",
" <td>17.406221</td>\n",
" <td>32.520000</td>\n",
" <td>27.919151</td>\n",
" <td>222.979996</td>\n",
" <td>61.306370</td>\n",
" <td>6.664506</td>\n",
" <td>103.129997</td>\n",
" <td>12.044519</td>\n",
" <td>NaN</td>\n",
" <td>37.519814</td>\n",
" <td>...</td>\n",
" <td>271900.0</td>\n",
" <td>10516100.0</td>\n",
" <td>NaN</td>\n",
" <td>27552100.0</td>\n",
" <td>11157600.0</td>\n",
" <td>14000.0</td>\n",
" <td>17424000.0</td>\n",
" <td>3467088.0</td>\n",
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" <td>NaN</td>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-27</th>\n",
" <td>156.570007</td>\n",
" <td>397.899994</td>\n",
" <td>128.369995</td>\n",
" <td>2763.340088</td>\n",
" <td>154.460007</td>\n",
" <td>36.250000</td>\n",
" <td>500.959991</td>\n",
" <td>62.790001</td>\n",
" <td>38.220001</td>\n",
" <td>140.070007</td>\n",
" <td>...</td>\n",
" <td>6382800.0</td>\n",
" <td>30582000.0</td>\n",
" <td>3488700.0</td>\n",
" <td>19520900.0</td>\n",
" <td>5832300.0</td>\n",
" <td>12200.0</td>\n",
" <td>32773000.0</td>\n",
" <td>1221900.0</td>\n",
" <td>3522700.0</td>\n",
" <td>1461900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-28</th>\n",
" <td>163.639999</td>\n",
" <td>410.529999</td>\n",
" <td>130.339996</td>\n",
" <td>2891.929932</td>\n",
" <td>154.220001</td>\n",
" <td>36.810001</td>\n",
" <td>513.109985</td>\n",
" <td>63.619999</td>\n",
" <td>39.820000</td>\n",
" <td>142.929993</td>\n",
" <td>...</td>\n",
" <td>4735300.0</td>\n",
" <td>33011700.0</td>\n",
" <td>4231900.0</td>\n",
" <td>17431000.0</td>\n",
" <td>4985300.0</td>\n",
" <td>13800.0</td>\n",
" <td>33683800.0</td>\n",
" <td>1155000.0</td>\n",
" <td>3585200.0</td>\n",
" <td>1555800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-29</th>\n",
" <td>157.649994</td>\n",
" <td>395.950012</td>\n",
" <td>126.540001</td>\n",
" <td>2485.629883</td>\n",
" <td>148.839996</td>\n",
" <td>35.680000</td>\n",
" <td>512.059998</td>\n",
" <td>62.430000</td>\n",
" <td>36.880001</td>\n",
" <td>146.940002</td>\n",
" <td>...</td>\n",
" <td>4670600.0</td>\n",
" <td>40990300.0</td>\n",
" <td>4580600.0</td>\n",
" <td>27143100.0</td>\n",
" <td>7028700.0</td>\n",
" <td>17000.0</td>\n",
" <td>34644900.0</td>\n",
" <td>1198900.0</td>\n",
" <td>3605900.0</td>\n",
" <td>1912200.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-02</th>\n",
" <td>157.960007</td>\n",
" <td>407.290009</td>\n",
" <td>127.730003</td>\n",
" <td>2490.000000</td>\n",
" <td>148.610001</td>\n",
" <td>36.139999</td>\n",
" <td>501.420013</td>\n",
" <td>60.330002</td>\n",
" <td>38.330002</td>\n",
" <td>145.949997</td>\n",
" <td>...</td>\n",
" <td>7684200.0</td>\n",
" <td>33309400.0</td>\n",
" <td>4651700.0</td>\n",
" <td>25009300.0</td>\n",
" <td>6672000.0</td>\n",
" <td>14700.0</td>\n",
" <td>36335200.0</td>\n",
" <td>1098500.0</td>\n",
" <td>3164700.0</td>\n",
" <td>1267000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-03</th>\n",
" <td>159.479996</td>\n",
" <td>407.579987</td>\n",
" <td>129.779999</td>\n",
" <td>2485.070068</td>\n",
" <td>153.580002</td>\n",
" <td>37.130001</td>\n",
" <td>500.929993</td>\n",
" <td>60.799999</td>\n",
" <td>38.900002</td>\n",
" <td>150.339996</td>\n",
" <td>...</td>\n",
" <td>6524200.0</td>\n",
" <td>33256100.0</td>\n",
" <td>2962200.0</td>\n",
" <td>21536800.0</td>\n",
" <td>5983500.0</td>\n",
" <td>9100.0</td>\n",
" <td>33894500.0</td>\n",
" <td>985000.0</td>\n",
" <td>2102300.0</td>\n",
" <td>1262700.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2517 rows × 560 columns</p>\n",
"</div>"
],
"text/plain": [
" Close \\\n",
" AAPL ADBE ALL AMZN BA \n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN NaN \n",
"2012-05-04 17.286030 32.610001 27.829645 223.990005 62.421898 \n",
"2012-05-07 17.415396 32.520000 27.878458 225.160004 62.520679 \n",
"2012-05-08 17.375639 32.669998 28.228365 223.899994 62.059765 \n",
"2012-05-09 17.406221 32.520000 27.919151 222.979996 61.306370 \n",
"... ... ... ... ... ... \n",
"2022-04-27 156.570007 397.899994 128.369995 2763.340088 154.460007 \n",
"2022-04-28 163.639999 410.529999 130.339996 2891.929932 154.220001 \n",
"2022-04-29 157.649994 395.950012 126.540001 2485.629883 148.839996 \n",
"2022-05-02 157.960007 407.290009 127.730003 2490.000000 148.610001 \n",
"2022-05-03 159.479996 407.579987 129.779999 2485.070068 153.580002 \n",
"\n",
" ... \\\n",
" BAC BIO BIP BYND CE ... \n",
"Date ... \n",
"2012-05-03 NaN NaN NaN NaN NaN ... \n",
"2012-05-04 6.673128 103.750000 12.067763 NaN 38.826321 ... \n",
"2012-05-07 6.862804 103.550003 12.110381 NaN 38.382442 ... \n",
"2012-05-08 6.716237 102.750000 11.928297 NaN 37.787819 ... \n",
"2012-05-09 6.664506 103.129997 12.044519 NaN 37.519814 ... \n",
"... ... ... ... ... ... ... \n",
"2022-04-27 36.250000 500.959991 62.790001 38.220001 140.070007 ... \n",
"2022-04-28 36.810001 513.109985 63.619999 39.820000 142.929993 ... \n",
"2022-04-29 35.680000 512.059998 62.430000 36.880001 146.940002 ... \n",
"2022-05-02 36.139999 501.420013 60.330002 38.330002 145.949997 ... \n",
"2022-05-03 37.130001 500.929993 60.799999 38.900002 150.339996 ... \n",
"\n",
" Volume \\\n",
" VXUS VZ W WFC WMT WTM \n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN NaN NaN \n",
"2012-05-04 55000.0 9941600.0 NaN 27154400.0 6944500.0 9500.0 \n",
"2012-05-07 81000.0 12062700.0 NaN 28211800.0 8658200.0 12000.0 \n",
"2012-05-08 98800.0 11314400.0 NaN 27069000.0 10273300.0 21700.0 \n",
"2012-05-09 271900.0 10516100.0 NaN 27552100.0 11157600.0 14000.0 \n",
"... ... ... ... ... ... ... \n",
"2022-04-27 6382800.0 30582000.0 3488700.0 19520900.0 5832300.0 12200.0 \n",
"2022-04-28 4735300.0 33011700.0 4231900.0 17431000.0 4985300.0 13800.0 \n",
"2022-04-29 4670600.0 40990300.0 4580600.0 27143100.0 7028700.0 17000.0 \n",
"2022-05-02 7684200.0 33309400.0 4651700.0 25009300.0 6672000.0 14700.0 \n",
"2022-05-03 6524200.0 33256100.0 2962200.0 21536800.0 5983500.0 9100.0 \n",
"\n",
" \n",
" XOM ZG ZM ZS \n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN \n",
"2012-05-04 15055500.0 2069156.0 NaN NaN \n",
"2012-05-07 12018100.0 2313851.0 NaN NaN \n",
"2012-05-08 19602400.0 3258312.0 NaN NaN \n",
"2012-05-09 17424000.0 3467088.0 NaN NaN \n",
"... ... ... ... ... \n",
"2022-04-27 32773000.0 1221900.0 3522700.0 1461900.0 \n",
"2022-04-28 33683800.0 1155000.0 3585200.0 1555800.0 \n",
"2022-04-29 34644900.0 1198900.0 3605900.0 1912200.0 \n",
"2022-05-02 36335200.0 1098500.0 3164700.0 1267000.0 \n",
"2022-05-03 33894500.0 985000.0 2102300.0 1262700.0 \n",
"\n",
"[2517 rows x 560 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = tickers.history(period=\"10y\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>2012-05-04</th>\n",
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" <td>517.116882</td>\n",
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" <th>2012-05-07</th>\n",
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" <td>225.160004</td>\n",
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" <td>6.862804</td>\n",
" <td>103.550003</td>\n",
" <td>12.110381</td>\n",
" <td>NaN</td>\n",
" <td>38.382442</td>\n",
" <td>...</td>\n",
" <td>32.647575</td>\n",
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" <td>25.291130</td>\n",
" <td>47.123810</td>\n",
" <td>522.576904</td>\n",
" <td>55.774429</td>\n",
" <td>12.793888</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-08</th>\n",
" <td>17.375639</td>\n",
" <td>32.669998</td>\n",
" <td>28.228365</td>\n",
" <td>223.899994</td>\n",
" <td>62.059765</td>\n",
" <td>6.716237</td>\n",
" <td>102.750000</td>\n",
" <td>11.928297</td>\n",
" <td>NaN</td>\n",
" <td>37.787819</td>\n",
" <td>...</td>\n",
" <td>32.200356</td>\n",
" <td>25.920763</td>\n",
" <td>NaN</td>\n",
" <td>25.026903</td>\n",
" <td>47.012344</td>\n",
" <td>534.158569</td>\n",
" <td>55.470730</td>\n",
" <td>13.380106</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-09</th>\n",
" <td>17.406221</td>\n",
" <td>32.520000</td>\n",
" <td>27.919151</td>\n",
" <td>222.979996</td>\n",
" <td>61.306370</td>\n",
" <td>6.664506</td>\n",
" <td>103.129997</td>\n",
" <td>12.044519</td>\n",
" <td>NaN</td>\n",
" <td>37.519814</td>\n",
" <td>...</td>\n",
" <td>31.782938</td>\n",
" <td>25.728996</td>\n",
" <td>NaN</td>\n",
" <td>24.634323</td>\n",
" <td>47.315331</td>\n",
" <td>532.726868</td>\n",
" <td>55.001972</td>\n",
" <td>13.442470</td>\n",
" <td>NaN</td>\n",
" <td>NaN</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>2022-04-27</th>\n",
" <td>156.570007</td>\n",
" <td>397.899994</td>\n",
" <td>128.369995</td>\n",
" <td>2763.340088</td>\n",
" <td>154.460007</td>\n",
" <td>36.250000</td>\n",
" <td>500.959991</td>\n",
" <td>62.790001</td>\n",
" <td>38.220001</td>\n",
" <td>140.070007</td>\n",
" <td>...</td>\n",
" <td>55.540001</td>\n",
" <td>48.459999</td>\n",
" <td>76.279999</td>\n",
" <td>44.580002</td>\n",
" <td>154.240005</td>\n",
" <td>1048.910034</td>\n",
" <td>84.639999</td>\n",
" <td>37.700001</td>\n",
" <td>97.620003</td>\n",
" <td>204.759995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-28</th>\n",
" <td>163.639999</td>\n",
" <td>410.529999</td>\n",
" <td>130.339996</td>\n",
" <td>2891.929932</td>\n",
" <td>154.220001</td>\n",
" <td>36.810001</td>\n",
" <td>513.109985</td>\n",
" <td>63.619999</td>\n",
" <td>39.820000</td>\n",
" <td>142.929993</td>\n",
" <td>...</td>\n",
" <td>56.360001</td>\n",
" <td>48.400002</td>\n",
" <td>83.389999</td>\n",
" <td>45.169998</td>\n",
" <td>156.210007</td>\n",
" <td>1071.930054</td>\n",
" <td>87.199997</td>\n",
" <td>39.610001</td>\n",
" <td>102.559998</td>\n",
" <td>213.589996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-29</th>\n",
" <td>157.649994</td>\n",
" <td>395.950012</td>\n",
" <td>126.540001</td>\n",
" <td>2485.629883</td>\n",
" <td>148.839996</td>\n",
" <td>35.680000</td>\n",
" <td>512.059998</td>\n",
" <td>62.430000</td>\n",
" <td>36.880001</td>\n",
" <td>146.940002</td>\n",
" <td>...</td>\n",
" <td>55.860001</td>\n",
" <td>46.299999</td>\n",
" <td>76.940002</td>\n",
" <td>43.630001</td>\n",
" <td>152.990005</td>\n",
" <td>1048.020020</td>\n",
" <td>85.250000</td>\n",
" <td>38.650002</td>\n",
" <td>99.570000</td>\n",
" <td>202.740005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-02</th>\n",
" <td>157.960007</td>\n",
" <td>407.290009</td>\n",
" <td>127.730003</td>\n",
" <td>2490.000000</td>\n",
" <td>148.610001</td>\n",
" <td>36.139999</td>\n",
" <td>501.420013</td>\n",
" <td>60.330002</td>\n",
" <td>38.330002</td>\n",
" <td>145.949997</td>\n",
" <td>...</td>\n",
" <td>55.689999</td>\n",
" <td>46.230000</td>\n",
" <td>87.389999</td>\n",
" <td>43.669998</td>\n",
" <td>151.979996</td>\n",
" <td>1050.099976</td>\n",
" <td>86.410004</td>\n",
" <td>39.799999</td>\n",
" <td>104.790001</td>\n",
" <td>208.229996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-03</th>\n",
" <td>159.479996</td>\n",
" <td>407.579987</td>\n",
" <td>129.779999</td>\n",
" <td>2485.070068</td>\n",
" <td>153.580002</td>\n",
" <td>37.130001</td>\n",
" <td>500.929993</td>\n",
" <td>60.799999</td>\n",
" <td>38.900002</td>\n",
" <td>150.339996</td>\n",
" <td>...</td>\n",
" <td>56.169998</td>\n",
" <td>47.169998</td>\n",
" <td>89.849998</td>\n",
" <td>44.160000</td>\n",
" <td>152.509995</td>\n",
" <td>1048.599976</td>\n",
" <td>88.190002</td>\n",
" <td>42.480000</td>\n",
" <td>103.529999</td>\n",
" <td>204.699997</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2517 rows × 80 columns</p>\n",
"</div>"
],
"text/plain": [
" AAPL ADBE ALL AMZN BA \\\n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN NaN \n",
"2012-05-04 17.286030 32.610001 27.829645 223.990005 62.421898 \n",
"2012-05-07 17.415396 32.520000 27.878458 225.160004 62.520679 \n",
"2012-05-08 17.375639 32.669998 28.228365 223.899994 62.059765 \n",
"2012-05-09 17.406221 32.520000 27.919151 222.979996 61.306370 \n",
"... ... ... ... ... ... \n",
"2022-04-27 156.570007 397.899994 128.369995 2763.340088 154.460007 \n",
"2022-04-28 163.639999 410.529999 130.339996 2891.929932 154.220001 \n",
"2022-04-29 157.649994 395.950012 126.540001 2485.629883 148.839996 \n",
"2022-05-02 157.960007 407.290009 127.730003 2490.000000 148.610001 \n",
"2022-05-03 159.479996 407.579987 129.779999 2485.070068 153.580002 \n",
"\n",
" BAC BIO BIP BYND CE ... \\\n",
"Date ... \n",
"2012-05-03 NaN NaN NaN NaN NaN ... \n",
"2012-05-04 6.673128 103.750000 12.067763 NaN 38.826321 ... \n",
"2012-05-07 6.862804 103.550003 12.110381 NaN 38.382442 ... \n",
"2012-05-08 6.716237 102.750000 11.928297 NaN 37.787819 ... \n",
"2012-05-09 6.664506 103.129997 12.044519 NaN 37.519814 ... \n",
"... ... ... ... ... ... ... \n",
"2022-04-27 36.250000 500.959991 62.790001 38.220001 140.070007 ... \n",
"2022-04-28 36.810001 513.109985 63.619999 39.820000 142.929993 ... \n",
"2022-04-29 35.680000 512.059998 62.430000 36.880001 146.940002 ... \n",
"2022-05-02 36.139999 501.420013 60.330002 38.330002 145.949997 ... \n",
"2022-05-03 37.130001 500.929993 60.799999 38.900002 150.339996 ... \n",
"\n",
" VXUS VZ W WFC WMT \\\n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN NaN \n",
"2012-05-04 32.483589 25.735390 NaN 24.936304 46.733696 \n",
"2012-05-07 32.647575 25.907972 NaN 25.291130 47.123810 \n",
"2012-05-08 32.200356 25.920763 NaN 25.026903 47.012344 \n",
"2012-05-09 31.782938 25.728996 NaN 24.634323 47.315331 \n",
"... ... ... ... ... ... \n",
"2022-04-27 55.540001 48.459999 76.279999 44.580002 154.240005 \n",
"2022-04-28 56.360001 48.400002 83.389999 45.169998 156.210007 \n",
"2022-04-29 55.860001 46.299999 76.940002 43.630001 152.990005 \n",
"2022-05-02 55.689999 46.230000 87.389999 43.669998 151.979996 \n",
"2022-05-03 56.169998 47.169998 89.849998 44.160000 152.509995 \n",
"\n",
" WTM XOM ZG ZM ZS \n",
"Date \n",
"2012-05-03 NaN NaN NaN NaN NaN \n",
"2012-05-04 517.116882 55.833855 12.500780 NaN NaN \n",
"2012-05-07 522.576904 55.774429 12.793888 NaN NaN \n",
"2012-05-08 534.158569 55.470730 13.380106 NaN NaN \n",
"2012-05-09 532.726868 55.001972 13.442470 NaN NaN \n",
"... ... ... ... ... ... \n",
"2022-04-27 1048.910034 84.639999 37.700001 97.620003 204.759995 \n",
"2022-04-28 1071.930054 87.199997 39.610001 102.559998 213.589996 \n",
"2022-04-29 1048.020020 85.250000 38.650002 99.570000 202.740005 \n",
"2022-05-02 1050.099976 86.410004 39.799999 104.790001 208.229996 \n",
"2022-05-03 1048.599976 88.190002 42.480000 103.529999 204.699997 \n",
"\n",
"[2517 rows x 80 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Close\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"\n",
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"\n",
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"\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; &#x27;AMZN&#x27; ... &#x27;XOM&#x27; &#x27;ZG&#x27; &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan nan ... 88.19 42.48 103.5 204.7</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cfd8952b-606f-453c-9348-d34b5b5c0bd4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cfd8952b-606f-453c-9348-d34b5b5c0bd4' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2573484e-27f1-411d-a7c0-235272cb71d7' class='xr-section-summary-in' type='checkbox' checked><label for='section-2573484e-27f1-411d-a7c0-235272cb71d7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-1e200a64-f69a-49e4-bade-38755ecb34e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e200a64-f69a-49e4-bade-38755ecb34e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03d4c5ab-ab3a-4195-a2af-1dc94503ef83' class='xr-var-data-in' type='checkbox'><label for='data-03d4c5ab-ab3a-4195-a2af-1dc94503ef83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-7386c038-1f5e-48c4-b5c4-6f619b15d3fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7386c038-1f5e-48c4-b5c4-6f619b15d3fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a57f067-c61e-4bb2-b34e-70befb00783a' class='xr-var-data-in' type='checkbox'><label for='data-1a57f067-c61e-4bb2-b34e-70befb00783a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d0af4d0-fdfc-423d-bd89-d534cb2ec513' class='xr-section-summary-in' type='checkbox' checked><label for='section-8d0af4d0-fdfc-423d-bd89-d534cb2ec513' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-4c16e57c-c04d-4366-8ff6-598d02d7a869' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c16e57c-c04d-4366-8ff6-598d02d7a869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d49c5d2-4ad4-4459-b016-d9da8d7dbd7c' class='xr-var-data-in' type='checkbox'><label for='data-2d49c5d2-4ad4-4459-b016-d9da8d7dbd7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2dadf6d2-da22-44e7-b2f1-adff18ab42ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2dadf6d2-da22-44e7-b2f1-adff18ab42ca' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' 'AMZN' ... 'XOM' 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan nan ... 88.19 42.48 103.5 204.7"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xa = xarray.DataArray(df[\"Close\"], dims=[\"date\", \"symbol\"])\n",
"xa.coords[\"symbol\"] = xa.symbol.astype(str)\n",
"ds = xarray.Dataset(data_vars={\"adjclose\": xa})\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
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"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; &#x27;AMZN&#x27; ... &#x27;ZG&#x27; &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1e9fd1da-1df8-47c5-b6e1-6647211d4efc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1e9fd1da-1df8-47c5-b6e1-6647211d4efc' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ff69641-285b-4377-ad20-66fd2f1ea04f' class='xr-section-summary-in' type='checkbox' checked><label for='section-0ff69641-285b-4377-ad20-66fd2f1ea04f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-9b1297e8-cac3-49f6-b3ae-8407c6e544c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b1297e8-cac3-49f6-b3ae-8407c6e544c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edf07be0-c9f9-4b3d-adbb-d657d97e3663' class='xr-var-data-in' type='checkbox'><label for='data-edf07be0-c9f9-4b3d-adbb-d657d97e3663' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-3938451d-ffab-497a-909b-c02705fd8084' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3938451d-ffab-497a-909b-c02705fd8084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6940de5-b4f6-4d4a-893c-3395e64e141e' class='xr-var-data-in' type='checkbox'><label for='data-e6940de5-b4f6-4d4a-893c-3395e64e141e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19dd92e6-aa0b-482f-986d-927caaad94d6' class='xr-section-summary-in' type='checkbox' checked><label for='section-19dd92e6-aa0b-482f-986d-927caaad94d6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-78c7eeb0-537b-42ef-83b7-a23cc7d2e520' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78c7eeb0-537b-42ef-83b7-a23cc7d2e520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26df0a24-c614-42b2-8548-4842d942e654' class='xr-var-data-in' type='checkbox'><label for='data-26df0a24-c614-42b2-8548-4842d942e654' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-e4f3c3e2-42dc-4a71-83b2-aeb20e2888df' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4f3c3e2-42dc-4a71-83b2-aeb20e2888df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-918a7835-82cc-43f5-b708-d774acb411a8' class='xr-var-data-in' type='checkbox'><label for='data-918a7835-82cc-43f5-b708-d774acb411a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"120\" x2=\"31\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 31.737134090121618,0.0 31.737134090121618,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"15.868567\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
" <text x=\"51.737134\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,51.737134,60.000000)\">2517</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e647c49-74ef-4240-b298-fd7c53a14d6d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e647c49-74ef-4240-b298-fd7c53a14d6d' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' 'AMZN' ... 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fill cells that are missing only for some series for any reason\n",
"# Fill cells before a symbol became available with cash\n",
"ds[\"adjclose_filled\"] = ds.adjclose.chunk().ffill(\"date\").bfill(\"date\")\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" <th>date</th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th>2012-05-03</th>\n",
" <td>17.286030</td>\n",
" <td>32.610001</td>\n",
" <td>27.829645</td>\n",
" <td>223.990005</td>\n",
" <td>62.421898</td>\n",
" <td>6.673128</td>\n",
" <td>103.750000</td>\n",
" <td>12.067763</td>\n",
" <td>65.750000</td>\n",
" <td>38.826321</td>\n",
" <td>...</td>\n",
" <td>32.483589</td>\n",
" <td>25.735390</td>\n",
" <td>37.720001</td>\n",
" <td>24.936304</td>\n",
" <td>46.733696</td>\n",
" <td>517.116882</td>\n",
" <td>55.833855</td>\n",
" <td>12.500780</td>\n",
" <td>62.000000</td>\n",
" <td>33.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-04</th>\n",
" <td>17.286030</td>\n",
" <td>32.610001</td>\n",
" <td>27.829645</td>\n",
" <td>223.990005</td>\n",
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" <td>517.116882</td>\n",
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" <td>12.500780</td>\n",
" <td>62.000000</td>\n",
" <td>33.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-07</th>\n",
" <td>17.415396</td>\n",
" <td>32.520000</td>\n",
" <td>27.878458</td>\n",
" <td>225.160004</td>\n",
" <td>62.520679</td>\n",
" <td>6.862804</td>\n",
" <td>103.550003</td>\n",
" <td>12.110381</td>\n",
" <td>65.750000</td>\n",
" <td>38.382442</td>\n",
" <td>...</td>\n",
" <td>32.647575</td>\n",
" <td>25.907972</td>\n",
" <td>37.720001</td>\n",
" <td>25.291130</td>\n",
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" <th>2012-05-08</th>\n",
" <td>17.375639</td>\n",
" <td>32.669998</td>\n",
" <td>28.228365</td>\n",
" <td>223.899994</td>\n",
" <td>62.059765</td>\n",
" <td>6.716237</td>\n",
" <td>102.750000</td>\n",
" <td>11.928297</td>\n",
" <td>65.750000</td>\n",
" <td>37.787819</td>\n",
" <td>...</td>\n",
" <td>32.200356</td>\n",
" <td>25.920763</td>\n",
" <td>37.720001</td>\n",
" <td>25.026903</td>\n",
" <td>47.012344</td>\n",
" <td>534.158569</td>\n",
" <td>55.470730</td>\n",
" <td>13.380106</td>\n",
" <td>62.000000</td>\n",
" <td>33.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-05-09</th>\n",
" <td>17.406221</td>\n",
" <td>32.520000</td>\n",
" <td>27.919151</td>\n",
" <td>222.979996</td>\n",
" <td>61.306370</td>\n",
" <td>6.664506</td>\n",
" <td>103.129997</td>\n",
" <td>12.044519</td>\n",
" <td>65.750000</td>\n",
" <td>37.519814</td>\n",
" <td>...</td>\n",
" <td>31.782938</td>\n",
" <td>25.728996</td>\n",
" <td>37.720001</td>\n",
" <td>24.634323</td>\n",
" <td>47.315331</td>\n",
" <td>532.726868</td>\n",
" <td>55.001972</td>\n",
" <td>13.442470</td>\n",
" <td>62.000000</td>\n",
" <td>33.000000</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>2022-04-27</th>\n",
" <td>156.570007</td>\n",
" <td>397.899994</td>\n",
" <td>128.369995</td>\n",
" <td>2763.340088</td>\n",
" <td>154.460007</td>\n",
" <td>36.250000</td>\n",
" <td>500.959991</td>\n",
" <td>62.790001</td>\n",
" <td>38.220001</td>\n",
" <td>140.070007</td>\n",
" <td>...</td>\n",
" <td>55.540001</td>\n",
" <td>48.459999</td>\n",
" <td>76.279999</td>\n",
" <td>44.580002</td>\n",
" <td>154.240005</td>\n",
" <td>1048.910034</td>\n",
" <td>84.639999</td>\n",
" <td>37.700001</td>\n",
" <td>97.620003</td>\n",
" <td>204.759995</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-28</th>\n",
" <td>163.639999</td>\n",
" <td>410.529999</td>\n",
" <td>130.339996</td>\n",
" <td>2891.929932</td>\n",
" <td>154.220001</td>\n",
" <td>36.810001</td>\n",
" <td>513.109985</td>\n",
" <td>63.619999</td>\n",
" <td>39.820000</td>\n",
" <td>142.929993</td>\n",
" <td>...</td>\n",
" <td>56.360001</td>\n",
" <td>48.400002</td>\n",
" <td>83.389999</td>\n",
" <td>45.169998</td>\n",
" <td>156.210007</td>\n",
" <td>1071.930054</td>\n",
" <td>87.199997</td>\n",
" <td>39.610001</td>\n",
" <td>102.559998</td>\n",
" <td>213.589996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-04-29</th>\n",
" <td>157.649994</td>\n",
" <td>395.950012</td>\n",
" <td>126.540001</td>\n",
" <td>2485.629883</td>\n",
" <td>148.839996</td>\n",
" <td>35.680000</td>\n",
" <td>512.059998</td>\n",
" <td>62.430000</td>\n",
" <td>36.880001</td>\n",
" <td>146.940002</td>\n",
" <td>...</td>\n",
" <td>55.860001</td>\n",
" <td>46.299999</td>\n",
" <td>76.940002</td>\n",
" <td>43.630001</td>\n",
" <td>152.990005</td>\n",
" <td>1048.020020</td>\n",
" <td>85.250000</td>\n",
" <td>38.650002</td>\n",
" <td>99.570000</td>\n",
" <td>202.740005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-02</th>\n",
" <td>157.960007</td>\n",
" <td>407.290009</td>\n",
" <td>127.730003</td>\n",
" <td>2490.000000</td>\n",
" <td>148.610001</td>\n",
" <td>36.139999</td>\n",
" <td>501.420013</td>\n",
" <td>60.330002</td>\n",
" <td>38.330002</td>\n",
" <td>145.949997</td>\n",
" <td>...</td>\n",
" <td>55.689999</td>\n",
" <td>46.230000</td>\n",
" <td>87.389999</td>\n",
" <td>43.669998</td>\n",
" <td>151.979996</td>\n",
" <td>1050.099976</td>\n",
" <td>86.410004</td>\n",
" <td>39.799999</td>\n",
" <td>104.790001</td>\n",
" <td>208.229996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2022-05-03</th>\n",
" <td>159.479996</td>\n",
" <td>407.579987</td>\n",
" <td>129.779999</td>\n",
" <td>2485.070068</td>\n",
" <td>153.580002</td>\n",
" <td>37.130001</td>\n",
" <td>500.929993</td>\n",
" <td>60.799999</td>\n",
" <td>38.900002</td>\n",
" <td>150.339996</td>\n",
" <td>...</td>\n",
" <td>56.169998</td>\n",
" <td>47.169998</td>\n",
" <td>89.849998</td>\n",
" <td>44.160000</td>\n",
" <td>152.509995</td>\n",
" <td>1048.599976</td>\n",
" <td>88.190002</td>\n",
" <td>42.480000</td>\n",
" <td>103.529999</td>\n",
" <td>204.699997</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2517 rows × 80 columns</p>\n",
"</div>"
],
"text/plain": [
"symbol AAPL ADBE ALL AMZN BA \\\n",
"date \n",
"2012-05-03 17.286030 32.610001 27.829645 223.990005 62.421898 \n",
"2012-05-04 17.286030 32.610001 27.829645 223.990005 62.421898 \n",
"2012-05-07 17.415396 32.520000 27.878458 225.160004 62.520679 \n",
"2012-05-08 17.375639 32.669998 28.228365 223.899994 62.059765 \n",
"2012-05-09 17.406221 32.520000 27.919151 222.979996 61.306370 \n",
"... ... ... ... ... ... \n",
"2022-04-27 156.570007 397.899994 128.369995 2763.340088 154.460007 \n",
"2022-04-28 163.639999 410.529999 130.339996 2891.929932 154.220001 \n",
"2022-04-29 157.649994 395.950012 126.540001 2485.629883 148.839996 \n",
"2022-05-02 157.960007 407.290009 127.730003 2490.000000 148.610001 \n",
"2022-05-03 159.479996 407.579987 129.779999 2485.070068 153.580002 \n",
"\n",
"symbol BAC BIO BIP BYND CE ... \\\n",
"date ... \n",
"2012-05-03 6.673128 103.750000 12.067763 65.750000 38.826321 ... \n",
"2012-05-04 6.673128 103.750000 12.067763 65.750000 38.826321 ... \n",
"2012-05-07 6.862804 103.550003 12.110381 65.750000 38.382442 ... \n",
"2012-05-08 6.716237 102.750000 11.928297 65.750000 37.787819 ... \n",
"2012-05-09 6.664506 103.129997 12.044519 65.750000 37.519814 ... \n",
"... ... ... ... ... ... ... \n",
"2022-04-27 36.250000 500.959991 62.790001 38.220001 140.070007 ... \n",
"2022-04-28 36.810001 513.109985 63.619999 39.820000 142.929993 ... \n",
"2022-04-29 35.680000 512.059998 62.430000 36.880001 146.940002 ... \n",
"2022-05-02 36.139999 501.420013 60.330002 38.330002 145.949997 ... \n",
"2022-05-03 37.130001 500.929993 60.799999 38.900002 150.339996 ... \n",
"\n",
"symbol VXUS VZ W WFC WMT \\\n",
"date \n",
"2012-05-03 32.483589 25.735390 37.720001 24.936304 46.733696 \n",
"2012-05-04 32.483589 25.735390 37.720001 24.936304 46.733696 \n",
"2012-05-07 32.647575 25.907972 37.720001 25.291130 47.123810 \n",
"2012-05-08 32.200356 25.920763 37.720001 25.026903 47.012344 \n",
"2012-05-09 31.782938 25.728996 37.720001 24.634323 47.315331 \n",
"... ... ... ... ... ... \n",
"2022-04-27 55.540001 48.459999 76.279999 44.580002 154.240005 \n",
"2022-04-28 56.360001 48.400002 83.389999 45.169998 156.210007 \n",
"2022-04-29 55.860001 46.299999 76.940002 43.630001 152.990005 \n",
"2022-05-02 55.689999 46.230000 87.389999 43.669998 151.979996 \n",
"2022-05-03 56.169998 47.169998 89.849998 44.160000 152.509995 \n",
"\n",
"symbol WTM XOM ZG ZM ZS \n",
"date \n",
"2012-05-03 517.116882 55.833855 12.500780 62.000000 33.000000 \n",
"2012-05-04 517.116882 55.833855 12.500780 62.000000 33.000000 \n",
"2012-05-07 522.576904 55.774429 12.793888 62.000000 33.000000 \n",
"2012-05-08 534.158569 55.470730 13.380106 62.000000 33.000000 \n",
"2012-05-09 532.726868 55.001972 13.442470 62.000000 33.000000 \n",
"... ... ... ... ... ... \n",
"2022-04-27 1048.910034 84.639999 37.700001 97.620003 204.759995 \n",
"2022-04-28 1071.930054 87.199997 39.610001 102.559998 213.589996 \n",
"2022-04-29 1048.020020 85.250000 38.650002 99.570000 202.740005 \n",
"2022-05-02 1050.099976 86.410004 39.799999 104.790001 208.229996 \n",
"2022-05-03 1048.599976 88.190002 42.480000 103.529999 204.699997 \n",
"\n",
"[2517 rows x 80 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.adjclose_filled.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
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".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; &#x27;AMZN&#x27; ... &#x27;ZG&#x27; &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" adjclose_rel (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4fb08b8b-0038-4e25-8792-1fb12544faf7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4fb08b8b-0038-4e25-8792-1fb12544faf7' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a78ba601-9d34-4278-a489-f9b5fc5991eb' class='xr-section-summary-in' type='checkbox' checked><label for='section-a78ba601-9d34-4278-a489-f9b5fc5991eb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-9ca58362-6666-4f85-b18c-741362844576' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ca58362-6666-4f85-b18c-741362844576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c0609c6-d47f-4dd0-a74e-d0b1f2aaf8af' class='xr-var-data-in' type='checkbox'><label for='data-8c0609c6-d47f-4dd0-a74e-d0b1f2aaf8af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-f0b965c9-0e89-453c-8e8c-2d2d9e1f0f7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0b965c9-0e89-453c-8e8c-2d2d9e1f0f7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5b5e722-9b7c-4abc-acf6-0d6508eb44ee' class='xr-var-data-in' type='checkbox'><label for='data-e5b5e722-9b7c-4abc-acf6-0d6508eb44ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2ad47202-7cc1-4a1a-afc4-49d0d9d0e20b' class='xr-section-summary-in' type='checkbox' checked><label for='section-2ad47202-7cc1-4a1a-afc4-49d0d9d0e20b' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-8edfa842-19bd-44df-9f4e-c365c003ed26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8edfa842-19bd-44df-9f4e-c365c003ed26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4440a6ed-dc4a-44f6-8eea-ea03bba3db2e' class='xr-var-data-in' type='checkbox'><label for='data-4440a6ed-dc4a-44f6-8eea-ea03bba3db2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-5af0323c-2707-454a-ac62-945c0c162b71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5af0323c-2707-454a-ac62-945c0c162b71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e84b9d29-bc86-4503-a1f2-1d0696b96e4d' class='xr-var-data-in' type='checkbox'><label for='data-e84b9d29-bc86-4503-a1f2-1d0696b96e4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"31\" y2=\"0\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 31.737134090121618,0.0 31.737134090121618,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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" <!-- Text -->\n",
" <text x=\"15.868567\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_rel</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-6778a0a0-af4d-4dd0-9d5f-8a95b9922a89' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6778a0a0-af4d-4dd0-9d5f-8a95b9922a89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee0c67a-5253-40d3-8b4a-312827b38f33' class='xr-var-data-in' type='checkbox'><label for='data-1ee0c67a-5253-40d3-8b4a-312827b38f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 12 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"31\" y2=\"0\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
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"\n",
" <!-- Text -->\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e34a3d13-60af-4303-b2c5-9df25d3a4a0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e34a3d13-60af-4303-b2c5-9df25d3a4a0e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 2012-05-04 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' 'AMZN' ... 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" adjclose_rel (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"adjclose_rel\"] = ds.adjclose_filled / ds.adjclose_filled.isel(date=0)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='date'>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1440x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds.adjclose_rel.to_pandas().plot(figsize=(20, 15), grid=True)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZG&#x27; &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" adjclose_rel (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" log_daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-26baac9b-8a25-49a0-bf1a-748618b1b566' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26baac9b-8a25-49a0-bf1a-748618b1b566' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-31ad93ae-39a3-42d7-991e-65541c9ed9b6' class='xr-section-summary-in' type='checkbox' checked><label for='section-31ad93ae-39a3-42d7-991e-65541c9ed9b6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-5d7e0385-cce9-4464-a6a3-67a7daa3bf7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d7e0385-cce9-4464-a6a3-67a7daa3bf7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b5254b-6693-4e82-9e79-dfa3bdd38921' class='xr-var-data-in' type='checkbox'><label for='data-50b5254b-6693-4e82-9e79-dfa3bdd38921' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-088f48c4-e9b2-43a1-bfae-d72adfe699ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-088f48c4-e9b2-43a1-bfae-d72adfe699ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec89c5af-e6aa-4805-b554-fb2d07491b2a' class='xr-var-data-in' type='checkbox'><label for='data-ec89c5af-e6aa-4805-b554-fb2d07491b2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d17ee4be-6b58-4b3d-a2eb-b715beb19432' class='xr-section-summary-in' type='checkbox' checked><label for='section-d17ee4be-6b58-4b3d-a2eb-b715beb19432' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-7e4b3cce-3455-43ca-8451-b38e9f2f9860' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e4b3cce-3455-43ca-8451-b38e9f2f9860' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af0bd567-7c9b-4e8c-bef3-93ab8f89a21b' class='xr-var-data-in' type='checkbox'><label for='data-af0bd567-7c9b-4e8c-bef3-93ab8f89a21b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-7cc31588-2ca1-4dda-bb1c-49810b339ff8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cc31588-2ca1-4dda-bb1c-49810b339ff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdbea751-d761-40ef-b2ed-9510ad1fb55c' class='xr-var-data-in' type='checkbox'><label for='data-cdbea751-d761-40ef-b2ed-9510ad1fb55c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
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" <!-- Text -->\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_rel</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-2482dc19-7d0e-43d7-87eb-08ab96f4c232' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2482dc19-7d0e-43d7-87eb-08ab96f4c232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6459933d-3b64-439a-8637-cabc9991a708' class='xr-var-data-in' type='checkbox'><label for='data-6459933d-3b64-439a-8637-cabc9991a708' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
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" <table>\n",
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" <td> </td>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 12 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" adjclose_rel (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" log_daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"daily_returns\"] = ds.adjclose_filled / ds.adjclose_filled.shift(date=1)\n",
"ds[\"log_daily_returns\"] = numpy.log(ds.daily_returns)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='date'>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1440x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds.daily_returns.to_pandas().plot(figsize=(20, 15), grid=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"dr_1y = ds.log_daily_returns.isel(date=slice(-252, None))\n",
"ds[\"mean_log_returns\"] = dr_1y.mean(\"date\")\n",
"ds[\"volatility\"] = dr_1y.std(\"date\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZG&#x27; &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" adjclose_rel (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" log_daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" mean_log_returns (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" volatility (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" sharpe (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c55716bf-6f58-4ba9-8f93-9fa8085a5bfe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c55716bf-6f58-4ba9-8f93-9fa8085a5bfe' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-77c166ec-6173-4983-a687-5448cff77022' class='xr-section-summary-in' type='checkbox' checked><label for='section-77c166ec-6173-4983-a687-5448cff77022' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-5339c8f1-b55c-4a5c-8284-5b8cddf347a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5339c8f1-b55c-4a5c-8284-5b8cddf347a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-011366e7-f360-4f51-9d32-ed6b790388d3' class='xr-var-data-in' type='checkbox'><label for='data-011366e7-f360-4f51-9d32-ed6b790388d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-3731a0ea-b790-4e30-b75e-9d50016b7273' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3731a0ea-b790-4e30-b75e-9d50016b7273' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-973a20a8-3a4a-42b8-be95-737563b60a71' class='xr-var-data-in' type='checkbox'><label for='data-973a20a8-3a4a-42b8-be95-737563b60a71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-039b5a46-5354-4e5a-a3ac-bd5ca3d60a66' class='xr-section-summary-in' type='checkbox' checked><label for='section-039b5a46-5354-4e5a-a3ac-bd5ca3d60a66' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-af5205b5-e6a3-4417-8f55-1fca11308cfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af5205b5-e6a3-4417-8f55-1fca11308cfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-901c2728-bd3b-42bf-8dae-6f05f51b6c1f' class='xr-var-data-in' type='checkbox'><label for='data-901c2728-bd3b-42bf-8dae-6f05f51b6c1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-cb5987cf-2bc6-4133-a42e-200a8c53c38f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb5987cf-2bc6-4133-a42e-200a8c53c38f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-329911a4-1bbd-46b1-b801-d42a932e831c' class='xr-var-data-in' type='checkbox'><label for='data-329911a4-1bbd-46b1-b801-d42a932e831c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" adjclose_rel (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" log_daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" mean_log_returns (symbol) float64 dask.array<chunksize=(80,), meta=np.ndarray>\n",
" volatility (symbol) float64 dask.array<chunksize=(80,), meta=np.ndarray>\n",
" sharpe (symbol) float64 dask.array<chunksize=(80,), meta=np.ndarray>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"sharpe\"] = ds.mean_log_returns / ds.volatility\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
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" <th>sharpe</th>\n",
" </tr>\n",
" <tr>\n",
" <th>symbol</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RDFN</th>\n",
" <td>-0.006646</td>\n",
" <td>0.041919</td>\n",
" <td>-0.158549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZM</th>\n",
" <td>-0.004286</td>\n",
" <td>0.033537</td>\n",
" <td>-0.127788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TDOC</th>\n",
" <td>-0.005631</td>\n",
" <td>0.048567</td>\n",
" <td>-0.115939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DIS</th>\n",
" <td>-0.001921</td>\n",
" <td>0.016707</td>\n",
" <td>-0.114971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PTON</th>\n",
" <td>-0.006604</td>\n",
" <td>0.058286</td>\n",
" <td>-0.113300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KO</th>\n",
" <td>0.000727</td>\n",
" <td>0.009630</td>\n",
" <td>0.075494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PEP</th>\n",
" <td>0.000721</td>\n",
" <td>0.009447</td>\n",
" <td>0.076352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>XOM</th>\n",
" <td>0.001796</td>\n",
" <td>0.018568</td>\n",
" <td>0.096727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NVO</th>\n",
" <td>0.001739</td>\n",
" <td>0.017736</td>\n",
" <td>0.098031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CVX</th>\n",
" <td>0.001880</td>\n",
" <td>0.016093</td>\n",
" <td>0.116792</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>80 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
"measure mean_log_returns volatility sharpe\n",
"symbol \n",
"RDFN -0.006646 0.041919 -0.158549\n",
"ZM -0.004286 0.033537 -0.127788\n",
"TDOC -0.005631 0.048567 -0.115939\n",
"DIS -0.001921 0.016707 -0.114971\n",
"PTON -0.006604 0.058286 -0.113300\n",
"... ... ... ...\n",
"KO 0.000727 0.009630 0.075494\n",
"PEP 0.000721 0.009447 0.076352\n",
"XOM 0.001796 0.018568 0.096727\n",
"NVO 0.001739 0.017736 0.098031\n",
"CVX 0.001880 0.016093 0.116792\n",
"\n",
"[80 rows x 3 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[[\"mean_log_returns\", \"volatility\", \"sharpe\"]].to_array(\"measure\").T.to_pandas().sort_values(\"sharpe\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Coordinates:\n",
" symbol &lt;U5 &#x27;TSLA&#x27;\n",
"Data variables:\n",
" mean_log_returns float64 0.00119\n",
" volatility float64 0.03446\n",
" sharpe float64 0.03454</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2a9b8132-0a7d-4a21-93ff-0985fd83150f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a9b8132-0a7d-4a21-93ff-0985fd83150f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-87249519-5ee2-4669-9872-409e4e70ecee' class='xr-section-summary-in' type='checkbox' checked><label for='section-87249519-5ee2-4669-9872-409e4e70ecee' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>symbol</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;TSLA&#x27;</div><input id='attrs-9b52fe2a-2332-4b20-a22e-40749ff1874a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b52fe2a-2332-4b20-a22e-40749ff1874a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e08f6a3-cc1c-4aeb-bad1-2843645419be' class='xr-var-data-in' type='checkbox'><label for='data-9e08f6a3-cc1c-4aeb-bad1-2843645419be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;TSLA&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90005ba5-3cb6-4da4-8c40-ea8478f4b6c4' class='xr-section-summary-in' type='checkbox' checked><label for='section-90005ba5-3cb6-4da4-8c40-ea8478f4b6c4' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mean_log_returns</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.00119</div><input id='attrs-91316ff7-a5b2-4d60-a3d3-d2a260dd1b12' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91316ff7-a5b2-4d60-a3d3-d2a260dd1b12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45931f32-f651-4e10-8d95-e5c74435564b' class='xr-var-data-in' type='checkbox'><label for='data-45931f32-f651-4e10-8d95-e5c74435564b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.00119041)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volatility</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03446</div><input id='attrs-cadaced4-e389-4c4d-97df-bf1eac0bf6f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cadaced4-e389-4c4d-97df-bf1eac0bf6f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-018c6250-22b4-4fd9-91fa-6619c4d0eedf' class='xr-var-data-in' type='checkbox'><label for='data-018c6250-22b4-4fd9-91fa-6619c4d0eedf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.03446437)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sharpe</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.03454</div><input id='attrs-8b68980d-b965-49ce-ac00-7620e844ba9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b68980d-b965-49ce-ac00-7620e844ba9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e0cc965-ddbb-4d17-8d8d-acb7a1f1ed1c' class='xr-var-data-in' type='checkbox'><label for='data-6e0cc965-ddbb-4d17-8d8d-acb7a1f1ed1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.03454035)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e695ef4-612a-4408-aaf7-b396549402c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8e695ef4-612a-4408-aaf7-b396549402c9' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
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"ds[[\"mean_log_returns\", \"volatility\", \"sharpe\"]].sel(symbol=\"TSLA\").compute()"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
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" volatility (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" sharpe (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" cov (symbol, symbol2) float64 dask.array&lt;chunksize=(80, 80), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5ed55bf-217e-4a2c-93c7-d1a7dfe01d02' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5ed55bf-217e-4a2c-93c7-d1a7dfe01d02' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li><li><span class='xr-has-index'>symbol2</span>: 80</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ed6f98a0-9504-4ced-80ec-899e1b5a78b2' class='xr-section-summary-in' type='checkbox' checked><label for='section-ed6f98a0-9504-4ced-80ec-899e1b5a78b2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-9e60c303-77ae-4e0c-8a35-20185c9576c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e60c303-77ae-4e0c-8a35-20185c9576c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73918c4b-334a-429a-aa31-d9afdc9e45b0' class='xr-var-data-in' type='checkbox'><label for='data-73918c4b-334a-429a-aa31-d9afdc9e45b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-406ca439-158f-4dc4-b365-d7d6dfb86813' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-406ca439-158f-4dc4-b365-d7d6dfb86813' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6409eccc-11e5-4bbf-af3e-05e644947f56' class='xr-var-data-in' type='checkbox'><label for='data-6409eccc-11e5-4bbf-af3e-05e644947f56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol2</span></div><div class='xr-var-dims'>(symbol2)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-2865b32a-4173-4ce0-b285-f82295bdd174' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2865b32a-4173-4ce0-b285-f82295bdd174' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f2cfe74-f067-4d22-b24f-c663b8d441d3' class='xr-var-data-in' type='checkbox'><label for='data-9f2cfe74-f067-4d22-b24f-c663b8d441d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddd4b21f-fca5-4fa5-a985-7390b0491c66' class='xr-section-summary-in' type='checkbox' checked><label for='section-ddd4b21f-fca5-4fa5-a985-7390b0491c66' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-9c386faf-0962-46c7-ac47-7f70a0e27367' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c386faf-0962-46c7-ac47-7f70a0e27367' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26188db0-8771-4279-a974-4a9590b06d0b' class='xr-var-data-in' type='checkbox'><label for='data-26188db0-8771-4279-a974-4a9590b06d0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-5f2edfa5-3427-4602-a68a-20b43117cc7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f2edfa5-3427-4602-a68a-20b43117cc7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbf1b315-f8e9-4619-a8a7-c8493603db40' class='xr-var-data-in' type='checkbox'><label for='data-bbf1b315-f8e9-4619-a8a7-c8493603db40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_rel</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-7cbda2d2-be36-4bcd-9c1e-b64f20ec3ef1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cbda2d2-be36-4bcd-9c1e-b64f20ec3ef1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6cc09e0-75c1-4183-99f7-1e261d22e8bf' class='xr-var-data-in' type='checkbox'><label for='data-d6cc09e0-75c1-4183-99f7-1e261d22e8bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 12 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daily_returns</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-0adfb22b-8d0e-4a3c-aff5-df03b6f02a6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0adfb22b-8d0e-4a3c-aff5-df03b6f02a6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5a03951-6100-4768-89b6-85fa281f9f51' class='xr-var-data-in' type='checkbox'><label for='data-c5a03951-6100-4768-89b6-85fa281f9f51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 16 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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"\n",
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"</svg>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80, symbol2: 80)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZG' 'ZM' 'ZS'\n",
" * symbol2 (symbol2) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZG' 'ZM' 'ZS'\n",
"Data variables:\n",
" adjclose (date, symbol) float64 nan nan nan ... 42.48 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
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" sharpe (symbol) float64 dask.array<chunksize=(80,), meta=np.ndarray>\n",
" cov (symbol, symbol2) float64 dask.array<chunksize=(80, 80), meta=np.ndarray>"
]
},
"execution_count": 17,
"metadata": {},
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}
],
"source": [
"ds[\"symbol2\"] = ds.symbol.data\n",
"ds[\"cov\"] = ((\"symbol\", \"symbol2\"), cov)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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" <td>0.131016</td>\n",
" <td>-0.098899</td>\n",
" <td>0.329094</td>\n",
" <td>0.501157</td>\n",
" <td>0.619902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALL</th>\n",
" <td>0.192816</td>\n",
" <td>-0.011789</td>\n",
" <td>1.000000</td>\n",
" <td>0.114652</td>\n",
" <td>0.292772</td>\n",
" <td>0.524627</td>\n",
" <td>0.101653</td>\n",
" <td>0.243520</td>\n",
" <td>0.050545</td>\n",
" <td>0.477043</td>\n",
" <td>...</td>\n",
" <td>0.317660</td>\n",
" <td>0.507751</td>\n",
" <td>0.168248</td>\n",
" <td>0.476060</td>\n",
" <td>0.283823</td>\n",
" <td>0.373608</td>\n",
" <td>0.293905</td>\n",
" <td>-0.033029</td>\n",
" <td>-0.046458</td>\n",
" <td>-0.072657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AMZN</th>\n",
" <td>0.581783</td>\n",
" <td>0.521407</td>\n",
" <td>0.114652</td>\n",
" <td>1.000000</td>\n",
" <td>0.390370</td>\n",
" <td>0.309127</td>\n",
" <td>0.266765</td>\n",
" <td>0.250411</td>\n",
" <td>0.385641</td>\n",
" <td>0.133332</td>\n",
" <td>...</td>\n",
" <td>0.448521</td>\n",
" <td>0.094008</td>\n",
" <td>0.452149</td>\n",
" <td>0.254210</td>\n",
" <td>0.152313</td>\n",
" <td>0.140825</td>\n",
" <td>0.098649</td>\n",
" <td>0.323598</td>\n",
" <td>0.424980</td>\n",
" <td>0.516260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BA</th>\n",
" <td>0.372049</td>\n",
" <td>0.257752</td>\n",
" <td>0.292772</td>\n",
" <td>0.390370</td>\n",
" <td>1.000000</td>\n",
" <td>0.518976</td>\n",
" <td>0.079437</td>\n",
" <td>0.231533</td>\n",
" <td>0.352271</td>\n",
" <td>0.467778</td>\n",
" <td>...</td>\n",
" <td>0.622880</td>\n",
" <td>0.178324</td>\n",
" <td>0.351169</td>\n",
" <td>0.519915</td>\n",
" <td>0.114558</td>\n",
" <td>0.290328</td>\n",
" <td>0.285798</td>\n",
" <td>0.298657</td>\n",
" <td>0.253778</td>\n",
" <td>0.276329</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>WTM</th>\n",
" <td>0.268931</td>\n",
" <td>0.131016</td>\n",
" <td>0.373608</td>\n",
" <td>0.140825</td>\n",
" <td>0.290328</td>\n",
" <td>0.371929</td>\n",
" <td>0.121077</td>\n",
" <td>0.237756</td>\n",
" <td>0.090280</td>\n",
" <td>0.386862</td>\n",
" <td>...</td>\n",
" <td>0.382133</td>\n",
" <td>0.216749</td>\n",
" <td>0.134980</td>\n",
" <td>0.323291</td>\n",
" <td>0.151590</td>\n",
" <td>1.000000</td>\n",
" <td>0.190762</td>\n",
" <td>0.107587</td>\n",
" <td>0.061631</td>\n",
" <td>0.158013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>XOM</th>\n",
" <td>0.056498</td>\n",
" <td>-0.098899</td>\n",
" <td>0.293905</td>\n",
" <td>0.098649</td>\n",
" <td>0.285798</td>\n",
" <td>0.355073</td>\n",
" <td>-0.202585</td>\n",
" <td>0.225809</td>\n",
" <td>-0.024906</td>\n",
" <td>0.299507</td>\n",
" <td>...</td>\n",
" <td>0.228780</td>\n",
" <td>0.272306</td>\n",
" <td>0.075405</td>\n",
" <td>0.329124</td>\n",
" <td>0.060384</td>\n",
" <td>0.190762</td>\n",
" <td>1.000000</td>\n",
" <td>-0.040831</td>\n",
" <td>-0.088041</td>\n",
" <td>-0.148244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZG</th>\n",
" <td>0.298180</td>\n",
" <td>0.329094</td>\n",
" <td>-0.033029</td>\n",
" <td>0.323598</td>\n",
" <td>0.298657</td>\n",
" <td>0.116420</td>\n",
" <td>0.311624</td>\n",
" <td>0.110677</td>\n",
" <td>0.397864</td>\n",
" <td>0.099128</td>\n",
" <td>...</td>\n",
" <td>0.353255</td>\n",
" <td>-0.001238</td>\n",
" <td>0.403417</td>\n",
" <td>0.045220</td>\n",
" <td>0.027083</td>\n",
" <td>0.107587</td>\n",
" <td>-0.040831</td>\n",
" <td>1.000000</td>\n",
" <td>0.409488</td>\n",
" <td>0.385577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZM</th>\n",
" <td>0.380788</td>\n",
" <td>0.501157</td>\n",
" <td>-0.046458</td>\n",
" <td>0.424980</td>\n",
" <td>0.253778</td>\n",
" <td>0.074559</td>\n",
" <td>0.382268</td>\n",
" <td>0.090331</td>\n",
" <td>0.512084</td>\n",
" <td>0.037804</td>\n",
" <td>...</td>\n",
" <td>0.330002</td>\n",
" <td>-0.055616</td>\n",
" <td>0.528272</td>\n",
" <td>0.084766</td>\n",
" <td>-0.047450</td>\n",
" <td>0.061631</td>\n",
" <td>-0.088041</td>\n",
" <td>0.409488</td>\n",
" <td>1.000000</td>\n",
" <td>0.517405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ZS</th>\n",
" <td>0.475915</td>\n",
" <td>0.619902</td>\n",
" <td>-0.072657</td>\n",
" <td>0.516260</td>\n",
" <td>0.276329</td>\n",
" <td>0.071075</td>\n",
" <td>0.349237</td>\n",
" <td>0.192938</td>\n",
" <td>0.440370</td>\n",
" <td>0.073268</td>\n",
" <td>...</td>\n",
" <td>0.348589</td>\n",
" <td>-0.125929</td>\n",
" <td>0.430227</td>\n",
" <td>0.076630</td>\n",
" <td>-0.039891</td>\n",
" <td>0.158013</td>\n",
" <td>-0.148244</td>\n",
" <td>0.385577</td>\n",
" <td>0.517405</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>80 rows × 80 columns</p>\n",
"</div>"
],
"text/plain": [
"symbol2 AAPL ADBE ALL AMZN BA BAC BIO \\\n",
"symbol \n",
"AAPL 1.000000 0.617308 0.192816 0.581783 0.372049 0.327786 0.413973 \n",
"ADBE 0.617308 1.000000 -0.011789 0.521407 0.257752 0.172397 0.518421 \n",
"ALL 0.192816 -0.011789 1.000000 0.114652 0.292772 0.524627 0.101653 \n",
"AMZN 0.581783 0.521407 0.114652 1.000000 0.390370 0.309127 0.266765 \n",
"BA 0.372049 0.257752 0.292772 0.390370 1.000000 0.518976 0.079437 \n",
"... ... ... ... ... ... ... ... \n",
"WTM 0.268931 0.131016 0.373608 0.140825 0.290328 0.371929 0.121077 \n",
"XOM 0.056498 -0.098899 0.293905 0.098649 0.285798 0.355073 -0.202585 \n",
"ZG 0.298180 0.329094 -0.033029 0.323598 0.298657 0.116420 0.311624 \n",
"ZM 0.380788 0.501157 -0.046458 0.424980 0.253778 0.074559 0.382268 \n",
"ZS 0.475915 0.619902 -0.072657 0.516260 0.276329 0.071075 0.349237 \n",
"\n",
"symbol2 BIP BYND CE ... VXUS VZ W \\\n",
"symbol ... \n",
"AAPL 0.313850 0.318104 0.280586 ... 0.612679 0.115999 0.396889 \n",
"ADBE 0.219319 0.360457 0.171507 ... 0.466805 -0.094651 0.463974 \n",
"ALL 0.243520 0.050545 0.477043 ... 0.317660 0.507751 0.168248 \n",
"AMZN 0.250411 0.385641 0.133332 ... 0.448521 0.094008 0.452149 \n",
"BA 0.231533 0.352271 0.467778 ... 0.622880 0.178324 0.351169 \n",
"... ... ... ... ... ... ... ... \n",
"WTM 0.237756 0.090280 0.386862 ... 0.382133 0.216749 0.134980 \n",
"XOM 0.225809 -0.024906 0.299507 ... 0.228780 0.272306 0.075405 \n",
"ZG 0.110677 0.397864 0.099128 ... 0.353255 -0.001238 0.403417 \n",
"ZM 0.090331 0.512084 0.037804 ... 0.330002 -0.055616 0.528272 \n",
"ZS 0.192938 0.440370 0.073268 ... 0.348589 -0.125929 0.430227 \n",
"\n",
"symbol2 WFC WMT WTM XOM ZG ZM ZS \n",
"symbol \n",
"AAPL 0.333594 0.227479 0.268931 0.056498 0.298180 0.380788 0.475915 \n",
"ADBE 0.185310 0.156559 0.131016 -0.098899 0.329094 0.501157 0.619902 \n",
"ALL 0.476060 0.283823 0.373608 0.293905 -0.033029 -0.046458 -0.072657 \n",
"AMZN 0.254210 0.152313 0.140825 0.098649 0.323598 0.424980 0.516260 \n",
"BA 0.519915 0.114558 0.290328 0.285798 0.298657 0.253778 0.276329 \n",
"... ... ... ... ... ... ... ... \n",
"WTM 0.323291 0.151590 1.000000 0.190762 0.107587 0.061631 0.158013 \n",
"XOM 0.329124 0.060384 0.190762 1.000000 -0.040831 -0.088041 -0.148244 \n",
"ZG 0.045220 0.027083 0.107587 -0.040831 1.000000 0.409488 0.385577 \n",
"ZM 0.084766 -0.047450 0.061631 -0.088041 0.409488 1.000000 0.517405 \n",
"ZS 0.076630 -0.039891 0.158013 -0.148244 0.385577 0.517405 1.000000 \n",
"\n",
"[80 rows x 80 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.cov.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"N_WEIGHTS = 10\n",
"N_PORTFOLIOS = 500_000\n",
"CHUNK_SIZE = 100_000\n",
"\n",
"rng = da.random.RandomState(0)\n",
"\n",
"ds[\"weights_pos\"] = (\n",
" (\"portfolio\", \"symbol_i\"), \n",
" rng.randint(0, ds.sizes[\"symbol\"], size=(N_PORTFOLIOS, N_WEIGHTS), chunks=(CHUNK_SIZE, -1)),\n",
")\n",
"ds[\"weights_raw\"] = (\n",
" (\"portfolio\", \"symbol_i\"),\n",
" rng.random((N_PORTFOLIOS, N_WEIGHTS), chunks=(CHUNK_SIZE, -1)),\n",
")\n",
"\n",
"import numba\n",
"\n",
"@numba.guvectorize([\"i8[:],f8[:],f8[:],f8[:]\"], \"(i),(i),(j)->(j)\", nopython=True)\n",
"def weights(weights_pos, weights_raw, symbol, out):\n",
" for j in range(out.size):\n",
" out[j] = 0\n",
" for i in range(weights_pos.size):\n",
" j = weights_pos[i]\n",
" out[j] += weights_raw[i]\n",
"\n",
"ds[\"weights\"] = xarray.apply_ufunc(\n",
" weights,\n",
" ds.weights_pos,\n",
" ds.weights_raw,\n",
" xarray.zeros_like(ds.symbol, dtype=float),\n",
" input_core_dims=[(\"symbol_i\",), (\"symbol_i\", ), (\"symbol\", )],\n",
" output_core_dims=[(\"symbol\", )],\n",
" output_dtypes=[float],\n",
" dask=\"parallelized\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"\n",
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" 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>symbol</th>\n",
" <th>AAPL</th>\n",
" <th>ADBE</th>\n",
" <th>ALL</th>\n",
" <th>AMZN</th>\n",
" <th>BA</th>\n",
" <th>BAC</th>\n",
" <th>BIO</th>\n",
" <th>BIP</th>\n",
" <th>BYND</th>\n",
" <th>CE</th>\n",
" <th>...</th>\n",
" <th>VXUS</th>\n",
" <th>VZ</th>\n",
" <th>W</th>\n",
" <th>WFC</th>\n",
" <th>WMT</th>\n",
" <th>WTM</th>\n",
" <th>XOM</th>\n",
" <th>ZG</th>\n",
" <th>ZM</th>\n",
" <th>ZS</th>\n",
" </tr>\n",
" <tr>\n",
" <th>portfolio</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>0</th>\n",
" <td>0.054185</td>\n",
" <td>0.0</td>\n",
" <td>0.128997</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.198553</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.046797</td>\n",
" <td>0.006623</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.004428</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.124935</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.055561</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.222691</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.004295</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.0</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>499995</th>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.145873</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.145117</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <th>499996</th>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>...</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.125847</td>\n",
" <td>0.00000</td>\n",
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" <tr>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
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" <tr>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.014263</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.13327</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>499999</th>\n",
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"<p>500000 rows × 80 columns</p>\n",
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"text/plain": [
"symbol AAPL ADBE ALL AMZN BA BAC BIO \\\n",
"portfolio \n",
"0 0.054185 0.0 0.128997 0.000000 0.000000 0.000000 0.198553 \n",
"1 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.0 0.046797 0.006623 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.0 0.055561 0.000000 0.000000 0.000000 0.000000 \n",
"... ... ... ... ... ... ... ... \n",
"499995 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"499996 0.000000 0.0 0.000000 0.000000 0.000000 0.140986 0.000000 \n",
"499997 0.000000 0.0 0.000000 0.000000 0.149146 0.000000 0.000000 \n",
"499998 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"499999 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
"symbol BIP BYND CE ... VXUS VZ W \\\n",
"portfolio ... \n",
"0 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"3 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.0 0.222691 ... 0.000000 0.004295 0.000000 \n",
"... ... ... ... ... ... ... ... \n",
"499995 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.145873 \n",
"499996 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"499997 0.000000 0.0 0.000000 ... 0.098218 0.000000 0.000000 \n",
"499998 0.014263 0.0 0.000000 ... 0.000000 0.000000 0.112033 \n",
"499999 0.000000 0.0 0.000000 ... 0.000000 0.000000 0.000000 \n",
"\n",
"symbol WFC WMT WTM XOM ZG ZM ZS \n",
"portfolio \n",
"0 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.0 \n",
"1 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.0 \n",
"2 0.000000 0.000000 0.000000 0.004428 0.000000 0.00000 0.0 \n",
"3 0.000000 0.000000 0.000000 0.124935 0.000000 0.00000 0.0 \n",
"4 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.0 \n",
"... ... ... ... ... ... ... ... \n",
"499995 0.000000 0.000000 0.145117 0.000000 0.000000 0.00000 0.0 \n",
"499996 0.000000 0.000000 0.000000 0.000000 0.125847 0.00000 0.0 \n",
"499997 0.000000 0.000000 0.235927 0.000000 0.000000 0.00000 0.0 \n",
"499998 0.000000 0.000000 0.000000 0.000000 0.000000 0.13327 0.0 \n",
"499999 0.070521 0.244686 0.000000 0.000000 0.000000 0.00000 0.0 \n",
"\n",
"[500000 rows x 80 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"norm_weights\"] = ds.weights / ds.weights.sum(\"symbol\")\n",
"ds.norm_weights.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80, symbol2: 80,\n",
" portfolio: 500000, symbol_i: 10)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
" * symbol2 (symbol2) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Dimensions without coordinates: portfolio, symbol_i\n",
"Data variables: (12/16)\n",
" adjclose (date, symbol) float64 nan nan ... 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" adjclose_rel (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" log_daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" mean_log_returns (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" ... ...\n",
" weights_raw (portfolio, symbol_i) float64 dask.array&lt;chunksize=(100000, 10), meta=np.ndarray&gt;\n",
" weights (portfolio, symbol) float64 dask.array&lt;chunksize=(100000, 80), meta=np.ndarray&gt;\n",
" norm_weights (portfolio, symbol) float64 dask.array&lt;chunksize=(100000, 80), meta=np.ndarray&gt;\n",
" ptf_mean_log_daily_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_mean_log_yearly_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_mean_yearly_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4d87576-4e02-4e84-971e-bd3cc3def420' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4d87576-4e02-4e84-971e-bd3cc3def420' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 2517</li><li><span class='xr-has-index'>symbol</span>: 80</li><li><span class='xr-has-index'>symbol2</span>: 80</li><li><span>portfolio</span>: 500000</li><li><span>symbol_i</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-99517960-f3c5-4fb6-bd9f-646133598b45' class='xr-section-summary-in' type='checkbox' checked><label for='section-99517960-f3c5-4fb6-bd9f-646133598b45' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-05-03 ... 2022-05-03</div><input id='attrs-4f8f5da1-5bc2-43f9-a60c-f9239f5f8eda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f8f5da1-5bc2-43f9-a60c-f9239f5f8eda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80eda0d1-9558-42d2-87cb-86c0531d58a1' class='xr-var-data-in' type='checkbox'><label for='data-80eda0d1-9558-42d2-87cb-86c0531d58a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-05-03T00:00:00.000000000&#x27;, &#x27;2012-05-04T00:00:00.000000000&#x27;,\n",
" &#x27;2012-05-07T00:00:00.000000000&#x27;, ..., &#x27;2022-04-29T00:00:00.000000000&#x27;,\n",
" &#x27;2022-05-02T00:00:00.000000000&#x27;, &#x27;2022-05-03T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol</span></div><div class='xr-var-dims'>(symbol)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-e341fa1b-0e0d-47ec-97f9-ed4d21a6ab00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e341fa1b-0e0d-47ec-97f9-ed4d21a6ab00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c3deb6c-9cb0-43fb-98fb-e840aa1e88f1' class='xr-var-data-in' type='checkbox'><label for='data-1c3deb6c-9cb0-43fb-98fb-e840aa1e88f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>symbol2</span></div><div class='xr-var-dims'>(symbol2)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;</div><input id='attrs-489d1ece-c7ab-4da9-8567-85b3ba35917f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-489d1ece-c7ab-4da9-8567-85b3ba35917f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b69966b1-7a28-4137-9e07-342c533349a9' class='xr-var-data-in' type='checkbox'><label for='data-b69966b1-7a28-4137-9e07-342c533349a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AAPL&#x27;, &#x27;ADBE&#x27;, &#x27;ALL&#x27;, &#x27;AMZN&#x27;, &#x27;BA&#x27;, &#x27;BAC&#x27;, &#x27;BIO&#x27;, &#x27;BIP&#x27;, &#x27;BYND&#x27;, &#x27;CE&#x27;,\n",
" &#x27;CMCSA&#x27;, &#x27;CRM&#x27;, &#x27;CSCO&#x27;, &#x27;CTRE&#x27;, &#x27;CVX&#x27;, &#x27;DIS&#x27;, &#x27;ETSY&#x27;, &#x27;F&#x27;, &#x27;FB&#x27;, &#x27;FCX&#x27;,\n",
" &#x27;FVRR&#x27;, &#x27;GM&#x27;, &#x27;GOOGL&#x27;, &#x27;HD&#x27;, &#x27;IAC&#x27;, &#x27;IBM&#x27;, &#x27;INTC&#x27;, &#x27;IRBT&#x27;, &#x27;ISRG&#x27;,\n",
" &#x27;JNJ&#x27;, &#x27;JPM&#x27;, &#x27;KO&#x27;, &#x27;LULU&#x27;, &#x27;MA&#x27;, &#x27;MAT&#x27;, &#x27;MELI&#x27;, &#x27;MO&#x27;, &#x27;MRK&#x27;, &#x27;MRNA&#x27;,\n",
" &#x27;MSFT&#x27;, &#x27;NFLX&#x27;, &#x27;NRG&#x27;, &#x27;NVO&#x27;, &#x27;NVS&#x27;, &#x27;OKE&#x27;, &#x27;PEP&#x27;, &#x27;PFE&#x27;, &#x27;PG&#x27;, &#x27;PINS&#x27;,\n",
" &#x27;PM&#x27;, &#x27;PTON&#x27;, &#x27;RDEIY&#x27;, &#x27;RDFN&#x27;, &#x27;RHHBY&#x27;, &#x27;ROKU&#x27;, &#x27;SAP&#x27;, &#x27;SE&#x27;, &#x27;SNY&#x27;,\n",
" &#x27;SQ&#x27;, &#x27;STZ&#x27;, &#x27;T&#x27;, &#x27;TDOC&#x27;, &#x27;TSLA&#x27;, &#x27;UL&#x27;, &#x27;UNH&#x27;, &#x27;UPWK&#x27;, &#x27;V&#x27;, &#x27;VEA&#x27;,\n",
" &#x27;VIRT&#x27;, &#x27;VTI&#x27;, &#x27;VXUS&#x27;, &#x27;VZ&#x27;, &#x27;W&#x27;, &#x27;WFC&#x27;, &#x27;WMT&#x27;, &#x27;WTM&#x27;, &#x27;XOM&#x27;, &#x27;ZG&#x27;,\n",
" &#x27;ZM&#x27;, &#x27;ZS&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e9a5489-a89d-4228-907d-2aa8d992709c' class='xr-section-summary-in' type='checkbox' ><label for='section-8e9a5489-a89d-4228-907d-2aa8d992709c' class='xr-section-summary' >Data variables: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>adjclose</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 42.48 103.5 204.7</div><input id='attrs-9e78868e-51d9-4547-8dde-3f1933fbdeda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e78868e-51d9-4547-8dde-3f1933fbdeda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b047d4e6-8fb9-4903-b8aa-7c87a97f12e2' class='xr-var-data-in' type='checkbox'><label for='data-b047d4e6-8fb9-4903-b8aa-7c87a97f12e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, ..., nan,\n",
" nan, nan],\n",
" [ 17.28602982, 32.61000061, 27.82964516, ..., 12.50078011,\n",
" nan, nan],\n",
" [ 17.41539574, 32.52000046, 27.87845802, ..., 12.79388809,\n",
" nan, nan],\n",
" ...,\n",
" [157.6499939 , 395.95001221, 126.54000092, ..., 38.65000153,\n",
" 99.56999969, 202.74000549],\n",
" [157.96000671, 407.29000854, 127.73000336, ..., 39.79999924,\n",
" 104.79000092, 208.22999573],\n",
" [159.47999573, 407.57998657, 129.77999878, ..., 42.47999954,\n",
" 103.52999878, 204.69999695]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_filled</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-776998dc-bcea-4166-abce-b7a00a660d27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-776998dc-bcea-4166-abce-b7a00a660d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f86060e0-23b0-4310-8df9-e60d146fe83b' class='xr-var-data-in' type='checkbox'><label for='data-f86060e0-23b0-4310-8df9-e60d146fe83b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 9 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"81\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>adjclose_rel</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-4bab4379-5945-4416-836f-449707fc5564' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bab4379-5945-4416-836f-449707fc5564' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-695293a0-7698-4751-ab1e-3b3e76e1e9aa' class='xr-var-data-in' type='checkbox'><label for='data-695293a0-7698-4751-ab1e-3b3e76e1e9aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 12 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daily_returns</span></div><div class='xr-var-dims'>(date, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;</div><input id='attrs-885c8f49-2d26-4f44-b201-cae4d7443af7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-885c8f49-2d26-4f44-b201-cae4d7443af7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d0b3dae-61d2-47d4-8715-92b21cc2a95a' class='xr-var-data-in' type='checkbox'><label for='data-6d0b3dae-61d2-47d4-8715-92b21cc2a95a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table>\n",
" <thead>\n",
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" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.54 MiB </td>\n",
" <td> 1.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Count </th>\n",
" <td> 16 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
" <td> numpy.ndarray </td>\n",
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" </table>\n",
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" <td> 1.54 MiB </td>\n",
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" <td> (2517, 80) </td>\n",
" <td> (2517, 80) </td>\n",
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" <tr>\n",
" <th> Count </th>\n",
" <td> 17 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
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" <tr>\n",
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" <th> Count </th>\n",
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" <td> 1 Chunks </td>\n",
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" <th> Type </th>\n",
" <td> float64 </td>\n",
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" </table>\n",
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" <table>\n",
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" <th> Chunk </th>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
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" <td> 640 B </td>\n",
" <td> 640 B </td>\n",
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" <td> (80,) </td>\n",
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" <th> Count </th>\n",
" <td> 21 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
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" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
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" \n",
" <tr>\n",
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" <td> 640 B </td>\n",
" <td> 640 B </td>\n",
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" <td> (80,) </td>\n",
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" <td> 24 Tasks </td>\n",
" <td> 1 Chunks </td>\n",
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" <tr>\n",
" <th> Type </th>\n",
" <td> float64 </td>\n",
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" <tr>\n",
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" <td> 34 Tasks </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>weights</span></div><div class='xr-var-dims'>(portfolio, symbol)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100000, 80), meta=np.ndarray&gt;</div><input id='attrs-d20ca51c-41f4-49f4-abc4-8da08bc3d10b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d20ca51c-41f4-49f4-abc4-8da08bc3d10b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3851068-b2aa-4efd-8084-783476fe2ea2' class='xr-var-data-in' type='checkbox'><label for='data-d3851068-b2aa-4efd-8084-783476fe2ea2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (date: 2517, symbol: 80, symbol2: 80,\n",
" portfolio: 500000, symbol_i: 10)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZM' 'ZS'\n",
" * symbol2 (symbol2) <U5 'AAPL' 'ADBE' 'ALL' ... 'ZM' 'ZS'\n",
"Dimensions without coordinates: portfolio, symbol_i\n",
"Data variables: (12/16)\n",
" adjclose (date, symbol) float64 nan nan ... 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" adjclose_rel (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" log_daily_returns (date, symbol) float64 dask.array<chunksize=(2517, 80), meta=np.ndarray>\n",
" mean_log_returns (symbol) float64 dask.array<chunksize=(80,), meta=np.ndarray>\n",
" ... ...\n",
" weights_raw (portfolio, symbol_i) float64 dask.array<chunksize=(100000, 10), meta=np.ndarray>\n",
" weights (portfolio, symbol) float64 dask.array<chunksize=(100000, 80), meta=np.ndarray>\n",
" norm_weights (portfolio, symbol) float64 dask.array<chunksize=(100000, 80), meta=np.ndarray>\n",
" ptf_mean_log_daily_returns (portfolio) float64 dask.array<chunksize=(100000,), meta=np.ndarray>\n",
" ptf_mean_log_yearly_returns (portfolio) float64 dask.array<chunksize=(100000,), meta=np.ndarray>\n",
" ptf_mean_yearly_returns (portfolio) float64 dask.array<chunksize=(100000,), meta=np.ndarray>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds[\"ptf_mean_log_daily_returns\"] = xarray.dot(ds.mean_log_returns, ds.norm_weights, dims=\"symbol\")\n",
"ds[\"ptf_mean_log_yearly_returns\"] = ds.ptf_mean_log_daily_returns * 252\n",
"ds[\"ptf_mean_yearly_returns\"] = numpy.exp(ds.ptf_mean_log_yearly_returns)\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import numba\n",
"\n",
"@numba.guvectorize([\"f8[:],f8[:,:],f8[:]\"], \"(i),(i,j)->()\", nopython=True)\n",
"def volatility(w: np.ndarray, c: np.ndarray, out: np.ndarray) -> None:\n",
" n = w.size\n",
" acc1 = 0.0\n",
" for i in range(n):\n",
" acc2 = 0.0\n",
" for j in range(n):\n",
" acc2 += w[j] * c[j, i]\n",
" acc1 += acc2 * w[i]\n",
" out[0] = acc1\n",
"\n",
"ds[\"ptf_volatility\"] = xarray.apply_ufunc(\n",
" volatility,\n",
" ds.norm_weights,\n",
" ds.cov,\n",
" input_core_dims=[(\"symbol\",), (\"symbol\", \"symbol2\")],\n",
" output_core_dims=[()],\n",
" output_dtypes=[float],\n",
" dask=\"parallelized\",\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"ds[\"ptf_sharpe\"] = ds.ptf_mean_log_daily_returns / ds.ptf_volatility"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (date: 2517, symbol: 80, symbol2: 80,\n",
" portfolio: 500000, symbol_i: 10)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 2012-05-03 ... 2022-05-03\n",
" * symbol (symbol) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
" * symbol2 (symbol2) &lt;U5 &#x27;AAPL&#x27; &#x27;ADBE&#x27; &#x27;ALL&#x27; ... &#x27;ZM&#x27; &#x27;ZS&#x27;\n",
"Dimensions without coordinates: portfolio, symbol_i\n",
"Data variables: (12/18)\n",
" adjclose (date, symbol) float64 nan nan ... 103.5 204.7\n",
" adjclose_filled (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" adjclose_rel (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" log_daily_returns (date, symbol) float64 dask.array&lt;chunksize=(2517, 80), meta=np.ndarray&gt;\n",
" mean_log_returns (symbol) float64 dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
" ... ...\n",
" norm_weights (portfolio, symbol) float64 dask.array&lt;chunksize=(100000, 80), meta=np.ndarray&gt;\n",
" ptf_mean_log_daily_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_mean_log_yearly_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_mean_yearly_returns (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_volatility (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;\n",
" ptf_sharpe (portfolio) float64 dask.array&lt;chunksize=(100000,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7db7df80-6ad2-4cbc-8175-93fb2ca07763' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7db7df80-6ad2-4cbc-8175-93fb2ca07763' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-sect
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