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tej87681088/輝達概念股英文.ipynb

Last active July 31, 2024 01:54
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輝達概念股英文.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tejapi\n",
"import pandas as pd\n",
"\n",
"tejapi.ApiConfig.api_key = \"Your Key\"\n",
"tejapi.ApiConfig.api_base = \"https://api.tej.com.tw\"\n",
"tejapi.ApiConfig.ignoretz = True"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"coid_list = ['6579', '2353', '6166', '2395', '3711', '3515', '2357', '2417', '3088', '8210', '2324', '5371', '2308', '3048', '5484', '2317', '9921', '2376', '3312', '2356', '6117', '6125', '2449', '6245', '2465', '2301', '2454', '3706', '2377', '6922', '8234', '6569', '4938', '2382', '2359', '3030', '3540', '2330', '2303', '3037', '3231', '6669', '5474'] \n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"tablebefore = tejapi.get (\n",
" 'TWN/APRCD2',\n",
" coid = coid_list ,\n",
" mdate = {'gte':'2024-05-02','lte':'2024-06-02'}, \n",
" paginate = True, \n",
" opts={'columns':['coid','mdate','roia']},\n",
" )\n",
"tableafter = tejapi.get (\n",
" 'TWN/APRCD2',\n",
" coid = coid_list ,\n",
" mdate = {'gte':'2024-06-02','lte':'2024-07-02'}, \n",
" paginate = True, \n",
" opts={'columns':['coid','mdate','roia']},\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>coid</th>\n",
" <th>mdate</th>\n",
" <th>roia</th>\n",
" </tr>\n",
" <tr>\n",
" <th>None</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2301</td>\n",
" <td>2024-05-02</td>\n",
" <td>-0.9000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2301</td>\n",
" <td>2024-05-03</td>\n",
" <td>0.6054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2301</td>\n",
" <td>2024-05-06</td>\n",
" <td>-0.3009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2301</td>\n",
" <td>2024-05-07</td>\n",
" <td>-0.3018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2301</td>\n",
" <td>2024-05-08</td>\n",
" <td>2.4218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>941</th>\n",
" <td>9921</td>\n",
" <td>2024-05-27</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>942</th>\n",
" <td>9921</td>\n",
" <td>2024-05-28</td>\n",
" <td>1.5982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>943</th>\n",
" <td>9921</td>\n",
" <td>2024-05-29</td>\n",
" <td>0.6742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>944</th>\n",
" <td>9921</td>\n",
" <td>2024-05-30</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>945</th>\n",
" <td>9921</td>\n",
" <td>2024-05-31</td>\n",
" <td>-2.9018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>946 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" coid mdate roia\n",
"None \n",
"0 2301 2024-05-02 -0.9000\n",
"1 2301 2024-05-03 0.6054\n",
"2 2301 2024-05-06 -0.3009\n",
"3 2301 2024-05-07 -0.3018\n",
"4 2301 2024-05-08 2.4218\n",
"... ... ... ...\n",
"941 9921 2024-05-27 0.0000\n",
"942 9921 2024-05-28 1.5982\n",
"943 9921 2024-05-29 0.6742\n",
"944 9921 2024-05-30 0.0000\n",
"945 9921 2024-05-31 -2.9018\n",
"\n",
"[946 rows x 3 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"dfbefore = pd.DataFrame(tablebefore)\n",
"\n",
"dfafter = pd.DataFrame(tableafter)\n",
"\n",
"\n",
"dfbefore\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"average_return_rate_before = dfbefore.groupby('coid')['roia'].mean()\n",
"\n",
"average_return_rate_after= dfafter.groupby('coid')['roia'].mean()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"std_return_rate_before = dfbefore.groupby('coid')['roia'].std()\n",
"\n",
"\n",
"std_return_rate_after = dfafter.groupby('coid')['roia'].std()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"sharpe_ratio_before = average_return_rate_before / std_return_rate_before *100 #ignore risk-free rate\n",
"\n",
"dfbefore = pd.DataFrame(sharpe_ratio_before).reset_index()\n",
"dfbefore.rename(columns={'roia': 'Sharpe ratio bf %'}, inplace=True)\n",
"\n",
"\n",
"sharpe_ratio_after = average_return_rate_after / std_return_rate_after *100\n",
"dfafter = pd.DataFrame(sharpe_ratio_after).reset_index()\n",
"dfafter.rename(columns={'roia': 'Sharpe ratio aft %'}, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>coid</th>\n",
" <th>Sharpe ratio bf %</th>\n",
" <th>Sharpe ratio aft %</th>\n",
" <th>Sharpe difference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2301</td>\n",
" <td>12.312575</td>\n",
" <td>6.798626</td>\n",
" <td>-5.513949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2303</td>\n",
" <td>27.862752</td>\n",
" <td>5.378234</td>\n",
" <td>-22.484518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2308</td>\n",
" <td>3.825403</td>\n",
" <td>45.939685</td>\n",
" <td>42.114282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2317</td>\n",
" <td>19.818377</td>\n",
" <td>39.003323</td>\n",
" <td>19.184947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2324</td>\n",
" <td>6.891955</td>\n",
" <td>-49.955756</td>\n",
" <td>-56.847711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2330</td>\n",
" <td>13.414805</td>\n",
" <td>41.007175</td>\n",
" <td>27.592370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2353</td>\n",
" <td>23.543538</td>\n",
" <td>-30.757161</td>\n",
" <td>-54.300699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2356</td>\n",
" <td>2.572368</td>\n",
" <td>13.339620</td>\n",
" <td>10.767252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2357</td>\n",
" <td>25.830189</td>\n",
" <td>-17.348829</td>\n",
" <td>-43.179018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2359</td>\n",
" <td>49.813958</td>\n",
" <td>-1.975209</td>\n",
" <td>-51.789166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2376</td>\n",
" <td>12.772852</td>\n",
" <td>-14.807080</td>\n",
" <td>-27.579932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2377</td>\n",
" <td>36.730038</td>\n",
" <td>-16.522807</td>\n",
" <td>-53.252845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2382</td>\n",
" <td>12.332701</td>\n",
" <td>19.536106</td>\n",
" <td>7.203405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2395</td>\n",
" <td>-18.231988</td>\n",
" <td>14.881022</td>\n",
" <td>33.113010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2417</td>\n",
" <td>26.463620</td>\n",
" <td>8.520332</td>\n",
" <td>-17.943288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2449</td>\n",
" <td>-16.422706</td>\n",
" <td>41.646036</td>\n",
" <td>58.068742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2454</td>\n",
" <td>40.171417</td>\n",
" <td>24.167831</td>\n",
" <td>-16.003586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2465</td>\n",
" <td>-5.089301</td>\n",
" <td>-3.138268</td>\n",
" <td>1.951033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3030</td>\n",
" <td>55.275946</td>\n",
" <td>-1.394916</td>\n",
" <td>-56.670862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3037</td>\n",
" <td>1.574759</td>\n",
" <td>-5.962473</td>\n",
" <td>-7.537233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3048</td>\n",
" <td>49.999567</td>\n",
" <td>25.276329</td>\n",
" <td>-24.723238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3088</td>\n",
" <td>37.037549</td>\n",
" <td>4.931176</td>\n",
" <td>-32.106372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3231</td>\n",
" <td>-2.180548</td>\n",
" <td>-4.559746</td>\n",
" <td>-2.379198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3312</td>\n",
" <td>41.557451</td>\n",
" <td>29.633543</td>\n",
" <td>-11.923908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3515</td>\n",
" <td>8.997059</td>\n",
" <td>0.649883</td>\n",
" <td>-8.347175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3540</td>\n",
" <td>8.523439</td>\n",
" <td>9.200291</td>\n",
" <td>0.676853</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3706</td>\n",
" <td>9.463943</td>\n",
" <td>-22.657390</td>\n",
" <td>-32.121333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3711</td>\n",
" <td>15.173648</td>\n",
" <td>11.686580</td>\n",
" <td>-3.487068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>4938</td>\n",
" <td>31.068200</td>\n",
" <td>4.059777</td>\n",
" <td>-27.008424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5371</td>\n",
" <td>10.004917</td>\n",
" <td>-37.668300</td>\n",
" <td>-47.673217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>5474</td>\n",
" <td>34.325634</td>\n",
" <td>7.677580</td>\n",
" <td>-26.648054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>5484</td>\n",
" <td>14.863654</td>\n",
" <td>127.149583</td>\n",
" <td>112.285930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>6117</td>\n",
" <td>57.174050</td>\n",
" <td>-16.335131</td>\n",
" <td>-73.509181</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>6125</td>\n",
" <td>32.692142</td>\n",
" <td>-10.628760</td>\n",
" <td>-43.320902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>6166</td>\n",
" <td>22.956420</td>\n",
" <td>5.203130</td>\n",
" <td>-17.753290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>6245</td>\n",
" <td>-6.261627</td>\n",
" <td>6.850900</td>\n",
" <td>13.112527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>6569</td>\n",
" <td>22.571157</td>\n",
" <td>26.293804</td>\n",
" <td>3.722647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>6579</td>\n",
" <td>17.345672</td>\n",
" <td>10.129950</td>\n",
" <td>-7.215723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>6669</td>\n",
" <td>8.539067</td>\n",
" <td>11.917708</td>\n",
" <td>3.378641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>6922</td>\n",
" <td>42.266872</td>\n",
" <td>10.458184</td>\n",
" <td>-31.808687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>8210</td>\n",
" <td>9.192942</td>\n",
" <td>-2.215687</td>\n",
" <td>-11.408629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>8234</td>\n",
" <td>4.649493</td>\n",
" <td>12.815652</td>\n",
" <td>8.166159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>9921</td>\n",
" <td>-0.048282</td>\n",
" <td>-17.426459</td>\n",
" <td>-17.378177</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coid Sharpe ratio bf % Sharpe ratio aft % Sharpe difference \n",
"0 2301 12.312575 6.798626 -5.513949\n",
"1 2303 27.862752 5.378234 -22.484518\n",
"2 2308 3.825403 45.939685 42.114282\n",
"3 2317 19.818377 39.003323 19.184947\n",
"4 2324 6.891955 -49.955756 -56.847711\n",
"5 2330 13.414805 41.007175 27.592370\n",
"6 2353 23.543538 -30.757161 -54.300699\n",
"7 2356 2.572368 13.339620 10.767252\n",
"8 2357 25.830189 -17.348829 -43.179018\n",
"9 2359 49.813958 -1.975209 -51.789166\n",
"10 2376 12.772852 -14.807080 -27.579932\n",
"11 2377 36.730038 -16.522807 -53.252845\n",
"12 2382 12.332701 19.536106 7.203405\n",
"13 2395 -18.231988 14.881022 33.113010\n",
"14 2417 26.463620 8.520332 -17.943288\n",
"15 2449 -16.422706 41.646036 58.068742\n",
"16 2454 40.171417 24.167831 -16.003586\n",
"17 2465 -5.089301 -3.138268 1.951033\n",
"18 3030 55.275946 -1.394916 -56.670862\n",
"19 3037 1.574759 -5.962473 -7.537233\n",
"20 3048 49.999567 25.276329 -24.723238\n",
"21 3088 37.037549 4.931176 -32.106372\n",
"22 3231 -2.180548 -4.559746 -2.379198\n",
"23 3312 41.557451 29.633543 -11.923908\n",
"24 3515 8.997059 0.649883 -8.347175\n",
"25 3540 8.523439 9.200291 0.676853\n",
"26 3706 9.463943 -22.657390 -32.121333\n",
"27 3711 15.173648 11.686580 -3.487068\n",
"28 4938 31.068200 4.059777 -27.008424\n",
"29 5371 10.004917 -37.668300 -47.673217\n",
"30 5474 34.325634 7.677580 -26.648054\n",
"31 5484 14.863654 127.149583 112.285930\n",
"32 6117 57.174050 -16.335131 -73.509181\n",
"33 6125 32.692142 -10.628760 -43.320902\n",
"34 6166 22.956420 5.203130 -17.753290\n",
"35 6245 -6.261627 6.850900 13.112527\n",
"36 6569 22.571157 26.293804 3.722647\n",
"37 6579 17.345672 10.129950 -7.215723\n",
"38 6669 8.539067 11.917708 3.378641\n",
"39 6922 42.266872 10.458184 -31.808687\n",
"40 8210 9.192942 -2.215687 -11.408629\n",
"41 8234 4.649493 12.815652 8.166159\n",
"42 9921 -0.048282 -17.426459 -17.378177"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df = pd.merge(dfbefore, dfafter, on='coid')\n",
"merged_df['Sharpe difference '] = merged_df['Sharpe ratio aft %']-merged_df['Sharpe ratio bf %']\n",
"\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>coid</th>\n",
" <th>Sharpe ratio bf %</th>\n",
" <th>Sharpe ratio aft %</th>\n",
" <th>Sharpe difference</th>\n",
" <th>increased or not</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2301</td>\n",
" <td>12.312575</td>\n",
" <td>6.798626</td>\n",
" <td>-5.513949</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2303</td>\n",
" <td>27.862752</td>\n",
" <td>5.378234</td>\n",
" <td>-22.484518</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2308</td>\n",
" <td>3.825403</td>\n",
" <td>45.939685</td>\n",
" <td>42.114282</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2317</td>\n",
" <td>19.818377</td>\n",
" <td>39.003323</td>\n",
" <td>19.184947</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2324</td>\n",
" <td>6.891955</td>\n",
" <td>-49.955756</td>\n",
" <td>-56.847711</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2330</td>\n",
" <td>13.414805</td>\n",
" <td>41.007175</td>\n",
" <td>27.592370</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2353</td>\n",
" <td>23.543538</td>\n",
" <td>-30.757161</td>\n",
" <td>-54.300699</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2356</td>\n",
" <td>2.572368</td>\n",
" <td>13.339620</td>\n",
" <td>10.767252</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2357</td>\n",
" <td>25.830189</td>\n",
" <td>-17.348829</td>\n",
" <td>-43.179018</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2359</td>\n",
" <td>49.813958</td>\n",
" <td>-1.975209</td>\n",
" <td>-51.789166</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2376</td>\n",
" <td>12.772852</td>\n",
" <td>-14.807080</td>\n",
" <td>-27.579932</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2377</td>\n",
" <td>36.730038</td>\n",
" <td>-16.522807</td>\n",
" <td>-53.252845</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2382</td>\n",
" <td>12.332701</td>\n",
" <td>19.536106</td>\n",
" <td>7.203405</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2395</td>\n",
" <td>-18.231988</td>\n",
" <td>14.881022</td>\n",
" <td>33.113010</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2417</td>\n",
" <td>26.463620</td>\n",
" <td>8.520332</td>\n",
" <td>-17.943288</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2449</td>\n",
" <td>-16.422706</td>\n",
" <td>41.646036</td>\n",
" <td>58.068742</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2454</td>\n",
" <td>40.171417</td>\n",
" <td>24.167831</td>\n",
" <td>-16.003586</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2465</td>\n",
" <td>-5.089301</td>\n",
" <td>-3.138268</td>\n",
" <td>1.951033</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3030</td>\n",
" <td>55.275946</td>\n",
" <td>-1.394916</td>\n",
" <td>-56.670862</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3037</td>\n",
" <td>1.574759</td>\n",
" <td>-5.962473</td>\n",
" <td>-7.537233</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3048</td>\n",
" <td>49.999567</td>\n",
" <td>25.276329</td>\n",
" <td>-24.723238</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3088</td>\n",
" <td>37.037549</td>\n",
" <td>4.931176</td>\n",
" <td>-32.106372</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3231</td>\n",
" <td>-2.180548</td>\n",
" <td>-4.559746</td>\n",
" <td>-2.379198</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3312</td>\n",
" <td>41.557451</td>\n",
" <td>29.633543</td>\n",
" <td>-11.923908</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3515</td>\n",
" <td>8.997059</td>\n",
" <td>0.649883</td>\n",
" <td>-8.347175</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3540</td>\n",
" <td>8.523439</td>\n",
" <td>9.200291</td>\n",
" <td>0.676853</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3706</td>\n",
" <td>9.463943</td>\n",
" <td>-22.657390</td>\n",
" <td>-32.121333</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3711</td>\n",
" <td>15.173648</td>\n",
" <td>11.686580</td>\n",
" <td>-3.487068</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>4938</td>\n",
" <td>31.068200</td>\n",
" <td>4.059777</td>\n",
" <td>-27.008424</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>5371</td>\n",
" <td>10.004917</td>\n",
" <td>-37.668300</td>\n",
" <td>-47.673217</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>5474</td>\n",
" <td>34.325634</td>\n",
" <td>7.677580</td>\n",
" <td>-26.648054</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>5484</td>\n",
" <td>14.863654</td>\n",
" <td>127.149583</td>\n",
" <td>112.285930</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>6117</td>\n",
" <td>57.174050</td>\n",
" <td>-16.335131</td>\n",
" <td>-73.509181</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>6125</td>\n",
" <td>32.692142</td>\n",
" <td>-10.628760</td>\n",
" <td>-43.320902</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>6166</td>\n",
" <td>22.956420</td>\n",
" <td>5.203130</td>\n",
" <td>-17.753290</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>6245</td>\n",
" <td>-6.261627</td>\n",
" <td>6.850900</td>\n",
" <td>13.112527</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>6569</td>\n",
" <td>22.571157</td>\n",
" <td>26.293804</td>\n",
" <td>3.722647</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>6579</td>\n",
" <td>17.345672</td>\n",
" <td>10.129950</td>\n",
" <td>-7.215723</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>6669</td>\n",
" <td>8.539067</td>\n",
" <td>11.917708</td>\n",
" <td>3.378641</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>6922</td>\n",
" <td>42.266872</td>\n",
" <td>10.458184</td>\n",
" <td>-31.808687</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>8210</td>\n",
" <td>9.192942</td>\n",
" <td>-2.215687</td>\n",
" <td>-11.408629</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>8234</td>\n",
" <td>4.649493</td>\n",
" <td>12.815652</td>\n",
" <td>8.166159</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>9921</td>\n",
" <td>-0.048282</td>\n",
" <td>-17.426459</td>\n",
" <td>-17.378177</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" coid Sharpe ratio bf % Sharpe ratio aft % Sharpe difference \\\n",
"0 2301 12.312575 6.798626 -5.513949 \n",
"1 2303 27.862752 5.378234 -22.484518 \n",
"2 2308 3.825403 45.939685 42.114282 \n",
"3 2317 19.818377 39.003323 19.184947 \n",
"4 2324 6.891955 -49.955756 -56.847711 \n",
"5 2330 13.414805 41.007175 27.592370 \n",
"6 2353 23.543538 -30.757161 -54.300699 \n",
"7 2356 2.572368 13.339620 10.767252 \n",
"8 2357 25.830189 -17.348829 -43.179018 \n",
"9 2359 49.813958 -1.975209 -51.789166 \n",
"10 2376 12.772852 -14.807080 -27.579932 \n",
"11 2377 36.730038 -16.522807 -53.252845 \n",
"12 2382 12.332701 19.536106 7.203405 \n",
"13 2395 -18.231988 14.881022 33.113010 \n",
"14 2417 26.463620 8.520332 -17.943288 \n",
"15 2449 -16.422706 41.646036 58.068742 \n",
"16 2454 40.171417 24.167831 -16.003586 \n",
"17 2465 -5.089301 -3.138268 1.951033 \n",
"18 3030 55.275946 -1.394916 -56.670862 \n",
"19 3037 1.574759 -5.962473 -7.537233 \n",
"20 3048 49.999567 25.276329 -24.723238 \n",
"21 3088 37.037549 4.931176 -32.106372 \n",
"22 3231 -2.180548 -4.559746 -2.379198 \n",
"23 3312 41.557451 29.633543 -11.923908 \n",
"24 3515 8.997059 0.649883 -8.347175 \n",
"25 3540 8.523439 9.200291 0.676853 \n",
"26 3706 9.463943 -22.657390 -32.121333 \n",
"27 3711 15.173648 11.686580 -3.487068 \n",
"28 4938 31.068200 4.059777 -27.008424 \n",
"29 5371 10.004917 -37.668300 -47.673217 \n",
"30 5474 34.325634 7.677580 -26.648054 \n",
"31 5484 14.863654 127.149583 112.285930 \n",
"32 6117 57.174050 -16.335131 -73.509181 \n",
"33 6125 32.692142 -10.628760 -43.320902 \n",
"34 6166 22.956420 5.203130 -17.753290 \n",
"35 6245 -6.261627 6.850900 13.112527 \n",
"36 6569 22.571157 26.293804 3.722647 \n",
"37 6579 17.345672 10.129950 -7.215723 \n",
"38 6669 8.539067 11.917708 3.378641 \n",
"39 6922 42.266872 10.458184 -31.808687 \n",
"40 8210 9.192942 -2.215687 -11.408629 \n",
"41 8234 4.649493 12.815652 8.166159 \n",
"42 9921 -0.048282 -17.426459 -17.378177 \n",
"\n",
" increased or not \n",
"0 False \n",
"1 False \n",
"2 True \n",
"3 True \n",
"4 False \n",
"5 True \n",
"6 False \n",
"7 True \n",
"8 False \n",
"9 False \n",
"10 False \n",
"11 False \n",
"12 True \n",
"13 True \n",
"14 False \n",
"15 True \n",
"16 False \n",
"17 True \n",
"18 False \n",
"19 False \n",
"20 False \n",
"21 False \n",
"22 False \n",
"23 False \n",
"24 False \n",
"25 True \n",
"26 False \n",
"27 False \n",
"28 False \n",
"29 False \n",
"30 False \n",
"31 True \n",
"32 False \n",
"33 False \n",
"34 False \n",
"35 True \n",
"36 True \n",
"37 False \n",
"38 True \n",
"39 False \n",
"40 False \n",
"41 True \n",
"42 False "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df['increased or not'] = merged_df['Sharpe difference '].apply(lambda x: 'True' if x > 0 else 'False')\n",
"\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"sign_counts = merged_df['increased or not'].value_counts()\n",
"\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.bar(sign_counts.index, sign_counts.values, color='#00FF7F',alpha=0.8)\n",
"\n",
"\n",
"ax.set_xlabel('increased or not')\n",
"ax.set_ylabel('Number of companies')\n",
"ax.set_title('Distribution of Changes in Sharpe Ratio')\n",
"plt.grid(True)\n",
"plt.tight_layout()\n",
"# 顯示圖表\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1400x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"\n",
"plt.figure(figsize=(14, 8))\n",
"plt.bar(merged_df['coid'].astype(str), merged_df['Sharpe ratio bf %'], label='Sharpe ratio bf %', alpha=0.3)\n",
"plt.bar(merged_df['coid'].astype(str), merged_df['Sharpe ratio aft %'], label='Sharpe ratio aft %', alpha=0.8)\n",
"\n",
"\n",
"\n",
"plt.title('Sharpe ratios of each company before and after the speech.')\n",
"plt.xlabel('coid')\n",
"plt.ylabel('Sharpe ratio %')\n",
"plt.legend()\n",
"\n",
"plt.xticks(rotation=90)\n",
"plt.grid(True)\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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