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tej87681088/輝達概念股.ipynb

Created July 9, 2024 00:59
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輝達概念股.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import tejapi\n",
"import pandas as pd\n",
"\n",
"tejapi.ApiConfig.api_key = \"BYAxGg825SxMGvvkTsi6vMPcu1zT6p \"\n",
"tejapi.ApiConfig.api_base = \"http://10.10.10.66\"\n",
"tejapi.ApiConfig.ignoretz = True"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"tablebefore = tejapi.get (\n",
" 'TWN/APRCD2',\n",
" coid = coid_list ,\n",
" mdate = {'gte':'2024-05-02','lte':'2024-06-02'}, \n",
" chinese_column_name = True,\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",
" chinese_column_name = True,\n",
" paginate = True, \n",
" opts={'columns':['coid','mdate','roia']},\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>證券代碼</th>\n",
" <th>年月日</th>\n",
" <th>日報酬率 %</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": [
" 證券代碼 年月日 日報酬率 %\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": 6,
"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": 7,
"metadata": {},
"outputs": [],
"source": [
"average_return_rate_before = dfbefore.groupby('證券代碼')['日報酬率 %'].mean()\n",
"\n",
"average_return_rate_after= dfafter.groupby('證券代碼')['日報酬率 %'].mean()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"std_return_rate_before = dfbefore.groupby('證券代碼')['日報酬率 %'].std()\n",
"\n",
"\n",
"std_return_rate_after = dfafter.groupby('證券代碼')['日報酬率 %'].std()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"sharpe_ratio_before = average_return_rate_before / std_return_rate_before *100 #在此忽略無風險利率\n",
"\n",
"dfbefore = pd.DataFrame(sharpe_ratio_before).reset_index()\n",
"dfbefore.rename(columns={'日報酬率 %': '演講前夏普值 %'}, 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={'日報酬率 %': '演講後夏普值 %'}, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>證券代碼</th>\n",
" <th>演講前夏普值 %</th>\n",
" <th>演講後夏普值 %</th>\n",
" <th>夏普值差異</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": [
" 證券代碼 演講前夏普值 % 演講後夏普值 % 夏普值差異 \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": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df = pd.merge(dfbefore, dfafter, on='證券代碼')\n",
"merged_df['夏普值差異 '] = merged_df['演講後夏普值 %']-merged_df['演講前夏普值 %']\n",
"# 顯示結果\n",
"\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>證券代碼</th>\n",
" <th>演講前夏普值 %</th>\n",
" <th>演講後夏普值 %</th>\n",
" <th>夏普值差異</th>\n",
" <th>是否上升</th>\n",
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" <th>0</th>\n",
" <td>2301</td>\n",
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" <td>6.798626</td>\n",
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" <th>2</th>\n",
" <td>2308</td>\n",
" <td>3.825403</td>\n",
" <td>45.939685</td>\n",
" <td>42.114282</td>\n",
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" <td>2317</td>\n",
" <td>19.818377</td>\n",
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" <td>2324</td>\n",
" <td>6.891955</td>\n",
" <td>-49.955756</td>\n",
" <td>-56.847711</td>\n",
" <td>False</td>\n",
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" <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",
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" <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",
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" <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",
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" <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",
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" <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",
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" <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": [
" 證券代碼 演講前夏普值 % 演講後夏普值 % 夏普值差異 是否上升\n",
"0 2301 12.312575 6.798626 -5.513949 False\n",
"1 2303 27.862752 5.378234 -22.484518 False\n",
"2 2308 3.825403 45.939685 42.114282 True\n",
"3 2317 19.818377 39.003323 19.184947 True\n",
"4 2324 6.891955 -49.955756 -56.847711 False\n",
"5 2330 13.414805 41.007175 27.592370 True\n",
"6 2353 23.543538 -30.757161 -54.300699 False\n",
"7 2356 2.572368 13.339620 10.767252 True\n",
"8 2357 25.830189 -17.348829 -43.179018 False\n",
"9 2359 49.813958 -1.975209 -51.789166 False\n",
"10 2376 12.772852 -14.807080 -27.579932 False\n",
"11 2377 36.730038 -16.522807 -53.252845 False\n",
"12 2382 12.332701 19.536106 7.203405 True\n",
"13 2395 -18.231988 14.881022 33.113010 True\n",
"14 2417 26.463620 8.520332 -17.943288 False\n",
"15 2449 -16.422706 41.646036 58.068742 True\n",
"16 2454 40.171417 24.167831 -16.003586 False\n",
"17 2465 -5.089301 -3.138268 1.951033 True\n",
"18 3030 55.275946 -1.394916 -56.670862 False\n",
"19 3037 1.574759 -5.962473 -7.537233 False\n",
"20 3048 49.999567 25.276329 -24.723238 False\n",
"21 3088 37.037549 4.931176 -32.106372 False\n",
"22 3231 -2.180548 -4.559746 -2.379198 False\n",
"23 3312 41.557451 29.633543 -11.923908 False\n",
"24 3515 8.997059 0.649883 -8.347175 False\n",
"25 3540 8.523439 9.200291 0.676853 True\n",
"26 3706 9.463943 -22.657390 -32.121333 False\n",
"27 3711 15.173648 11.686580 -3.487068 False\n",
"28 4938 31.068200 4.059777 -27.008424 False\n",
"29 5371 10.004917 -37.668300 -47.673217 False\n",
"30 5474 34.325634 7.677580 -26.648054 False\n",
"31 5484 14.863654 127.149583 112.285930 True\n",
"32 6117 57.174050 -16.335131 -73.509181 False\n",
"33 6125 32.692142 -10.628760 -43.320902 False\n",
"34 6166 22.956420 5.203130 -17.753290 False\n",
"35 6245 -6.261627 6.850900 13.112527 True\n",
"36 6569 22.571157 26.293804 3.722647 True\n",
"37 6579 17.345672 10.129950 -7.215723 False\n",
"38 6669 8.539067 11.917708 3.378641 True\n",
"39 6922 42.266872 10.458184 -31.808687 False\n",
"40 8210 9.192942 -2.215687 -11.408629 False\n",
"41 8234 4.649493 12.815652 8.166159 True\n",
"42 9921 -0.048282 -17.426459 -17.378177 False"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"merged_df['是否上升'] = merged_df['夏普值差異 '].apply(lambda x: 'True' if x > 0 else 'False')\n",
"\n",
"merged_df"
]
},
{
"cell_type": "code",
"execution_count": 55,
"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['是否上升'].value_counts()\n",
"\n",
"# 繪製長條圖\n",
"fig, ax = plt.subplots()\n",
"ax.bar(sign_counts.index, sign_counts.values, color='#00FF7F',alpha=0.8)\n",
"\n",
"# 設定標籤和標題\n",
"ax.set_xlabel('是否上升')\n",
"ax.set_ylabel('家數')\n",
"ax.set_title('夏普值變化的分布')\n",
"plt.grid(True)\n",
"plt.tight_layout()\n",
"\n",
"# 顯示圖表\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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['證券代碼'].astype(str), merged_df['演講前夏普值 %'], label='演講前夏普值 %', alpha=0.3)\n",
"plt.bar(merged_df['證券代碼'].astype(str), merged_df['演講後夏普值 %'], label='演講後夏普值 %', alpha=0.8)\n",
"\n",
"\n",
"# 添加標題和標籤\n",
"plt.title('各公司演講前後夏普值及其差異')\n",
"plt.xlabel('證券代碼')\n",
"plt.ylabel('夏普值 %')\n",
"plt.legend()\n",
"\n",
"# 顯示圖表\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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