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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "markdown",
5 + "metadata": {},
6 + "source": [
7 + "## 데이터 수집\n",
8 + "데이터 : 코스피200 지수 종가/오픈/고가/저가/거래량 <br>\n",
9 + "수집 출처 : investing.com\n",
10 + "\n",
11 + "|<center>**Data Set**</center>|<center>**Period**</center>|<center>**Number of Data**</center>|\n",
12 + "|------|---|:---|\n",
13 + "|Training Data|2005/01/03 ~ 2018/12/28|3464|\n",
14 + "|Test Data|2019/01/02 ~ 2019/12/30|246|\n",
15 + "|Validation Data|2020/01/02 ~ 2020/09/29|188|"
16 + ]
17 + },
18 + {
19 + "cell_type": "code",
20 + "execution_count": 2,
21 + "metadata": {},
22 + "outputs": [
23 + {
24 + "ename": "ModuleNotFoundError",
25 + "evalue": "No module named 'date'",
26 + "output_type": "error",
27 + "traceback": [
28 + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
29 + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
30 + "\u001b[0;32m<ipython-input-2-7e78e5e4da43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
31 + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'date'"
32 + ]
33 + }
34 + ],
35 + "source": [
36 + "# module\n",
37 + "\n",
38 + "import pandas as pd\n",
39 + "from tqdm import tqdm\n",
40 + "import date"
41 + ]
42 + },
43 + {
44 + "cell_type": "code",
45 + "execution_count": null,
46 + "metadata": {},
47 + "outputs": [],
48 + "source": [
49 + "train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')\n",
50 + "test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_data.csv')\n",
51 + "validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_data.csv')"
52 + ]
53 + },
54 + {
55 + "cell_type": "code",
56 + "execution_count": null,
57 + "metadata": {},
58 + "outputs": [],
59 + "source": [
60 + "for i in tqdm(range(len(train_data))):\n",
61 + " train_data['date'][i] = train_data['date'][i].replace(' ','-') "
62 + ]
63 + },
64 + {
65 + "cell_type": "code",
66 + "execution_count": null,
67 + "metadata": {},
68 + "outputs": [],
69 + "source": [
70 + "train_data.head(3)"
71 + ]
72 + },
73 + {
74 + "cell_type": "code",
75 + "execution_count": null,
76 + "metadata": {},
77 + "outputs": [],
78 + "source": [
79 + "for i in range(len(train_data)):\n",
80 + " train_data['date'][i] = datetime.datetime.strptime(train_data['date'][i],\"%Y-%m-%d\").date()\n"
81 + ]
82 + },
83 + {
84 + "cell_type": "code",
85 + "execution_count": null,
86 + "metadata": {},
87 + "outputs": [],
88 + "source": [
89 + "test_data.head(3)"
90 + ]
91 + },
92 + {
93 + "cell_type": "code",
94 + "execution_count": null,
95 + "metadata": {},
96 + "outputs": [],
97 + "source": [
98 + "validation_data.head(3)"
99 + ]
100 + },
101 + {
102 + "cell_type": "code",
103 + "execution_count": null,
104 + "metadata": {},
105 + "outputs": [],
106 + "source": []
107 + }
108 + ],
109 + "metadata": {
110 + "kernelspec": {
111 + "display_name": "Python 3",
112 + "language": "python",
113 + "name": "python3"
114 + }
115 + },
116 + "nbformat": 4,
117 + "nbformat_minor": 2
118 +}
1 +{
2 + "cells": [],
3 + "metadata": {},
4 + "nbformat": 4,
5 + "nbformat_minor": 2
6 +}
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "markdown",
5 + "metadata": {},
6 + "source": [
7 + "## 데이터 수집\n",
8 + "데이터 : 코스피200 지수 종가/오픈/고가/저가/거래량 <br>\n",
9 + "수집 출처 : investing.com\n",
10 + "\n",
11 + "|<center>**Data Set**</center>|<center>**Period**</center>|<center>**Number of Data**</center>|\n",
12 + "|------|---|:---|\n",
13 + "|Training Data|2005/01/03 ~ 2018/12/28|3464|\n",
14 + "|Test Data|2019/01/02 ~ 2019/12/30|246|\n",
15 + "|Validation Data|2020/01/02 ~ 2020/09/29|188|"
16 + ]
17 + },
18 + {
19 + "cell_type": "code",
20 + "execution_count": 1,
21 + "metadata": {},
22 + "outputs": [],
23 + "source": [
24 + "# module\n",
25 + "import pandas as pd\n",
26 + "from tqdm import tqdm\n",
27 + "import matplotlib.pyplot as plt\n",
28 + "import seaborn as sns"
29 + ]
30 + },
31 + {
32 + "cell_type": "code",
33 + "execution_count": 53,
34 + "metadata": {},
35 + "outputs": [],
36 + "source": [
37 + "train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')"
38 + ]
39 + },
40 + {
41 + "cell_type": "code",
42 + "execution_count": 3,
43 + "metadata": {},
44 + "outputs": [],
45 + "source": [
46 + "train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')\n",
47 + "test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_data.csv')\n",
48 + "validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_data.csv')"
49 + ]
50 + },
51 + {
52 + "cell_type": "code",
53 + "execution_count": 54,
54 + "metadata": {},
55 + "outputs": [
56 + {
57 + "name": "stderr",
58 + "output_type": "stream",
59 + "text": [
60 + " 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
61 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
62 + "\n",
63 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
64 + " \n",
65 + "100%|██████████| 3464/3464 [10:11<00:00, 5.89it/s]\n"
66 + ]
67 + },
68 + {
69 + "data": {
70 + "text/plain": [
71 + "0 2018-12-28\n",
72 + "1 2018-12-27\n",
73 + "2 2018-12-26\n",
74 + "3 2018-12-24\n",
75 + "4 2018-12-21\n",
76 + "5 2018-12-20\n",
77 + "6 2018-12-19\n",
78 + "7 2018-12-18\n",
79 + "8 2018-12-17\n",
80 + "9 2018-12-14\n",
81 + "10 2018-12-13\n",
82 + "11 2018-12-12\n",
83 + "12 2018-12-11\n",
84 + "13 2018-12-10\n",
85 + "14 2018-12-07\n",
86 + "15 2018-12-06\n",
87 + "16 2018-12-05\n",
88 + "17 2018-12-04\n",
89 + "18 2018-12-03\n",
90 + "19 2018-11-30\n",
91 + "20 2018-11-29\n",
92 + "21 2018-11-28\n",
93 + "22 2018-11-27\n",
94 + "23 2018-11-26\n",
95 + "24 2018-11-23\n",
96 + "25 2018-11-22\n",
97 + "26 2018-11-21\n",
98 + "27 2018-11-20\n",
99 + "28 2018-11-19\n",
100 + "29 2018-11-16\n",
101 + " ... \n",
102 + "3434 2005-02-16\n",
103 + "3435 2005-02-15\n",
104 + "3436 2005-02-14\n",
105 + "3437 2005-02-11\n",
106 + "3438 2005-02-07\n",
107 + "3439 2005-02-04\n",
108 + "3440 2005-02-03\n",
109 + "3441 2005-02-02\n",
110 + "3442 2005-02-01\n",
111 + "3443 2005-01-31\n",
112 + "3444 2005-01-28\n",
113 + "3445 2005-01-27\n",
114 + "3446 2005-01-26\n",
115 + "3447 2005-01-25\n",
116 + "3448 2005-01-24\n",
117 + "3449 2005-01-21\n",
118 + "3450 2005-01-20\n",
119 + "3451 2005-01-19\n",
120 + "3452 2005-01-18\n",
121 + "3453 2005-01-17\n",
122 + "3454 2005-01-14\n",
123 + "3455 2005-01-13\n",
124 + "3456 2005-01-12\n",
125 + "3457 2005-01-11\n",
126 + "3458 2005-01-10\n",
127 + "3459 2005-01-07\n",
128 + "3460 2005-01-06\n",
129 + "3461 2005-01-05\n",
130 + "3462 2005-01-04\n",
131 + "3463 2005-01-03\n",
132 + "Name: date, Length: 3464, dtype: datetime64[ns]"
133 + ]
134 + },
135 + "execution_count": 54,
136 + "metadata": {},
137 + "output_type": "execute_result"
138 + }
139 + ],
140 + "source": [
141 + "for i in tqdm(range(len(train_data))):\n",
142 + " train_data['date'][i] = train_data['date'][i].replace(' ','') \n",
143 + "pd.to_datetime(train_data['date'], format='%Y%m%d')"
144 + ]
145 + },
146 + {
147 + "cell_type": "code",
148 + "execution_count": 56,
149 + "metadata": {},
150 + "outputs": [
151 + {
152 + "data": {
153 + "text/html": [
154 + "<div>\n",
155 + "<style scoped>\n",
156 + " .dataframe tbody tr th:only-of-type {\n",
157 + " vertical-align: middle;\n",
158 + " }\n",
159 + "\n",
160 + " .dataframe tbody tr th {\n",
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163 + "\n",
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165 + " text-align: right;\n",
166 + " }\n",
167 + "</style>\n",
168 + "<table border=\"1\" class=\"dataframe\">\n",
169 + " <thead>\n",
170 + " <tr style=\"text-align: right;\">\n",
171 + " <th></th>\n",
172 + " <th>date</th>\n",
173 + " <th>close</th>\n",
174 + " <th>open</th>\n",
175 + " <th>high</th>\n",
176 + " <th>low</th>\n",
177 + " <th>vol</th>\n",
178 + " </tr>\n",
179 + " </thead>\n",
180 + " <tbody>\n",
181 + " <tr>\n",
182 + " <th>0</th>\n",
183 + " <td>20181228</td>\n",
184 + " <td>261.98</td>\n",
185 + " <td>261.61</td>\n",
186 + " <td>262.85</td>\n",
187 + " <td>261.27</td>\n",
188 + " <td>62.70M</td>\n",
189 + " </tr>\n",
190 + " <tr>\n",
191 + " <th>1</th>\n",
192 + " <td>20181227</td>\n",
193 + " <td>260.57</td>\n",
194 + " <td>261.53</td>\n",
195 + " <td>261.71</td>\n",
196 + " <td>259.73</td>\n",
197 + " <td>84.46M</td>\n",
198 + " </tr>\n",
199 + " <tr>\n",
200 + " <th>2</th>\n",
201 + " <td>20181226</td>\n",
202 + " <td>260.98</td>\n",
203 + " <td>261.09</td>\n",
204 + " <td>262.49</td>\n",
205 + " <td>259.35</td>\n",
206 + " <td>92.49M</td>\n",
207 + " </tr>\n",
208 + " </tbody>\n",
209 + "</table>\n",
210 + "</div>"
211 + ],
212 + "text/plain": [
213 + " date close open high low vol\n",
214 + "0 20181228 261.98 261.61 262.85 261.27 62.70M\n",
215 + "1 20181227 260.57 261.53 261.71 259.73 84.46M\n",
216 + "2 20181226 260.98 261.09 262.49 259.35 92.49M"
217 + ]
218 + },
219 + "execution_count": 56,
220 + "metadata": {},
221 + "output_type": "execute_result"
222 + }
223 + ],
224 + "source": [
225 + "train_data.head(3)"
226 + ]
227 + },
228 + {
229 + "cell_type": "code",
230 + "execution_count": 9,
231 + "metadata": {},
232 + "outputs": [
233 + {
234 + "name": "stderr",
235 + "output_type": "stream",
236 + "text": [
237 + " 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
238 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
239 + "\n",
240 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
241 + " \n",
242 + "100%|██████████| 246/246 [00:14<00:00, 18.37it/s]\n"
243 + ]
244 + },
245 + {
246 + "data": {
247 + "text/html": [
248 + "<div>\n",
249 + "<style scoped>\n",
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253 + "\n",
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260 + " }\n",
261 + "</style>\n",
262 + "<table border=\"1\" class=\"dataframe\">\n",
263 + " <thead>\n",
264 + " <tr style=\"text-align: right;\">\n",
265 + " <th></th>\n",
266 + " <th>date</th>\n",
267 + " <th>close</th>\n",
268 + " <th>open</th>\n",
269 + " <th>high</th>\n",
270 + " <th>low</th>\n",
271 + " <th>vol</th>\n",
272 + " </tr>\n",
273 + " </thead>\n",
274 + " <tbody>\n",
275 + " <tr>\n",
276 + " <th>0</th>\n",
277 + " <td>20191230</td>\n",
278 + " <td>293.77</td>\n",
279 + " <td>294.60</td>\n",
280 + " <td>295.49</td>\n",
281 + " <td>293.40</td>\n",
282 + " <td>54.97M</td>\n",
283 + " </tr>\n",
284 + " <tr>\n",
285 + " <th>1</th>\n",
286 + " <td>20191227</td>\n",
287 + " <td>295.20</td>\n",
288 + " <td>292.37</td>\n",
289 + " <td>296.95</td>\n",
290 + " <td>291.28</td>\n",
291 + " <td>77.51M</td>\n",
292 + " </tr>\n",
293 + " <tr>\n",
294 + " <th>2</th>\n",
295 + " <td>20191226</td>\n",
296 + " <td>294.06</td>\n",
297 + " <td>293.12</td>\n",
298 + " <td>294.07</td>\n",
299 + " <td>291.79</td>\n",
300 + " <td>68.48M</td>\n",
301 + " </tr>\n",
302 + " </tbody>\n",
303 + "</table>\n",
304 + "</div>"
305 + ],
306 + "text/plain": [
307 + " date close open high low vol\n",
308 + "0 20191230 293.77 294.60 295.49 293.40 54.97M\n",
309 + "1 20191227 295.20 292.37 296.95 291.28 77.51M\n",
310 + "2 20191226 294.06 293.12 294.07 291.79 68.48M"
311 + ]
312 + },
313 + "execution_count": 9,
314 + "metadata": {},
315 + "output_type": "execute_result"
316 + }
317 + ],
318 + "source": [
319 + "for i in tqdm(range(len(test_data))):\n",
320 + " test_data['date'][i] = test_data['date'][i].replace(' ','') \n",
321 + "pd.to_datetime(test_data['date'], format='%Y%m%d')\n",
322 + "\n",
323 + "\n",
324 + "test_data.head(3)"
325 + ]
326 + },
327 + {
328 + "cell_type": "code",
329 + "execution_count": 10,
330 + "metadata": {
331 + "scrolled": true
332 + },
333 + "outputs": [
334 + {
335 + "name": "stderr",
336 + "output_type": "stream",
337 + "text": [
338 + " 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
339 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
340 + "\n",
341 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
342 + " \n",
343 + "100%|██████████| 187/187 [00:11<00:00, 15.68it/s]\n"
344 + ]
345 + },
346 + {
347 + "data": {
348 + "text/html": [
349 + "<div>\n",
350 + "<style scoped>\n",
351 + " .dataframe tbody tr th:only-of-type {\n",
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354 + "\n",
355 + " .dataframe tbody tr th {\n",
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357 + " }\n",
358 + "\n",
359 + " .dataframe thead th {\n",
360 + " text-align: right;\n",
361 + " }\n",
362 + "</style>\n",
363 + "<table border=\"1\" class=\"dataframe\">\n",
364 + " <thead>\n",
365 + " <tr style=\"text-align: right;\">\n",
366 + " <th></th>\n",
367 + " <th>date</th>\n",
368 + " <th>close</th>\n",
369 + " <th>open</th>\n",
370 + " <th>high</th>\n",
371 + " <th>low</th>\n",
372 + " <th>vol</th>\n",
373 + " </tr>\n",
374 + " </thead>\n",
375 + " <tbody>\n",
376 + " <tr>\n",
377 + " <th>0</th>\n",
378 + " <td>20200929</td>\n",
379 + " <td>309.44</td>\n",
380 + " <td>310.01</td>\n",
381 + " <td>311.34</td>\n",
382 + " <td>308.52</td>\n",
383 + " <td>138.82M</td>\n",
384 + " </tr>\n",
385 + " <tr>\n",
386 + " <th>1</th>\n",
387 + " <td>20200928</td>\n",
388 + " <td>307.03</td>\n",
389 + " <td>306.49</td>\n",
390 + " <td>308.34</td>\n",
391 + " <td>304.64</td>\n",
392 + " <td>173.18M</td>\n",
393 + " </tr>\n",
394 + " <tr>\n",
395 + " <th>2</th>\n",
396 + " <td>20200925</td>\n",
397 + " <td>303.57</td>\n",
398 + " <td>305.38</td>\n",
399 + " <td>305.55</td>\n",
400 + " <td>301.93</td>\n",
401 + " <td>156.86M</td>\n",
402 + " </tr>\n",
403 + " </tbody>\n",
404 + "</table>\n",
405 + "</div>"
406 + ],
407 + "text/plain": [
408 + " date close open high low vol\n",
409 + "0 20200929 309.44 310.01 311.34 308.52 138.82M\n",
410 + "1 20200928 307.03 306.49 308.34 304.64 173.18M\n",
411 + "2 20200925 303.57 305.38 305.55 301.93 156.86M"
412 + ]
413 + },
414 + "execution_count": 10,
415 + "metadata": {},
416 + "output_type": "execute_result"
417 + }
418 + ],
419 + "source": [
420 + "for i in tqdm(range(len(validation_data))):\n",
421 + " validation_data['date'][i] = validation_data['date'][i].replace(' ','') \n",
422 + "pd.to_datetime(validation_data['date'], format='%Y%m%d')\n",
423 + "\n",
424 + "validation_data.head(3)"
425 + ]
426 + },
427 + {
428 + "cell_type": "markdown",
429 + "metadata": {},
430 + "source": [
431 + "## 데이터프레임 행 역순으로 변경"
432 + ]
433 + },
434 + {
435 + "cell_type": "markdown",
436 + "metadata": {},
437 + "source": [
438 + "### train_data"
439 + ]
440 + },
441 + {
442 + "cell_type": "code",
443 + "execution_count": 57,
444 + "metadata": {},
445 + "outputs": [
446 + {
447 + "data": {
448 + "text/html": [
449 + "<div>\n",
450 + "<style scoped>\n",
451 + " .dataframe tbody tr th:only-of-type {\n",
452 + " vertical-align: middle;\n",
453 + " }\n",
454 + "\n",
455 + " .dataframe tbody tr th {\n",
456 + " vertical-align: top;\n",
457 + " }\n",
458 + "\n",
459 + " .dataframe thead th {\n",
460 + " text-align: right;\n",
461 + " }\n",
462 + "</style>\n",
463 + "<table border=\"1\" class=\"dataframe\">\n",
464 + " <thead>\n",
465 + " <tr style=\"text-align: right;\">\n",
466 + " <th></th>\n",
467 + " <th>date</th>\n",
468 + " <th>close</th>\n",
469 + " <th>open</th>\n",
470 + " <th>high</th>\n",
471 + " <th>low</th>\n",
472 + " <th>vol</th>\n",
473 + " </tr>\n",
474 + " </thead>\n",
475 + " <tbody>\n",
476 + " <tr>\n",
477 + " <th>0</th>\n",
478 + " <td>20050103</td>\n",
479 + " <td>115.36</td>\n",
480 + " <td>115.65</td>\n",
481 + " <td>115.90</td>\n",
482 + " <td>114.99</td>\n",
483 + " <td>101.24M</td>\n",
484 + " </tr>\n",
485 + " <tr>\n",
486 + " <th>1</th>\n",
487 + " <td>20050104</td>\n",
488 + " <td>114.77</td>\n",
489 + " <td>115.00</td>\n",
490 + " <td>115.80</td>\n",
491 + " <td>114.50</td>\n",
492 + " <td>119.97M</td>\n",
493 + " </tr>\n",
494 + " <tr>\n",
495 + " <th>2</th>\n",
496 + " <td>20050105</td>\n",
497 + " <td>114.65</td>\n",
498 + " <td>113.41</td>\n",
499 + " <td>114.65</td>\n",
500 + " <td>113.17</td>\n",
501 + " <td>142.31M</td>\n",
502 + " </tr>\n",
503 + " <tr>\n",
504 + " <th>3</th>\n",
505 + " <td>20050106</td>\n",
506 + " <td>112.94</td>\n",
507 + " <td>113.88</td>\n",
508 + " <td>114.97</td>\n",
509 + " <td>112.94</td>\n",
510 + " <td>147.88M</td>\n",
511 + " </tr>\n",
512 + " <tr>\n",
513 + " <th>4</th>\n",
514 + " <td>20050107</td>\n",
515 + " <td>112.71</td>\n",
516 + " <td>113.61</td>\n",
517 + " <td>113.94</td>\n",
518 + " <td>112.18</td>\n",
519 + " <td>117.39M</td>\n",
520 + " </tr>\n",
521 + " <tr>\n",
522 + " <th>5</th>\n",
523 + " <td>20050110</td>\n",
524 + " <td>113.27</td>\n",
525 + " <td>113.01</td>\n",
526 + " <td>113.36</td>\n",
527 + " <td>112.07</td>\n",
528 + " <td>98.12M</td>\n",
529 + " </tr>\n",
530 + " <tr>\n",
531 + " <th>6</th>\n",
532 + " <td>20050111</td>\n",
533 + " <td>114.66</td>\n",
534 + " <td>113.33</td>\n",
535 + " <td>114.66</td>\n",
536 + " <td>112.73</td>\n",
537 + " <td>133.20M</td>\n",
538 + " </tr>\n",
539 + " <tr>\n",
540 + " <th>7</th>\n",
541 + " <td>20050112</td>\n",
542 + " <td>114.09</td>\n",
543 + " <td>114.71</td>\n",
544 + " <td>114.94</td>\n",
545 + " <td>113.79</td>\n",
546 + " <td>124.80M</td>\n",
547 + " </tr>\n",
548 + " <tr>\n",
549 + " <th>8</th>\n",
550 + " <td>20050113</td>\n",
551 + " <td>115.01</td>\n",
552 + " <td>114.26</td>\n",
553 + " <td>115.08</td>\n",
554 + " <td>113.69</td>\n",
555 + " <td>106.38M</td>\n",
556 + " </tr>\n",
557 + " <tr>\n",
558 + " <th>9</th>\n",
559 + " <td>20050114</td>\n",
560 + " <td>117.82</td>\n",
561 + " <td>114.23</td>\n",
562 + " <td>117.82</td>\n",
563 + " <td>113.91</td>\n",
564 + " <td>151.41M</td>\n",
565 + " </tr>\n",
566 + " <tr>\n",
567 + " <th>10</th>\n",
568 + " <td>20050117</td>\n",
569 + " <td>119.91</td>\n",
570 + " <td>118.96</td>\n",
571 + " <td>120.28</td>\n",
572 + " <td>118.70</td>\n",
573 + " <td>189.66M</td>\n",
574 + " </tr>\n",
575 + " <tr>\n",
576 + " <th>11</th>\n",
577 + " <td>20050118</td>\n",
578 + " <td>119.50</td>\n",
579 + " <td>119.91</td>\n",
580 + " <td>120.42</td>\n",
581 + " <td>119.49</td>\n",
582 + " <td>172.12M</td>\n",
583 + " </tr>\n",
584 + " <tr>\n",
585 + " <th>12</th>\n",
586 + " <td>20050119</td>\n",
587 + " <td>118.90</td>\n",
588 + " <td>119.85</td>\n",
589 + " <td>120.40</td>\n",
590 + " <td>118.66</td>\n",
591 + " <td>177.45M</td>\n",
592 + " </tr>\n",
593 + " <tr>\n",
594 + " <th>13</th>\n",
595 + " <td>20050120</td>\n",
596 + " <td>118.02</td>\n",
597 + " <td>118.05</td>\n",
598 + " <td>118.74</td>\n",
599 + " <td>117.72</td>\n",
600 + " <td>165.45M</td>\n",
601 + " </tr>\n",
602 + " <tr>\n",
603 + " <th>14</th>\n",
604 + " <td>20050121</td>\n",
605 + " <td>119.40</td>\n",
606 + " <td>118.14</td>\n",
607 + " <td>119.41</td>\n",
608 + " <td>117.62</td>\n",
609 + " <td>153.51M</td>\n",
610 + " </tr>\n",
611 + " <tr>\n",
612 + " <th>15</th>\n",
613 + " <td>20050124</td>\n",
614 + " <td>119.83</td>\n",
615 + " <td>119.19</td>\n",
616 + " <td>120.66</td>\n",
617 + " <td>118.34</td>\n",
618 + " <td>215.32M</td>\n",
619 + " </tr>\n",
620 + " <tr>\n",
621 + " <th>16</th>\n",
622 + " <td>20050125</td>\n",
623 + " <td>118.91</td>\n",
624 + " <td>118.92</td>\n",
625 + " <td>119.25</td>\n",
626 + " <td>118.35</td>\n",
627 + " <td>190.17M</td>\n",
628 + " </tr>\n",
629 + " <tr>\n",
630 + " <th>17</th>\n",
631 + " <td>20050126</td>\n",
632 + " <td>120.51</td>\n",
633 + " <td>119.36</td>\n",
634 + " <td>120.95</td>\n",
635 + " <td>119.16</td>\n",
636 + " <td>194.21M</td>\n",
637 + " </tr>\n",
638 + " <tr>\n",
639 + " <th>18</th>\n",
640 + " <td>20050127</td>\n",
641 + " <td>120.16</td>\n",
642 + " <td>120.65</td>\n",
643 + " <td>121.00</td>\n",
644 + " <td>119.61</td>\n",
645 + " <td>184.68M</td>\n",
646 + " </tr>\n",
647 + " <tr>\n",
648 + " <th>19</th>\n",
649 + " <td>20050128</td>\n",
650 + " <td>119.65</td>\n",
651 + " <td>120.14</td>\n",
652 + " <td>120.25</td>\n",
653 + " <td>118.98</td>\n",
654 + " <td>156.89M</td>\n",
655 + " </tr>\n",
656 + " <tr>\n",
657 + " <th>20</th>\n",
658 + " <td>20050131</td>\n",
659 + " <td>121.06</td>\n",
660 + " <td>119.45</td>\n",
661 + " <td>121.21</td>\n",
662 + " <td>119.45</td>\n",
663 + " <td>141.84M</td>\n",
664 + " </tr>\n",
665 + " <tr>\n",
666 + " <th>21</th>\n",
667 + " <td>20050201</td>\n",
668 + " <td>120.00</td>\n",
669 + " <td>120.74</td>\n",
670 + " <td>120.97</td>\n",
671 + " <td>119.91</td>\n",
672 + " <td>132.38M</td>\n",
673 + " </tr>\n",
674 + " <tr>\n",
675 + " <th>22</th>\n",
676 + " <td>20050202</td>\n",
677 + " <td>119.74</td>\n",
678 + " <td>120.30</td>\n",
679 + " <td>120.37</td>\n",
680 + " <td>119.41</td>\n",
681 + " <td>154.26M</td>\n",
682 + " </tr>\n",
683 + " <tr>\n",
684 + " <th>23</th>\n",
685 + " <td>20050203</td>\n",
686 + " <td>120.64</td>\n",
687 + " <td>119.88</td>\n",
688 + " <td>120.80</td>\n",
689 + " <td>119.71</td>\n",
690 + " <td>166.09M</td>\n",
691 + " </tr>\n",
692 + " <tr>\n",
693 + " <th>24</th>\n",
694 + " <td>20050204</td>\n",
695 + " <td>121.30</td>\n",
696 + " <td>120.50</td>\n",
697 + " <td>121.65</td>\n",
698 + " <td>120.18</td>\n",
699 + " <td>153.74M</td>\n",
700 + " </tr>\n",
701 + " <tr>\n",
702 + " <th>25</th>\n",
703 + " <td>20050207</td>\n",
704 + " <td>123.20</td>\n",
705 + " <td>122.41</td>\n",
706 + " <td>123.27</td>\n",
707 + " <td>122.25</td>\n",
708 + " <td>142.40M</td>\n",
709 + " </tr>\n",
710 + " <tr>\n",
711 + " <th>26</th>\n",
712 + " <td>20050211</td>\n",
713 + " <td>122.84</td>\n",
714 + " <td>122.60</td>\n",
715 + " <td>122.99</td>\n",
716 + " <td>121.39</td>\n",
717 + " <td>140.35M</td>\n",
718 + " </tr>\n",
719 + " <tr>\n",
720 + " <th>27</th>\n",
721 + " <td>20050214</td>\n",
722 + " <td>124.94</td>\n",
723 + " <td>124.06</td>\n",
724 + " <td>125.44</td>\n",
725 + " <td>123.65</td>\n",
726 + " <td>235.22M</td>\n",
727 + " </tr>\n",
728 + " <tr>\n",
729 + " <th>28</th>\n",
730 + " <td>20050215</td>\n",
731 + " <td>125.48</td>\n",
732 + " <td>124.99</td>\n",
733 + " <td>125.49</td>\n",
734 + " <td>124.37</td>\n",
735 + " <td>236.83M</td>\n",
736 + " </tr>\n",
737 + " <tr>\n",
738 + " <th>29</th>\n",
739 + " <td>20050216</td>\n",
740 + " <td>125.85</td>\n",
741 + " <td>126.08</td>\n",
742 + " <td>126.82</td>\n",
743 + " <td>125.13</td>\n",
744 + " <td>218.70M</td>\n",
745 + " </tr>\n",
746 + " <tr>\n",
747 + " <th>...</th>\n",
748 + " <td>...</td>\n",
749 + " <td>...</td>\n",
750 + " <td>...</td>\n",
751 + " <td>...</td>\n",
752 + " <td>...</td>\n",
753 + " <td>...</td>\n",
754 + " </tr>\n",
755 + " <tr>\n",
756 + " <th>3434</th>\n",
757 + " <td>20181116</td>\n",
758 + " <td>271.65</td>\n",
759 + " <td>272.95</td>\n",
760 + " <td>273.52</td>\n",
761 + " <td>271.06</td>\n",
762 + " <td>74.60M</td>\n",
763 + " </tr>\n",
764 + " <tr>\n",
765 + " <th>3435</th>\n",
766 + " <td>20181119</td>\n",
767 + " <td>272.31</td>\n",
768 + " <td>272.01</td>\n",
769 + " <td>274.05</td>\n",
770 + " <td>270.83</td>\n",
771 + " <td>75.01M</td>\n",
772 + " </tr>\n",
773 + " <tr>\n",
774 + " <th>3436</th>\n",
775 + " <td>20181120</td>\n",
776 + " <td>269.58</td>\n",
777 + " <td>268.54</td>\n",
778 + " <td>270.58</td>\n",
779 + " <td>268.54</td>\n",
780 + " <td>80.90M</td>\n",
781 + " </tr>\n",
782 + " <tr>\n",
783 + " <th>3437</th>\n",
784 + " <td>20181121</td>\n",
785 + " <td>268.27</td>\n",
786 + " <td>265.96</td>\n",
787 + " <td>268.49</td>\n",
788 + " <td>264.95</td>\n",
789 + " <td>81.84M</td>\n",
790 + " </tr>\n",
791 + " <tr>\n",
792 + " <th>3438</th>\n",
793 + " <td>20181122</td>\n",
794 + " <td>267.76</td>\n",
795 + " <td>268.51</td>\n",
796 + " <td>269.11</td>\n",
797 + " <td>267.15</td>\n",
798 + " <td>64.41M</td>\n",
799 + " </tr>\n",
800 + " <tr>\n",
801 + " <th>3439</th>\n",
802 + " <td>20181123</td>\n",
803 + " <td>266.33</td>\n",
804 + " <td>268.12</td>\n",
805 + " <td>268.54</td>\n",
806 + " <td>264.86</td>\n",
807 + " <td>58.35M</td>\n",
808 + " </tr>\n",
809 + " <tr>\n",
810 + " <th>3440</th>\n",
811 + " <td>20181126</td>\n",
812 + " <td>269.79</td>\n",
813 + " <td>266.43</td>\n",
814 + " <td>269.95</td>\n",
815 + " <td>266.41</td>\n",
816 + " <td>75.65M</td>\n",
817 + " </tr>\n",
818 + " <tr>\n",
819 + " <th>3441</th>\n",
820 + " <td>20181127</td>\n",
821 + " <td>272.08</td>\n",
822 + " <td>270.90</td>\n",
823 + " <td>272.47</td>\n",
824 + " <td>269.27</td>\n",
825 + " <td>71.91M</td>\n",
826 + " </tr>\n",
827 + " <tr>\n",
828 + " <th>3442</th>\n",
829 + " <td>20181128</td>\n",
830 + " <td>273.45</td>\n",
831 + " <td>272.64</td>\n",
832 + " <td>273.53</td>\n",
833 + " <td>271.35</td>\n",
834 + " <td>79.83M</td>\n",
835 + " </tr>\n",
836 + " <tr>\n",
837 + " <th>3443</th>\n",
838 + " <td>20181129</td>\n",
839 + " <td>274.16</td>\n",
840 + " <td>277.08</td>\n",
841 + " <td>277.54</td>\n",
842 + " <td>273.40</td>\n",
843 + " <td>80.65M</td>\n",
844 + " </tr>\n",
845 + " <tr>\n",
846 + " <th>3444</th>\n",
847 + " <td>20181130</td>\n",
848 + " <td>271.35</td>\n",
849 + " <td>274.53</td>\n",
850 + " <td>275.51</td>\n",
851 + " <td>270.88</td>\n",
852 + " <td>137.53M</td>\n",
853 + " </tr>\n",
854 + " <tr>\n",
855 + " <th>3445</th>\n",
856 + " <td>20181203</td>\n",
857 + " <td>276.18</td>\n",
858 + " <td>275.70</td>\n",
859 + " <td>276.98</td>\n",
860 + " <td>273.59</td>\n",
861 + " <td>89.07M</td>\n",
862 + " </tr>\n",
863 + " <tr>\n",
864 + " <th>3446</th>\n",
865 + " <td>20181204</td>\n",
866 + " <td>273.29</td>\n",
867 + " <td>275.08</td>\n",
868 + " <td>275.72</td>\n",
869 + " <td>272.03</td>\n",
870 + " <td>80.80M</td>\n",
871 + " </tr>\n",
872 + " <tr>\n",
873 + " <th>3447</th>\n",
874 + " <td>20181205</td>\n",
875 + " <td>271.49</td>\n",
876 + " <td>269.53</td>\n",
877 + " <td>272.38</td>\n",
878 + " <td>269.47</td>\n",
879 + " <td>75.64M</td>\n",
880 + " </tr>\n",
881 + " <tr>\n",
882 + " <th>3448</th>\n",
883 + " <td>20181206</td>\n",
884 + " <td>267.18</td>\n",
885 + " <td>270.18</td>\n",
886 + " <td>270.18</td>\n",
887 + " <td>266.61</td>\n",
888 + " <td>86.09M</td>\n",
889 + " </tr>\n",
890 + " <tr>\n",
891 + " <th>3449</th>\n",
892 + " <td>20181207</td>\n",
893 + " <td>268.28</td>\n",
894 + " <td>268.97</td>\n",
895 + " <td>269.66</td>\n",
896 + " <td>267.03</td>\n",
897 + " <td>68.45M</td>\n",
898 + " </tr>\n",
899 + " <tr>\n",
900 + " <th>3450</th>\n",
901 + " <td>20181210</td>\n",
902 + " <td>265.35</td>\n",
903 + " <td>264.55</td>\n",
904 + " <td>266.20</td>\n",
905 + " <td>263.63</td>\n",
906 + " <td>77.30M</td>\n",
907 + " </tr>\n",
908 + " <tr>\n",
909 + " <th>3451</th>\n",
910 + " <td>20181211</td>\n",
911 + " <td>264.69</td>\n",
912 + " <td>265.87</td>\n",
913 + " <td>265.99</td>\n",
914 + " <td>263.87</td>\n",
915 + " <td>70.04M</td>\n",
916 + " </tr>\n",
917 + " <tr>\n",
918 + " <th>3452</th>\n",
919 + " <td>20181212</td>\n",
920 + " <td>268.47</td>\n",
921 + " <td>266.00</td>\n",
922 + " <td>268.84</td>\n",
923 + " <td>265.55</td>\n",
924 + " <td>83.69M</td>\n",
925 + " </tr>\n",
926 + " <tr>\n",
927 + " <th>3453</th>\n",
928 + " <td>20181213</td>\n",
929 + " <td>269.41</td>\n",
930 + " <td>269.07</td>\n",
931 + " <td>271.06</td>\n",
932 + " <td>268.22</td>\n",
933 + " <td>145.06M</td>\n",
934 + " </tr>\n",
935 + " <tr>\n",
936 + " <th>3454</th>\n",
937 + " <td>20181214</td>\n",
938 + " <td>265.55</td>\n",
939 + " <td>269.69</td>\n",
940 + " <td>269.69</td>\n",
941 + " <td>264.44</td>\n",
942 + " <td>99.73M</td>\n",
943 + " </tr>\n",
944 + " <tr>\n",
945 + " <th>3455</th>\n",
946 + " <td>20181217</td>\n",
947 + " <td>266.33</td>\n",
948 + " <td>265.81</td>\n",
949 + " <td>267.19</td>\n",
950 + " <td>265.41</td>\n",
951 + " <td>71.45M</td>\n",
952 + " </tr>\n",
953 + " <tr>\n",
954 + " <th>3456</th>\n",
955 + " <td>20181218</td>\n",
956 + " <td>265.18</td>\n",
957 + " <td>264.42</td>\n",
958 + " <td>266.83</td>\n",
959 + " <td>263.93</td>\n",
960 + " <td>90.33M</td>\n",
961 + " </tr>\n",
962 + " <tr>\n",
963 + " <th>3457</th>\n",
964 + " <td>20181219</td>\n",
965 + " <td>267.31</td>\n",
966 + " <td>266.02</td>\n",
967 + " <td>267.55</td>\n",
968 + " <td>265.26</td>\n",
969 + " <td>81.75M</td>\n",
970 + " </tr>\n",
971 + " <tr>\n",
972 + " <th>3458</th>\n",
973 + " <td>20181220</td>\n",
974 + " <td>264.81</td>\n",
975 + " <td>265.13</td>\n",
976 + " <td>266.34</td>\n",
977 + " <td>263.12</td>\n",
978 + " <td>81.34M</td>\n",
979 + " </tr>\n",
980 + " <tr>\n",
981 + " <th>3459</th>\n",
982 + " <td>20181221</td>\n",
983 + " <td>265.06</td>\n",
984 + " <td>263.66</td>\n",
985 + " <td>265.06</td>\n",
986 + " <td>263.15</td>\n",
987 + " <td>92.60M</td>\n",
988 + " </tr>\n",
989 + " <tr>\n",
990 + " <th>3460</th>\n",
991 + " <td>20181224</td>\n",
992 + " <td>264.45</td>\n",
993 + " <td>263.51</td>\n",
994 + " <td>265.09</td>\n",
995 + " <td>262.96</td>\n",
996 + " <td>61.96M</td>\n",
997 + " </tr>\n",
998 + " <tr>\n",
999 + " <th>3461</th>\n",
1000 + " <td>20181226</td>\n",
1001 + " <td>260.98</td>\n",
1002 + " <td>261.09</td>\n",
1003 + " <td>262.49</td>\n",
1004 + " <td>259.35</td>\n",
1005 + " <td>92.49M</td>\n",
1006 + " </tr>\n",
1007 + " <tr>\n",
1008 + " <th>3462</th>\n",
1009 + " <td>20181227</td>\n",
1010 + " <td>260.57</td>\n",
1011 + " <td>261.53</td>\n",
1012 + " <td>261.71</td>\n",
1013 + " <td>259.73</td>\n",
1014 + " <td>84.46M</td>\n",
1015 + " </tr>\n",
1016 + " <tr>\n",
1017 + " <th>3463</th>\n",
1018 + " <td>20181228</td>\n",
1019 + " <td>261.98</td>\n",
1020 + " <td>261.61</td>\n",
1021 + " <td>262.85</td>\n",
1022 + " <td>261.27</td>\n",
1023 + " <td>62.70M</td>\n",
1024 + " </tr>\n",
1025 + " </tbody>\n",
1026 + "</table>\n",
1027 + "<p>3464 rows × 6 columns</p>\n",
1028 + "</div>"
1029 + ],
1030 + "text/plain": [
1031 + " date close open high low vol\n",
1032 + "0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
1033 + "1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
1034 + "2 20050105 114.65 113.41 114.65 113.17 142.31M\n",
1035 + "3 20050106 112.94 113.88 114.97 112.94 147.88M\n",
1036 + "4 20050107 112.71 113.61 113.94 112.18 117.39M\n",
1037 + "5 20050110 113.27 113.01 113.36 112.07 98.12M\n",
1038 + "6 20050111 114.66 113.33 114.66 112.73 133.20M\n",
1039 + "7 20050112 114.09 114.71 114.94 113.79 124.80M\n",
1040 + "8 20050113 115.01 114.26 115.08 113.69 106.38M\n",
1041 + "9 20050114 117.82 114.23 117.82 113.91 151.41M\n",
1042 + "10 20050117 119.91 118.96 120.28 118.70 189.66M\n",
1043 + "11 20050118 119.50 119.91 120.42 119.49 172.12M\n",
1044 + "12 20050119 118.90 119.85 120.40 118.66 177.45M\n",
1045 + "13 20050120 118.02 118.05 118.74 117.72 165.45M\n",
1046 + "14 20050121 119.40 118.14 119.41 117.62 153.51M\n",
1047 + "15 20050124 119.83 119.19 120.66 118.34 215.32M\n",
1048 + "16 20050125 118.91 118.92 119.25 118.35 190.17M\n",
1049 + "17 20050126 120.51 119.36 120.95 119.16 194.21M\n",
1050 + "18 20050127 120.16 120.65 121.00 119.61 184.68M\n",
1051 + "19 20050128 119.65 120.14 120.25 118.98 156.89M\n",
1052 + "20 20050131 121.06 119.45 121.21 119.45 141.84M\n",
1053 + "21 20050201 120.00 120.74 120.97 119.91 132.38M\n",
1054 + "22 20050202 119.74 120.30 120.37 119.41 154.26M\n",
1055 + "23 20050203 120.64 119.88 120.80 119.71 166.09M\n",
1056 + "24 20050204 121.30 120.50 121.65 120.18 153.74M\n",
1057 + "25 20050207 123.20 122.41 123.27 122.25 142.40M\n",
1058 + "26 20050211 122.84 122.60 122.99 121.39 140.35M\n",
1059 + "27 20050214 124.94 124.06 125.44 123.65 235.22M\n",
1060 + "28 20050215 125.48 124.99 125.49 124.37 236.83M\n",
1061 + "29 20050216 125.85 126.08 126.82 125.13 218.70M\n",
1062 + "... ... ... ... ... ... ...\n",
1063 + "3434 20181116 271.65 272.95 273.52 271.06 74.60M\n",
1064 + "3435 20181119 272.31 272.01 274.05 270.83 75.01M\n",
1065 + "3436 20181120 269.58 268.54 270.58 268.54 80.90M\n",
1066 + "3437 20181121 268.27 265.96 268.49 264.95 81.84M\n",
1067 + "3438 20181122 267.76 268.51 269.11 267.15 64.41M\n",
1068 + "3439 20181123 266.33 268.12 268.54 264.86 58.35M\n",
1069 + "3440 20181126 269.79 266.43 269.95 266.41 75.65M\n",
1070 + "3441 20181127 272.08 270.90 272.47 269.27 71.91M\n",
1071 + "3442 20181128 273.45 272.64 273.53 271.35 79.83M\n",
1072 + "3443 20181129 274.16 277.08 277.54 273.40 80.65M\n",
1073 + "3444 20181130 271.35 274.53 275.51 270.88 137.53M\n",
1074 + "3445 20181203 276.18 275.70 276.98 273.59 89.07M\n",
1075 + "3446 20181204 273.29 275.08 275.72 272.03 80.80M\n",
1076 + "3447 20181205 271.49 269.53 272.38 269.47 75.64M\n",
1077 + "3448 20181206 267.18 270.18 270.18 266.61 86.09M\n",
1078 + "3449 20181207 268.28 268.97 269.66 267.03 68.45M\n",
1079 + "3450 20181210 265.35 264.55 266.20 263.63 77.30M\n",
1080 + "3451 20181211 264.69 265.87 265.99 263.87 70.04M\n",
1081 + "3452 20181212 268.47 266.00 268.84 265.55 83.69M\n",
1082 + "3453 20181213 269.41 269.07 271.06 268.22 145.06M\n",
1083 + "3454 20181214 265.55 269.69 269.69 264.44 99.73M\n",
1084 + "3455 20181217 266.33 265.81 267.19 265.41 71.45M\n",
1085 + "3456 20181218 265.18 264.42 266.83 263.93 90.33M\n",
1086 + "3457 20181219 267.31 266.02 267.55 265.26 81.75M\n",
1087 + "3458 20181220 264.81 265.13 266.34 263.12 81.34M\n",
1088 + "3459 20181221 265.06 263.66 265.06 263.15 92.60M\n",
1089 + "3460 20181224 264.45 263.51 265.09 262.96 61.96M\n",
1090 + "3461 20181226 260.98 261.09 262.49 259.35 92.49M\n",
1091 + "3462 20181227 260.57 261.53 261.71 259.73 84.46M\n",
1092 + "3463 20181228 261.98 261.61 262.85 261.27 62.70M\n",
1093 + "\n",
1094 + "[3464 rows x 6 columns]"
1095 + ]
1096 + },
1097 + "execution_count": 57,
1098 + "metadata": {},
1099 + "output_type": "execute_result"
1100 + }
1101 + ],
1102 + "source": [
1103 + "train_data = train_data.loc[::-1]\n",
1104 + "train_data = train_data.reset_index(drop=True)"
1105 + ]
1106 + },
1107 + {
1108 + "cell_type": "code",
1109 + "execution_count": 60,
1110 + "metadata": {},
1111 + "outputs": [
1112 + {
1113 + "data": {
1114 + "text/html": [
1115 + "<div>\n",
1116 + "<style scoped>\n",
1117 + " .dataframe tbody tr th:only-of-type {\n",
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1120 + "\n",
1121 + " .dataframe tbody tr th {\n",
1122 + " vertical-align: top;\n",
1123 + " }\n",
1124 + "\n",
1125 + " .dataframe thead th {\n",
1126 + " text-align: right;\n",
1127 + " }\n",
1128 + "</style>\n",
1129 + "<table border=\"1\" class=\"dataframe\">\n",
1130 + " <thead>\n",
1131 + " <tr style=\"text-align: right;\">\n",
1132 + " <th></th>\n",
1133 + " <th>date</th>\n",
1134 + " <th>close</th>\n",
1135 + " <th>open</th>\n",
1136 + " <th>high</th>\n",
1137 + " <th>low</th>\n",
1138 + " <th>vol</th>\n",
1139 + " </tr>\n",
1140 + " </thead>\n",
1141 + " <tbody>\n",
1142 + " <tr>\n",
1143 + " <th>0</th>\n",
1144 + " <td>20050103</td>\n",
1145 + " <td>115.36</td>\n",
1146 + " <td>115.65</td>\n",
1147 + " <td>115.90</td>\n",
1148 + " <td>114.99</td>\n",
1149 + " <td>101.24M</td>\n",
1150 + " </tr>\n",
1151 + " <tr>\n",
1152 + " <th>1</th>\n",
1153 + " <td>20050104</td>\n",
1154 + " <td>114.77</td>\n",
1155 + " <td>115.00</td>\n",
1156 + " <td>115.80</td>\n",
1157 + " <td>114.50</td>\n",
1158 + " <td>119.97M</td>\n",
1159 + " </tr>\n",
1160 + " <tr>\n",
1161 + " <th>2</th>\n",
1162 + " <td>20050105</td>\n",
1163 + " <td>114.65</td>\n",
1164 + " <td>113.41</td>\n",
1165 + " <td>114.65</td>\n",
1166 + " <td>113.17</td>\n",
1167 + " <td>142.31M</td>\n",
1168 + " </tr>\n",
1169 + " </tbody>\n",
1170 + "</table>\n",
1171 + "</div>"
1172 + ],
1173 + "text/plain": [
1174 + " date close open high low vol\n",
1175 + "0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
1176 + "1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
1177 + "2 20050105 114.65 113.41 114.65 113.17 142.31M"
1178 + ]
1179 + },
1180 + "execution_count": 60,
1181 + "metadata": {},
1182 + "output_type": "execute_result"
1183 + }
1184 + ],
1185 + "source": [
1186 + "train_data.head(3)"
1187 + ]
1188 + },
1189 + {
1190 + "cell_type": "markdown",
1191 + "metadata": {},
1192 + "source": [
1193 + "### test_data"
1194 + ]
1195 + },
1196 + {
1197 + "cell_type": "code",
1198 + "execution_count": 41,
1199 + "metadata": {},
1200 + "outputs": [],
1201 + "source": [
1202 + "test_data = test_data.loc[::-1]\n",
1203 + "test_data.reset_index(drop=True)"
1204 + ]
1205 + },
1206 + {
1207 + "cell_type": "code",
1208 + "execution_count": 49,
1209 + "metadata": {},
1210 + "outputs": [
1211 + {
1212 + "data": {
1213 + "text/html": [
1214 + "<div>\n",
1215 + "<style scoped>\n",
1216 + " .dataframe tbody tr th:only-of-type {\n",
1217 + " vertical-align: middle;\n",
1218 + " }\n",
1219 + "\n",
1220 + " .dataframe tbody tr th {\n",
1221 + " vertical-align: top;\n",
1222 + " }\n",
1223 + "\n",
1224 + " .dataframe thead th {\n",
1225 + " text-align: right;\n",
1226 + " }\n",
1227 + "</style>\n",
1228 + "<table border=\"1\" class=\"dataframe\">\n",
1229 + " <thead>\n",
1230 + " <tr style=\"text-align: right;\">\n",
1231 + " <th></th>\n",
1232 + " <th>date</th>\n",
1233 + " <th>close</th>\n",
1234 + " <th>open</th>\n",
1235 + " <th>high</th>\n",
1236 + " <th>low</th>\n",
1237 + " <th>vol</th>\n",
1238 + " </tr>\n",
1239 + " </thead>\n",
1240 + " <tbody>\n",
1241 + " <tr>\n",
1242 + " <th>0</th>\n",
1243 + " <td>20190102</td>\n",
1244 + " <td>258.23</td>\n",
1245 + " <td>263.35</td>\n",
1246 + " <td>263.77</td>\n",
1247 + " <td>257.47</td>\n",
1248 + " <td>69.95M</td>\n",
1249 + " </tr>\n",
1250 + " <tr>\n",
1251 + " <th>1</th>\n",
1252 + " <td>20190103</td>\n",
1253 + " <td>255.77</td>\n",
1254 + " <td>258.19</td>\n",
1255 + " <td>258.70</td>\n",
1256 + " <td>255.35</td>\n",
1257 + " <td>90.52M</td>\n",
1258 + " </tr>\n",
1259 + " <tr>\n",
1260 + " <th>2</th>\n",
1261 + " <td>20190104</td>\n",
1262 + " <td>257.89</td>\n",
1263 + " <td>255.62</td>\n",
1264 + " <td>258.12</td>\n",
1265 + " <td>254.55</td>\n",
1266 + " <td>91.20M</td>\n",
1267 + " </tr>\n",
1268 + " </tbody>\n",
1269 + "</table>\n",
1270 + "</div>"
1271 + ],
1272 + "text/plain": [
1273 + " date close open high low vol\n",
1274 + "0 20190102 258.23 263.35 263.77 257.47 69.95M\n",
1275 + "1 20190103 255.77 258.19 258.70 255.35 90.52M\n",
1276 + "2 20190104 257.89 255.62 258.12 254.55 91.20M"
1277 + ]
1278 + },
1279 + "execution_count": 49,
1280 + "metadata": {},
1281 + "output_type": "execute_result"
1282 + }
1283 + ],
1284 + "source": [
1285 + "test_data.head(3)"
1286 + ]
1287 + },
1288 + {
1289 + "cell_type": "markdown",
1290 + "metadata": {},
1291 + "source": [
1292 + "### validation_data"
1293 + ]
1294 + },
1295 + {
1296 + "cell_type": "code",
1297 + "execution_count": 48,
1298 + "metadata": {},
1299 + "outputs": [],
1300 + "source": [
1301 + "validation_data = validation_data.loc[::-1]\n",
1302 + "validation_data = validation_data.reset_index(drop=True)"
1303 + ]
1304 + },
1305 + {
1306 + "cell_type": "code",
1307 + "execution_count": 50,
1308 + "metadata": {},
1309 + "outputs": [
1310 + {
1311 + "data": {
1312 + "text/html": [
1313 + "<div>\n",
1314 + "<style scoped>\n",
1315 + " .dataframe tbody tr th:only-of-type {\n",
1316 + " vertical-align: middle;\n",
1317 + " }\n",
1318 + "\n",
1319 + " .dataframe tbody tr th {\n",
1320 + " vertical-align: top;\n",
1321 + " }\n",
1322 + "\n",
1323 + " .dataframe thead th {\n",
1324 + " text-align: right;\n",
1325 + " }\n",
1326 + "</style>\n",
1327 + "<table border=\"1\" class=\"dataframe\">\n",
1328 + " <thead>\n",
1329 + " <tr style=\"text-align: right;\">\n",
1330 + " <th></th>\n",
1331 + " <th>date</th>\n",
1332 + " <th>close</th>\n",
1333 + " <th>open</th>\n",
1334 + " <th>high</th>\n",
1335 + " <th>low</th>\n",
1336 + " <th>vol</th>\n",
1337 + " </tr>\n",
1338 + " </thead>\n",
1339 + " <tbody>\n",
1340 + " <tr>\n",
1341 + " <th>0</th>\n",
1342 + " <td>20200102</td>\n",
1343 + " <td>290.35</td>\n",
1344 + " <td>294.19</td>\n",
1345 + " <td>294.57</td>\n",
1346 + " <td>289.96</td>\n",
1347 + " <td>66.16M</td>\n",
1348 + " </tr>\n",
1349 + " <tr>\n",
1350 + " <th>1</th>\n",
1351 + " <td>20200103</td>\n",
1352 + " <td>290.74</td>\n",
1353 + " <td>293.10</td>\n",
1354 + " <td>294.88</td>\n",
1355 + " <td>289.04</td>\n",
1356 + " <td>79.77M</td>\n",
1357 + " </tr>\n",
1358 + " <tr>\n",
1359 + " <th>2</th>\n",
1360 + " <td>20200106</td>\n",
1361 + " <td>288.43</td>\n",
1362 + " <td>287.73</td>\n",
1363 + " <td>289.41</td>\n",
1364 + " <td>287.49</td>\n",
1365 + " <td>69.96M</td>\n",
1366 + " </tr>\n",
1367 + " </tbody>\n",
1368 + "</table>\n",
1369 + "</div>"
1370 + ],
1371 + "text/plain": [
1372 + " date close open high low vol\n",
1373 + "0 20200102 290.35 294.19 294.57 289.96 66.16M\n",
1374 + "1 20200103 290.74 293.10 294.88 289.04 79.77M\n",
1375 + "2 20200106 288.43 287.73 289.41 287.49 69.96M"
1376 + ]
1377 + },
1378 + "execution_count": 50,
1379 + "metadata": {},
1380 + "output_type": "execute_result"
1381 + }
1382 + ],
1383 + "source": [
1384 + "validation_data.head(3)"
1385 + ]
1386 + },
1387 + {
1388 + "cell_type": "markdown",
1389 + "metadata": {},
1390 + "source": [
1391 + "## 거래량 단위 K , M -> 숫자로 변환"
1392 + ]
1393 + },
1394 + {
1395 + "cell_type": "markdown",
1396 + "metadata": {},
1397 + "source": [
1398 + "### test_data"
1399 + ]
1400 + },
1401 + {
1402 + "cell_type": "code",
1403 + "execution_count": 72,
1404 + "metadata": {},
1405 + "outputs": [],
1406 + "source": [
1407 + "def vol_char_to_num(data):\n",
1408 + " for i in tqdm(range(len(data))):\n",
1409 + " unit = data['vol'][i][-1]\n",
1410 + " data['vol'][i] = data['vol'][i][:-1]\n",
1411 + " if unit == 'K':\n",
1412 + " data['vol'][i] = float(data['vol'][i]) * 1000\n",
1413 + " elif unit == 'M':\n",
1414 + " data['vol'][i] = float(data['vol'][i]) * 1000000"
1415 + ]
1416 + },
1417 + {
1418 + "cell_type": "code",
1419 + "execution_count": 77,
1420 + "metadata": {},
1421 + "outputs": [
1422 + {
1423 + "name": "stderr",
1424 + "output_type": "stream",
1425 + "text": [
1426 + " 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
1427 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1428 + "\n",
1429 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1430 + " after removing the cwd from sys.path.\n",
1431 + "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
1432 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1433 + "\n",
1434 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1435 + " \n",
1436 + " 42%|████▏ | 1445/3464 [12:44<17:43, 1.90it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
1437 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1438 + "\n",
1439 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1440 + " \n",
1441 + "100%|██████████| 3464/3464 [30:22<00:00, 1.85it/s]\n"
1442 + ]
1443 + }
1444 + ],
1445 + "source": [
1446 + "vol_char_to_num(train_data)"
1447 + ]
1448 + },
1449 + {
1450 + "cell_type": "code",
1451 + "execution_count": 73,
1452 + "metadata": {},
1453 + "outputs": [
1454 + {
1455 + "name": "stderr",
1456 + "output_type": "stream",
1457 + "text": [
1458 + " 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
1459 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1460 + "\n",
1461 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1462 + " after removing the cwd from sys.path.\n",
1463 + "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
1464 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1465 + "\n",
1466 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1467 + " \n",
1468 + "100%|██████████| 246/246 [02:14<00:00, 1.95it/s]\n"
1469 + ]
1470 + }
1471 + ],
1472 + "source": [
1473 + "vol_char_to_num(test_data)"
1474 + ]
1475 + },
1476 + {
1477 + "cell_type": "code",
1478 + "execution_count": 78,
1479 + "metadata": {},
1480 + "outputs": [
1481 + {
1482 + "name": "stderr",
1483 + "output_type": "stream",
1484 + "text": [
1485 + " 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
1486 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1487 + "\n",
1488 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1489 + " after removing the cwd from sys.path.\n",
1490 + "/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
1491 + "A value is trying to be set on a copy of a slice from a DataFrame\n",
1492 + "\n",
1493 + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
1494 + " \n",
1495 + "100%|██████████| 187/187 [01:37<00:00, 1.95it/s]\n"
1496 + ]
1497 + }
1498 + ],
1499 + "source": [
1500 + "vol_char_to_num(validation_data)"
1501 + ]
1502 + },
1503 + {
1504 + "cell_type": "markdown",
1505 + "metadata": {},
1506 + "source": [
1507 + "### csv파일로 옮기기 "
1508 + ]
1509 + },
1510 + {
1511 + "cell_type": "code",
1512 + "execution_count": 79,
1513 + "metadata": {},
1514 + "outputs": [],
1515 + "source": [
1516 + "train_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv\")"
1517 + ]
1518 + },
1519 + {
1520 + "cell_type": "code",
1521 + "execution_count": 81,
1522 + "metadata": {},
1523 + "outputs": [],
1524 + "source": [
1525 + "test_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv\")"
1526 + ]
1527 + },
1528 + {
1529 + "cell_type": "code",
1530 + "execution_count": 42,
1531 + "metadata": {
1532 + "scrolled": true
1533 + },
1534 + "outputs": [
1535 + {
1536 + "ename": "NameError",
1537 + "evalue": "name 'validation_data' is not defined",
1538 + "output_type": "error",
1539 + "traceback": [
1540 + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
1541 + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
1542 + "\u001b[0;32m<ipython-input-42-84d885a6a29a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
1543 + "\u001b[0;31mNameError\u001b[0m: name 'validation_data' is not defined"
1544 + ]
1545 + }
1546 + ],
1547 + "source": [
1548 + "validation_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
1549 + ]
1550 + },
1551 + {
1552 + "cell_type": "code",
1553 + "execution_count": 44,
1554 + "metadata": {},
1555 + "outputs": [],
1556 + "source": [
1557 + "validation_data = pd.read_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
1558 + ]
1559 + },
1560 + {
1561 + "cell_type": "markdown",
1562 + "metadata": {},
1563 + "source": [
1564 + "## 2. Data Scaling\n",
1565 + "- sklearn 패키지에 있는 MinMaxScaler활용"
1566 + ]
1567 + },
1568 + {
1569 + "cell_type": "code",
1570 + "execution_count": 111,
1571 + "metadata": {},
1572 + "outputs": [],
1573 + "source": [
1574 + "from sklearn.preprocessing import MinMaxScaler\n",
1575 + "scaler = MinMaxScaler()\n",
1576 + "scale_cols = ['close','open','high','low','vol']"
1577 + ]
1578 + },
1579 + {
1580 + "cell_type": "code",
1581 + "execution_count": 94,
1582 + "metadata": {},
1583 + "outputs": [],
1584 + "source": [
1585 + "\n",
1586 + "##----train_data----##\n",
1587 + "train_data_scaled = scaler.fit_transform(train_data[scale_cols])\n",
1588 + "train_data_scaled = pd.DataFrame(train_data_scaled)\n",
1589 + "train_data_scaled.columns = ['close','open','high','low','vol']\n",
1590 + "\n",
1591 + "train_data_scaled.head(3)"
1592 + ]
1593 + },
1594 + {
1595 + "cell_type": "code",
1596 + "execution_count": 104,
1597 + "metadata": {},
1598 + "outputs": [],
1599 + "source": [
1600 + "##----test_data----##\n",
1601 + "test_data_scaled = scaler.fit_transform(test_data[scale_cols])\n",
1602 + "test_data_scaled = pd.DataFrame(test_data_scaled)\n",
1603 + "test_data_scaled.columns = ['close','open','high','low','vol']"
1604 + ]
1605 + },
1606 + {
1607 + "cell_type": "code",
1608 + "execution_count": 105,
1609 + "metadata": {},
1610 + "outputs": [
1611 + {
1612 + "data": {
1613 + "text/html": [
1614 + "<div>\n",
1615 + "<style scoped>\n",
1616 + " .dataframe tbody tr th:only-of-type {\n",
1617 + " vertical-align: middle;\n",
1618 + " }\n",
1619 + "\n",
1620 + " .dataframe tbody tr th {\n",
1621 + " vertical-align: top;\n",
1622 + " }\n",
1623 + "\n",
1624 + " .dataframe thead th {\n",
1625 + " text-align: right;\n",
1626 + " }\n",
1627 + "</style>\n",
1628 + "<table border=\"1\" class=\"dataframe\">\n",
1629 + " <thead>\n",
1630 + " <tr style=\"text-align: right;\">\n",
1631 + " <th></th>\n",
1632 + " <th>close</th>\n",
1633 + " <th>open</th>\n",
1634 + " <th>high</th>\n",
1635 + " <th>low</th>\n",
1636 + " <th>vol</th>\n",
1637 + " </tr>\n",
1638 + " </thead>\n",
1639 + " <tbody>\n",
1640 + " <tr>\n",
1641 + " <th>0</th>\n",
1642 + " <td>0.143847</td>\n",
1643 + " <td>0.277840</td>\n",
1644 + " <td>0.242639</td>\n",
1645 + " <td>0.165408</td>\n",
1646 + " <td>0.207573</td>\n",
1647 + " </tr>\n",
1648 + " <tr>\n",
1649 + " <th>1</th>\n",
1650 + " <td>0.087047</td>\n",
1651 + " <td>0.163147</td>\n",
1652 + " <td>0.126912</td>\n",
1653 + " <td>0.116907</td>\n",
1654 + " <td>0.381423</td>\n",
1655 + " </tr>\n",
1656 + " <tr>\n",
1657 + " <th>2</th>\n",
1658 + " <td>0.135996</td>\n",
1659 + " <td>0.106024</td>\n",
1660 + " <td>0.113673</td>\n",
1661 + " <td>0.098604</td>\n",
1662 + " <td>0.387170</td>\n",
1663 + " </tr>\n",
1664 + " </tbody>\n",
1665 + "</table>\n",
1666 + "</div>"
1667 + ],
1668 + "text/plain": [
1669 + " close open high low vol\n",
1670 + "0 0.143847 0.277840 0.242639 0.165408 0.207573\n",
1671 + "1 0.087047 0.163147 0.126912 0.116907 0.381423\n",
1672 + "2 0.135996 0.106024 0.113673 0.098604 0.387170"
1673 + ]
1674 + },
1675 + "execution_count": 105,
1676 + "metadata": {},
1677 + "output_type": "execute_result"
1678 + }
1679 + ],
1680 + "source": [
1681 + "test_data_scaled.head(3)"
1682 + ]
1683 + },
1684 + {
1685 + "cell_type": "code",
1686 + "execution_count": 47,
1687 + "metadata": {},
1688 + "outputs": [],
1689 + "source": [
1690 + "##----val_data----##\n",
1691 + "val_data_scaled = scaler.fit_transform(validation_data[scale_cols])\n",
1692 + "val_data_scaled = pd.DataFrame(val_data_scaled)\n",
1693 + "val_data_scaled.columns = ['close','open','high','low','vol']"
1694 + ]
1695 + },
1696 + {
1697 + "cell_type": "code",
1698 + "execution_count": 106,
1699 + "metadata": {},
1700 + "outputs": [],
1701 + "source": [
1702 + "train_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_scaled.csv\")\n",
1703 + "test_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_scaled.csv\")\n",
1704 + "val_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_scaled.csv\")"
1705 + ]
1706 + },
1707 + {
1708 + "cell_type": "markdown",
1709 + "metadata": {},
1710 + "source": [
1711 + "## 3. 이동평균 데이터 \n",
1712 + "\n",
1713 + "- 단순이동평균\n",
1714 + "- 지수이동평균\n",
1715 + "\n",
1716 + "종가이동평균 : ma, 거래량이동평균 : vma\n"
1717 + ]
1718 + },
1719 + {
1720 + "cell_type": "code",
1721 + "execution_count": 2,
1722 + "metadata": {},
1723 + "outputs": [],
1724 + "source": [
1725 + "train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv')\n",
1726 + "test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv')\n",
1727 + "validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')"
1728 + ]
1729 + },
1730 + {
1731 + "cell_type": "markdown",
1732 + "metadata": {},
1733 + "source": [
1734 + "### (1) 단순이동평균 "
1735 + ]
1736 + },
1737 + {
1738 + "cell_type": "markdown",
1739 + "metadata": {},
1740 + "source": [
1741 + "#### 종가"
1742 + ]
1743 + },
1744 + {
1745 + "cell_type": "code",
1746 + "execution_count": 99,
1747 + "metadata": {},
1748 + "outputs": [],
1749 + "source": [
1750 + "##----train_data----##\n",
1751 + "train_SMA_5 = train['close'].rolling(5).mean()\n",
1752 + "train_SMA_10 = train['close'].rolling(10).mean()\n",
1753 + "train_SMA_20 = train['close'].rolling(20).mean()\n",
1754 + "train_SMA_60 = train['close'].rolling(60).mean()\n",
1755 + "train_SMA_120 = train['close'].rolling(120).mean()\n",
1756 + "\n",
1757 + "train_SMA = pd.DataFrame({'5':train_SMA_5,'10':train_SMA_10,'20':train_SMA_20,'60':train_SMA_60,'120':train_SMA_120})\n",
1758 + "train_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_SMA.csv\")"
1759 + ]
1760 + },
1761 + {
1762 + "cell_type": "code",
1763 + "execution_count": 100,
1764 + "metadata": {},
1765 + "outputs": [],
1766 + "source": [
1767 + "##----test_data----##\n",
1768 + "test_SMA_5 = test['close'].rolling(5).mean()\n",
1769 + "test_SMA_10 = test['close'].rolling(10).mean()\n",
1770 + "test_SMA_20 = test['close'].rolling(20).mean()\n",
1771 + "test_SMA_60 = test['close'].rolling(60).mean()\n",
1772 + "test_SMA_120 = test['close'].rolling(120).mean()\n",
1773 + "\n",
1774 + "test_SMA = pd.DataFrame({'5':test_SMA_5,'10':test_SMA_10,'20':test_SMA_20,'60':test_SMA_60,'120':test_SMA_120})\n",
1775 + "test_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_SMA.csv\")"
1776 + ]
1777 + },
1778 + {
1779 + "cell_type": "code",
1780 + "execution_count": 101,
1781 + "metadata": {},
1782 + "outputs": [],
1783 + "source": [
1784 + "##----val_data----##\n",
1785 + "val_SMA_5 = validation['close'].rolling(5).mean()\n",
1786 + "val_SMA_10 = validation['close'].rolling(10).mean()\n",
1787 + "val_SMA_20 = validation['close'].rolling(20).mean()\n",
1788 + "val_SMA_60 = validation['close'].rolling(60).mean()\n",
1789 + "val_SMA_120 = validation['close'].rolling(120).mean()\n",
1790 + "\n",
1791 + "val_SMA = pd.DataFrame({'5':val_SMA_5,'10':val_SMA_10,'20':val_SMA_20,'60':val_SMA_60,'120':val_SMA_120})\n",
1792 + "val_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_SMA.csv\")"
1793 + ]
1794 + },
1795 + {
1796 + "cell_type": "markdown",
1797 + "metadata": {},
1798 + "source": [
1799 + "#### 거래량"
1800 + ]
1801 + },
1802 + {
1803 + "cell_type": "code",
1804 + "execution_count": 102,
1805 + "metadata": {},
1806 + "outputs": [],
1807 + "source": [
1808 + "##----train_data----##\n",
1809 + "train_SVMA_5 = train['vol'].rolling(5).mean()\n",
1810 + "train_SVMA_10 = train['vol'].rolling(10).mean()\n",
1811 + "train_SVMA_20 = train['vol'].rolling(20).mean()\n",
1812 + "train_SVMA_60 = train['vol'].rolling(60).mean()\n",
1813 + "train_SVMA_120 = train['vol'].rolling(120).mean()\n",
1814 + "\n",
1815 + "train_SVMA = pd.DataFrame({'5':train_SVMA_5,'10':train_SVMA_10,'20':train_SVMA_20,'60':train_SVMA_60,'120':train_SVMA_120})\n",
1816 + "train_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_SVMA.csv\")"
1817 + ]
1818 + },
1819 + {
1820 + "cell_type": "code",
1821 + "execution_count": 103,
1822 + "metadata": {},
1823 + "outputs": [],
1824 + "source": [
1825 + "##----test_data----##\n",
1826 + "test_SVMA_5 = test['vol'].rolling(5).mean()\n",
1827 + "test_SVMA_10 = test['vol'].rolling(10).mean()\n",
1828 + "test_SVMA_20 = test['vol'].rolling(20).mean()\n",
1829 + "test_SVMA_60 = test['vol'].rolling(60).mean()\n",
1830 + "test_SVMA_120 = test['vol'].rolling(120).mean()\n",
1831 + "\n",
1832 + "test_SVMA = pd.DataFrame({'5':test_SVMA_5,'10':test_SVMA_10,'20':test_SVMA_20,'60':test_SVMA_60,'120':test_SVMA_120})\n",
1833 + "test_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_SVMA.csv\")"
1834 + ]
1835 + },
1836 + {
1837 + "cell_type": "code",
1838 + "execution_count": 104,
1839 + "metadata": {},
1840 + "outputs": [],
1841 + "source": [
1842 + "##----val_data----##\n",
1843 + "\n",
1844 + "val_SVMA_5 = validation['vol'].rolling(5).mean()\n",
1845 + "val_SVMA_10 = validation['vol'].rolling(10).mean()\n",
1846 + "val_SVMA_20 = validation['vol'].rolling(20).mean()\n",
1847 + "val_SVMA_60 = validation['vol'].rolling(60).mean()\n",
1848 + "val_SVMA_120 = validation['vol'].rolling(120).mean()\n",
1849 + "\n",
1850 + "val_SVMA = pd.DataFrame({'5':val_SVMA_5,'10':val_SVMA_10,'20':val_SVMA_20,'60':val_SVMA_60,'120':val_SVMA_120})\n",
1851 + "val_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_SVMA.csv\")"
1852 + ]
1853 + },
1854 + {
1855 + "cell_type": "markdown",
1856 + "metadata": {},
1857 + "source": [
1858 + "### (2) 지수이동평균 "
1859 + ]
1860 + },
1861 + {
1862 + "cell_type": "markdown",
1863 + "metadata": {},
1864 + "source": [
1865 + "#### 종가"
1866 + ]
1867 + },
1868 + {
1869 + "cell_type": "code",
1870 + "execution_count": 105,
1871 + "metadata": {},
1872 + "outputs": [],
1873 + "source": [
1874 + "##----train_data----##\n",
1875 + "train_EMA_5 = train['close'].ewm(span=5).mean()\n",
1876 + "train_EMA_10 = train['close'].ewm(span=10).mean()\n",
1877 + "train_EMA_20 = train['close'].ewm(span=20).mean()\n",
1878 + "train_EMA_60 = train['close'].ewm(span=60).mean()\n",
1879 + "train_EMA_120 = train['close'].ewm(span=120).mean()\n",
1880 + "\n",
1881 + "train_EMA = pd.DataFrame({'5':train_EMA_5,'10':train_EMA_10,'20':train_EMA_20,'60':train_EMA_60,'120':train_EMA_120})\n",
1882 + "train_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_EMA.csv\")"
1883 + ]
1884 + },
1885 + {
1886 + "cell_type": "code",
1887 + "execution_count": 106,
1888 + "metadata": {},
1889 + "outputs": [],
1890 + "source": [
1891 + "##----test_data----##\n",
1892 + "test_EMA_5 = test['close'].ewm(span=5).mean()\n",
1893 + "test_EMA_10 = test['close'].ewm(span=10).mean()\n",
1894 + "test_EMA_20 = test['close'].ewm(span=20).mean()\n",
1895 + "test_EMA_60 = test['close'].ewm(span=60).mean()\n",
1896 + "test_EMA_120 = test['close'].ewm(span=120).mean()\n",
1897 + "\n",
1898 + "test_EMA = pd.DataFrame({'5':test_EMA_5,'10':test_EMA_10,'20':test_EMA_20,'60':test_EMA_60,'120':test_EMA_120})\n",
1899 + "test_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_EMA.csv\")"
1900 + ]
1901 + },
1902 + {
1903 + "cell_type": "code",
1904 + "execution_count": 107,
1905 + "metadata": {},
1906 + "outputs": [],
1907 + "source": [
1908 + "##----val_data----##\n",
1909 + "val_EMA_5 = validation['close'].ewm(span=5).mean()\n",
1910 + "val_EMA_10 = validation['close'].ewm(span=10).mean()\n",
1911 + "val_EMA_20 = validation['close'].ewm(span=20).mean()\n",
1912 + "val_EMA_60 = validation['close'].ewm(span=60).mean()\n",
1913 + "val_EMA_120 = validation['close'].ewm(span=120).mean()\n",
1914 + "\n",
1915 + "val_EMA = pd.DataFrame({'5':val_EMA_5,'10':val_EMA_10,'20':val_EMA_20,'60':val_EMA_60,'120':val_EMA_120})\n",
1916 + "val_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_EMA.csv\")"
1917 + ]
1918 + },
1919 + {
1920 + "cell_type": "markdown",
1921 + "metadata": {},
1922 + "source": [
1923 + "#### 거래량"
1924 + ]
1925 + },
1926 + {
1927 + "cell_type": "code",
1928 + "execution_count": 108,
1929 + "metadata": {},
1930 + "outputs": [],
1931 + "source": [
1932 + "##----train_data----##\n",
1933 + "train_EVMA_5 = train['vol'].ewm(span=5).mean()\n",
1934 + "train_EVMA_10 = train['vol'].ewm(span=10).mean()\n",
1935 + "train_EVMA_20 = train['vol'].ewm(span=20).mean()\n",
1936 + "train_EVMA_60 = train['vol'].ewm(span=60).mean()\n",
1937 + "train_EVMA_120 = train['vol'].ewm(span=120).mean()\n",
1938 + "\n",
1939 + "train_EVMA = pd.DataFrame({'5':train_EVMA_5,'10':train_EVMA_10,'20':train_EVMA_20,'60':train_EVMA_60,'120':train_EVMA_120})\n",
1940 + "train_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_EVMA.csv\")"
1941 + ]
1942 + },
1943 + {
1944 + "cell_type": "code",
1945 + "execution_count": 109,
1946 + "metadata": {},
1947 + "outputs": [],
1948 + "source": [
1949 + "##----test_data----##\n",
1950 + "test_EVMA_5 = test['vol'].ewm(span=5).mean()\n",
1951 + "test_EVMA_10 = test['vol'].ewm(span=10).mean()\n",
1952 + "test_EVMA_20 = test['vol'].ewm(span=20).mean()\n",
1953 + "test_EVMA_60 = test['vol'].ewm(span=60).mean()\n",
1954 + "test_EVMA_120 = test['vol'].ewm(span=120).mean()\n",
1955 + "\n",
1956 + "test_EVMA = pd.DataFrame({'5':test_EVMA_5,'10':test_EVMA_10,'20':test_EVMA_20,'60':test_EVMA_60,'120':test_EVMA_120})\n",
1957 + "test_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_EVMA.csv\")"
1958 + ]
1959 + },
1960 + {
1961 + "cell_type": "code",
1962 + "execution_count": 110,
1963 + "metadata": {},
1964 + "outputs": [],
1965 + "source": [
1966 + "##----val_data----##\n",
1967 + "val_EVMA_5 = validation['vol'].ewm(span=5).mean()\n",
1968 + "val_EVMA_10 = validation['vol'].ewm(span=10).mean()\n",
1969 + "val_EVMA_20 = validation['vol'].ewm(span=20).mean()\n",
1970 + "val_EVMA_60 = validation['vol'].ewm(span=60).mean()\n",
1971 + "val_EVMA_120 = validation['vol'].ewm(span=120).mean()\n",
1972 + "\n",
1973 + "val_EVMA = pd.DataFrame({'5':val_EVMA_5,'10':val_EVMA_10,'20':val_EVMA_20,'60':val_EVMA_60,'120':val_EVMA_120})\n",
1974 + "val_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_EVMA.csv\")"
1975 + ]
1976 + },
1977 + {
1978 + "cell_type": "markdown",
1979 + "metadata": {},
1980 + "source": [
1981 + "### (3) scaling"
1982 + ]
1983 + },
1984 + {
1985 + "cell_type": "markdown",
1986 + "metadata": {},
1987 + "source": [
1988 + "#### scaling 단순이동평균 - 종가"
1989 + ]
1990 + },
1991 + {
1992 + "cell_type": "code",
1993 + "execution_count": 113,
1994 + "metadata": {},
1995 + "outputs": [],
1996 + "source": [
1997 + "scale_cols2 = ['5','10','20','60','120']"
1998 + ]
1999 + },
2000 + {
2001 + "cell_type": "code",
2002 + "execution_count": 143,
2003 + "metadata": {},
2004 + "outputs": [],
2005 + "source": [
2006 + "##----train_data----##\n",
2007 + "train_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/train_SMA.csv')\n",
2008 + "\n",
2009 + "train_SMA_scaled = scaler.fit_transform(train_SMA[scale_cols2])\n",
2010 + "train_SMA_scaled = pd.DataFrame(train_SMA_scaled)\n",
2011 + "train_SMA_scaled.columns = ['5','10','20','60','120']\n",
2012 + "\n",
2013 + "##----test_data----##\n",
2014 + "test_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SMA.csv')\n",
2015 + "\n",
2016 + "test_SMA_scaled = scaler.fit_transform(test_SMA[scale_cols2])\n",
2017 + "test_SMA_scaled = pd.DataFrame(test_SMA_scaled)\n",
2018 + "test_SMA_scaled.columns = ['5','10','20','60','120']\n",
2019 + "\n",
2020 + "##----val_data----##\n",
2021 + "val_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SMA.csv')\n",
2022 + "\n",
2023 + "val_SMA_scaled = scaler.fit_transform(val_SMA[scale_cols2])\n",
2024 + "val_SMA_scaled = pd.DataFrame(val_SMA_scaled)\n",
2025 + "val_SMA_scaled.columns = ['5','10','20','60','120']"
2026 + ]
2027 + },
2028 + {
2029 + "cell_type": "markdown",
2030 + "metadata": {},
2031 + "source": [
2032 + "#### scaling 단순이동평균 - 거래량"
2033 + ]
2034 + },
2035 + {
2036 + "cell_type": "code",
2037 + "execution_count": 144,
2038 + "metadata": {},
2039 + "outputs": [],
2040 + "source": [
2041 + "##----train_data----##\n",
2042 + "train_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/train_SVMA.csv')\n",
2043 + "\n",
2044 + "train_SVMA_scaled = scaler.fit_transform(train_SVMA[scale_cols2])\n",
2045 + "train_SVMA_scaled = pd.DataFrame(train_SVMA_scaled)\n",
2046 + "train_SVMA_scaled.columns = ['5','10','20','60','120']\n",
2047 + "\n",
2048 + "##----test_data----##\n",
2049 + "test_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SVMA.csv')\n",
2050 + "\n",
2051 + "test_SVMA_scaled = scaler.fit_transform(test_SVMA[scale_cols2])\n",
2052 + "test_SVMA_scaled = pd.DataFrame(test_SVMA_scaled)\n",
2053 + "test_SVMA_scaled.columns = ['5','10','20','60','120']\n",
2054 + "\n",
2055 + "##----val_data----##\n",
2056 + "val_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SVMA.csv')\n",
2057 + "\n",
2058 + "val_SVMA_scaled = scaler.fit_transform(val_SVMA[scale_cols2])\n",
2059 + "val_SVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
2060 + "val_SVMA_scaled.columns = ['5','10','20','60','120']"
2061 + ]
2062 + },
2063 + {
2064 + "cell_type": "code",
2065 + "execution_count": 145,
2066 + "metadata": {},
2067 + "outputs": [],
2068 + "source": [
2069 + "train_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_SMA_scaled.csv\")\n",
2070 + "test_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SMA_scaled.csv\")\n",
2071 + "val_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SMA_scaled.csv\")\n",
2072 + "train_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_SVMA_scaled.csv\")\n",
2073 + "test_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SVMA_scaled.csv\")\n",
2074 + "val_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SVMA_scaled.csv\")"
2075 + ]
2076 + },
2077 + {
2078 + "cell_type": "markdown",
2079 + "metadata": {},
2080 + "source": [
2081 + "#### scaling 지수이동평균 - 종가"
2082 + ]
2083 + },
2084 + {
2085 + "cell_type": "code",
2086 + "execution_count": 146,
2087 + "metadata": {},
2088 + "outputs": [],
2089 + "source": [
2090 + "##----train_data----##\n",
2091 + "train_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/train_EMA.csv')\n",
2092 + "\n",
2093 + "train_EMA_scaled = scaler.fit_transform(train_EMA[scale_cols2])\n",
2094 + "train_EMA_scaled = pd.DataFrame(train_EMA_scaled)\n",
2095 + "train_EMA_scaled.columns = ['5','10','20','60','120']\n",
2096 + "\n",
2097 + "##----test_data----##\n",
2098 + "test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv')\n",
2099 + "\n",
2100 + "test_EMA_scaled = scaler.fit_transform(test_EMA[scale_cols2])\n",
2101 + "test_EMA_scaled = pd.DataFrame(test_EMA_scaled)\n",
2102 + "test_EMA_scaled.columns = ['5','10','20','60','120']\n",
2103 + "\n",
2104 + "##----val_data----##\n",
2105 + "val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv')\n",
2106 + "\n",
2107 + "val_EMA_scaled = scaler.fit_transform(val_EMA[scale_cols2])\n",
2108 + "val_EMA_scaled = pd.DataFrame(val_EMA_scaled)\n",
2109 + "val_EMA_scaled.columns = ['5','10','20','60','120']"
2110 + ]
2111 + },
2112 + {
2113 + "cell_type": "markdown",
2114 + "metadata": {},
2115 + "source": [
2116 + "#### scaling 지수이동평균 - 거래량"
2117 + ]
2118 + },
2119 + {
2120 + "cell_type": "code",
2121 + "execution_count": 147,
2122 + "metadata": {},
2123 + "outputs": [],
2124 + "source": [
2125 + "##----train_data----##\n",
2126 + "train_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/train_EVMA.csv')\n",
2127 + "\n",
2128 + "train_EVMA_scaled = scaler.fit_transform(train_EVMA[scale_cols2])\n",
2129 + "train_EVMA_scaled = pd.DataFrame(train_EVMA_scaled)\n",
2130 + "train_EVMA_scaled.columns = ['5','10','20','60','120']\n",
2131 + "\n",
2132 + "##----test_data----##\n",
2133 + "test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv')\n",
2134 + "\n",
2135 + "test_EVMA_scaled = scaler.fit_transform(test_EVMA[scale_cols2])\n",
2136 + "test_EVMA_scaled = pd.DataFrame(test_EVMA_scaled)\n",
2137 + "test_EVMA_scaled.columns = ['5','10','20','60','120']\n",
2138 + "\n",
2139 + "##----val_data----##\n",
2140 + "val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
2141 + "\n",
2142 + "val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
2143 + "val_EVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
2144 + "val_EVMA_scaled.columns = ['5','10','20','60','120']"
2145 + ]
2146 + },
2147 + {
2148 + "cell_type": "code",
2149 + "execution_count": 148,
2150 + "metadata": {},
2151 + "outputs": [],
2152 + "source": [
2153 + "train_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_EMA_scaled.csv\")\n",
2154 + "test_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EMA_scaled.csv\")\n",
2155 + "val_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EMA_scaled.csv\")\n",
2156 + "train_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_EVMA_scaled.csv\")\n",
2157 + "test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EVMA_scaled.csv\")\n",
2158 + "val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EVMA_scaled.csv\")"
2159 + ]
2160 + }
2161 + ],
2162 + "metadata": {
2163 + "kernelspec": {
2164 + "display_name": "Python 3",
2165 + "language": "python",
2166 + "name": "python3"
2167 + },
2168 + "language_info": {
2169 + "codemirror_mode": {
2170 + "name": "ipython",
2171 + "version": 3
2172 + },
2173 + "file_extension": ".py",
2174 + "mimetype": "text/x-python",
2175 + "name": "python",
2176 + "nbconvert_exporter": "python",
2177 + "pygments_lexer": "ipython3",
2178 + "version": "3.7.3"
2179 + }
2180 + },
2181 + "nbformat": 4,
2182 + "nbformat_minor": 2
2183 +}
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This diff could not be displayed because it is too large.
This diff could not be displayed because it is too large.
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This diff could not be displayed because it is too large.
This diff could not be displayed because it is too large.
1 +,5,10,20,60,120
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1 +,5,10,20,60,120
2 +0,0.7207787436198625,0.7003700860565538,0.6797465304554713,0.7135560972586541,0.7984496281539215
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7 +5,0.7321502866739424,0.7088378895657743,0.6871847194950673,0.7226627277138853,0.8096917759974822
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10 +8,0.7898983932964707,0.7600163999688472,0.73589043247582,0.7855669880934357,0.8884330234272282
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This diff could not be displayed because it is too large.
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81 +79,20190429,286.79,282.0,286.79,281.81,66980000.00000001
82 +80,20190430,284.92,286.25,286.59,282.61,77990000.0
83 +81,20190502,286.12,283.37,286.87,283.14,84180000.0
84 +82,20190503,283.67,285.64,285.95,283.14,67790000.0
85 +83,20190507,281.31,280.89,281.66,278.95,97720000.0
86 +84,20190508,280.15,277.85,281.84,277.84,75000000.0
87 +85,20190509,271.0,278.83,279.63,271.0,124010000.0
88 +86,20190510,272.06,273.37,274.32,269.71,89440000.0
89 +87,20190513,268.72,269.8,271.54,268.55,66870000.00000001
90 +88,20190514,268.57,266.03,270.42,265.55,82190000.0
91 +89,20190515,269.64,268.42,271.0,267.62,77020000.0
92 +90,20190516,265.99,269.83,269.96,265.24,75960000.0
93 +91,20190517,264.35,267.85,268.06,263.94,75100000.0
94 +92,20190520,264.96,265.9,267.65,264.33,64230000.00000001
95 +93,20190521,266.12,265.25,268.55,264.79,81020000.0
96 +94,20190522,266.65,267.01,267.6,263.95,61660000.0
97 +95,20190523,266.38,266.4,267.12,264.5,71660000.0
98 +96,20190524,264.42,264.7,266.11,263.1,67230000.0
99 +97,20190527,264.4,264.18,265.85,263.17,53140000.0
100 +98,20190528,264.7,264.38,266.16,263.27,147260000.0
101 +99,20190529,261.37,264.1,264.1,260.22,82790000.0
102 +100,20190530,263.93,261.58,264.23,261.58,70340000.0
103 +101,20190531,263.89,262.48,265.21,261.53,77790000.0
104 +102,20190603,268.16,262.68,268.55,262.33,83610000.0
105 +103,20190604,267.66,267.84,268.47,266.72,70290000.0
106 +104,20190605,267.99,270.1,270.46,267.46,67510000.0
107 +105,20190607,268.27,267.97,269.63,266.27,67970000.0
108 +106,20190610,272.1,270.32,272.2,268.68,60890000.0
109 +107,20190611,273.21,271.48,273.69,271.24,61240000.0
110 +108,20190612,272.74,273.14,274.32,272.0,66920000.0
111 +109,20190613,271.13,271.96,272.46,268.38,95800000.0
112 +110,20190614,270.48,270.19,271.55,268.96,66709999.99999999
113 +111,20190617,270.09,269.83,271.59,269.14,57060000.0
114 +112,20190618,271.36,269.77,271.87,269.77,65720000.0
115 +113,20190619,275.31,274.4,275.6,274.08,69730000.0
116 +114,20190620,276.01,274.63,276.09,274.43,55340000.0
117 +115,20190621,275.48,276.17,276.26,274.6,77280000.0
118 +116,20190624,275.58,275.91,276.73,275.13,55540000.0
119 +117,20190625,275.28,275.33,277.02,274.19,60510000.0
120 +118,20190626,275.53,275.21,276.36,274.66,58820000.0
121 +119,20190627,277.75,275.47,278.63,275.09,66300000.0
122 +120,20190628,277.5,278.03,278.56,276.58,67600000.0
123 +121,20190701,277.27,279.59,279.68,276.85,74360000.0
124 +122,20190702,275.97,276.06,277.46,274.63,65540000.00000001
125 +123,20190703,272.37,275.57,276.69,271.9,97520000.0
126 +124,20190704,274.54,273.18,274.61,271.93,77520000.0
127 +125,20190705,274.35,274.74,275.23,272.41,60020000.0
128 +126,20190708,268.55,271.38,271.67,268.38,75100000.0
129 +127,20190709,267.72,269.6,270.22,267.72,71880000.0
130 +128,20190710,268.75,269.29,270.33,268.6,67510000.0
131 +129,20190711,271.78,270.58,272.97,270.46,107370000.0
132 +130,20190712,272.68,272.21,273.23,271.28,55660000.0
133 +131,20190715,272.64,272.06,273.38,270.99,83610000.0
134 +132,20190716,274.05,272.79,274.54,272.14,137050000.0
135 +133,20190717,271.48,272.13,272.28,270.29,117950000.0
136 +134,20190718,270.67,271.05,271.63,269.73,80140000.0
137 +135,20190719,274.55,272.71,274.95,272.21,87940000.0
138 +136,20190722,274.55,273.9,275.35,273.57,60910000.0
139 +137,20190723,275.92,274.93,276.56,274.84,95080000.0
140 +138,20190724,273.22,275.12,276.48,272.91,84290000.0
141 +139,20190725,273.04,274.08,274.62,271.25,95650000.0
142 +140,20190726,272.25,271.41,272.56,270.57,89840000.0
143 +141,20190729,267.75,271.21,271.91,267.07,78750000.0
144 +142,20190730,268.95,268.69,269.79,268.33,64530000.0
145 +143,20190731,266.34,268.46,269.14,265.0,96240000.0
146 +144,20190801,265.73,264.97,267.44,264.47,72580000.0
147 +145,20190802,262.99,262.61,263.94,261.75,99660000.0
148 +146,20190805,257.19,261.1,261.4,256.76,131039999.99999999
149 +147,20190806,253.41,250.85,257.04,250.24,123170000.0
150 +148,20190807,252.0,254.46,254.9,251.08,90740000.0
151 +149,20190808,252.4,254.11,255.33,252.37,163710000.0
152 +150,20190809,254.85,255.13,256.15,254.41,117690000.0
153 +151,20190812,255.35,255.57,256.43,254.43,69380000.0
154 +152,20190813,252.83,253.5,254.58,252.63,76820000.0
155 +153,20190814,254.86,255.84,256.82,254.59,82240000.0
156 +154,20190816,253.9,252.76,253.94,251.46,71830000.0
157 +155,20190819,255.11,255.2,255.84,254.25,66019999.99999999
158 +156,20190820,258.05,255.53,258.62,254.74,81690000.0
159 +157,20190821,258.38,257.15,259.49,257.11,65480000.00000001
160 +158,20190822,256.28,258.53,258.73,256.25,67250000.0
161 +159,20190823,256.11,255.11,256.88,254.91,54780000.0
162 +160,20190826,252.19,251.8,253.14,250.99,62610000.0
163 +161,20190827,253.5,253.74,254.96,252.53,93380000.0
164 +162,20190828,255.53,254.2,255.98,253.65,58340000.0
165 +163,20190829,254.31,255.68,256.28,253.6,64849999.99999999
166 +164,20190830,259.0,256.85,259.61,256.72,94160000.0
167 +165,20190902,259.08,258.25,259.74,257.8,54890000.0
168 +166,20190903,258.25,258.55,259.67,257.4,64319999.99999999
169 +167,20190904,261.69,257.89,261.69,257.76,69290000.0
170 +168,20190905,264.44,262.53,266.0,262.27,88990000.0
171 +169,20190906,265.53,266.2,266.36,264.74,59200000.0
172 +170,20190909,267.4,266.11,268.67,265.92,62210000.0
173 +171,20190910,268.76,267.96,269.13,267.35,66340000.0
174 +172,20190911,270.84,270.35,271.35,269.58,103900000.0
175 +173,20190916,272.02,271.48,272.03,269.59,103340000.0
176 +174,20190917,271.68,271.46,272.26,270.9,73740000.0
177 +175,20190918,273.06,271.88,273.72,271.04,67600000.0
178 +176,20190919,274.92,273.11,276.15,273.03,70500000.0
179 +177,20190920,276.44,275.01,276.67,274.46,74030000.0
180 +178,20190923,276.65,275.86,277.11,275.64,60190000.0
181 +179,20190924,277.97,276.41,278.17,275.55,68270000.0
182 +180,20190925,274.42,276.67,277.04,274.12,70840000.0
183 +181,20190926,274.89,275.61,276.38,273.5,89590000.0
184 +182,20190927,271.33,273.32,273.4,270.52,57740000.0
185 +183,20190930,273.55,270.61,273.85,270.06,54690000.0
186 +184,20191001,274.42,273.43,275.38,272.81,52630000.0
187 +185,20191002,268.55,272.39,272.42,268.55,66209999.99999999
188 +186,20191004,267.39,268.14,270.16,267.39,62880000.0
189 +187,20191007,267.67,269.12,269.75,266.83,53570000.0
190 +188,20191008,271.36,268.9,271.51,267.97,75080000.0
191 +189,20191010,268.8,268.79,270.78,266.23,98180000.0
192 +190,20191011,271.3,271.68,272.68,270.58,59640000.0
193 +191,20191014,274.23,274.71,275.75,273.97,71050000.0
194 +192,20191015,274.34,273.85,275.09,273.83,53050000.0
195 +193,20191016,276.32,276.26,277.59,275.24,65379999.99999999
196 +194,20191017,275.57,275.92,276.68,274.62,53870000.0
197 +195,20191018,272.81,275.99,277.06,272.28,71470000.0
198 +196,20191021,273.79,273.87,274.1,272.67,45390000.0
199 +197,20191022,277.41,275.4,278.19,274.93,63880000.0
200 +198,20191023,276.23,276.83,277.29,275.34,65290000.00000001
201 +199,20191024,276.7,277.7,277.84,274.31,73680000.0
202 +200,20191025,277.07,277.6,277.67,275.7,58280000.0
203 +201,20191028,277.78,278.16,279.42,277.29,59070000.0
204 +202,20191029,277.55,279.25,279.49,276.81,57150000.0
205 +203,20191030,275.51,277.49,277.49,274.95,72530000.0
206 +204,20191031,275.82,276.84,279.04,275.82,78340000.0
207 +205,20191101,278.43,276.17,278.55,275.59,58540000.0
208 +206,20191104,282.54,279.9,282.77,279.9,77000000.0
209 +207,20191105,284.38,282.99,284.46,282.27,76360000.0
210 +208,20191106,284.52,284.31,286.43,283.7,77950000.0
211 +209,20191107,284.37,284.04,284.82,283.05,70720000.0
212 +210,20191108,283.13,285.81,286.12,282.57,73680000.0
213 +211,20191111,280.85,283.3,283.3,280.4,61560000.0
214 +212,20191112,283.65,281.9,283.65,280.79,65569999.99999999
215 +213,20191113,281.28,282.28,282.49,280.62,60130000.0
216 +214,20191114,283.93,281.9,283.93,281.1,79760000.0
217 +215,20191115,287.29,283.85,287.72,283.3,71650000.0
218 +216,20191118,287.18,287.1,287.78,285.94,56110000.0
219 +217,20191119,286.23,285.74,286.34,283.73,68170000.0
220 +218,20191120,282.21,284.27,285.29,282.0,73550000.0
221 +219,20191121,278.28,281.89,282.33,276.98,81750000.0
222 +220,20191122,279.43,278.57,279.89,278.14,74350000.0
223 +221,20191125,282.19,281.77,283.53,281.48,64510000.00000001
224 +222,20191126,281.83,283.09,284.87,281.77,119520000.0
225 +223,20191127,282.85,283.56,284.02,281.27,60900000.0
226 +224,20191128,281.23,281.23,282.8,281.05,61620000.0
227 +225,20191129,276.78,282.08,282.1,276.34,74600000.0
228 +226,20191202,277.44,278.27,279.44,276.97,66610000.0
229 +227,20191203,276.19,274.93,276.63,273.88,67280000.0
230 +228,20191204,274.15,274.36,274.68,272.53,81920000.0
231 +229,20191205,273.3,275.65,276.63,272.53,66510000.00000001
232 +230,20191206,276.56,275.19,277.0,274.75,70110000.0
233 +231,20191209,278.03,278.31,278.75,277.02,70520000.0
234 +232,20191210,279.31,276.8,279.75,276.62,56670000.0
235 +233,20191211,280.28,279.19,281.03,278.45,66849999.99999999
236 +234,20191212,285.11,282.62,285.43,282.62,133650000.0
237 +235,20191213,290.11,289.34,290.11,288.32,99620000.0
238 +236,20191216,289.76,289.54,290.8,289.36,73740000.0
239 +237,20191217,294.4,291.76,294.5,290.52,94370000.0
240 +238,20191218,294.22,294.31,295.85,293.05,76910000.0
241 +239,20191219,294.31,295.06,295.84,292.82,70190000.0
242 +240,20191220,295.31,295.58,295.86,293.95,75970000.0
243 +241,20191223,294.87,295.84,296.12,293.81,62090000.0
244 +242,20191224,292.9,295.18,295.25,292.47,60610000.0
245 +243,20191226,294.06,293.12,294.07,291.79,68480000.0
246 +244,20191227,295.2,292.37,296.95,291.28,77510000.0
247 +245,20191230,293.77,294.6,295.49,293.4,54970000.0
This diff could not be displayed because it is too large.
1 +,date,close,open,high,low,vol
2 +0,20200102,290.35,294.19,294.57,289.96,66160000.0
3 +1,20200103,290.74,293.1,294.88,289.04,79770000.0
4 +2,20200106,288.43,287.73,289.41,287.49,69960000.0
5 +3,20200107,291.23,290.01,292.26,289.64,65349999.99999999
6 +4,20200108,289.42,288.94,291.01,287.3,124530000.0
7 +5,20200109,294.41,293.83,294.41,292.1,111790000.0
8 +6,20200110,297.06,294.8,297.17,294.17,94160000.0
9 +7,20200113,300.13,296.62,300.31,296.53,81680000.0
10 +8,20200114,301.53,302.2,303.61,300.69,92120000.0
11 +9,20200115,299.74,299.91,301.13,298.82,71770000.0
12 +10,20200116,302.78,299.93,302.91,298.95,85710000.0
13 +11,20200117,303.3,305.07,306.05,302.51,81540000.0
14 +12,20200120,305.58,304.99,307.54,304.77,78040000.0
15 +13,20200121,302.11,304.94,306.09,301.85,67630000.0
16 +14,20200122,306.08,301.79,306.52,301.16,79330000.0
17 +15,20200123,302.33,303.77,304.72,301.71,86910000.0
18 +16,20200128,292.77,294.98,296.3,291.3,130169999.99999999
19 +17,20200129,293.98,294.38,295.67,292.45,85730000.0
20 +18,20200130,288.37,293.27,294.11,287.09,101540000.0
21 +19,20200131,284.53,290.24,291.47,284.53,101460000.0
22 +20,20200203,285.05,280.17,286.24,279.78,111060000.0
23 +21,20200204,290.68,285.19,291.38,285.11,105450000.0
24 +22,20200205,292.02,293.57,294.26,290.3,102640000.0
25 +23,20200206,300.65,294.74,300.98,294.32,110220000.0
26 +24,20200207,298.21,299.59,300.05,296.01,108110000.0
27 +25,20200210,296.24,294.07,296.73,293.33,72220000.0
28 +26,20200211,299.28,297.91,301.1,297.48,78350000.0
29 +27,20200212,301.54,299.83,302.2,298.09,110630000.0
30 +28,20200213,300.93,302.79,304.43,300.48,115070000.0
31 +29,20200214,303.01,300.95,304.2,299.18,80530000.0
32 +30,20200217,302.76,302.84,304.05,300.99,84280000.0
33 +31,20200218,297.74,300.26,301.29,297.2,84750000.0
34 +32,20200219,298.33,299.68,300.38,295.53,84260000.0
35 +33,20200220,296.65,300.34,301.35,295.01,90010000.0
36 +34,20200221,292.42,292.16,295.24,291.96,100710000.0
37 +35,20200224,281.02,285.51,286.62,281.02,132820000.0
38 +36,20200225,284.24,280.48,284.63,279.77,117030000.0
39 +37,20200226,279.94,278.45,281.98,277.68,119550000.0
40 +38,20200227,277.09,279.46,281.09,276.07,110500000.0
41 +39,20200228,268.02,272.33,273.98,267.23,202610000.0
42 +40,20200302,270.37,269.59,272.6,265.7,163720000.0
43 +41,20200303,271.56,277.44,277.54,270.79,146720000.0
44 +42,20200304,278.13,270.13,278.9,270.13,208460000.0
45 +43,20200305,281.38,280.66,282.05,277.91,147440000.0
46 +44,20200306,275.1,276.74,278.31,274.0,140900000.0
47 +45,20200309,263.11,266.39,267.75,261.76,182670000.0
48 +46,20200310,264.67,261.94,265.57,260.75,173090000.0
49 +47,20200311,257.01,264.63,265.04,255.69,194210000.0
50 +48,20200312,247.62,253.93,255.49,243.76,242470000.0
51 +49,20200313,240.65,231.71,246.19,227.36,298900000.0
52 +50,20200316,232.97,245.21,245.21,232.76,175050000.0
53 +51,20200317,226.89,222.94,234.56,222.46,216730000.0
54 +52,20200318,215.83,228.63,230.04,215.81,208040000.0
55 +53,20200319,199.28,220.72,220.85,196.27,303560000.0
56 +54,20200320,213.67,204.84,214.08,200.54,269350000.0
57 +55,20200323,201.87,200.8,206.66,198.67,210300000.0
58 +56,20200324,220.34,207.65,220.34,205.41,225730000.0
59 +57,20200325,232.89,228.37,233.42,224.74,306110000.0
60 +58,20200326,229.34,231.91,236.61,229.22,290840000.0
61 +59,20200327,233.79,239.11,239.48,227.01,249100000.0
62 +60,20200330,232.45,227.05,234.7,225.09,167230000.0
63 +61,20200331,236.82,235.2,237.47,232.5,240440000.0
64 +62,20200401,226.35,234.23,237.52,226.34,228580000.0
65 +63,20200402,231.84,227.47,232.22,223.6,181790000.0
66 +64,20200403,231.7,232.48,234.45,229.23,181040000.0
67 +65,20200406,240.81,234.4,241.19,233.75,190740000.0
68 +66,20200407,244.87,245.65,247.39,240.68,224450000.0
69 +67,20200408,241.89,243.71,246.99,241.77,173120000.0
70 +68,20200409,245.61,246.13,246.46,243.11,175940000.0
71 +69,20200410,248.0,245.14,248.21,244.02,286110000.0
72 +70,20200413,243.4,246.59,246.9,243.39,195900000.0
73 +71,20200414,247.45,246.2,248.55,244.58,193120000.0
74 +72,20200416,247.1,244.99,247.87,243.61,197540000.0
75 +73,20200417,255.02,252.52,257.37,252.38,287360000.0
76 +74,20200420,252.14,253.85,255.93,251.46,211000000.0
77 +75,20200421,249.4,250.26,251.88,245.11,257160000.00000003
78 +76,20200422,251.88,246.16,252.18,244.56,184500000.0
79 +77,20200423,253.74,253.42,255.22,251.58,163810000.0
80 +78,20200424,250.28,252.47,252.86,249.19,154310000.0
81 +79,20200427,254.84,251.18,255.79,250.61,151360000.0
82 +80,20200428,256.39,255.91,257.34,253.22,172420000.0
83 +81,20200429,258.15,256.58,259.67,256.04,184480000.0
84 +82,20200504,250.6,252.44,253.7,250.34,174040000.0
85 +83,20200506,255.0,253.76,255.01,251.26,133280000.0
86 +84,20200507,254.46,253.88,256.14,253.37,114360000.0
87 +85,20200508,256.62,256.66,258.29,256.23,125220000.0
88 +86,20200511,254.95,257.67,258.87,254.59,144300000.0
89 +87,20200512,253.37,255.33,255.56,250.98,166800000.0
90 +88,20200513,255.85,250.03,255.85,249.95,141250000.0
91 +89,20200514,253.65,253.26,254.39,252.09,167640000.0
92 +90,20200515,253.79,255.34,255.49,252.02,228900000.0
93 +91,20200518,255.44,254.55,256.31,252.95,223000000.0
94 +92,20200519,261.79,261.12,262.34,259.96,253330000.0
95 +93,20200520,262.72,260.48,263.25,260.48,140300000.0
96 +94,20200521,263.74,264.52,264.85,263.07,159480000.0
97 +95,20200522,259.62,263.82,264.07,258.34,166030000.0
98 +96,20200525,262.76,260.95,262.82,259.16,125430000.0
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