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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터 수집\n",
"데이터 : 코스피200 지수 종가/오픈/고가/저가/거래량 <br>\n",
"수집 출처 : investing.com\n",
"\n",
"|<center>**Data Set**</center>|<center>**Period**</center>|<center>**Number of Data**</center>|\n",
"|------|---|:---|\n",
"|Training Data|2005/01/03 ~ 2018/12/28|3464|\n",
"|Test Data|2019/01/02 ~ 2019/12/30|246|\n",
"|Validation Data|2020/01/02 ~ 2020/09/29|188|"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'date'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\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",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'date'"
]
}
],
"source": [
"# module\n",
"\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import date"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')\n",
"test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_data.csv')\n",
"validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in tqdm(range(len(train_data))):\n",
" train_data['date'][i] = train_data['date'][i].replace(' ','-') "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i in range(len(train_data)):\n",
" train_data['date'][i] = datetime.datetime.strptime(train_data['date'][i],\"%Y-%m-%d\").date()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"validation_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터 수집\n",
"데이터 : 코스피200 지수 종가/오픈/고가/저가/거래량 <br>\n",
"수집 출처 : investing.com\n",
"\n",
"|<center>**Data Set**</center>|<center>**Period**</center>|<center>**Number of Data**</center>|\n",
"|------|---|:---|\n",
"|Training Data|2005/01/03 ~ 2018/12/28|3464|\n",
"|Test Data|2019/01/02 ~ 2019/12/30|246|\n",
"|Validation Data|2020/01/02 ~ 2020/09/29|188|"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# module\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_data.csv')\n",
"test_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_data.csv')\n",
"validation_data = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 3464/3464 [10:11<00:00, 5.89it/s]\n"
]
},
{
"data": {
"text/plain": [
"0 2018-12-28\n",
"1 2018-12-27\n",
"2 2018-12-26\n",
"3 2018-12-24\n",
"4 2018-12-21\n",
"5 2018-12-20\n",
"6 2018-12-19\n",
"7 2018-12-18\n",
"8 2018-12-17\n",
"9 2018-12-14\n",
"10 2018-12-13\n",
"11 2018-12-12\n",
"12 2018-12-11\n",
"13 2018-12-10\n",
"14 2018-12-07\n",
"15 2018-12-06\n",
"16 2018-12-05\n",
"17 2018-12-04\n",
"18 2018-12-03\n",
"19 2018-11-30\n",
"20 2018-11-29\n",
"21 2018-11-28\n",
"22 2018-11-27\n",
"23 2018-11-26\n",
"24 2018-11-23\n",
"25 2018-11-22\n",
"26 2018-11-21\n",
"27 2018-11-20\n",
"28 2018-11-19\n",
"29 2018-11-16\n",
" ... \n",
"3434 2005-02-16\n",
"3435 2005-02-15\n",
"3436 2005-02-14\n",
"3437 2005-02-11\n",
"3438 2005-02-07\n",
"3439 2005-02-04\n",
"3440 2005-02-03\n",
"3441 2005-02-02\n",
"3442 2005-02-01\n",
"3443 2005-01-31\n",
"3444 2005-01-28\n",
"3445 2005-01-27\n",
"3446 2005-01-26\n",
"3447 2005-01-25\n",
"3448 2005-01-24\n",
"3449 2005-01-21\n",
"3450 2005-01-20\n",
"3451 2005-01-19\n",
"3452 2005-01-18\n",
"3453 2005-01-17\n",
"3454 2005-01-14\n",
"3455 2005-01-13\n",
"3456 2005-01-12\n",
"3457 2005-01-11\n",
"3458 2005-01-10\n",
"3459 2005-01-07\n",
"3460 2005-01-06\n",
"3461 2005-01-05\n",
"3462 2005-01-04\n",
"3463 2005-01-03\n",
"Name: date, Length: 3464, dtype: datetime64[ns]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in tqdm(range(len(train_data))):\n",
" train_data['date'][i] = train_data['date'][i].replace(' ','') \n",
"pd.to_datetime(train_data['date'], format='%Y%m%d')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20181228</td>\n",
" <td>261.98</td>\n",
" <td>261.61</td>\n",
" <td>262.85</td>\n",
" <td>261.27</td>\n",
" <td>62.70M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20181227</td>\n",
" <td>260.57</td>\n",
" <td>261.53</td>\n",
" <td>261.71</td>\n",
" <td>259.73</td>\n",
" <td>84.46M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20181226</td>\n",
" <td>260.98</td>\n",
" <td>261.09</td>\n",
" <td>262.49</td>\n",
" <td>259.35</td>\n",
" <td>92.49M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20181228 261.98 261.61 262.85 261.27 62.70M\n",
"1 20181227 260.57 261.53 261.71 259.73 84.46M\n",
"2 20181226 260.98 261.09 262.49 259.35 92.49M"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 246/246 [00:14<00:00, 18.37it/s]\n"
]
},
{
"data": {
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"\n",
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" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20191230</td>\n",
" <td>293.77</td>\n",
" <td>294.60</td>\n",
" <td>295.49</td>\n",
" <td>293.40</td>\n",
" <td>54.97M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20191227</td>\n",
" <td>295.20</td>\n",
" <td>292.37</td>\n",
" <td>296.95</td>\n",
" <td>291.28</td>\n",
" <td>77.51M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20191226</td>\n",
" <td>294.06</td>\n",
" <td>293.12</td>\n",
" <td>294.07</td>\n",
" <td>291.79</td>\n",
" <td>68.48M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20191230 293.77 294.60 295.49 293.40 54.97M\n",
"1 20191227 295.20 292.37 296.95 291.28 77.51M\n",
"2 20191226 294.06 293.12 294.07 291.79 68.48M"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in tqdm(range(len(test_data))):\n",
" test_data['date'][i] = test_data['date'][i].replace(' ','') \n",
"pd.to_datetime(test_data['date'], format='%Y%m%d')\n",
"\n",
"\n",
"test_data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 187/187 [00:11<00:00, 15.68it/s]\n"
]
},
{
"data": {
"text/html": [
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"\n",
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" 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>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20200929</td>\n",
" <td>309.44</td>\n",
" <td>310.01</td>\n",
" <td>311.34</td>\n",
" <td>308.52</td>\n",
" <td>138.82M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200928</td>\n",
" <td>307.03</td>\n",
" <td>306.49</td>\n",
" <td>308.34</td>\n",
" <td>304.64</td>\n",
" <td>173.18M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20200925</td>\n",
" <td>303.57</td>\n",
" <td>305.38</td>\n",
" <td>305.55</td>\n",
" <td>301.93</td>\n",
" <td>156.86M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20200929 309.44 310.01 311.34 308.52 138.82M\n",
"1 20200928 307.03 306.49 308.34 304.64 173.18M\n",
"2 20200925 303.57 305.38 305.55 301.93 156.86M"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in tqdm(range(len(validation_data))):\n",
" validation_data['date'][i] = validation_data['date'][i].replace(' ','') \n",
"pd.to_datetime(validation_data['date'], format='%Y%m%d')\n",
"\n",
"validation_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 데이터프레임 행 역순으로 변경"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### train_data"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.90</td>\n",
" <td>114.99</td>\n",
" <td>101.24M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20050104</td>\n",
" <td>114.77</td>\n",
" <td>115.00</td>\n",
" <td>115.80</td>\n",
" <td>114.50</td>\n",
" <td>119.97M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20050105</td>\n",
" <td>114.65</td>\n",
" <td>113.41</td>\n",
" <td>114.65</td>\n",
" <td>113.17</td>\n",
" <td>142.31M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20050106</td>\n",
" <td>112.94</td>\n",
" <td>113.88</td>\n",
" <td>114.97</td>\n",
" <td>112.94</td>\n",
" <td>147.88M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20050107</td>\n",
" <td>112.71</td>\n",
" <td>113.61</td>\n",
" <td>113.94</td>\n",
" <td>112.18</td>\n",
" <td>117.39M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20050110</td>\n",
" <td>113.27</td>\n",
" <td>113.01</td>\n",
" <td>113.36</td>\n",
" <td>112.07</td>\n",
" <td>98.12M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20050111</td>\n",
" <td>114.66</td>\n",
" <td>113.33</td>\n",
" <td>114.66</td>\n",
" <td>112.73</td>\n",
" <td>133.20M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20050112</td>\n",
" <td>114.09</td>\n",
" <td>114.71</td>\n",
" <td>114.94</td>\n",
" <td>113.79</td>\n",
" <td>124.80M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20050113</td>\n",
" <td>115.01</td>\n",
" <td>114.26</td>\n",
" <td>115.08</td>\n",
" <td>113.69</td>\n",
" <td>106.38M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20050114</td>\n",
" <td>117.82</td>\n",
" <td>114.23</td>\n",
" <td>117.82</td>\n",
" <td>113.91</td>\n",
" <td>151.41M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20050117</td>\n",
" <td>119.91</td>\n",
" <td>118.96</td>\n",
" <td>120.28</td>\n",
" <td>118.70</td>\n",
" <td>189.66M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20050118</td>\n",
" <td>119.50</td>\n",
" <td>119.91</td>\n",
" <td>120.42</td>\n",
" <td>119.49</td>\n",
" <td>172.12M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20050119</td>\n",
" <td>118.90</td>\n",
" <td>119.85</td>\n",
" <td>120.40</td>\n",
" <td>118.66</td>\n",
" <td>177.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20050120</td>\n",
" <td>118.02</td>\n",
" <td>118.05</td>\n",
" <td>118.74</td>\n",
" <td>117.72</td>\n",
" <td>165.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20050121</td>\n",
" <td>119.40</td>\n",
" <td>118.14</td>\n",
" <td>119.41</td>\n",
" <td>117.62</td>\n",
" <td>153.51M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>20050124</td>\n",
" <td>119.83</td>\n",
" <td>119.19</td>\n",
" <td>120.66</td>\n",
" <td>118.34</td>\n",
" <td>215.32M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20050125</td>\n",
" <td>118.91</td>\n",
" <td>118.92</td>\n",
" <td>119.25</td>\n",
" <td>118.35</td>\n",
" <td>190.17M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20050126</td>\n",
" <td>120.51</td>\n",
" <td>119.36</td>\n",
" <td>120.95</td>\n",
" <td>119.16</td>\n",
" <td>194.21M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20050127</td>\n",
" <td>120.16</td>\n",
" <td>120.65</td>\n",
" <td>121.00</td>\n",
" <td>119.61</td>\n",
" <td>184.68M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20050128</td>\n",
" <td>119.65</td>\n",
" <td>120.14</td>\n",
" <td>120.25</td>\n",
" <td>118.98</td>\n",
" <td>156.89M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20050131</td>\n",
" <td>121.06</td>\n",
" <td>119.45</td>\n",
" <td>121.21</td>\n",
" <td>119.45</td>\n",
" <td>141.84M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20050201</td>\n",
" <td>120.00</td>\n",
" <td>120.74</td>\n",
" <td>120.97</td>\n",
" <td>119.91</td>\n",
" <td>132.38M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20050202</td>\n",
" <td>119.74</td>\n",
" <td>120.30</td>\n",
" <td>120.37</td>\n",
" <td>119.41</td>\n",
" <td>154.26M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>20050203</td>\n",
" <td>120.64</td>\n",
" <td>119.88</td>\n",
" <td>120.80</td>\n",
" <td>119.71</td>\n",
" <td>166.09M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20050204</td>\n",
" <td>121.30</td>\n",
" <td>120.50</td>\n",
" <td>121.65</td>\n",
" <td>120.18</td>\n",
" <td>153.74M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20050207</td>\n",
" <td>123.20</td>\n",
" <td>122.41</td>\n",
" <td>123.27</td>\n",
" <td>122.25</td>\n",
" <td>142.40M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20050211</td>\n",
" <td>122.84</td>\n",
" <td>122.60</td>\n",
" <td>122.99</td>\n",
" <td>121.39</td>\n",
" <td>140.35M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20050214</td>\n",
" <td>124.94</td>\n",
" <td>124.06</td>\n",
" <td>125.44</td>\n",
" <td>123.65</td>\n",
" <td>235.22M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20050215</td>\n",
" <td>125.48</td>\n",
" <td>124.99</td>\n",
" <td>125.49</td>\n",
" <td>124.37</td>\n",
" <td>236.83M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20050216</td>\n",
" <td>125.85</td>\n",
" <td>126.08</td>\n",
" <td>126.82</td>\n",
" <td>125.13</td>\n",
" <td>218.70M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3434</th>\n",
" <td>20181116</td>\n",
" <td>271.65</td>\n",
" <td>272.95</td>\n",
" <td>273.52</td>\n",
" <td>271.06</td>\n",
" <td>74.60M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3435</th>\n",
" <td>20181119</td>\n",
" <td>272.31</td>\n",
" <td>272.01</td>\n",
" <td>274.05</td>\n",
" <td>270.83</td>\n",
" <td>75.01M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3436</th>\n",
" <td>20181120</td>\n",
" <td>269.58</td>\n",
" <td>268.54</td>\n",
" <td>270.58</td>\n",
" <td>268.54</td>\n",
" <td>80.90M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3437</th>\n",
" <td>20181121</td>\n",
" <td>268.27</td>\n",
" <td>265.96</td>\n",
" <td>268.49</td>\n",
" <td>264.95</td>\n",
" <td>81.84M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3438</th>\n",
" <td>20181122</td>\n",
" <td>267.76</td>\n",
" <td>268.51</td>\n",
" <td>269.11</td>\n",
" <td>267.15</td>\n",
" <td>64.41M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3439</th>\n",
" <td>20181123</td>\n",
" <td>266.33</td>\n",
" <td>268.12</td>\n",
" <td>268.54</td>\n",
" <td>264.86</td>\n",
" <td>58.35M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3440</th>\n",
" <td>20181126</td>\n",
" <td>269.79</td>\n",
" <td>266.43</td>\n",
" <td>269.95</td>\n",
" <td>266.41</td>\n",
" <td>75.65M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3441</th>\n",
" <td>20181127</td>\n",
" <td>272.08</td>\n",
" <td>270.90</td>\n",
" <td>272.47</td>\n",
" <td>269.27</td>\n",
" <td>71.91M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3442</th>\n",
" <td>20181128</td>\n",
" <td>273.45</td>\n",
" <td>272.64</td>\n",
" <td>273.53</td>\n",
" <td>271.35</td>\n",
" <td>79.83M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3443</th>\n",
" <td>20181129</td>\n",
" <td>274.16</td>\n",
" <td>277.08</td>\n",
" <td>277.54</td>\n",
" <td>273.40</td>\n",
" <td>80.65M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3444</th>\n",
" <td>20181130</td>\n",
" <td>271.35</td>\n",
" <td>274.53</td>\n",
" <td>275.51</td>\n",
" <td>270.88</td>\n",
" <td>137.53M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3445</th>\n",
" <td>20181203</td>\n",
" <td>276.18</td>\n",
" <td>275.70</td>\n",
" <td>276.98</td>\n",
" <td>273.59</td>\n",
" <td>89.07M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3446</th>\n",
" <td>20181204</td>\n",
" <td>273.29</td>\n",
" <td>275.08</td>\n",
" <td>275.72</td>\n",
" <td>272.03</td>\n",
" <td>80.80M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3447</th>\n",
" <td>20181205</td>\n",
" <td>271.49</td>\n",
" <td>269.53</td>\n",
" <td>272.38</td>\n",
" <td>269.47</td>\n",
" <td>75.64M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3448</th>\n",
" <td>20181206</td>\n",
" <td>267.18</td>\n",
" <td>270.18</td>\n",
" <td>270.18</td>\n",
" <td>266.61</td>\n",
" <td>86.09M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3449</th>\n",
" <td>20181207</td>\n",
" <td>268.28</td>\n",
" <td>268.97</td>\n",
" <td>269.66</td>\n",
" <td>267.03</td>\n",
" <td>68.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3450</th>\n",
" <td>20181210</td>\n",
" <td>265.35</td>\n",
" <td>264.55</td>\n",
" <td>266.20</td>\n",
" <td>263.63</td>\n",
" <td>77.30M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3451</th>\n",
" <td>20181211</td>\n",
" <td>264.69</td>\n",
" <td>265.87</td>\n",
" <td>265.99</td>\n",
" <td>263.87</td>\n",
" <td>70.04M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3452</th>\n",
" <td>20181212</td>\n",
" <td>268.47</td>\n",
" <td>266.00</td>\n",
" <td>268.84</td>\n",
" <td>265.55</td>\n",
" <td>83.69M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3453</th>\n",
" <td>20181213</td>\n",
" <td>269.41</td>\n",
" <td>269.07</td>\n",
" <td>271.06</td>\n",
" <td>268.22</td>\n",
" <td>145.06M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3454</th>\n",
" <td>20181214</td>\n",
" <td>265.55</td>\n",
" <td>269.69</td>\n",
" <td>269.69</td>\n",
" <td>264.44</td>\n",
" <td>99.73M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3455</th>\n",
" <td>20181217</td>\n",
" <td>266.33</td>\n",
" <td>265.81</td>\n",
" <td>267.19</td>\n",
" <td>265.41</td>\n",
" <td>71.45M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3456</th>\n",
" <td>20181218</td>\n",
" <td>265.18</td>\n",
" <td>264.42</td>\n",
" <td>266.83</td>\n",
" <td>263.93</td>\n",
" <td>90.33M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3457</th>\n",
" <td>20181219</td>\n",
" <td>267.31</td>\n",
" <td>266.02</td>\n",
" <td>267.55</td>\n",
" <td>265.26</td>\n",
" <td>81.75M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3458</th>\n",
" <td>20181220</td>\n",
" <td>264.81</td>\n",
" <td>265.13</td>\n",
" <td>266.34</td>\n",
" <td>263.12</td>\n",
" <td>81.34M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3459</th>\n",
" <td>20181221</td>\n",
" <td>265.06</td>\n",
" <td>263.66</td>\n",
" <td>265.06</td>\n",
" <td>263.15</td>\n",
" <td>92.60M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3460</th>\n",
" <td>20181224</td>\n",
" <td>264.45</td>\n",
" <td>263.51</td>\n",
" <td>265.09</td>\n",
" <td>262.96</td>\n",
" <td>61.96M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3461</th>\n",
" <td>20181226</td>\n",
" <td>260.98</td>\n",
" <td>261.09</td>\n",
" <td>262.49</td>\n",
" <td>259.35</td>\n",
" <td>92.49M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3462</th>\n",
" <td>20181227</td>\n",
" <td>260.57</td>\n",
" <td>261.53</td>\n",
" <td>261.71</td>\n",
" <td>259.73</td>\n",
" <td>84.46M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3463</th>\n",
" <td>20181228</td>\n",
" <td>261.98</td>\n",
" <td>261.61</td>\n",
" <td>262.85</td>\n",
" <td>261.27</td>\n",
" <td>62.70M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3464 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
"1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
"2 20050105 114.65 113.41 114.65 113.17 142.31M\n",
"3 20050106 112.94 113.88 114.97 112.94 147.88M\n",
"4 20050107 112.71 113.61 113.94 112.18 117.39M\n",
"5 20050110 113.27 113.01 113.36 112.07 98.12M\n",
"6 20050111 114.66 113.33 114.66 112.73 133.20M\n",
"7 20050112 114.09 114.71 114.94 113.79 124.80M\n",
"8 20050113 115.01 114.26 115.08 113.69 106.38M\n",
"9 20050114 117.82 114.23 117.82 113.91 151.41M\n",
"10 20050117 119.91 118.96 120.28 118.70 189.66M\n",
"11 20050118 119.50 119.91 120.42 119.49 172.12M\n",
"12 20050119 118.90 119.85 120.40 118.66 177.45M\n",
"13 20050120 118.02 118.05 118.74 117.72 165.45M\n",
"14 20050121 119.40 118.14 119.41 117.62 153.51M\n",
"15 20050124 119.83 119.19 120.66 118.34 215.32M\n",
"16 20050125 118.91 118.92 119.25 118.35 190.17M\n",
"17 20050126 120.51 119.36 120.95 119.16 194.21M\n",
"18 20050127 120.16 120.65 121.00 119.61 184.68M\n",
"19 20050128 119.65 120.14 120.25 118.98 156.89M\n",
"20 20050131 121.06 119.45 121.21 119.45 141.84M\n",
"21 20050201 120.00 120.74 120.97 119.91 132.38M\n",
"22 20050202 119.74 120.30 120.37 119.41 154.26M\n",
"23 20050203 120.64 119.88 120.80 119.71 166.09M\n",
"24 20050204 121.30 120.50 121.65 120.18 153.74M\n",
"25 20050207 123.20 122.41 123.27 122.25 142.40M\n",
"26 20050211 122.84 122.60 122.99 121.39 140.35M\n",
"27 20050214 124.94 124.06 125.44 123.65 235.22M\n",
"28 20050215 125.48 124.99 125.49 124.37 236.83M\n",
"29 20050216 125.85 126.08 126.82 125.13 218.70M\n",
"... ... ... ... ... ... ...\n",
"3434 20181116 271.65 272.95 273.52 271.06 74.60M\n",
"3435 20181119 272.31 272.01 274.05 270.83 75.01M\n",
"3436 20181120 269.58 268.54 270.58 268.54 80.90M\n",
"3437 20181121 268.27 265.96 268.49 264.95 81.84M\n",
"3438 20181122 267.76 268.51 269.11 267.15 64.41M\n",
"3439 20181123 266.33 268.12 268.54 264.86 58.35M\n",
"3440 20181126 269.79 266.43 269.95 266.41 75.65M\n",
"3441 20181127 272.08 270.90 272.47 269.27 71.91M\n",
"3442 20181128 273.45 272.64 273.53 271.35 79.83M\n",
"3443 20181129 274.16 277.08 277.54 273.40 80.65M\n",
"3444 20181130 271.35 274.53 275.51 270.88 137.53M\n",
"3445 20181203 276.18 275.70 276.98 273.59 89.07M\n",
"3446 20181204 273.29 275.08 275.72 272.03 80.80M\n",
"3447 20181205 271.49 269.53 272.38 269.47 75.64M\n",
"3448 20181206 267.18 270.18 270.18 266.61 86.09M\n",
"3449 20181207 268.28 268.97 269.66 267.03 68.45M\n",
"3450 20181210 265.35 264.55 266.20 263.63 77.30M\n",
"3451 20181211 264.69 265.87 265.99 263.87 70.04M\n",
"3452 20181212 268.47 266.00 268.84 265.55 83.69M\n",
"3453 20181213 269.41 269.07 271.06 268.22 145.06M\n",
"3454 20181214 265.55 269.69 269.69 264.44 99.73M\n",
"3455 20181217 266.33 265.81 267.19 265.41 71.45M\n",
"3456 20181218 265.18 264.42 266.83 263.93 90.33M\n",
"3457 20181219 267.31 266.02 267.55 265.26 81.75M\n",
"3458 20181220 264.81 265.13 266.34 263.12 81.34M\n",
"3459 20181221 265.06 263.66 265.06 263.15 92.60M\n",
"3460 20181224 264.45 263.51 265.09 262.96 61.96M\n",
"3461 20181226 260.98 261.09 262.49 259.35 92.49M\n",
"3462 20181227 260.57 261.53 261.71 259.73 84.46M\n",
"3463 20181228 261.98 261.61 262.85 261.27 62.70M\n",
"\n",
"[3464 rows x 6 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data = train_data.loc[::-1]\n",
"train_data = train_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20050103</td>\n",
" <td>115.36</td>\n",
" <td>115.65</td>\n",
" <td>115.90</td>\n",
" <td>114.99</td>\n",
" <td>101.24M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20050104</td>\n",
" <td>114.77</td>\n",
" <td>115.00</td>\n",
" <td>115.80</td>\n",
" <td>114.50</td>\n",
" <td>119.97M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20050105</td>\n",
" <td>114.65</td>\n",
" <td>113.41</td>\n",
" <td>114.65</td>\n",
" <td>113.17</td>\n",
" <td>142.31M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20050103 115.36 115.65 115.90 114.99 101.24M\n",
"1 20050104 114.77 115.00 115.80 114.50 119.97M\n",
"2 20050105 114.65 113.41 114.65 113.17 142.31M"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### test_data"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"test_data = test_data.loc[::-1]\n",
"test_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20190102</td>\n",
" <td>258.23</td>\n",
" <td>263.35</td>\n",
" <td>263.77</td>\n",
" <td>257.47</td>\n",
" <td>69.95M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20190103</td>\n",
" <td>255.77</td>\n",
" <td>258.19</td>\n",
" <td>258.70</td>\n",
" <td>255.35</td>\n",
" <td>90.52M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20190104</td>\n",
" <td>257.89</td>\n",
" <td>255.62</td>\n",
" <td>258.12</td>\n",
" <td>254.55</td>\n",
" <td>91.20M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20190102 258.23 263.35 263.77 257.47 69.95M\n",
"1 20190103 255.77 258.19 258.70 255.35 90.52M\n",
"2 20190104 257.89 255.62 258.12 254.55 91.20M"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### validation_data"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"validation_data = validation_data.loc[::-1]\n",
"validation_data = validation_data.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>date</th>\n",
" <th>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20200102</td>\n",
" <td>290.35</td>\n",
" <td>294.19</td>\n",
" <td>294.57</td>\n",
" <td>289.96</td>\n",
" <td>66.16M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20200103</td>\n",
" <td>290.74</td>\n",
" <td>293.10</td>\n",
" <td>294.88</td>\n",
" <td>289.04</td>\n",
" <td>79.77M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20200106</td>\n",
" <td>288.43</td>\n",
" <td>287.73</td>\n",
" <td>289.41</td>\n",
" <td>287.49</td>\n",
" <td>69.96M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date close open high low vol\n",
"0 20200102 290.35 294.19 294.57 289.96 66.16M\n",
"1 20200103 290.74 293.10 294.88 289.04 79.77M\n",
"2 20200106 288.43 287.73 289.41 287.49 69.96M"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"validation_data.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 거래량 단위 K , M -> 숫자로 변환"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### test_data"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"def vol_char_to_num(data):\n",
" for i in tqdm(range(len(data))):\n",
" unit = data['vol'][i][-1]\n",
" data['vol'][i] = data['vol'][i][:-1]\n",
" if unit == 'K':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000\n",
" elif unit == 'M':\n",
" data['vol'][i] = float(data['vol'][i]) * 1000000"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/3464 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
" 42%|████▏ | 1445/3464 [12:44<17:43, 1.90it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 3464/3464 [30:22<00:00, 1.85it/s]\n"
]
}
],
"source": [
"vol_char_to_num(train_data)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/246 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 246/246 [02:14<00:00, 1.95it/s]\n"
]
}
],
"source": [
"vol_char_to_num(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/187 [00:00<?, ?it/s]/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n",
"/Users/yangyoonji/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" \n",
"100%|██████████| 187/187 [01:37<00:00, 1.95it/s]\n"
]
}
],
"source": [
"vol_char_to_num(validation_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### csv파일로 옮기기 "
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"train_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"test_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'validation_data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\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",
"\u001b[0;31mNameError\u001b[0m: name 'validation_data' is not defined"
]
}
],
"source": [
"validation_data.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"validation_data = pd.read_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Data Scaling\n",
"- sklearn 패키지에 있는 MinMaxScaler활용"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"scaler = MinMaxScaler()\n",
"scale_cols = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"\n",
"##----train_data----##\n",
"train_data_scaled = scaler.fit_transform(train_data[scale_cols])\n",
"train_data_scaled = pd.DataFrame(train_data_scaled)\n",
"train_data_scaled.columns = ['close','open','high','low','vol']\n",
"\n",
"train_data_scaled.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_data_scaled = scaler.fit_transform(test_data[scale_cols])\n",
"test_data_scaled = pd.DataFrame(test_data_scaled)\n",
"test_data_scaled.columns = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 105,
"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>close</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>vol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.143847</td>\n",
" <td>0.277840</td>\n",
" <td>0.242639</td>\n",
" <td>0.165408</td>\n",
" <td>0.207573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.087047</td>\n",
" <td>0.163147</td>\n",
" <td>0.126912</td>\n",
" <td>0.116907</td>\n",
" <td>0.381423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.135996</td>\n",
" <td>0.106024</td>\n",
" <td>0.113673</td>\n",
" <td>0.098604</td>\n",
" <td>0.387170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" close open high low vol\n",
"0 0.143847 0.277840 0.242639 0.165408 0.207573\n",
"1 0.087047 0.163147 0.126912 0.116907 0.381423\n",
"2 0.135996 0.106024 0.113673 0.098604 0.387170"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data_scaled.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_data_scaled = scaler.fit_transform(validation_data[scale_cols])\n",
"val_data_scaled = pd.DataFrame(val_data_scaled)\n",
"val_data_scaled.columns = ['close','open','high','low','vol']"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"train_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train_scaled.csv\")\n",
"test_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test_scaled.csv\")\n",
"val_data_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation_scaled.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. 이동평균 데이터 \n",
"\n",
"- 단순이동평균\n",
"- 지수이동평균\n",
"\n",
"종가이동평균 : ma, 거래량이동평균 : vma\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/train.csv')\n",
"test = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/test.csv')\n",
"validation = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/validation.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (1) 단순이동평균 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 종가"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_SMA_5 = train['close'].rolling(5).mean()\n",
"train_SMA_10 = train['close'].rolling(10).mean()\n",
"train_SMA_20 = train['close'].rolling(20).mean()\n",
"train_SMA_60 = train['close'].rolling(60).mean()\n",
"train_SMA_120 = train['close'].rolling(120).mean()\n",
"\n",
"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",
"train_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_SMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_SMA_5 = test['close'].rolling(5).mean()\n",
"test_SMA_10 = test['close'].rolling(10).mean()\n",
"test_SMA_20 = test['close'].rolling(20).mean()\n",
"test_SMA_60 = test['close'].rolling(60).mean()\n",
"test_SMA_120 = test['close'].rolling(120).mean()\n",
"\n",
"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",
"test_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_SMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_SMA_5 = validation['close'].rolling(5).mean()\n",
"val_SMA_10 = validation['close'].rolling(10).mean()\n",
"val_SMA_20 = validation['close'].rolling(20).mean()\n",
"val_SMA_60 = validation['close'].rolling(60).mean()\n",
"val_SMA_120 = validation['close'].rolling(120).mean()\n",
"\n",
"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",
"val_SMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_SMA.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 거래량"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_SVMA_5 = train['vol'].rolling(5).mean()\n",
"train_SVMA_10 = train['vol'].rolling(10).mean()\n",
"train_SVMA_20 = train['vol'].rolling(20).mean()\n",
"train_SVMA_60 = train['vol'].rolling(60).mean()\n",
"train_SVMA_120 = train['vol'].rolling(120).mean()\n",
"\n",
"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",
"train_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_SVMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_SVMA_5 = test['vol'].rolling(5).mean()\n",
"test_SVMA_10 = test['vol'].rolling(10).mean()\n",
"test_SVMA_20 = test['vol'].rolling(20).mean()\n",
"test_SVMA_60 = test['vol'].rolling(60).mean()\n",
"test_SVMA_120 = test['vol'].rolling(120).mean()\n",
"\n",
"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",
"test_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_SVMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"\n",
"val_SVMA_5 = validation['vol'].rolling(5).mean()\n",
"val_SVMA_10 = validation['vol'].rolling(10).mean()\n",
"val_SVMA_20 = validation['vol'].rolling(20).mean()\n",
"val_SVMA_60 = validation['vol'].rolling(60).mean()\n",
"val_SVMA_120 = validation['vol'].rolling(120).mean()\n",
"\n",
"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",
"val_SVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_SVMA.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (2) 지수이동평균 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 종가"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_EMA_5 = train['close'].ewm(span=5).mean()\n",
"train_EMA_10 = train['close'].ewm(span=10).mean()\n",
"train_EMA_20 = train['close'].ewm(span=20).mean()\n",
"train_EMA_60 = train['close'].ewm(span=60).mean()\n",
"train_EMA_120 = train['close'].ewm(span=120).mean()\n",
"\n",
"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",
"train_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_EMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EMA_5 = test['close'].ewm(span=5).mean()\n",
"test_EMA_10 = test['close'].ewm(span=10).mean()\n",
"test_EMA_20 = test['close'].ewm(span=20).mean()\n",
"test_EMA_60 = test['close'].ewm(span=60).mean()\n",
"test_EMA_120 = test['close'].ewm(span=120).mean()\n",
"\n",
"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",
"test_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_EMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_EMA_5 = validation['close'].ewm(span=5).mean()\n",
"val_EMA_10 = validation['close'].ewm(span=10).mean()\n",
"val_EMA_20 = validation['close'].ewm(span=20).mean()\n",
"val_EMA_60 = validation['close'].ewm(span=60).mean()\n",
"val_EMA_120 = validation['close'].ewm(span=120).mean()\n",
"\n",
"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",
"val_EMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_EMA.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 거래량"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_EVMA_5 = train['vol'].ewm(span=5).mean()\n",
"train_EVMA_10 = train['vol'].ewm(span=10).mean()\n",
"train_EVMA_20 = train['vol'].ewm(span=20).mean()\n",
"train_EVMA_60 = train['vol'].ewm(span=60).mean()\n",
"train_EVMA_120 = train['vol'].ewm(span=120).mean()\n",
"\n",
"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",
"train_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/train_EVMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"##----test_data----##\n",
"test_EVMA_5 = test['vol'].ewm(span=5).mean()\n",
"test_EVMA_10 = test['vol'].ewm(span=10).mean()\n",
"test_EVMA_20 = test['vol'].ewm(span=20).mean()\n",
"test_EVMA_60 = test['vol'].ewm(span=60).mean()\n",
"test_EVMA_120 = test['vol'].ewm(span=120).mean()\n",
"\n",
"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",
"test_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/test_EVMA.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [],
"source": [
"##----val_data----##\n",
"val_EVMA_5 = validation['vol'].ewm(span=5).mean()\n",
"val_EVMA_10 = validation['vol'].ewm(span=10).mean()\n",
"val_EVMA_20 = validation['vol'].ewm(span=20).mean()\n",
"val_EVMA_60 = validation['vol'].ewm(span=60).mean()\n",
"val_EVMA_120 = validation['vol'].ewm(span=120).mean()\n",
"\n",
"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",
"val_EVMA.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/val_EVMA.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### (3) scaling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### scaling 단순이동평균 - 종가"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"scale_cols2 = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/train_SMA.csv')\n",
"\n",
"train_SMA_scaled = scaler.fit_transform(train_SMA[scale_cols2])\n",
"train_SMA_scaled = pd.DataFrame(train_SMA_scaled)\n",
"train_SMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----test_data----##\n",
"test_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SMA.csv')\n",
"\n",
"test_SMA_scaled = scaler.fit_transform(test_SMA[scale_cols2])\n",
"test_SMA_scaled = pd.DataFrame(test_SMA_scaled)\n",
"test_SMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_SMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SMA.csv')\n",
"\n",
"val_SMA_scaled = scaler.fit_transform(val_SMA[scale_cols2])\n",
"val_SMA_scaled = pd.DataFrame(val_SMA_scaled)\n",
"val_SMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### scaling 단순이동평균 - 거래량"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/train_SVMA.csv')\n",
"\n",
"train_SVMA_scaled = scaler.fit_transform(train_SVMA[scale_cols2])\n",
"train_SVMA_scaled = pd.DataFrame(train_SVMA_scaled)\n",
"train_SVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----test_data----##\n",
"test_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/test_SVMA.csv')\n",
"\n",
"test_SVMA_scaled = scaler.fit_transform(test_SVMA[scale_cols2])\n",
"test_SVMA_scaled = pd.DataFrame(test_SVMA_scaled)\n",
"test_SVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_SVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Simple/val_SVMA.csv')\n",
"\n",
"val_SVMA_scaled = scaler.fit_transform(val_SVMA[scale_cols2])\n",
"val_SVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
"val_SVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [],
"source": [
"train_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_SMA_scaled.csv\")\n",
"test_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SMA_scaled.csv\")\n",
"val_SMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SMA_scaled.csv\")\n",
"train_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_SVMA_scaled.csv\")\n",
"test_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_SVMA_scaled.csv\")\n",
"val_SVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_SVMA_scaled.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### scaling 지수이동평균 - 종가"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/train_EMA.csv')\n",
"\n",
"train_EMA_scaled = scaler.fit_transform(train_EMA[scale_cols2])\n",
"train_EMA_scaled = pd.DataFrame(train_EMA_scaled)\n",
"train_EMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----test_data----##\n",
"test_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EMA.csv')\n",
"\n",
"test_EMA_scaled = scaler.fit_transform(test_EMA[scale_cols2])\n",
"test_EMA_scaled = pd.DataFrame(test_EMA_scaled)\n",
"test_EMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EMA.csv')\n",
"\n",
"val_EMA_scaled = scaler.fit_transform(val_EMA[scale_cols2])\n",
"val_EMA_scaled = pd.DataFrame(val_EMA_scaled)\n",
"val_EMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### scaling 지수이동평균 - 거래량"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [],
"source": [
"##----train_data----##\n",
"train_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/train_EVMA.csv')\n",
"\n",
"train_EVMA_scaled = scaler.fit_transform(train_EVMA[scale_cols2])\n",
"train_EVMA_scaled = pd.DataFrame(train_EVMA_scaled)\n",
"train_EVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----test_data----##\n",
"test_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/test_EVMA.csv')\n",
"\n",
"test_EVMA_scaled = scaler.fit_transform(test_EVMA[scale_cols2])\n",
"test_EVMA_scaled = pd.DataFrame(test_EVMA_scaled)\n",
"test_EVMA_scaled.columns = ['5','10','20','60','120']\n",
"\n",
"##----val_data----##\n",
"val_EVMA = pd.read_csv('/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA/Exponential/val_EVMA.csv')\n",
"\n",
"val_EVMA_scaled = scaler.fit_transform(val_EVMA[scale_cols2])\n",
"val_EVMA_scaled = pd.DataFrame(val_SVMA_scaled)\n",
"val_EVMA_scaled.columns = ['5','10','20','60','120']"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"train_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_EMA_scaled.csv\")\n",
"test_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EMA_scaled.csv\")\n",
"val_EMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EMA_scaled.csv\")\n",
"train_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/train_EVMA_scaled.csv\")\n",
"test_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/test_EVMA_scaled.csv\")\n",
"val_EVMA_scaled.to_csv(\"/Users/yangyoonji/Documents/2020_2학기/캡스톤디자인/data/MA_scaled/val_EVMA_scaled.csv\")"
]
}
],
"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",
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"nbformat": 4,
"nbformat_minor": 2
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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142,78737099.509912,82448741.95329647,82098247.48981042,78795304.98198041,79022234.70121422
143,84571399.67327467,84956243.41633418,83445081.80395383,79372006.19150035,79335217.43915962
144,80574266.44884978,82706017.34063654,82410311.59273274,79147531.45723526,79212623.95558658
145,86936177.63256653,85788559.64233957,84053139.80072078,79825287.08928047,79583102.50183807
146,101637451.75504434,94016094.25282456,88528080.69260226,81517050.143154,80513951.47389805
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148,102789978.55779748,97757385.40891598,91723758.73149092,83151718.75248002,81454914.38862774
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150,121294434.91457665,111192629.90183724,100399632.53475803,86862953.04179546,83560964.96202242
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152,94933082.18425627,98723000.18222106,95481058.63884214,85973840.00260678,83190063.34799169
153,90702054.78950417,95726091.05818075,94220005.17986296,85850693.18308754,83173053.7105959
154,84411369.85966945,91381347.22942048,92087623.34360652,85388362.66124132,82970250.02717084
155,78280913.23977964,86770193.18770754,89604992.13757762,84749813.47548708,82667603.30299222
156,79417275.49318643,85846521.69903342,88851183.2495523,84648953.82510895,82650171.31157126
157,74771516.99545762,82143517.75375456,86625355.97147058,84017204.80496584,82344402.13639772
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159,66436229.775758944,74952768.08309181,81922932.59733455,82519661.41902205,81591081.73519641
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161,74567213.23367065,76467059.62587139,81349928.8994743,82243116.38072407,81469197.9859328
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166,67653213.26533934,70568343.96463281,75814416.9578957,79942915.02498235,80263045.79586883
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170,67282980.39809172,69472339.64564958,73654107.27451344,78660881.99805602,79547879.03351581
171,66968653.598727815,68902823.34644057,72957525.6060476,78255607.77256688,79316399.38796788
172,79279102.39915188,75265946.37436047,75904428.01836537,79099046.2173207,79746819.4080831
173,87299401.59943458,80370319.7608404,78517339.70712902,79896243.8506837,80159494.52466825
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175,77719734.04419315,77062114.88122374,77065940.18127665,79296163.40811451,79830012.43674116
176,75313156.0294621,75869003.0846376,76440612.5322974,79006972.81892894,79667280.60030253
177,74885437.35297473,75534638.88743076,76211030.38215661,78843360.10639042,79569046.17753774
178,69986958.23531649,72744704.54426153,74685217.93950711,78230204.01929432,79231652.38629629
179,69414638.82354432,71931121.89985034,74074244.79324482,77902828.92212635,79040975.46360417
180,69889759.21569622,71732736.09987755,73766221.47542134,77670704.55598532,78898443.6888021
181,76456506.14379747,74979511.35444526,75273248.0200799,78062408.93711244,79084103.41720256
182,70217670.76253165,71845054.74454613,73603414.8567558,77394603.49398103,78713771.01334529
183,65041780.50835443,68725953.88190137,71802137.23324154,76648572.79106846,78297287.04639027
184,60904520.33890295,65799416.81246476,69976219.38489585,75859423.73945571,77852669.53200829
185,62673013.55926863,65874068.301107526,69617531.82147771,75542406.1947985,77651151.12106878
186,62742009.03951242,65329692.246360704,68975862.11941633,75126429.28692363,77395682.23130259
187,59684672.69300828,63191566.38338602,67508637.14577807,74418317.5103994,76983929.58772148
188,64816448.46200552,65353099.76822493,68229719.32676573,74440051.90595104,76951050.85634708
189,75937632.30800368,71321627.08309312,71082127.02566093,75219794.952211,77317379.96256821
190,70505088.20533578,69197694.8861671,69992400.63682753,74708103.49864133,77012558.79047488
191,70686725.47022386,69534477.6341367,70093124.3861558,74587966.18225698,76909816.4038255
192,64807816.98014924,66537299.88247548,68469969.67608288,73880667.34430723,76498970.31074221
193,64998544.65343282,66326881.722025394,68175686.84870179,73601523.29827802,76307642.99825618
194,61289029.76895521,64061994.136202596,66813240.47760354,72953614.60302457,75921813.76880768
195,64682686.5126368,65408904.29325667,67256741.38584003,72904900.70679134,75845313.06440106
196,58251791.00842454,61769103.51266454,65174194.581488885,72001502.97831102,75322306.62018277
197,60127860.67228303,62152902.87399826,65050937.95437521,71734861.68347964,75125935.34374784
198,61848573.78152203,62723284.169634946,65073705.76829524,71523276.51661749,74957239.16176414
199,65792382.52101469,64715414.32061041,65893352.83964287,71594078.8117556,74935346.84454319
200,63288255.014009796,63545338.98959034,65168271.61548995,71157013.98300382,74650042.92795908
201,61882170.00933987,62731640.991483,64587483.84162244,70760246.36225016,74383319.11307517
202,60304780.00622658,61716797.17484973,63879152.046118796,70313494.65236376,74088466.05847073
203,64379853.33748438,63682834.05214978,64703042.32855992,70386247.93863398,74061816.99243067
204,69033235.55832292,66347773.31539528,66001800.203626506,70647307.74644876,74134930.27038765
205,65535490.37221528,64928178.16714159,65291152.56439571,70249933.05759455,73868565.47842251
206,69356993.58147685,67123054.86402494,66406280.89271794,70471469.84676795,73922021.58945155
207,71691329.0543179,68802499.43420222,67354254.14189337,70664724.66267131,73963617.30588178
208,73777552.70287861,70465681.35525636,68363372.7958774,70903811.41243176,74031595.0085497
209,72758368.46858574,70511921.10884611,68587813.48215154,70897779.31174627,73975153.65795209
210,73065578.97905716,71087935.45269227,69072783.62703547,70989080.13588032,73970125.7779377
211,69230385.9860381,69355583.55220276,68357280.42402425,70679666.18781315,73758828.8024666
212,68010257.3240254,68667295.63362044,68091825.14539927,70511997.89586888,73619473.66305795
213,65383504.882683605,67115060.06387126,67333556.08355409,70171332.83239487,73390025.82126093
214,70175669.92178907,69414140.05225831,68517026.93327403,70485958.05786388,73498323.22058006
215,70667113.2811927,69820660.0427568,68815405.32070322,70524151.86338848,73466914.28605373
216,65814742.1874618,67327812.76225556,67605366.71828404,70051215.4369818,73172101.5668956
217,66599828.12497453,67480937.71457273,67659141.3165607,69989493.16225381,73087178.03658803
218,68916552.08331634,68584403.58465041,68220175.47705808,70106309.95230044,73095032.10525917
219,73194368.05554423,70978148.38744125,69508730.19388165,70488319.71439438,73241841.78789687
220,73579578.70369615,71591212.3169974,69969803.50886431,70615012.19417539,73260630.74869086
221,70556385.8024641,70303719.16845241,69449822.22218923,70414725.51802589,73112325.91958986
222,86877590.53497607,79252133.86509743,74218410.58294798,72025686.28379843,73898509.22269078
223,78218393.68998404,75915382.25326154,72949990.52719635,71660701.11868203,73678394.68816516
224,72685595.7933227,73316221.84357762,71870943.81014134,71331315.75190687,73474282.53829132
225,73323730.5288818,73549636.05383624,72130853.92350025,71438543.04871851,73493329.94822115
226,71085820.35258786,72287884.04404783,71605058.31166686,71280148.26355879,73376907.90927947
227,69817213.5683919,71377359.67240277,71193147.99621937,71148930.23942904,73273826.77013199
228,73851475.71226127,73294203.36832954,72214752.94907404,71502250.96981093,73419953.37398939
229,71404317.14150752,72060711.84681508,71671443.14434563,71338493.99750549,73303213.54992093
230,70972878.09433834,71706036.96557596,71522734.27344203,71298197.27133828,73249285.69234169
231,70821918.7295589,71490393.88092579,71427235.7712016,71272671.43485771,73203209.33175845
232,66104612.4863726,68795776.81166655,70021784.74526809,70793692.56166492,72924190.87385134
233,66353074.99091506,68441999.20954536,69719710.00760312,70664338.20346631,72821716.94373758
234,88785383.3272767,80297999.35326439,75808309.05486967,72730260.00470783,73847563.12938038
235,92396922.21818447,83811090.3799436,78076089.14500731,73612228.64536986,74282059.7533357
236,86177948.14545631,81979983.03813566,77663128.2740336,73616419.42267917,74272924.20175363
237,88908632.09697087,84232713.39483827,79254258.91467376,74297108.59597898,74611518.45709977
238,84909088.06464724,82901310.95941313,79030996.16088617,74382806.86768514,74650230.77835631
239,80002725.3764315,80590163.5122471,78188996.5264849,74245291.69301854,74575132.61203586
240,78658483.58428766,79750133.78274763,77977663.52395546,74301857.84644176,74598611.14874089
241,73135655.72285844,76539200.3677026,76464552.71210435,73901343.46597376,74388128.86181106
242,68960437.1485723,73642982.1190294,74954595.31091018,73465429.49525711,74156353.49598584
243,68800291.43238153,72704258.0973877,74337967.1860463,73301924.82363744,74060893.98297602
244,71703527.62158768,73578029.35240813,74640065.54928677,73439933.66132462,74118881.05931881
245,66125685.08105845,70194751.28833392,72766725.9731262,72834195.82410148,73797037.32302998
This diff could not be displayed because it is too large.
This diff could not be displayed because it is too large.
,5,10,20,60,120
0,290.35,290.35,290.35,290.35,290.35
1,290.584,290.5645,290.55475000000007,290.54825,290.546625
2,289.5636842105263,289.7064451827242,289.77455453788514,289.8185019905564,289.82929191453906
3,290.25584615384616,290.20838861386136,290.1947191396509,290.18921293853555,290.1882715884893
4,289.93497630331757,289.9820636850258,290.0073211724861,290.0249461750425,290.02945313941797
5,291.57021052631586,291.1321516949973,290.93608530215727,290.81802184973026,290.79029987199664
6,293.5138999514328,292.5605250091097,292.0939659935443,291.80133725502986,291.73137153925194
7,295.80881046788267,294.28261704890076,293.48303485094357,292.9678554203964,292.84345190583
8,297.7668055917793,295.85939396220533,294.7738074085934,294.0508935955951,293.8742088942418
9,298.4361444205084,296.67453918434455,295.52167298162396,294.70886229726636,294.50575039822564
10,299.90103187339736,297.92180849886745,296.55738416856485,295.5708889925896,295.3221916365514
11,301.0428214167197,298.99636131761093,297.475918281278,296.3394962064098,296.049628010287
12,302.5630254373694,300.2885271508089,298.5364510771458,297.20097793158453,296.85826347616677
13,302.4114979025865,300.64093361251196,298.98801278373537,297.6325551286248,297.27536787957797
14,303.6371308618896,301.68112442692944,299.8571219978324,298.3364108298715,297.93327321119904
15,303.2007562208907,301.80405941776127,300.1521122086799,298.6531568680743,298.24374096121045
16,299.7203049644773,300.1054579490509,299.29218144944457,298.2072872253213,297.87705368000996
17,297.80557439401184,298.9608383551318,298.6862499571252,297.9001201404354,297.6285299142352
18,294.6589635152324,296.9917388870737,297.53127577968763,297.2341883927812,297.0647287304202
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20,289.20400125333197,292.90629446816445,294.9007281157027,295.64046395332474,295.7022919028046
21,289.6960666036228,292.4965568491797,294.4487671742239,295.327534158755,295.4318603606577
22,290.4707800997638,292.40904394962286,294.19173576114457,295.12501498766215,295.2547536510838
23,293.8640549712014,293.9196328513784,294.86803858694657,295.4539520265556,295.52524612601485
24,295.3127606859635,294.70490273413446,295.214718536387,295.61376204178976,295.65547077416966
25,295.62184861766895,294.98553267899104,295.32018044471397,295.6491820551754,295.6829449841399
26,296.84125387446517,295.76982366323807,295.72441152604813,295.84977242984456,295.847015027901
27,298.4075209614982,296.82276783934464,296.3140493141282,296.1572327272525,296.09934664936424
28,299.2483538852009,297.5717612622915,296.77919564419824,296.4097547333716,296.3076668079656
29,300.50224246261536,298.5629398873923,297.4036158971055,296.7520783917076,296.58921451428773
30,301.25483092495716,299.32756154251757,297.9377477570261,297.0578474231871,296.84199286334444
31,300.08321790105003,299.0384438039588,297.9181165607325,297.09194764148356,296.87790116705827
32,299.49881103100006,298.9094642206802,297.9588414880684,297.1527901812074,296.9346349008873
33,298.54920637577214,298.4982048207208,297.82989887464606,297.12847884193206,296.923759318796
34,296.5061361807462,297.3920914281754,297.2986756487537,296.904300020915,296.7553272978676
35,291.34408842376706,294.41317657242973,295.7049085808639,296.15906895931994,296.17888587936926
36,288.9760582263905,292.56240454350774,294.58542015992845,295.6076624853543,295.7501414720657
37,285.9640382046931,290.26630172738544,293.1588048074366,294.89247416663335,295.1931697088525
38,283.0060250685214,287.8696538667824,291.5969298471368,294.0901554545088,294.56712434009455
39,278.0106829272469,284.25944686040583,289.3097565298411,292.9294844903441,293.6653492968259
40,275.4637884646077,281.73341792099797,287.4756784427467,291.93676110999144,292.8875972131249
41,274.1625255907682,279.88330109664304,285.93690549704536,291.0500349514753,292.1873462354179
42,275.4850170959501,279.5644620476826,285.183200633815,290.4937682718443,291.73320448546883
43,277.45001143240324,279.89460817402943,284.8165055109482,290.10536679368545,291.4039252713173
44,276.6666742789397,279.0227568371354,283.88076789478197,289.4721080582906,290.8931877869471
45,272.147782816748,276.12924484470017,281.88259114102476,288.36994756072755,290.0355411166612
46,269.6551885313051,274.045578761495,280.22830950042214,287.3879472704924,289.26359539538015
47,265.4401256726629,270.9479976560639,277.99876671332805,286.1400377394029,288.295487053514
48,259.50008376780136,266.70631593300106,275.08393387389884,284.5706696606965,287.09083789021696
49,253.21672250201317,261.9685959464911,271.78235839648363,282.7953930056341,285.73320905922003
50,246.46781499428576,256.69593456016884,268.06336585929125,280.79670337679835,284.21010551132036
51,239.94187665830836,251.27651445748967,264.1204356852863,278.6500252608285,282.5756250402552
52,231.9045844351374,244.83153867191857,259.49837695883707,276.16587647165,280.6949184762732
53,221.0297229533893,236.54927778717797,253.73739047913185,273.1459149943942,278.4272859798505
54,218.57648196841956,232.38934213682657,249.90586165782247,270.8248579886972,276.6438108244144
55,213.00765464484633,226.84029775756943,245.31411721750686,268.1505666905591,274.6069017997951
56,215.45176976345525,225.6584127025341,242.9276828316269,266.3073714660572,273.1442878336463
57,221.26451317599256,226.9732583487845,241.96882405899422,265.02644811404286,272.070537078357
58,223.9563421174382,227.40357811670773,240.76279127866883,263.6660907058779,270.9421818346769
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79,74854000.0,75178000.0,74714500.0,82192666.66666667,
80,76976000.0,73364000.0,75005000.0,81247000.0,
81,76090000.0,74515000.0,74634500.0,80091666.66666667,
82,74732000.0,72329000.0,74334000.0,79311000.0,
83,78932000.0,75714000.0,75975500.0,79261500.0,
84,80536000.0,77695000.0,76497000.0,79033000.0,
85,89740000.0,83358000.0,79386000.0,79767000.0,
86,90792000.0,83441000.0,80104000.0,79722500.0,
87,90608000.0,82670000.0,79213500.0,79282166.66666667,
88,87502000.0,83217000.0,79870000.0,78778833.33333333,
89,87906000.0,84221000.0,79699500.0,78776000.0,
90,78296000.0,84018000.0,78691000.0,78817333.33333333,
91,75428000.0,83110000.0,78812500.0,79023166.66666667,
92,74900000.0,82754000.0,77541500.0,78617333.33333333,
93,74666000.0,81084000.0,78399000.0,78497000.0,
94,71594000.0,79750000.0,78722500.0,78352333.33333333,
95,70734000.0,74515000.0,78936500.0,78323000.0,
96,69160000.0,72294000.0,77867500.0,78088000.0,
97,66942000.0,70921000.0,76795500.0,77761333.33333333,
98,80190000.0,77428000.0,80322500.0,78064166.66666667,
99,84416000.0,78005000.0,81113000.0,77651500.0,
100,84152000.0,77443000.0,80730500.0,77510333.33333333,
101,86264000.0,77712000.0,80411000.0,77553000.0,
102,92358000.0,79650000.0,81202000.0,77635833.33333333,
103,76964000.0,78577000.0,79830500.0,77613333.33333333,
104,73908000.0,79162000.0,79456000.0,77658000.0,
105,73434000.0,78793000.0,76654000.0,77724833.33333333,
106,70054000.0,78159000.0,75226500.0,77714833.33333333,
107,65580000.0,78969000.0,74945000.0,77138500.0,
108,64906000.0,70935000.0,74181500.0,76201666.66666667,
109,70564000.0,72236000.0,75120500.0,76783333.33333333,
110,70312000.0,71873000.0,74658000.0,76972000.0,
111,69546000.0,69800000.0,73756000.0,76903500.0,
112,70442000.0,68011000.0,73830500.0,76380666.66666667,
113,71004000.0,67955000.0,73266000.0,75580666.66666667,
114,62912000.0,66738000.0,72950000.0,75214333.33333333,
115,65026000.0,67669000.0,73231000.0,75406500.0,
116,64722000.0,67134000.0,72646500.0,75202666.66666667,
117,63680000.0,67061000.0,73015000.0,75095833.33333333,
118,61498000.0,66251000.0,68593000.0,74704833.33333333,
119,63690000.0,63301000.0,67768500.0,74532000.0,81489416.66666667
120,61754000.0,63390000.0,67631500.0,74455666.66666667,81469833.33333333
121,65518000.0,65120000.0,67460000.0,74168500.0,81335166.66666667
122,66524000.0,65102000.0,66556500.0,74030833.33333333,81121333.33333333
123,74264000.0,67881000.0,67918000.0,74574666.66666667,81164666.66666667
124,76508000.0,70099000.0,68418500.0,74790500.0,81086416.66666667
125,74992000.0,68373000.0,68021000.0,74687000.0,80763666.66666667
126,75140000.0,70329000.0,68731500.0,74687333.33333333,80557583.33333333
127,76408000.0,71466000.0,69263500.0,74474000.0,80480916.66666667
128,70406000.0,72335000.0,69293000.0,74448166.66666667,80419083.33333333
129,76376000.0,76442000.0,69871500.0,74897166.66666667,80587416.66666667
130,75504000.0,75248000.0,69319000.0,74222666.66666667,80213750.0
131,77206000.0,76173000.0,70646500.0,74405000.0,80187583.33333333
132,90240000.0,83324000.0,74213000.0,75195000.0,80671583.33333333
133,100328000.0,85367000.0,76624000.0,76096333.33333333,80933250.0
134,94882000.0,85629000.0,77864000.0,76512166.66666667,81027166.66666667
135,101338000.0,88421000.0,78397000.0,76854833.33333333,80927000.0
136,96798000.0,87002000.0,78665500.0,76393166.66666667,80327416.66666667
137,88404000.0,89322000.0,80394000.0,76734833.33333333,80157166.66666667
138,81672000.0,91000000.0,81667500.0,76861000.0,80059833.33333333
139,84774000.0,89828000.0,83135000.0,77338833.33333333,79765750.0
140,85154000.0,93246000.0,84247000.0,77536333.33333333,79391666.66666667
141,88722000.0,92760000.0,84466500.0,77445833.33333333,78768750.0
142,82612000.0,85508000.0,84416000.0,77391500.0,78351250.0
143,85002000.0,83337000.0,84352000.0,77366833.33333333,78314166.66666667
144,80388000.0,82581000.0,84105000.0,77326500.0,78179750.0
145,82352000.0,83753000.0,86087000.0,76920666.66666667,78343833.33333333
146,92810000.0,90766000.0,88884000.0,77614000.0,78668250.0
147,104538000.0,93575000.0,91448500.0,78552333.33333333,78917250.0
148,103438000.0,94220000.0,92610000.0,78694833.33333333,78736833.33333333
149,121664000.0,101026000.0,95427000.0,80139666.66666667,79457833.33333333
150,125270000.0,103811000.0,98528500.0,80835166.66666667,79826250.0
151,112938000.0,102874000.0,97817000.0,80739833.33333333,79881500.0
152,103668000.0,104103000.0,94805500.0,80949666.66666667,79783500.0
153,101968000.0,102703000.0,93020000.0,80970000.0,79733500.0
154,83592000.0,102628000.0,92604500.0,81139500.0,79745916.66666667
155,73258000.0,99264000.0,91508500.0,81045500.0,79684250.0
156,75720000.0,94329000.0,92547500.0,81286500.0,79687250.0
157,73452000.0,88560000.0,91067500.0,81492166.66666667,79626750.0
158,70454000.0,86211000.0,90215500.0,80158666.66666667,79111416.66666667
159,67044000.0,75318000.0,88172000.0,79691833.33333333,78671666.66666667
160,66362000.0,69810000.0,86810500.0,79563000.0,78536666.66666667
161,68700000.0,72210000.0,87542000.0,79822833.33333333,78687916.66666667
162,67272000.0,70362000.0,87232500.0,79401666.66666667,78518750.0
163,66792000.0,68623000.0,85663000.0,79311000.0,78462166.66666667
164,74668000.0,70856000.0,86742000.0,79755166.66666667,78706583.33333333
165,73124000.0,69743000.0,84503500.0,79537166.66666667,78631000.0
166,67312000.0,68006000.0,81167500.0,79594333.33333333,78654583.33333333
167,69502000.0,68387000.0,78473500.0,79728500.0,78433500.0
168,74330000.0,70561000.0,78386000.0,80096333.33333333,78149000.0
169,67338000.0,71003000.0,73160500.0,79486333.33333333,78134833.33333333
170,68802000.0,70963000.0,70386500.0,79411333.33333333,78191666.66666667
171,69206000.0,68259000.0,70234500.0,79566000.0,78234750.0
172,76128000.0,72815000.0,71588500.0,80202333.33333333,78291500.0
173,78998000.0,76664000.0,72643500.0,80762500.0,78171583.33333333
174,81906000.0,74622000.0,72739000.0,81069166.66666667,78141750.0
175,82984000.0,75893000.0,72818000.0,80907833.33333333,78157166.66666667
176,83816000.0,76511000.0,72258500.0,81157166.66666667,78179916.66666667
177,77842000.0,76985000.0,72686000.0,81382500.0,78239166.66666667
178,69212000.0,74105000.0,72333000.0,81405333.33333333,78055083.33333333
179,68118000.0,75012000.0,73007500.0,81438166.66666667,77985083.33333333
180,68766000.0,75875000.0,73419000.0,81492166.66666667,77973916.66666667
181,72584000.0,78200000.0,73229500.0,81746000.0,77957250.0
182,69326000.0,73584000.0,73199500.0,81616000.0,77823416.66666667
183,68226000.0,68719000.0,72691500.0,80902166.66666667,77738416.66666667
184,65098000.0,66608000.0,70615000.0,80487333.33333333,77638916.66666667
185,64172000.0,66469000.0,71181000.0,80590500.0,77638750.0
186,58830000.0,65707000.0,71109000.0,80386833.33333333,77537083.33333333
187,57996000.0,63661000.0,70323000.0,80081666.66666667,77277833.33333333
188,62074000.0,65150000.0,69627500.0,80207833.33333333,77328000.0
189,71184000.0,68141000.0,71576500.0,80054666.66666667,77475916.66666667
190,69870000.0,67021000.0,71448000.0,80121000.0,77171833.33333333
191,71504000.0,65167000.0,71683500.0,79911666.66666667,77158333.33333333
192,71400000.0,64698000.0,69141000.0,78511666.66666667,76853333.33333333
193,69460000.0,65767000.0,67243000.0,77635500.0,76865916.66666667
194,60598000.0,65891000.0,66249500.0,77197666.66666667,76854916.66666667
195,62964000.0,66417000.0,66443000.0,76923166.66666667,76889000.0
196,57832000.0,64668000.0,65187500.0,76664500.0,76528833.33333333
197,59998000.0,65699000.0,64680000.0,76144500.0,76439666.66666667
198,59980000.0,64720000.0,64935000.0,75827833.33333333,76344416.66666667
199,63942000.0,62270000.0,65205500.0,75461666.66666667,76400250.0
200,61304000.0,62134000.0,64577500.0,74935666.66666667,76236000.0
201,64040000.0,60936000.0,63051500.0,74607666.66666667,76026750.0
202,62694000.0,61346000.0,63022000.0,74484666.66666667,75938083.33333333
203,64142000.0,62061000.0,63914000.0,74089500.0,75728166.66666667
204,65074000.0,64508000.0,65199500.0,74185500.0,75756000.0
205,65126000.0,63215000.0,64816000.0,73500166.66666667,75210416.66666667
206,68712000.0,66376000.0,65522000.0,72599500.0,75106750.0
207,72554000.0,67624000.0,66661500.0,71819333.33333333,75185833.33333333
208,73638000.0,68890000.0,66805000.0,71606166.66666667,75150500.0
209,72114000.0,68594000.0,65432000.0,70056333.33333333,75098000.0
210,75142000.0,70134000.0,66134000.0,69322833.33333333,75079000.0
211,72054000.0,70383000.0,65659500.0,69192500.0,74966166.66666667
212,69896000.0,71225000.0,66285500.0,69005000.0,74977333.33333333
213,66332000.0,69985000.0,66023000.0,68636500.0,74803250.0
214,68140000.0,70127000.0,67317500.0,68768666.66666667,74954083.33333333
215,67734000.0,71438000.0,67326500.0,68862500.0,74954000.0
216,66644000.0,69349000.0,67862500.0,68436166.66666667,74861333.33333333
217,67164000.0,68530000.0,68077000.0,68481000.0,74986583.33333333
218,69848000.0,68090000.0,68490000.0,68586000.0,74372333.33333333
219,70246000.0,69193000.0,68893500.0,69035500.0,74363666.66666667
220,70786000.0,69260000.0,69697000.0,69231166.66666667,74397083.33333333
221,72466000.0,69555000.0,69969000.0,68750000.0,74286416.66666667
222,82736000.0,74950000.0,73087500.0,69769666.66666667,74585666.66666667
223,80206000.0,75027000.0,72506000.0,69703833.33333333,74507416.66666667
224,76180000.0,73213000.0,71670000.0,69161500.0,74458333.33333333
225,76230000.0,73508000.0,72473000.0,69490000.0,74513583.33333333
226,76650000.0,74558000.0,71953500.0,69528166.66666667,74561250.0
227,66202000.0,74469000.0,71499500.0,69494666.66666667,74611583.33333333
228,70406000.0,75306000.0,71698000.0,69376833.33333333,74736583.33333333
229,71384000.0,73782000.0,71487500.0,69498666.66666667,74492500.0
230,70486000.0,73358000.0,71309000.0,69630333.33333333,74520833.33333333
231,71268000.0,73959000.0,71757000.0,69700000.0,74633000.0
232,69146000.0,67674000.0,71312000.0,68912833.33333333,74557583.33333333
233,66132000.0,68269000.0,71648000.0,68304666.66666667,74533583.33333333
234,79560000.0,75472000.0,74342500.0,69303166.66666667,75186166.66666667
235,85462000.0,77974000.0,75741000.0,69836833.33333333,75372333.33333333
236,86106000.0,78687000.0,76622500.0,69890833.33333333,75524000.0
237,93646000.0,81396000.0,77932500.0,70229833.33333333,75806166.66666667
238,95658000.0,80895000.0,78100500.0,70508500.0,75956916.66666667
239,82966000.0,81263000.0,77522500.0,70540500.0,75989333.33333333
240,78236000.0,81849000.0,77603500.0,70626000.0,76059083.33333333
241,75906000.0,81006000.0,77482500.0,70167666.66666667,75956833.33333333
242,69154000.0,81400000.0,74537000.0,70215500.0,75915750.0
243,67468000.0,81563000.0,74916000.0,70445333.33333333,75673750.0
244,68932000.0,75949000.0,75710500.0,70860000.0,75673666.66666667
245,64732000.0,71484000.0,74729000.0,70672666.66666667,75631583.33333333
This diff could not be displayed because it is too large.
This diff could not be displayed because it is too large.
,5,10,20,60,120
0,,,,,
1,,,,,
2,,,,,
3,,,,,
4,290.034,,,,
5,290.846,,,,
6,292.11,,,,
7,294.44999999999993,,,,
8,296.50999999999993,,,,
9,298.57399999999996,294.304,,,
10,300.24799999999993,295.54699999999997,,,
11,301.4959999999999,296.803,,,
12,302.5859999999999,298.518,,,
13,302.7019999999999,299.606,,,
14,303.9699999999999,301.272,,,
15,303.8799999999999,302.06399999999996,,,
16,301.7739999999999,301.635,,,
17,299.4539999999999,301.02,,,
18,296.70599999999985,299.704,,,
19,292.39599999999984,298.183,296.2434999999999,,
20,288.9399999999999,296.41,295.97849999999994,,
21,288.5219999999999,295.148,295.97549999999995,,
22,288.1299999999999,293.79200000000003,296.1549999999999,,
23,290.5859999999999,293.646,296.6259999999999,,
24,293.3219999999999,292.85900000000004,297.06549999999993,,
25,295.5599999999999,292.25,297.1569999999999,,
26,297.27999999999986,292.90099999999995,297.2679999999999,,
27,299.18399999999986,293.657,297.3384999999999,,
28,299.2399999999999,294.91299999999995,297.3084999999999,,
29,300.1999999999999,296.76099999999997,297.4719999999999,,
30,301.50399999999985,298.532,297.47099999999995,,
31,301.19599999999986,299.23799999999994,297.1929999999999,,
32,300.55399999999986,299.86899999999997,296.8304999999999,,
33,299.6979999999998,299.46899999999994,296.55749999999995,,
34,297.5799999999998,298.89,295.87449999999995,,
35,293.23199999999986,297.36799999999994,294.8089999999999,,
36,290.5319999999998,295.8639999999999,294.3824999999999,,
37,286.85399999999987,293.70399999999995,293.6804999999998,,
38,282.94199999999984,291.32,293.11649999999986,,
39,278.0619999999998,287.821,292.2909999999998,,
40,275.9319999999998,284.582,291.55699999999985,,
41,273.3959999999998,281.96399999999994,290.60099999999983,,
42,273.03399999999976,279.94399999999996,289.9064999999999,,
43,273.89199999999977,278.417,288.94299999999987,,
44,275.3079999999998,276.68499999999995,287.7874999999999,,
45,273.8559999999999,274.89399999999995,286.1309999999999,,
46,272.4779999999999,272.937,284.4004999999999,,
47,268.2539999999999,270.644,282.1739999999999,,
48,261.50199999999984,267.697,279.5084999999999,,
49,254.6119999999999,264.96,276.39049999999986,,
50,248.58399999999992,261.21999999999997,272.9009999999999,,
51,241.02799999999988,256.753,269.35849999999994,,
52,232.79199999999986,250.52299999999997,265.23349999999994,,
53,223.12399999999988,242.31299999999996,260.3649999999999,,
54,217.7279999999999,236.17,256.4274999999999,,
55,211.50799999999987,230.04599999999996,252.46999999999989,,
56,210.19799999999987,225.61299999999997,249.27499999999992,,
57,213.60999999999984,223.20099999999994,246.92249999999996,,
58,219.62199999999984,221.37299999999996,244.53499999999994,,
59,223.64599999999982,220.68699999999995,242.82349999999997,277.1193333333333,
60,229.7619999999998,220.63499999999993,240.92749999999995,276.15433333333334,
61,233.05799999999982,221.628,239.19049999999993,275.2556666666667,
62,231.74999999999983,222.67999999999998,236.60149999999993,274.22099999999995,
63,232.24999999999983,225.93599999999998,234.12449999999995,273.23116666666664,
64,231.83199999999982,227.73899999999995,231.9544999999999,272.26916666666665,
65,233.50399999999982,231.63299999999995,230.83949999999996,271.37583333333333,
66,235.1139999999998,234.08599999999993,229.84949999999995,270.506,
67,238.22199999999984,234.98599999999993,229.09349999999995,269.53533333333337,
68,240.97599999999989,236.61299999999991,228.99299999999994,268.6033333333333,
69,244.23599999999988,238.03399999999993,229.36049999999994,267.741,
70,244.7539999999999,239.12899999999996,229.88199999999992,266.7513333333333,
71,245.2699999999999,240.19199999999992,230.9099999999999,265.8205,
72,246.3119999999999,242.2669999999999,232.47349999999992,264.84583333333336,
73,248.19399999999987,244.5849999999999,235.26049999999995,264.061,
74,249.02199999999985,246.6289999999999,237.18399999999997,263.162,
75,250.22199999999984,247.48799999999991,239.56049999999996,262.27983333333333,
76,251.10799999999986,248.18899999999994,241.13749999999996,261.5983333333333,
77,252.43599999999986,249.37399999999994,242.17999999999992,260.92766666666665,
78,251.4879999999999,249.84099999999995,243.22699999999992,260.2928333333333,
79,252.02799999999988,250.52499999999995,244.27949999999993,259.79799999999994,
80,253.42599999999985,251.82399999999993,245.47649999999993,259.3203333333333,
81,254.67999999999984,252.89399999999995,246.54299999999995,258.7781666666666,
82,254.05199999999982,253.24399999999997,247.75549999999993,258.08783333333326,
83,254.9959999999998,253.24199999999996,248.91349999999994,257.32699999999994,
84,254.91999999999985,253.474,250.05149999999995,256.5978333333333,
85,254.9659999999999,254.19599999999997,250.84199999999993,255.93749999999997,
86,254.32599999999994,254.50299999999993,251.34599999999992,255.19866666666664,
87,254.87999999999994,254.4659999999999,251.9199999999999,254.39583333333331,
88,255.0499999999999,255.02299999999985,252.43199999999993,253.64449999999997,
89,254.88799999999992,254.90399999999985,252.7144999999999,252.8218333333333,
90,254.32199999999995,254.64399999999986,253.23399999999992,252.00566666666663,
91,254.41999999999993,254.37299999999988,253.6334999999999,251.30066666666664,
92,256.1039999999999,255.49199999999988,254.36799999999988,250.69166666666666,
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154,0.0,0.03231409329065027,0.11530431450684908,0.40795824455300966,0.5658602208400971
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161,0.0023891048031003947,0.004870027915856667,0.03272968401557108,0.31141137236344285,0.4925546727007628
162,0.01110770750897938,0.005471220518726838,0.025437565691239783,0.2993353771042546,0.482802069746203
163,0.006685544746370731,0.0,0.015223671937860672,0.2861262632161239,0.47236922012135096
164,0.04308178690764208,0.01844728800541251,0.01988439349949722,0.2792442383992544,0.4654503761059523
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166,0.07767772812216034,0.04254361931367434,0.02590791139459192,0.2653104391221017,0.45152563774220766
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183,0.4877509528355004,0.48105015664449535,0.4122539499078659,0.39978287064608153,0.4881520678981026
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187,0.38292125826046064,0.4200217258796224,0.4026414809270449,0.4091371116325071,0.4889322981852011
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190,0.39907540334921965,0.4156918594792822,0.40758271018684766,0.41925655396477346,0.49156723851614004
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194,0.5027359302827321,0.48349475291590505,0.46050894852742097,0.453458487763994,0.5073406406945455
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197,0.5191463604925115,0.5050208880144815,0.4854529696816705,0.47474751860185727,0.5175703791065249
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204,0.5509436703484027,0.556283079138117,0.5493351560298585,0.5321832758546208,0.5483575417151769
205,0.5689185615119454,0.5685577739121088,0.5607215470722142,0.5415750663418581,0.5536627800376106
206,0.6153805602945441,0.5986895282079514,0.5832061755144302,0.5558043371743455,0.561685404288994
207,0.6617909628728942,0.6323363176990808,0.6090034463626335,0.571869896110508,0.5708245196029011
208,0.6939056894874067,0.660549799881724,0.632758815237807,0.5875829845438219,0.5798985698586954
209,0.7140571591279716,0.6829003893374335,0.6538071455591687,0.602592140365358,0.5887107949222443
210,0.7170891279081655,0.6951263740542926,0.6691753272636927,0.6155559287844152,0.5965221127136129
211,0.6999835492711783,0.6939852883143933,0.6763216111173236,0.6252396420055053,0.6026412441195763
212,0.7120689947587673,0.7067374883251984,0.691086915888734,0.6381105004369783,0.6105621953731895
213,0.7002440583985798,0.7055870411550176,0.6974209661810846,0.6475918768346443,0.6167295079939041
214,0.7145915839682608,0.7175984131200552,0.7110067705010827,0.6600791233505099,0.6245939841065997
215,0.7523435985094062,0.7438488780126802,0.733258231949808,0.6763622121755031,0.6346085993648618
216,0.7765888195950481,0.7647888740645792,0.7530644501166321,0.6919729559047614,0.6443736123162296
217,0.7847827623366754,0.777278196380129,0.7681684195007374,0.705882039084134,0.6533222176389124
218,0.7565216616856585,0.7678478118283225,0.7699180335011846,0.714302780356487,0.6593818729881864
219,0.7047122076343406,0.7409230228740098,0.7598519089690328,0.7175281435408518,0.6626653568930632
220,0.6798199070521838,0.7245146100784225,0.7541532352089089,0.7220869693693945,0.6666729137621257
221,0.6863786450823381,0.7245798490283706,0.7571783456703258,0.7299505037400511,0.6724848476381773
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223,0.6971895064693454,0.7264631642506867,0.7633825608874822,0.7453018951582031,0.6840154324133536
224,0.689904950171484,0.7214818409840253,0.7626831041640081,0.7512019580704372,0.6888756110417216
225,0.6477175856004749,0.6956555416096197,0.7488597958559513,0.7513392545445505,0.6906350600374243
226,0.625129407537214,0.6777508752829453,0.7383093319546497,0.7522980108280048,0.6928112871161449
227,0.5995843879746898,0.6569918637194814,0.7250584869174617,0.7516609973450095,0.6941031923401102
228,0.5654408407397984,0.6300361236601848,0.7070227615759865,0.7484919892556796,0.6939914972707673
229,0.5355478366693687,0.6038268046502653,0.6881851970517392,0.7443632901738368,0.6933064122465975
230,0.5429672660488176,0.5983170159796858,0.6808048145666898,0.7444495912559184,0.6948388568509305
231,0.560245363705711,0.6009940602658865,0.6784846258246535,0.7463725658723952,0.6973391216814928
232,0.5825019992844762,0.6094407420804364,0.6801795191541729,0.7498341612105914,0.7006617370301385
233,0.6054770740661084,0.6210928211178128,0.6845882193554385,0.7543959562098372,0.7045827498709745
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235,0.7404812198470161,0.7057891433031038,0.7342769061783017,0.7812212417267066,0.7220713777457046
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238,0.9090700108166638,0.8468189491723122,0.8344428523508078,0.8371014171335833,0.7575215257223338
239,0.9408865742692791,0.8838870660126172,0.8657468279087208,0.8563555349832139,0.7699414903586064
240,0.9704866039206346,0.9191033165370897,0.8970336222382365,0.8762291761995868,0.7828229196598286
241,0.9865288025877117,0.9457659842524935,0.924036495967492,0.8949002749155603,0.7951870663609846
242,0.9806972966203604,0.9579519453572178,0.9426282923753693,0.9104939722378091,0.8060118736995285
243,0.986540851301009,0.9735921150683033,0.9628878552478852,0.9270274687648321,0.8174330121229918
244,1.0,0.9919606996314947,0.9845970666880923,0.9444449062301725,0.829426693849701
245,0.99697651388938,1.0,1.0,0.9595018024567352,0.8402524914130218
This diff could not be displayed because it is too large.
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0,0.7207787436198625,0.7003700860565538,0.6797465304554713,0.7135560972586541,0.7984496281539215
1,0.7229594665502643,0.7026923259293882,0.6823450757529765,0.7174135870078828,0.8034700414229876
2,0.7134508082666902,0.693402772970154,0.672443374902119,0.7032143664986821,0.7851544226533065
3,0.7199012929733104,0.6988369596820156,0.6777758132420271,0.7104275502654254,0.794320227197109
4,0.7169110013462923,0.6963866996635613,0.6753974879363471,0.7072312962613205,0.790265126120997
5,0.7321502866739424,0.7088378895657743,0.6871847194950673,0.7226627277138853,0.8096917759974822
6,0.7502641674386104,0.7243018777397818,0.7018797398890855,0.7417957874677414,0.8337200973474177
7,0.7716511930216992,0.7429457509308954,0.7195088404864243,0.7644935522354137,0.8621147726360983
8,0.7898983932964707,0.7600163999688472,0.73589043247582,0.7855669880934357,0.8884330234272282
9,0.7961361818948378,0.7688414010737521,0.7453818247491495,0.798369548103576,0.9045581313563655
10,0.8097879496682265,0.7823447042641867,0.758526354343279,0.8151426080822457,0.9254042721131537
11,0.8204286617999192,0.7939781280457852,0.7701837536067146,0.8300979394744008,0.9439778574817703
12,0.8345959428542786,0.8079674941071682,0.7836433018829134,0.8468603955854199,0.9646246939610625
13,0.8331838079410805,0.8117827497566394,0.7893742105480737,0.8552578967888698,0.9752745934218217
14,0.8446058844308608,0.8230441604074383,0.8004043432040433,0.86895331243828,0.9920728472382745
15,0.8405391656878172,0.8243750906520826,0.8041481548045692,0.875116462982926,1.0
16,0.8081036961877881,0.805985533268891,0.7932345087455186,0.8664408636063206,0.9906373976242255
17,0.7902596925101995,0.7935935460417278,0.7855444455954506,0.8604640974966209,0.984291856561927
18,0.7609353912435062,0.7722754980309965,0.7708863126649517,0.8475065948975677,0.9698963577985955
19,0.7294608789486652,0.7472941473799053,0.7527168550526038,0.8308512528715299,0.9512348499606444
20,0.7100988045383632,0.7280452792531866,0.737501222251717,0.8164963770499059,0.9351093473593011
21,0.7146845233103096,0.7236093396904515,0.7317652467736431,0.8104074818429394,0.9282044364752906
22,0.7219043330775168,0.7226618993886413,0.7285031819640309,0.80646692382774,0.9236823824395088
23,0.753527377185639,0.7390159776129677,0.737086349376372,0.8128672832510615,0.9305888491163126
24,0.767028342021477,0.7475175395368479,0.7414861713659082,0.815976819168287,0.9339138665431719
25,0.7699088340735305,0.7505557215762755,0.742824620918773,0.8166660112921322,0.9346153637499937
26,0.781272872556215,0.7590466856579683,0.7479548420445652,0.8205690393581122,0.9388045534499545
27,0.795869430470004,0.7704461679405958,0.7554381167769044,0.8265515107101731,0.9452473202425749
28,0.8037054289202408,0.7785549902774629,0.7613414320816911,0.8314650090711284,0.9505663452315467
29,0.8153908288045864,0.7892857806005882,0.7692661417855193,0.8381258413684778,0.9577550844086336
30,0.82240444810794,0.7975637987598647,0.7760449736919623,0.8440754045693044,0.9642092574676902
31,0.8114858011558386,0.7944337253779299,0.7757958281348762,0.8447389165114236,0.9651261018386226
32,0.806039521402836,0.7930373546037051,0.7763126807278922,0.8459227726188709,0.9665746805550581
33,0.7971898434264604,0.7885849397940845,0.7746762303163828,0.8454497297838426,0.966296995028598
34,0.7781498005943501,0.7766098319896773,0.767934312780032,0.8410877247191886,0.9619964312508253
35,0.7300429804598452,0.7443592266013712,0.7477073241618011,0.8265872395695801,0.9472781902017795
36,0.7079745285311987,0.7243222261300915,0.7334995518939977,0.8158581358067503,0.9363310872826958
37,0.6799045237100654,0.699463944024139,0.715393937165961,0.801942213444911,0.9221099636690608
38,0.6523378268105795,0.6735171317955326,0.6955717011055098,0.7863309337090483,0.9061251882012904
39,0.6057845911153643,0.6344319725442782,0.6665444802594211,0.7637469420495622,0.8831002249822753
40,0.5820492442948904,0.6070844428331976,0.6432676304988569,0.7444308247051463,0.8632419318809061
41,0.5699223477672726,0.5870545357986097,0.6237385892193474,0.727177169891279,0.8453624694516266
42,0.5822470808895452,0.5836026907985512,0.6141730887160328,0.7163534976869199,0.8337668978433301
43,0.6005595091272091,0.5871769495326511,0.6095192479973837,0.7087960968264753,0.825359433197665
44,0.593259332662279,0.5777380314080851,0.5976435137742486,0.6964743360174204,0.812318806402792
45,0.5511462974845058,0.5464120224507054,0.5722840373050975,0.6750288236776738,0.7904205700458977
46,0.5279169918671804,0.5238536426833771,0.5512890396106589,0.6559213530430092,0.7707105304165607
47,0.48863543533330644,0.4903183216933029,0.5229932255691994,0.6316398992011791,0.7459918834215244
48,0.43327823195946125,0.44439663206547,0.4860001844560826,0.601103600203059,0.7152336550986442
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This diff could not be displayed because it is too large.
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0,,,,,
1,,,,,
2,,,,,
3,,,,,
4,0.7230866769314381,,,,
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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.
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1,,,,,
2,,,,,
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4,0.015527973186991229,,,,
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129,0.456707457861925,0.4385418981995999,0.4526363843871275,0.4625943719972545,0.5238336713995944
130,0.47748787808820126,0.47477217159368745,0.45857110248801725,0.48135438114847773,0.08679851250845166
131,0.4765643038559215,0.47143809735496855,0.46199497831545333,0.47471974376572845,0.32302231237322515
132,0.5091202955437542,0.4876639253167383,0.488472951380964,0.5010295126973228,0.7746788370520623
133,0.4497806511198341,0.4729939986663707,0.4368865555809176,0.4587051018073671,0.6132521974306964
134,0.43107827291618595,0.4489886641475893,0.4220497603286919,0.4458933882406777,0.29369506423258956
135,0.520664973447241,0.4858857523894198,0.49783154530928986,0.5026309768931592,0.3596179851250845
136,0.520664973447241,0.5123360746832626,0.5069618808491221,0.5337451384122627,0.13116970926301558
137,0.552297390902794,0.5352300511224719,0.5345811458571106,0.5628002745367189,0.4199628127112915
138,0.489956130223967,0.539453211824851,0.5327550787491449,0.5186456188515223,0.3287694388100068
139,0.48580004617871175,0.516336963769727,0.49029901848892976,0.48066803935026314,0.4247802569303584
140,0.46755945509120256,0.45699044232051644,0.4432777904587999,0.46511095859071094,0.37567613252197435
141,0.363657353959824,0.4525450100022228,0.4284409952065751,0.3850377487989016,0.28194726166328604
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143,0.3311013622719914,0.39142031562569457,0.36521342159324366,0.33768016472203133,0.4297667342799189
144,0.3170168552297392,0.3138475216714838,0.3264094955489618,0.32555479295355827,0.2298005409060176
145,0.2537520203186334,0.26139142031562645,0.24651905957543985,0.26332646991535125,0.4586713995943205
146,0.11983375663818929,0.2278284063125149,0.18854142889751202,0.14916495081217107,0.7238843813387423
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148,0.0,0.08024005334518858,0.04017347637525681,0.01921757035003413,0.38328262339418523
149,0.009235742322789342,0.07246054678817604,0.049988587080576075,0.04873026767330124,1.0
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153,0.06603555760794233,0.11091353634140955,0.08399908696644598,0.0995195607412489,0.3114435429344152
154,0.043869776033248264,0.04245387863969796,0.01826067107966267,0.027911233127430712,0.2234617985125084
155,0.07180789655968578,0.09668815292287203,0.061629764893860184,0.09174102036147325,0.17435767410412434
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157,0.14731009004848694,0.14003111802622747,0.14494407669481912,0.15717227179135218,0.16979377958079794
158,0.09882244285384356,0.17070460102244933,0.12759643916913976,0.13749714024250714,0.18475321162947939
159,0.09489725236665869,0.09468770837964069,0.08536863729742095,0.10684053992221454,0.07936105476673427
160,0.004386977603324738,0.021115803511891684,0.0,0.01715854495538771,0.1455375253549696
161,0.03463403371045892,0.06423649699933343,0.04154302670623178,0.05239075726378406,0.40559499661933746
162,0.0815054259986141,0.07446099133140738,0.06482538233280088,0.0780141843971629,0.10944895199459093
163,0.053336411914107096,0.10735719048677517,0.07167313398767394,0.07687028140013652,0.16446923597025015
164,0.16162549064881038,0.13336296954878968,0.14768317735676817,0.14824982841455103,0.41218728870858695
165,0.16347263911336807,0.16448099577683983,0.1506505364072135,0.17295813315030895,0.08029073698444894
166,0.14430847379358092,0.1711491442542794,0.14905272768774314,0.16380690917410146,0.1599898580121703
167,0.22373585776956784,0.1564792176039118,0.1951609221638897,0.17204301075268802,0.20199459093982425
168,0.287231586238744,0.25961324738830793,0.2935402876055697,0.27522306108441974,0.36849222447599733
169,0.3123989840683432,0.34118693042898407,0.30175758959141774,0.3317318691374975,0.11671737660581477
170,0.355576079427383,0.33918648588575273,0.35448527733394286,0.3587279798673073,0.14215686274509803
171,0.3869776033248664,0.38030673482996225,0.3649851632047483,0.3914436055822472,0.17706220419202162
172,0.4350034634033699,0.43342965103356423,0.4156585254508114,0.44246167924959945,0.4945064232589588
173,0.4622489032555981,0.4585463436319195,0.43118009586852324,0.442690459849004,0.48977349560513866
174,0.45439852228122835,0.4581018004000885,0.43643003880392595,0.47266071837108115,0.239604462474645
175,0.48626183329485073,0.4674372082685041,0.4697557635243106,0.47586364676275483,0.187711291413117
176,0.529208035095821,0.49477661702600617,0.525222551928783,0.5213909860443824,0.21222109533468564
177,0.5643038559224189,0.5370082240497895,0.5370919881305642,0.5541066117593223,0.24205544286680186
178,0.5691526206418835,0.5559013114025344,0.5471353572243789,0.581102722489133,0.12508451656524683
179,0.599630570307089,0.5681262502778406,0.5713307464049313,0.5790436970944866,0.19337390128465176
180,0.5176633571923341,0.5739053122916209,0.5455375485049085,0.5463280713795466,0.21509465855307647
181,0.5285153544216108,0.5503445210046687,0.5304724948641866,0.5321436742164263,0.3735632183908046
182,0.44631724774878734,0.4994443209602135,0.4624514950924441,0.46396705559368545,0.10437795807978367
183,0.49757561764026814,0.43920871304734455,0.472723122574755,0.4534431480210479,0.07860040567951321
184,0.5176633571923341,0.5018893087352749,0.5076466560146091,0.5163578128574695,0.0611899932386748
185,0.3821288386054027,0.478773060680151,0.4400821730198592,0.418897277510867,0.17596348884381335
186,0.3553451858693135,0.3843076239164258,0.38849577721981365,0.39235872797986726,0.14781947261663292
187,0.3618102054952663,0.40609024227606216,0.37913718329148605,0.37954701441317695,0.06913455037187288
188,0.4470099284229967,0.40120026672593934,0.41931065966674286,0.4056280027453676,0.25092968221771467
189,0.38790117755714615,0.3987552789508788,0.40264779730655054,0.3658201784488675,0.4461629479377958
190,0.44562456707457887,0.46299177595021224,0.44601689112074894,0.4653397391901164,0.12043610547667344
191,0.5132763795890094,0.5303400755723491,0.5160922163889525,0.5428963623884702,0.21686950642325892
192,0.5158162087277756,0.5112247166036905,0.5010271627482306,0.5396934339967965,0.06473968897903992
193,0.5615331332255824,0.5647921760391199,0.5580917598721751,0.5719514985129264,0.16894861392832988
194,0.5442161163703529,0.5572349410980229,0.5373202465190596,0.557767101349806,0.07167004732927656
195,0.4804894943431073,0.558790842409425,0.5459940652818993,0.5042324410889947,0.22041920216362404
196,0.5031170630339412,0.5116692598355197,0.4784295822871494,0.5131548844657976,0.0
197,0.5867005310551834,0.5456768170704605,0.5717872631819221,0.5648592999313662,0.15627112914131175
198,0.5594550912029552,0.5774616581462553,0.5512440082173029,0.5742393045069774,0.16818796484110893
199,0.5703070884322319,0.5967992887308293,0.5637982195845694,0.5506749027682449,0.23909736308316426
200,0.5788501500808119,0.5945765725716834,0.5599178269801417,0.5824754060855639,0.10894185260311023
201,0.5952435927037625,0.6070237830629042,0.5998630449669031,0.6188515213909866,0.1156186612576065
202,0.5899330408681598,0.6312513891976002,0.6014608536863735,0.6078700526195382,0.09939148073022314
203,0.5428307550219342,0.592131584796622,0.5558091759872177,0.5653168611301762,0.22937795807978362
204,0.5499884553220964,0.577683929762169,0.5911892262040634,0.5852207732784258,0.2784820824881677
205,0.6102516739782962,0.5627917314958886,0.5800045651677701,0.5799588194921066,0.11113928329952671
206,0.7051489263449549,0.6456990442320514,0.6763296051129881,0.6785632578357355,0.26715686274509803
207,0.7476333410297844,0.7143809735496784,0.7149052727687737,0.7327842598947605,0.2617478025693036
208,0.7508658508427608,0.7437208268504119,0.7598721753024424,0.7654998856097004,0.275185936443543
209,0.7474024474717149,0.737719493220717,0.7231225747546226,0.7506291466483646,0.21408045977011497
210,0.7187716462710689,0.7770615692376088,0.752796165259074,0.7396476778769161,0.23909736308316426
211,0.6661279150311712,0.7212713936430326,0.6884272997032648,0.6900022878059939,0.1366632860040568
212,0.7307781112906939,0.6901533674149816,0.6964163433006156,0.698924731182796,0.1705544286680189
213,0.6760563380281681,0.6985996888197379,0.6699383702351067,0.6950354609929077,0.12457741717376608
214,0.7372431309166476,0.6901533674149816,0.7028075781784988,0.7060169297643561,0.29048343475321164
215,0.8148233664280768,0.7334963325183388,0.7893175074183985,0.7563486616334938,0.22194050033806628
216,0.8122835372893098,0.8057346076905993,0.7906870577493716,0.8167467398764581,0.09060175794455716
217,0.7903486492726852,0.7755056679262067,0.7578178498059804,0.7661862274079159,0.19252873563218392
218,0.6975294389286528,0.7428317403867526,0.7338507190139243,0.7266071837108212,0.2379986477349561
219,0.6067882706072494,0.689931095799067,0.6662862360191735,0.6117593228094265,0.30730223123732253
220,0.6333410297852691,0.6161369193154043,0.6105911892262039,0.6382978723404253,0.24475997295469915
221,0.6970676518125138,0.687263836408091,0.6936772426386666,0.7147105925417527,0.16159567275185943
222,0.6887554837220033,0.7166036897088244,0.7242638666971013,0.721345229924502,0.6265212981744424
223,0.712306626645117,0.7270504556568129,0.7048619036749599,0.7099061999542435,0.1310851926977688
224,0.6749018702378207,0.6752611691487003,0.677014380278476,0.7048730267673307,0.13717038539553755
225,0.5721542368967896,0.6941542565014451,0.6610362930837717,0.5971173644474943,0.2468728870858688
226,0.5873932117293927,0.6094687708379638,0.6003195617438939,0.611530542210021,0.17934415145368493
227,0.5585315169706764,0.5352300511224719,0.5361789545765809,0.5408373369938229,0.1850067613252197
228,0.5114292311244508,0.5225605690153374,0.4916685688199047,0.5099519560741239,0.3087390128465179
229,0.4918032786885247,0.5512336074683262,0.5361789545765809,0.5099519560741239,0.1784989858012171
230,0.5670745786192564,0.5410091131362531,0.5446245149509243,0.5607412491420725,0.20892494929006084
231,0.6010159316555059,0.6103578573016231,0.5845697329376858,0.6126744452070456,0.21239012846517918
232,0.6305703070884316,0.5767948432985115,0.6073955717872632,0.6035232212308399,0.09533468559837727
233,0.6529669822211952,0.6299177595021117,0.6366126455147221,0.6453900709219855,0.1813725490196078
234,0.7644885707688758,0.7061569237608367,0.737046336452865,0.7407915808739425,0.7459432048681542
235,0.8799353498037403,0.8555234496554789,0.8438712622688893,0.8711965225348886,0.4583333333333333
236,0.8718540752712993,0.8599688819737734,0.8596210910750974,0.8949897048730273,0.239604462474645
237,0.9789886862156534,0.9093131807068247,0.9440766948185351,0.9215282544040262,0.4139621365787695
238,0.9748326021703999,0.9659924427650592,0.9748915772654652,0.9794097460535349,0.26639621365787697
239,0.9769106441930271,0.9826628139586582,0.974663318876968,0.9741477922672157,0.20960108181203513
240,1.0,0.9942209379862197,0.9751198356539605,1.0,0.2584516565246789
241,0.9898406834449318,1.0,0.9810545537548512,0.9967970716083281,0.14114266396213654
242,0.9443546525051945,0.9853300733496342,0.9611960739557182,0.9661404712880355,0.12863421230561195
243,0.9711383052412836,0.9395421204712164,0.9342615841132167,0.9505833905284842,0.19514874915483432
244,0.9974601708612321,0.9228717492776184,1.0,0.9389155799588194,0.2714672075726842
245,0.9644423920572605,0.9724383196265851,0.9666742752796171,0.9874170670327151,0.08096686950642329
This diff could not be displayed because it is too large.
,close,open,high,low,vol
0,0.7339619600257901,0.7468809980806144,0.735279357644697,0.7480239520958081,0.0019700839109814128
1,0.7371050934880723,0.7381637875879721,0.7378721980595515,0.7406786427145711,0.035072358020187294
2,0.7184880722114764,0.6952175303902755,0.6921211107393777,0.728303393213573,0.011212452876079304
3,0.7410541586073502,0.7134516954574537,0.715958514553362,0.7454690618762474,0.0
4,0.726466795615732,0.7048944337811902,0.7055035128805616,0.7267864271457085,0.1439377356196036
5,0.7666827852998066,0.7440019193857965,0.733941117430579,0.7651097804391218,0.11295147756293328
6,0.7880399742101869,0.7517594369801663,0.7570257611241218,0.7816367265469062,0.0700717499695975
7,0.8127820760799482,0.7663147792706335,0.783288725326196,0.8004790419161674,0.03971786452632861
8,0.8240651192778852,0.8109404990403071,0.8108899297423886,0.8336926147704593,0.06511005715675547
9,0.809638942617666,0.7926263595649397,0.7901472064235529,0.8187624750499001,0.015614739146296991
10,0.8341392649903285,0.792786308381318,0.8050351288056208,0.8198003992015968,0.04951964003405085
11,0.8383301096067053,0.8338931541906589,0.8312980930076945,0.8482235528942115,0.03937735619603552
12,0.8567053513862024,0.8332533589251441,0.8437604550016726,0.8662674650698603,0.030864647938708523
13,0.8287395228884591,0.8328534868841972,0.8316326530612239,0.8429540918163674,0.005545421379058751
14,0.8607350096711797,0.8076615483045428,0.8352291736366675,0.8374451097804394,0.034002188982123344
15,0.8305125725338491,0.8234964811260397,0.8201739712278353,0.8418363273453091,0.05243828286513441
16,0.7534655061250806,0.7531989763275753,0.7497490799598527,0.7587225548902199,0.15765535692569627
17,0.7632172791747263,0.7484005118362125,0.7444797591167613,0.7679041916167664,0.04956828408123559
18,0.7180045132172792,0.7395233525271914,0.7314319170291068,0.7251097804391218,0.08802140338076131
19,0.6870567375886523,0.7152911068458094,0.7093509534961526,0.7046706586826346,0.0878268271920224
20,0.691247582205029,0.6347568777991046,0.665607226497156,0.6667465069860279,0.11117596984069078
21,0.7366215344938751,0.674904030710173,0.7085981933757106,0.709301397205589,0.09753131460537517
22,0.7474210186976145,0.7419225847728728,0.7326865172298425,0.7507385229540919,0.09069682597592121
23,0.8169729206963245,0.7512795905310303,0.7888926062228168,0.7828343313373252,0.10913291985893231
24,0.797308188265635,0.790067178502879,0.7811140849782536,0.7963273453093811,0.10400097288094373
25,0.7814313346228239,0.7459213051823419,0.7533456005352959,0.7749301397205588,0.016709230207953313
26,0.8059316569954864,0.7766314779270636,0.7898962863834058,0.8080638722554891,0.03161863067007176
27,0.8241457124435849,0.7919865642994244,0.7990966878554697,0.8129341317365266,0.11013012282621915
28,0.8192295293359122,0.8156589891234807,0.8177484108397455,0.8320159680638726,0.1209291013012283
29,0.8359929078014183,0.8009436980166347,0.8158246905319502,0.8216367265469062,0.03692083181320688
30,0.8339780786589295,0.8160588611644275,0.8145700903312145,0.8360878243512977,0.04604159066034297
31,0.7935203094777565,0.7954254638515674,0.7914854466376713,0.805828343313373,0.047184725769184005
32,0.7982753062540293,0.7907869481765835,0.7838742054198724,0.7924950099800399,0.045992946613158225
33,0.7847356544165052,0.7960652591170823,0.7919872867179658,0.7883433133732536,0.05997811017876689
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,date,close,open,high,low,vol
0,20190102,258.23,263.35,263.77,257.47,69950000.0
1,20190103,255.77,258.19,258.7,255.35,90520000.0
2,20190104,257.89,255.62,258.12,254.55,91200000.0
3,20190107,261.57,261.11,263.13,260.76,92320000.0
4,20190108,259.65,261.29,262.43,259.39,86910000.0
5,20190109,265.6,260.95,266.05,260.87,98750000.0
6,20190110,265.55,265.73,266.85,264.61,99830000.0
7,20190111,267.13,266.58,267.58,265.56,81080000.0
8,20190114,264.99,266.22,266.7,264.23,74930000.0
9,20190115,269.93,265.62,270.02,265.37,87170000.0
10,20190116,271.23,269.96,271.28,269.05,100500000.0
11,20190117,271.88,271.66,273.05,270.81,86750000.0
12,20190118,273.88,273.02,273.95,272.56,78970000.0
13,20190121,274.0,274.99,275.14,272.29,86550000.0
14,20190122,272.71,274.08,274.6,271.45,68870000.0
15,20190123,273.87,271.35,274.51,271.03,99960000.0
16,20190124,276.61,274.1,276.64,273.74,132860000.00000001
17,20190125,281.67,277.3,281.72,277.04,115510000.0
18,20190128,281.64,282.56,283.46,280.47,95970000.0
19,20190129,283.0,281.12,283.0,279.65,130940000.0
20,20190130,286.62,282.86,286.63,282.24,134730000.0
21,20190131,285.89,288.85,288.87,285.89,153500000.0
22,20190201,285.79,287.07,288.34,285.43,114630000.0
23,20190207,285.65,287.54,287.67,284.97,100690000.0
24,20190208,281.49,283.85,284.01,280.95,88710000.0
25,20190211,282.1,281.75,282.19,280.02,79970000.0
26,20190212,283.69,282.12,284.32,281.43,92110000.0
27,20190213,284.97,285.09,285.59,284.13,93290000.0
28,20190214,288.68,284.25,288.68,282.91,112390000.0
29,20190215,283.98,286.42,286.42,282.81,77190000.0
30,20190218,285.9,285.6,287.02,284.64,73480000.0
31,20190219,284.91,285.36,286.12,283.16,62750000.0
32,20190220,288.4,286.55,289.22,286.21,88580000.0
33,20190221,288.41,287.43,289.28,286.05,88240000.0
34,20190222,288.74,287.29,288.74,286.54,70340000.0
35,20190225,289.0,290.19,290.32,287.84,73420000.0
36,20190226,287.89,288.52,289.2,287.06,81330000.0
37,20190227,288.76,288.9,289.41,288.09,72740000.0
38,20190228,283.8,288.13,288.62,283.8,129090000.0
39,20190304,282.51,286.12,286.68,282.16,107550000.0
40,20190305,280.45,281.42,281.89,279.53,78810000.0
41,20190306,279.85,280.09,280.26,278.72,75230000.0
42,20190307,278.87,279.33,280.98,277.44,78640000.0
43,20190308,274.84,277.18,278.0,274.84,71640000.0
44,20190311,275.06,275.5,276.28,273.88,64830000.0
45,20190312,277.73,277.41,278.48,276.94,63960000.0
46,20190313,276.0,276.92,277.12,274.04,61490000.0
47,20190314,276.64,276.79,277.4,274.78,95820000.0
48,20190315,279.87,276.42,280.5,276.31,123130000.0
49,20190318,279.93,280.27,280.77,278.92,60900000.0
50,20190319,279.82,280.3,280.4,278.63,55390000.0
51,20190320,279.92,278.84,280.07,276.34,61170000.0
52,20190321,282.38,281.14,284.39,280.63,97090000.0
53,20190322,283.13,283.92,284.55,282.09,117730000.0
54,20190325,277.45,279.12,279.21,277.19,77320000.0
55,20190326,277.6,278.43,278.49,276.91,65750000.0
56,20190327,277.28,277.01,278.16,274.87,67770000.0
57,20190328,275.08,275.02,276.2,273.9,66920000.0
58,20190329,276.48,276.33,277.01,274.32,82280000.0
59,20190401,280.32,278.41,280.93,278.41,76670000.0
60,20190402,281.72,281.67,282.47,281.0,72180000.0
61,20190403,285.65,281.88,285.65,281.22,91590000.0
62,20190404,286.15,285.29,287.75,284.97,73800000.0
63,20190405,286.68,286.03,287.53,285.93,64890000.0
64,20190408,286.38,287.78,287.83,285.09,64569999.99999999
65,20190409,286.71,286.51,288.02,285.38,66230000.00000001
66,20190410,287.92,285.44,288.01,285.1,75080000.0
67,20190411,287.42,288.33,288.71,286.38,84680000.0
68,20190412,288.37,286.89,288.62,285.83,69060000.0
69,20190415,289.38,289.54,290.74,289.12,80430000.0
70,20190416,290.62,289.18,290.76,288.77,96130000.0
71,20190417,290.33,290.68,291.21,289.84,72670000.0
72,20190418,285.82,290.88,291.15,285.38,89650000.0
73,20190419,285.89,287.18,287.48,285.72,63870000.0
74,20190422,285.87,286.06,287.59,284.52,55190000.0
75,20190423,286.47,285.85,286.83,285.42,67380000.0
76,20190424,283.7,287.82,287.82,281.96,88610000.0
77,20190425,282.83,282.8,284.22,281.9,74580000.0
78,20190426,281.34,280.86,282.47,279.7,76720000.0
79,20190429,286.79,282.0,286.79,281.81,66980000.00000001
80,20190430,284.92,286.25,286.59,282.61,77990000.0
81,20190502,286.12,283.37,286.87,283.14,84180000.0
82,20190503,283.67,285.64,285.95,283.14,67790000.0
83,20190507,281.31,280.89,281.66,278.95,97720000.0
84,20190508,280.15,277.85,281.84,277.84,75000000.0
85,20190509,271.0,278.83,279.63,271.0,124010000.0
86,20190510,272.06,273.37,274.32,269.71,89440000.0
87,20190513,268.72,269.8,271.54,268.55,66870000.00000001
88,20190514,268.57,266.03,270.42,265.55,82190000.0
89,20190515,269.64,268.42,271.0,267.62,77020000.0
90,20190516,265.99,269.83,269.96,265.24,75960000.0
91,20190517,264.35,267.85,268.06,263.94,75100000.0
92,20190520,264.96,265.9,267.65,264.33,64230000.00000001
93,20190521,266.12,265.25,268.55,264.79,81020000.0
94,20190522,266.65,267.01,267.6,263.95,61660000.0
95,20190523,266.38,266.4,267.12,264.5,71660000.0
96,20190524,264.42,264.7,266.11,263.1,67230000.0
97,20190527,264.4,264.18,265.85,263.17,53140000.0
98,20190528,264.7,264.38,266.16,263.27,147260000.0
99,20190529,261.37,264.1,264.1,260.22,82790000.0
100,20190530,263.93,261.58,264.23,261.58,70340000.0
101,20190531,263.89,262.48,265.21,261.53,77790000.0
102,20190603,268.16,262.68,268.55,262.33,83610000.0
103,20190604,267.66,267.84,268.47,266.72,70290000.0
104,20190605,267.99,270.1,270.46,267.46,67510000.0
105,20190607,268.27,267.97,269.63,266.27,67970000.0
106,20190610,272.1,270.32,272.2,268.68,60890000.0
107,20190611,273.21,271.48,273.69,271.24,61240000.0
108,20190612,272.74,273.14,274.32,272.0,66920000.0
109,20190613,271.13,271.96,272.46,268.38,95800000.0
110,20190614,270.48,270.19,271.55,268.96,66709999.99999999
111,20190617,270.09,269.83,271.59,269.14,57060000.0
112,20190618,271.36,269.77,271.87,269.77,65720000.0
113,20190619,275.31,274.4,275.6,274.08,69730000.0
114,20190620,276.01,274.63,276.09,274.43,55340000.0
115,20190621,275.48,276.17,276.26,274.6,77280000.0
116,20190624,275.58,275.91,276.73,275.13,55540000.0
117,20190625,275.28,275.33,277.02,274.19,60510000.0
118,20190626,275.53,275.21,276.36,274.66,58820000.0
119,20190627,277.75,275.47,278.63,275.09,66300000.0
120,20190628,277.5,278.03,278.56,276.58,67600000.0
121,20190701,277.27,279.59,279.68,276.85,74360000.0
122,20190702,275.97,276.06,277.46,274.63,65540000.00000001
123,20190703,272.37,275.57,276.69,271.9,97520000.0
124,20190704,274.54,273.18,274.61,271.93,77520000.0
125,20190705,274.35,274.74,275.23,272.41,60020000.0
126,20190708,268.55,271.38,271.67,268.38,75100000.0
127,20190709,267.72,269.6,270.22,267.72,71880000.0
128,20190710,268.75,269.29,270.33,268.6,67510000.0
129,20190711,271.78,270.58,272.97,270.46,107370000.0
130,20190712,272.68,272.21,273.23,271.28,55660000.0
131,20190715,272.64,272.06,273.38,270.99,83610000.0
132,20190716,274.05,272.79,274.54,272.14,137050000.0
133,20190717,271.48,272.13,272.28,270.29,117950000.0
134,20190718,270.67,271.05,271.63,269.73,80140000.0
135,20190719,274.55,272.71,274.95,272.21,87940000.0
136,20190722,274.55,273.9,275.35,273.57,60910000.0
137,20190723,275.92,274.93,276.56,274.84,95080000.0
138,20190724,273.22,275.12,276.48,272.91,84290000.0
139,20190725,273.04,274.08,274.62,271.25,95650000.0
140,20190726,272.25,271.41,272.56,270.57,89840000.0
141,20190729,267.75,271.21,271.91,267.07,78750000.0
142,20190730,268.95,268.69,269.79,268.33,64530000.0
143,20190731,266.34,268.46,269.14,265.0,96240000.0
144,20190801,265.73,264.97,267.44,264.47,72580000.0
145,20190802,262.99,262.61,263.94,261.75,99660000.0
146,20190805,257.19,261.1,261.4,256.76,131039999.99999999
147,20190806,253.41,250.85,257.04,250.24,123170000.0
148,20190807,252.0,254.46,254.9,251.08,90740000.0
149,20190808,252.4,254.11,255.33,252.37,163710000.0
150,20190809,254.85,255.13,256.15,254.41,117690000.0
151,20190812,255.35,255.57,256.43,254.43,69380000.0
152,20190813,252.83,253.5,254.58,252.63,76820000.0
153,20190814,254.86,255.84,256.82,254.59,82240000.0
154,20190816,253.9,252.76,253.94,251.46,71830000.0
155,20190819,255.11,255.2,255.84,254.25,66019999.99999999
156,20190820,258.05,255.53,258.62,254.74,81690000.0
157,20190821,258.38,257.15,259.49,257.11,65480000.00000001
158,20190822,256.28,258.53,258.73,256.25,67250000.0
159,20190823,256.11,255.11,256.88,254.91,54780000.0
160,20190826,252.19,251.8,253.14,250.99,62610000.0
161,20190827,253.5,253.74,254.96,252.53,93380000.0
162,20190828,255.53,254.2,255.98,253.65,58340000.0
163,20190829,254.31,255.68,256.28,253.6,64849999.99999999
164,20190830,259.0,256.85,259.61,256.72,94160000.0
165,20190902,259.08,258.25,259.74,257.8,54890000.0
166,20190903,258.25,258.55,259.67,257.4,64319999.99999999
167,20190904,261.69,257.89,261.69,257.76,69290000.0
168,20190905,264.44,262.53,266.0,262.27,88990000.0
169,20190906,265.53,266.2,266.36,264.74,59200000.0
170,20190909,267.4,266.11,268.67,265.92,62210000.0
171,20190910,268.76,267.96,269.13,267.35,66340000.0
172,20190911,270.84,270.35,271.35,269.58,103900000.0
173,20190916,272.02,271.48,272.03,269.59,103340000.0
174,20190917,271.68,271.46,272.26,270.9,73740000.0
175,20190918,273.06,271.88,273.72,271.04,67600000.0
176,20190919,274.92,273.11,276.15,273.03,70500000.0
177,20190920,276.44,275.01,276.67,274.46,74030000.0
178,20190923,276.65,275.86,277.11,275.64,60190000.0
179,20190924,277.97,276.41,278.17,275.55,68270000.0
180,20190925,274.42,276.67,277.04,274.12,70840000.0
181,20190926,274.89,275.61,276.38,273.5,89590000.0
182,20190927,271.33,273.32,273.4,270.52,57740000.0
183,20190930,273.55,270.61,273.85,270.06,54690000.0
184,20191001,274.42,273.43,275.38,272.81,52630000.0
185,20191002,268.55,272.39,272.42,268.55,66209999.99999999
186,20191004,267.39,268.14,270.16,267.39,62880000.0
187,20191007,267.67,269.12,269.75,266.83,53570000.0
188,20191008,271.36,268.9,271.51,267.97,75080000.0
189,20191010,268.8,268.79,270.78,266.23,98180000.0
190,20191011,271.3,271.68,272.68,270.58,59640000.0
191,20191014,274.23,274.71,275.75,273.97,71050000.0
192,20191015,274.34,273.85,275.09,273.83,53050000.0
193,20191016,276.32,276.26,277.59,275.24,65379999.99999999
194,20191017,275.57,275.92,276.68,274.62,53870000.0
195,20191018,272.81,275.99,277.06,272.28,71470000.0
196,20191021,273.79,273.87,274.1,272.67,45390000.0
197,20191022,277.41,275.4,278.19,274.93,63880000.0
198,20191023,276.23,276.83,277.29,275.34,65290000.00000001
199,20191024,276.7,277.7,277.84,274.31,73680000.0
200,20191025,277.07,277.6,277.67,275.7,58280000.0
201,20191028,277.78,278.16,279.42,277.29,59070000.0
202,20191029,277.55,279.25,279.49,276.81,57150000.0
203,20191030,275.51,277.49,277.49,274.95,72530000.0
204,20191031,275.82,276.84,279.04,275.82,78340000.0
205,20191101,278.43,276.17,278.55,275.59,58540000.0
206,20191104,282.54,279.9,282.77,279.9,77000000.0
207,20191105,284.38,282.99,284.46,282.27,76360000.0
208,20191106,284.52,284.31,286.43,283.7,77950000.0
209,20191107,284.37,284.04,284.82,283.05,70720000.0
210,20191108,283.13,285.81,286.12,282.57,73680000.0
211,20191111,280.85,283.3,283.3,280.4,61560000.0
212,20191112,283.65,281.9,283.65,280.79,65569999.99999999
213,20191113,281.28,282.28,282.49,280.62,60130000.0
214,20191114,283.93,281.9,283.93,281.1,79760000.0
215,20191115,287.29,283.85,287.72,283.3,71650000.0
216,20191118,287.18,287.1,287.78,285.94,56110000.0
217,20191119,286.23,285.74,286.34,283.73,68170000.0
218,20191120,282.21,284.27,285.29,282.0,73550000.0
219,20191121,278.28,281.89,282.33,276.98,81750000.0
220,20191122,279.43,278.57,279.89,278.14,74350000.0
221,20191125,282.19,281.77,283.53,281.48,64510000.00000001
222,20191126,281.83,283.09,284.87,281.77,119520000.0
223,20191127,282.85,283.56,284.02,281.27,60900000.0
224,20191128,281.23,281.23,282.8,281.05,61620000.0
225,20191129,276.78,282.08,282.1,276.34,74600000.0
226,20191202,277.44,278.27,279.44,276.97,66610000.0
227,20191203,276.19,274.93,276.63,273.88,67280000.0
228,20191204,274.15,274.36,274.68,272.53,81920000.0
229,20191205,273.3,275.65,276.63,272.53,66510000.00000001
230,20191206,276.56,275.19,277.0,274.75,70110000.0
231,20191209,278.03,278.31,278.75,277.02,70520000.0
232,20191210,279.31,276.8,279.75,276.62,56670000.0
233,20191211,280.28,279.19,281.03,278.45,66849999.99999999
234,20191212,285.11,282.62,285.43,282.62,133650000.0
235,20191213,290.11,289.34,290.11,288.32,99620000.0
236,20191216,289.76,289.54,290.8,289.36,73740000.0
237,20191217,294.4,291.76,294.5,290.52,94370000.0
238,20191218,294.22,294.31,295.85,293.05,76910000.0
239,20191219,294.31,295.06,295.84,292.82,70190000.0
240,20191220,295.31,295.58,295.86,293.95,75970000.0
241,20191223,294.87,295.84,296.12,293.81,62090000.0
242,20191224,292.9,295.18,295.25,292.47,60610000.0
243,20191226,294.06,293.12,294.07,291.79,68480000.0
244,20191227,295.2,292.37,296.95,291.28,77510000.0
245,20191230,293.77,294.6,295.49,293.4,54970000.0
This diff could not be displayed because it is too large.
,date,close,open,high,low,vol
0,20200102,290.35,294.19,294.57,289.96,66160000.0
1,20200103,290.74,293.1,294.88,289.04,79770000.0
2,20200106,288.43,287.73,289.41,287.49,69960000.0
3,20200107,291.23,290.01,292.26,289.64,65349999.99999999
4,20200108,289.42,288.94,291.01,287.3,124530000.0
5,20200109,294.41,293.83,294.41,292.1,111790000.0
6,20200110,297.06,294.8,297.17,294.17,94160000.0
7,20200113,300.13,296.62,300.31,296.53,81680000.0
8,20200114,301.53,302.2,303.61,300.69,92120000.0
9,20200115,299.74,299.91,301.13,298.82,71770000.0
10,20200116,302.78,299.93,302.91,298.95,85710000.0
11,20200117,303.3,305.07,306.05,302.51,81540000.0
12,20200120,305.58,304.99,307.54,304.77,78040000.0
13,20200121,302.11,304.94,306.09,301.85,67630000.0
14,20200122,306.08,301.79,306.52,301.16,79330000.0
15,20200123,302.33,303.77,304.72,301.71,86910000.0
16,20200128,292.77,294.98,296.3,291.3,130169999.99999999
17,20200129,293.98,294.38,295.67,292.45,85730000.0
18,20200130,288.37,293.27,294.11,287.09,101540000.0
19,20200131,284.53,290.24,291.47,284.53,101460000.0
20,20200203,285.05,280.17,286.24,279.78,111060000.0
21,20200204,290.68,285.19,291.38,285.11,105450000.0
22,20200205,292.02,293.57,294.26,290.3,102640000.0
23,20200206,300.65,294.74,300.98,294.32,110220000.0
24,20200207,298.21,299.59,300.05,296.01,108110000.0
25,20200210,296.24,294.07,296.73,293.33,72220000.0
26,20200211,299.28,297.91,301.1,297.48,78350000.0
27,20200212,301.54,299.83,302.2,298.09,110630000.0
28,20200213,300.93,302.79,304.43,300.48,115070000.0
29,20200214,303.01,300.95,304.2,299.18,80530000.0
30,20200217,302.76,302.84,304.05,300.99,84280000.0
31,20200218,297.74,300.26,301.29,297.2,84750000.0
32,20200219,298.33,299.68,300.38,295.53,84260000.0
33,20200220,296.65,300.34,301.35,295.01,90010000.0
34,20200221,292.42,292.16,295.24,291.96,100710000.0
35,20200224,281.02,285.51,286.62,281.02,132820000.0
36,20200225,284.24,280.48,284.63,279.77,117030000.0
37,20200226,279.94,278.45,281.98,277.68,119550000.0
38,20200227,277.09,279.46,281.09,276.07,110500000.0
39,20200228,268.02,272.33,273.98,267.23,202610000.0
40,20200302,270.37,269.59,272.6,265.7,163720000.0
41,20200303,271.56,277.44,277.54,270.79,146720000.0
42,20200304,278.13,270.13,278.9,270.13,208460000.0
43,20200305,281.38,280.66,282.05,277.91,147440000.0
44,20200306,275.1,276.74,278.31,274.0,140900000.0
45,20200309,263.11,266.39,267.75,261.76,182670000.0
46,20200310,264.67,261.94,265.57,260.75,173090000.0
47,20200311,257.01,264.63,265.04,255.69,194210000.0
48,20200312,247.62,253.93,255.49,243.76,242470000.0
49,20200313,240.65,231.71,246.19,227.36,298900000.0
50,20200316,232.97,245.21,245.21,232.76,175050000.0
51,20200317,226.89,222.94,234.56,222.46,216730000.0
52,20200318,215.83,228.63,230.04,215.81,208040000.0
53,20200319,199.28,220.72,220.85,196.27,303560000.0
54,20200320,213.67,204.84,214.08,200.54,269350000.0
55,20200323,201.87,200.8,206.66,198.67,210300000.0
56,20200324,220.34,207.65,220.34,205.41,225730000.0
57,20200325,232.89,228.37,233.42,224.74,306110000.0
58,20200326,229.34,231.91,236.61,229.22,290840000.0
59,20200327,233.79,239.11,239.48,227.01,249100000.0
60,20200330,232.45,227.05,234.7,225.09,167230000.0
61,20200331,236.82,235.2,237.47,232.5,240440000.0
62,20200401,226.35,234.23,237.52,226.34,228580000.0
63,20200402,231.84,227.47,232.22,223.6,181790000.0
64,20200403,231.7,232.48,234.45,229.23,181040000.0
65,20200406,240.81,234.4,241.19,233.75,190740000.0
66,20200407,244.87,245.65,247.39,240.68,224450000.0
67,20200408,241.89,243.71,246.99,241.77,173120000.0
68,20200409,245.61,246.13,246.46,243.11,175940000.0
69,20200410,248.0,245.14,248.21,244.02,286110000.0
70,20200413,243.4,246.59,246.9,243.39,195900000.0
71,20200414,247.45,246.2,248.55,244.58,193120000.0
72,20200416,247.1,244.99,247.87,243.61,197540000.0
73,20200417,255.02,252.52,257.37,252.38,287360000.0
74,20200420,252.14,253.85,255.93,251.46,211000000.0
75,20200421,249.4,250.26,251.88,245.11,257160000.00000003
76,20200422,251.88,246.16,252.18,244.56,184500000.0
77,20200423,253.74,253.42,255.22,251.58,163810000.0
78,20200424,250.28,252.47,252.86,249.19,154310000.0
79,20200427,254.84,251.18,255.79,250.61,151360000.0
80,20200428,256.39,255.91,257.34,253.22,172420000.0
81,20200429,258.15,256.58,259.67,256.04,184480000.0
82,20200504,250.6,252.44,253.7,250.34,174040000.0
83,20200506,255.0,253.76,255.01,251.26,133280000.0
84,20200507,254.46,253.88,256.14,253.37,114360000.0
85,20200508,256.62,256.66,258.29,256.23,125220000.0
86,20200511,254.95,257.67,258.87,254.59,144300000.0
87,20200512,253.37,255.33,255.56,250.98,166800000.0
88,20200513,255.85,250.03,255.85,249.95,141250000.0
89,20200514,253.65,253.26,254.39,252.09,167640000.0
90,20200515,253.79,255.34,255.49,252.02,228900000.0
91,20200518,255.44,254.55,256.31,252.95,223000000.0
92,20200519,261.79,261.12,262.34,259.96,253330000.0
93,20200520,262.72,260.48,263.25,260.48,140300000.0
94,20200521,263.74,264.52,264.85,263.07,159480000.0
95,20200522,259.62,263.82,264.07,258.34,166030000.0
96,20200525,262.76,260.95,262.82,259.16,125430000.0
97,20200526,267.31,263.48,267.31,262.95,175420000.0
98,20200527,267.64,266.71,269.17,265.86,247180000.0
99,20200528,268.29,270.17,271.44,264.85,248930000.0
100,20200529,268.32,266.62,269.61,265.23,269470000.0
101,20200601,273.19,269.29,273.39,268.93,171510000.0
102,20200602,276.08,272.65,276.47,272.22,251030000.0
103,20200603,285.91,279.14,287.49,278.98,476500000.0
104,20200604,286.45,291.17,292.9,284.93,364580000.0
105,20200605,290.62,286.3,291.5,284.5,282060000.0
106,20200608,290.77,295.41,295.68,289.71,286660000.0
107,20200609,291.32,293.96,294.87,287.83,268570000.0
108,20200610,291.9,291.03,292.92,289.6,229070000.0
109,20200611,288.62,289.98,292.08,284.29,275900000.0
110,20200612,281.78,276.97,282.54,276.05,232830000.0
111,20200615,267.95,278.61,280.87,267.95,262440000.0
112,20200616,282.59,275.89,282.59,274.0,247890000.0
113,20200617,283.02,282.08,284.9,278.33,337220000.0
114,20200618,281.91,281.74,283.39,280.02,181590000.0
115,20200619,283.37,284.56,284.7,278.53,189130000.0
116,20200622,281.42,281.01,283.71,280.34,139840000.0
117,20200623,281.94,284.64,286.04,279.26,163680000.0
118,20200624,286.7,283.71,288.3,283.3,200570000.0
119,20200625,279.73,282.38,284.36,279.73,152980000.0
120,20200626,283.38,283.36,284.58,280.65,169010000.0
121,20200629,278.04,279.36,281.77,277.06,137760000.0
122,20200630,280.09,282.55,283.98,280.09,179500000.0
123,20200701,280.26,283.1,283.84,279.44,133800000.00000001
124,20200702,283.86,281.3,283.89,280.71,111370000.0
125,20200703,285.89,285.59,286.09,283.36,100220000.0
126,20200706,290.62,286.84,291.14,286.31,128120000.0
127,20200707,286.77,292.74,292.98,286.76,156130000.0
128,20200708,285.97,287.2,288.41,285.18,119450000.0
129,20200709,287.25,287.8,289.41,287.08,140610000.0
130,20200710,285.06,287.87,288.09,283.51,136770000.0
131,20200713,289.84,287.92,290.49,286.86,127050000.0
132,20200714,289.63,288.42,289.63,286.89,126300000.0
133,20200715,292.27,292.94,294.42,290.89,168350000.0
134,20200716,289.25,292.34,292.74,288.46,129449999.99999999
135,20200717,291.57,289.9,292.1,289.9,118640000.0
136,20200720,290.81,292.76,292.76,289.01,161820000.0
137,20200721,295.16,294.03,296.11,293.2,366990000.0
138,20200722,294.04,294.71,296.14,293.39,327320000.0
139,20200723,292.37,293.62,293.62,290.49,250980000.0
140,20200724,290.66,289.84,292.74,289.42,216210000.0
141,20200727,293.51,291.03,295.26,290.82,193240000.0
142,20200728,300.13,296.53,301.34,296.38,301280000.0
143,20200729,301.25,300.57,303.51,299.61,264440000.0
144,20200730,301.85,303.13,304.13,301.49,191760000.0
145,20200731,299.32,303.69,304.16,298.89,201960000.0
146,20200803,299.46,299.57,300.33,297.57,179730000.0
147,20200804,303.04,302.5,304.27,301.48,219870000.0
148,20200805,306.64,303.94,306.67,302.91,210810000.0
149,20200806,311.32,307.88,312.84,307.88,254200000.0
150,20200807,312.57,311.99,314.11,310.0,299120000.0
151,20200810,316.77,312.75,317.84,312.08,261180000.0
152,20200811,321.02,317.63,322.64,317.56,297600000.0
153,20200812,322.68,320.31,322.68,317.3,293140000.0
154,20200813,323.33,325.84,326.22,319.66,285740000.0
155,20200814,319.24,321.59,322.66,316.28,260399999.99999997
156,20200818,312.84,319.57,321.69,311.2,247520000.0
157,20200819,313.54,315.96,316.82,313.18,159930000.0
158,20200820,301.59,310.45,312.77,301.15,249740000.0
159,20200821,306.16,306.17,309.88,303.15,166300000.0
160,20200824,309.33,306.86,310.27,303.39,142210000.0
161,20200825,313.59,312.45,314.14,310.57,197820000.0
162,20200826,314.19,313.61,315.08,309.74,193540000.0
163,20200827,311.38,314.46,315.17,311.37,175570000.0
164,20200828,312.24,313.86,315.95,311.06,256410000.00000003
165,20200831,307.14,315.46,316.11,307.12,312180000.0
166,20200901,309.81,309.08,311.15,307.63,342810000.0
167,20200902,311.5,311.69,312.92,308.52,337040000.0
168,20200903,316.43,314.73,317.46,314.45,342180000.0
169,20200904,312.03,308.25,312.64,308.04,408030000.0
170,20200907,313.67,312.08,314.44,310.93,264670000.00000003
171,20200908,317.38,316.31,317.43,314.56,227270000.0
172,20200909,313.77,313.54,315.9,312.9,198500000.0
173,20200910,316.53,317.92,318.38,315.46,234270000.0
174,20200911,316.45,315.64,316.77,313.2,165160000.0
175,20200914,320.98,319.83,321.22,318.54,176780000.0
176,20200915,323.36,321.82,323.57,320.54,152450000.0
177,20200916,322.31,323.14,324.19,321.52,146880000.0
178,20200917,318.0,321.3,322.23,316.92,178300000.0
179,20200918,318.39,319.2,319.81,316.62,153540000.0
180,20200921,315.89,318.13,320.86,314.78,165860000.0
181,20200922,308.82,315.53,316.02,307.4,304360000.0
182,20200923,309.64,311.5,311.76,303.29,208050000.0
183,20200924,302.48,304.85,306.98,301.95,181250000.0
184,20200925,303.57,305.38,305.55,301.93,156860000.0
185,20200928,307.03,306.49,308.34,304.64,173180000.0
186,20200929,309.44,310.01,311.34,308.52,138820000.0
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