1691 lines
52 KiB
Plaintext
1691 lines
52 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 64,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tushare as ts\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import MySQLdb\n",
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"import time\n",
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"import datetime\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"from imp import reload\n",
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"import chan"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 113,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"build end\n",
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"handle end\n"
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]
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}
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],
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"source": [
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"df=pd.DataFrame(columns=['ts_code','trade_time','trade_date','high','low','open','close'])\n",
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"ts_code_list=[]\n",
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"trade_time_list=[]\n",
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"trade_date_list=[]\n",
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"high_list=[]\n",
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"low_list=[]\n",
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"open_list=[]\n",
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"close_list=[]\n",
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"base_time=datetime.datetime.now()\n",
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"v=20\n",
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"for i in range(600000):\n",
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" rd=random.randint(0,20)-10\n",
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" ts_code='000000.SZ'\n",
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" time_time=base_time+datetime.timedelta(minutes=i-600000)\n",
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" trade_time=time_time.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
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" trade_date=time_time.strftime(\"%Y-%m-%d\")\n",
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" high=v if rd<=0 else v+rd\n",
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" low=v if rd>0 else v-rd\n",
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" open=v\n",
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" close=v+rd\n",
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" ts_code_list.append(ts_code)\n",
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" trade_time_list.append(trade_time)\n",
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" trade_date_list.append(trade_date)\n",
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" high_list.append(high)\n",
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" low_list.append(low)\n",
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" open_list.append(open)\n",
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" close_list.append(close)\n",
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" v=v+rd\n",
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"df = pd.DataFrame({'ts_code':ts_code_list, 'trade_time':trade_time_list, 'trade_date':trade_date_list, 'high':high_list, 'low':low_list, 'open':open_list, 'close':close_list})\n",
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"print('build end')\n",
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"reload(chan) #重新加载外部chan.py\n",
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"df=chan.build(df)\n",
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"print('handle end')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 100,
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"metadata": {
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"collapsed": true
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},
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"outputs": [
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{
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"data": {
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
|
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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" '000000.SZ',\n",
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|
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" '000000.SZ',\n",
|
|
" '000000.SZ',\n",
|
|
" ...]"
|
|
]
|
|
},
|
|
"execution_count": 100,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ts_code_list"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 114,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"√找到两个连续符合中枢 000000.SZ 2018-09-24 15:52:55 4 共找到： 1\n",
|
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"√找到两个连续符合中枢 000000.SZ 2018-09-29 07:53:55 16 共找到： 2\n",
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"√找到两个连续符合中枢 000000.SZ 2018-10-15 04:53:55 51 共找到： 3\n",
|
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"√找到两个连续符合中枢 000000.SZ 2018-10-24 09:02:55 72 共找到： 4\n",
|
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"√找到两个连续符合中枢 000000.SZ 2018-10-25 18:09:55 76 共找到： 5\n",
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"√找到两个连续符合中枢 000000.SZ 2018-11-01 17:43:55 93 共找到： 6\n",
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"√找到两个连续符合中枢 000000.SZ 2018-11-03 11:22:55 97 共找到： 7\n",
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"√找到两个连续符合中枢 000000.SZ 2018-11-21 13:33:55 136 共找到： 8\n",
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"√找到两个连续符合中枢 000000.SZ 2018-12-07 00:30:55 168 共找到： 9\n",
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"√找到两个连续符合中枢 000000.SZ 2018-12-10 12:08:55 180 共找到： 10\n",
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"√找到两个连续符合中枢 000000.SZ 2018-12-17 22:11:55 193 共找到： 11\n",
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"√找到两个连续符合中枢 000000.SZ 2018-12-22 04:05:55 203 共找到： 12\n",
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"√找到两个连续符合中枢 000000.SZ 2018-12-30 14:09:55 225 共找到： 13\n",
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"√找到两个连续符合中枢 000000.SZ 2019-01-10 17:23:55 251 共找到： 14\n",
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"√找到两个连续符合中枢 000000.SZ 2019-01-21 10:51:55 274 共找到： 15\n",
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"√找到两个连续符合中枢 000000.SZ 2019-01-29 03:49:55 294 共找到： 16\n",
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"√找到两个连续符合中枢 000000.SZ 2019-02-24 09:41:55 359 共找到： 17\n",
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"√找到两个连续符合中枢 000000.SZ 2019-02-27 07:56:55 366 共找到： 18\n",
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"√找到两个连续符合中枢 000000.SZ 2019-03-03 22:57:55 373 共找到： 19\n",
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"√找到两个连续符合中枢 000000.SZ 2019-03-12 20:57:55 392 共找到： 20\n",
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"√找到两个连续符合中枢 000000.SZ 2019-03-29 09:06:55 432 共找到： 21\n",
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"√找到两个连续符合中枢 000000.SZ 2019-04-10 05:54:55 457 共找到： 22\n",
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"√找到两个连续符合中枢 000000.SZ 2019-04-25 19:06:55 496 共找到： 23\n",
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"√找到两个连续符合中枢 000000.SZ 2019-05-01 20:54:55 511 共找到： 24\n",
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"√找到两个连续符合中枢 000000.SZ 2019-05-10 22:20:55 534 共找到： 25\n",
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"√找到两个连续符合中枢 000000.SZ 2019-05-12 02:58:55 536 共找到： 26\n",
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"√找到两个连续符合中枢 000000.SZ 2019-05-21 16:35:55 553 共找到： 27\n",
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"√找到两个连续符合中枢 000000.SZ 2019-05-30 16:23:55 575 共找到： 28\n",
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"√找到两个连续符合中枢 000000.SZ 2019-06-21 11:58:55 619 共找到： 29\n",
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"√找到两个连续符合中枢 000000.SZ 2019-06-22 05:49:55 621 共找到： 30\n",
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"√找到两个连续符合中枢 000000.SZ 2019-06-27 19:51:55 635 共找到： 31\n",
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"√找到两个连续符合中枢 000000.SZ 2019-06-29 05:03:55 637 共找到： 32\n",
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"√找到两个连续符合中枢 000000.SZ 2019-07-05 15:27:55 653 共找到： 33\n",
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"√找到两个连续符合中枢 000000.SZ 2019-07-28 19:33:55 707 共找到： 34\n",
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"√找到两个连续符合中枢 000000.SZ 2019-08-03 19:18:55 718 共找到： 35\n",
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"√找到两个连续符合中枢 000000.SZ 2019-08-18 15:41:55 755 共找到： 36\n",
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"√找到两个连续符合中枢 000000.SZ 2019-08-23 02:53:55 765 共找到： 37\n",
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"√找到两个连续符合中枢 000000.SZ 2019-09-15 10:42:55 818 共找到： 38\n",
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"√找到两个连续符合中枢 000000.SZ 2019-09-18 04:46:55 825 共找到： 39\n",
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"√找到两个连续符合中枢 000000.SZ 2019-10-03 08:43:55 861 共找到： 40\n",
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"√找到两个连续符合中枢 000000.SZ 2019-10-19 23:40:55 906 共找到： 41\n",
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"√找到两个连续符合中枢 000000.SZ 2019-10-25 04:41:55 913 共找到： 42\n",
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"√找到两个连续符合中枢 000000.SZ 2019-11-02 13:46:55 932 共找到： 43\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"result_df=pd.DataFrame()\n",
|
|
"found_qs_count=0 #找到的趋势数\n",
|
|
"found_count=0 #符合趋势的本组中枢已找到个数\n",
|
|
"zs_no_pre=0\n",
|
|
"zs_zg_pre=0\n",
|
|
"zs_zd_pre=0 \n",
|
|
"zs_gg_pre=0\n",
|
|
"zs_dd_pre=0\n",
|
|
"zs_direction_pre=''\n",
|
|
"for n, k in df.loc[(df[\"line\"]=='bottom') | (df[\"line\"]=='top')].iterrows():\n",
|
|
" #print(k.zs_no, n, zs_no_pre==0 , k.zs_no>zs_no_pre)\n",
|
|
" if k.zs_no!=0 and (zs_no_pre==0 or k.zs_no>zs_no_pre): #新中枢\n",
|
|
" #print(k.zs_no,\"新中枢\",k.zs_no,k.zs_direction)\n",
|
|
" if found_count==0 and (k.zs_direction=='downdown' or k.zs_direction=='upup'):\n",
|
|
" found_count=found_count+1\n",
|
|
" else: #前面已经找到一个待确认的\n",
|
|
" if zs_direction_pre=='downdown' and k.zs_direction=='downdown' and k.gg<zs_dd_pre: #与前面的方向一致\n",
|
|
" found_count=found_count+1\n",
|
|
" #print('下降趋势延申')\n",
|
|
" elif zs_direction_pre=='upup' and k.zs_direction=='upup' and k.dd>zs_gg_pre: #与前面的方向一致\n",
|
|
" found_count=found_count+1\n",
|
|
" #print('上升趋势延申')\n",
|
|
" elif k.zs_direction=='upup' or k.zs_direction=='downdown': #盘整中枢，结束\n",
|
|
" found_count=0\n",
|
|
" #print('×盘整中枢，重新找')\n",
|
|
" elif zs_direction_pre!=k.zs_direction: #与前面的方向不一致，作为第一个继续往下找\n",
|
|
" found_count=1\n",
|
|
" #print('×与之前方向不同，作为第一个往下找')\n",
|
|
" else: #不成趋势\n",
|
|
" found_count=1\n",
|
|
" #print('×条件不符合，作为第一个往下找')\n",
|
|
" if found_count>=2: #找到两个，记录\n",
|
|
" found_qs_count=found_qs_count+1\n",
|
|
" result_df=result_df.append({'ts_code':k.ts_code, 'trade_time':k.zs_confirm_time, 'trade_price':k.zs_confirm_price, 'n':n, 'direction': k.zs_direction},ignore_index=True)\n",
|
|
" print('√找到两个连续符合中枢',k.ts_code, k.zs_confirm_time, k.zs_no,'共找到：',found_qs_count)\n",
|
|
" found_count=0\n",
|
|
" zs_no_pre=k.zs_no\n",
|
|
" zs_zg_pre=k.zg\n",
|
|
" zs_zd_pre=k.zg \n",
|
|
" zs_gg_pre=k.gg\n",
|
|
" zs_dd_pre=k.dd\n",
|
|
" zs_direction_pre=k.zs_direction\n",
|
|
" elif k.zs_no==0 and found_count>0:\n",
|
|
" line_point=k.high if k.line=='top' else k.low\n",
|
|
" if (zs_direction_pre=='upup' and line_point<zs_gg_pre) or (zs_direction_pre=='downdown' and line_point>zs_dd_pre): #连接线段触碰前中枢\n",
|
|
" found_count=0\n",
|
|
" #print('×连接不符合')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 115,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"direction\n",
|
|
"downdown -1461.333333\n",
|
|
"upup -783.840000\n",
|
|
"Name: trade_price, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 115,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result_df.groupby('direction')['trade_price'].mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 112,
|
|
"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>direction</th>\n",
|
|
" <th>n</th>\n",
|
|
" <th>trade_price</th>\n",
|
|
" <th>trade_time</th>\n",
|
|
" <th>ts_code</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>45429.0</td>\n",
|
|
" <td>1290.0</td>\n",
|
|
" <td>2018-10-23 08:21:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>48185.0</td>\n",
|
|
" <td>1407.0</td>\n",
|
|
" <td>2018-10-25 07:29:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>51333.0</td>\n",
|
|
" <td>929.0</td>\n",
|
|
" <td>2018-10-27 23:29:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>53204.0</td>\n",
|
|
" <td>629.0</td>\n",
|
|
" <td>2018-10-29 00:41:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>54092.0</td>\n",
|
|
" <td>470.0</td>\n",
|
|
" <td>2018-10-29 09:50:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>54601.0</td>\n",
|
|
" <td>214.0</td>\n",
|
|
" <td>2018-10-29 22:19:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>58285.0</td>\n",
|
|
" <td>-421.0</td>\n",
|
|
" <td>2018-11-01 10:22:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>63365.0</td>\n",
|
|
" <td>472.0</td>\n",
|
|
" <td>2018-11-04 23:05:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>64607.0</td>\n",
|
|
" <td>817.0</td>\n",
|
|
" <td>2018-11-05 20:53:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>9</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>66698.0</td>\n",
|
|
" <td>184.0</td>\n",
|
|
" <td>2018-11-07 09:26:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>10</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>70424.0</td>\n",
|
|
" <td>400.0</td>\n",
|
|
" <td>2018-11-09 21:17:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>11</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>76868.0</td>\n",
|
|
" <td>1034.0</td>\n",
|
|
" <td>2018-11-14 10:30:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>12</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>82272.0</td>\n",
|
|
" <td>1133.0</td>\n",
|
|
" <td>2018-11-18 11:25:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>13</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>84398.0</td>\n",
|
|
" <td>1476.0</td>\n",
|
|
" <td>2018-11-19 11:40:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>14</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>100205.0</td>\n",
|
|
" <td>1383.0</td>\n",
|
|
" <td>2018-11-30 11:21:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>15</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>105761.0</td>\n",
|
|
" <td>2088.0</td>\n",
|
|
" <td>2018-12-04 09:59:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>16</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>113752.0</td>\n",
|
|
" <td>1378.0</td>\n",
|
|
" <td>2018-12-10 00:32:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>17</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>170353.0</td>\n",
|
|
" <td>1172.0</td>\n",
|
|
" <td>2019-01-18 03:43:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>174766.0</td>\n",
|
|
" <td>1300.0</td>\n",
|
|
" <td>2019-01-21 05:57:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>175605.0</td>\n",
|
|
" <td>1606.0</td>\n",
|
|
" <td>2019-01-22 10:34:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>20</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>183268.0</td>\n",
|
|
" <td>2016.0</td>\n",
|
|
" <td>2019-01-27 05:57:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>192731.0</td>\n",
|
|
" <td>2224.0</td>\n",
|
|
" <td>2019-02-02 18:27:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>22</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>257518.0</td>\n",
|
|
" <td>6.0</td>\n",
|
|
" <td>2019-03-19 18:26:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>23</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>273238.0</td>\n",
|
|
" <td>2494.0</td>\n",
|
|
" <td>2019-03-30 19:52:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>290749.0</td>\n",
|
|
" <td>2474.0</td>\n",
|
|
" <td>2019-04-11 21:15:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>295643.0</td>\n",
|
|
" <td>2766.0</td>\n",
|
|
" <td>2019-04-15 05:07:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>26</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>296349.0</td>\n",
|
|
" <td>3052.0</td>\n",
|
|
" <td>2019-04-15 19:37:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>27</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>333239.0</td>\n",
|
|
" <td>4771.0</td>\n",
|
|
" <td>2019-05-11 08:41:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>341253.0</td>\n",
|
|
" <td>5130.0</td>\n",
|
|
" <td>2019-05-17 00:20:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>29</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>399803.0</td>\n",
|
|
" <td>3068.0</td>\n",
|
|
" <td>2019-06-26 15:12:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>30</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>413327.0</td>\n",
|
|
" <td>2339.0</td>\n",
|
|
" <td>2019-07-06 03:03:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>429276.0</td>\n",
|
|
" <td>1670.0</td>\n",
|
|
" <td>2019-07-17 00:40:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>431950.0</td>\n",
|
|
" <td>2285.0</td>\n",
|
|
" <td>2019-07-18 20:08:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>33</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>440072.0</td>\n",
|
|
" <td>1691.0</td>\n",
|
|
" <td>2019-07-24 18:03:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>34</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>443888.0</td>\n",
|
|
" <td>1244.0</td>\n",
|
|
" <td>2019-07-27 09:26:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>35</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>467004.0</td>\n",
|
|
" <td>457.0</td>\n",
|
|
" <td>2019-08-12 09:48:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>36</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>484840.0</td>\n",
|
|
" <td>-161.0</td>\n",
|
|
" <td>2019-08-24 22:35:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>37</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>497936.0</td>\n",
|
|
" <td>285.0</td>\n",
|
|
" <td>2019-09-03 00:26:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>38</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>501125.0</td>\n",
|
|
" <td>-401.0</td>\n",
|
|
" <td>2019-09-05 01:05:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>39</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>502218.0</td>\n",
|
|
" <td>-679.0</td>\n",
|
|
" <td>2019-09-06 07:36:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>40</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>517298.0</td>\n",
|
|
" <td>-1184.0</td>\n",
|
|
" <td>2019-09-16 03:25:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>41</th>\n",
|
|
" <td>upup</td>\n",
|
|
" <td>518586.0</td>\n",
|
|
" <td>-825.0</td>\n",
|
|
" <td>2019-09-17 03:41:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>42</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>571627.0</td>\n",
|
|
" <td>-670.0</td>\n",
|
|
" <td>2019-10-24 03:29:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>43</th>\n",
|
|
" <td>downdown</td>\n",
|
|
" <td>596568.0</td>\n",
|
|
" <td>-1453.0</td>\n",
|
|
" <td>2019-11-10 12:18:12</td>\n",
|
|
" <td>000000.SZ</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" direction n trade_price trade_time ts_code\n",
|
|
"0 upup 45429.0 1290.0 2018-10-23 08:21:12 000000.SZ\n",
|
|
"1 upup 48185.0 1407.0 2018-10-25 07:29:12 000000.SZ\n",
|
|
"2 downdown 51333.0 929.0 2018-10-27 23:29:12 000000.SZ\n",
|
|
"3 downdown 53204.0 629.0 2018-10-29 00:41:12 000000.SZ\n",
|
|
"4 downdown 54092.0 470.0 2018-10-29 09:50:12 000000.SZ\n",
|
|
"5 downdown 54601.0 214.0 2018-10-29 22:19:12 000000.SZ\n",
|
|
"6 downdown 58285.0 -421.0 2018-11-01 10:22:12 000000.SZ\n",
|
|
"7 upup 63365.0 472.0 2018-11-04 23:05:12 000000.SZ\n",
|
|
"8 upup 64607.0 817.0 2018-11-05 20:53:12 000000.SZ\n",
|
|
"9 downdown 66698.0 184.0 2018-11-07 09:26:12 000000.SZ\n",
|
|
"10 upup 70424.0 400.0 2018-11-09 21:17:12 000000.SZ\n",
|
|
"11 upup 76868.0 1034.0 2018-11-14 10:30:12 000000.SZ\n",
|
|
"12 upup 82272.0 1133.0 2018-11-18 11:25:12 000000.SZ\n",
|
|
"13 upup 84398.0 1476.0 2018-11-19 11:40:12 000000.SZ\n",
|
|
"14 upup 100205.0 1383.0 2018-11-30 11:21:12 000000.SZ\n",
|
|
"15 upup 105761.0 2088.0 2018-12-04 09:59:12 000000.SZ\n",
|
|
"16 downdown 113752.0 1378.0 2018-12-10 00:32:12 000000.SZ\n",
|
|
"17 downdown 170353.0 1172.0 2019-01-18 03:43:12 000000.SZ\n",
|
|
"18 upup 174766.0 1300.0 2019-01-21 05:57:12 000000.SZ\n",
|
|
"19 upup 175605.0 1606.0 2019-01-22 10:34:12 000000.SZ\n",
|
|
"20 upup 183268.0 2016.0 2019-01-27 05:57:12 000000.SZ\n",
|
|
"21 upup 192731.0 2224.0 2019-02-02 18:27:12 000000.SZ\n",
|
|
"22 upup 257518.0 6.0 2019-03-19 18:26:12 000000.SZ\n",
|
|
"23 upup 273238.0 2494.0 2019-03-30 19:52:12 000000.SZ\n",
|
|
"24 upup 290749.0 2474.0 2019-04-11 21:15:12 000000.SZ\n",
|
|
"25 upup 295643.0 2766.0 2019-04-15 05:07:12 000000.SZ\n",
|
|
"26 upup 296349.0 3052.0 2019-04-15 19:37:12 000000.SZ\n",
|
|
"27 downdown 333239.0 4771.0 2019-05-11 08:41:12 000000.SZ\n",
|
|
"28 downdown 341253.0 5130.0 2019-05-17 00:20:12 000000.SZ\n",
|
|
"29 upup 399803.0 3068.0 2019-06-26 15:12:12 000000.SZ\n",
|
|
"30 downdown 413327.0 2339.0 2019-07-06 03:03:12 000000.SZ\n",
|
|
"31 upup 429276.0 1670.0 2019-07-17 00:40:12 000000.SZ\n",
|
|
"32 upup 431950.0 2285.0 2019-07-18 20:08:12 000000.SZ\n",
|
|
"33 upup 440072.0 1691.0 2019-07-24 18:03:12 000000.SZ\n",
|
|
"34 downdown 443888.0 1244.0 2019-07-27 09:26:12 000000.SZ\n",
|
|
"35 downdown 467004.0 457.0 2019-08-12 09:48:12 000000.SZ\n",
|
|
"36 upup 484840.0 -161.0 2019-08-24 22:35:12 000000.SZ\n",
|
|
"37 downdown 497936.0 285.0 2019-09-03 00:26:12 000000.SZ\n",
|
|
"38 downdown 501125.0 -401.0 2019-09-05 01:05:12 000000.SZ\n",
|
|
"39 downdown 502218.0 -679.0 2019-09-06 07:36:12 000000.SZ\n",
|
|
"40 downdown 517298.0 -1184.0 2019-09-16 03:25:12 000000.SZ\n",
|
|
"41 upup 518586.0 -825.0 2019-09-17 03:41:12 000000.SZ\n",
|
|
"42 downdown 571627.0 -670.0 2019-10-24 03:29:12 000000.SZ\n",
|
|
"43 downdown 596568.0 -1453.0 2019-11-10 12:18:12 000000.SZ"
|
|
]
|
|
},
|
|
"execution_count": 112,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"result_df"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python [conda root]",
|
|
"language": "python",
|
|
"name": "conda-root-py"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|