Skip to content

A
alpha-mind
Commit c102774f authored by Dr.李's avatar Dr.李
Browse files
Options

fixed typo cont...

parent a842747f
Show whitespace changes
Inline Side-by-side
Showing with 234 additions and 34 deletions
notebooks/full factors strategy with dask cluster.ipynb
View file @ c102774f
......@@ -326,23 +326,226 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'predictions' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-16-72383b872457>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mref_date\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrade_dates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mpredictions1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mpredictions2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0mcodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 14\u001b[0m \u001b[0mindustry_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_industry_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mref_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcodes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindustry_category\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindustry_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mindustry_exp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindustry_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindustries\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'predictions' is not defined"
"name": "stderr",
"output_type": "stream",
"text": [
"2018-02-10 21:47:59,659 - ALPHA_MIND - INFO - 2010-02-04 is finished\n",
"2018-02-10 21:48:00,252 - ALPHA_MIND - INFO - 2010-02-25 is finished\n",
"2018-02-10 21:48:00,872 - ALPHA_MIND - INFO - 2010-03-11 is finished\n",
"2018-02-10 21:48:01,374 - ALPHA_MIND - INFO - 2010-03-25 is finished\n",
"2018-02-10 21:48:01,901 - ALPHA_MIND - INFO - 2010-04-09 is finished\n",
"2018-02-10 21:48:02,409 - ALPHA_MIND - INFO - 2010-04-23 is finished\n",
"2018-02-10 21:48:02,911 - ALPHA_MIND - INFO - 2010-05-10 is finished\n",
"2018-02-10 21:48:03,423 - ALPHA_MIND - INFO - 2010-05-24 is finished\n",
"2018-02-10 21:48:03,935 - ALPHA_MIND - INFO - 2010-06-07 is finished\n",
"2018-02-10 21:48:04,526 - ALPHA_MIND - INFO - 2010-06-24 is finished\n",
"2018-02-10 21:48:05,062 - ALPHA_MIND - INFO - 2010-07-08 is finished\n",
"2018-02-10 21:48:05,598 - ALPHA_MIND - INFO - 2010-07-22 is finished\n",
"2018-02-10 21:48:06,225 - ALPHA_MIND - INFO - 2010-08-05 is finished\n",
"2018-02-10 21:48:06,755 - ALPHA_MIND - INFO - 2010-08-19 is finished\n",
"2018-02-10 21:48:07,286 - ALPHA_MIND - INFO - 2010-09-02 is finished\n",
"2018-02-10 21:48:07,890 - ALPHA_MIND - INFO - 2010-09-16 is finished\n",
"2018-02-10 21:48:08,402 - ALPHA_MIND - INFO - 2010-10-12 is finished\n",
"2018-02-10 21:48:08,904 - ALPHA_MIND - INFO - 2010-10-26 is finished\n",
"2018-02-10 21:48:09,465 - ALPHA_MIND - INFO - 2010-11-09 is finished\n",
"2018-02-10 21:48:09,968 - ALPHA_MIND - INFO - 2010-11-23 is finished\n",
"2018-02-10 21:48:10,492 - ALPHA_MIND - INFO - 2010-12-07 is finished\n",
"2018-02-10 21:48:10,987 - ALPHA_MIND - INFO - 2010-12-21 is finished\n",
"2018-02-10 21:48:11,534 - ALPHA_MIND - INFO - 2011-01-05 is finished\n",
"2018-02-10 21:48:12,161 - ALPHA_MIND - INFO - 2011-01-19 is finished\n",
"2018-02-10 21:48:12,704 - ALPHA_MIND - INFO - 2011-02-09 is finished\n",
"2018-02-10 21:48:13,242 - ALPHA_MIND - INFO - 2011-02-23 is finished\n",
"2018-02-10 21:48:13,798 - ALPHA_MIND - INFO - 2011-03-09 is finished\n",
"2018-02-10 21:48:14,316 - ALPHA_MIND - INFO - 2011-03-23 is finished\n",
"2018-02-10 21:48:14,828 - ALPHA_MIND - INFO - 2011-04-08 is finished\n",
"2018-02-10 21:48:15,378 - ALPHA_MIND - INFO - 2011-04-22 is finished\n",
"2018-02-10 21:48:15,889 - ALPHA_MIND - INFO - 2011-05-09 is finished\n",
"2018-02-10 21:48:16,403 - ALPHA_MIND - INFO - 2011-05-23 is finished\n",
"2018-02-10 21:48:16,924 - ALPHA_MIND - INFO - 2011-06-07 is finished\n",
"2018-02-10 21:48:17,441 - ALPHA_MIND - INFO - 2011-06-21 is finished\n",
"2018-02-10 21:48:18,071 - ALPHA_MIND - INFO - 2011-07-05 is finished\n",
"2018-02-10 21:48:18,638 - ALPHA_MIND - INFO - 2011-07-19 is finished\n",
"2018-02-10 21:48:19,189 - ALPHA_MIND - INFO - 2011-08-02 is finished\n",
"2018-02-10 21:48:19,783 - ALPHA_MIND - INFO - 2011-08-16 is finished\n",
"2018-02-10 21:48:20,381 - ALPHA_MIND - INFO - 2011-08-30 is finished\n",
"2018-02-10 21:48:20,909 - ALPHA_MIND - INFO - 2011-09-14 is finished\n",
"2018-02-10 21:48:21,461 - ALPHA_MIND - INFO - 2011-09-28 is finished\n",
"2018-02-10 21:48:21,999 - ALPHA_MIND - INFO - 2011-10-19 is finished\n",
"2018-02-10 21:48:22,694 - ALPHA_MIND - INFO - 2011-11-02 is finished\n",
"2018-02-10 21:48:23,290 - ALPHA_MIND - INFO - 2011-11-16 is finished\n",
"2018-02-10 21:48:23,949 - ALPHA_MIND - INFO - 2011-11-30 is finished\n",
"2018-02-10 21:48:24,543 - ALPHA_MIND - INFO - 2011-12-14 is finished\n",
"2018-02-10 21:48:25,087 - ALPHA_MIND - INFO - 2011-12-28 is finished\n",
"2018-02-10 21:48:25,627 - ALPHA_MIND - INFO - 2012-01-13 is finished\n",
"2018-02-10 21:48:26,194 - ALPHA_MIND - INFO - 2012-02-03 is finished\n",
"2018-02-10 21:48:26,785 - ALPHA_MIND - INFO - 2012-02-17 is finished\n",
"2018-02-10 21:48:27,351 - ALPHA_MIND - INFO - 2012-03-02 is finished\n",
"2018-02-10 21:48:27,886 - ALPHA_MIND - INFO - 2012-03-16 is finished\n",
"2018-02-10 21:48:28,394 - ALPHA_MIND - INFO - 2012-03-30 is finished\n",
"2018-02-10 21:48:28,912 - ALPHA_MIND - INFO - 2012-04-18 is finished\n",
"2018-02-10 21:48:29,524 - ALPHA_MIND - INFO - 2012-05-04 is finished\n",
"2018-02-10 21:48:30,067 - ALPHA_MIND - INFO - 2012-05-18 is finished\n",
"2018-02-10 21:48:30,603 - ALPHA_MIND - INFO - 2012-06-01 is finished\n",
"2018-02-10 21:48:31,185 - ALPHA_MIND - INFO - 2012-06-15 is finished\n",
"2018-02-10 21:48:31,805 - ALPHA_MIND - INFO - 2012-07-02 is finished\n",
"2018-02-10 21:48:32,378 - ALPHA_MIND - INFO - 2012-07-16 is finished\n",
"2018-02-10 21:48:32,900 - ALPHA_MIND - INFO - 2012-07-30 is finished\n",
"2018-02-10 21:48:33,494 - ALPHA_MIND - INFO - 2012-08-13 is finished\n",
"2018-02-10 21:48:34,090 - ALPHA_MIND - INFO - 2012-08-27 is finished\n",
"2018-02-10 21:48:34,634 - ALPHA_MIND - INFO - 2012-09-10 is finished\n",
"2018-02-10 21:48:35,230 - ALPHA_MIND - INFO - 2012-09-24 is finished\n",
"2018-02-10 21:48:35,866 - ALPHA_MIND - INFO - 2012-10-15 is finished\n",
"2018-02-10 21:48:36,423 - ALPHA_MIND - INFO - 2012-10-29 is finished\n",
"2018-02-10 21:48:36,944 - ALPHA_MIND - INFO - 2012-11-12 is finished\n",
"2018-02-10 21:48:37,468 - ALPHA_MIND - INFO - 2012-11-26 is finished\n",
"2018-02-10 21:48:38,028 - ALPHA_MIND - INFO - 2012-12-10 is finished\n",
"2018-02-10 21:48:38,558 - ALPHA_MIND - INFO - 2012-12-24 is finished\n",
"2018-02-10 21:48:39,070 - ALPHA_MIND - INFO - 2013-01-10 is finished\n",
"2018-02-10 21:48:39,615 - ALPHA_MIND - INFO - 2013-01-24 is finished\n",
"2018-02-10 21:48:40,157 - ALPHA_MIND - INFO - 2013-02-07 is finished\n",
"2018-02-10 21:48:40,755 - ALPHA_MIND - INFO - 2013-02-28 is finished\n",
"2018-02-10 21:48:41,439 - ALPHA_MIND - INFO - 2013-03-14 is finished\n",
"2018-02-10 21:48:41,997 - ALPHA_MIND - INFO - 2013-03-28 is finished\n",
"2018-02-10 21:48:42,546 - ALPHA_MIND - INFO - 2013-04-15 is finished\n",
"2018-02-10 21:48:43,090 - ALPHA_MIND - INFO - 2013-05-02 is finished\n",
"2018-02-10 21:48:43,635 - ALPHA_MIND - INFO - 2013-05-16 is finished\n",
"2018-02-10 21:48:44,161 - ALPHA_MIND - INFO - 2013-05-30 is finished\n",
"2018-02-10 21:48:44,720 - ALPHA_MIND - INFO - 2013-06-18 is finished\n",
"2018-02-10 21:48:45,311 - ALPHA_MIND - INFO - 2013-07-02 is finished\n",
"2018-02-10 21:48:45,899 - ALPHA_MIND - INFO - 2013-07-16 is finished\n",
"2018-02-10 21:48:46,465 - ALPHA_MIND - INFO - 2013-07-30 is finished\n",
"2018-02-10 21:48:47,123 - ALPHA_MIND - INFO - 2013-08-13 is finished\n",
"2018-02-10 21:48:47,723 - ALPHA_MIND - INFO - 2013-08-27 is finished\n",
"2018-02-10 21:48:48,312 - ALPHA_MIND - INFO - 2013-09-10 is finished\n",
"2018-02-10 21:48:48,881 - ALPHA_MIND - INFO - 2013-09-26 is finished\n",
"2018-02-10 21:48:49,419 - ALPHA_MIND - INFO - 2013-10-17 is finished\n",
"2018-02-10 21:48:49,963 - ALPHA_MIND - INFO - 2013-10-31 is finished\n",
"2018-02-10 21:48:50,512 - ALPHA_MIND - INFO - 2013-11-14 is finished\n",
"2018-02-10 21:48:51,096 - ALPHA_MIND - INFO - 2013-11-28 is finished\n",
"2018-02-10 21:48:51,676 - ALPHA_MIND - INFO - 2013-12-12 is finished\n",
"2018-02-10 21:48:52,285 - ALPHA_MIND - INFO - 2013-12-26 is finished\n",
"2018-02-10 21:48:52,991 - ALPHA_MIND - INFO - 2014-01-10 is finished\n",
"2018-02-10 21:48:53,526 - ALPHA_MIND - INFO - 2014-01-24 is finished\n",
"2018-02-10 21:48:54,062 - ALPHA_MIND - INFO - 2014-02-14 is finished\n",
"2018-02-10 21:48:54,641 - ALPHA_MIND - INFO - 2014-02-28 is finished\n",
"2018-02-10 21:48:55,226 - ALPHA_MIND - INFO - 2014-03-14 is finished\n",
"2018-02-10 21:48:55,807 - ALPHA_MIND - INFO - 2014-03-28 is finished\n",
"2018-02-10 21:48:56,384 - ALPHA_MIND - INFO - 2014-04-14 is finished\n",
"2018-02-10 21:48:56,958 - ALPHA_MIND - INFO - 2014-04-28 is finished\n",
"2018-02-10 21:48:57,521 - ALPHA_MIND - INFO - 2014-05-14 is finished\n",
"2018-02-10 21:48:58,086 - ALPHA_MIND - INFO - 2014-05-28 is finished\n",
"2018-02-10 21:48:58,723 - ALPHA_MIND - INFO - 2014-06-12 is finished\n",
"2018-02-10 21:48:59,297 - ALPHA_MIND - INFO - 2014-06-26 is finished\n",
"2018-02-10 21:48:59,833 - ALPHA_MIND - INFO - 2014-07-10 is finished\n",
"2018-02-10 21:49:00,363 - ALPHA_MIND - INFO - 2014-07-24 is finished\n",
"2018-02-10 21:49:00,886 - ALPHA_MIND - INFO - 2014-08-07 is finished\n",
"2018-02-10 21:49:01,417 - ALPHA_MIND - INFO - 2014-08-21 is finished\n",
"2018-02-10 21:49:01,954 - ALPHA_MIND - INFO - 2014-09-04 is finished\n",
"2018-02-10 21:49:02,527 - ALPHA_MIND - INFO - 2014-09-19 is finished\n",
"2018-02-10 21:49:03,112 - ALPHA_MIND - INFO - 2014-10-10 is finished\n",
"2018-02-10 21:49:03,654 - ALPHA_MIND - INFO - 2014-10-24 is finished\n",
"2018-02-10 21:49:04,219 - ALPHA_MIND - INFO - 2014-11-07 is finished\n",
"2018-02-10 21:49:04,897 - ALPHA_MIND - INFO - 2014-11-21 is finished\n",
"2018-02-10 21:49:05,447 - ALPHA_MIND - INFO - 2014-12-05 is finished\n",
"2018-02-10 21:49:05,967 - ALPHA_MIND - INFO - 2014-12-19 is finished\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-02-10 21:49:06,487 - ALPHA_MIND - INFO - 2015-01-06 is finished\n",
"2018-02-10 21:49:07,019 - ALPHA_MIND - INFO - 2015-01-20 is finished\n",
"2018-02-10 21:49:07,548 - ALPHA_MIND - INFO - 2015-02-03 is finished\n",
"2018-02-10 21:49:08,076 - ALPHA_MIND - INFO - 2015-02-17 is finished\n",
"2018-02-10 21:49:08,647 - ALPHA_MIND - INFO - 2015-03-10 is finished\n",
"2018-02-10 21:49:09,235 - ALPHA_MIND - INFO - 2015-03-24 is finished\n",
"2018-02-10 21:49:09,790 - ALPHA_MIND - INFO - 2015-04-08 is finished\n",
"2018-02-10 21:49:10,358 - ALPHA_MIND - INFO - 2015-04-22 is finished\n",
"2018-02-10 21:49:11,040 - ALPHA_MIND - INFO - 2015-05-07 is finished\n",
"2018-02-10 21:49:11,651 - ALPHA_MIND - INFO - 2015-05-21 is finished\n",
"2018-02-10 21:49:12,205 - ALPHA_MIND - INFO - 2015-06-04 is finished\n",
"2018-02-10 21:49:12,866 - ALPHA_MIND - INFO - 2015-06-18 is finished\n",
"2018-02-10 21:49:13,476 - ALPHA_MIND - INFO - 2015-07-03 is finished\n",
"2018-02-10 21:49:14,142 - ALPHA_MIND - INFO - 2015-07-17 is finished\n",
"2018-02-10 21:49:14,699 - ALPHA_MIND - INFO - 2015-07-31 is finished\n",
"2018-02-10 21:49:15,317 - ALPHA_MIND - INFO - 2015-08-14 is finished\n",
"2018-02-10 21:49:15,911 - ALPHA_MIND - INFO - 2015-08-28 is finished\n",
"2018-02-10 21:49:16,470 - ALPHA_MIND - INFO - 2015-09-15 is finished\n",
"2018-02-10 21:49:17,148 - ALPHA_MIND - INFO - 2015-09-29 is finished\n",
"2018-02-10 21:49:17,722 - ALPHA_MIND - INFO - 2015-10-20 is finished\n",
"2018-02-10 21:49:18,272 - ALPHA_MIND - INFO - 2015-11-03 is finished\n",
"2018-02-10 21:49:18,913 - ALPHA_MIND - INFO - 2015-11-17 is finished\n",
"2018-02-10 21:49:19,445 - ALPHA_MIND - INFO - 2015-12-01 is finished\n",
"2018-02-10 21:49:20,024 - ALPHA_MIND - INFO - 2015-12-15 is finished\n",
"2018-02-10 21:49:20,597 - ALPHA_MIND - INFO - 2015-12-29 is finished\n",
"2018-02-10 21:49:21,183 - ALPHA_MIND - INFO - 2016-01-13 is finished\n",
"2018-02-10 21:49:21,741 - ALPHA_MIND - INFO - 2016-01-27 is finished\n",
"2018-02-10 21:49:22,327 - ALPHA_MIND - INFO - 2016-02-17 is finished\n",
"2018-02-10 21:49:22,978 - ALPHA_MIND - INFO - 2016-03-02 is finished\n",
"2018-02-10 21:49:23,530 - ALPHA_MIND - INFO - 2016-03-16 is finished\n",
"2018-02-10 21:49:24,066 - ALPHA_MIND - INFO - 2016-03-30 is finished\n",
"2018-02-10 21:49:24,624 - ALPHA_MIND - INFO - 2016-04-14 is finished\n",
"2018-02-10 21:49:25,173 - ALPHA_MIND - INFO - 2016-04-28 is finished\n",
"2018-02-10 21:49:25,721 - ALPHA_MIND - INFO - 2016-05-13 is finished\n",
"2018-02-10 21:49:26,264 - ALPHA_MIND - INFO - 2016-05-27 is finished\n",
"2018-02-10 21:49:26,828 - ALPHA_MIND - INFO - 2016-06-14 is finished\n",
"2018-02-10 21:49:27,378 - ALPHA_MIND - INFO - 2016-06-28 is finished\n",
"2018-02-10 21:49:27,941 - ALPHA_MIND - INFO - 2016-07-12 is finished\n",
"2018-02-10 21:49:28,566 - ALPHA_MIND - INFO - 2016-07-26 is finished\n",
"2018-02-10 21:49:29,118 - ALPHA_MIND - INFO - 2016-08-09 is finished\n",
"2018-02-10 21:49:29,695 - ALPHA_MIND - INFO - 2016-08-23 is finished\n",
"2018-02-10 21:49:30,281 - ALPHA_MIND - INFO - 2016-09-06 is finished\n",
"2018-02-10 21:49:30,864 - ALPHA_MIND - INFO - 2016-09-22 is finished\n",
"2018-02-10 21:49:31,452 - ALPHA_MIND - INFO - 2016-10-13 is finished\n",
"2018-02-10 21:49:32,024 - ALPHA_MIND - INFO - 2016-10-27 is finished\n",
"2018-02-10 21:49:32,621 - ALPHA_MIND - INFO - 2016-11-10 is finished\n",
"2018-02-10 21:49:33,225 - ALPHA_MIND - INFO - 2016-11-24 is finished\n",
"2018-02-10 21:49:33,768 - ALPHA_MIND - INFO - 2016-12-08 is finished\n",
"2018-02-10 21:49:34,423 - ALPHA_MIND - INFO - 2016-12-22 is finished\n",
"2018-02-10 21:49:35,017 - ALPHA_MIND - INFO - 2017-01-06 is finished\n",
"2018-02-10 21:49:35,555 - ALPHA_MIND - INFO - 2017-01-20 is finished\n",
"2018-02-10 21:49:36,100 - ALPHA_MIND - INFO - 2017-02-10 is finished\n",
"2018-02-10 21:49:36,683 - ALPHA_MIND - INFO - 2017-02-24 is finished\n",
"2018-02-10 21:49:37,246 - ALPHA_MIND - INFO - 2017-03-10 is finished\n",
"2018-02-10 21:49:37,817 - ALPHA_MIND - INFO - 2017-03-24 is finished\n",
"2018-02-10 21:49:38,457 - ALPHA_MIND - INFO - 2017-04-11 is finished\n",
"2018-02-10 21:49:39,041 - ALPHA_MIND - INFO - 2017-04-25 is finished\n",
"2018-02-10 21:49:39,602 - ALPHA_MIND - INFO - 2017-05-10 is finished\n",
"2018-02-10 21:49:40,172 - ALPHA_MIND - INFO - 2017-05-24 is finished\n",
"2018-02-10 21:49:40,848 - ALPHA_MIND - INFO - 2017-06-09 is finished\n",
"2018-02-10 21:49:41,402 - ALPHA_MIND - INFO - 2017-06-23 is finished\n",
"2018-02-10 21:49:41,948 - ALPHA_MIND - INFO - 2017-07-07 is finished\n",
"2018-02-10 21:49:42,515 - ALPHA_MIND - INFO - 2017-07-21 is finished\n",
"2018-02-10 21:49:43,073 - ALPHA_MIND - INFO - 2017-08-04 is finished\n",
"2018-02-10 21:49:43,619 - ALPHA_MIND - INFO - 2017-08-18 is finished\n",
"2018-02-10 21:49:44,182 - ALPHA_MIND - INFO - 2017-09-01 is finished\n",
"2018-02-10 21:49:44,741 - ALPHA_MIND - INFO - 2017-09-15 is finished\n",
"2018-02-10 21:49:45,334 - ALPHA_MIND - INFO - 2017-09-29 is finished\n",
"2018-02-10 21:49:45,884 - ALPHA_MIND - INFO - 2017-10-20 is finished\n",
"2018-02-10 21:49:46,443 - ALPHA_MIND - INFO - 2017-11-03 is finished\n",
"2018-02-10 21:49:47,125 - ALPHA_MIND - INFO - 2017-11-17 is finished\n",
"2018-02-10 21:49:47,732 - ALPHA_MIND - INFO - 2017-12-01 is finished\n",
"2018-02-10 21:49:48,303 - ALPHA_MIND - INFO - 2017-12-15 is finished\n",
"2018-02-10 21:49:48,857 - ALPHA_MIND - INFO - 2017-12-29 is finished\n",
"2018-02-10 21:49:49,428 - ALPHA_MIND - INFO - 2018-01-15 is finished\n",
"2018-02-10 21:49:49,938 - ALPHA_MIND - INFO - 2018-01-29 is finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 1min 50s\n"
]
}
],
"source": [
"#%%time\n",
"%%time\n",
"\n",
"engine = SqlEngine(data_source)\n",
"\n",
......@@ -395,14 +598,14 @@
" lbound=lbound,\n",
" ubound=ubound)\n",
" except ValueError:\n",
" alpha_logger.info('{0} full re-balance'.format(date))\n",
" alpha_logger.info('{0} full re-balance'.format(ref_date))\n",
" target_pos, _ = er_portfolio_analysis(er,\n",
" industry_names,\n",
" None,\n",
" constraint,\n",
" False,\n",
" benchmark_w,\n",
" method=method,\n",
" method='risk_neutral',\n",
" lbound=lbound,\n",
" ubound=ubound)\n",
" \n",
......@@ -425,31 +628,28 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 18,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Empty data passed with indices specified.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m 4309\u001b[0m \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mform_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4310\u001b[1;33m \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4311\u001b[0m \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, blocks, axes, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m 2794\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdo_integrity_check\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2795\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36m_verify_integrity\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 3005\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mmgr_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3006\u001b[1;33m \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3007\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mtot_items\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m 4277\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mblock_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4278\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty data passed with indices specified.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4279\u001b[0m raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n",
"\u001b[1;31mValueError\u001b[0m: Empty data passed with indices specified.",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-14-ca83bdfc6e39>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mret_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'returns'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mrets\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'turn_over'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mturn_overs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_dates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# index return\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m index_return = engine.fetch_dx_return_index_range(benchmark,\n\u001b[0;32m 5\u001b[0m \u001b[0mdates\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrade_dates\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 273\u001b[0m dtype=dtype, copy=copy)\n\u001b[0;32m 274\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 276\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 277\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[1;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[0;32m 409\u001b[0m \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 413\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m 5504\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_ensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5506\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m 4312\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4313\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4314\u001b[1;33m \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32md:\\ProgramData\\IntelPython3_2018\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m 4276\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4277\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mblock_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4278\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty data passed with indices specified.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4279\u001b[0m raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n\u001b[0;32m 4280\u001b[0m passed, implied))\n",
"\u001b[1;31mValueError\u001b[0m: Empty data passed with indices specified."
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1f100392908>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAF2CAYAAADJIOaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ4QQQgghxM7+D798SbyHSgnRAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1f1017afdd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment