[
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2018 Yumo Xu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# stocknet-code\n\nThis repository releases the code for stock movement prediction from tweets and historical stock prices. Please cite the following paper [[bib](https://aclanthology.info/papers/P18-1183/p18-1183.bib)] if you use this code,  \n\nYumo Xu and Shay B. Cohen. 2018. [Stock Movement Prediction from Tweets and Historical Prices](http://aclweb.org/anthology/P18-1183). In Proceedings of the 56st Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia, volume 1.\n> Stock movement prediction is a challenging problem: the market is highly *stochastic*, and we make *temporally-dependent* predictions from *chaotic* data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with  temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.\n\nShould you have any query please contact me at [yumo.xu@ed.ac.uk](mailto:yumo.xu@ed.ac.uk).\n\n## Dependencies\n- Python 2.7.11\n- Tensorflow 1.4.0\n- Scipy 1.0.0\n- NLTK 3.2.5\n\n## Directories\n- src: source files;\n- res: resource files including,\n    - Vocabulary file `vocab.txt`;\n    - Pre-trained embeddings of [GloVe](https://github.com/stanfordnlp/GloVe). We used the GloVe obtained from the Twitter corpora which you could download [here](http://nlp.stanford.edu/data/wordvecs/glove.twitter.27B.zip).\n- data: datasets consisting of tweets and prices which you could download [here](https://github.com/yumoxu/stocknet-dataset).\n\n## Configurations\nModel configurations are listed in `config.yml` where you could set `variant_type` to *hedge, tech, fund* or *discriminative* to get four corresponding model variants, HedgeFundAnalyst, TechincalAnalyst, FundamentalAnalyst or DiscriminativeAnalyst described in the paper. \n\nAdditionally, when you set `variant_type=hedge, alpha=0`, you would acquire IndependentAnalyst without any auxiliary effects. \n\n## Running\n\nAfter configuration, use `sh src/run.sh` in your terminal to start model learning and test the model after the training is completed. If you would like to do them separately, simply comment out `exe.train_and_dev()` or `exe.restore_and_test()` in `Main.py`."
  },
  {
    "path": "data/.gitkeep",
    "content": ""
  },
  {
    "path": "res/vocab.txt",
    "content": "[\"fawn\", \"mid-week\", \"1,800\", \"marketmovers\", \"eurcad\", \"4bot\", \"woods\", \"hanging\", \"woody\", \"mdbx\", \"igual\", \"skillsatwork\", \"befouls\", \"volumeshiftup\", \"bringing\", \"wednesday\", \"tcbi\", \"fuera\", \"tom'w\", \"\\u2705\", \"tom's\", \"\\ud83d\\ude0f\", \"nagsusumamo\", \"270\", \"271\", \"272\", \"274\", \"275\", \"277\", \"278\", \"279\", \"trendfollowing\", \"214.50\", \"errors\", \"cooking\", \"\\u262f\", \"designing\", \"shocks\", \"27b\", \"27c\", \"ulpix\", \"affiliates\", \"ching\", \"china\", \"gileads\", \"gunit\", \"chino\", \"65.79\", \"doldrums\", \"\\ud83d\\udc63\", \"kids\", \"controversy\", \"kidd\", \"climber\", \"e8\", \"golden\", \"projection\", \"wikimedia\", \"stern\", \"ivabradine\", \"dna\", \"musiq\", \"dnb\", \"dno\", \"dnn\", \"dnt\", \"grahamz\", \"music\", \"therefore\", \"dns\", \"dnr\", \"excu\", \"psxp\", \"yahoo\", \"5.16\", \"5.15\", \"yahoy\", \"exch\", \"locked\", \"100.00\", \"locker\", \"india's\", \"wang\", \"dividendinvesting\", \"#3uuudd33\", \"51.8\", \"want\", \"117m\", \"pinto\", \"117k\", \"51.5\", \"absolute\", \"mcmuffin\", \"duniya\", \"travel\", \"playback\", \"1175\", \"resultados\", \"wrong\", \"sedg\", \"sentiquant\", \"opted\", \"amaya\", \"\\u0e43\\u0e08\\u0e40\\u0e22\", \"18th\", \"boleto\", \"nonfarmpayroll\", \"#promo\", \"clear--fixed\", \"peut\", \"partnered\", \"mabthera\", \"rewarded\", \"welcomes\", \"fir\", \"fis\", \"ex-dividend\", \"uop\", \"fiw\", \"fit\", \"unitedhealthcare\", \"screaming\", \"fix\", \"ensaio\", \"inar\", \"inap\", \"secede\", \"fib\", \"fig\", \"cndo\", \"gevo\", \"fii\", \"fin\", \"fio\", \"vouchers\", \"\\u0627\\u0628\\u0644\", \"twits\", \"effects\", \"25/05\", \"yourself\", \"1,341\", \"telekom\", \"arrow\", \"\\uff7e\", \"madmoney\", \"302.50\", \"17,573\", \"osis\", \"gdxj\", \"392\", \"391\", \"390\", \"397\", \"396\", \"395\", \"394\", \"2day\", \"399\", \"medical's\", \"sucampo\", \"touts\", \"guaire\", \"snapchat-like\", \"stock's\", \"encourage\", \"adapt\", \"nextera\", \"39c\", \"abbott\", \"gameplan\", \"3740\", \"corrects\", \"estimate\", \"gameplay\", \"strats\", \"accts\", \"competes\", \"yester\", \"greatvalues\", \"chromebooks\", \"flymusic\", \"kfx\", \"kfy\", \"kfc\", \"chevron\", \"bundyelvis\", \"dgx\", \"service\", \"forrester\", \"needed\", \"mind-boggling\", \"master\", \"lifesciences\", \"genesis\", \"muzikkzone\", \"2014-07-\", \"rewards\", \"shambles\", \"descendi\\u00f3\", \"task\", \"positively\", \"\\u00ed\", \"regulator\", \"ahmet\", \"feeling\", \"realmoneypro\", \"agparaguay\", \"algotrade\", \"longs\", \"lts.to\", \"ka-ching\", \"spectrum\", \"l'attente\", \"pacientes\", \"2.79\", \"affairs\", \"06:280\", \"mostly\", \"vgk\", \"2.71\", \"2.73\", \"2.72\", \"2.75\", \"2.77\", \"2.76\", \"vgz\", \"vgt\", \"shipments\", \"45th\", \"juventus\", \"limitless\", \"metrics\", \"simplify\", \"mouth\", \"icloud\", \"tiai\\u00e4\", \"681b\", \"6,818\", \"tast\", \"tecl\", \"teco\", \"tech\", \"chamakta\", \"teck\", \"tecd\", \"scream\", \"saying\", \"20/15\", \"htwr\", \"620.00\", \"620.01\", \"tempted\", \"cheaply\", \"eliminated\", \"peck\", \"norcal\", \"rico\", \"rick\", \"rich\", \"rice\", \"rica\", \"plate\", \"plata\", \"7-18\", \"7-12\", \"7-10\", \"d-up\", \"prvchi\", \"am\\u00e9liore\", \"10:08\", \"nicely\", \"pretzel\", \"compras\", \"patch\", \"compran\", \"skechers\", \"ahorran\", \"sb568\", \"rdwr\", \"royfberger\", \"sentiments\", \"lots\", \"irs\", \"irm\", \"ira\", \"ire\", \"lote\", \"discipline\", \"onpennies\", \"extend\", \"nature\", \"vuzi\", \"extent\", \"cus\", \"centralbanks\", \"f135\", \"veev\", \"automate\", \"heating\", \"ryurx\", \"high-yielding\", \"lookin\", \"veel\", \"colgate-palmolive\", \"obscenity\", \"11.19\", \"corporateamerica\", \"11.11\", \"felicidad\", \"financeppch\", \"smackdown\", \"frc\", \"pagaran\", \"3.87\", \"3.86\", \"fre\", \"3.84\", \"union\", \"fri\", \"fro\", \"frm\", \".\", \"muck\", \"much\", \"investingpfe\", \"wiseguy\", \"frt\", \"frx\", \"+3.00\", \"ovti\", \"dave\", \"doubts\", \"spin\", \"spil\", \"+1.09\", \"+1.03\", \"+1.02\", \"avanir\", \"+1.07\", \"employ\", \"droppin\", \"cuh\", \"unos\", \"portfoliomanagement\", \"0lj\", \"hone\", \"hong\", \"alimentation\", \"\\ud83c\\udfa4\", \"acquire\", \"4.9\", \"split\", \"investaapl\", \"outspends\", \"d\\u68af\\u86aa\\u4fa0\\uff96\\uff9a\\uff94\\uff69\", \"workforce\", \"505b\", \"bp's\", \"\\u00e4r\", \"muaah\", \"corporati\", \"\\ud83c\\uddf8\", \"corporate\", \"plaque\", \"smvi\", \"airbag\", \"hai\", \"upsides\", \"hal\", \"ham\", \"han\", \"hao\", \"haa\", \"hab\", \"had\", \"madhosh\", \"hay\", \"haz\", \"beloved\", \"har\", \"has\", \"hat\", \"hav\", \"t's\", \"municipal\", \"survival\", \"\\ud83d\\udc4c\", \"pbmd\", \"shadow\", \"replace\", \"12:30\", \"12:33\", \"aeye\", \"gadg\", \"retake\", \"beneficial\", \"crowd\", \"richytee\", \"czech\", \"axisbank.in\", \"crown\", \"jnj's\", \"p\\u00e9rdidas\", \"captive\", \"\\u2796\", \"billboard\", \"chantier\", \"hse.to\", \"bottom\", \"ecopetrol\", \"satish_kumar\", \"opponents\", \"wayfair\", \"starring\", \"13,936\", \"stokes\", \"aboard\", \"adep\", \"acol\", \"ades\", \"cell-based\", \"stoked\", \"informaci\\u00f3n\", \"acor\", \"mbi\", \"maxwell\", \"dyson\", \"shoote\", \"mbt\", \"shoots\", \"hxu.to\", \"mbr\", \"bombardier\", \"altitude\", \"tame\", \"2,757\", \"greatness\", \"anci\", \"verde\", \"safeguard\", \"embusteros\", \"congratulations\", \"azn\", \"azo\", \"smashes\", \"break-up\", \"azz\", \"pesos\", \"smashed\", \"30m\", \"\\u2601\", \"passenger\", \"publica\", \"minas\", \"genzyme's\", \"eventual\", \"rspp\", \"populations\", \"cyou\", \"pasadena\", \"role\", \"ubnt\", \"roll\", \"intend\", \"set-ups\", \"outage\", \"devon\", \"intent\", \"pauljsingh\", \"mike's\", \"\\u22ee\", \"4thofjuly\", \"diinn\", \"depuis\", \"osx\", \"childr\", \"childs\", \"cincinnati\", \"chain\", \"whoever\", \"osu\", \"osh\", \"osk\", \"pc-on-a-stick\", \"<=-p\", \"chair\", \"ex-financials\", \"690\", \"charter-twc\", \"bullcity\", \"crater\", \"datefeb\", \"hedgefunds\", \"over-the-top\", \"downloading\", \"os2\", \"biopharmaceuticals\", \"choice\", \"bhar\", \"gloomy\", \"minuto\", \"stays\", \"exact\", \"minute\", \"catalonia\", \"cooks\", \"skewed\", \"yndx\", \"145.62\", \"shortsqueeze\", \"twgp\", \"orbitz\", \"11:45\", \"11:44\", \"11:47\", \"11:46\", \"chopping\", \"shirts\", \"11:43\", \"11:42\", \"11:48\", \"\\u061f\", \"cumplea\\u00f1os\", \"headset\", \"qorvo\", \"profitURL\", \"\\ud83d\\udca8\", \"humanrightsday\", \"xeljanz\", \"boost\", \"303\", \"climbs\", \"address\", \"xtreme\", \"on\\u03b4\", \"revenues\", \"rmrk\", \"impacted\", \"hsu.to\", \"liderando\", \"influx\", \"hclp\", \"raise\", \"umpq\", \"petroleo\", \"sistemas\", \"rejecting\", \"instag\", \"azsey\", \"2015-2016\", \"opposes\", \"working\", \"aoxing\", \"vrx.to\", \"\\u20b1\", \"opposed\", \"optionmonster\", \"ree\", \"beneficio\", \"21.000\", \"72.61\", \"consoles\", \"94th\", \"cuanto\", \"rebounding\", \"depot's\", \"currency\", \"following\", \"64gb\", \"admired\", \"mirrors\", \"locks\", \"incremental\", \"week-end\", \"usna\", \"listens\", \"dfevx\", \"toucharcade\", \"\\u0645\\u0627\\u0634\\u0627\\u0621\", \"thanking\", \"investvlo\", \"bevyz\", \"colocan\", \"cecaf\", \"zaoff\", \"fueled\", \"atau\", \"pulley\", \"denbury\", \"oculus\", \"intradia\", \"quadrant\", \"wolves\", \"pulled\", \"investorsobserver\", \"years\", \"episodes\", \"jin\", \"jim\", \"troubles\", \"faves\", \"107.00\", \"talita\", \"jis\", \"nsa\", \"troubled\", \"no-brainer\", \"modestly\", \"recipients\", \"civilian\", \"cr\\u00e9dito\", \"drilling\", \"makeamericagreatagain\", \"aprox\", \"sorted\", \"\\\\\", \"stockwars\", \"didg\", \"vrnt\", \"didn\", \"105.00\", \"instability\", \"quarter\", \"ni\\u00f1os\", \"chevron's\", \"exelon\", \"prismo\", \"nsp\", \"sponsor\", \"entering\", \"disasters\", \"troll\", \"cytk\", \"stockanalysis\", \"799\", \"cytx\", \"seriously\", \"trauma\", \"internet\", \"ezch\", \"cytr\", \"rayank\", \"curbs\", \"tienen\", \"incentives\", \"crazies\", \"grandma\", \"benioff\", \"brasil\", \"initiate\", \"calidad\", \"imbruvica\", \"79k\", \"wal-mart's\", \"emotion\", \"saving\", \"onb\", \"one\", \"sammantic\", \"ons\", \"gooogl\", \"teardown\", \"broadcom\", \"tesla's\", \"fuelband\", \"-10.53\", \"surges\", \"corevalve\", \"immelt\", \"vorapaxar\", \"surged\", \"1.59\", \"dltr\", \"non-stop\", \"crossroads\", \"so-so\", \"rehab\", \"daratumumab\", \"shakeup\", \"rzx\", \"supersized\", \"cbli\", \"emea\", \"locations\", \"turner\", \"grnh\", \"emes\", \"invite\", \"warriors\", \"bgeix\", \"dnrg\", \"intends\", \"mayer\", \"opposite\", \"paysandu\", \"buffet\", \"printed\", \"phil\", \"jitters\", \"phir\", \"eprs\", \"wynn\", \"hcsg\", \"infuriate\", \"friction\", \"inconsistent\", \"aggressive\", \"raisethewage\", \"625p\", \"cerrado\", \"monda\", \"awaiting\", \"ytd\", \"ma50\", \"14:13\", \"14:17\", \"nmrx\", \"mhfi\", \"could've\", \"tiko\", \"tradelikeachamp\", \"sdow\", \"vision\", \"asna\", \"-1.78\", \"-1.74\", \"drreddy.in\", \"weeklytop\", \"642,719\", \"impressions\", \"rel\\u00e8ve\", \"dexter\", \"venden\", \"835\", \"alarming\", \"vaccines\", \"29/14\", \"refreshed\", \"nsrgy\", \"vender\", \"nuan\", \"punta\", \"punto\", \"awards\", \"re.to\", \"rope-a-dope\", \"flipped\", \"s\", \"workplace\", \"grooming\", \"can't\", \"axgn\", \"i'making\", \"droga\", \"curr\", \"83.25\", \"ukwu\", \"west\", \"orfg\", \"buy-signal\", \"wants\", \"stocksxlk\", \"stocksxli\", \"k1ll3r\", \"formed\", \"photos\", \"tightened\", \"hashtag\", \"stocksxlp\", \"former\", \"runnin\", \"vrsn\", \"breakoutstocks\", \"vrsk\", \"single\", \"squeezes\", \"squeezed\", \"situation\", \"ballpark\", \"appetizing\", \"engaged\", \"04/15\", \"limping\", \"pahc\", \"iwatch\", \"5.59\", \"technology\", \"mpc\", \"verified\", \"\\u03b4\", \"rt2gain_fast\", \"sreh\", \"managements\", \"edged\", \"assigns\", \"singapore\", \"\\u0633\\u0627\\u0639\\u0647\", \"108.50\", \"fima\", \"defa\", \"edges\", \"wired\", \"tracking\", \"60,000\", \"nothin\", \"sunnyvale\", \"iger's\", \"dimension\", \"777x\", \"jenners\", \"being\", \"divestitures\", \"hou.to\", \"excuses\", \"needle-free\", \"perdi\\u00f3\", \"dicks\", \"netapp\", \"192.27\", \"wallstjesus\", \"vimc\", \"zion\", \"rompe\", \"sums\", \"unveil\", \"downtrend\", \"bcei\", \"traffic\", \"mal-donado\", \"world\", \"ziop\", \"overthewall\", \"enero\", \"shutter\", \"superiority\", \"i-phones\", \"37.47\", \"obstruct\", \"couldn't\", \"grub\", \"ezu\", \"tvs\", \"diving\", \"shinsei\", \"perdio\", \"blow-out\", \"microsoft's\", \"911\", \"restoring\", \"retains\", \"ez2\", \"leadership\", \"thailand\", \"\\u0438\\u0434\\u0435\\u0438\", \"retraced\", \"245.00\", \"91c\", \"gtgl\", \"qre\", \"bjk\", \"qrm\", \"pivot\", \"mindless\", \"sealed\", \"brazilian\", \"bubble\", \"\\udbb8\\udf34\", \"continents\", \"scouting\", \"mh370\", \"yhoo\", \"osir\", \"with\", \"pull\", \"rush\", \"stockiq\", \"rusl\", \"rage\", \"dirty\", \"31.0\", \"russ\", \"mihna\", \"trips\", \"puls\", \"delhaize\", \"mundo\", \"watcher\", \"08:10\", \"08:11\", \"follow-up\", \"dividendanalysis\", \"watched\", \"cream\", \"charobs\", \"cvsh\", \"noticiero\", \"portfoliobuzz\", \"2watch\", \"friggin\", \"puppy\", \"mtdr\", \"01:42\", \"01:44\", \"sounding\", \"06:10\", \"06:11\", \"iwo\", \"nissan\", \"brotherhood\", \"rp\", \"gillis\", \"b3AT_USER\", \"sinverguenza\", \"tricky\", \"posible\", \"snda\", \"tricks\", \"rs\", \"sndk\", \"ocfn\", \"eigi\", \"ssds\", \"head-to-head\", \"rightsmile\", \"scl\", \"sco\", \"sci\", \"caused\", \"scg\", \"hznp\", \"scc\", \"macroenvironment\", \"mason\", \"causes\", \"scr\", \"\\u2020\", \"wless\", \"cartera\", \"stocksfb\", \"midwest\", \"\\ud83d\\ude44\", \"iamgold\", \"baton\", \"pplt\", \"24th\", \"edr.to\", \"sanw\", \"sanp\", \"zbra\", \"4-7\", \"sand\", \"sane\", \"sana\", \"small\", \"sanm\", \"mm's\", \"kemper\", \"199\", \"pasa\", \"195\", \"194\", \"paso\", \"190\", \"193\", \"past\", \"displays\", \"pass\", \"\\ud83d\\udc98\", \"investment\", \"quicken\", \"clock\", \"bioceo\", \"otov\", \"hasn\", \"full\", \"hash\", \"hasi\", \"threatened\", \"vvip\", \"btzo\", \"deaths\", \"november\", \"fult\", \"19b\", \"coolkids\", \"experience\", \"fastenal\", \"prior\", \"countif\", \"er.to\", \"skepticism\", \"friday's\", \"habra\", \"tax-free\", \"depart\", \"follower\", \"\\u00a1\", \"mmr-deficient\", \"attendance\", \"thefed\", \"mori\", \"morn\", \"morl\", \"sp500\", \"more\", \"nomas\", \"door\", \"initiated\", \"company\", \"corrected\", \"initiates\", \"doob\", \"doom\", \"uncool\", \"negativity\", \"installing\", \"chief's\", \"learn\", \"knocked\", \"345k\", \"\\ue67a\", \"scramble\", \"mnta\", \"aliant\", \"mntx\", \"huge\", \"respective\", \"gannett\", \"shipped\", \"dismissed\", \"neck-line\", \"cmcsa\", \"ingelheim's\", \"dismisses\", \"nkd_f\", \"intended\", \"toyotas\", \"variant\", \"n\\u00e3o\", \"resemble\", \"11.5\", \"trellis\", \"67.00\", \"02:30\", \"02:31\", \"pennystocksxlu\", \"papel\", \"installed\", \"stylus\", \"resorts\", \"paper\", \"scott\", \"signs\", \"rt2gain_\", \"smiling\", \"scratch-resilient\", \"roots\", \"6479\", \"\\ud83d\\udcaf\", \"castisos\", \"sauce\", \"colleague\", \"abandons\", \"gadget\", \"featuresamazonfiretv\", \"58.00\", \"58.01\", \"-1.278\", \"stockmarketlei\", \"denny\", \"backtesting\", \"piping\", \"shocking\", \"reactions\", \"spinnaker\", \"300mm\", \"gilberto\", \"new-drug\", \"gfi\", \"advertisers\", \"operation\", \"16.35\", \"scoopy\", \"tr4gedy\", \"scoops\", \"bedrijfsdossier\", \"valeurs\", \"research\", \"lvmuy\", \"votos\", \"inquirer\", \"offline\", \"occurs\", \"fastacash\", \"10.78\", \"pairs\", \"peanutbuddajamm\", \"government's\", \"3/31\", \"12.31\", \"myopinion\", \"sb101\", \"aveo\", \"amsg\", \"dft\", \"swipe\", \"moderately\", \"dfs\", \"+0.48\", \"saint\", \"+0.44\", \"+0.47\", \"dfa\", \"avew\", \"+0.43\", \"150bn\", \"29.99\", \"interviewed\", \"havells\", \"nigga\", \"vela\", \"theater\", \"traps\", \"y0u\", \"getaway\", \"charges\", \"dismantling\", \"emirates\", \"cct\", \"44m\", \"75.97\", \"wafd\", \"sell\", \"sharps\", \"qdef\", \"168b\", \"ptry\", \"self\", \"alsn\", \"also\", \"fltx\", \"bsft\", \"singles\", \"trille\", \"cce\", \"virus\", \"shake-up\", \"semi-monopoly\", \"deviations\", \"seize\", \"sometimes\", \"barred\", \"barrel\", \"charged\", \"ugi\", \"ugh\", \"ugl\", \"uga\", \"bookings\", \"finisar\", \"stuffit\", \"xvxfine\", \"occupation\", \"wraps\", \"realtimesearch\", \"ingram\", \"\\u0664\\u0669\", \"secular\", \"remedy\", \"compass\", \"rafale\", \"especia\", \"bytes\", \"tanked\", \"greekdebt\", \"tanker\", \"prospectus\", \"rumored\", \"insane\", \"d7034\", \"318\", \"sipc\", \"313\", \"312\", \"311\", \"activists\", \"316\", \"315\", \"314\", \"go90\", \"analog\", \"project\", \"trex\", \"idle\", \"15th\", \"2dafulle\", \"untouched\", \"31c\", \"tres\", \"last\", \"apuro\", \"investwlb\", \"nanotech\", \"oscar's\", \"connection\", \"mcdiarmid\", \"opay\", \"\\ud83d\\udc81\", \"bell\", \"olla\", \"acted\", \"ultrashort\", \"quickpay\", \"nr7\", \"understands\", \"94.50\", \"initiation\", \"iliganonko\", \"2015-08-\", \"niles\", \"nrg\", \"nrf\", \"nrz\", \"moderation\", \"pttrx\", \"awake\", \"clabe\", \"229b\", \"despite\", \"nrp\", \"presses\", \"96.59\", \"expanded\", \"budget\", \"39.47\", \"caltex\", \"vod\", \"30\", \"e-mix\", \"vol\", \"02/27\", \"von\", \"voo\", \"motors\", \"02/23\", \"02/20\", \"39.42\", \"vou\", \"vow\", \"vos\", \"vox\", \"4h4h4h4h4\", \"voz\", \"\\ud83d\\ude2d\", \"cchackal\", \"weekday\", \"ljpc\", \"calculation\", \"nabi\", \"a\\u00f1o\", \"nabs\", \"knl\", \"knd\", \"20sma\", \"knx\", \"mobilepotpurri\", \"televen\", \"patentlyapple\", \"hercules\", \"blizzard\", \"reap\", \"multi-year\", \"parents\", \"retracements\", \"venmo\", \"indonesian\", \"shehzad\", \"emergency\", \"couple\", \"hayat\", \"abound\", \"-7.68\", \"emergence\", \"chorus\", \"marques\", \"alexandre\", \"spins\", \"concedes\", \"ijr\", \"ijs\", \"methods\", \"bounce\", \"streamers\", \"methode\", \"ncom\", \"mideast\", \"greener\", \"ijh\", \"ijj\", \"ijk\", \"investopedia\", \"ptoi\", \"measurements\", \"alny\", \"ptos\", \"17.98\", \"masterstroke\", \"eplus\", \"17.96\", \"bested\", \"biomarkers\", \"peptidream\", \"eye-care\", \"tenientico\", \"tradingblock\", \"fourhorsemen\", \"melbourne\", \"deserves\", \"1gb\", \"42.50\", \"intg\", \"chk\", \"inta\", \"intc\", \"terrorists\", \"intl\", \"into\", \"soportar\", \"tif\", \"intu\", \"intt\", \"isoray\", \"i\\u03c0g\", \"dow_jones\", \"contributor\", \"katie\", \"bioc\", \"biof\", \"biod\", \"spam\", \"socl\", \"biol\", \"soch\", \"bios\", \"biot\", \"spat\", \"hawaiian\", \"atlantic\", \"topline\", \"considerable\", \"ministerio\", \"fades\", \"drt.to\", \"bolster\", \"tradingsignal\", 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\"currency-rigging\", \"evhc\", \"\\u062d\\u0627\\u0644\\u064a\\u0627\", \"telecoms\", \"rahy\", \"bearishness\", \"trip\", \"radically\", \"tip\", \"digitimes\", \"tim\", \"universal's\", \"tie\", \"vanished\", \"depot\", \"tia\", \"tic\", \"nuance\", \"phoenix\", \"rosneft\", \"n855\", \"scharf\", \"younger\", \"longer\", \"rydex\", \"tstyle\", \"359.80\", \"wwav\", \"serious\", \"neighbors\", \"iovine\", \"remarkable\", \"\\u270a\", \"alternatives\", \"wendy's\", \"tsem\", \"\\u0643\\u0633\\u0631\", \"essentials\", \"comunidad\", \"bigstocksnbonds\", \"1940\", \"appleearnings\", \"69.000\", \"armageddon\", \"gtls\", \"goodyear\", \"tc0a\", \"5/15\", \"5/12\", \"artwork\", \"242.86\", \"5/19\", \"5/18\", \"fanzone\", \"highearnings\", \"194b\", \"7,179\", \"\\u30fb\", \"bahia\", \"usafricabf\", \"rockbar\", \"two-week\", \"strza\", \"farnam\", \"slowed\", \"spin-off\", \"121.00\", \"drinks\", \"+1.71\", \"applepay\", \"stands\", \"informe\", \"64bit\", \"\\u0631\\u0627\\u062d\", \"reply\", \"1234\", \"1230\", \"tj12\", \"water\", \"unanimously\", \"baseball\", \"supplying\", \"preorder\", \"craziness\", \"abdhakeemsabani\", \"auto-renewal\", \"navigation\", \"tweak\", \"australias\", \"third-quarter\", \"frothy\", \"memory\", \"australian\", \"blackberrys\", \"sessions\", \"clicking\", \"investewj\", \"beers\", \"sequoia\", \"ddrante\", \"tribunal\", \"dialed\", \"realizadas\", \"flagship\", \"dialer\", \"ferry\", \"modifies\", \"michelada\", \"streak\", \"spxpm\", \"trump\", \"stream\", \"abinbev\", \"73.23\", \"jetblue\", \"secured\", \"alway\", \"yungkensushi\", \"08:040\", \"endorsement\", \"secures\", \"einhorn\", \"raspao\", \"clone\", \"birthday\", \"leva\", \"kaisa\", \"avengers\", \"classroom\", \"branches\", \"2086\", \"purification\", \"devs\", \"bmws\", \"imagining\", \"exclusive\", \"acts\", \"scammers\", \"constituents\", \"midnight\", \"kthirum\", \"motions\", \"-2.71\", \"-2.70\", \"faceshift\", \"9th\", \"swann\", \"aumn\", \"he'd\", \"businessinsider\", \"millon\", \"whirlpool\", \"non-apocalypse\", \"bahana\", \"momo\", \"iron-ore\", \"-2.869\", \"26.1\", \"rethinking\", \"newsxin\", \"headwinds\", \"corvex\", \"cadivi\", \"distributor\", \"\\ue41f\", \"hysteria\", \"hacker\", \"factors\", \"gerstein\", \"factory\", \"-4.35\", \"kent\", \"600b\", \"bolts\", \"dow's\", \"hull\", \"zweig\", \"beraber\", \"previews\", \"\\ud83c\\udf51\", \"accommodation\", \"motion\", \"discontinued\", \"merrill\", \"surviving\", \"\\u2206\", \"sistema\", \"fetches\", \"talimogene\", \"enrollment\", \"earths\", \"trillion\", \"haggle\", \"plastic\", \"sstk\", \"lobbyists\", \"white\", \"exploring\", \"9.00\", \"screwing\", \"434b\", \"corning\", \"wide\", \"perd\\u00f3n\", \"sikha\", \"poisoning\", \"paulson\", \"ebr\", \"thestreet's\", \"jase's\", \"downdraft\", \"gamechanger\", \"prosperity\", \"conversion\", \"+2.94\", \"55.87\", \"bleeds\", \"lock-up\", \"synjardy\", \"strengths\", \"estimize\", \"facebook-owned\", \"multiple\", \"ukrainian\", \"+0.95\", \"sculley\", \"seu\", \"15,900\", \"erbb\", \"senior\", \"slope\", \"itna\", \"angelo-net-teknoloj-komunyx\", \"vytorin\", \"artna\", \"hack\", \"hace\", \"nviv\", \"woof\", \"wood\", \"alexion\", \"parabolic\", \"polgarstocks\", \"tock\", \"toch\", \"stockmarketcat\", \"toca\", \"expectation\", \"sss\", \"nobl\", \"ssl\", \"sso\", \"ssn\", \"ssh\", \"lighter\", \"sse\", \"ssd\", \"cpix\", \"ssb\", \"xic.to\", \"aluminum\", \"29.23\", \":'(\", \":')\", \"verisign\", \"ignored\", \"548m\", \"encourages\", \"lnt\", \"0.331\", \"android-URL\", \"lnn\", \"lnk\", \"stanley\", \"lng\", \"\\u00edndice\", \"mortgage\", \"crystal\", \"ways\", \"review\", \"inhibitors\", \"\\u27b2\", \"mtn\", \"f\\u00f6r\", \"arrival\", \"multiplied\", \"comp\", \"avecina\", \"cheap\", \"uspto\", \"comm\", \"como\", \"emotional\", \"cities\", \"come\", \"reaction\", \"superstar\", \"gimmicky\", \"wally\", \"adios\", \"welive\", \"mtd\", \"pakistan\", \"ariad\", \"sixth\", \"bizarre\", \"soriot\", \"howard\", \"84.29\", \"mxwl\", \"fmcc\", \"shopi\", \"fsnn\", \"refractory\", \"shops\", \"telephone\", \"kimberly-clark\", \"immunoonc\", \"\\u261d\", \"10:39\", \"mispricing\", \"10:32\", \"10:31\", \"10:30\", \"islt\", \"10:35\", \"reminder\", \"isle\", \"screenshot\", \"co-hosts\", \"drives\", \"driven\", \"brick-and-mortar\", \"hebben\", \"breakdowns\", \"t.kommenter.evar\", \"components\", \"tweedy\", \"walmart's\", \"nasdaqbidu\", \"captures\", \"investingxlp\", \"inception\", \"violent\", \"kill\", \"45.25\", \"irbt\", \"captured\", \"blow\", \"hint\", \"we've\", \"everyone's\", \"freeseas\", \"blox\", \"blog\", \"hing\", \"samples\", \"styles\", \"correcting\", \"marketcorrection\", \"manalo\", \"holocau\", \"madrid\", \"mohnish\", \"stemcells\", \"drake\", \"confronts\", \"intact\", \"teamnosleep\", \"slice\", \"caribbean\", \"esperaremos\", \"vzc\", \"gva\", \"stops\", \"inspect\", \"loudest\", \"seven-year\", \"wuba\", \"gekocht\", \"cvrs\", \"hedgies\", \"baffled\", \"\\u257a\", \"\\ud83c\\uddea\", \"baffles\", \"gregory\", \"parature\", \"\\u2606\", \"ysty\", \"bbb\", \"devaluacion\", \"06:255\", \"stockmarketgpro\", \"c.ro\", \"omega\", \"pgf.to\", \"serge_poznanski\", \"sidesteps\", \"deteriorating\", \"valley\", \"hare\", \"viropro\", \"fundamentals\", \"cannibis\", \"dumbphones\", \"amcx\", \"bubbleburst\", \"amco\", \"amcn\", \"amcf\", \"100.88\", \"amcc\", \"toilet\", \"invirtieron\", \"cinema\", \"britain\", \"intro\", \"intri\", \"contra\\u00edda\", \"spends\", \"4.5\", \"4.4\", \"4.7\", \"igt\", \"4.1\", \"4.0\", \"4.3\", \"4.2\", \"ctct\", \"4.8\", \"rubber\", \"5\", \"prime_time\", \"ign\", \"igm\", \"oppenheimer's\", \"stalwart\", \"requested\", \"imp\", \"stockm\", \"ptbi\", \"stocka\", \"symbol\", \"intermed\", \"codecon\", \"stocky\", \"twenty-first\", \"stocks\", \"booked\", \"lineup\", \"calls\", \"11/7\", \"synergies\", \"11/2\", \"11/1\", \"calle\", \"cwco\", \"clauses\", \"00:06\", \"fa0\", \"small-cap\", \"00:03\", \"00:00\", \"executing\", \"synthetic\", \"investor's\", \"lining\", \"opportunities\", \"ajax-p\", \"libertad\", \"faz\", \"fax\", \"sonc\", \"song\", \"far\", \"fas\", \"fap\", \"fav\", \"turbo-charge\", \"fat\", \"cihn\", \"sons\", \"sont\", \"uwm\", \"fam\", \"fab\", \"sony\", \"hemp\", \"faa\", \"ticker\", \"ranting\", \"stimulus\", \"stupidcheap\", \"entourage\", \"wallflower\", \"nbr\", \"synergy\", \"nbi\", \"06:437\", \"gerspach\", \"idti\", \"nbl\", \"nbc\", \"nba\", \"nbg\", \"will\", \"pinterest\", \"muzikk\", \"sbu\", \"hovering\", \"vamos\", \"busine\", \"zona\", \"shouldn\", \"shoulda\", \"perosphere\", \"newmont\", \"allows\", \"divisi\\u00f3n\", \"dailyrecap\", \"damned\", \"suddenly\", \"semiconductors\", \"primeair\", \"smaa\", \"appliances\", \"\\u0645\\u0642\\u0627\\u0628\\u0644\", \"mma\", \"cae.to\", \"prochain\", \"branded\", \"grabbing\", \"rebelswithoutaco\", \"operators\", \"03/24\", \"domination\", \"heu.to\", \"stick\", \"03/22\", \"03/29\", \"caravon\", \"d\\u00e9but\", \"originations\", \"marketmakers\", \"68.2\", \"gtomy\", \"joints\", \"trial\", \"retest\", \"avd\", \"kahp\\u00e9\", \"sociales\", \"5.0\", \"5.1\", \"5.2\", \"5.3\", \"5.4\", \"5.5\", \"5.6\", \"5.7\", \"5.8\", \"hsgx\", \"stemming\", \"whore\", \"buzzword\", \"sandwich\", \"5sos\", \"reflect\", \"conmebol\", \"shortcomings\", \"\\uff74\\uff6b\\uff82\", \"departure\", \"grande\", \"culti\", \"microprocessors\", \"revlimid\", \"don's\", \"don't\", \"khuda\", \"disabled\", \"scientist\", \"compramos\", \"lncgy\", \"tet.to\", \"contextualizing\", \"trovagene\", \"ant\", \"return\", \"\\u043f\\u0440\\u043e\", \"clippers\", \"framework\", \"ficar\", \"milestone\", \"accumulate\", \"inelastic\", \"2,600\", \"stated\", \"cubicles\", \"d3u\", \"raytheon\", \"tzoo\", \"pirates\", \"generation\", \"searchcap\", \"carriers\", \"10.000\", \"\\u25ba\", \"etak\", \"cost-cutting\", \"fiber\", \"atlanticcity\", \"3.02\", \"3.01\", \"3.00\", \"miracleman\", \"3.05\", \"3.04\", \"obra\", \"obre\", \"-3.6\", \"\\u24e4\", \"thread\", \"500.000\", \"threat\", \"brodalumab\", \"3dp\", \"relate\", \"fancy\", \"plummet\", \"seadrill\", \"182\", \"script\", \"shareerx\", \"interact\", \"regulation\", \"passed\", \"finances\", \"headache\", \"shareowner\", \"nasdaqair\", \"option\", \"dethroned\", \"exploded\", \"2015020\", \"treasurer\", \"convinced\", \"explodes\", \"throttle\", \"double\", \"mhealth\", \"cloud-computing\", \"-0.61\", \"-0.62\", \"stall\", \"stalk\", \"-0.65\", \"-0.66\", \"dang\", \"-0.69\", \"+1.89\", \"zn_f\", \"galt\", \"cleaner\", \"imports\", \"dediu\", \"nnan\", \"gale\", \"alike\", \"cvr\", \"importa\", \"canaccord\", \"ungs\", \"comprado\", \"parece\", \"clovis\", \"104k\", \"buff\", \"\\ud83d\\udc45\", \"hoff\", \"reach\", \"251.00\", \"react\", \"sandra\", \"hoft\", \"machete\", \"achievement\", \"1044\", \"windows\", \"coincides\", \"dewdiligence\", \"reestablished\", \"wdhr\", \"assistance\", \"bending\", \"acgx\", \"fone\", \"dollar's\", \"solaredge\", \"fonu\", \"font\", \"acgl\", \"seasonality\", \"viagra\", \"ezcorp\", \"hip\", \"hablando\", \"hir\", \"his\", \"hit\", \"hiv\", \"5,000\", \"longest\", \"hig\", \"hii\", \"winsome\", \"cartoone\", \"grmar\", \"patria\", \"cartoons\", \"arp\", \"bars\", \"art\", \"dump\", \"arw\", \"bary\", \"intelligence\", \"pva\", \"ara\", \"arb\", \"upnorth\", \"dumb\", \"are\", \"pvg\", \"arg\", \"pvh\", \"matin\", \"optum\", \"arm\", \"arn\", \"aro\", \"recording\", \"dosti\", \"ichan\", \"vonv\", \"unions\", \"chromebox\", \"use\", \"consecutive\", \"atra\", \"ulcerative\", \"atro\", \"rapid\", \"opposition\", \"treelighting\", \"presi\\u00f3n\", \"pcar\", \"odiyx\", \"c\", \"134.54\", \"36.55\", \"movers\", \"111.50\", \"jq1234\", \"became\", \"fraternity\", \"15.89\", \"15.88\", \"cramer's\", \"1,453\", \"finds\", \"ubpix\", \"epicfail\", \"currencytrader\", \"nasdaqsedg\", \"34.55\", \"macworld\", \"802.11\", \"overlook\", \"-23-2014\", \"whom\", \"reduction\", \"complicated\", \"whoa\", \"likefolio\", \"slideshow\", \"schwab\", \"bxmt\", \"sa's\", \"236m\", \"stolen\", \"bitgold\", \"mikaela\", \"pitchfork\", \"gbpjpy\", \"abot\", \"wireline\", \"behavior\", \"114.20\", \"engineers\", \"lqdt\", \"y0ungbo\", \"765\", \"knoc\", \"661.67\", \"rit\", \"currentc\", \"rio\", \"cAT_USER\", \"hypersolar\", \"rig\", \"rid\", \"yosemite\", \"lengthy\", \"nyoko\", \"lengths\", \"demandados\", \"bpop\", \"orientated\", \"widely\", \"mgkaron\", \"negotiate\", \"protesters\", \"smartlens\", \"moving\", \"noodle\", \"blanco\", \"kellogg\", \"analysis\", \"edgy\", \"edge\", \"noodls\", \"legere\", \"endeavour\", \"sunrisetrader\", \"vigilant\", \"carbonation\", \"retweeet\", \"67b\", \"tigress\", \"beatz\", \"dlgnf\", \"violations\", \"675\", \"670\", \"aggregation\", \"672\", \"kasi\", \"164.61\", \"softbank\", \"capitals\", \"ignore\", \"capitali\", \"boligarcas\", \"hinted\", \"traits\", \"qlik\", \"emps\", \"kpti\", \"investwall\", \"prow\", \"prov\", \"pros\", \"prom\", \"blackmonday\", \"ticking\", \"switcheroo\", \"prog\", \"prof\", \"prod\", \"brookfield\", \"angelo\", \"942.86\", \"\\u0e33\\u0e25\\u0e32\\u0e22\\u0e1e\", \"intense\", \"roundup\", \"subprime\", \"freunde\", \"mondelez\", \"entrepreneurs\", \"completing\", \"profit.ly\", \"chewytrader\", \"01-21-\", \"retweeted\", \"aeromexico\", \"11:12\", \"because\", \"elra\", \"baiano\", \"tilson\", \"intraday\", \"winnebago\", \"amidst\", \"bruh\", \"\\u0628\\u0644\\u0627\\u0643\", \"wnc\", \"perplexing\", \"wnr\", \"wns\", \"crank\", \"aktien\", \"familia\", \"crane\", \"batuna\", \"211,000\", \"z\", \"pirs\", \"called\", \"dill\", \"cinv\", \"price-to-book\", \"soars\", \"shortcuts\", \"bluechip\", \"fegr\", \"263.35\", \"gbpcad\", \"malicious\", \"2da\", \"2do\", \"cutters\", \"-8.8\", \"-8.6\", \"xiaomi\", \"#x27\", \"lgnd\", \"cenovus\", \"scroll\", \"tnxp\", \"adjustment\", \"ctfunkup\", \"squeezing\", \"swelling\", \"visualizing\", \"fences\", \"sniper\", \"60-70\", \"715\", \"engineered\", \"710\", \"\\u21d1\", \"wishes\", \"campe\\u00e3o\", \"132013\", \"notez\", \"notes\", \"palmeiras\", \"deals\", \"105.80\", \"crmd\", \"noted\", \"crme\", \"13:18\", \"cfo\", \"cfm\", \"cfk\", \"cfg\", \"cfd\", \"rrd\", \"rrc\", \"cfa\", \"cfx\", \"jared\", \"manipulating\", \"rrs\", \"citi's\", \"universities\", \"hourly\", \"waiting\", \"relocate\", \"oshkosh\", \"mcgi\", \"ilmn\", \"\\u00e2\\u00fe\", \"ukraine\", \"micromax\", \"skyrocketed\", \"adjusts\", \"oversold\", \"grandes\", \"\\u0665:\\u0663\\u0660\", \"2,100\", \"ym_f\", \"scales\", \"confession\", \"scaled\", \"motor\", \"apply\", \"epzm\", \"dejar\", \"icel\", \"cisconews\", \"subsidies\", \"automobile\", \"cxrx\", \"74.94\", \"newceo\", \"pitter-patter\", \"mindeset\", \"epazd\", \"akrx\", \"cooperate\", \"bcra\", \"rabbit\", \"306.85\", \"esl.to\", \"bcrm\", \"women\", \"annoy\", \"sph\", \"7pm\", \"wtic\", \"annou\", \"dxm\", \"dllr\", \"dlls\", \"ruiz\", \"thi.ca\", \"proof\", \"0.9\", \"0.8\", \"tap\", \"tar\", \"tas\", \"0.1\", \"0.0\", \"flaring\", \"0.2\", \"0.5\", \"0.4\", \"0.7\", \"0.6\", \"tad\", \"rapi\", \"tag\", \"\\u2794\", \"tab\", \"tac\", \"tal\", \"tan\", \"seatbelts\", \"tak\", \"japa\", \"guru\", \"grfs\", \"spe\", \"47.8\", \"gure\", \"cbds\", \"guri\", \"47.5\", \"82.50\", \"hurdles\", \"outlier\", \"bestsellers\", \"130.33\", \"discomfort\", \"10/30\", \"10/31\", \"wt\", \"76.00\", \"76.01\", \"audrealford\", \"9,368\", \"mentorship\", \"acc\\u00e9l\\u00e9rer\", \"1.300\", \"74.05\", \"\\ud83d\\ude08\", \"2prosper\", \"unjustified\", \"ocul\", \"sgol\", \"underwater\", \"qasp\", \"byu\", \"choose\", \"byi\", \"covered\", \"byd\", \"bye\", \"virtualreality\", \"pending\", \"violating\", \"crash\", \"organovo\", \"spikes\", \"trae\", \"trad\", \"18.000\", \"dunya\", \"stockta\", \"tisi\", \"trak\", \"makulit\", \"trap\", \"tras\", \"180\", \"eases\", \"traveler's\", \"wsti\", \"theraflu\", \"pam\", \"category\", \"respects\", \"71.55\", \"dive\", \"sp3\", \"fleetcor\", \"york\", \"must-have\", \"conflicts\", \"intraweek\", \"300pts\", \"horace\", \"conversation\", \"adrian\", \"aos\", \"48,500\", \"wowser\", \"divy\", \"04:792\", \"repurposing\", \"aapl's\", \"0.79\", \"0.78\", \"nomination\", \"overdraft\", \"myspace\", \"08:33\", \"arista\", \"\\u0628\\u0639\\u062f\", \"\\u0628\\u0639\\u0636\", \"\\u03bb\", \"vper\", \"165.00\", \"doomed\", \"greenlight\", \"riocf\", \"institution\", \"riches\", \"richer\", \"followme\", \"dow-related\", \"lifelock\", \"e-books\", \"1.4\", \"1.5\", \"1.6\", \"futures\", \"1.0\", \"1.1\", \"8fge\", \"1.3\", \"ghx\", \"\\ud83d\\udc8a\", \"1.8\", \"1.9\", \"c-suite\", \"tapimmune\", \"afraid\", \"allergies\", \"agency\", \"ahold\", \"funds\", \"\\u0142\\u00f8\", \"25000\", \"centre\", \"gfig\", \"delve\", \"sean\", \"dent\", \"denk\", \"denn\", \"dena\", \"typical\", \"nasdaqslv\", \"timothysykes\", \"btig\", \"99s\", \"purely\", \"\\uff49\\uff4e\\uff44\\uff49\\uff45\\uff53\", \"playbook\", \"chicken\", \"debate\", \"hamburglar\", \"tfb_cats\", \"operational\", \"take-two\", \"candidates\", \"iheart\", \"thousands\", \"dopo\", \"sued\", \"10:00\", \"dope\", \"flirting\", \"999\", \"100bn\", \"oneq\", \"ones\", \"sues\", \"words\", \"jaime\", \"darden\", \"tyco\", \"laughs\", \"11am\", \"generations\", \"view\", \"voorwaarden\", \"merits\", \"bpts\", \"unfairly\", \"radioshack\", \"031\", \"closer\", \"closes\", \"biofuel\", \"closet\", \"puertas\", \"\\u00a8\", \"complementary\", \"supera\", \"xray\", \"genius\", \"prod.pdub\", \"pli.to\", \"closed\", \"pants\", \"beverages\", \"aerol\\u00edneas\", \"declining\", \"tiers\", \"fv_stupid\", \"hempmelon\", \"postpaid\", \"grain\", \"atleast\", \"grail\", \"canopy\", \"safely\", \"lvgi\", \"withdraws\", \"llegada\", \"llegado\", \"tormenta\", \"122.00\", \"122.07\", \"bbm\", \"bbl\", \"bbh\", \"bbg\", \"\\u00f0\\u03b4\\u0142\\u0142\\u03b4\", \"bbd\", \"bbc\", \"amgen\", \"\\u0639\\u0646\", \"agreements\", \"bby\", \"hydrophi\", \"bbw\", \"expectativas\", \"bbt\", \"bbq\", \"bbp\", \"verizon\", \"exploding\", \"43.46\", \"norway\", \"vanilla\", \"choices\", \"h8s\", \"readies\", \"riche\", \"laherparepvec\", \"wild\", \"4nflx\", \"vape\", \"d'affaires\", \"41.46\", \"dxssx\", \"vintage\", \"adverse\", \"retailing\", \"yoon\", \"premiere\", \"19:35\", \"issuance\", \"tnke\", \"mtls\", \"certian\", \"stockmarketmro\", \"\\ud83d\\udd3d\", \"theset\", \"english\", \"breaker\", \"contenders\", \"rocker\", \"rocket\", \"boot\", \"wrek\", \"-1.038\", \"rocked\", \"wres\", \"-1.031\", \"happiness\", \"skt\", \"shts\", \"sky\", \"skx\", \"hall\", \"skg\", \"skf\", \"adoption\", \"skm\", \"earthquakes\", \"\\u00bf\", \"exclusive-microsoft's\", \"branch\", \"acuerdos\", \"knoo\", \"09/12\", \"siemens\", \"09/11\", \"09/16\", \"know\", \"tay\", \"09/18\", \"republica\", \"msft\", \"2015-85\", \"taxpayers\", \"exadirectioners\", \"j's\", \"\\u0628\\u0628\\u0627\\u0644\\u0643\", \"ystrdays\", \"shabby\", \"chatrooms\", \"deified\", \"searching\", \"trendline\", \"115\", \"114\", \"117\", \"116\", \"111\", \"110\", \"113\", \"112\", \"wednesday's\", \"empire\", \"119\", \"118\", \"leaf\", \"csal\", \"lead\", \"leak\", \"lean\", \"lear\", \"leap\", \"leader\", \"says\", \"b\\u00f6rse\", \"11m\", \"653.75\", \"qp_technology\", \"11k\", \"mitm\", \"mitk\", \"poetf\", \"11x\", \"throne\", \"getting\", \"high-end\", \"bmbg\", \"shipping\", \"surge\", \"dependency\", \"warrants\", \"headphone\", \"brush\", \"registration\", \"eh\", \"lfap\", \"owoo\", \"transglobe\", \"honeywell's\", \"\\u0627\\u0644\\u0623\\u0646\\u0636\\u0645\\u0627\\u0645\", \"svcs\", \"appointing\", \"i've\", \"685.71\", \"hruff\", \"edvmf\", \"triple-digit\", \"lvs\", \"windfall\", \"200,000\", \"broadly\", \"ex\", \"ttwo\", \"meaningless\", \"frames\", \"et\", \"disposables\", \"percent\", \"oprah\", \"illinois\", \"roars\", \"book\", \"boom\", \"sick\", \"megacap\", \"lance\", \"junk\", \"agios\", \"juno\", \"the_sweete\", \"578.57\", \"june\", \"maihenh\", \"12.25\", \"4.24\", \"4.25\", \"ap's\", \"emera\", \"teamkeepdatshit\", \"hullabal\", \"bac's\", \"moynihan's\", \"expands\", \"jawbone\"]"
  },
  {
    "path": "src/ConfigLoader.py",
    "content": "#!/usr/local/bin/python\nimport logging\nimport logging.config\nimport yaml\nimport itertools\nimport os\nimport io\nimport json\nimport sys\n\n\nclass PathParser:\n\n    def __init__(self, config_path):\n        self.root = '../'\n        self.log = os.path.join(self.root, config_path['log'])\n\n        self.data = os.path.join(self.root, config_path['data'])\n        self.res = os.path.join(self.root, config_path['res'])\n        self.graphs = os.path.join(self.root, config_path['graphs'])\n        self.checkpoints = os.path.join(self.root, config_path['checkpoints'])\n\n        self.glove = os.path.join(self.res, config_path['glove'])\n\n        self.retrieved = os.path.join(self.data, config_path['tweet_retrieved'])\n        self.preprocessed = os.path.join(self.data, config_path['tweet_preprocessed'])\n        self.movement = os.path.join(self.data, config_path['price'])\n        self.vocab = os.path.join(self.res, config_path['vocab_tweet'])\n\nconfig_fp = os.path.join(os.path.dirname(__file__), 'config.yml')\nconfig = yaml.load(file(config_fp, 'r'))\nconfig_model = config['model']\n\ndates = config['dates']\n\nconfig_stocks = config['stocks']  # a list of lists\nlist_of_lists = [config_stocks[key] for key in config_stocks]\nstock_symbols = list(itertools.chain.from_iterable(list_of_lists))\nss_size = len(stock_symbols)\n\npath_parser = PathParser(config_path=config['paths'])\n\n# logger\nlogger = logging.getLogger('my_logger')\nlogger.setLevel(logging.DEBUG)\nlog_fp = os.path.join(path_parser.log, '{0}.log'.format('model'))\nfile_handler = logging.FileHandler(log_fp)\nconsole_handler = logging.StreamHandler(sys.stdout)\nformatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')\nfile_handler.setFormatter(formatter)\nconsole_handler.setFormatter(formatter)\nlogger.addHandler(file_handler)\nlogger.addHandler(console_handler)\n\nwith io.open(str(path_parser.vocab), 'r', encoding='utf-8') as vocab_f:\n    vocab = json.load(vocab_f)\n    vocab_size = len(vocab) + 1  # for unk\n"
  },
  {
    "path": "src/DataPipe.py",
    "content": "#!/usr/local/bin/python\nimport os\nimport io\nimport json\nimport numpy as np\nfrom datetime import datetime, timedelta\nimport random\nfrom ConfigLoader import logger, path_parser, config_model, dates, stock_symbols, vocab, vocab_size\n\n\nclass DataPipe:\n\n    def __init__(self):\n        # load path\n        self.movement_path = path_parser.movement\n        self.tweet_path = path_parser.preprocessed\n        self.vocab_path = path_parser.vocab\n        self.glove_path = path_parser.glove\n\n        # load dates\n        self.train_start_date = dates['train_start_date']\n        self.train_end_date = dates['train_end_date']\n        self.dev_start_date = dates['dev_start_date']\n        self.dev_end_date = dates['dev_end_date']\n        self.test_start_date = dates['test_start_date']\n        self.test_end_date = dates['test_end_date']\n\n        # load model config\n        self.batch_size = config_model['batch_size']\n        self.shuffle = config_model['shuffle']\n\n        self.max_n_days = config_model['max_n_days']\n        self.max_n_words = config_model['max_n_words']\n        self.max_n_msgs = config_model['max_n_msgs']\n\n        self.word_embed_type = config_model['word_embed_type']\n        self.word_embed_size = config_model['word_embed_size']\n        self.stock_embed_size = config_model['stock_embed_size']\n        self.init_stock_with_word= config_model['init_stock_with_word']\n        self.price_embed_size = config_model['word_embed_size']\n        self.y_size = config_model['y_size']\n\n        assert self.word_embed_type in ('rand', 'glove')\n\n    @staticmethod\n    def _convert_token_to_id(token, token_id_dict):\n        if token not in token_id_dict:\n            token = 'UNK'\n        return token_id_dict[token]\n\n    def _get_start_end_date(self, phase):\n        \"\"\"\n            phase: train, dev, test, unit_test\n            => start_date & end_date\n        \"\"\"\n        assert phase in {'train', 'dev', 'test', 'whole', 'unit_test'}\n        if phase == 'train':\n            return self.train_start_date, self.train_end_date\n        elif phase == 'dev':\n            return self.dev_start_date, self.dev_end_date\n        elif phase == 'test':\n            return self.test_start_date, self.test_end_date\n        elif phase == 'whole':\n            return self.train_start_date, self.test_end_date\n        else:\n            return '2012-07-23', '2012-08-05'  # '2014-07-23', '2014-08-05'\n\n    def _get_batch_size(self, phase):\n        \"\"\"\n            phase: train, dev, test, unit_test\n        \"\"\"\n        if phase == 'train':\n            return self.batch_size\n        elif phase == 'unit_test':\n            return 5\n        else:\n            return 1\n\n    def index_token(self, token_list, key='id', type='word'):\n        assert key in ('id', 'token')\n        assert type in ('word', 'stock')\n        indexed_token_dict = dict()\n\n        if type == 'word':\n            token_list_cp = list(token_list)  # un-change the original input\n            token_list_cp.insert(0, 'UNK')  # for unknown tokens\n        else:\n            token_list_cp = token_list\n\n        if key == 'id':\n            for id in range(len(token_list_cp)):\n                indexed_token_dict[id] = token_list_cp[id]\n        else:\n            for id in range(len(token_list_cp)):\n                indexed_token_dict[token_list_cp[id]] = id\n\n        # id_token_dict = dict(zip(token_id_dict.values(), token_id_dict.keys()))\n        return indexed_token_dict\n\n    def build_stock_id_word_id_dict(self):\n        # load vocab, user, stock list\n        stock_id_word_id_dict = dict()\n\n        vocab_id_dict = self.index_token(vocab, key='token')\n        id_stock_dict = self.index_token(stock_symbols, type='stock')\n\n        for (stock_id, stock_symbol) in id_stock_dict.items():\n            stock_symbol = stock_symbol.lower()\n            if stock_symbol in vocab_id_dict:\n                stock_id_word_id_dict[stock_id] = vocab_id_dict[stock_symbol]\n            else:\n                stock_id_word_id_dict[stock_id] = None\n        return stock_id_word_id_dict\n\n    def _convert_words_to_ids(self, words, vocab_id_dict):\n        \"\"\"\n            Replace each word in the data set with its index in the dictionary\n\n        :param words: words in tweet\n        :param vocab_id_dict: dict, vocab-id\n        :return:\n        \"\"\"\n        return [self._convert_token_to_id(w, vocab_id_dict) for w in words]\n\n    def _get_prices_and_ts(self, ss, main_target_date):\n\n        def _get_mv_class(data, use_one_hot=False):\n            mv = float(data[1])\n            if self.y_size == 2:\n                if mv <= 1e-7:\n                    return [1.0, 0.0] if use_one_hot else 0\n                else:\n                    return [0.0, 1.0] if use_one_hot else 1\n\n            if self.y_size == 3:\n                threshold_1, threshold_2 = -0.004, 0.005\n                if mv < threshold_1:\n                    return [1.0, 0.0, 0.0] if use_one_hot else 0\n                elif mv < threshold_2:\n                    return [0.0, 1.0, 0.0] if use_one_hot else 1\n                else:\n                    return [0.0, 0.0, 1.0] if use_one_hot else 2\n\n        def _get_y(data):\n            return _get_mv_class(data, use_one_hot=True)\n\n        def _get_prices(data):\n            return [float(p) for p in data[3:6]]\n\n        def _get_mv_percents(data):\n            return _get_mv_class(data)\n\n        ts, ys, prices, mv_percents, main_mv_percent = list(), list(), list(), list(), 0.0\n        d_t_min = main_target_date - timedelta(days=self.max_n_days-1)\n\n        stock_movement_path = os.path.join(str(self.movement_path), '{}.txt'.format(ss))\n        with io.open(stock_movement_path, 'r', encoding='utf8') as movement_f:\n            for line in movement_f:  # descend\n                data = line.split('\\t')\n                t = datetime.strptime(data[0], '%Y-%m-%d').date()\n                # logger.info(t)\n                if t == main_target_date:\n                    # logger.info(t)\n                    ts.append(t)\n                    ys.append(_get_y(data))\n                    main_mv_percent = data[1]\n                    if -0.005 <= float(main_mv_percent) < 0.0055:  # discard sample with low movement percent\n                        return None\n                if d_t_min <= t < main_target_date:\n                    ts.append(t)\n                    ys.append(_get_y(data))\n                    prices.append(_get_prices(data))  # high, low, close\n                    mv_percents.append(_get_mv_percents(data))\n                if t < d_t_min:  # one additional line for x_1_prices. not a referred trading day\n                    prices.append(_get_prices(data))\n                    mv_percents.append(_get_mv_percents(data))\n                    break\n\n        T = len(ts)\n        if len(ys) != T or len(prices) != T or len(mv_percents) != T:  # ensure data legibility\n            return None\n\n        # ascend\n        for item in (ts, ys, mv_percents, prices):\n            item.reverse()\n\n        prices_and_ts = {\n            'T': T,\n            'ts': ts,\n            'ys': ys,\n            'main_mv_percent': main_mv_percent,\n            'mv_percents': mv_percents,\n            'prices': prices,\n        }\n\n        return prices_and_ts\n\n    def _get_unaligned_corpora(self, ss, main_target_date, vocab_id_dict):\n        def get_ss_index(word_seq, ss):\n            ss = ss.lower()\n            ss_index = len(word_seq) - 1  # init\n            if ss in word_seq:\n                ss_index = word_seq.index(ss)\n            else:\n                if '$' in word_seq:\n                    dollar_index = word_seq.index('$')\n                    if dollar_index is not len(word_seq) - 1 and ss in word_seq[dollar_index + 1]:\n                        ss_index = dollar_index + 1\n                    else:\n                        for index in range(dollar_index + 1, len(word_seq)):\n                            if ss in word_seq[index]:\n                                ss_index = index\n                                break\n            return ss_index\n\n        unaligned_corpora = list()  # list of sets: (d, msgs, ss_indices)\n        stock_tweet_path = os.path.join(str(self.tweet_path), ss)\n\n        d_d_max = main_target_date - timedelta(days=1)\n        d_d_min = main_target_date - timedelta(days=self.max_n_days)\n\n        d = d_d_max  # descend\n        while d >= d_d_min:\n            msg_fp = os.path.join(stock_tweet_path, d.isoformat())\n            if os.path.exists(msg_fp):\n                word_mat = np.zeros([self.max_n_msgs, self.max_n_words], dtype=np.int32)\n                n_word_vec = np.zeros([self.max_n_msgs, ], dtype=np.int32)\n                ss_index_vec = np.zeros([self.max_n_msgs, ], dtype=np.int32)\n                msg_id = 0\n                with open(msg_fp, 'r') as tweet_f:\n                    for line in tweet_f:\n                        msg_dict = json.loads(line)\n                        text = msg_dict['text']\n                        if not text:\n                            continue\n\n                        words = text[:self.max_n_words]\n                        word_ids = self._convert_words_to_ids(words, vocab_id_dict)\n                        n_words = len(word_ids)\n\n                        n_word_vec[msg_id] = n_words\n                        word_mat[msg_id, :n_words] = word_ids\n                        ss_index_vec[msg_id] = get_ss_index(words, ss)\n\n                        msg_id += 1\n                        if msg_id == self.max_n_msgs:\n                            break\n                corpus = [d, word_mat[:msg_id], ss_index_vec[:msg_id], n_word_vec[:msg_id], msg_id]\n                unaligned_corpora.append(corpus)\n            d -= timedelta(days=1)\n\n        unaligned_corpora.reverse()  # ascend\n        return unaligned_corpora\n\n    def _trading_day_alignment(self, ts, T, unaligned_corpora):\n        aligned_word_tensor = np.zeros([T, self.max_n_msgs, self.max_n_words], dtype=np.int32)\n        aligned_ss_index_mat = np.zeros([T, self.max_n_msgs], dtype=np.int32)\n        aligned_n_words_mat = np.zeros([T, self.max_n_msgs], dtype=np.int32)\n        aligned_n_msgs_vec = np.zeros([T, ], dtype=np.int32)\n\n        # list for gathering\n        aligned_msgs = [[] for _ in range(T)]\n        aligned_ss_indices = [[] for _ in range(T)]\n        aligned_n_words = [[] for _ in range(T)]\n        aligned_n_msgs = [[] for _ in range(T)]\n\n        corpus_t_indices = []\n        max_threshold = 0\n\n        for corpus in unaligned_corpora:\n            d = corpus[0]\n            for t in range(T):\n                if d < ts[t]:\n                    corpus_t_indices.append(t)\n                    break\n\n        assert len(corpus_t_indices) == len(unaligned_corpora)\n\n        for i in range(len(unaligned_corpora)):\n            corpus, t = unaligned_corpora[i], corpus_t_indices[i]\n            word_mat, ss_index_vec, n_word_vec, n_msgs = corpus[1:]\n            aligned_msgs[t].extend(word_mat)\n            aligned_ss_indices[t].extend(ss_index_vec)\n            aligned_n_words[t].append(n_word_vec)\n            aligned_n_msgs[t].append(n_msgs)\n\n        def is_eligible():\n            n_fails = len([0 for n_msgs in aligned_n_msgs if sum(n_msgs) == 0])\n            return n_fails <= max_threshold\n\n        if not is_eligible():\n            return None\n\n        # gather into nd_array and clip exceeded part\n        for t in range(T):\n            n_msgs = sum(aligned_n_msgs[t])\n\n            if aligned_msgs[t] and aligned_ss_indices[t] and aligned_n_words[t]:\n                msgs, ss_indices, n_word = np.vstack(aligned_msgs[t]), np.hstack(aligned_ss_indices[t]), np.hstack(aligned_n_words[t])\n                assert len(msgs) == len(ss_indices) == len(n_word)\n                n_msgs = min(n_msgs, self.max_n_msgs)  # clip length\n                aligned_n_msgs_vec[t] = n_msgs\n                aligned_word_tensor[t, :n_msgs] = msgs[:n_msgs]\n                aligned_ss_index_mat[t, :n_msgs] = ss_indices[:n_msgs]\n                aligned_n_words_mat[t, :n_msgs] = n_word[:n_msgs]\n\n        aligned_info_dict = {\n            'msgs': aligned_word_tensor,\n            'ss_indices': aligned_ss_index_mat,\n            'n_words': aligned_n_words_mat,\n            'n_msgs': aligned_n_msgs_vec,\n        }\n\n        return aligned_info_dict\n\n    def sample_gen_from_one_stock(self, vocab_id_dict, stock_id_dict, s, phase):\n        \"\"\"\n            generate samples for the given stock.\n\n            => tuple, (x:dict, y_:int, price_seq: list of floats, prediction_date_str:str)\n        \"\"\"\n        start_date, end_date = self._get_start_end_date(phase)\n        stock_movement_path = os.path.join(str(self.movement_path), '{}.txt'.format(s))\n        main_target_dates = []\n\n        with open(stock_movement_path, 'r') as movement_f:\n            for line in movement_f:\n                data = line.split('\\t')\n                main_target_date = datetime.strptime(data[0], '%Y-%m-%d').date()\n                main_target_date_str = main_target_date.isoformat()\n\n                if start_date <= main_target_date_str < end_date:\n                    main_target_dates.append(main_target_date)\n\n        if self.shuffle:  # shuffle data\n            random.shuffle(main_target_dates)\n\n        for main_target_date in main_target_dates:\n            # logger.info('start _get_unaligned_corpora')\n            unaligned_corpora = self._get_unaligned_corpora(s, main_target_date, vocab_id_dict)\n            # logger.info('start _get_prices_and_ts')\n            prices_and_ts = self._get_prices_and_ts(s, main_target_date)\n            if not prices_and_ts:\n                continue\n\n            # logger.info('start _trading_day_alignment')\n            aligned_info_dict = self._trading_day_alignment(prices_and_ts['ts'], prices_and_ts['T'], unaligned_corpora)\n            if not aligned_info_dict:\n                continue\n\n            sample_dict = {\n                # meta info\n                'stock': self._convert_token_to_id(s, stock_id_dict),\n                'main_target_date': main_target_date.isoformat(),\n                'T': prices_and_ts['T'],\n                # target\n                'ys': prices_and_ts['ys'],\n                'main_mv_percent': prices_and_ts['main_mv_percent'],\n                'mv_percents': prices_and_ts['mv_percents'],\n                # source\n                'prices': prices_and_ts['prices'],\n                'msgs': aligned_info_dict['msgs'],\n                'ss_indices': aligned_info_dict['ss_indices'],\n                'n_words': aligned_info_dict['n_words'],\n                'n_msgs': aligned_info_dict['n_msgs'],\n            }\n\n            yield sample_dict\n\n    def batch_gen(self, phase):\n        batch_size = self._get_batch_size(phase)\n        # prepare vocab, user, stock dict\n        vocab_id_dict = self.index_token(vocab, key='token')\n        stock_id_dict = self.index_token(stock_symbols, key='token', type='stock')\n        generators = [self.sample_gen_from_one_stock(vocab_id_dict, stock_id_dict, s, phase) for s in stock_symbols]\n        # logger.info('{0} Generators prepared...'.format(len(generators)))\n\n        while True:\n            # start_time = time.time()\n            # logger.info('start to collect a batch...')\n            stock_batch = np.zeros([batch_size, ], dtype=np.int32)\n            T_batch = np.zeros([batch_size, ], dtype=np.int32)\n            y_batch = np.zeros([batch_size, self.max_n_days, self.y_size], dtype=np.float32)\n            main_mv_percent_batch = np.zeros([batch_size, ], dtype=np.float32)\n            mv_percent_batch = np.zeros([batch_size, self.max_n_days], dtype=np.float32)\n            price_batch = np.zeros([batch_size, self.max_n_days, 3], dtype=np.float32)\n            word_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs, self.max_n_words], dtype=np.int32)\n            ss_index_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs], dtype=np.int32)\n            n_msgs_batch = np.zeros([batch_size, self.max_n_days], dtype=np.int32)\n            n_words_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs], dtype=np.int32)\n\n            sample_id = 0\n            while sample_id < batch_size:\n                gen_id = random.randint(0, len(generators)-1)\n                try:\n                    sample_dict = next(generators[gen_id])\n                    T = sample_dict['T']\n                    # meta\n                    stock_batch[sample_id] = sample_dict['stock']\n                    T_batch[sample_id] = T\n                    # target\n                    y_batch[sample_id, :T] = sample_dict['ys']\n                    main_mv_percent_batch[sample_id] = sample_dict['main_mv_percent']\n                    mv_percent_batch[sample_id, :T] = sample_dict['mv_percents']\n                    # source\n                    price_batch[sample_id, :T] = sample_dict['prices']\n                    word_batch[sample_id, :T] = sample_dict['msgs']\n                    ss_index_batch[sample_id, :T] = sample_dict['ss_indices']\n                    n_msgs_batch[sample_id, :T] = sample_dict['n_msgs']\n                    n_words_batch[sample_id, :T] = sample_dict['n_words']\n\n                    sample_id += 1\n                except StopIteration:\n                    del generators[gen_id]\n                    if generators:\n                        continue\n                    else:\n                        raise StopIteration\n\n            batch_dict = {\n                # meta\n                'batch_size': sample_id,\n                'stock_batch': stock_batch,\n                'T_batch': T_batch,\n                # target\n                'y_batch': y_batch,\n                'main_mv_percent_batch': main_mv_percent_batch,\n                'mv_percent_batch': mv_percent_batch,\n                # source\n                'price_batch': price_batch,\n                'word_batch': word_batch,\n                'ss_index_batch': ss_index_batch,\n                'n_msgs_batch': n_msgs_batch,\n                'n_words_batch': n_words_batch,\n            }\n\n            yield batch_dict\n\n    def batch_gen_by_stocks(self, phase):\n        batch_size = 2000\n        vocab_id_dict = self.index_token(vocab, key='token')\n        stock_id_dict = self.index_token(stock_symbols, key='token', type='stock')\n\n        for s in stock_symbols:\n            gen = self.sample_gen_from_one_stock(vocab_id_dict, stock_id_dict, s, phase)\n\n            stock_batch = np.zeros([batch_size, ], dtype=np.int32)\n            T_batch = np.zeros([batch_size, ], dtype=np.int32)\n            n_msgs_batch = np.zeros([batch_size, self.max_n_days], dtype=np.int32)\n            n_words_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs], dtype=np.int32)\n            y_batch = np.zeros([batch_size, self.max_n_days, self.y_size], dtype=np.float32)\n            price_batch = np.zeros([batch_size, self.max_n_days, 3], dtype=np.float32)\n            mv_percent_batch = np.zeros([batch_size, self.max_n_days], dtype=np.float32)\n            word_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs, self.max_n_words], dtype=np.int32)\n            ss_index_batch = np.zeros([batch_size, self.max_n_days, self.max_n_msgs], dtype=np.int32)\n            main_mv_percent_batch = np.zeros([batch_size, ], dtype=np.float32)\n\n            sample_id = 0\n            while True:\n                try:\n                    sample_info_dict = next(gen)\n                    T = sample_info_dict['T']\n\n                    # meta\n                    stock_batch[sample_id] = sample_info_dict['stock']\n                    T_batch[sample_id] = sample_info_dict['T']\n                    # target\n                    y_batch[sample_id, :T] = sample_info_dict['ys']\n                    main_mv_percent_batch[sample_id] = sample_info_dict['main_mv_percent']\n                    mv_percent_batch[sample_id, :T] = sample_info_dict['mv_percents']\n                    # source\n                    price_batch[sample_id, :T] = sample_info_dict['prices']\n                    word_batch[sample_id, :T] = sample_info_dict['msgs']\n                    ss_index_batch[sample_id, :T] = sample_info_dict['ss_indices']\n                    n_msgs_batch[sample_id, :T] = sample_info_dict['n_msgs']\n                    n_words_batch[sample_id, :T] = sample_info_dict['n_words']\n\n                    sample_id += 1\n                except StopIteration:\n                    break\n\n            n_sample_threshold = 1\n            if sample_id < n_sample_threshold:\n                continue\n\n            batch_dict = {\n                # meta\n                's': s,\n                'batch_size': sample_id,\n                'stock_batch': stock_batch[:sample_id],\n                'T_batch': T_batch[:sample_id],\n                # target\n                'y_batch': y_batch[:sample_id],\n                'main_mv_percent_batch': main_mv_percent_batch[:sample_id],\n                'mv_percent_batch': mv_percent_batch[:sample_id],\n                # source\n                'price_batch': price_batch[:sample_id],\n                'word_batch': word_batch[:sample_id],\n                'ss_index_batch': ss_index_batch[:sample_id],\n                'n_msgs_batch': n_msgs_batch[:sample_id],\n                'n_words_batch': n_words_batch[:sample_id],\n            }\n\n            yield batch_dict\n\n    def sample_mv_percents(self, phase):\n        main_mv_percents = []\n        for s in stock_symbols:\n            start_date, end_date = self._get_start_end_date(phase)\n            stock_mv_path = os.path.join(str(self.movement_path), '{}.txt'.format(s))\n            main_target_dates = []\n\n            with open(stock_mv_path, 'r') as movement_f:\n                for line in movement_f:\n                    data = line.split('\\t')\n                    main_target_date = datetime.strptime(data[0], '%Y-%m-%d').date()\n                    main_target_date_str = main_target_date.isoformat()\n\n                    if start_date <= main_target_date_str < end_date:\n                        main_target_dates.append(main_target_date)\n\n            for main_target_date in main_target_dates:\n                prices_and_ts = self._get_prices_and_ts(s, main_target_date)\n                if not prices_and_ts:\n                    continue\n                main_mv_percents.append(prices_and_ts['main_mv_percent'])\n\n            logger.info('finished: {}'.format(s))\n\n        return main_mv_percents\n\n    def init_word_table(self):\n        word_table_init = np.random.random((vocab_size, self.word_embed_size)) * 2 - 1  # [-1.0, 1.0]\n\n        if self.word_embed_type is not 'rand':\n            n_replacement = 0\n            vocab_id_dict = self.index_token(vocab, key='token')\n\n            with io.open(self.glove_path, 'r', encoding='utf-8') as f:\n                for line in f:\n                    tuples = line.split()\n                    word, embed = tuples[0], [float(embed_col) for embed_col in tuples[1:]]\n                    if word in ['<unk>', 'unk']:  # unify UNK\n                        word = 'UNK'\n                    if word in vocab_id_dict:\n                        n_replacement += 1\n                        word_id = vocab_id_dict[word]\n                        word_table_init[word_id] = embed\n\n            logger.info('ASSEMBLE: word table #replacement: {}'.format(n_replacement))\n        return word_table_init\n"
  },
  {
    "path": "src/Executor.py",
    "content": "#!/usr/local/bin/python\nimport os\nimport tensorflow as tf\nimport metrics as metrics\nimport stat_logger as stat_logger\nfrom DataPipe import DataPipe\nfrom ConfigLoader import logger\n\n\nclass Executor:\n\n    def __init__(self, model, silence_step=200, skip_step=20):\n        self.model = model\n        self.silence_step = silence_step\n        self.skip_step = skip_step\n        self.pipe = DataPipe()\n\n        self.saver = tf.train.Saver()\n        self.tf_config = tf.ConfigProto(allow_soft_placement=True)\n        self.tf_config.gpu_options.allow_growth = True\n\n    def unit_test_train(self):\n        with tf.Session() as sess:\n            word_table_init = self.pipe.init_word_table()\n            feed_table_init = {self.model.word_table_init: word_table_init}\n            sess.run(tf.global_variables_initializer(), feed_dict=feed_table_init)\n            logger.info('Word table init: done!')\n\n            logger.info('Model: {0}, start a new session!'.format(self.model.model_name))\n\n            n_iter = self.model.global_step.eval()\n\n            # forward\n            train_batch_loss_list = list()\n            train_epoch_size = 0.0\n            train_epoch_n_acc = 0.0\n            train_batch_gen = self.pipe.batch_gen(phase='train')\n            train_batch_dict = next(train_batch_gen)\n\n            while n_iter < 100:\n                feed_dict = {self.model.is_training_phase: True,\n                             self.model.batch_size: train_batch_dict['batch_size'],\n                             self.model.stock_ph: train_batch_dict['stock_batch'],\n                             self.model.T_ph: train_batch_dict['T_batch'],\n                             self.model.n_words_ph: train_batch_dict['n_words_batch'],\n                             self.model.n_msgs_ph: train_batch_dict['n_msgs_batch'],\n                             self.model.y_ph: train_batch_dict['y_batch'],\n                             self.model.price_ph: train_batch_dict['price_batch'],\n                             self.model.mv_percent_ph: train_batch_dict['mv_percent_batch'],\n                             self.model.word_ph: train_batch_dict['word_batch'],\n                             self.model.ss_index_ph: train_batch_dict['ss_index_batch'],\n                             }\n\n                ops = [self.model.y_T, self.model.y_T_, self.model.loss, self.model.optimize]\n                train_batch_y, train_batch_y_, train_batch_loss, _ = sess.run(ops, feed_dict)\n\n                # training batch stat\n                train_epoch_size += float(train_batch_dict['batch_size'])\n                train_batch_loss_list.append(train_batch_loss)\n                train_batch_n_acc = sess.run(metrics.n_accurate(y=train_batch_y, y_=train_batch_y_))\n                train_epoch_n_acc += float(train_batch_n_acc)\n\n                stat_logger.print_batch_stat(n_iter, train_batch_loss, train_batch_n_acc,\n                                             train_batch_dict['batch_size'])\n                n_iter += 1\n\n    def generation(self, sess, phase):\n        generation_gen = self.pipe.batch_gen_by_stocks(phase)\n\n        gen_loss_list = list()\n        gen_size, gen_n_acc = 0.0, 0.0\n        y_list, y_list_ = list(), list()\n\n        for gen_batch_dict in generation_gen:\n\n            feed_dict = {self.model.is_training_phase: False,\n                         self.model.batch_size: gen_batch_dict['batch_size'],\n                         self.model.stock_ph: gen_batch_dict['stock_batch'],\n                         self.model.T_ph: gen_batch_dict['T_batch'],\n                         self.model.n_words_ph: gen_batch_dict['n_words_batch'],\n                         self.model.n_msgs_ph: gen_batch_dict['n_msgs_batch'],\n                         self.model.y_ph: gen_batch_dict['y_batch'],\n                         self.model.price_ph: gen_batch_dict['price_batch'],\n                         self.model.mv_percent_ph: gen_batch_dict['mv_percent_batch'],\n                         self.model.word_ph: gen_batch_dict['word_batch'],\n                         self.model.ss_index_ph: gen_batch_dict['ss_index_batch'],\n                         self.model.dropout_mel_in: 0.0,\n                         self.model.dropout_mel: 0.0,\n                         self.model.dropout_ce: 0.0,\n                         self.model.dropout_vmd_in: 0.0,\n                         self.model.dropout_vmd: 0.0,\n                         }\n\n            gen_batch_y, gen_batch_y_, gen_batch_loss = sess.run([self.model.y_T, self.model.y_T_, self.model.loss],\n                                                                 feed_dict=feed_dict)\n\n            # gather\n            y_list.append(gen_batch_y)\n            y_list_.append(gen_batch_y_)\n            gen_loss_list.append(gen_batch_loss)  # list of floats\n\n            gen_batch_n_acc = float(sess.run(metrics.n_accurate(y=gen_batch_y, y_=gen_batch_y_)))  # float\n            gen_n_acc += gen_batch_n_acc\n\n            batch_size = float(gen_batch_dict['batch_size'])\n            gen_size += batch_size\n\n        results = metrics.eval_res(gen_n_acc, gen_size, gen_loss_list, y_list, y_list_)\n        return results\n\n    def train_and_dev(self):\n        with tf.Session(config=self.tf_config) as sess:\n            # prep: writer and init\n            writer = tf.summary.FileWriter(self.model.tf_graph_path, sess.graph)\n\n            # init all vars with tables\n            feed_table_init = {self.model.word_table_init: self.pipe.init_word_table()}\n            sess.run(tf.global_variables_initializer(), feed_dict=feed_table_init)\n            logger.info('Word table init: done!')\n\n            # prep: checkpoint\n            checkpoint = tf.train.get_checkpoint_state(os.path.dirname(self.model.tf_checkpoint_file_path))\n            if checkpoint and checkpoint.model_checkpoint_path:\n                # restore partial saved vars\n                reader = tf.train.NewCheckpointReader(checkpoint.model_checkpoint_path)\n                restore_dict = dict()\n                for v in tf.all_variables():\n                    tensor_name = v.name.split(':')[0]\n                    if reader.has_tensor(tensor_name):\n                        print('has tensor: {0}'.format(tensor_name))\n                        restore_dict[tensor_name] = v\n\n                checkpoint_saver = tf.train.Saver(restore_dict)\n                checkpoint_saver.restore(sess, checkpoint.model_checkpoint_path)\n                logger.info('Model: {0}, session restored!'.format(self.model.model_name))\n            else:\n                logger.info('Model: {0}, start a new session!'.format(self.model.model_name))\n\n            for epoch in range(self.model.n_epochs):\n                logger.info('Epoch: {0}/{1} start'.format(epoch+1, self.model.n_epochs))\n\n                # training phase\n                train_batch_loss_list = list()\n                epoch_size, epoch_n_acc = 0.0, 0.0\n\n                train_batch_gen = self.pipe.batch_gen(phase='train')  # a new gen for a new epoch\n\n                for train_batch_dict in train_batch_gen:\n\n                    # logger.info('train: batch_size: {0}'.format(train_batch_dict['batch_size']))\n\n                    feed_dict = {self.model.is_training_phase: True,\n                                 self.model.batch_size: train_batch_dict['batch_size'],\n                                 self.model.stock_ph: train_batch_dict['stock_batch'],\n                                 self.model.T_ph: train_batch_dict['T_batch'],\n                                 self.model.n_words_ph: train_batch_dict['n_words_batch'],\n                                 self.model.n_msgs_ph: train_batch_dict['n_msgs_batch'],\n                                 self.model.y_ph: train_batch_dict['y_batch'],\n                                 self.model.price_ph: train_batch_dict['price_batch'],\n                                 self.model.mv_percent_ph: train_batch_dict['mv_percent_batch'],\n                                 self.model.word_ph: train_batch_dict['word_batch'],\n                                 self.model.ss_index_ph: train_batch_dict['ss_index_batch'],\n                                 }\n\n                    ops = [self.model.y_T, self.model.y_T_, self.model.loss, self.model.optimize,\n                           self.model.global_step]\n                    train_batch_y, train_batch_y_, train_batch_loss, _, n_iter = sess.run(ops, feed_dict)\n\n                    # training batch stat\n                    epoch_size += float(train_batch_dict['batch_size'])\n                    train_batch_loss_list.append(train_batch_loss)  # list of floats\n                    train_batch_n_acc = sess.run(metrics.n_accurate(y=train_batch_y, y_=train_batch_y_))  # float\n                    epoch_n_acc += float(train_batch_n_acc)\n\n                    # save model and generation\n                    if n_iter >= self.silence_step and n_iter % self.skip_step == 0:\n                        stat_logger.print_batch_stat(n_iter, train_batch_loss, train_batch_n_acc,\n                                                     train_batch_dict['batch_size'])\n                        self.saver.save(sess, self.model.tf_saver_path, n_iter)\n                        res = self.generation(sess, phase='dev')\n                        stat_logger.print_eval_res(res)\n\n                # print training epoch stat\n                epoch_loss, epoch_acc = metrics.basic_train_stat(train_batch_loss_list, epoch_n_acc, epoch_size)\n                stat_logger.print_epoch_stat(epoch_loss=epoch_loss, epoch_acc=epoch_acc)\n\n        writer.close()\n\n    def restore_and_test(self):\n        with tf.Session(config=self.tf_config) as sess:\n            checkpoint = tf.train.get_checkpoint_state(os.path.dirname(self.model.tf_checkpoint_file_path))\n            if checkpoint and checkpoint.model_checkpoint_path:\n                logger.info('Model: {0}, session restored!'.format(self.model.model_name))\n                self.saver.restore(sess, checkpoint.model_checkpoint_path)\n            else:\n                logger.info('Model: {0}: NOT found!'.format(self.model.model_name))\n                raise IOError\n\n            res = self.generation(sess, phase='test')\n            stat_logger.print_eval_res(res)\n"
  },
  {
    "path": "src/Main.py",
    "content": "#!/usr/local/bin/python\nfrom Model import Model\nfrom Executor import Executor\n\n\nif __name__ == '__main__':\n    model = Model()\n    model.assemble_graph()\n\n    silence_step = 0\n    skip_step = 20\n\n    exe = Executor(model, silence_step=silence_step, skip_step=skip_step)\n\n    exe.train_and_dev()\n    exe.restore_and_test()\n"
  },
  {
    "path": "src/Model.py",
    "content": "#!/usr/local/bin/python\nfrom __future__ import print_function\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport neural as neural\nimport tensorflow.contrib.distributions as ds\nfrom tensorflow.contrib.layers import batch_norm\nfrom ConfigLoader import logger, ss_size, vocab_size, config_model, path_parser\n\n\nclass Model:\n\n    def __init__(self):\n        logger.info('INIT: #stock: {0}, #vocab+1: {1}'.format(ss_size, vocab_size))\n\n        # model config\n        self.mode = config_model['mode']\n        self.opt = config_model['opt']\n        self.lr = config_model['lr']\n        self.decay_step = config_model['decay_step']\n        self.decay_rate = config_model['decay_rate']\n        self.momentum = config_model['momentum']\n\n        self.kl_lambda_anneal_rate = config_model['kl_lambda_anneal_rate']\n        self.kl_lambda_start_step = config_model['kl_lambda_start_step']\n        self.use_constant_kl_lambda = config_model['use_constant_kl_lambda']\n        self.constant_kl_lambda = config_model['constant_kl_lambda']\n\n        self.daily_att = config_model['daily_att']\n        self.alpha = config_model['alpha']\n\n        self.clip = config_model['clip']\n        self.n_epochs = config_model['n_epochs']\n        self.batch_size_for_name = config_model['batch_size']\n\n        self.max_n_days = config_model['max_n_days']\n        self.max_n_msgs = config_model['max_n_msgs']\n        self.max_n_words = config_model['max_n_words']\n\n        self.weight_init = config_model['weight_init']\n        uniform = True if self.weight_init == 'xavier-uniform' else False\n        self.initializer = tf.contrib.layers.xavier_initializer(uniform=uniform)\n        self.bias_initializer = tf.constant_initializer(0.0, dtype=tf.float32)\n\n        self.word_embed_type = config_model['word_embed_type']\n\n        self.y_size = config_model['y_size']\n        self.word_embed_size = config_model['word_embed_size']\n        self.stock_embed_size = config_model['stock_embed_size']\n        self.price_embed_size = config_model['word_embed_size']\n\n        self.mel_cell_type = config_model['mel_cell_type']\n        self.variant_type = config_model['variant_type']\n        self.vmd_cell_type = config_model['vmd_cell_type']\n\n        self.vmd_rec = config_model['vmd_rec']\n\n        self.mel_h_size = config_model['mel_h_size']\n        self.msg_embed_size = config_model['mel_h_size']\n        self.corpus_embed_size = config_model['mel_h_size']\n\n        self.h_size = config_model['h_size']\n        self.z_size = config_model['h_size']\n        self.g_size = config_model['g_size']\n        self.use_in_bn= config_model['use_in_bn']\n        self.use_o_bn = config_model['use_o_bn']\n        self.use_g_bn = config_model['use_g_bn']\n\n        self.dropout_train_mel_in = config_model['dropout_mel_in']\n        self.dropout_train_mel = config_model['dropout_mel']\n        self.dropout_train_ce = config_model['dropout_ce']\n        self.dropout_train_vmd_in = config_model['dropout_vmd_in']\n        self.dropout_train_vmd = config_model['dropout_vmd']\n\n        self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')\n\n        # model name\n        name_pattern_max_n = 'days-{0}.msgs-{1}-words-{2}'\n        name_max_n = name_pattern_max_n.format(self.max_n_days, self.max_n_msgs, self.max_n_words)\n\n        name_pattern_input_type = 'word_embed-{0}.vmd_in-{1}'\n        name_input_type = name_pattern_input_type.format(self.word_embed_type, self.variant_type)\n\n        name_pattern_key = 'alpha-{0}.anneal-{1}.rec-{2}'\n        name_key = name_pattern_key.format(self.alpha, self.kl_lambda_anneal_rate, self.vmd_rec)\n\n        name_pattern_train = 'batch-{0}.opt-{1}.lr-{2}-drop-{3}-cell-{4}'\n        name_train = name_pattern_train.format(self.batch_size_for_name, self.opt, self.lr, self.dropout_train_mel_in, self.mel_cell_type)\n\n        name_tuple = (self.mode, name_max_n, name_input_type, name_key, name_train)\n        self.model_name = '_'.join(name_tuple)\n\n        # paths\n        self.tf_graph_path = os.path.join(path_parser.graphs, self.model_name)  # summary\n        self.tf_checkpoints_path = os.path.join(path_parser.checkpoints, self.model_name)  # checkpoints\n        self.tf_checkpoint_file_path = os.path.join(self.tf_checkpoints_path, 'checkpoint')  # for restore\n        self.tf_saver_path = os.path.join(self.tf_checkpoints_path, 'sess')  # for save\n\n        # verification\n        assert self.opt in ('sgd', 'adam')\n        assert self.mel_cell_type in ('ln-lstm', 'gru', 'basic')\n        assert self.vmd_cell_type in ('ln-lstm', 'gru')\n        assert self.variant_type in ('hedge', 'fund', 'tech', 'discriminative')\n        assert self.vmd_rec in ('zh', 'h')\n        assert self.weight_init in ('xavier-uniform', 'xavier-normal')\n\n    def _build_placeholders(self):\n        with tf.name_scope('placeholder'):\n            self.is_training_phase = tf.placeholder(dtype=tf.bool, shape=())\n            self.batch_size = tf.placeholder(dtype=tf.int32, shape=())\n\n            # init\n            self.word_table_init = tf.placeholder(dtype=tf.float32, shape=[vocab_size, self.word_embed_size])\n\n            # model\n            self.stock_ph = tf.placeholder(dtype=tf.int32, shape=[None])\n            self.T_ph = tf.placeholder(dtype=tf.int32, shape=[None, ])\n            self.n_words_ph = tf.placeholder(dtype=tf.int32, shape=[None, self.max_n_days, self.max_n_msgs])\n            self.n_msgs_ph = tf.placeholder(dtype=tf.int32, shape=[None, self.max_n_days])\n            self.y_ph = tf.placeholder(dtype=tf.float32, shape=[None, self.max_n_days, self.y_size])  # 2-d vectorised movement\n            self.mv_percent_ph = tf.placeholder(dtype=tf.int32, shape=[None, self.max_n_days])  # movement percent\n            self.price_ph = tf.placeholder(dtype=tf.float32, shape=[None, self.max_n_days, 3])  # high, low, close\n            self.word_ph = tf.placeholder(dtype=tf.int32, shape=[None, self.max_n_days, self.max_n_msgs, self.max_n_words])\n            self.ss_index_ph = tf.placeholder(dtype=tf.int32, shape=[None, self.max_n_days, self.max_n_msgs])\n\n            # dropout\n            self.dropout_mel_in = tf.placeholder_with_default(self.dropout_train_mel_in, shape=())\n            self.dropout_mel = tf.placeholder_with_default(self.dropout_train_mel, shape=())\n            self.dropout_ce = tf.placeholder_with_default(self.dropout_train_ce, shape=())\n            self.dropout_vmd_in = tf.placeholder_with_default(self.dropout_train_vmd_in, shape=())\n            self.dropout_vmd = tf.placeholder_with_default(self.dropout_train_vmd, shape=())\n\n    def _build_embeds(self):\n        with tf.name_scope('embeds'):\n            with tf.variable_scope('embeds'):\n                word_table = tf.get_variable('word_table', initializer=self.word_table_init, trainable=False)\n                self.word_embed = tf.nn.embedding_lookup(word_table, self.word_ph, name='word_embed')\n\n    def _create_msg_embed_layer_in(self):\n        \"\"\"\n            acquire the inputs for MEL.\n\n            Input:\n                word_embed: batch_size * max_n_days * max_n_msgs * max_n_words * word_embed_size\n\n            Output:\n                mel_in: same as word_embed\n        \"\"\"\n        with tf.name_scope('mel_in'):\n            with tf.variable_scope('mel_in'):\n                mel_in = self.word_embed\n                if self.use_in_bn:\n                    mel_in = neural.bn(mel_in, self.is_training_phase, bn_scope='bn-mel_inputs')\n                self.mel_in = tf.nn.dropout(mel_in, keep_prob=1-self.dropout_mel_in)\n\n    def _create_msg_embed_layer(self):\n        \"\"\"\n            Input:\n                mel_in: same as word_embed\n\n            Output:\n                msg_embed: batch_size * max_n_days * max_n_msgs * msg_embed_size\n        \"\"\"\n\n        def _for_one_trading_day(daily_in, daily_ss_index_vec, daily_mask):\n            \"\"\"\n                daily_in: max_n_msgs * max_n_words * word_embed_size\n            \"\"\"\n            out, _ = tf.nn.bidirectional_dynamic_rnn(mel_cell_f, mel_cell_b, daily_in, daily_mask,\n                                                     mel_init_f, mel_init_b, dtype=tf.float32)\n            out_f, out_b = out\n            ss_indices = tf.reshape(daily_ss_index_vec, [-1, 1])\n\n            msg_ids = tf.constant(range(0, self.max_n_msgs), dtype=tf.int32, shape=[self.max_n_msgs, 1])  # [0, 1, 2, ...]\n            out_id = tf.concat([msg_ids, ss_indices], axis=1)\n            # fw, bw and average\n            mel_h_f, mel_h_b = tf.gather_nd(out_f, out_id), tf.gather_nd(out_b, out_id)\n            msg_embed = (mel_h_f + mel_h_b) / 2\n\n            return msg_embed\n\n        def _for_one_sample(sample, sample_ss_index, sample_mask):\n            return neural.iter(size=self.max_n_days, func=_for_one_trading_day,\n                               iter_arg=sample, iter_arg2=sample_ss_index, iter_arg3=sample_mask)\n\n        def _for_one_batch():\n            return neural.iter(size=self.batch_size, func=_for_one_sample,\n                               iter_arg=self.mel_in, iter_arg2=self.ss_index_ph, iter_arg3=self.n_words_ph)\n\n        with tf.name_scope('mel'):\n            with tf.variable_scope('mel_iter', reuse=tf.AUTO_REUSE):\n                if self.mel_cell_type == 'ln-lstm':\n                    mel_cell_f = tf.contrib.rnn.LayerNormBasicLSTMCell(self.mel_h_size)\n                    mel_cell_b = tf.contrib.rnn.LayerNormBasicLSTMCell(self.mel_h_size)\n                elif self.mel_cell_type == 'gru':\n                    mel_cell_f = tf.contrib.rnn.GRUCell(self.mel_h_size)\n                    mel_cell_b = tf.contrib.rnn.GRUCell(self.mel_h_size)\n                else:\n                    mel_cell_f = tf.contrib.rnn.BasicRNNCell(self.mel_h_size)\n                    mel_cell_b = tf.contrib.rnn.BasicRNNCell(self.mel_h_size)\n\n                mel_cell_f = tf.contrib.rnn.DropoutWrapper(mel_cell_f, output_keep_prob=1.0-self.dropout_mel)\n                mel_cell_b = tf.contrib.rnn.DropoutWrapper(mel_cell_b, output_keep_prob=1.0-self.dropout_mel)\n\n                mel_init_f = mel_cell_f.zero_state([self.max_n_msgs], tf.float32)\n                mel_init_b = mel_cell_f.zero_state([self.max_n_msgs], tf.float32)\n\n                msg_embed_shape = (self.batch_size, self.max_n_days, self.max_n_msgs, self.msg_embed_size)\n                msg_embed = tf.reshape(_for_one_batch(), shape=msg_embed_shape)\n                self.msg_embed = tf.nn.dropout(msg_embed, keep_prob=1-self.dropout_mel, name='msg_embed')\n\n    def _create_corpus_embed(self):\n        \"\"\"\n            msg_embed: batch_size * max_n_days * max_n_msgs * msg_embed_size\n\n            => corpus_embed: batch_size * max_n_days * corpus_embed_size\n        \"\"\"\n        with tf.name_scope('corpus_embed'):\n            with tf.variable_scope('u_t'):\n                proj_u = self._linear(self.msg_embed, self.msg_embed_size, 'tanh', use_bias=False)\n                w_u = tf.get_variable('w_u', shape=(self.msg_embed_size, 1), initializer=self.initializer)\n            u = tf.reduce_mean(tf.tensordot(proj_u, w_u, axes=1), axis=-1)  # batch_size * max_n_days * max_n_msgs\n\n            mask_msgs = tf.sequence_mask(self.n_msgs_ph, maxlen=self.max_n_msgs, dtype=tf.bool, name='mask_msgs')\n            ninf = tf.fill(tf.shape(mask_msgs), np.NINF)\n            masked_score = tf.where(mask_msgs, u, ninf)\n            u = neural.softmax(masked_score)  # batch_size * max_n_days * max_n_msgs\n            u = tf.where(tf.is_nan(u), tf.zeros_like(u), u)  # replace nan with 0.0\n\n            u = tf.expand_dims(u, axis=-2)  # batch_size * max_n_days * 1 * max_n_msgs\n            corpus_embed = tf.matmul(u, self.msg_embed)  # batch_size * max_n_days * 1 * msg_embed_size\n            corpus_embed = tf.reduce_mean(corpus_embed, axis=-2)  # batch_size * max_n_days * msg_embed_size\n            self.corpus_embed = tf.nn.dropout(corpus_embed, keep_prob=1-self.dropout_ce, name='corpus_embed')\n\n    def _build_mie(self):\n        \"\"\"\n            Create market information encoder.\n\n            corpus_embed: batch_size * max_n_days * corpus_embed_size\n            price: batch_size * max_n_days * 3\n            => x: batch_size * max_n_days * x_size\n        \"\"\"\n        with tf.name_scope('mie'):\n            self.price = self.price_ph\n            self.price_size = 3\n\n            if self.variant_type == 'tech':\n                self.x = self.price\n                self.x_size = self.price_size\n            else:\n                self._create_msg_embed_layer_in()\n                self._create_msg_embed_layer()\n                self._create_corpus_embed()\n                if self.variant_type == 'fund':\n                    self.x = self.corpus_embed\n                    self.x_size = self.corpus_embed_size\n                else:\n                    self.x = tf.concat([self.corpus_embed, self.price], axis=2)\n                    self.x_size = self.corpus_embed_size + self.price_size\n\n    def _create_vmd_with_h_rec(self):\n        with tf.name_scope('vmd'):\n            with tf.variable_scope('vmd_h_rec'):\n                x = tf.nn.dropout(self.x, keep_prob=1-self.dropout_vmd_in)\n                x = tf.transpose(x, [1, 0, 2])  # max_n_days * batch_size * x_size\n                y_ = tf.transpose(self.y_ph, [1, 0, 2])  # max_n_days * batch_size * y_size\n\n                self.mask_aux_trading_days = tf.sequence_mask(self.T_ph - 1, self.max_n_days, dtype=tf.bool,\n                                                              name='mask_aux_trading_days')\n\n                def _loop_body(t, ta_h_s, ta_z_prior, ta_z_post, ta_kl):\n\n                    with tf.variable_scope('iter_body', reuse=tf.AUTO_REUSE):\n\n                        def _init():\n                            h_s_init = tf.nn.tanh(tf.random_normal(shape=[self.batch_size, self.h_size]))\n                            h_z_init = tf.nn.tanh(tf.random_normal(shape=[self.batch_size, self.z_size]))\n\n                            z_init, _ = self._z(arg=h_z_init, is_prior=False)\n\n                            return h_s_init, z_init\n\n                        def _subsequent():\n                            h_s_t_1 = tf.reshape(ta_h_s.read(t-1), [self.batch_size, self.h_size])\n                            z_t_1 = tf.reshape(ta_z_post.read(t-1), [self.batch_size, self.z_size])\n\n                            return h_s_t_1, z_t_1\n\n                        h_s_t_1, z_t_1 = tf.cond(t >= 1, _subsequent, _init)\n\n                        gate_args = [x[t], h_s_t_1, z_t_1]\n\n                        with tf.variable_scope('gru_r'):\n                            r = self._linear(gate_args, self.h_size, 'sigmoid')\n                        with tf.variable_scope('gru_u'):\n                            u = self._linear(gate_args, self.h_size, 'sigmoid')\n\n                        h_args = [x[t], tf.multiply(r, h_s_t_1), z_t_1]\n\n                        with tf.variable_scope('gru_h'):\n                            h_tilde = self._linear(h_args, self.h_size, 'tanh')\n\n                        h_s_t = tf.multiply(1 - u, h_s_t_1) + tf.multiply(u, h_tilde)\n\n                        with tf.variable_scope('h_z_prior'):\n                            h_z_prior_t = self._linear([x[t], h_s_t], self.z_size, 'tanh')\n                        with tf.variable_scope('z_prior'):\n                            z_prior_t, z_prior_t_pdf = self._z(h_z_prior_t, is_prior=True)\n\n                        with tf.variable_scope('h_z_post'):\n                            h_z_post_t = self._linear([x[t], h_s_t, y_[t]], self.z_size, 'tanh')\n                        with tf.variable_scope('z_post'):\n                            z_post_t, z_post_t_pdf = self._z(h_z_post_t, is_prior=False)\n\n                    kl_t = ds.kl_divergence(z_post_t_pdf, z_prior_t_pdf)\n\n                    # write\n                    ta_h_s = ta_h_s.write(t, h_s_t)\n                    ta_z_prior = ta_z_prior.write(t, z_prior_t)  # write: batch_size * z_size\n                    ta_z_post = ta_z_post.write(t, z_post_t)  # write: batch_size * z_size\n                    ta_kl = ta_kl.write(t, kl_t)  # write: batch_size * 1\n\n                    return t + 1, ta_h_s, ta_z_prior, ta_z_post, ta_kl\n\n                ta_h_s_init = tf.TensorArray(tf.float32, size=self.max_n_days, clear_after_read=False)\n                ta_z_prior_init = tf.TensorArray(tf.float32, size=self.max_n_days)\n                ta_z_post_init = tf.TensorArray(tf.float32, size=self.max_n_days, clear_after_read=False)\n                ta_kl_init = tf.TensorArray(tf.float32, size=self.max_n_days)\n\n                loop_init = (0, ta_h_s_init, ta_z_prior_init, ta_z_post_init, ta_kl_init)\n                loop_cond = lambda t, *args: t < self.max_n_days\n                _, ta_h_s, ta_z_prior, ta_z_post, ta_kl = tf.while_loop(loop_cond, _loop_body, loop_init)\n\n                h_s = tf.reshape(ta_h_s.stack(), shape=(self.max_n_days, self.batch_size, self.h_size))\n                z_shape = (self.max_n_days, self.batch_size, self.z_size)\n                z_prior = tf.reshape(ta_z_prior.stack(), shape=z_shape)\n                z_post = tf.reshape(ta_z_post.stack(), shape=z_shape)\n                kl = tf.reshape(ta_kl.stack(), shape=z_shape)\n\n                x = tf.transpose(x, [1, 0, 2])  # batch_size * max_n_days * x_size\n                h_s = tf.transpose(h_s, [1, 0, 2])  # batch_size * max_n_days * vmd_h_size\n                z_prior = tf.transpose(z_prior, [1, 0, 2])  # batch_size * max_n_days * z_size\n                z_post = tf.transpose(z_post, [1, 0, 2])  # batch_size * max_n_days * z_size\n                self.kl = tf.reduce_sum(tf.transpose(kl, [1, 0, 2]), axis=2)  # batch_size * max_n_days\n\n                with tf.variable_scope('g'):\n                    self.g = self._linear([x, h_s, z_post], self.g_size, 'tanh', use_bn=False)\n\n                with tf.variable_scope('y'):\n                    self.y = self._linear(self.g, self.y_size, 'softmax')\n\n                sample_index = tf.reshape(tf.range(self.batch_size), (self.batch_size, 1), name='sample_index')\n                self.indexed_T = tf.concat([sample_index, tf.reshape(self.T_ph-1, (self.batch_size, 1))], axis=1)\n\n                def _infer_func():\n                    g_T = tf.gather_nd(params=self.g, indices=self.indexed_T)  # batch_size * g_size\n\n                    if not self.daily_att:\n                        y_T = tf.gather_nd(params=self.y, indices=self.indexed_T)  # batch_size * y_size\n                        return g_T, y_T\n\n                    return g_T\n\n                def _gen_func():\n                    # use prior for g\n                    z_prior_T = tf.gather_nd(params=z_prior, indices=self.indexed_T)  # batch_size * z_size\n                    h_s_T = tf.gather_nd(params=h_s, indices=self.indexed_T)\n                    x_T = tf.gather_nd(params=x, indices=self.indexed_T)\n\n                    with tf.variable_scope('g', reuse=tf.AUTO_REUSE):\n                        g_T = self._linear([x_T, h_s_T, z_prior_T], self.g_size, 'tanh', use_bn=False)\n\n                    if not self.daily_att:\n                        with tf.variable_scope('y', reuse=tf.AUTO_REUSE):\n                            y_T = self._linear(g_T, self.y_size, 'softmax')\n                        return g_T, y_T\n\n                    return g_T\n\n                if not self.daily_att:\n                    self.g_T, self.y_T = tf.cond(tf.equal(self.is_training_phase, True), _infer_func, _gen_func)\n                else:\n                    self.g_T = tf.cond(tf.equal(self.is_training_phase, True), _infer_func, _gen_func)\n\n    def _create_vmd_with_zh_rec(self):\n        \"\"\"\n            Create a variational movement decoder.\n\n            x: batch_size * max_n_days * vmd_in_size\n            => vmd_h: batch_size * max_n_days * vmd_h_size\n            => z: batch_size * max_n_days * vmd_z_size\n            => y: batch_size * max_n_days * 2\n        \"\"\"\n        with tf.name_scope('vmd'):\n            with tf.variable_scope('vmd_zh_rec', reuse=tf.AUTO_REUSE):\n                x = tf.nn.dropout(self.x, keep_prob=1-self.dropout_vmd_in)\n\n                self.mask_aux_trading_days = tf.sequence_mask(self.T_ph - 1, self.max_n_days, dtype=tf.bool,\n                                                              name='mask_aux_trading_days')\n\n                if self.vmd_cell_type == 'ln-lstm':\n                    cell = tf.contrib.rnn.LayerNormBasicLSTMCell(self.h_size)\n                else:\n                    cell = tf.contrib.rnn.GRUCell(self.h_size)\n                cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=1.0-self.dropout_vmd)\n\n                init_state = None\n                # calculate vmd_h, batch_size * max_n_days * vmd_h_size\n                h_s, _ = tf.nn.dynamic_rnn(cell, x, sequence_length=self.T_ph, initial_state=init_state, dtype=tf.float32)\n\n                # forward max_n_days\n                x = tf.transpose(x, [1, 0, 2])  # max_n_days * batch_size * x_size\n                h_s = tf.transpose(h_s, [1, 0, 2])  # max_n_days * batch_size * vmd_h_size\n                y_ = tf.transpose(self.y_ph, [1, 0, 2])  # max_n_days * batch_size * y_size\n\n                def _loop_body(t, ta_z_prior, ta_z_post, ta_kl):\n                    \"\"\"\n                        iter body. iter over trading days.\n                    \"\"\"\n                    with tf.variable_scope('iter_body', reuse=tf.AUTO_REUSE):\n\n                        init = lambda: tf.random_normal(shape=[self.batch_size, self.z_size], name='z_post_t_1')\n                        subsequent = lambda: tf.reshape(ta_z_post.read(t-1), [self.batch_size, self.z_size])\n\n                        z_post_t_1 = tf.cond(t >= 1, subsequent, init)\n\n                        with tf.variable_scope('h_z_prior'):\n                            h_z_prior_t = self._linear([x[t], h_s[t], z_post_t_1], self.z_size, 'tanh')\n                        with tf.variable_scope('z_prior'):\n                            z_prior_t, z_prior_t_pdf = self._z(h_z_prior_t, is_prior=True)\n\n                        with tf.variable_scope('h_z_post'):\n                            h_z_post_t = self._linear([x[t], h_s[t], y_[t], z_post_t_1], self.z_size, 'tanh')\n                        with tf.variable_scope('z_post'):\n                            z_post_t, z_post_t_pdf = self._z(h_z_post_t, is_prior=False)\n\n                    kl_t = ds.kl_divergence(z_post_t_pdf, z_prior_t_pdf)  # batch_size * z_size\n\n                    ta_z_prior = ta_z_prior.write(t, z_prior_t)  # write: batch_size * z_size\n                    ta_z_post = ta_z_post.write(t, z_post_t)  # write: batch_size * z_size\n                    ta_kl = ta_kl.write(t, kl_t)  # write: batch_size * 1\n\n                    return t + 1, ta_z_prior, ta_z_post, ta_kl\n\n                # loop_init\n                ta_z_prior_init = tf.TensorArray(tf.float32, size=self.max_n_days)\n                ta_z_post_init = tf.TensorArray(tf.float32, size=self.max_n_days, clear_after_read=False)\n                ta_kl_init = tf.TensorArray(tf.float32, size=self.max_n_days)\n\n                loop_init = (0, ta_z_prior_init, ta_z_post_init, ta_kl_init)\n                cond = lambda t, *args: t < self.max_n_days\n\n                _, ta_z_prior, ta_z_post, ta_kl = tf.while_loop(cond, _loop_body, loop_init)\n\n                z_shape = (self.max_n_days, self.batch_size, self.z_size)\n                z_prior = tf.reshape(ta_z_prior.stack(), shape=z_shape)\n                z_post = tf.reshape(ta_z_post.stack(), shape=z_shape)\n                kl = tf.reshape(ta_kl.stack(), shape=z_shape)\n\n                h_s = tf.transpose(h_s, [1, 0, 2])  # batch_size * max_n_days * vmd_h_size\n                z_prior = tf.transpose(z_prior, [1, 0, 2])  # batch_size * max_n_days * z_size\n                z_post = tf.transpose(z_post, [1, 0, 2])  # batch_size * max_n_days * z_size\n                self.kl = tf.reduce_sum(tf.transpose(kl, [1, 0, 2]), axis=2)  # batch_size * max_n_days\n\n                with tf.variable_scope('g'):\n                    self.g = self._linear([h_s, z_post], self.g_size, 'tanh')  # batch_size * max_n_days * g_size\n\n                with tf.variable_scope('y'):\n                    self.y = self._linear(self.g, self.y_size, 'softmax')  # batch_size * max_n_days * y_size\n\n                sample_index = tf.reshape(tf.range(self.batch_size), (self.batch_size, 1), name='sample_index')\n\n                self.indexed_T = tf.concat([sample_index, tf.reshape(self.T_ph-1, (self.batch_size, 1))], axis=1)\n\n                def _infer_func():\n                    g_T = tf.gather_nd(params=self.g, indices=self.indexed_T)  # batch_size * g_size\n\n                    if not self.daily_att:\n                        y_T = tf.gather_nd(params=self.y, indices=self.indexed_T)  # batch_size * y_size\n                        return g_T, y_T\n\n                    return g_T\n\n                def _gen_func():\n                    # use prior for g & y\n                    z_prior_T = tf.gather_nd(params=z_prior, indices=self.indexed_T)  # batch_size * z_size\n                    h_s_T = tf.gather_nd(params=h_s, indices=self.indexed_T)\n\n                    with tf.variable_scope('g', reuse=tf.AUTO_REUSE):\n                        g_T = self._linear([h_s_T, z_prior_T], self.g_size, 'tanh', use_bn=False)\n\n                    if not self.daily_att:\n                        with tf.variable_scope('y', reuse=tf.AUTO_REUSE):\n                            y_T = self._linear(g_T, self.y_size, 'softmax')\n                        return g_T, y_T\n\n                    return g_T\n\n                if not self.daily_att:\n                    self.g_T, self.y_T = tf.cond(tf.equal(self.is_training_phase, True), _infer_func, _gen_func)\n                else:\n                    self.g_T = tf.cond(tf.equal(self.is_training_phase, True), _infer_func, _gen_func)\n\n    def _create_discriminative_vmd(self):\n        \"\"\"\n            Create a discriminative movement decoder.\n\n            x: batch_size * max_n_days * vmd_in_size\n            => vmd_h: batch_size * max_n_days * vmd_h_size\n            => z: batch_size * max_n_days * vmd_z_size\n            => y: batch_size * max_n_days * 2\n        \"\"\"\n        with tf.name_scope('vmd'):\n            with tf.variable_scope('vmd_zh_rec', reuse=tf.AUTO_REUSE):\n                x = tf.nn.dropout(self.x, keep_prob=1-self.dropout_vmd_in)\n\n                self.mask_aux_trading_days = tf.sequence_mask(self.T_ph - 1, self.max_n_days, dtype=tf.bool,\n                                                              name='mask_aux_trading_days')\n\n                if self.vmd_cell_type == 'ln-lstm':\n                    cell = tf.contrib.rnn.LayerNormBasicLSTMCell(self.h_size)\n                else:\n                    cell = tf.contrib.rnn.GRUCell(self.h_size)\n                cell = tf.contrib.rnn.DropoutWrapper(cell, output_keep_prob=1.0-self.dropout_vmd)\n\n                init_state = None\n                h_s, _ = tf.nn.dynamic_rnn(cell, x, sequence_length=self.T_ph, initial_state=init_state, dtype=tf.float32)\n\n                # forward max_n_days\n                x = tf.transpose(x, [1, 0, 2])  # max_n_days * batch_size * x_size\n                h_s = tf.transpose(h_s, [1, 0, 2])  # max_n_days * batch_size * vmd_h_size\n\n                def _loop_body(t, ta_z):\n                    \"\"\"\n                        iter body. iter over trading days.\n                    \"\"\"\n                    with tf.variable_scope('iter_body', reuse=tf.AUTO_REUSE):\n\n                        init = lambda: tf.random_normal(shape=[self.batch_size, self.z_size], name='z_post_t_1')\n                        subsequent = lambda: tf.reshape(ta_z.read(t-1), [self.batch_size, self.z_size])\n\n                        z_t_1 = tf.cond(t >= 1, subsequent, init)\n\n                        with tf.variable_scope('h_z'):\n                            h_z_t = self._linear([x[t], h_s[t], z_t_1], self.z_size, 'tanh')\n                        with tf.variable_scope('z'):\n                            z_t = self._linear(h_z_t, self.z_size, 'tanh')\n\n                    ta_z = ta_z.write(t, z_t)  # write: batch_size * z_size\n                    return t + 1, ta_z\n\n                # loop_init\n                ta_z_init = tf.TensorArray(tf.float32, size=self.max_n_days, clear_after_read=False)\n\n                loop_init = (0, ta_z_init)\n                cond = lambda t, *args: t < self.max_n_days\n\n                _, ta_z_init = tf.while_loop(cond, _loop_body, loop_init)\n\n                z_shape = (self.max_n_days, self.batch_size, self.z_size)\n                z = tf.reshape(ta_z_init.stack(), shape=z_shape)\n\n                h_s = tf.transpose(h_s, [1, 0, 2])  # batch_size * max_n_days * vmd_h_size\n                z = tf.transpose(z, [1, 0, 2])  # batch_size * max_n_days * z_size\n\n                with tf.variable_scope('g'):\n                    self.g = self._linear([h_s, z], self.g_size, 'tanh')  # batch_size * max_n_days * g_size\n\n                with tf.variable_scope('y'):\n                    self.y = self._linear(self.g, self.y_size, 'softmax')  # batch_size * max_n_days * y_size\n\n                # get g_T\n                sample_index = tf.reshape(tf.range(self.batch_size), (self.batch_size, 1), name='sample_index')\n                self.indexed_T = tf.concat([sample_index, tf.reshape(self.T_ph-1, (self.batch_size, 1))], axis=1)\n                self.g_T = tf.gather_nd(params=self.g, indices=self.indexed_T)\n\n    def _build_vmd(self):\n        if self.variant_type == 'discriminative':\n            self._create_discriminative_vmd()\n        else:\n            if self.vmd_rec == 'h':\n                self._create_vmd_with_h_rec()\n            else:\n                self._create_vmd_with_zh_rec()\n\n    def _build_temporal_att(self):\n        \"\"\"\n            g: batch_size * max_n_days * g_size\n            g_T: batch_size * g_size\n        \"\"\"\n        with tf.name_scope('tda'):\n            with tf.variable_scope('tda'):\n                with tf.variable_scope('v_i'):\n                    proj_i = self._linear([self.g], self.g_size, 'tanh', use_bias=False)\n                    w_i = tf.get_variable('w_i', shape=(self.g_size, 1), initializer=self.initializer)\n                v_i = tf.reduce_sum(tf.tensordot(proj_i, w_i, axes=1), axis=-1)  # batch_size * max_n_days\n\n                with tf.variable_scope('v_d'):\n                    proj_d = self._linear([self.g], self.g_size, 'tanh', use_bias=False)\n                g_T = tf.expand_dims(self.g_T, axis=-1)  # batch_size * g_size * 1\n                v_d = tf.reduce_sum(tf.matmul(proj_d, g_T), axis=-1)  # batch_size * max_n_days\n\n                aux_score = tf.multiply(v_i, v_d, name='v_stared')\n                ninf = tf.fill(tf.shape(aux_score), np.NINF)\n                masked_aux_score = tf.where(self.mask_aux_trading_days, aux_score, ninf)\n                v_stared = tf.nn.softmax(masked_aux_score)\n\n                # v_stared: batch_size * max_n_days\n                self.v_stared = tf.where(tf.is_nan(v_stared), tf.zeros_like(v_stared), v_stared)\n\n                if self.daily_att == 'y':\n                    context = tf.transpose(self.y, [0, 2, 1])  # batch_size * y_size * max_n_days\n                else:\n                    context = tf.transpose(self.g, [0, 2, 1])  # batch_size * g_size * max_n_days\n\n                v_stared = tf.expand_dims(self.v_stared, -1)  # batch_size * max_n_days * 1\n                att_c = tf.reduce_sum(tf.matmul(context, v_stared), axis=-1)  # batch_size * g_size / y_size\n                with tf.variable_scope('y_T'):\n                    self.y_T = self._linear([att_c, self.g_T], self.y_size, 'softmax')\n\n    def _create_generative_ata(self):\n        \"\"\"\n             calculate loss.\n\n             g: batch_size * max_n_days * g_size\n             y: batch_size * max_n_days * y_size\n             kl_loss: batch_size * max_n_days\n             => loss: batch_size\n        \"\"\"\n        with tf.name_scope('ata'):\n            with tf.variable_scope('ata'):\n                v_aux = self.alpha * self.v_stared  # batch_size * max_n_days\n\n                minor = 0.0  # 0.0, 1e-7\n                likelihood_aux = tf.reduce_sum(tf.multiply(self.y_ph, tf.log(self.y + minor)), axis=2)  # batch_size * max_n_days\n\n                kl_lambda = self._kl_lambda()\n                obj_aux = likelihood_aux - kl_lambda * self.kl  # batch_size * max_n_days\n\n                # deal with T specially, likelihood_T: batch_size, 1\n                self.y_T_ = tf.gather_nd(params=self.y_ph, indices=self.indexed_T)  # batch_size * y_size\n                likelihood_T = tf.reduce_sum(tf.multiply(self.y_T_, tf.log(self.y_T + minor)), axis=1, keep_dims=True)\n\n                kl_T = tf.reshape(tf.gather_nd(params=self.kl, indices=self.indexed_T), shape=[self.batch_size, 1])\n                obj_T = likelihood_T - kl_lambda * kl_T\n\n                obj = obj_T + tf.reduce_sum(tf.multiply(obj_aux, v_aux), axis=1, keep_dims=True)  # batch_size * 1\n                self.loss = tf.reduce_mean(-obj, axis=[0, 1])\n\n    def _create_discriminative_ata(self):\n        \"\"\"\n             calculate discriminative loss.\n\n             g: batch_size * max_n_days * g_size\n             y: batch_size * max_n_days * y_size\n             => loss: batch_size\n        \"\"\"\n        with tf.name_scope('ata'):\n            with tf.variable_scope('ata'):\n                v_aux = self.alpha * self.v_stared  # batch_size * max_n_days\n\n                minor = 0.0  # 0.0, 1e-7\n                likelihood_aux = tf.reduce_sum(tf.multiply(self.y_ph, tf.log(self.y + minor)), axis=2)  # batch_size * max_n_days\n\n                # deal with T specially, likelihood_T: batch_size, 1\n                self.y_T_ = tf.gather_nd(params=self.y_ph, indices=self.indexed_T)  # batch_size * y_size\n                likelihood_T = tf.reduce_sum(tf.multiply(self.y_T_, tf.log(self.y_T + minor)), axis=1, keep_dims=True)\n\n                obj = likelihood_T + tf.reduce_sum(tf.multiply(likelihood_aux, v_aux), axis=1, keep_dims=True)  # batch_size * 1\n                self.loss = tf.reduce_mean(-obj, axis=[0, 1])\n\n    def _build_ata(self):\n        if self.variant_type == 'discriminative':\n            self._create_discriminative_ata()\n        else:\n            self._create_generative_ata()\n\n    def _create_optimizer(self):\n        with tf.name_scope('optimizer'):\n            if self.opt == 'sgd':\n                decayed_lr = tf.train.exponential_decay(learning_rate=self.lr, global_step=self.global_step,\n                                                        decay_steps=self.decay_step, decay_rate=self.decay_rate)\n                optimizer = tf.train.MomentumOptimizer(learning_rate=decayed_lr, momentum=self.momentum)\n            else:\n                optimizer = tf.train.AdamOptimizer(self.lr)\n            gradients, variables = zip(*optimizer.compute_gradients(self.loss))\n            gradients, _ = tf.clip_by_global_norm(gradients, self.clip)\n            self.optimize = optimizer.apply_gradients(zip(gradients, variables))\n            self.global_step = tf.assign_add(self.global_step, 1)\n\n    def assemble_graph(self):\n        logger.info('Start graph assembling...')\n        with tf.device('/device:GPU:0'):\n            self._build_placeholders()\n            self._build_embeds()\n            self._build_mie()\n            self._build_vmd()\n            self._build_temporal_att()\n            self._build_ata()\n            self._create_optimizer()\n\n    def _kl_lambda(self):\n        def _nonzero_kl_lambda():\n            if self.use_constant_kl_lambda:\n                return self.constant_kl_lambda\n            else:\n                return tf.minimum(self.kl_lambda_anneal_rate * global_step, 1.0)\n\n        global_step = tf.cast(self.global_step, tf.float32)\n\n        return tf.cond(global_step < self.kl_lambda_start_step, lambda: 0.0, _nonzero_kl_lambda)\n\n    def _linear(self, args, output_size, activation=None, use_bias=True, use_bn=False):\n        if type(args) not in (list, tuple):\n            args = [args]\n\n        shape = [a if a else -1 for a in args[0].get_shape().as_list()[:-1]]\n        shape.append(output_size)\n\n        sizes = [a.get_shape()[-1].value for a in args]\n        total_arg_size = sum(sizes)\n        scope = tf.get_variable_scope()\n        x = args[0] if len(args) == 1 else tf.concat(args, -1)\n\n        with tf.variable_scope(scope):\n            weight = tf.get_variable('weight', [total_arg_size, output_size], dtype=tf.float32, initializer=self.initializer)\n            res = tf.tensordot(x, weight, axes=1)\n            if use_bias:\n                bias = tf.get_variable('bias', [output_size], dtype=tf.float32, initializer=self.bias_initializer)\n                res = tf.nn.bias_add(res, bias)\n\n        res = tf.reshape(res, shape)\n\n        if use_bn:\n            res = batch_norm(res, center=True, scale=True, decay=0.99, updates_collections=None,\n                             is_training=self.is_training_phase, scope=scope)\n\n        if activation == 'tanh':\n            res = tf.nn.tanh(res)\n        elif activation == 'sigmoid':\n            res = tf.nn.sigmoid(res)\n        elif activation == 'relu':\n            res = tf.nn.relu(res)\n        elif activation == 'softmax':\n            res = tf.nn.softmax(res)\n\n        return res\n\n    def _z(self, arg, is_prior):\n        mean = self._linear(arg, self.z_size)\n        stddev = self._linear(arg, self.z_size)\n        stddev = tf.sqrt(tf.exp(stddev))\n        epsilon = tf.random_normal(shape=[self.batch_size, self.z_size])\n\n        z = mean if is_prior else mean + tf.multiply(stddev, epsilon)\n        pdf_z = ds.Normal(loc=mean, scale=stddev)\n\n        return z, pdf_z\n"
  },
  {
    "path": "src/config.yml",
    "content": "---\nmodel:\n  mode: 'all'\n  variant_type: 'hedge' # hedge, tech, fund, discriminative\n  alpha: 0.5 # 0.1, 0.3, 0.5, 0.7, 0.9, 1.0\n  dropout_vmd_in: 0.3\n  dropout_mel_in: 0.3\n  n_epochs: 15\n  batch_size: 32\n\n  kl_lambda_anneal_rate: 0.005  # 0.005, 0.002, 0.001, 0.0005\n  kl_lambda_start_step: 0\n  daily_att: 'y'  # y, g\n\n  vmd_rec: 'zh'  # zh or h\n\n  opt: 'adam'  # sgd, adam\n  lr: 0.001\n  clip: 15.0\n\n  y_size: 2\n  use_constant_kl_lambda: 0\n  constant_kl_lambda: 0.1\n\n  word_embed_type: 'glove'\n  weight_init: 'xavier-uniform'\n  mel_cell_type: 'gru'  # basic, gru, ln-lstm\n  vmd_cell_type: 'gru'  # basic, gru, ln-lstm\n\n  word_embed_size: 50\n  stock_embed_size: 150\n  init_stock_with_word: 0\n  mel_h_size: 100\n  h_size: 150\n  g_size: 50\n\n  shuffle: 1\n  max_n_days: 5\n  max_n_msgs: 30\n  max_n_words: 40\n\n  decay_step: 100\n  decay_rate: 0.96\n  momentum: 0.9\n\n  use_in_bn: 0\n  use_o_bn: 0\n  use_g_bn: 0\n  dropout_mel: 0.0\n  dropout_ce: 0.0\n  dropout_vmd: 0.0\n\ndates:\n  train_start_date: '2014-01-01'\n  train_end_date: '2015-08-01'\n  dev_start_date: '2015-08-01'\n  dev_end_date: '2015-10-01'\n  test_start_date: '2015-10-01'\n  test_end_date: '2016-01-01'\n\nstocks:\n  materials: ['XOM', 'RDS-B', 'PTR', 'CVX', 'TOT', 'BP', 'BHP', 'SNP', 'SLB', 'BBL']\n  consumer_goods: ['AAPL', 'PG', 'BUD', 'KO', 'PM', 'TM', 'PEP', 'UN', 'UL', 'MO']\n  healthcare: ['JNJ', 'PFE', 'NVS', 'UNH', 'MRK', 'AMGN', 'MDT', 'ABBV', 'SNY', 'CELG']\n  services: ['AMZN', 'BABA', 'WMT', 'CMCSA', 'HD', 'DIS', 'MCD', 'CHTR', 'UPS', 'PCLN']\n  utilities: ['NEE', 'DUK', 'D', 'SO', 'NGG', 'AEP', 'PCG', 'EXC', 'SRE', 'PPL']\n  cong: ['IEP', 'HRG', 'CODI', 'REX', 'SPLP', 'PICO', 'AGFS', 'GMRE']\n  finance: ['BCH', 'BSAC', 'BRK-A', 'JPM', 'WFC', 'BAC', 'V', 'C', 'HSBC', 'MA']\n  industrial_goods: ['GE', 'MMM', 'BA', 'HON', 'UTX', 'LMT', 'CAT', 'GD', 'DHR', 'ABB']\n  tech: ['GOOG', 'MSFT', 'FB', 'T', 'CHL', 'ORCL', 'TSM', 'VZ', 'INTC', 'CSCO']\n\npaths:\n  data: 'data/'\n  tweet_retrieved: 'tweet/raw'\n  tweet_preprocessed: 'tweet/preprocessed'\n  price: 'price/preprocessed'\n  res: 'res/'\n  glove: 'glove.twitter.27B.50d.txt'\n  vocab_tweet: 'vocab.txt'\n  graphs: 'graphs'\n  checkpoints: 'checkpoints'\n  log: 'log/'\n"
  },
  {
    "path": "src/metrics.py",
    "content": "#!/usr/local/bin/python\nimport tensorflow as tf\nimport numpy as np\nimport math\n\n\ndef n_accurate(y, y_):\n    \"\"\"\n        y, y_: Tensor, shape: [batch_size, y_size];\n    \"\"\"\n    correct_y_batch = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n    n_accurate = tf.reduce_sum(tf.cast(correct_y_batch, tf.float32))  # similar to numpy.count_nonzero()\n    return n_accurate\n\n\ndef eval_acc(n_acc, total):\n    return float(n_acc) / total\n\n\ndef create_confusion_matrix(y, y_, is_distribution=True):\n    \"\"\"\n        By batch. shape: [n_batch, batch_size, y_size]\n    \"\"\"\n    n_samples = float(y_.shape[0])   # get dimension list\n    if is_distribution:\n        label_ref = np.argmax(y_, 1)  # 1-d array of 0 and 1\n        label_hyp = np.argmax(y, 1)\n    else:\n        label_ref, label_hyp = y, y_\n\n    # p & n in prediction\n    p_in_hyp = np.sum(label_hyp)\n    n_in_hyp = n_samples - p_in_hyp\n\n    # Positive class: up\n    tp = np.sum(np.multiply(label_ref, label_hyp))  # element-wise, both 1 can remain\n    fp = p_in_hyp - tp  # predicted positive, but false\n\n    # Negative class: down\n    tn = n_samples - np.count_nonzero(label_ref + label_hyp)  # both 0 can remain\n    fn = n_in_hyp - tn  # predicted negative, but false\n\n    return float(tp), float(fp), float(tn), float(fn)\n\n\ndef eval_mcc(tp, fp, tn, fn):\n    core_de = (tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)\n    return (tp * tn - fp * fn) / math.sqrt(core_de) if core_de else None\n\n\ndef eval_res(gen_n_acc, gen_size, gen_loss_list, y_list, y_list_, use_mcc=None):\n    gen_acc = eval_acc(n_acc=gen_n_acc, total=gen_size)\n    gen_loss = np.average(gen_loss_list)\n    results = {'loss': gen_loss,\n               'acc': gen_acc,\n               }\n\n    if use_mcc:\n        gen_y, gen_y_ = np.vstack(y_list), np.vstack(y_list_)\n        tp, fp, tn, fn = create_confusion_matrix(y=gen_y, y_=gen_y_)\n        results['mcc'] = eval_mcc(tp, fp, tn, fn)\n\n    return results\n\n\ndef basic_train_stat(train_batch_loss_list, train_epoch_n_acc, train_epoch_size):\n    train_epoch_loss = np.average(train_batch_loss_list)\n    train_epoch_acc = eval_acc(n_acc=train_epoch_n_acc, total=train_epoch_size)\n    return train_epoch_loss, train_epoch_acc\n\n\n"
  },
  {
    "path": "src/neural.py",
    "content": "#!/usr/local/bin/python\nimport tensorflow as tf\nimport math\nimport tensorflow.contrib.distributions as ds\n\n\ndef bn(input, is_training_phase, bn_scope):\n    return tf.contrib.layers.batch_norm(input, center=True, scale=True, decay=0.99, updates_collections=None,\n                                        is_training=is_training_phase, scope=bn_scope)\n\n\ndef tanh(input, bn_scope=None, is_training_phase=None, dropout_value=None, name=\"relu\"):\n    if bn_scope:\n        input = bn(input, is_training_phase, bn_scope)\n\n    value = tf.tanh(input)\n\n    if dropout_value is not None:\n        return tf.nn.dropout(value, keep_prob=1-dropout_value, name=name)\n    else:\n        return value\n\n\ndef softmax(input, bn_scope=None, is_training_phase=None):\n    if bn_scope and is_training_phase is not None:\n        input = tf.contrib.layers.batch_norm(input, center=True, scale=True, decay=0.99, updates_collections=None,\n                                             is_training=is_training_phase, scope=bn_scope)\n    return tf.nn.softmax(input)\n\n\ndef iter(size, func, iter_arg, iter_arg2=None, iter_arg3=None, **kwargs):\n    ta = tf.TensorArray(tf.float32, size=size)\n    loop_init = (0, ta)\n    cond = lambda i, _: i < size\n    if iter_arg3 is not None:\n        body = lambda i, ta: (i + 1, ta.write(i, func(iter_arg[i], iter_arg2[i], iter_arg3[i], **kwargs)))\n    else:\n        body = lambda i, ta: (i + 1, ta.write(i, func(labels=iter_arg[:, i], logits=iter_arg2[:, i])))\n    _, ta_final = tf.while_loop(cond, body, loop_init)\n\n    return ta_final.stack()\n\n\ndef get_z(input, batch_size, z_size, W_mean, W_stddev, b_mean, b_stddev, is_prior):\n    mean = tf.tensordot(input, W_mean, axes=1) + b_mean\n    stddev = tf.tensordot(input, W_stddev, axes=1) + b_stddev\n    stddev = tf.sqrt(tf.exp(stddev))\n    epsilon = tf.random_normal(shape=[batch_size, z_size], name='epsilon')\n\n    z = mean if is_prior else mean + tf.multiply(stddev, epsilon)\n\n    pdf_z = ds.Normal(loc=mean, scale=stddev)\n\n    return z, pdf_z\n\n\ndef get_r(max_len_st, alpha, with_lt=None):\n    \"\"\"\n        r for [0, max_len_st]\n    \"\"\"\n    r = lambda t: math.exp(alpha * (t - max_len_st))\n    seq_len = max_len_st + 1 if with_lt else max_len_st\n    r_arr = [r(t) for t in range(seq_len)]\n    r_vec = tf.constant(r_arr, shape=(seq_len, 1), dtype=tf.float32)\n    return r_vec\n"
  },
  {
    "path": "src/run.sh",
    "content": "#!/bin/sh\nexport CUDA_VISIBLE_DEVICES=0\npython Main.py\n"
  },
  {
    "path": "src/stat_logger.py",
    "content": "#!/usr/local/bin/python\nimport metrics as metrics\nfrom ConfigLoader import logger\n\n\ndef print_batch_stat(n_iter, train_batch_loss, train_batch_n_acc, train_batch_size):\n    iter_str = '\\titer: {0}'.format(n_iter)\n    loss_str = 'batch loss: {:.6f}'.format(train_batch_loss) if type(train_batch_loss) is float else 'batch loss: {}'.format(train_batch_loss)\n    train_batch_acc = metrics.eval_acc(n_acc=train_batch_n_acc, total=train_batch_size)\n    acc_str = 'batch acc: {:.6f}'.format(train_batch_acc)\n    logger.info(', '.join((iter_str, loss_str, acc_str)))\n\n\ndef print_epoch_stat(epoch_loss, epoch_acc):\n    epoch_stat_pattern = 'Epoch: loss: {0:.6f}, acc: {1:.6f}'\n    logger.info(epoch_stat_pattern.format(epoch_loss, epoch_acc))\n\n\ndef print_eval_res(result_dict, use_mcc=None):\n    eval_loss, eval_acc = result_dict['loss'], result_dict['acc']\n    iter_str = '\\tEval'\n    loss_str = 'loss: {:.6f}'.format(eval_loss) if type(eval_loss) is float else 'eval loss: {}'.format(eval_loss)\n    acc_str = 'acc: {:.6f}'.format(eval_acc)\n    info_list = [iter_str, loss_str, acc_str]\n    if use_mcc:\n        mcc = result_dict['mcc']\n        mcc_str = 'mcc: {:.6f}'.format(mcc) if mcc else 'mcc: {}'.format(mcc)\n        info_list.append(mcc_str)\n    logger.info(', '.join(info_list))\n"
  }
]