앞에서 정규화에 대해서 알아보았습니다. 학습할 데이터가 적으니 이번에는 여러개의 종목을 하나로 합쳐서 학습해보도록 하겠습니다. 학습할 데이터를 합친다는 개념은 수치에 대한 회귀를 바탕으로 종목에 관계없이 같다는 가정하에서 출발 하는 것입니다.
어떤 종목을 선택하는지는 config.py에 list 형태로 미리 정의해 두었습니다. 이전에는 하나의 종목만 입력을 해두었고, 여기에 여러개의 종목을 넣었습니다.
predict_stock_list_for_db=[
"005930"
]
여러 종목 넣음
predict_stock_list_for_db=[
"005930",#삼성전자
"035720",#카카오
"036570",#엔씨소프트
"000080",#하이트진로
"009150",#삼성전기
"005490",#POSCO
"010130",#고려아연
"009540",#현대중곡업
"000720",#현대걸설
"009240",#한샘
"005380",#현대자동차
"097950",#CJ 제이제당
"090430",#아모레퍼시픽
"139480",#이마트
"008770",#호텔신라
"000660",#SK하이닉스
"011070",#LG이노텍
"122630",#KODEX 레버리지
"114800",#KODEX 인버스
"039490" #키움증권
]
한꺼번에 데이터들을 연결해서 붙여서 테스트를 하였습니다.
C:\Users\USER\Documents\python\stock>python predict_simul_normal_reg.py 20180724.0 havetobuy 532 46500.0 buy 45337.5 47662.5 20180727.0 havetobuy 529 46900.0 buy 45727.5 48072.5 20180802.0 sell 525 46900.0 - 45500.0 20180810.0 sell 519 46500.0 - 44850.0 20180823.0 havetobuy 511 46100.0 buy 44947.5 47252.5 20180824.0 havetobuy 510 46200.0 buy 45045.0 47355.0 20180830.0 sell 506 46100.0 + 47950.0 20180830.0 sell 506 46200.0 + 47950.0 20181106.0 havetobuy 463 43800.0 buy 42705.0 44895.0 20181108.0 sell 461 43800.0 + 45050.0 20190117.0 havetobuy 414 41450.0 buy 40413.75 42486.25 20190121.0 sell 412 41450.0 + 42750.0 20190222.0 havetobuy 391 46950.0 buy 45776.25 48123.75 20190228.0 sell 387 46950.0 - 45100.0 20190307.0 havetobuy 383 44000.0 buy 42900.0 45100.0 20190308.0 havetobuy 382 44450.0 buy 43338.75 45561.25 20190320.0 sell 374 44450.0 - 43100.0 20190321.0 sell 373 44000.0 + 46250.0 20190326.0 havetobuy 370 45500.0 buy 44362.5 46637.5 20190327.0 sell 369 45500.0 - 44250.0 20190410.0 havetobuy 359 46650.0 buy 45483.75 47816.25 20190419.0 sell 352 46650.0 - 45250.0 20190524.0 havetobuy 329 43850.0 buy 42753.75 44946.25 20190527.0 sell 328 43850.0 - 42350.0 20190709.0 havetobuy 298 44400.0 44700.0 could not buy 20190821.0 havetobuy 268 44450.0 buy 43338.75 45561.25 20190826.0 sell 265 44450.0 - 42950.0 20190910.0 havetobuy 254 46900.0 buy 45727.5 48072.5 20190919.0 sell 249 46900.0 + 49200.0 20190920.0 havetobuy 248 49150.0 buy 47921.25 50378.75 20190930.0 sell 242 49150.0 - 47900.0 20191017.0 havetobuy 231 50700.0 buy 49432.5 51967.5 20191024.0 sell 226 50700.0 + 52500.0 20191118.0 havetobuy 209 53700.0 buy 52357.5 55042.5 20191120.0 sell 207 53700.0 - 52000.0 20191212.0 havetobuy 191 51900.0 52700.0 could not buy 20200103.0 havetobuy 178 55200.0 buy 53820.0 56580.0 20200108.0 sell 175 55200.0 + 57400.0 20200313.0 havetobuy 130 50800.0 buy 49530.0 52070.0 20200316.0 sell 129 50800.0 - 48800.0 20200325.0 havetobuy 122 46950.0 47150.0 could not buy 20200327.0 havetobuy 120 47800.0 buy 46605.0 48995.0 20200330.0 sell 119 47800.0 - 46550.0 20200423.0 havetobuy 102 49850.0 buy 48603.75 51096.25 20200504.0 sell 97 49850.0 - 48500.0 20200528.0 havetobuy 80 49900.0 buy 48652.5 51147.5 20200601.0 sell 78 49900.0 + 51200.0 20200608.0 havetobuy 73 55500.0 buy 54112.5 56887.5 20200611.0 sell 70 55500.0 - 53200.0 20200708.0 havetobuy 51 53400.0 buy 52065.0 54735.0 20200715.0 sell 46 53400.0 + 55000.0 -10.0 20180509.0 havetobuy 583 112000.0 buy 109200.0 114800.0 20180510.0 sell 582 112000.0 + 118000.0 20180529.0 havetobuy 570 108000.0 buy 105300.0 110700.0 20180530.0 sell 569 108000.0 - 103500.0 20180615.0 havetobuy 559 112000.0 buy 109200.0 114800.0 20180618.0 sell 558 112000.0 - 106000.0 20180808.0 havetobuy 521 120000.0 buy 117000.0 123000.0 20180809.0 sell 520 120000.0 + 126000.0 20180813.0 havetobuy 518 128000.0 buy 124800.0 131200.0 20180814.0 sell 517 128000.0 - 123000.0 20180820.0 havetobuy 514 127000.0 buy 123825.0 130175.0 20180822.0 sell 512 127000.0 - 122500.0 20180830.0 havetobuy 506 126500.0 buy 123337.5 129662.5 20180831.0 sell 505 126500.0 - 123000.0 20181122.0 havetobuy 451 107500.0 buy 104812.5 110187.5 20181126.0 sell 449 107500.0 + 113000.0 20190104.0 havetobuy 423 102500.0 buy 99937.5 105062.5 20190107.0 sell 422 102500.0 + 107000.0 20190122.0 havetobuy 411 98800.0 99300.0 could not buy 20190130.0 havetobuy 405 102000.0 buy 99450.0 104550.0 20190131.0 sell 404 102000.0 - 98800.0 20190520.0 havetobuy 333 124500.0 125500.0 could not buy 20190715.0 havetobuy 294 131500.0 buy 128212.5 134787.5 20190719.0 sell 290 131500.0 + 135500.0 20190816.0 havetobuy 271 130500.0 buy 127237.5 133762.5 20190822.0 sell 267 130500.0 + 135000.0 20190827.0 havetobuy 264 132500.0 133000.0 could not buy 20200210.0 havetobuy 154 167500.0 buy 163312.5 171687.5 20200212.0 sell 152 167500.0 + 172000.0 20200710.0 havetobuy 49 355500.0 buy 346612.5 364387.5 20200713.0 sell 48 355500.0 - 345000.0 20200831.0 havetobuy 14 405500.0 buy 395362.5 415637.5 20200901.0 sell 13 405500.0 + 416000.0 2.5 20180504.0 havetobuy 585 355000.0 buy 346125.0 363875.0 20180508.0 sell 584 355000.0 - 340500.0 20180524.0 havetobuy 573 352000.0 buy 343200.0 360800.0 20180525.0 sell 572 352000.0 + 367000.0 20180705.0 havetobuy 545 365500.0 buy 356362.5 374637.5 20180709.0 sell 543 365500.0 + 380000.0 20180730.0 havetobuy 528 386500.0 buy 376837.5 396162.5 20180802.0 sell 525 386500.0 - 369500.0 20180829.0 havetobuy 507 393500.0 buy 383662.5 403337.5 20180831.0 sell 505 393500.0 - 382000.0 20180913.0 havetobuy 496 423500.0 buy 412912.5 434087.5 20180914.0 sell 495 423500.0 + 436000.0 20181004.0 havetobuy 485 423500.0 buy 412912.5 434087.5 20181008.0 sell 483 423500.0 - 408500.0 20181010.0 havetobuy 482 420500.0 buy 409987.5 431012.5 20181011.0 sell 481 420500.0 - 404500.0 20181023.0 havetobuy 473 426500.0 buy 415837.5 437162.5 20181025.0 sell 471 426500.0 - 408000.0 20181102.0 havetobuy 465 431000.0 buy 420225.0 441775.0 20181105.0 sell 464 431000.0 + 452500.0 20181105.0 havetobuy 464 443500.0 buy 432412.5 454587.5 20181106.0 sell 463 443500.0 + 458500.0 20190122.0 havetobuy 411 498000.0 buy 485550.0 510450.0 20190123.0 sell 410 498000.0 - 483500.0 20190128.0 havetobuy 407 467000.0 buy 455325.0 478675.0 20190211.0 sell 400 467000.0 - 447000.0 20190315.0 havetobuy 377 437500.0 buy 426562.5 448437.5 20190318.0 sell 376 437500.0 + 449500.0 20190723.0 havetobuy 288 500000.0 buy 487500.0 512500.0 20190726.0 sell 285 500000.0 - 482500.0 20200113.0 havetobuy 172 608000.0 buy 592800.0 623200.0 20200116.0 sell 169 608000.0 + 637000.0 20200212.0 havetobuy 152 644000.0 buy 627900.0 660100.0 20200213.0 sell 151 644000.0 - 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29000.0 20200204.0 havetobuy 158 30300.0 buy 29542.5 31057.5 20200205.0 sell 157 30300.0 + 31200.0 20200207.0 havetobuy 155 29600.0 29700.0 could not buy 20200218.0 havetobuy 148 29700.0 buy 28957.5 30442.5 20200219.0 sell 147 29700.0 + 31750.0 20200330.0 havetobuy 119 25500.0 buy 24862.5 26137.5 20200331.0 sell 118 25500.0 + 27300.0 20200331.0 havetobuy 118 25850.0 26150.0 could not buy -2.5 20180626.0 havetobuy 552 153500.0 buy 149662.5 157337.5 20180627.0 sell 551 153500.0 - 147000.0 20190107.0 havetobuy 422 92000.0 93800.0 could not buy 20190108.0 havetobuy 421 95500.0 buy 93112.5 97887.5 20190109.0 sell 420 95500.0 + 99300.0 20190208.0 havetobuy 401 109500.0 buy 106762.5 112237.5 20190211.0 sell 400 109500.0 - 105000.0 20190610.0 havetobuy 319 95500.0 buy 93112.5 97887.5 20190617.0 sell 314 95500.0 - 92900.0 20190712.0 havetobuy 295 90600.0 91100.0 could not buy 20190909.0 havetobuy 255 97200.0 buy 94770.0 99630.0 20190919.0 sell 249 97200.0 + 101500.0 20191113.0 havetobuy 212 111000.0 buy 108225.0 113775.0 20191118.0 sell 209 111000.0 + 114500.0 20191120.0 havetobuy 207 111500.0 buy 108712.5 114287.5 20191121.0 sell 206 111500.0 - 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13040.0 20200323.0 havetobuy 124 7350.0 buy 7166.25 7533.75 20200324.0 sell 123 7350.0 - 6645.0 20200423.0 havetobuy 102 9855.0 buy 9608.625 10101.375 20200427.0 sell 100 9855.0 + 10200.0 20200521.0 havetobuy 85 10730.0 10775.0 could not buy 20200528.0 havetobuy 80 11130.0 buy 10851.75 11408.25 20200529.0 havetobuy 79 11185.0 buy 10905.375 11464.625 20200601.0 sell 78 11130.0 + 11585.0 20200601.0 sell 78 11185.0 + 11585.0 20200910.0 havetobuy 6 15020.0 15180.0 could not buy 20200914.0 havetobuy 4 15250.0 15465.0 could not buy -2.5 20180511.0 havetobuy 581 6115.0 buy 5962.125 6267.875 20180514.0 havetobuy 580 6075.0 buy 5923.125 6226.875 20180530.0 sell 569 6115.0 + 6295.0 20180530.0 sell 569 6075.0 + 6295.0 20180604.0 havetobuy 566 6190.0 buy 6035.25 6344.75 20180618.0 sell 558 6190.0 + 6355.0 20180702.0 havetobuy 548 6445.0 buy 6283.875 6606.125 20180703.0 sell 547 6445.0 + 6630.0 20180704.0 havetobuy 546 6575.0 buy 6410.625 6739.375 20180719.0 havetobuy 535 6505.0 buy 6342.375 6667.625 20180813.0 sell 518 6505.0 + 6680.0 20180816.0 sell 516 6575.0 + 6745.0 20180905.0 havetobuy 502 6450.0 6470.0 could not buy 20180907.0 havetobuy 500 6560.0 6565.0 could not buy 20181004.0 havetobuy 485 6490.0 buy 6327.75 6652.25 20181010.0 sell 482 6490.0 + 6700.0 20181010.0 havetobuy 482 6630.0 buy 6464.25 6795.75 20181011.0 sell 481 6630.0 + 6965.0 20181012.0 havetobuy 480 6940.0 buy 6766.5 7113.5 20181015.0 havetobuy 479 6880.0 buy 6708.0 7052.0 20181023.0 sell 473 6880.0 + 7080.0 20181025.0 sell 471 6940.0 + 7305.0 20190102.0 havetobuy 425 7240.0 buy 7059.0 7421.0 20190104.0 sell 423 7240.0 + 7435.0 20190109.0 havetobuy 420 7290.0 buy 7107.75 7472.25 20190110.0 sell 419 7290.0 - 7105.0 20190311.0 havetobuy 381 6915.0 buy 6742.125 7087.875 20190315.0 havetobuy 377 6870.0 buy 6698.25 7041.75 20190321.0 sell 373 6915.0 - 6685.0 20190321.0 sell 373 6870.0 - 6685.0 20190409.0 havetobuy 360 6640.0 buy 6474.0 6806.0 20190417.0 havetobuy 354 6545.0 buy 6381.375 6708.625 20190424.0 sell 349 6545.0 + 6745.0 20190507.0 sell 342 6640.0 + 6825.0 20190514.0 havetobuy 337 7070.0 buy 6893.25 7246.75 20190529.0 sell 326 7070.0 + 7315.0 20190711.0 havetobuy 296 7055.0 buy 6878.625 7231.375 20190712.0 havetobuy 295 6970.0 buy 6795.75 7144.25 20190723.0 sell 288 7055.0 - 6865.0 20190723.0 havetobuy 288 6915.0 buy 6742.125 7087.875 20190729.0 sell 284 6915.0 + 7110.0 20190731.0 sell 282 6970.0 + 7160.0 20190903.0 havetobuy 259 7335.0 buy 7151.625 7518.375 20190905.0 sell 257 7335.0 - 7135.0 20191028.0 havetobuy 224 6835.0 buy 6664.125 7005.875 20191105.0 sell 218 6835.0 - 6650.0 20200113.0 havetobuy 172 6285.0 buy 6127.875 6442.125 20200117.0 sell 168 6285.0 - 6100.0 20200131.0 havetobuy 160 6480.0 buy 6318.0 6642.0 20200203.0 sell 159 6480.0 + 6655.0 20200226.0 havetobuy 142 6540.0 6595.0 could not buy 20200420.0 havetobuy 105 6890.0 buy 6717.75 7062.25 20200421.0 sell 104 6890.0 + 7185.0 20200511.0 havetobuy 93 6825.0 buy 6654.375 6995.625 20200513.0 sell 91 6825.0 + 7020.0 20200617.0 havetobuy 66 6115.0 buy 5962.125 6267.875 20200619.0 havetobuy 64 6140.0 buy 5986.5 6293.5 20200706.0 sell 53 6115.0 - 5915.0 20200706.0 sell 53 6140.0 - 5915.0 20200901.0 havetobuy 13 5565.0 buy 5425.875 5704.125 20200902.0 havetobuy 12 5520.0 buy 5382.0 5658.0 20200903.0 sell 11 5565.0 - 5390.0 20200910.0 sell 6 5520.0 - 5375.0 20200914.0 havetobuy 4 5400.0 buy 5265.0 5535.0 20.0 20180803.0 havetobuy 524 90400.0 90800.0 could not buy 20180810.0 havetobuy 519 90800.0 buy 88530.0 93070.0 20180813.0 sell 518 90800.0 - 82000.0 20180822.0 havetobuy 512 89600.0 buy 87360.0 91840.0 20180827.0 sell 509 89600.0 + 94700.0 20180914.0 havetobuy 495 91900.0 92000.0 could not buy 20180918.0 havetobuy 493 91500.0 buy 89212.5 93787.5 20180919.0 havetobuy 492 90600.0 buy 88335.0 92865.0 20180920.0 sell 491 91500.0 + 98000.0 20180920.0 sell 491 90600.0 + 98000.0 20181017.0 havetobuy 477 75300.0 75500.0 could not buy 20181023.0 havetobuy 473 76900.0 buy 74977.5 78822.5 20181024.0 sell 472 76900.0 - 73000.0 20181219.0 havetobuy 432 76300.0 buy 74392.5 78207.5 20181220.0 havetobuy 431 78500.0 buy 76537.5 80462.5 20181221.0 sell 430 76300.0 - 74200.0 20181221.0 sell 430 78500.0 - 74200.0 20181228.0 havetobuy 426 77000.0 77300.0 could not buy 20190108.0 havetobuy 421 77600.0 buy 75660.0 79540.0 20190114.0 sell 417 77600.0 - 75400.0 20190122.0 havetobuy 411 82600.0 buy 80535.0 84665.0 20190123.0 sell 410 82600.0 - 79700.0 20190220.0 havetobuy 393 80700.0 buy 78682.5 82717.5 20190226.0 sell 389 80700.0 + 84100.0 20190318.0 havetobuy 376 83300.0 83600.0 could not buy 20190402.0 havetobuy 365 85900.0 86800.0 could not buy 20190405.0 havetobuy 362 93400.0 buy 91065.0 95735.0 20190409.0 sell 360 93400.0 - 89200.0 20190528.0 havetobuy 327 81900.0 buy 79852.5 83947.5 20190529.0 sell 326 81900.0 - 79200.0 20190627.0 havetobuy 306 82000.0 buy 79950.0 84050.0 20190703.0 sell 302 82000.0 - 79100.0 20190708.0 havetobuy 299 80100.0 buy 78097.5 82102.5 20190709.0 sell 298 80100.0 - 76500.0 20190807.0 havetobuy 277 68400.0 buy 66690.0 70110.0 20190809.0 sell 275 68400.0 + 71100.0 20191230.0 havetobuy 180 80000.0 buy 78000.0 82000.0 20200108.0 sell 175 80000.0 - 76600.0 20200316.0 havetobuy 129 55000.0 buy 53625.0 56375.0 20200317.0 sell 128 55000.0 - 53000.0 20200317.0 havetobuy 128 56000.0 buy 54600.0 57400.0 20200318.0 sell 127 56000.0 - 54100.0 20200318.0 havetobuy 127 57300.0 buy 55867.5 58732.5 20200319.0 sell 126 57300.0 - 50000.0 20200602.0 havetobuy 77 82500.0 buy 80437.5 84562.5 20200603.0 sell 76 82500.0 + 95600.0 20200715.0 havetobuy 46 92900.0 93200.0 could not buy 20200821.0 havetobuy 20 103500.0 buy 100912.5 106087.5 20200824.0 sell 19 103500.0 + 107500.0 -17.5 -87.5
모든 종목 최종 합계는 -87.5%가 됩니다.
C:\Users\USER\Documents\python\stock>python predict_simul_normal_reg.py 20180724.0 havetobuy 532 46500.0 buy 45337.5 47662.5 20180727.0 havetobuy 529 46900.0 buy 45727.5 48072.5 20180802.0 sell 525 46900.0 - 45500.0 20180810.0 sell 519 46500.0 - 44850.0 20180823.0 havetobuy 511 46100.0 buy 44947.5 47252.5 20180824.0 havetobuy 510 46200.0 buy 45045.0 47355.0 20180830.0 sell 506 46100.0 + 47950.0 20180830.0 sell 506 46200.0 + 47950.0 20181106.0 havetobuy 463 43800.0 buy 42705.0 44895.0 20181108.0 sell 461 43800.0 + 45050.0 20190117.0 havetobuy 414 41450.0 buy 40413.75 42486.25 20190121.0 sell 412 41450.0 + 42750.0 20190222.0 havetobuy 391 46950.0 buy 45776.25 48123.75 20190228.0 sell 387 46950.0 - 45100.0 20190307.0 havetobuy 383 44000.0 buy 42900.0 45100.0 20190308.0 havetobuy 382 44450.0 buy 43338.75 45561.25 20190320.0 sell 374 44450.0 - 43100.0 20190321.0 sell 373 44000.0 + 46250.0 20190326.0 havetobuy 370 45500.0 buy 44362.5 46637.5 20190327.0 sell 369 45500.0 - 44250.0 20190410.0 havetobuy 359 46650.0 buy 45483.75 47816.25 20190419.0 sell 352 46650.0 - 45250.0 20190524.0 havetobuy 329 43850.0 buy 42753.75 44946.25 20190527.0 sell 328 43850.0 - 42350.0 20190709.0 havetobuy 298 44400.0 44700.0 could not buy 20190821.0 havetobuy 268 44450.0 buy 43338.75 45561.25 20190826.0 sell 265 44450.0 - 42950.0 20190910.0 havetobuy 254 46900.0 buy 45727.5 48072.5 20190919.0 sell 249 46900.0 + 49200.0 20190920.0 havetobuy 248 49150.0 buy 47921.25 50378.75 20190930.0 sell 242 49150.0 - 47900.0 20191017.0 havetobuy 231 50700.0 buy 49432.5 51967.5 20191024.0 sell 226 50700.0 + 52500.0 20191118.0 havetobuy 209 53700.0 buy 52357.5 55042.5 20191120.0 sell 207 53700.0 - 52000.0 20191212.0 havetobuy 191 51900.0 52700.0 could not buy 20200103.0 havetobuy 178 55200.0 buy 53820.0 56580.0 20200108.0 sell 175 55200.0 + 57400.0 20200313.0 havetobuy 130 50800.0 buy 49530.0 52070.0 20200316.0 sell 129 50800.0 - 48800.0 20200325.0 havetobuy 122 46950.0 47150.0 could not buy 20200327.0 havetobuy 120 47800.0 buy 46605.0 48995.0 20200330.0 sell 119 47800.0 - 46550.0 20200423.0 havetobuy 102 49850.0 buy 48603.75 51096.25 20200504.0 sell 97 49850.0 - 48500.0 20200528.0 havetobuy 80 49900.0 buy 48652.5 51147.5 20200601.0 sell 78 49900.0 + 51200.0 20200608.0 havetobuy 73 55500.0 buy 54112.5 56887.5 20200611.0 sell 70 55500.0 - 53200.0 20200708.0 havetobuy 51 53400.0 buy 52065.0 54735.0 20200715.0 sell 46 53400.0 + 55000.0 -10.0
학습은 전체 종목한뒤 첫 한종목만 확인했을때는 -10%입니다.
predict_stock_list_for_db=[
"005930"
]
C:\Users\USER\Documents\python\stock>python predict_simul_normal_reg.py 20180509.0 havetobuy 583 52600.0 buy 51285.0 53915.0 20180510.0 sell 582 52600.0 - 50600.0 20180525.0 havetobuy 572 51400.0 buy 50115.0 52685.0 20180528.0 sell 571 51400.0 + 53000.0 20180801.0 havetobuy 526 46250.0 buy 45093.75 47406.25 20180810.0 sell 519 46250.0 - 44850.0 20180823.0 havetobuy 511 46100.0 buy 44947.5 47252.5 20180830.0 sell 506 46100.0 + 47950.0 20181106.0 havetobuy 463 43800.0 buy 42705.0 44895.0 20181108.0 sell 461 43800.0 + 45050.0 20190215.0 havetobuy 396 47500.0 buy 46312.5 48687.5 20190218.0 sell 395 47500.0 - 45850.0 20190701.0 havetobuy 304 47000.0 buy 45825.0 48175.0 20190703.0 sell 302 47000.0 - 45200.0 20191010.0 havetobuy 236 48900.0 buy 47677.5 50122.5 20191014.0 sell 234 48900.0 + 50300.0 20191030.0 havetobuy 222 51100.0 buy 49822.5 52377.5 20191105.0 sell 218 51100.0 + 52700.0 20191118.0 havetobuy 209 53700.0 buy 52357.5 55042.5 20191120.0 sell 207 53700.0 - 52000.0 20200103.0 havetobuy 178 55200.0 buy 53820.0 56580.0 20200108.0 sell 175 55200.0 + 57400.0 20200206.0 havetobuy 156 59500.0 59700.0 could not buy 20200312.0 havetobuy 131 52100.0 buy 50797.5 53402.5 20200313.0 sell 130 52100.0 - 46850.0 20200427.0 havetobuy 100 49350.0 buy 48116.25 50583.75 20200512.0 sell 92 49350.0 - 47550.0 20200605.0 havetobuy 74 54600.0 buy 53235.0 55965.0 20200608.0 sell 73 54600.0 + 56500.0 20200813.0 havetobuy 25 59000.0 buy 57525.0 60475.0 20200820.0 sell 21 59000.0 - 55300.0 -2.5 -2.5
첫번째 한 종목만 학습한 경우 -2.5%
onehot encoding
종목 구분을 넣어 보도록 하겠습니다. 첫번째 종목은 1 이고 다음항목은 2 이고 그 다음은 3 이런식으로 간단하게 넣으면 좋겠지만, 이쪽 세계에서는 의미 하는 바가 좀 다릅니다. 위의 예제를 보면, 첫번째 종목과 두번째 종목이 차이가 1이 나게 되고 마치막과 처음과의 차이는 2가 나게됩니다. 이러한 문제를 해결하기위해서 one-hot encoding 을 하게되는데 이건 검색 해보면 원리가 잘 나와 있습니다. 여기에서 하고자 하는것은 dataframe에 어떻게 넣는지 입니다.
3개의 종목이 있다고 가정하면 최종적으로는 삼성전자는 1 0 0 의 값을 가지도록 하는것입니다.
"005930",#삼성전자:1 => onehot encoding 1 0 0
"035720",#카카오:2 => onehot encoding 0 1 0
"036570",#엔씨소프트:3 => onehot encoding 0 0 1
get_dummies 함수가 핵심이며 아래와 같이 데이터를 만들어 냅니다.
import pandas as pd one_hot_list = [] sindex = 0 for sindex in range(3): one_hot_list.append(sindex+1) print(one_hot_list) # 1,2 df_one = pd.DataFrame(one_hot_list, columns = ['sindex']) one_hot_encoded = pd.get_dummies(df_one.sindex,prefix='index_one_hot') print(one_hot_encoded)
결과
C:\Users\USER\Documents\python\stock>python testonehot.py [1, 2, 3] index_one_hot_1 index_one_hot_2 index_one_hot_3 0 1 0 0 1 0 1 0 2 0 0 1
그리고 item을 만들때마다 뒤쪽에 onehot encoding 한것을 뒤에 붙여줍니다. 그러면 코드상으로는 이런 형태가 되고
# onehot start if len(one_hot_encoded.iloc[sindex])==1: df_base[one_hot_encoded.columns]=one_hot_encoded.iloc[sindex] else: df_base[one_hot_encoded.columns]=one_hot_encoded.iloc[sindex].tolist() # onehot end
전체코드는 아래와 같습니다.
predict_feature_normal_reg.py
import config import platform import sqlite3 import pandas as pd import os from sklearn import preprocessing CSV_PREDICT_FNAME = "data_a/predict.csv" CSV_TEST_FNAME = "data_a/test.csv" CSV_TRAIN_FNAME = "data_a/train.csv" print(platform.architecture()) if platform.architecture()[0]!='64bit' : print("Please use 64bit python") exit(-1) """ DB 날짜(N-0) 가격(N-0) 거래량(N-0) 날짜(N-1) 가격(N-1) 거래량(N-1) 날짜(N-2) 가격(N-2) 거래량(N-2) 날짜(N-3) 가격(N-3) 거래량(N-3) 날짜(N-4) 가격(N-4) 거래량(N-4) 날짜(N-5) 가격(N-5) 거래량(N-5) ... 갯수는 X_M_PREDAYES : 예측할때 앞의 몇일을 가지고 학습 데이터를 만들지 결정하는 값 price(N-0),vol(N-0),price(N-1),vol(N-1),price(N-2),vol(N-2),price(N-3),vol(N-3)...N-M 가격(N-0) 거래량(N-0)|가격(N-1) 거래량(N-1)|가격(N-2) 거래량(N-2)|가격(N-3) 거래량(N-3)...N-M 갯수는 Y_O_POSTDAYES : 예측할때 뒤의 몇일을 가지고 결과를 만들지 결정하는 값 Y(N) : price(N+1),price(N+1),price(N+2),price(N+3)...N+O 이값중 price(N) 보다 목표% 보다 커지면 Y(N):1(구매해야함) 이 된다. """ def make_input_data(stockname,type): query = cur.execute("SELECT * FROM "+GetDBRealTableName(stockname,type)+" ORDER BY Time DESC") cols = [column[0] for column in query.description] df = pd.DataFrame.from_records(data=query.fetchall(), columns=cols) return df def GetDBRealTableName(stockname,type): if type=="MIN" : return '"'+stockname+"_MIN"+'"' if type=="DAY" : return '"'+stockname+"_DAY"+'"' return "" def date_to_int(datestr): return int(datestr) def make_lag_df(train_df,laglabel,step): train_df[laglabel+"_lag"+str(step)]=train_df[laglabel].shift(step*-1) return train_df def join_with_prev(df,prev_df,how): df = df.merge(prev_df,how=how) return df # 날짜 날짜-1 날짜-2 가격 ... #갯수는 Y_O_POSTDAYES : 예측할때 뒤의 몇일을 가지고 결과를 만들지 결정하는 값 #Y(N) : price(N+1),price(N+1),price(N+2),price(N+3)...N+O 이값중 price(N) 보다 목표% 보다 커지면 Y(N):1(구매해야함) 이 된다. # O = 2 # price(N+2),price(N+1),price(0),price(N-1),price(N-2),price(N-3)... # price(0),price(N-1),price(N-2),price(N-3),price(N-4),price(N-5)... # 2 1 def resultx(x): sum=0 for i in range(config.Y_O_POSTDAYES-1,1-1-1,-1): sum = sum + x[4*i+0] #0 Price_lag return sum/config.Y_O_POSTDAYES # mean def resultnormalx(x): scaler = preprocessing.MinMaxScaler() x = x.values y=scaler.fit_transform(x.reshape(-1,1)) return pd.Series(y.reshape(1,-1).flatten()) def resulty(y): # y price return y[1]/y[0] if __name__ == '__main__': con = sqlite3.connect(config.db_name) cur = con.cursor() one_hot_list = [] sindex = 0 for sindex in range(len(config.predict_stock_list_for_db)): one_hot_list.append(sindex+1) # 1,2 df_one = pd.DataFrame(one_hot_list, columns = ['sindex']) one_hot_encoded = pd.get_dummies(df_one.sindex,prefix='index_one_hot') print(one_hot_encoded) sindex = 0 for stockname in config.predict_stock_list_for_db : df = make_input_data(stockname,"DAY") df_base = df #df[['Time','Price','Vol']] #df_base["int_time"] = df_base["Time"].map(date_to_int).astype(int) lag_want_list = df.columns.tolist() lag_want_list.remove("Time") for step in range(0,config.X_M_PREDAYES+config.Y_O_POSTDAYES+1): for lagcolname in lag_want_list: df_base = make_lag_df(df_base,lagcolname, step) # onehot start if len(one_hot_encoded.iloc[sindex])==1: df_base[one_hot_encoded.columns]=one_hot_encoded.iloc[sindex] else: df_base[one_hot_encoded.columns]=one_hot_encoded.iloc[sindex].tolist() # onehot end print(df_base) # set Y check_col = [] for i in range(0,config.X_M_PREDAYES+config.Y_O_POSTDAYES+1): check_col.append("Price_lag"+str(i)) #0 check_col.append("SPrice_lag"+str(i))#1 check_col.append("HPrice_lag"+str(i))#2 check_col.append("LPrice_lag"+str(i))#3 df_base['Y'] = df_base[check_col].apply(resultx, axis=1) df_base['Y'] = df_base['Y'].shift(config.Y_O_POSTDAYES) # normalize check_col = [] for i in range(0,config.X_M_PREDAYES+config.Y_O_POSTDAYES+1): check_col.append("Price_lag"+str(i)) #0 check_col.append("SPrice_lag"+str(i))#1 check_col.append("HPrice_lag"+str(i))#2 check_col.append("LPrice_lag"+str(i))#3 df_base[check_col] = df_base[check_col].apply(resultnormalx, axis=1) check_col = [] for i in range(0,config.X_M_PREDAYES+config.Y_O_POSTDAYES+1): check_col.append("Vol_lag"+str(i)) #0 df_base[check_col] = df_base[check_col].apply(resultnormalx, axis=1) df_base['Y'] = df_base[['Y','Price']].apply(resulty, axis=1) # Save #df_base = df_base.drop(['Time','Price','Vol','SPrice','HPrice','LPrice'],axis=1) #예측데이터 predict = df_base.head(config.Y_O_POSTDAYES).drop(['Y'],axis=1) if sindex == 0: predict.to_csv(CSV_PREDICT_FNAME, encoding='utf-8', index=False) else: predict.to_csv(CSV_PREDICT_FNAME, mode = 'a', encoding='utf-8', index=False, header=False) df_base.drop(df_base.head(config.Y_O_POSTDAYES).index, inplace=True) #검증데이터 test = df_base.head(config.COUNT_TEST_DATA) if sindex == 0: test.to_csv(CSV_TEST_FNAME, encoding='utf-8', index=False) else: test.to_csv(CSV_TEST_FNAME, mode = 'a', encoding='utf-8', index=False, header=False) df_base.drop(df_base.head(config.COUNT_TEST_DATA).index, inplace=True) #훈련데이터 if sindex == 0: df_base.to_csv(CSV_TRAIN_FNAME, encoding='utf-8', index=False) else: df_base.to_csv(CSV_TRAIN_FNAME, mode = 'a', encoding='utf-8', index=False, header=False) sindex = sindex + 1 con.close()
전체 테스트
C:\Users\USER\Documents\python\stock>python predict_simul_normal_reg.py 20180425.0 havetobuy 591 50460.0 buy 49198.5 51721.5 20180426.0 sell 590 50460.0 + 52160.0 20180524.0 havetobuy 573 51800.0 buy 50505.0 53095.0 20180530.0 sell 569 51800.0 - 49100.0 20180813.0 havetobuy 518 45400.0 buy 44265.0 46535.0 20180816.0 sell 516 45400.0 - 43700.0 20180823.0 havetobuy 511 46100.0 buy 44947.5 47252.5 20180824.0 havetobuy 510 46200.0 buy 45045.0 47355.0 20180830.0 sell 506 46100.0 + 47950.0 20180830.0 sell 506 46200.0 + 47950.0 20180927.0 havetobuy 489 47400.0 buy 46215.0 48585.0 20181001.0 sell 487 47400.0 - 45800.0 20181024.0 havetobuy 472 43050.0 buy 41973.75 44126.25 20181025.0 sell 471 43050.0 - 40550.0 20181106.0 havetobuy 463 43800.0 buy 42705.0 44895.0 20181108.0 sell 461 43800.0 + 45050.0 20181127.0 havetobuy 448 42600.0 buy 41535.0 43665.0 20181129.0 sell 446 42600.0 + 43850.0 20181203.0 havetobuy 444 41850.0 42400.0 could not buy 20190117.0 havetobuy 414 41450.0 buy 40413.75 42486.25 20190121.0 sell 412 41450.0 + 42750.0 20190308.0 havetobuy 382 44450.0 buy 43338.75 45561.25 20190311.0 havetobuy 381 43800.0 buy 42705.0 44895.0 20190312.0 sell 380 43800.0 + 44950.0 20190315.0 havetobuy 377 43850.0 buy 42753.75 44946.25 20190320.0 sell 374 44450.0 - 43100.0 20190321.0 sell 373 43850.0 + 46250.0 20190527.0 havetobuy 328 42700.0 buy 41632.5 43767.5 20190529.0 sell 326 42700.0 - 41300.0 20190625.0 havetobuy 308 45500.0 buy 44362.5 46637.5 20190628.0 sell 305 45500.0 + 47000.0 20190715.0 havetobuy 294 46300.0 buy 45142.5 47457.5 20190717.0 havetobuy 292 46850.0 buy 45678.75 48021.25 20190718.0 sell 291 46850.0 - 45650.0 20190718.0 havetobuy 291 46050.0 buy 44898.75 47201.25 20190722.0 sell 289 46050.0 + 47300.0 20190723.0 sell 288 46300.0 + 47550.0 20190829.0 havetobuy 262 44150.0 buy 43046.25 45253.75 20190905.0 sell 257 44150.0 + 46100.0 20190910.0 havetobuy 254 46900.0 buy 45727.5 48072.5 20190919.0 sell 249 46900.0 + 49200.0 20191029.0 havetobuy 223 51300.0 buy 50017.5 52582.5 20191105.0 sell 218 51300.0 + 52700.0 20200311.0 havetobuy 132 54600.0 buy 53235.0 55965.0 20200312.0 sell 131 54600.0 - 49300.0 20200327.0 havetobuy 120 47800.0 buy 46605.0 48995.0 20200330.0 sell 119 47800.0 - 46550.0 20200421.0 havetobuy 104 50100.0 buy 48847.5 51352.5 20200422.0 sell 103 50100.0 - 48350.0 20200423.0 havetobuy 102 49850.0 buy 48603.75 51096.25 20200429.0 havetobuy 98 50100.0 buy 48847.5 51352.5 20200504.0 sell 97 49850.0 - 48500.0 20200504.0 sell 97 50100.0 - 48500.0 20200605.0 havetobuy 74 54600.0 buy 53235.0 55965.0 20200608.0 sell 73 54600.0 + 56500.0 20200608.0 havetobuy 73 55500.0 buy 54112.5 56887.5 20200611.0 sell 70 55500.0 - 53200.0 20200730.0 havetobuy 35 59000.0 buy 57525.0 60475.0 20200803.0 sell 33 59000.0 - 56700.0 2.5 20180604.0 havetobuy 566 104500.0 buy 101887.5 107112.5 20180605.0 sell 565 104500.0 + 112000.0 20180615.0 havetobuy 559 112000.0 buy 109200.0 114800.0 20180618.0 sell 558 112000.0 - 106000.0 20180810.0 havetobuy 519 125500.0 buy 122362.5 128637.5 20180817.0 sell 515 125500.0 + 129000.0 20180921.0 havetobuy 490 117500.0 118500.0 could not buy 20181012.0 havetobuy 480 98500.0 buy 96037.5 100962.5 20181015.0 sell 479 98500.0 + 102000.0 20181015.0 havetobuy 479 101000.0 buy 98475.0 103525.0 20181022.0 sell 474 101000.0 + 104000.0 20181114.0 havetobuy 457 101500.0 buy 98962.5 104037.5 20181116.0 sell 455 101500.0 + 106000.0 20181121.0 havetobuy 452 107000.0 buy 104325.0 109675.0 20181126.0 sell 449 107000.0 + 113000.0 20190121.0 havetobuy 412 99600.0 buy 97110.0 102090.0 20190122.0 sell 411 99600.0 + 102500.0 20190226.0 havetobuy 389 98300.0 98400.0 could not buy 20190228.0 havetobuy 387 105000.0 buy 102375.0 107625.0 20190304.0 sell 386 105000.0 + 109000.0 20190410.0 havetobuy 359 100500.0 buy 97987.5 103012.5 20190415.0 sell 356 100500.0 + 110500.0 20190529.0 havetobuy 326 123000.0 buy 119925.0 126075.0 20190604.0 sell 322 123000.0 - 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482500.0 20191017.0 havetobuy 231 506000.0 buy 493350.0 518650.0 20191018.0 sell 230 506000.0 + 520000.0 20200129.0 havetobuy 162 644000.0 buy 627900.0 660100.0 20200130.0 sell 161 644000.0 + 665000.0 20200204.0 havetobuy 158 640000.0 buy 624000.0 656000.0 20200205.0 sell 157 640000.0 + 664000.0 20200219.0 havetobuy 147 655000.0 660000.0 could not buy 20200525.0 havetobuy 83 790000.0 792000.0 could not buy 20200612.0 havetobuy 69 825000.0 buy 804375.0 845625.0 20200615.0 sell 68 825000.0 + 853000.0 20200812.0 havetobuy 26 840000.0 buy 819000.0 861000.0 20200813.0 sell 25 840000.0 + 905000.0 17.5 20180612.0 havetobuy 561 20850.0 buy 20328.75 21371.25 20180618.0 sell 558 20850.0 - 20150.0 20180627.0 havetobuy 551 19800.0 buy 19305.0 20295.0 20180628.0 sell 550 19800.0 + 20950.0 20180823.0 havetobuy 511 16750.0 buy 16331.25 17168.75 20180831.0 havetobuy 505 16900.0 buy 16477.5 17322.5 20180904.0 sell 503 16900.0 - 16450.0 20180907.0 havetobuy 500 16650.0 buy 16233.75 17066.25 20180913.0 sell 496 16650.0 + 17100.0 20180914.0 sell 495 16750.0 + 17250.0 20181030.0 havetobuy 468 15600.0 buy 15210.0 15990.0 20181105.0 sell 464 15600.0 - 15200.0 20190121.0 havetobuy 412 16100.0 buy 15697.5 16502.5 20190128.0 sell 407 16100.0 + 16600.0 20190524.0 havetobuy 329 20850.0 buy 20328.75 21371.25 20190528.0 havetobuy 327 20550.0 buy 20036.25 21063.75 20190529.0 sell 326 20850.0 - 19850.0 20190529.0 sell 326 20550.0 - 19850.0 20190731.0 havetobuy 282 21600.0 buy 21060.0 22140.0 20190801.0 sell 281 21600.0 - 20400.0 20190828.0 havetobuy 263 21700.0 buy 21157.5 22242.5 20190902.0 sell 260 21700.0 + 23750.0 20191120.0 havetobuy 207 30400.0 buy 29640.0 31160.0 20191122.0 sell 205 30400.0 - 29550.0 20200206.0 havetobuy 156 30650.0 buy 29883.75 31416.25 20200207.0 sell 155 30650.0 - 29700.0 20200207.0 havetobuy 155 29600.0 29700.0 could not buy 20200211.0 havetobuy 153 29850.0 buy 29103.75 30596.25 20200212.0 sell 152 29850.0 - 28950.0 20200326.0 havetobuy 121 24400.0 buy 23790.0 25010.0 20200327.0 sell 120 24400.0 + 26700.0 20200527.0 havetobuy 81 36100.0 buy 35197.5 37002.5 20200601.0 sell 78 36100.0 + 38750.0 20200716.0 havetobuy 45 43350.0 buy 42266.25 44433.75 20200717.0 sell 44 43350.0 - 41500.0 20200724.0 havetobuy 39 42300.0 buy 41242.5 43357.5 20200727.0 havetobuy 38 41600.0 41700.0 could not buy 20200730.0 sell 35 42300.0 - 41050.0 20200902.0 havetobuy 12 38100.0 buy 37147.5 39052.5 20200904.0 sell 10 38100.0 - 37000.0 -12.5 20180502.0 havetobuy 587 118500.0 buy 115537.5 121462.5 20180503.0 sell 586 118500.0 - 109000.0 20180702.0 havetobuy 548 148500.0 buy 144787.5 152212.5 20180703.0 sell 547 148500.0 + 154500.0 20180918.0 havetobuy 493 152000.0 buy 148200.0 155800.0 20180919.0 sell 492 152000.0 - 147500.0 20190108.0 havetobuy 421 95500.0 buy 93112.5 97887.5 20190109.0 sell 420 95500.0 + 99300.0 20190313.0 havetobuy 379 101500.0 buy 98962.5 104037.5 20190314.0 havetobuy 378 100500.0 buy 97987.5 103012.5 20190320.0 sell 374 101500.0 - 98300.0 20190321.0 sell 373 100500.0 + 104500.0 20190703.0 havetobuy 302 98100.0 buy 95647.5 100552.5 20190704.0 sell 301 98100.0 - 95000.0 20190909.0 havetobuy 255 97200.0 buy 94770.0 99630.0 20190919.0 sell 249 97200.0 + 101500.0 20191120.0 havetobuy 207 111500.0 buy 108712.5 114287.5 20191121.0 sell 206 111500.0 - 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129000.0 20181025.0 havetobuy 471 121500.0 buy 118462.5 124537.5 20181026.0 sell 470 121500.0 + 131000.0 20181121.0 havetobuy 452 96500.0 buy 94087.5 98912.5 20181123.0 sell 450 96500.0 - 93900.0 20181214.0 havetobuy 435 92200.0 buy 89895.0 94505.0 20181218.0 sell 433 92200.0 - 89100.0 20190320.0 havetobuy 374 101000.0 buy 98475.0 103525.0 20190321.0 sell 373 101000.0 + 109000.0 20190610.0 havetobuy 319 94200.0 94300.0 could not buy 20190904.0 havetobuy 258 99900.0 buy 97402.5 102397.5 20190905.0 sell 257 99900.0 + 107000.0 20190924.0 havetobuy 246 106000.0 buy 103350.0 108650.0 20190925.0 sell 245 106000.0 + 115000.0 20200128.0 havetobuy 163 152500.0 buy 148687.5 156312.5 20200129.0 sell 162 152500.0 + 160000.0 20200210.0 havetobuy 154 150500.0 buy 146737.5 154262.5 20200213.0 sell 151 150500.0 + 158000.0 20200401.0 havetobuy 117 114000.0 buy 111150.0 116850.0 20200402.0 sell 116 114000.0 - 107000.0 20200402.0 havetobuy 116 108500.0 buy 105787.5 111212.5 20200403.0 sell 115 108500.0 + 115000.0 20200603.0 havetobuy 76 154000.0 buy 150150.0 157850.0 20200608.0 sell 73 154000.0 + 160000.0 20200826.0 havetobuy 17 152000.0 buy 148200.0 155800.0 20200827.0 sell 16 152000.0 - 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14230.0 20200528.0 havetobuy 80 11130.0 buy 10851.75 11408.25 20200601.0 sell 78 11130.0 + 11585.0 20200605.0 havetobuy 74 12720.0 buy 12402.0 13038.0 20200608.0 sell 73 12720.0 + 13630.0 20200907.0 havetobuy 9 14855.0 buy 14483.625 15226.375 20200908.0 sell 8 14855.0 + 15365.0 20200914.0 havetobuy 4 15250.0 15465.0 could not buy 10.0 20180509.0 havetobuy 583 6140.0 buy 5986.5 6293.5 20180530.0 sell 569 6140.0 + 6295.0 20180604.0 havetobuy 566 6190.0 buy 6035.25 6344.75 20180611.0 havetobuy 562 6150.0 buy 5996.25 6303.75 20180618.0 sell 558 6190.0 + 6355.0 20180618.0 sell 558 6150.0 + 6355.0 20180704.0 havetobuy 546 6575.0 buy 6410.625 6739.375 20180816.0 sell 516 6575.0 + 6745.0 20181010.0 havetobuy 482 6630.0 buy 6464.25 6795.75 20181011.0 sell 481 6630.0 + 6965.0 20181012.0 havetobuy 480 6940.0 buy 6766.5 7113.5 20181025.0 sell 471 6940.0 + 7305.0 20181025.0 havetobuy 471 7100.0 buy 6922.5 7277.5 20181026.0 sell 470 7100.0 + 7390.0 20181228.0 havetobuy 426 7280.0 buy 7098.0 7462.0 20190108.0 havetobuy 421 7240.0 buy 7059.0 7421.0 20190111.0 sell 418 7280.0 - 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5315.0 10.0 20180531.0 havetobuy 568 122500.0 123000.0 could not buy 20180810.0 havetobuy 519 90800.0 buy 88530.0 93070.0 20180813.0 sell 518 90800.0 - 82000.0 20180822.0 havetobuy 512 89600.0 buy 87360.0 91840.0 20180827.0 sell 509 89600.0 + 94700.0 20180920.0 havetobuy 491 92800.0 buy 90480.0 95120.0 20180921.0 sell 490 92800.0 + 97600.0 20181219.0 havetobuy 432 76300.0 buy 74392.5 78207.5 20181221.0 sell 430 76300.0 - 74200.0 20190318.0 havetobuy 376 83300.0 83600.0 could not buy 20190402.0 havetobuy 365 85900.0 86800.0 could not buy 20190514.0 havetobuy 337 80000.0 buy 78000.0 82000.0 20190517.0 sell 334 80000.0 + 83200.0 20190627.0 havetobuy 306 82000.0 buy 79950.0 84050.0 20190703.0 sell 302 82000.0 - 79100.0 20190709.0 havetobuy 298 78400.0 buy 76440.0 80360.0 20190710.0 sell 297 78400.0 - 75800.0 20190826.0 havetobuy 265 65900.0 buy 64252.5 67547.5 20190827.0 sell 264 65900.0 - 62900.0 20200305.0 havetobuy 136 67700.0 buy 66007.5 69392.5 20200309.0 sell 134 67700.0 - 64500.0 20200316.0 havetobuy 129 55000.0 buy 53625.0 56375.0 20200317.0 sell 128 55000.0 - 53000.0 20200715.0 havetobuy 46 92900.0 93200.0 could not buy 20200811.0 havetobuy 27 118000.0 buy 115050.0 120950.0 20200812.0 sell 26 118000.0 - 111000.0 20200821.0 havetobuy 20 103500.0 buy 100912.5 106087.5 20200824.0 sell 19 103500.0 + 107500.0 -10.0 17.5
17.5%
predict_simul_normal_reg.py
# import pandas as pd import config BASE_PATH = "data_a/" def resultx(x): pprice = x[0]+x[0] * config.P_SELLP/100.0# Price 팔아야 하는 목표값을 계산한다 if pprice <= x[2] : return 1 # 목표값보다 예상값(PreY)이 커지면 구매표시를 한다. return 0 def result_changeY(y): # y price return y[0]*y[1] def simul(test_df): # 실제 구매했을때 얼마나 손익이 날 수 있는지 시뮬레이션을 하도록한다 rowcount = test_df.shape[0] havetobuy = False price = 0 buylist=[] total = 0 for i in range(rowcount-1,0-1,-1): LPrice = test_df.iloc[i]['LPrice'] HPrice = test_df.iloc[i]['HPrice'] Time = test_df.iloc[i]['Time'] # 이미 보유 하고 있으면 if len(buylist) > 0 : # 팔 항목 있는지 살펴본다. removelist = [] for buiedprice in buylist: pprice = buiedprice+buiedprice * config.P_SELLP/100.0 mprice = buiedprice-buiedprice * config.M_SELLP/100.0 if LPrice < mprice : total=total-config.M_SELLP removelist.append(buiedprice) print(Time,"sell",i,buiedprice,"-",LPrice) elif HPrice > pprice : total=total+config.P_SELLP removelist.append(buiedprice) print(Time,"sell",i,buiedprice,"+",HPrice) if len(removelist)>0: for item in removelist: buylist.remove(item) if havetobuy == True and len(buylist) < config.MONEY_TOTAL: LPrice = test_df.iloc[i]['LPrice'] if price >= LPrice : buylist.append(price) pprice = price+price * config.P_SELLP/100.0 mprice = price-price * config.M_SELLP/100.0 print(Time,"havetobuy",i,price,"buy",mprice,pprice) else: print(Time,"havetobuy",i,price,LPrice,"could not buy") # 사려고 하는 가격 보다 최저가가 높아서 사지 못함 if test_df.iloc[i]['Buy'] > 0.5 : havetobuy = True price = test_df.iloc[i]['Price'] else: havetobuy = False price = test_df.iloc[i]['Price'] print(total) return total test_df = pd.read_csv(BASE_PATH+'test_prey.csv', header=0, encoding='utf8') test_df['Y'] = test_df[['Price','Y']].apply(result_changeY, axis=1) test_df['PreY'] = test_df[['Price','PreY']].apply(result_changeY, axis=1) test_df['Buy'] = test_df[['Price','Y','PreY']].apply(resultx, axis=1) test_df.to_csv(BASE_PATH+"buy.csv", encoding='utf-8', index=False) sumtotal = 0 while(True): rowcount = test_df.shape[0] if rowcount==0 : break df = test_df.head(config.COUNT_TEST_DATA) sumtotal = sumtotal + simul(df) test_df.drop(test_df.head(config.COUNT_TEST_DATA).index, inplace=True) print(sumtotal)
정리
실행 순서
1. predcit_feature_normal_reg.py
2. predict_xgboostr_reg.py ( 이전 소스 사용 )
3. predict_simul_normal_reg.py
첫번째 한 종목만 학습한 경우 -2.5% ( 데이터를 새로 가져와서 기준일이 변경되어 다릅니다. )
전체 합쳐서 학습한경우 첫번째 종목 -10%
전체 합쳐서 학습한경우 전체 -87.5%
one-hot encoding 학습한 경우 첫번째 종목 2.5%
one-hot encoding 학습한 경우 전체 17.5%
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