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How to use close method in differencify
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indicators.py
Source:indicators.py 
1# coding:utf-82#3# The MIT License (MIT)4#5# Copyright (c) 2016-2021 yutiansut/QUANTAXIS6#7# Permission is hereby granted, free of charge, to any person obtaining a copy8# of this software and associated documentation files (the "Software"), to deal9# in the Software without restriction, including without limitation the rights10# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell11# copies of the Software, and to permit persons to whom the Software is12# furnished to do so, subject to the following conditions:13#14# The above copyright notice and this permission notice shall be included in all15# copies or substantial portions of the Software.16#17# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR18# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,19# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE20# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER21# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,22# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE23# SOFTWARE.24from functools import reduce25import numpy as np26import pandas as pd27from QUANTAXIS.QAIndicator.base import *28"""29DataFrame ç±»30以ä¸çå½æ°é½å¯ä»¥è¢«ç´æ¥add_func31"""32"""331. è¶åææ 34åå«è¶å¿è·è¸ªç±»ææ ,主è¦ç¨äºè·è¸ªå¹¶é¢æµè¡ä»·çåå±è¶å¿35å
å«ç主è¦ææ 361. 移å¨å¹³å线 MA372. ææ°å¹³æ»ç§»å¨å¹³å线 MACD383. è¶åææ DMI394. çå¸çº¿ PBX405. å¹³å线差 DMA416. å¨åææ (å¨é线) MTM427. ææ°å¹³å线 EXPMA438. ä½³åºææ CHO44"""45def QA_indicator_MA(DataFrame,*args,**kwargs):46 """MA47 48 Arguments:49 DataFrame {[type]} -- [description]50 51 Returns:52 [type] -- [description]53 """54 CLOSE = DataFrame['close']55 return pd.DataFrame({'MA{}'.format(N): MA(CLOSE, N) for N in list(args)})56def QA_indicator_MA_VOL(DataFrame,*args,**kwargs):57 """MA_VOLU58 59 Arguments:60 DataFrame {[type]} -- [description]61 62 Returns:63 [type] -- [description]64 """65 VOL = DataFrame['volume']66 return pd.DataFrame({'MA_VOL{}'.format(N): MA(VOL, N) for N in list(args)})67def QA_indicator_EMA(DataFrame, N):68 CLOSE = DataFrame['close']69 return pd.DataFrame({'EMA': EMA(CLOSE, N)})70def QA_indicator_SMA(DataFrame, N):71 CLOSE = DataFrame['close']72 return pd.DataFrame({'SMA': SMA(CLOSE, N)})73def QA_indicator_MACD(DataFrame, short=12, long=26, mid=9):74 """75 MACD CALC76 """77 CLOSE = DataFrame['close']78 DIF = EMA(CLOSE, short)-EMA(CLOSE, long)79 DEA = EMA(DIF, mid)80 MACD = (DIF-DEA)*281 return pd.DataFrame({'DIF': DIF, 'DEA': DEA, 'MACD': MACD})82def QA_indicator_DMI(DataFrame, M1=14, M2=6):83 """84 è¶åææ DMI85 """86 HIGH = DataFrame.high87 LOW = DataFrame.low88 CLOSE = DataFrame.close89 OPEN = DataFrame.open90 TR = SUM(MAX(MAX(HIGH-LOW, ABS(HIGH-REF(CLOSE, 1))),91 ABS(LOW-REF(CLOSE, 1))), M1)92 HD = HIGH-REF(HIGH, 1)93 LD = REF(LOW, 1)-LOW94 DMP = SUM(IFAND(HD>0,HD>LD,HD,0), M1)95 DMM = SUM(IFAND(LD>0,LD>HD,LD,0), M1)96 DI1 = DMP*100/TR97 DI2 = DMM*100/TR98 ADX = MA(ABS(DI2-DI1)/(DI1+DI2)*100, M2)99 ADXR = (ADX+REF(ADX, M2))/2100 return pd.DataFrame({101 'DI1': DI1, 'DI2': DI2,102 'ADX': ADX, 'ADXR': ADXR103 })104def QA_indicator_PBX(DataFrame, N1=3, N2=5, N3=8, N4=13, N5=18, N6=24):105 'çå¸çº¿'106 C = DataFrame['close']107 PBX1 = (EMA(C, N1) + EMA(C, 2 * N1) + EMA(C, 4 * N1)) / 3108 PBX2 = (EMA(C, N2) + EMA(C, 2 * N2) + EMA(C, 4 * N2)) / 3109 PBX3 = (EMA(C, N3) + EMA(C, 2 * N3) + EMA(C, 4 * N3)) / 3110 PBX4 = (EMA(C, N4) + EMA(C, 2 * N4) + EMA(C, 4 * N4)) / 3111 PBX5 = (EMA(C, N5) + EMA(C, 2 * N5) + EMA(C, 4 * N5)) / 3112 PBX6 = (EMA(C, N6) + EMA(C, 2 * N6) + EMA(C, 4 * N6)) / 3113 DICT = {'PBX1': PBX1, 'PBX2': PBX2, 'PBX3': PBX3,114 'PBX4': PBX4, 'PBX5': PBX5, 'PBX6': PBX6}115 return pd.DataFrame(DICT)116def QA_indicator_DMA(DataFrame, M1=10, M2=50, M3=10):117 """118 å¹³å线差 DMA119 """120 CLOSE = DataFrame.close121 DDD = MA(CLOSE, M1) - MA(CLOSE, M2)122 AMA = MA(DDD, M3)123 return pd.DataFrame({124 'DDD': DDD, 'AMA': AMA125 })126def QA_indicator_MTM(DataFrame, N=12, M=6):127 'å¨é线'128 C = DataFrame.close129 mtm = C - REF(C, N)130 MTMMA = MA(mtm, M)131 DICT = {'MTM': mtm, 'MTMMA': MTMMA}132 return pd.DataFrame(DICT)133def QA_indicator_EXPMA(DataFrame, P1=5, P2=10, P3=20, P4=60):134 """ ææ°å¹³å线 EXPMA"""135 CLOSE = DataFrame.close136 MA1 = EMA(CLOSE, P1)137 MA2 = EMA(CLOSE, P2)138 MA3 = EMA(CLOSE, P3)139 MA4 = EMA(CLOSE, P4)140 return pd.DataFrame({141 'MA1': MA1, 'MA2': MA2, 'MA3': MA3, 'MA4': MA4142 })143def QA_indicator_CHO(DataFrame, N1=10, N2=20, M=6):144 """145 ä½³åºææ CHO146 """147 HIGH = DataFrame.high148 LOW = DataFrame.low149 CLOSE = DataFrame.close150 VOL = DataFrame.volume151 MID = SUM(VOL*(2*CLOSE-HIGH-LOW)/(HIGH+LOW), 0)152 CHO = MA(MID, N1)-MA(MID, N2)153 MACHO = MA(CHO, M)154 return pd.DataFrame({155 'CHO': CHO, 'MACHO': MACHO156 })157"""1582. åè¶åææ 159主è¦ææè¶å¿ç转æç¹160éæºææ KDJ161ä¹ç¦»ç BIAS162åå¨éç ROC163顺å¿ææ CCI164å¨å»ææ W&R165éè¡é(åå¨éç) OSC166ç¸å¯¹å¼ºå¼±ææ RSI167å¨æä¹°åææ ADTM168"""169def QA_indicator_KDJ(DataFrame, N=9, M1=3, M2=3):170 C = DataFrame['close']171 H = DataFrame['high']172 L = DataFrame['low']173 RSV = ((C - LLV(L, N)) / (HHV(H, N) - LLV(L, N)) * 100).groupby('code').fillna(method='ffill')174 K = SMA(RSV, M1)175 D = SMA(K, M2)176 J = 3 * K - 2 * D177 DICT = {'KDJ_K': K, 'KDJ_D': D, 'KDJ_J': J}178 return pd.DataFrame(DICT)179def QA_indicator_BIAS(DataFrame, N1, N2, N3):180 'ä¹ç¦»ç'181 CLOSE = DataFrame['close']182 BIAS1 = (CLOSE - MA(CLOSE, N1)) / MA(CLOSE, N1) * 100183 BIAS2 = (CLOSE - MA(CLOSE, N2)) / MA(CLOSE, N2) * 100184 BIAS3 = (CLOSE - MA(CLOSE, N3)) / MA(CLOSE, N3) * 100185 DICT = {'BIAS1': BIAS1, 'BIAS2': BIAS2, 'BIAS3': BIAS3}186 return pd.DataFrame(DICT)187def QA_indicator_ROC(DataFrame, N=12, M=6):188 'åå¨çææ '189 C = DataFrame['close']190 roc = 100 * (C - REF(C, N)) / REF(C, N)191 ROCMA = MA(roc, M)192 DICT = {'ROC': roc, 'ROCMA': ROCMA}193 return pd.DataFrame(DICT)194def QA_indicator_CCI(DataFrame, N=14):195 """196 TYP:=(HIGH+LOW+CLOSE)/3;197 CCI:(TYP-MA(TYP,N))/(0.015*AVEDEV(TYP,N));198 """199 typ = (DataFrame['high'] + DataFrame['low'] + DataFrame['close']) / 3200 ## æ¤å¤AVEDEVå¯è½ä¸º0å¼ å æ¤å¯¼è´åºé +0.0000000000001201 cci = ((typ - MA(typ, N)) / (0.015 * AVEDEV(typ, N) + 0.00000001))202 a = 100203 b = -100204 return pd.DataFrame({205 'CCI': cci, 'a': a, 'b': b206 })207def QA_indicator_WR(DataFrame, N, N1):208 'å¨å»ææ '209 HIGH = DataFrame['high']210 LOW = DataFrame['low']211 CLOSE = DataFrame['close']212 WR1 = 100 * (HHV(HIGH, N) - CLOSE) / (HHV(HIGH, N) - LLV(LOW, N))213 WR2 = 100 * (HHV(HIGH, N1) - CLOSE) / (HHV(HIGH, N1) - LLV(LOW, N1))214 DICT = {'WR1': WR1, 'WR2': WR2}215 return pd.DataFrame(DICT)216def QA_indicator_OSC(DataFrame, N=20, M=6):217 """åå¨éç线218 éè¡éææ OSCï¼ä¹å«åå¨éç线ãå±äºè¶
ä¹°è¶
åç±»ææ ,æ¯ä»ç§»å¨å¹³å线åçæ´¾çåºæ¥çä¸ç§åæææ ã219 å®ååºå½æ¥æ¶çä»·ä¸ä¸æ®µæ¶é´å
å¹³åæ¶çä»·ç差离å¼,ä»èæµåºè¡ä»·çéè¡å¹
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§ç§»å¨å¹³å线åçï¼æ ¹æ®OSCçå¼å¯æ¨æä»·æ ¼çè¶å¿ï¼å¦æè¿ç¦»å¹³å线ï¼å°±å¾å¯è½åå¹³å线åå½ã221 """222 C = DataFrame['close']223 OS = (C - MA(C, N)) * 100224 MAOSC = EMA(OS, M)225 DICT = {'OSC': OS, 'MAOSC': MAOSC}226 return pd.DataFrame(DICT)227def QA_indicator_RSI(DataFrame, N1=12, N2=26, N3=9):228 'ç¸å¯¹å¼ºå¼±ææ RSI1:SMA(MAX(CLOSE-LC,0),N1,1)/SMA(ABS(CLOSE-LC),N1,1)*100;'229 CLOSE = DataFrame['close']230 LC = REF(CLOSE, 1)231 RSI1 = SMA(MAX(CLOSE - LC, 0), N1) / SMA(ABS(CLOSE - LC), N1) * 100232 RSI2 = SMA(MAX(CLOSE - LC, 0), N2) / SMA(ABS(CLOSE - LC), N2) * 100233 RSI3 = SMA(MAX(CLOSE - LC, 0), N3) / SMA(ABS(CLOSE - LC), N3) * 100234 DICT = {'RSI1': RSI1, 'RSI2': RSI2, 'RSI3': RSI3}235 return pd.DataFrame(DICT)236def QA_indicator_ADTM(DataFrame, N=23, M=8):237 'å¨æä¹°åæ°ææ '238 HIGH = DataFrame.high239 LOW = DataFrame.low240 OPEN = DataFrame.open241 DTM = IF(OPEN > REF(OPEN, 1), MAX((HIGH - OPEN), (OPEN - REF(OPEN, 1))), 0)242 DBM = IF(OPEN < REF(OPEN, 1), MAX((OPEN - LOW), (OPEN - REF(OPEN, 1))), 0)243 STM = SUM(DTM, N)244 SBM = SUM(DBM, N)245 ADTM1 = IF(STM > SBM, (STM - SBM) / STM,246 IF(STM != SBM, (STM - SBM) / SBM, 0))247 MAADTM = MA(ADTM1, M)248 DICT = {'ADTM': ADTM1, 'MAADTM': MAADTM}249 return pd.DataFrame(DICT)250"""2513. éè½ææ 252éè¿æ交éç大å°åååç å¤è¶å¿åå253容éææ VR254éç¸å¯¹å¼ºå¼± VRSI255è½éææ CR256人æ°ææ¿ææ ARBR257æ交éæ åå·® VSTD"""258def QA_indicator_VR(DataFrame, M1=26, M2=100, M3=200):259 VOL = DataFrame.volume260 CLOSE = DataFrame.close261 LC = REF(CLOSE, 1)262 VR = SUM(IF(CLOSE > LC, VOL, 0), M1)/SUM(IF(CLOSE <= LC, VOL, 0), M1)*100263 a = M2264 b = M3265 return pd.DataFrame({266 'VR': VR, 'a': a, 'b': b267 })268def QA_indicator_VRSI(DataFrame, N=6):269 VOL = DataFrame.volume270 vrsi = SMA(MAX(VOL-REF(VOL, 1), 0), N, 1) / \271 SMA(ABS(VOL-REF(VOL, 1)), N, 1)*100272 return pd.DataFrame({'VRSI': vrsi})273def QA_indicator_CR(DataFrame, N=26, M1=5, M2=10, M3=20):274 HIGH = DataFrame.high275 LOW = DataFrame.low276 CLOSE = DataFrame.close277 VOL = DataFrame.volume278 MID = (HIGH+LOW+CLOSE)/3279 CR = SUM(MAX(0, HIGH-REF(MID, 1)), N)/SUM(MAX(0, REF(MID, 1)-LOW), N)*100280 MA1 = REF(MA(CR, M1), M1/2.5+1)281 MA2 = REF(MA(CR, M2), M2/2.5+1)282 MA3 = REF(MA(CR, M3), M3/2.5+1)283 return pd.DataFrame({284 'CR': CR, 'MA1': MA1, 'MA2': MA2, 'MA3': MA3285 })286def QA_indicator_ARBR(DataFrame, M1=26, M2=70, M3=150):287 HIGH = DataFrame.high288 LOW = DataFrame.low289 CLOSE = DataFrame.close290 OPEN = DataFrame.open291 AR = SUM(HIGH-OPEN, M1)/SUM(OPEN-LOW, M1)*100292 BR = SUM(MAX(0, HIGH-REF(CLOSE, 1)), M1) / \293 SUM(MAX(0, REF(CLOSE, 1)-LOW), M1)*100294 a = M2295 b = M3296 return pd.DataFrame({297 'AR': AR, 'BR': BR, 'a': a, 'b': b298 })299def QA_indicator_VSTD(DataFrame, N=10):300 VOL = DataFrame.volume301 vstd = STD(VOL, N)302 return pd.DataFrame({'VSTD': vstd})303"""3044. éä»·ææ 305éè¿æ交éåè¡ä»·åå¨å
³ç³»åææªæ¥è¶å¿306éè¡åéææ ASI307ä»·éè¶å¿PVT308è½éæ½®OBV309éä»·è¶å¿VPT310"""311def QA_indicator_ASI(DataFrame, M1=26, M2=10):312 """313 LC=REF(CLOSE,1);314 AA=ABS(HIGH-LC);315 BB=ABS(LOW-LC);316 CC=ABS(HIGH-REF(LOW,1));317 DD=ABS(LC-REF(OPEN,1));318 R=IF(AA>BB AND AA>CC,AA+BB/2+DD/4,IF(BB>CC AND BB>AA,BB+AA/2+DD/4,CC+DD/4));319 X=(CLOSE-LC+(CLOSE-OPEN)/2+LC-REF(OPEN,1));320 SI=16*X/R*MAX(AA,BB);321 ASI:SUM(SI,M1);322 ASIT:MA(ASI,M2);323 """324 CLOSE = DataFrame['close']325 HIGH = DataFrame['high']326 LOW = DataFrame['low']327 OPEN = DataFrame['open']328 LC = REF(CLOSE, 1)329 AA = ABS(HIGH - LC)330 BB = ABS(LOW-LC)331 CC = ABS(HIGH - REF(LOW, 1))332 DD = ABS(LC - REF(OPEN, 1))333 R = IFAND(AA > BB, AA > CC, AA+BB/2+DD/4,334 IFAND(BB > CC, BB > AA, BB+AA/2+DD/4, CC+DD/4))335 X = (CLOSE - LC + (CLOSE - OPEN) / 2 + LC - REF(OPEN, 1))336 SI = 16*X/R*MAX(AA, BB)337 ASI = SUM(SI, M1)338 ASIT = MA(ASI, M2)339 return pd.DataFrame({340 'ASI': ASI, 'ASIT': ASIT341 })342def QA_indicator_PVT(DataFrame):343 CLOSE = DataFrame.close344 VOL = DataFrame.volume345 PVT = SUM((CLOSE-REF(CLOSE, 1))/REF(CLOSE, 1)*VOL, 0)346 return pd.DataFrame({'PVT': PVT})347def QA_indicator_OBV(DataFrame):348 """è½éæ½®"""349 VOL = DataFrame.volume350 CLOSE = DataFrame.close351 return pd.DataFrame({352 'OBV': np.cumsum(IF(CLOSE > REF(CLOSE, 1), VOL, IF(CLOSE < REF(CLOSE, 1), -VOL, 0)))/10000353 })354def QA_indicator_VPT(DataFrame, N=51, M=6):355 VOL = DataFrame.volume356 CLOSE = DataFrame.close357 VPT = SUM(VOL*(CLOSE-REF(CLOSE, 1))/REF(CLOSE, 1), 0)358 MAVPT = MA(VPT, M)359 return pd.DataFrame({360 'VPT': VPT, 'MAVPT': MAVPT361 })362"""3635. ååæ¯æææ 364主è¦ç¨äºåæè¡ä»·ç®åæ¶å°çåååæ¯æ365å¸æ带 BOLL366麦å
ææ MIKE367"""368def QA_indicator_BOLL(DataFrame, N=20, P=2):369 'å¸æ线'370 C = DataFrame['close']371 boll = MA(C, N)372 UB = boll + P * STD(C, N)373 LB = boll - P * STD(C, N)374 DICT = {'BOLL': boll, 'UB': UB, 'LB': LB}375 return pd.DataFrame(DICT)376def QA_indicator_MIKE(DataFrame, N=12):377 """378 MIKEææ 379 ææ 说æ380 MIKEæ¯å¦å¤ä¸ç§å½¢å¼çè·¯å¾ææ ã381 ä¹°ååå382 1 WEAK-Sï¼MEDIUM-Sï¼STRONG-Sä¸æ¡çº¿ä»£è¡¨å级ãä¸çº§ã强åæ¯æã383 2 WEAK-Rï¼MEDIUM-Rï¼STRONG-Rä¸æ¡çº¿ä»£è¡¨å级ãä¸çº§ã强åååã384 """385 HIGH = DataFrame.high386 LOW = DataFrame.low387 CLOSE = DataFrame.close388 TYP = (HIGH+LOW+CLOSE)/3389 LL = LLV(LOW, N)390 HH = HHV(HIGH, N)391 WR = TYP+(TYP-LL)392 MR = TYP+(HH-LL)393 SR = 2*HH-LL394 WS = TYP-(HH-TYP)395 MS = TYP-(HH-LL)396 SS = 2*LL-HH397 return pd.DataFrame({398 'WR': WR, 'MR': MR, 'SR': SR,399 'WS': WS, 'MS': MS, 'SS': SS400 })401def QA_indicator_BBI(DataFrame, N1=3, N2=6, N3=12, N4=24):402 'å¤ç©ºææ '403 C = DataFrame['close']404 bbi = (MA(C, N1) + MA(C, N2) + MA(C, N3) + MA(C, N4)) / 4405 DICT = {'BBI': bbi}406 return pd.DataFrame(DICT)407def QA_indicator_MFI(DataFrame, N=14):408 """409 èµéææ 410 TYP := (HIGH + LOW + CLOSE)/3;411 V1:=SUM(IF(TYP>REF(TYP,1),TYP*VOL,0),N)/SUM(IF(TYP<REF(TYP,1),TYP*VOL,0),N);412 MFI:100-(100/(1+V1));413 èµå¼: (æé«ä»· + æä½ä»· + æ¶çä»·)/3414 V1èµå¼:å¦æTYP>1æ¥åçTYP,è¿åTYP*æ交é(æ),å¦åè¿å0çNæ¥ç´¯å/å¦æTYP<1æ¥åçTYP,è¿åTYP*æ交é(æ),å¦åè¿å0çNæ¥ç´¯å415 è¾åºèµéæµéææ :100-(100/(1+V1))416 """417 C = DataFrame['close']418 H = DataFrame['high']419 L = DataFrame['low']420 VOL = DataFrame['volume']421 TYP = (C + H + L) / 3422 V1 = SUM(IF(TYP > REF(TYP, 1), TYP * VOL, 0), N) / \423 SUM(IF(TYP < REF(TYP, 1), TYP * VOL, 0), N)424 mfi = 100 - (100 / (1 + V1))425 DICT = {'MFI': mfi}426 return pd.DataFrame(DICT)427def QA_indicator_ATR(DataFrame, N=14):428 """429 è¾åºTR:(æé«ä»·-æä½ä»·)åæ¨æ¶-æé«ä»·çç»å¯¹å¼çè¾å¤§å¼åæ¨æ¶-æä½ä»·çç»å¯¹å¼çè¾å¤§å¼430 è¾åºçå®æ³¢å¹
:TRçNæ¥ç®å移å¨å¹³å431 ç®æ³ï¼ä»æ¥æ¯å¹
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ï¼æ±çå®æ³¢å¹
çNæ¥ç§»å¨å¹³å432 åæ°ï¼Nã天æ°ï¼ä¸è¬å14433 """434 C = DataFrame['close']435 H = DataFrame['high']436 L = DataFrame['low']437 TR = MAX(MAX((H - L), ABS(REF(C, 1) - H)), ABS(REF(C, 1) - L))438 atr = MA(TR, N)439 return pd.DataFrame({'TR': TR, 'ATR': atr})440def QA_indicator_SKDJ(DataFrame, N=9, M=3):441 """442 1.ææ >80 æ¶ï¼åæ¡£æºç大ï¼ææ <20 æ¶ï¼åå¼¹æºç大ï¼443 2.Kå¨20å·¦å³åä¸äº¤åDæ¶ï¼è§ä¸ºä¹°è¿ä¿¡å·åèï¼ 444 3.Kå¨80å·¦å³åä¸äº¤åDæ¶ï¼è§ä¸ºååºä¿¡å·åèï¼445 4.SKDJæ³¢å¨äº50å·¦å³çä»»ä½è®¯å·ï¼å
¶ä½ç¨ä¸å¤§ã446 """447 CLOSE = DataFrame['close']448 LOWV = LLV(DataFrame['low'], N)449 HIGHV = HHV(DataFrame['high'], N)450 RSV = EMA((CLOSE - LOWV) / (HIGHV - LOWV) * 100, M)451 K = EMA(RSV, M)452 D = MA(K, M)453 DICT = {'RSV': RSV, 'SKDJ_K': K, 'SKDJ_D': D}454 return pd.DataFrame(DICT)455def QA_indicator_DDI(DataFrame, N=13, N1=26, M=1, M1=5):456 """457 'æ¹åæ å离差ææ°'458 åæDDIæ±ç¶çº¿ï¼ç±çº¢å绿(æ£åè´)ï¼ååºä¿¡å·åèï¼ç±ç»¿å红ï¼ä¹°å
¥ä¿¡å·åèã459 """460 H = DataFrame['high']461 L = DataFrame['low']462 DMZ = IF((H + L) > (REF(H, 1) + REF(L, 1)), 463 MAX(ABS(H - REF(H, 1)), ABS(L - REF(L, 1))), 0)464 DMF = IF((H + L) < (REF(H, 1) + REF(L, 1)),465 MAX(ABS(H - REF(H, 1)), ABS(L - REF(L, 1))), 0)466 DIZ = SUM(DMZ, N) / (SUM(DMZ, N) + SUM(DMF, N))467 DIF = SUM(DMF, N) / (SUM(DMF, N) + SUM(DMZ, N))468 ddi = DIZ - DIF469 ADDI = SMA(ddi, N1, M)470 AD = MA(ADDI, M1)471 DICT = {'DDI': ddi, 'ADDI': ADDI, 'AD': AD}472 return pd.DataFrame(DICT)473def QA_indicator_shadow(DataFrame):474 """475 ä¸ä¸å½±çº¿ææ 476 """477 return {478 'LOW': lower_shadow(DataFrame), 'UP': upper_shadow(DataFrame),479 'BODY': body(DataFrame), 'BODY_ABS': body_abs(DataFrame), 'PRICE_PCG': price_pcg(DataFrame)480 }481def lower_shadow(DataFrame): # ä¸å½±çº¿482 return abs(DataFrame.low - MIN(DataFrame.open, DataFrame.close))483def upper_shadow(DataFrame): # ä¸å½±çº¿484 return abs(DataFrame.high - MAX(DataFrame.open, DataFrame.close))485def body_abs(DataFrame):486 return abs(DataFrame.open - DataFrame.close)487def body(DataFrame):488 return DataFrame.close - DataFrame.open489def price_pcg(DataFrame):490 return body(DataFrame) / DataFrame.open491def amplitude(DataFrame):492 return (DataFrame.high - DataFrame.low) / DataFrame.low493"""4946. 大çææ 495éè¿æ¶¨è·å®¶æ°ç 究大çææ°çèµ°å¿496涨è·æ¯ç ADR497ç»å¯¹å¹
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notes.py
Source:notes.py
...34 def set_mon_open(self, open):35 self.__mon_open = open36 def get_mon_open(self):37 return self.__mon_open38 def set_mon_close(self, close):39 self.__mon_close = close40 def get_mon_close(self):41 return self.__mon_close42 def set_mon_break_start(self, bstart):43 self.__mon_bstart = bstart44 def get_mon_break_start(self):45 return self.__mon_bstart46 def set_mon_break_end(self, bend):47 self.__mon_bend = bend48 def get_mon_break_end(self):49 return self.__mon_bend50 def set_tues_open(self, open):51 self.__tues_open = open52 def get_tues_open(self):53 return self.__tues_open54 def set_tues_close(self, close):55 self.__tues_close = close56 def get_tues_close(self):57 return self.__tues_close58 def set_tues_break_start(self, bstart):59 self.__tues_bstart = bstart60 def get_tues_break_start(self):61 return self.__tues_bstart62 def set_tues_break_end(self, bend):63 self.__tues_bend = bend64 def get_tues_break_end(self):65 return self.__tues_bend66 def set_wed_open(self, open):67 self.__wed_open = open68 def get_wed_open(self):69 return self.__wed_open70 def set_wed_close(self, close):71 self.__wed_close = close72 def get_wed_close(self):73 return self.__wed_close74 def set_wed_break_start(self, bstart):75 self.__wed_bstart = bstart76 def get_wed_break_start(self):77 return self.__wed_bstart78 def set_wed_break_end(self, bend):79 self.__wed_bend = bend80 def get_wed_break_end(self):81 return self.__wed_bend82 def set_thur_open(self, open):83 self.__thur_open = open84 def get_thur_open(self):85 return self.__thur_open86 def set_thur_close(self, close):87 self.__thur_close = close88 def get_thur_close(self):89 return self.__thur_close90 def set_thur_break_start(self, bstart):91 self.__thur_bstart = bstart92 def get_thur_break_start(self):93 return self.__thur_bstart94 def set_thur_break_end(self, bend):95 self.__thur_bend = bend96 def get_thur_break_end(self):97 return self.__thur_bend98 def set_fri_open(self, open):99 self.__fri_open = open100 def get_fri_open(self):101 return self.__fri_open102 def set_fri_close(self, close):103 self.__fri_close = close104 def get_fri_close(self):105 return self.__fri_close106 def set_fri_break_start(self, bstart):107 self.__fri_bstart = bstart108 def get_fri_break_start(self):109 return self.__fri_bstart110 def set_fri_break_end(self, bend):111 self.__fri_bend = bend112 def get_fri_break_end(self):113 return self.__fri_bend114 def set_sat_open(self, open):115 self.__sat_open = open116 def get_sat_open(self):117 return self.__sat_open118 def set_sat_close(self, close):119 self.__sat_close = close120 def get_sat_close(self):121 return self.__sat_close122 def set_sat_break_start(self, bstart):123 self.__sat_bstart = bstart124 def get_sat_break_start(self):125 return self.__sat_bstart126 def set_sat_break_end(self, bend):127 self.__sat_bend = bend128 def get_sat_break_end(self):129 return self.__sat_bend130 def set_sun_open(self, open):131 self.__sun_open = open132 def get_sun_open(self):133 return self.__sun_open134 def set_sun_close(self, close):135 self.__sun_close = close136 def get_sun_close(self):137 return self.__sun_close138 def set_sun_break_start(self, bstart):139 self.__sun_bstart = bstart140 def get_sun_break_start(self):141 return self.__sun_bstart142 def set_sun_break_end(self, bend):143 self.__sun_bend = bend144 def get_sun_break_end(self):145 return self.__sun_bend146OperatingHour.__init__(self, mon_open, mon_close, mon_bstart, mon_bend, tues_open, tues_close, tues_bstart,147 tues_bend, wed_open, wed_close, wed_bstart, wed_bend, thur_open, thur_close, thur_bstart,148 thur_bend, fri_open, fri_close, fri_bstart, fri_bend, sat_open, sat_close, sat_bstart,149 sat_bend, sun_open, sun_close, sun_bstart, sun_bend)150mon_open = TimeField('Opening Time:', [validators.Optional()])151mon_close = TimeField('Closing Time:', [validators.Optional()])152mon_break_start = TimeField('Break Start Time:', [validators.Optional()])153mon_break_end = TimeField('Break End Time:', [validators.Optional()])154tues_open = TimeField('Opening Time:', [validators.Optional()])155tues_close = TimeField('Closing Time:', [validators.Optional()])156tues_break_start = TimeField('Break Start Time:', [validators.Optional()])157tues_break_end = TimeField('Break End Time:', [validators.Optional()])158wed_open = TimeField('Opening Time:', [validators.Optional()])159wed_close = TimeField('Closing Time:', [validators.Optional()])160wed_break_start = TimeField('Break Start Time:', [validators.Optional()])161wed_break_end = TimeField('Break End Time:', [validators.Optional()])162thur_open = TimeField('Opening Time:', [validators.Optional()])163thur_close = TimeField('Closing Time:', [validators.Optional()])164thur_break_start = TimeField('Break Start Time:', [validators.Optional()])165thur_break_end = TimeField('Break End Time:', [validators.Optional()])166fri_open = TimeField('Opening Time:', [validators.Optional()])167fri_close = TimeField('Closing Time:', [validators.Optional()])168fri_break_start = TimeField('Break Start Time:', [validators.Optional()])169fri_break_end = TimeField('Break End Time:', [validators.Optional()])170sat_open = TimeField('Opening Time:', [validators.Optional()])171sat_close = TimeField('Closing Time:', [validators.Optional()])172sat_break_start = TimeField('Break Start Time:', [validators.Optional()])173sat_break_end = TimeField('Break End Time:', [validators.Optional()])174sun_open = TimeField('Opening Time:', [validators.Optional()])175sun_close = TimeField('Closing Time:', [validators.Optional()])176sun_break_start = TimeField('Break Start Time:', [validators.Optional()])177sun_break_end = TimeField('Break End Time:', [validators.Optional()])178def edit_clinic_info():179 # create_user_form = ClinicInfoForm(request.form)180 # if request.method == 'POST' and create_user_form.validate():181 # users_dict = {}182 # db = shelve.open('clinic_storage.db', 'c')183 #184 # try:185 # users_dict = db['Users']186 # except:187 # print("Error in retrieving Users from storage.db.")188 #189 # user = User.ClinicInfo(create_user_form.mon_open.data, create_user_form.mon_close.data, create_user_form.mon_break_start.data, create_user_form.mon_break_end.data,190 # create_user_form.tues_open.data, create_user_form.tues_close.data, create_user_form.tues_break_start.data, create_user_form.tues_break_end.data,191 # create_user_form.wed_open.data, create_user_form.wed_close.data, create_user_form.wed_break_start.data, create_user_form.wed_break_end.data,192 # create_user_form.thur_open.data, create_user_form.thur_close.data, create_user_form.thur_break_start.data, create_user_form.thur_break_end.data,193 # create_user_form.fri_open.data, create_user_form.fri_close.data, create_user_form.fri_break_start.data, create_user_form.fri_break_end.data,194 # create_user_form.sat_open.data, create_user_form.sat_close.data, create_user_form.sat_break_start.data, create_user_form.sat_break_end.data,195 # create_user_form.sun_open.data, create_user_form.sun_close.data, create_user_form.sun_break_start.data, create_user_form.sun_break_end.data,196 # create_user_form.off_start.data, create_user_form.off_end.data, create_user_form.off_reason.data, create_user_form.mon_break_end.data,197 # create_user_form.password.data, create_user_form.cfm_password.data, create_user_form.name.data,198 # create_user_form.address.data, create_user_form.email.data, create_user_form.phone.data)199 # users_dict[user.get_clinic_id()] = user200 # db['Users'] = users_dict201 #202 # # Test codes203 # users_dict = db['Users']204 # user = users_dict[user.get_clinic_id()]205 # print(user.get_name(), "was stored in storage.db successfully with Clinic ID =", user.get_clinic_id())206 #207 # db.close()208 # return redirect(url_for('login_clinic'))...
utils.py
Source:utils.py
1# -*- coding:utf-8 -*-2# editor: gmj3# Date: 2019-12-03 14:144# desc: è¾
å©æ¹æ³éå5def cut_short_period(index_list):6 # é¤å»åºå·å¼å·®å¼å°äº4çåºé´7 while 1:8 max_index = len(index_list) - 19 # åªæä¸ä¸ªå¼ æè
以0å¼å¤´çåºé´åå²indexå表10 if max_index == 0 or (index_list[0] == 0 and max_index == 1):11 break12 for k, v in enumerate(index_list):13 if k < max_index:14 if k == 0:15 if index_list[1] - index_list[0] < 3:16 del index_list[k + 1]17 continue18 if index_list[k + 1] - index_list[k] < 4:19 if k == max_index - 1:20 # å¦ææ¯ç¬¬ä¸æ®µæ°æ®å°äº4ï¼ååªå é¤index_list[k + 1]21 del index_list[k + 1]22 else:23 del index_list[k + 1]24 del index_list[k]25 break26 if max_index + 1 == len(index_list):27 break28def deviation_judge(section_1: tuple, section_2: tuple = None):29 """30 æ ¹æ®ä»·æ ¼ å MACD kdj_D æ¥å¤ææ¯å¦è离31 以åå¤ææ¯å¹
åéè½çå¼å¸¸32 :param section_1: 离ç°å¨æè¿çåºé´ (DateFrameæ°æ®,index å表)33 :param section_2: åæ°ç¬¬ä¸ä¸ªåºé´34 :return: è离ç¶æ35 """36 deviation_status = {}37 if section_2:38 data_df1, peak_index1 = section_139 up_sign = 1 if data_df1['MACD'].iloc[peak_index1[0]] > 0 else -140 section_1_close_macd = (data_df1['close'].iloc[peak_index1[0]], data_df1['MACD'].iloc[peak_index1[0]],41 data_df1['kdj_D'].iloc[peak_index1[0]])42 data_df3, peak_index3 = section_243 section_2_close_macd = (data_df3['close'].iloc[peak_index3[0]], data_df3['MACD'].iloc[peak_index3[0]],44 data_df3['kdj_D'].iloc[peak_index3[0]])45 if len(peak_index3) > 1:46 # å¤æåºæ大çmacdå¼çindex47 close_macd_list = []48 macd_list = []49 for i in peak_index3:50 close_macd_list.append((data_df3['close'].iloc[i], data_df3['MACD'].iloc[i], data_df3['kdj_D'].iloc[i]))51 macd_list.append(abs(data_df3['MACD'].iloc[i]))52 max_macd = max(macd_list)53 for close_macd in close_macd_list:54 if close_macd[1] == max_macd:55 section_2_close_macd = close_macd56 # å¤æè离57 if (section_1_close_macd[0] - section_2_close_macd[0]) * (58 section_1_close_macd[1] - section_2_close_macd[1]) < 0:59 if up_sign > 0:60 deviation_status['macd_deviation_status'] = 'ä¸æ¶¨MACDè离'61 else:62 deviation_status['macd_deviation_status'] = 'ä¸è·MACDè离'63 if (section_1_close_macd[0] - section_2_close_macd[0]) * (64 section_1_close_macd[2] - section_2_close_macd[2]) < 0:65 if up_sign > 0:66 deviation_status['kdj_deviation_status'] = 'ä¸æ¶¨KDJè离'67 else:68 deviation_status['kdj_deviation_status'] = 'ä¸è·KDJè离'69 else:70 data_df1, peak_index = section_171 up_sign = 1 if data_df1['MACD'].iloc[peak_index[0]] > 0 else -172 if (data_df1['close'].iloc[peak_index[0]] - data_df1['close'].iloc[peak_index[1]]) * \73 (data_df1['MACD'].iloc[peak_index[0]] - data_df1['MACD'].iloc[peak_index[1]]) < 0:74 if up_sign > 0:75 deviation_status['macd_deviation_status'] = 'ä¸æ¶¨MACDè离'76 else:77 deviation_status['macd_deviation_status'] = 'ä¸è·MACDè离'78 if (data_df1['close'].iloc[peak_index[0]] - data_df1['close'].iloc[peak_index[1]]) * \79 (data_df1['kdj_D'].iloc[peak_index[0]] - data_df1['kdj_D'].iloc[peak_index[1]]) < 0:80 if up_sign > 0:81 deviation_status['kdj_deviation_status'] = 'ä¸æ¶¨KDJè离'82 else:83 deviation_status['kdj_deviation_status'] = 'ä¸è·KDJè离'84 # éè½å¼å¸¸ å æ¯å¹
å¼å¸¸æ示85 warning_res = swing_vol_warning(data_df1)86 if warning_res:87 deviation_status['swing_vol_warning'] = warning_res88 # deviation_status['macd_trend']='MACD ä¸æ¶¨è¶å¿' if up_sign else 'MACD ä¸è·è¶å¿'89 return deviation_status90def swing_vol_warning(data_df):91 # éè½å¼å¸¸ å æ¯å¹
å¼å¸¸æ示92 for row in range(data_df.shape[0]):93 row_data = data_df.iloc[row]94 if (row_data['vol'] / row_data['vol_MA_10']) >= 2 and row_data['swing'] > 0.05:95 return f"{row_data['trade_date']}-æ¯å¹
åéè½å¼å¸¸"96 else:97 return None98def get_peak_index(macd_section):99 """100 :param macd_section: ä¸ä¸ªmacdææ å¼åºé´çDataFrame101 :return: 该åºé´çæå¼index102 """103 # macd_section=macd_sec['MACD']104 peak_index = []105 # å¤æ符å·106 # macd_section['MACD'].sum()107 for s_i in range(1, macd_section['MACD'].shape[0] - 1):108 # å¤ææå¼ å¦æ 该å¼ä¸ä¸¤ä¾§å¼çå·®å¼ä¹ç§¯ç¬¦å·ä¸ºæ£ï¼å为æå¼109 if (macd_section['MACD'].iloc[s_i] - macd_section['MACD'].iloc[s_i - 1]) * (110 macd_section['MACD'].iloc[s_i] - macd_section['MACD'].iloc[s_i + 1]) > 0:111 # ä¸åæ¾æå¤§å¼ ä¸è·æ¾æå°å¼112 if (macd_section['MACD'].iloc[s_i] - macd_section['MACD'].iloc[s_i - 1]) * macd_section[113 'MACD'].sum() > 0: # åé¤å¹å¼114 peak_index.append(s_i)115 # 第ä¸åºé´çå¼è¿å¨æç»ååï¼é£ä¹æå¼å°±æ¯ç¬¬ä¸å¼116 if not peak_index:117 peak_index.append(0)118 cut_short_period(peak_index)119 return peak_index120def deviation_macd_judge(section_1: tuple, section_2: tuple = None):121 """122 æ ¹æ®ä»·æ ¼ å MACD æ¥å¤ææ¯å¦è离123 以åå¤ææ¯å¹
åéè½çå¼å¸¸124 :param section_1: 离ç°å¨æè¿çåºé´ (DateFrameæ°æ®,index å表)125 :param section_2: åæ°ç¬¬ä¸ä¸ªåºé´126 :return: è离ç¶æ127 """128 deviation_status = {}129 if section_2:130 data_df1, peak_index1 = section_1131 up_sign = 1 if data_df1['MACD'].iloc[peak_index1[0]] > 0 else -1132 section_1_close_macd = (data_df1['close'].iloc[peak_index1[0]], data_df1['MACD'].iloc[peak_index1[0]])133 data_df3, peak_index3 = section_2134 section_2_close_macd = (data_df3['close'].iloc[peak_index3[0]], data_df3['MACD'].iloc[peak_index3[0]])135 if len(peak_index3) > 1:136 # å¤æåºæ大çmacdå¼çindex137 close_macd_list = []138 macd_list = []139 for i in peak_index3:140 close_macd_list.append((data_df3['close'].iloc[i], data_df3['MACD'].iloc[i], data_df3['kdj_D'].iloc[i]))141 macd_list.append(abs(data_df3['MACD'].iloc[i]))142 max_macd = max(macd_list)143 for close_macd in close_macd_list:144 if close_macd[1] == max_macd:145 section_2_close_macd = close_macd146 # å¤æè离147 if (section_1_close_macd[0] - section_2_close_macd[0]) * (148 section_1_close_macd[1] - section_2_close_macd[1]) < 0:149 if up_sign > 0:150 deviation_status['macd_deviation_status'] = 'ä¸æ¶¨MACDè离'151 else:152 deviation_status['macd_deviation_status'] = 'ä¸è·MACDè离'153 else:154 data_df1, peak_index = section_1155 up_sign = 1 if data_df1['MACD'].iloc[peak_index[0]] > 0 else -1156 if (data_df1['close'].iloc[peak_index[0]] - data_df1['close'].iloc[peak_index[1]]) * \157 (data_df1['MACD'].iloc[peak_index[0]] - data_df1['MACD'].iloc[peak_index[1]]) < 0:158 if up_sign > 0:159 deviation_status['macd_deviation_status'] = 'ä¸æ¶¨MACDè离'160 else:161 deviation_status['macd_deviation_status'] = 'ä¸è·MACDè离'162 # éè½å¼å¸¸ å æ¯å¹
å¼å¸¸æ示163 warning_res = swing_vol_warning(data_df1)164 if warning_res:165 deviation_status['swing_vol_warning'] = warning_res...
test_early_close.py
Source:test_early_close.py
...32 self.assertRaises(db.DBNoSuchFileError, d2.open,33 self.filename+"2", db.DB_BTREE, db.DB_THREAD, 0666)34 d.put("test","this is a test")35 self.assertEqual(d.get("test"), "this is a test", "put!=get")36 dbenv.close() # This "close" should close the child db handle also37 self.assertRaises(db.DBError, d.get, "test")38 def test02_close_dbenv_before_dbcursor(self):39 dbenv = db.DBEnv()40 dbenv.open(self.homeDir,41 db.DB_INIT_CDB| db.DB_CREATE |db.DB_THREAD|db.DB_INIT_MPOOL,42 0666)43 d = db.DB(dbenv)44 d.open(self.filename, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)45 d.put("test","this is a test")46 d.put("test2","another test")47 d.put("test3","another one")48 self.assertEqual(d.get("test"), "this is a test", "put!=get")49 c=d.cursor()50 c.first()51 c.next()52 d.close() # This "close" should close the child db handle also53 # db.close should close the child cursor54 self.assertRaises(db.DBError,c.next)55 d = db.DB(dbenv)56 d.open(self.filename, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)57 c=d.cursor()58 c.first()59 c.next()60 dbenv.close()61 # The "close" should close the child db handle also, with cursors62 self.assertRaises(db.DBError, c.next)63 def test03_close_db_before_dbcursor_without_env(self):64 import os.path65 path=os.path.join(self.homeDir,self.filename)66 d = db.DB()67 d.open(path, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)68 d.put("test","this is a test")69 d.put("test2","another test")70 d.put("test3","another one")71 self.assertEqual(d.get("test"), "this is a test", "put!=get")72 c=d.cursor()73 c.first()74 c.next()75 d.close()76 # The "close" should close the child db handle also77 self.assertRaises(db.DBError, c.next)78 def test04_close_massive(self):79 dbenv = db.DBEnv()80 dbenv.open(self.homeDir,81 db.DB_INIT_CDB| db.DB_CREATE |db.DB_THREAD|db.DB_INIT_MPOOL,82 0666)83 dbs=[db.DB(dbenv) for i in xrange(16)]84 cursors=[]85 for i in dbs :86 i.open(self.filename, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)87 dbs[10].put("test","this is a test")88 dbs[10].put("test2","another test")89 dbs[10].put("test3","another one")90 self.assertEqual(dbs[4].get("test"), "this is a test", "put!=get")91 for i in dbs :92 cursors.extend([i.cursor() for j in xrange(32)])93 for i in dbs[::3] :94 i.close()95 for i in cursors[::3] :96 i.close()97 # Check for missing exception in DB! (after DB close)98 self.assertRaises(db.DBError, dbs[9].get, "test")99 # Check for missing exception in DBCursor! (after DB close)100 self.assertRaises(db.DBError, cursors[101].first)101 cursors[80].first()102 cursors[80].next()103 dbenv.close() # This "close" should close the child db handle also104 # Check for missing exception! (after DBEnv close)105 self.assertRaises(db.DBError, cursors[80].next)106 def test05_close_dbenv_delete_db_success(self):107 dbenv = db.DBEnv()108 dbenv.open(self.homeDir,109 db.DB_INIT_CDB| db.DB_CREATE |db.DB_THREAD|db.DB_INIT_MPOOL,110 0666)111 d = db.DB(dbenv)112 d.open(self.filename, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)113 dbenv.close() # This "close" should close the child db handle also114 del d115 try:116 import gc117 except ImportError:118 gc = None119 if gc:120 # force d.__del__ [DB_dealloc] to be called121 gc.collect()122 def test06_close_txn_before_dup_cursor(self) :123 dbenv = db.DBEnv()124 dbenv.open(self.homeDir,db.DB_INIT_TXN | db.DB_INIT_MPOOL |125 db.DB_INIT_LOG | db.DB_CREATE)126 d = db.DB(dbenv)127 txn = dbenv.txn_begin()128 d.open(self.filename, dbtype = db.DB_HASH, flags = db.DB_CREATE,129 txn=txn)130 d.put("XXX", "yyy", txn=txn)131 txn.commit()132 txn = dbenv.txn_begin()133 c1 = d.cursor(txn)134 c2 = c1.dup()135 self.assertEqual(("XXX", "yyy"), c1.first())136 # Not interested in warnings about implicit close.137 import warnings138 if sys.version_info < (2, 6) :139 # Completely resetting the warning state is140 # problematic with python >=2.6 with -3 (py3k warning),141 # because some stdlib modules selectively ignores warnings.142 warnings.simplefilter("ignore")143 txn.commit()144 warnings.resetwarnings()145 else :146 # When we drop support for python 2.4147 # we could use: (in 2.5 we need a __future__ statement)148 #149 # with warnings.catch_warnings():150 # warnings.simplefilter("ignore")151 # txn.commit()152 #153 # We can not use "with" as is, because it would be invalid syntax154 # in python 2.4 and (with no __future__) 2.5.155 # Here we simulate "with" following PEP 343 :156 w = warnings.catch_warnings()157 w.__enter__()158 try :159 warnings.simplefilter("ignore")160 txn.commit()161 finally :162 w.__exit__()163 self.assertRaises(db.DBCursorClosedError, c2.first)164 def test07_close_db_before_sequence(self):165 import os.path166 path=os.path.join(self.homeDir,self.filename)167 d = db.DB()168 d.open(path, db.DB_BTREE, db.DB_CREATE | db.DB_THREAD, 0666)169 dbs=db.DBSequence(d)170 d.close() # This "close" should close the child DBSequence also171 dbs.close() # If not closed, core dump (in Berkeley DB 4.6.*)172#----------------------------------------------------------------------173def test_suite():174 suite = unittest.TestSuite()175 suite.addTest(unittest.makeSuite(DBEnvClosedEarlyCrash))176 return suite177if __name__ == '__main__':...
data_manager.py
Source:data_manager.py
1import pandas as pd2import numpy as np3COLUMNS_CHART_DATA = ['date', 'open', 'high', 'low', 'close', 'volume']4COLUMNS_TRAINING_DATA_V1 = [5 'open_lastclose_ratio', 'high_close_ratio', 'low_close_ratio',6 'close_lastclose_ratio', 'volume_lastvolume_ratio',7 'close_ma5_ratio', 'volume_ma5_ratio',8 'close_ma10_ratio', 'volume_ma10_ratio',9 'close_ma20_ratio', 'volume_ma20_ratio',10 'close_ma60_ratio', 'volume_ma60_ratio',11 'close_ma120_ratio', 'volume_ma120_ratio',12]13COLUMNS_TRAINING_DATA_V1_RICH = [14 'open_lastclose_ratio', 'high_close_ratio', 'low_close_ratio',15 'close_lastclose_ratio', 'volume_lastvolume_ratio',16 'close_ma5_ratio', 'volume_ma5_ratio',17 'close_ma10_ratio', 'volume_ma10_ratio',18 'close_ma20_ratio', 'volume_ma20_ratio',19 'close_ma60_ratio', 'volume_ma60_ratio',20 'close_ma120_ratio', 'volume_ma120_ratio',21 'inst_lastinst_ratio', 'frgn_lastfrgn_ratio',22 'inst_ma5_ratio', 'frgn_ma5_ratio',23 'inst_ma10_ratio', 'frgn_ma10_ratio',24 'inst_ma20_ratio', 'frgn_ma20_ratio',25 'inst_ma60_ratio', 'frgn_ma60_ratio',26 'inst_ma120_ratio', 'frgn_ma120_ratio',27]28COLUMNS_TRAINING_DATA_V2 = [29 'per', 'pbr', 'roe',30 'open_lastclose_ratio', 'high_close_ratio', 'low_close_ratio',31 'close_lastclose_ratio', 'volume_lastvolume_ratio',32 'close_ma5_ratio', 'volume_ma5_ratio',33 'close_ma10_ratio', 'volume_ma10_ratio',34 'close_ma20_ratio', 'volume_ma20_ratio',35 'close_ma60_ratio', 'volume_ma60_ratio',36 'close_ma120_ratio', 'volume_ma120_ratio',37 'market_kospi_ma5_ratio', 'market_kospi_ma20_ratio', 38 'market_kospi_ma60_ratio', 'market_kospi_ma120_ratio', 39 'bond_k3y_ma5_ratio', 'bond_k3y_ma20_ratio', 40 'bond_k3y_ma60_ratio', 'bond_k3y_ma120_ratio'41]42def preprocess(data, ver='v1'):43 windows = [5, 10, 20, 60, 120]44 for window in windows:45 data['close_ma{}'.format(window)] = \46 data['close'].rolling(window).mean()47 data['volume_ma{}'.format(window)] = \48 data['volume'].rolling(window).mean()49 data['close_ma%d_ratio' % window] = \50 (data['close'] - data['close_ma%d' % window]) \51 / data['close_ma%d' % window]52 data['volume_ma%d_ratio' % window] = \53 (data['volume'] - data['volume_ma%d' % window]) \54 / data['volume_ma%d' % window]55 56 if ver == 'v1.rich':57 data['inst_ma{}'.format(window)] = \58 data['close'].rolling(window).mean()59 data['frgn_ma{}'.format(window)] = \60 data['volume'].rolling(window).mean()61 data['inst_ma%d_ratio' % window] = \62 (data['close'] - data['inst_ma%d' % window]) \63 / data['inst_ma%d' % window]64 data['frgn_ma%d_ratio' % window] = \65 (data['volume'] - data['frgn_ma%d' % window]) \66 / data['frgn_ma%d' % window]67 data['open_lastclose_ratio'] = np.zeros(len(data))68 data.loc[1:, 'open_lastclose_ratio'] = \69 (data['open'][1:].values - data['close'][:-1].values) \70 / data['close'][:-1].values71 data['high_close_ratio'] = \72 (data['high'].values - data['close'].values) \73 / data['close'].values74 data['low_close_ratio'] = \75 (data['low'].values - data['close'].values) \76 / data['close'].values77 data['close_lastclose_ratio'] = np.zeros(len(data))78 data.loc[1:, 'close_lastclose_ratio'] = \79 (data['close'][1:].values - data['close'][:-1].values) \80 / data['close'][:-1].values81 data['volume_lastvolume_ratio'] = np.zeros(len(data))82 data.loc[1:, 'volume_lastvolume_ratio'] = \83 (data['volume'][1:].values - data['volume'][:-1].values) \84 / data['volume'][:-1] \85 .replace(to_replace=0, method='ffill') \86 .replace(to_replace=0, method='bfill').values87 if ver == 'v1.rich':88 data['inst_lastinst_ratio'] = np.zeros(len(data))89 data.loc[1:, 'inst_lastinst_ratio'] = \90 (data['inst'][1:].values - data['inst'][:-1].values) \91 / data['inst'][:-1] \92 .replace(to_replace=0, method='ffill') \93 .replace(to_replace=0, method='bfill').values94 data['frgn_lastfrgn_ratio'] = np.zeros(len(data))95 data.loc[1:, 'frgn_lastfrgn_ratio'] = \96 (data['frgn'][1:].values - data['frgn'][:-1].values) \97 / data['frgn'][:-1] \98 .replace(to_replace=0, method='ffill') \99 .replace(to_replace=0, method='bfill').values100 return data101def load_data(fpath, date_from, date_to, ver='v1'):102 header = None if ver == 'v1' else 0103 data = pd.read_csv(fpath, thousands=',', header=header, 104 converters={'date': lambda x: str(x)})105 if ver == 'v1':106 data.columns = ['date', 'open', 'high', 'low', 'close', 'volume']107 # ë ì§ ì¤ë¦ì°¨ì ì ë ¬108 data = data.sort_values(by='date').reset_index()109 # ë°ì´í° ì ì²ë¦¬110 data = preprocess(data)111 112 # ê¸°ê° íí°ë§113 data['date'] = data['date'].str.replace('-', '')114 data = data[(data['date'] >= date_from) & (data['date'] <= date_to)]115 data = data.dropna()116 # ì°¨í¸ ë°ì´í° ë¶ë¦¬117 chart_data = data[COLUMNS_CHART_DATA]118 # íìµ ë°ì´í° ë¶ë¦¬119 training_data = None120 if ver == 'v1':121 training_data = data[COLUMNS_TRAINING_DATA_V1]122 elif ver == 'v1.rich':123 training_data = data[COLUMNS_TRAINING_DATA_V1_RICH]124 elif ver == 'v2':125 data.loc[:, ['per', 'pbr', 'roe']] = \126 data[['per', 'pbr', 'roe']].apply(lambda x: x / 100)127 training_data = data[COLUMNS_TRAINING_DATA_V2]128 training_data = training_data.apply(np.tanh)129 else:130 raise Exception('Invalid version.')131 ...
alphas.py
Source:alphas.py
1import numpy as np2import pandas as pd 3#设å®åºç¡å½æ°4def get_delta(x,N):5 lst=[]6 for i in range(1,N+2):7 lst.append(x)8 x=x.diff()9 return lst[-1]10def rank(array):11 s = pd.Series(array)12 return s.rank(ascending=False)[len(s)-1]13#ååªè¡ç¥¨çtime seriesåæææ 14def get_alpha1(df):15 df['a1']=(df['high']-df['low'])/abs(df['close']-df['open'])16 return df['a1']17def get_alpha2(df):18 df['a2']=(df['high']-df['close'])/(df['close']-df['low'])19 return df['a2']20def get_alpha3(df):21 df['a3']=(df['high']-df['open'])/(df['open']-df['close'])22 return df['a3']23# Alpha#1: (SignedPower(((returns < 0) ? stddev(returns, 20) : close), 2.)24def get_alpha4(df):25 filter4_1=df['returns'].rolling(window=20).std().where(df['returns']<0)26 df['ralpha4']=filter4_1.fillna(df['close'])27 return df['ralpha4']**228#Alpha#3:correlation(high,volume,5)29def get_alpha5(df):30 return df['high'].rolling(window=5).corr(df['volume'])31#Alpha#4:correlation(open,volume,10)32def get_alpha6(df):33 return df['open'].rolling(window=10).corr(df['volume'])34 35#Alpha#5:delta(sum(open,5)*sum(return,5),10)36def get_alpha7(df):37 return get_delta(df['open'].rolling(window=5).sum()*df['returns'].rolling(window=5).sum(),10)38#Alpha#9: ((0 < ts_min(delta(close, 1), 5)) ? delta(close, 1) : ((ts_max(delta(close, 1), 5) < 0) ? delta(close, 1) : (-1 * delta(close, 1)))) 39def get_alpha8(df):40 filter9_1=df['close'].diff().where((df['close'].diff().rolling(window=5).min())<0)41 filter9_2=filter9_1.fillna(df['close'].diff().where((df['close'].diff().rolling(window=5).max())>0))42 return filter9_2.fillna(-df['close'].diff())43#Alpha#12: (delta(volume, 1)) * (-1 * delta(close, 1))44def get_alpha9(df):45 return -1*df['volume'].diff()*df['close'].diff()46#Alpha#18: stddev(abs((close - open)), 5) + (close - open)) + correlation(close, open, 10)47def get_alpha10(df):48 return abs(df['close']-df['open']).rolling(window=5).std()+(df['close']-df['open'])+get_alpha7(df)49#Alpha#23: (((sum(high, 20) / 20) < high) ? (-1 * delta(high, 2)) : 0) 50def get_alpha11(df):51 df['alpha12']=-1*get_delta(df['high'],2).where(df['high'].rolling(window=20).mean()<df['high'])52 return df['alpha12'].fillna(0)53#Alpha#24: ((((delta((sum(close, 100) / 100), 100) / delay(close, 100)) < 0.05) || ((delta((sum(close, 100) / 100), 100) / delay(close, 100)) == 0.05)) ? (-1 * (close - ts_min(close, 100))) : (-1 * delta(close, 3))) 54def get_alpha12(df):55 nofilter13=df['close'].rolling(window=100).min()-df['close']56 filter13_1=nofilter13.where(get_delta(df['close'].rolling(window=100).mean(),100)/(df['close']-get_delta(df['close'],100))<0.05)57 filter13_2=filter13_1.fillna(nofilter13.where(get_delta(df['close'].rolling(window=100).mean(),100)/(df['close']-get_delta(df['close'],100))==0.05))58 return filter13_2.fillna(-get_delta(df['close'],3))59#Alpha#26: (-1 * ts_max(correlation(ts_rank(volume, 5), ts_rank(high, 5), 5), 3)) 60def get_alpha13(df):61 return df['volume'].rolling(window=5).apply(func=rank).rolling(window=5).corr(df['high'].rolling(window=5).apply(func=rank)).rolling(window=3).max()62#Alpha#35: ((Ts_Rank(volume, 32) * (1 - Ts_Rank(((close + high) - low), 16))) * (1 - Ts_Rank(returns, 32))) 63def get_alpha14(df):64 return df['volume'].rolling(window=32).apply(func=rank)*(1-(df['close']+df['high']-df['low']).rolling(window=32).apply(func=rank))*(1-df['returns'].rolling(window=32).apply(func=rank))65#Alpha#43: (ts_rank((volume / adv20), 20) * ts_rank((-1 * delta(close, 7)), 8)) 66def get_alpha15(df):67 return df['volume']/df['close']*df['volume'].rolling(window=20).mean().rolling(window=20).apply(func=rank)*-get_delta(df['close'],7).rolling(window=8).apply(func=rank)68#Alpha#55: correlation((close - ts_min(low, 12)) / (ts_max(high, 12) - ts_min(low, 12))), volume, 6)69def get_alpha16(df):70 return df['volume'].rolling(window=6).corr((df['close']-df['low'].rolling(window=12).min())/(df['high'].rolling(window=12).max()-df['low'].rolling(window=12).min()))71#Alpha#101: ((close - open) / ((high - low) + .001)) 72def get_alpha17(df):73 return (df['close']-df['open'])/(df['high']-df['low']+0.01)74#Alpha#28: (correlation(adv20, low, 5) + ((high + low) / 2)) - close75def get_alpha18(df):76 return df['low'].rolling(window=5).corr(df['close']*df['volume'].rolling(window=20).mean())+(df['high']+df['low'])/2-df['close']77#EMV:A=ï¼ä»æ¥æé«+ä»æ¥æä½ï¼/2;B=ï¼åæ¥æé«+åæ¥æä½ï¼/2;C=ä»æ¥æé«-ä»æ¥æä½;2.EM=ï¼A-Bï¼*C/ä»æ¥æ交é¢;3.EMV=Næ¥å
EMçç´¯å;4.MAEMV=EMVçMæ¥ç®å移å¨å¹³å.åæ°N为14ï¼åæ°M为978def get_alpha19(df):79 return get_delta((df['high']+df['low'])/2,1).rolling(window=14).sum().rolling(window=9).mean()80#MTMMAå¨éææ 81def get_alpha20(df):82 return get_delta(df['close'],12).rolling(window=6).mean()83#横æªé¢åæææ 84#Alpha#41: (((high * low)^0.5) - vwap) 85#Alpha#25: rank(((((-1 * returns) * adv20) * vwap) * (high - close))) 86#Alpha#84: SignedPower(Ts_Rank((vwap - ts_max(vwap, 15.3217)), 20.7127), delta(close, 4.96796)) ...
python.py
Source:python.py
...23 },24 'write' : {25 'call' : 'evaluate',26 'write' : """open("%(path)s", 'ab+').write(__import__("base64").urlsafe_b64decode('%(chunk_b64)s'))""",27 'truncate' : """open("%(path)s", 'w').close()"""28 },29 'read' : {30 'call': 'evaluate',31 'read' : """__import__("base64").b64encode(open("%(path)s", "rb").read())"""32 },33 'md5' : {34 'call': 'evaluate',35 'md5': """__import__("hashlib").md5(open("%(path)s", 'rb').read()).hexdigest()"""36 },37 'evaluate' : {38 'call': 'render',39 'evaluate': """%(code)s""",40 'test_os': """'-'.join([__import__('os').name, __import__('sys').platform])""",41 'test_os_expected': '^[\w-]+$'...
test1.py
Source:test1.py
1import pandas as pd2import numpy as np3import keras4import tensorflow as tf5from keras.preprocessing.sequence import TimeseriesGenerator6import plotly.graph_objects as go7#Implementation8filename = "GOOG.csv"9df = pd.read_csv(filename)10print(df.info())11df['Date'] = pd.to_datetime(df['Date'])12df.set_axis(df['Date'], inplace=True)13df.drop(columns=['Open', 'High', 'Low', 'Volume'], inplace=True)14#Data Preprocessing15close_data = df['Close'].values16close_data = close_data.reshape((-1,1))17split_percent = 0.8018split = int(split_percent * len(close_data))19close_train = close_data[:split]20close_test = close_data[split:]21date_train = df['Date'][:split]22date_test = df['Date'][split:]23print(len(close_train))24print(len(close_test))25look_back = 1526train_generator = TimeseriesGenerator(close_train, close_train, length=look_back, batch_size=20) 27test_generator = TimeseriesGenerator(close_test, close_test, length=look_back, batch_size=1)28#Neural Network29from keras.models import Sequential30from keras.layers import LSTM, Dense31model = Sequential()32model.add(33 LSTM(10,34 activation = 'relu',35 input_shape=(look_back,1))36)37model.add(Dense(1))38model.compile(optimizer = 'adam', loss = 'mse')39num_epochs = 2540model.fit_generator(train_generator, epochs = num_epochs, verbose = 1)41prediction = model.predict_generator(test_generator)42close_train = close_train.reshape((-1))43close_test = close_test.reshape((-1))44prediction = prediction.reshape((-1))45#print("Predicted:", [float(f"{x:.1f}") for x in prediction])46trace1 = go.Scatter(47 x = date_train,48 y = close_train,49 mode = 'lines',50 name = 'Data'51)52trace2 = go.Scatter(53 x = date_test,54 y = prediction,55 mode = 'lines',56 name = 'Prediction'57)58trace3 = go.Scatter(59 x = date_test,60 y = close_test,61 mode='lines',62 name = 'Ground Truth'63)64layout = go.Layout(65 title = "Google Stock",66 xaxis = {'title' : "Date"},67 yaxis = {'title' : "Close"}68)69fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)70fig.show()71close_data = close_data.reshape((-1))72def predict(num_prediction, model):73 prediction_list = close_data[-look_back:]74 75 for _ in range(num_prediction):76 x = prediction_list[-look_back:]77 x = x.reshape((1, look_back, 1))78 out = model.predict(x)[0][0]79 prediction_list = np.append(prediction_list, out)80 prediction_list = prediction_list[look_back-1:]81 82 return prediction_list83 84def predict_dates(num_prediction):85 last_date = df['Date'].values[-1]86 prediction_dates = pd.date_range(last_date, periods=num_prediction+1).tolist()87 return prediction_dates88num_prediction = 3089forecast = predict(num_prediction, model)...
Using AI Code Generation
1const differencify = require('differencify');2const differencifyConfig = {3 {4 },5 formatImageName: '{tag}-{logName}-{width}x{height}',6};7const init = differencify.init(differencifyConfig);8init.close();
Using AI Code Generation
1const differencify = require('differencify');2const differencify = require('differencify');3differencify.init(differencifyConfig);4differencify.close();5const differencify = require('differencify');6differencify.init(differencifyConfig);7differencify.close();8differencify.getDiffCount() will get the number of images that are different from the baseline image. This method should be called after using any
Using AI Code Generation
1const differencify = require('differencify');2const { expect } = require('chai');3const { close } = differencify.init(browser, { viewportChangePause: 1000 });4describe('Test', function() {5 it('test', function() {6 browser.saveFullPageScreen('google', { /* some options */ });7 expect(browser.checkFullPageScreen('google', { /* some options */ })).to.be.within(0, 1);8 close();9 });10});
Using AI Code Generation
1const differencify = require('differencify')2const diff = differencify.init({ autoSaveBaseline: true })3diff.close()4const differencify = require('differencify-webdriverio')5const diff = differencify.init({ autoSaveBaseline: true })6diff.close()
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