-
Notifications
You must be signed in to change notification settings - Fork 0
/
OptionPricing.py
839 lines (683 loc) · 35.6 KB
/
OptionPricing.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
#Android Studio is a full-featured IDE
import sys
from PySide.QtGui import *
from PySide.QtCore import *
from bs4 import BeautifulSoup
import urllib2
import numpy as np
from scipy.stats import norm
from scipy.integrate import quad
import urllib
import os
import csv
import cmath as cm
import matplotlib
import scipy
from scipy.sparse.linalg import dsolve
from datetime import *
matplotlib.rcParams['backend.qt4']='PySide'
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
class MatplotlibWidget(QWidget):
def __init__(self, parent=None):
super(MatplotlibWidget, self).__init__(parent)
self.figure = Figure()
self.canvas = FigureCanvas(self.figure)
self.axis = self.figure.add_subplot(111)
self.layoutVertical = QVBoxLayout(self)
self.layoutVertical.addWidget(self.canvas)
class GUI(QWidget):
def __init__(self):
super(GUI, self).__init__()
self.initUI()
def initUI(self):
self.errorMessageDialog = QErrorMessage(self)
self.errorMessageDialog.setWindowTitle('ERROR')
self.retrieving = False
self.retrieved = False
frameStyle = QFrame.Sunken | QFrame.Panel
self.setToolTip('This is a tool for <b>Option Pricing</b>')
#--------------------------------------
#Define Different Input Widget and Grid
groupBoxBackgroundInformation = QGroupBox('Background Information')
groupBoxRetrievedData = QGroupBox('Retrieved Data')
groupBoxInput = QGroupBox('Input Parameters')
groupBoxOutput = QGroupBox('Output Results')
gridBackgroundInformation = QGridLayout()
gridRetrievedData = QGridLayout()
gridInput = QGridLayout()
gridOutput = QGridLayout()
groupBoxBackgroundInformation.setLayout(gridBackgroundInformation)
groupBoxRetrievedData.setLayout(gridRetrievedData)
groupBoxInput.setLayout(gridInput)
groupBoxOutput.setLayout(gridOutput)
#Define Different Widget For Different Models
widgetInput_BlackScholes = QWidget()
widgetInput_Heston = QWidget()
widgetInput_Merton = QWidget()
widgetInput_TrendFollowing = QWidget()
gridInput_BlackScholes = QGridLayout()
gridInput_Heston = QGridLayout()
gridInput_Merton = QGridLayout()
gridInput_TrendFollowing = QGridLayout()
widgetInput_BlackScholes.setLayout(gridInput_BlackScholes)
widgetInput_Heston.setLayout(gridInput_Heston)
widgetInput_Merton.setLayout(gridInput_Merton)
widgetInput_TrendFollowing.setLayout(gridInput_TrendFollowing)
stackedInput = QStackedWidget()
stackedInput.addWidget(widgetInput_BlackScholes)
stackedInput.addWidget(widgetInput_Heston)
stackedInput.addWidget(widgetInput_Merton)
stackedInput.addWidget(widgetInput_TrendFollowing)
stackedInput.setStyleSheet("QStackedWidget { border: 2px gray; margin: 0px; }")
#--------------------------------
#Initialize all the labels and input boxes
#Initialize Data Retrieved Part
tickerLabel = QLabel(self)
tickerLabel.setText('A Valid Tiker')
self.tickerInput = QLineEdit(self)
self.retrieveButton = QPushButton('Retrieve', self)
self.retrieveButton.setToolTip('Retrieve data from <b>Yahoo Finance</b>')
self.retrieveButton.resize(self.retrieveButton.sizeHint())
self.retrieveButton.pressed.connect(self.retrieve)
stockPriceLabel = QLabel(self)
stockPriceLabel.setText('Current Stock Price')
self.stockPriceOutput = QLabel(self)
self.stockPriceOutput.setFrameStyle(frameStyle)
dividendRateLabel = QLabel(self)
dividendRateLabel.setText('Current Dividend Rate')
self.dividendRateOutput = QLabel(self)
self.dividendRateOutput.setFrameStyle(frameStyle)
historicalVolatilityLabel = QLabel(self)
historicalVolatilityLabel.setText('Historical Volatility (Yearly)')
self.historicalVolatilityOutput = QLabel(self)
self.historicalVolatilityOutput.setFrameStyle(frameStyle)
interestRateLabel = QLabel(self)
interestRateLabel.setText('Interest Rate')
self.interestRateOutput = QLabel(self)
self.interestRateOutput.setFrameStyle(frameStyle)
self.interestRate = getInterestRate()
self.interestRateOutput.setText(str(self.interestRate*100)+"%")
self.matplotlibWidget = MatplotlibWidget(self)
self.matplotlibWidget.canvas.draw()
#Initialize Input Parameters Part
modelLabel = QLabel(self)
modelLabel.setText('Option Pricing Model')
self.model = QComboBox(self)
self.model.addItem('Black Scholes')
self.model.addItem('Heston')
self.model.addItem('Merton Jump-Diffusion')
self.model.addItem('Optimal Trend Following')
self.model.activated.connect(lambda: stackedInput.setCurrentIndex(self.model.currentIndex()))
##WE HAVE DIFFERENT MODELS
##1. Input for Black Scholes Model
strikePriceLabel_BlackScholes = QLabel(self)
strikePriceLabel_BlackScholes.setText('Strike Price')
self.strikePriceInput_BlackScholes = QLineEdit(self)
timeToMaturityLabel_BlackScholes = QLabel(self)
timeToMaturityLabel_BlackScholes.setText('Time To Maturity')
self.timeToMaturityInput_BlackScholes = QLineEdit(self)
volatilityLabel_BlackScholes = QLabel(self)
volatilityLabel_BlackScholes.setText('Volatility')
self.volatilityInput_BlackScholes = QLineEdit(self)
##2. Input for Heston Model
strikePriceLabel_Heston = QLabel(self)
strikePriceLabel_Heston.setText('Strike Price')
self.strikePriceInput_Heston = QLineEdit(self)
timeToMaturityLabel_Heston = QLabel(self)
timeToMaturityLabel_Heston.setText('Time To Maturity')
self.timeToMaturityInput_Heston = QLineEdit(self)
meanInversionLabel_Heston = QLabel(self)
meanInversionLabel_Heston.setText('Mean Inversion')
self.meanInversionInput_Heston = QLineEdit(self)
longRunVarianceLabel_Heston = QLabel(self)
longRunVarianceLabel_Heston.setText('Long-run Variance')
self.longRunVarianceInput_Heston = QLineEdit(self)
currentVarianceLabel_Heston = QLabel(self)
currentVarianceLabel_Heston.setText('Current Variance')
self.currentVarianceInput_Heston = QLineEdit(self)
correlationLabel_Heston = QLabel(self)
correlationLabel_Heston.setText('Correlation of Z1(t) and Z2(t)')
self.correlationInput_Heston = QLineEdit(self)
volatilityOfVolatilityLabel_Heston = QLabel(self)
volatilityOfVolatilityLabel_Heston.setText('Volatility Of Volatility')
self.volatilityOfVolatilityInput_Heston = QLineEdit(self)
##3. Input for Merton Jump-Diffusion Model
strikePriceLabel_Merton = QLabel(self)
strikePriceLabel_Merton.setText('Strike Price')
self.strikePriceInput_Merton = QLineEdit(self)
timeToMaturityLabel_Merton = QLabel(self)
timeToMaturityLabel_Merton.setText('Time To Maturity')
self.timeToMaturityInput_Merton = QLineEdit(self)
volatilityLabel_Merton = QLabel(self)
volatilityLabel_Merton.setText('Volatility')
self.volatilityInput_Merton = QLineEdit(self)
expectedNumberOfJumpsLabel_Merton = QLabel(self)
expectedNumberOfJumpsLabel_Merton.setText('Expeceted Number Of Jumps (Per Year)')
self.expectedNumberOfJumpsInput_Merton = QLineEdit(self)
proportionOfTotalVolatilityLabel_Merton = QLabel(self)
proportionOfTotalVolatilityLabel_Merton.setText('Proportion Of Total Volatility')
self.proportionOfTotalVolatilityInput_Merton = QLineEdit(self)
##4. Input for Optimal Trend Following Model
expectedLengthOfBullMarketLabel_TrendFollowing = QLabel(self)
expectedLengthOfBullMarketLabel_TrendFollowing.setText('Expected Length Of Bull Market')
#expectedLengthOfBullMarketLabel_TrendFollowing.setToolTip('Switching Intensity From Bull to Bear')
self.expectedLengthOfBullMarketInput_TrendFollowing = QLineEdit(self)
expectedLengthOfBearMarketLabel_TrendFollowing = QLabel(self)
expectedLengthOfBearMarketLabel_TrendFollowing.setText('Expected Length Of Bear Market')
#expectedLengthOfBearMarketLabel_TrendFollowing.setToolTip('Switching Intensity From Bear to Bear')
self.expectedLengthOfBearMarketInput_TrendFollowing = QLineEdit(self)
expectedReturnRateBullLabel_TrendFollowing = QLabel(self)
expectedReturnRateBullLabel_TrendFollowing.setText('Expected Return Rate in Bull Market')
expectedReturnRateBullLabel_TrendFollowing.setToolTip('Positive!')
self.expectedReturnRateBullInput_TrendFollowing = QLineEdit(self)
expectedReturnRateBearLabel_TrendFollowing = QLabel(self)
expectedReturnRateBearLabel_TrendFollowing.setText('Expected Return Rate in Bear Market')
expectedReturnRateBearLabel_TrendFollowing.setToolTip('Negative!')
self.expectedReturnRateBearInput_TrendFollowing = QLineEdit(self)
volatilityLabel_TrendFollowing = QLabel(self)
volatilityLabel_TrendFollowing.setText('Volatility')
self.volatilityInput_TrendFollowing = QLineEdit(self)
ratioOfSlippageBuyLabel_TrendFollowing = QLabel(self)
ratioOfSlippageBuyLabel_TrendFollowing.setText('Ratio of Slippage per Transaction with Buy Order')
ratioOfSlippageBuyLabel_TrendFollowing.setToolTip('[0, 1]')
self.ratioOfSlippageBuyInput_TrendFollowing = QLineEdit(self)
ratioOfSlippageSellLabel_TrendFollowing = QLabel(self)
ratioOfSlippageSellLabel_TrendFollowing.setText('Ratio of Slippage per Transaction with Sell Order')
ratioOfSlippageSellLabel_TrendFollowing.setToolTip('[0, 1]')
self.ratioOfSlippageSellInput_TrendFollowing = QLineEdit(self)
interestedTimeIntervalLabel = QLabel(self)
interestedTimeIntervalLabel.setText('Interested Time Interval (years)')
self.interestedTimeIntervalInput_TrendFollowing = QLineEdit(self)
##Other
computeButton = QPushButton('Compute', self)
computeButton.setToolTip('Compute Option Price')
computeButton.resize(computeButton.sizeHint())
computeButton.clicked.connect(self.compute)
resetDefaultButton = QPushButton('Reset to Defaults', self)
resetDefaultButton.setToolTip('Reset to Default Values')
resetDefaultButton.resize(resetDefaultButton.sizeHint())
resetDefaultButton.clicked.connect(self.setDefault)
#Initialize Output Results Part
callOptionPriceLabel = QLabel(self)
callOptionPriceLabel.setText('Call Option Price')
self.callOptionPriceOutput = QLabel(self)
self.callOptionPriceOutput.setFrameStyle(frameStyle)
putOptionPriceLabel = QLabel(self)
putOptionPriceLabel.setText('Put Option Price')
self.putOptionPriceOutput = QLabel(self)
self.putOptionPriceOutput.setFrameStyle(frameStyle)
#----------------------
#Widget setup
#Set Column Width
gridBackgroundInformation.setColumnStretch(0, 250)
gridBackgroundInformation.setColumnStretch(1, 250)
gridRetrievedData.setColumnStretch(0, 250)
gridRetrievedData.setColumnStretch(1, 250)
gridInput.setColumnStretch(0, 250)
gridInput.setColumnStretch(1, 250)
gridOutput.setColumnStretch(0, 250)
gridOutput.setColumnStretch(1, 250)
gridInput_BlackScholes.setColumnStretch(0, 250)
gridInput_BlackScholes.setColumnStretch(1, 250)
gridInput_Heston.setColumnStretch(0, 250)
gridInput_Heston.setColumnStretch(1, 250)
gridInput_Merton.setColumnStretch(0, 250)
gridInput_Merton.setColumnStretch(1, 250)
gridInput_TrendFollowing.setColumnStretch(0, 250)
gridInput_TrendFollowing.setColumnStretch(1, 250)
#Background Information
gridBackgroundInformation.addWidget(interestRateLabel, 0, 0)
gridBackgroundInformation.addWidget(self.interestRateOutput, 0, 1)
gridBackgroundInformation.addWidget(tickerLabel, 1, 0)
gridBackgroundInformation.addWidget(self.tickerInput, 1, 1)
gridBackgroundInformation.addWidget(self.retrieveButton, 2, 1)
#Retrieved Data
gridRetrievedData.addWidget(stockPriceLabel, 0, 0)
gridRetrievedData.addWidget(self.stockPriceOutput, 0, 1)
gridRetrievedData.addWidget(dividendRateLabel, 1, 0)
gridRetrievedData.addWidget(self.dividendRateOutput, 1, 1)
gridRetrievedData.addWidget(historicalVolatilityLabel, 2, 0)
gridRetrievedData.addWidget(self.historicalVolatilityOutput, 2, 1)
#Input Result
#1. Black Scholes Model
gridInput_BlackScholes.addWidget(strikePriceLabel_BlackScholes, 0, 0)
gridInput_BlackScholes.addWidget(self.strikePriceInput_BlackScholes, 0, 1)
gridInput_BlackScholes.addWidget(timeToMaturityLabel_BlackScholes, 1, 0)
gridInput_BlackScholes.addWidget(self.timeToMaturityInput_BlackScholes, 1, 1)
gridInput_BlackScholes.addWidget(volatilityLabel_BlackScholes, 2, 0)
gridInput_BlackScholes.addWidget(self.volatilityInput_BlackScholes, 2, 1)
#2. Heston Model
gridInput_Heston.addWidget(strikePriceLabel_Heston, 0, 0)
gridInput_Heston.addWidget(self.strikePriceInput_Heston, 0, 1)
gridInput_Heston.addWidget(timeToMaturityLabel_Heston, 1, 0)
gridInput_Heston.addWidget(self.timeToMaturityInput_Heston, 1, 1)
gridInput_Heston.addWidget(meanInversionLabel_Heston, 2, 0)
gridInput_Heston.addWidget(self.meanInversionInput_Heston, 2, 1)
gridInput_Heston.addWidget(longRunVarianceLabel_Heston, 3, 0)
gridInput_Heston.addWidget(self.longRunVarianceInput_Heston, 3, 1)
gridInput_Heston.addWidget(currentVarianceLabel_Heston, 4, 0)
gridInput_Heston.addWidget(self.currentVarianceInput_Heston, 4, 1)
gridInput_Heston.addWidget(correlationLabel_Heston, 5, 0)
gridInput_Heston.addWidget(self.correlationInput_Heston, 5, 1)
gridInput_Heston.addWidget(volatilityOfVolatilityLabel_Heston, 6, 0)
gridInput_Heston.addWidget(self.volatilityOfVolatilityInput_Heston, 6, 1)
#3. Merton Jump-Diffusion Model
gridInput_Merton.addWidget(strikePriceLabel_Merton, 0, 0)
gridInput_Merton.addWidget(self.strikePriceInput_Merton, 0, 1)
gridInput_Merton.addWidget(timeToMaturityLabel_Merton, 1, 0)
gridInput_Merton.addWidget(self.timeToMaturityInput_Merton, 1, 1)
gridInput_Merton.addWidget(volatilityLabel_Merton, 2, 0)
gridInput_Merton.addWidget(self.volatilityInput_Merton, 2, 1)
gridInput_Merton.addWidget(expectedNumberOfJumpsLabel_Merton, 3, 0)
gridInput_Merton.addWidget(self.expectedNumberOfJumpsInput_Merton, 3, 1)
gridInput_Merton.addWidget(proportionOfTotalVolatilityLabel_Merton, 4, 0)
gridInput_Merton.addWidget(self.proportionOfTotalVolatilityInput_Merton, 4, 1)
#4. Optimal Trend Following Model
gridInput_TrendFollowing.addWidget(expectedLengthOfBullMarketLabel_TrendFollowing, 0, 0)
gridInput_TrendFollowing.addWidget(self.expectedLengthOfBullMarketInput_TrendFollowing, 0, 1)
gridInput_TrendFollowing.addWidget(expectedLengthOfBearMarketLabel_TrendFollowing, 1, 0)
gridInput_TrendFollowing.addWidget(self.expectedLengthOfBearMarketInput_TrendFollowing, 1, 1)
gridInput_TrendFollowing.addWidget(expectedReturnRateBullLabel_TrendFollowing, 2, 0)
gridInput_TrendFollowing.addWidget(self.expectedReturnRateBullInput_TrendFollowing, 2, 1)
gridInput_TrendFollowing.addWidget(expectedReturnRateBearLabel_TrendFollowing, 3, 0)
gridInput_TrendFollowing.addWidget(self.expectedReturnRateBearInput_TrendFollowing, 3, 1)
gridInput_TrendFollowing.addWidget(volatilityLabel_TrendFollowing, 4, 0)
gridInput_TrendFollowing.addWidget(self.volatilityInput_TrendFollowing, 4, 1)
gridInput_TrendFollowing.addWidget(ratioOfSlippageBuyLabel_TrendFollowing, 5, 0)
gridInput_TrendFollowing.addWidget(self.ratioOfSlippageBuyInput_TrendFollowing, 5, 1)
gridInput_TrendFollowing.addWidget(ratioOfSlippageSellLabel_TrendFollowing, 6, 0)
gridInput_TrendFollowing.addWidget(self.ratioOfSlippageSellInput_TrendFollowing, 6, 1)
gridInput_TrendFollowing.addWidget(interestedTimeIntervalLabel, 7, 0)
gridInput_TrendFollowing.addWidget(self.interestedTimeIntervalInput_TrendFollowing, 7, 1)
#Final combination
gridInput.addWidget(modelLabel, 0, 0)
gridInput.addWidget(self.model, 0, 1)
gridInput.addWidget(stackedInput, 1, 0, 1, 2)
gridInput.addWidget(resetDefaultButton, 2, 0)
gridInput.addWidget(computeButton, 2, 1)
#Output Result
gridOutput.addWidget(callOptionPriceLabel, 0, 0)
gridOutput.addWidget(self.callOptionPriceOutput, 0, 1)
gridOutput.addWidget(putOptionPriceLabel, 1, 0)
gridOutput.addWidget(self.putOptionPriceOutput, 1, 1)
leftVBox = QVBoxLayout()
rightVBox = QVBoxLayout()
leftVBox.addWidget(groupBoxBackgroundInformation)
leftVBox.addWidget(groupBoxRetrievedData)
leftVBox.addWidget(self.matplotlibWidget)
leftVBox.addStretch(1)
rightVBox.addWidget(groupBoxInput)
rightVBox.addWidget(groupBoxOutput)
rightVBox.addStretch(1)
mainHBox = QHBoxLayout()
mainHBox.addLayout(leftVBox)
mainHBox.addLayout(rightVBox)
mainHBox.setStretch(0,500)
mainHBox.setStretch(1,500)
self.setLayout(mainHBox)
self.setGeometry(300, 300, 1000, 450)
self.setWindowTitle('Option Pricing')
self.show()
def setDefault(self):
if (self.retrieved):
#1. Black Scholes
self.strikePriceInput_BlackScholes.setText(str(self.stockPrice))
self.timeToMaturityInput_BlackScholes.setText('1.00')
self.volatilityInput_BlackScholes.setText(str(self.historicalVolatility))
#2. Heston
self.strikePriceInput_Heston.setText(str(self.stockPrice))
self.timeToMaturityInput_Heston.setText('1.00')
self.meanInversionInput_Heston.setText('0.02')
self.longRunVarianceInput_Heston.setText('1.00')
self.currentVarianceInput_Heston.setText(str(self.historicalVolatility))
self.correlationInput_Heston.setText('0.00')
self.volatilityOfVolatilityInput_Heston.setText('1.00')
#3. Merton Jump Diffusion
self.strikePriceInput_Merton.setText(str(self.stockPrice))
self.timeToMaturityInput_Merton.setText('1.00')
self.volatilityInput_Merton.setText(str(self.historicalVolatility))
self.expectedNumberOfJumpsInput_Merton.setText('1')
self.proportionOfTotalVolatilityInput_Merton.setText('0.00')
#4. Optimal Trend Following
self.expectedLengthOfBullMarketInput_TrendFollowing.setText(str(1.0/0.36))
self.expectedLengthOfBearMarketInput_TrendFollowing.setText(str(1.0/2.53))
self.expectedReturnRateBullInput_TrendFollowing.setText('0.18')
self.expectedReturnRateBearInput_TrendFollowing.setText('-0.77')
self.ratioOfSlippageBuyInput_TrendFollowing.setText('0.001')
self.ratioOfSlippageSellInput_TrendFollowing.setText('0.001')
self.volatilityInput_TrendFollowing.setText(str(self.historicalVolatility))
self.interestedTimeIntervalInput_TrendFollowing.setText('10')
else:
self.errorMessageDialog.showMessage('<b>Please Retrieve Data First!!!</b>')
def retrieve(self):
if (self.retrieving == False):
self.retrieving = True
ticker = self.tickerInput.text()
if (ticker != ''):
url = "https://finance.yahoo.com/q?s="+ticker
[self.stockPrice, self.dividendRate] = getStockPrice(url)
if (self.stockPrice > -1e-6):
self.stockPriceOutput.setText(str(self.stockPrice))
self.dividendRateOutput.setText(str(self.dividendRate*100)+"%")
url = "http://www.google.com/finance/historical?q=" + ticker + "&output=csv"
url = generateURL(ticker, 20)
[self.historicalPrice, self.timeStamp] = getHistoricalPrice(url)
historicalReturn = []
for i in range(len(self.historicalPrice) - 1):
historicalReturn.append(np.log(self.historicalPrice[i+1]/self.historicalPrice[i]))
#print len(self.historicalPrice)
#Change volatility from daily to yearly
self.historicalVolatility = np.std(historicalReturn) * np.sqrt(250)
self.historicalVolatilityOutput.setText(str(self.historicalVolatility))
else:
self.errorMessageDialog.showMessage('<b>Invalid ticker!!!</b>')
else:
self.errorMessageDialog.showMessage('<b>Invalid ticker!!!</b>')
self.retrieving = False
self.retrieved = True
self.matplotlibWidget.axis.cla()
list_of_dates = [datetime.strptime(x, "%Y-%m-%d") for x in self.timeStamp]
self.dates = matplotlib.dates.date2num(list_of_dates)
self.matplotlibWidget.axis.plot_date(self.dates, self.historicalPrice, '-')
self.matplotlibWidget.canvas.draw()
self.setDefault()
def compute(self):
if (self.retrieved):
currentModel = self.model.currentText()
if (currentModel == 'Black Scholes'):
strikePrice = float(self.strikePriceInput_BlackScholes.text())
timeToMaturity = float(self.timeToMaturityInput_BlackScholes.text())
volatility = float(self.volatilityInput_BlackScholes.text())
[callOptionPrice, putOptionPrice] = BlackScholes(self.stockPrice, strikePrice,
timeToMaturity, volatility, self.interestRate, self.dividendRate)
elif currentModel == 'Heston':
strikePrice = float(self.strikePriceInput_Heston.text())
timeToMaturity = float(self.timeToMaturityInput_Heston.text())
meanInversion = float(self.meanInversionInput_Heston.text())
longRunVariance = float(self.longRunVarianceInput_Heston.text())
currentVariance = float(self.currentVarianceInput_Heston.text())
correlation = float(self.correlationInput_Heston.text())
volatilityOfVolatility = float(self.volatilityOfVolatilityInput_Heston.text())
[callOptionPrice, putOptionPrice] = HestonQuad_q(meanInversion, longRunVariance,
volatilityOfVolatility, correlation, currentVariance, self.interestRate,
timeToMaturity, self.stockPrice, strikePrice, self.dividendRate)
elif currentModel == 'Merton Jump-Diffusion':
strikePrice = float(self.strikePriceInput_Merton.text())
timeToMaturity = float(self.timeToMaturityInput_Merton.text())
volatility = float(self.volatilityInput_Merton.text())
expectedNumberOfJumps = int(self.expectedNumberOfJumpsInput_Merton.text())
proportionOfTotalVolatility = float(self.proportionOfTotalVolatilityInput_Merton.text())
if expectedNumberOfJumps > 0:
[callOptionPrice, putOptionPrice] = MertonJumpDiffusion(self.stockPrice, strikePrice,
timeToMaturity, volatility, self.interestRate, self.dividendRate,
expectedNumberOfJumps, proportionOfTotalVolatility)
else:
self.errorMessageDialog.showMessage('<b>Please Enter a Positive Expected Number Of Jumps</b>')
elif currentModel == 'Optimal Trend Following':
volatility = float(self.volatilityInput_TrendFollowing.text())
expectedLengthOfBullMarket = float(self.expectedLengthOfBullMarketInput_TrendFollowing.text())
expectedLengthOfBearMarket = float(self.expectedLengthOfBearMarketInput_TrendFollowing.text())
switchingIntensityFromBullToBear = 1.0/expectedLengthOfBullMarket
switchingIntensityFromBearToBull = 1.0/expectedLengthOfBearMarket
expectedReturnRateBull = float(self.expectedReturnRateBullInput_TrendFollowing.text())
expectedReturnRateBear = float(self.expectedReturnRateBearInput_TrendFollowing.text())
ratioOfSlippageBuy = float(self.ratioOfSlippageBuyInput_TrendFollowing.text())
ratioOfSlippageSell = float(self.ratioOfSlippageSellInput_TrendFollowing.text())
interestedTimeInterval = int(self.interestedTimeIntervalInput_TrendFollowing.text())
if ratioOfSlippageBuy > 1 or ratioOfSlippageBuy < 0:
self.errorMessageDialog.showMessage('<b>Please Enter a Valid Slippage Ratio with a Buy Order</b>')
elif ratioOfSlippageSell > 1 or ratioOfSlippageSell < 0:
self.errorMessageDialog.showMessage('<b>Please Enter a Valid Slippage Ratio with a Sell Order</b>')
elif expectedReturnRateBull < 0:
self.errorMessageDialog.showMessage('<b>Please Enter a Positive Expected Return Rate in Bull Market</b>')
elif expectedReturnRateBear > 0:
self.errorMessageDialog.showMessage('<b>Please Enter a Negative Expected Return Rate in Bear Market</b>')
else:
ticker = self.tickerInput.text()
url = generateURL(ticker, interestedTimeInterval)
historicalPrice, timeStamp = getHistoricalPrice(url)
# print url
# print len(historicalPrice)
list_of_dates = [datetime.strptime(x, "%Y-%m-%d") for x in timeStamp]
dates = matplotlib.dates.date2num(list_of_dates)
#def OptimalTrendFollowing(mu1, mu2, rho, Kb, Ks, sigma, lamda1, lamda2, s, timeStamp, T):
[buylist, selllist, valueOfPortfolio] = OptimalTrendFollowing(expectedReturnRateBull, expectedReturnRateBear,
self.interestRate, ratioOfSlippageBuy, ratioOfSlippageSell, volatility, switchingIntensityFromBullToBear,
switchingIntensityFromBearToBull, historicalPrice, timeStamp)
buytime = [dates[i] for i in buylist]
selltime = [dates[i] for i in selllist]
#print buytime
#print selltime
buyprice = [historicalPrice[i] for i in buylist]
sellprice = [historicalPrice[i] for i in selllist]
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(111)
maxPrice = max(historicalPrice)
seg = maxPrice/15.0
ax.plot_date(dates, historicalPrice,'-',color = 'c',label = "Historical Price")
ax.plot_date(dates, valueOfPortfolio, '-', color ='m',label = "Portfolio Value")
ax.vlines(buytime, buyprice, [x+seg for x in buyprice] , color = 'r', linestyles = 'dashed', label = 'Buy')
ax.vlines(selltime, [x-seg for x in sellprice], sellprice, color = 'g', linestyles = 'dashed', label = 'Sell')
ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d"))
plt.legend(bbox_to_anchor=(0.85, 0.95), loc=2, borderaxespad=0.)
plt.show()
callOptionPrice = "N/A"
putOptionPrice = "N/A"
self.callOptionPriceOutput.setText(str(callOptionPrice))
self.putOptionPriceOutput.setText(str(putOptionPrice))
else:
self.errorMessageDialog.showMessage('<b>Please Retrieve Data First!!!</b>')
def generateURL(ticker, T):
TD = date.today()
return "http://real-chart.finance.yahoo.com/table.csv?s=" + ticker + "&a=" + str(TD.month-1) + "&b=" + str(TD.day) + "&c=" + str(TD.year-T) + "&d=" + str(TD.month-1) + "&e=" + str(TD.day) + "&f=" + str(TD.year) + "&ignore=.csv"
def getStockPrice(url):
response = urllib2.urlopen(url)
if (response.geturl()!=url):
return [-1,-1]
content = response.read()
soup = BeautifulSoup(content).find("span", {"class" : "time_rtq_ticker"})
stockPrice = float(soup.string.replace(",",""))
soup = BeautifulSoup(content).find_all("tr", {"class" : "end"})
dividendRate = 0.0
if (len(soup) == 1):
soup = soup[0]
dividendRateString = soup.td.string
if (dividendRateString != "N/A (N/A) "):
temp = dividendRateString.split(" ")
temp = temp[1].split("(")
temp = temp[1].split("%")
dividendRate = float(temp[0])/100
return [stockPrice, dividendRate]
def getHistoricalPrice(url):
urllib.urlretrieve (url, "data.csv")
csvfile = file('data.csv','rb')
reader = csv.reader(csvfile)
counter = 0
closePrice = []
timeStamp = []
for line in reader:
counter = counter + 1
if (counter > 1):
closePrice.append(float(line[4]))
timeStamp.append(line[0])
closePrice = closePrice[::-1]
timeStamp = timeStamp[::-1]
csvfile.close()
os.remove("data.csv")
return [closePrice, timeStamp]
def getInterestRate():
url = "https://ycharts.com/indicators/10_year_treasury_rate"
response = urllib2.urlopen(url)
content = response.read()
soup = BeautifulSoup(content).find("div", {"id" : "pgNameVal"})
temp = soup.string.split("%")
return float(temp[0])/100
def BlackScholes(S, X, T, sigma, r, q):
d1 = (np.log(S/X)+(r-q+0.5*sigma*sigma)*T)/(sigma*np.sqrt(T))
d2 = d1-sigma*np.sqrt(T)
call = S*np.exp(-q*T)*norm.cdf(d1)-X*np.exp(-r*T)*norm.cdf(d2)
put = X*np.exp(-r*T)*norm.cdf(-d2)-S*np.exp(-q*T)*norm.cdf(-d1)
return [call, put]
def HestonQuad_q(kappa,theta,sigma,rho,v0,r,T,s0,K,q):
if T==0:
call = max(s0-K,0)
else:
call = s0*np.exp(-q*T)*HestonP(kappa,theta,sigma,rho,v0,r,T,s0,K,1,q) - K*np.exp(-r*T)*HestonP(kappa,theta,sigma,rho,v0,r,T,s0,K,2,q)
put = call - s0*np.exp(-q*T) + K*np.exp(-r*T)
return [call, put]
def HestonP(kappa, theta, sigma, rho, v0, r, T, s0, K, Type, q):
r = r-q
ans, err = quad(HestonPIntegrand,0,100,args=(kappa,theta,sigma,rho,v0,r,T,s0,K,Type,q))
return 0.5 + 1/np.pi*ans
def HestonPIntegrand(phi, kappa ,theta, sigma, rho, v0, r, T, s0, K, Type, q):
r = r-q
result = cm.exp(-1j*phi*cm.log(K)).real*Hestf(phi,kappa,theta,sigma,rho,v0,r,T,s0,Type,q)/(1j*phi)
return result.real
def Hestf(phi, kappa, theta, sigma, rho, v0, r, T, s0, Type, q):
r = r-q
if (Type == 1):
u = 0.5
b = kappa - rho*sigma
else:
u = -0.5
b = kappa
a = kappa*theta
x = cm.log(s0)
d = cm.sqrt((rho*sigma*phi*1j-b)**2-sigma**2*(2*u*phi*1j-phi**2))
g = (b-rho*sigma*phi*1j + d)/(b-rho*sigma*phi*1j - d)
C = r*phi*1j*T + a/sigma**2*((b- rho*sigma*phi*1j + d)*T - 2*cm.log((1-g*cm.exp(d*T))/(1-g)))
D = (b-rho*sigma*phi*1j + d)/sigma**2*((1-cm.exp(d*T))/(1-g*cm.exp(d*T)))
return cm.exp(C + D*v0 + 1j*phi*x)
def MertonJumpDiffusion(S, X, T, sigma, r, q, lamda, gamma):
factor = 1.0
power = 1.0
call = 0.0
put = 0.0
for i in range(0, 100):
coefficient = power/factor*np.exp(-lamda*T)
#print coefficient
delta2 = gamma * sigma**2 / lamda
sigma_i = np.sqrt(sigma**2 - lamda*delta2 + delta2*(i/T))
[europeanCall, europeanPut] = BlackScholes(S, X, T, sigma_i, r, q)
call = call + coefficient * europeanCall
put = put + coefficient * europeanPut
factor = factor * (i+1)
power = power * lamda * T
return [call, put]
def OptimalTrendFollowing(mu1, mu2, rho, Kb, Ks, sigma, lambda1, lambda2, s, timeStamp):
[pb, ps] = solveHJB(mu1, mu2, sigma, lambda1, lambda2, Kb, Ks, rho, 2)
# print "pb=", pb
# print "ps=", ps
p0 = (ps+pb)/2
#p0 = (rho-mu2+sigma**2/2)/(mu1-mu2)
N = len(s)
p = np.zeros(N)
p[0] = p0
hs = np.zeros(N)
hs[0] = int(1)
nbuy = 0
nsell = 0
#Assume we are holding one stock at t=0
M = 0.0
bstock = 1.0/(1+Kb)
dt = 1.0/250 # a different dt from the one in solveHJB
for j in range(1,N):
f = -(lambda1+lambda2)*p[j-1]+lambda2-(mu1-mu2)*p[j-1]*(1-p[j-1])*((mu1-mu2)*p[j-1]+mu2-sigma**2/2)/(sigma**2)
p[j]= min(max(p[j-1]+f*dt+(mu1-mu2)*p[j-1]*(1-p[j-1])*np.log(s[j]/s[j-1])/(sigma**2),0),1)
#print p
buytime = []
selltime = []
# print s
# hs = 1 -> Holding Stock
# hs = 0 -> Holding Cash
buylist = []
selllist = []
valueOfPortfolio = []
valueOfPortfolio.append(M)
for j in range(1,N):
# print hs[j-1]
# print bstock
if hs[j-1] == 0:
M = M*np.exp(rho*dt)
if hs[j-1] == 1 and p[j]<=ps:
nsell = nsell+1
M = s[j]*(1-Ks)*bstock
# print "Sell: " + str(M)
# print " Today: " + str(s[j])
# print " Yesterday: " + str(s[j-1])
bstock = 0
hs[j] = 0
selltime.append(timeStamp[j])
selllist.append(j)
elif hs[j-1] == 0 and p[j]>=pb:
nbuy = nbuy+1
bstock = M*1.0/s[j]/(1+Kb)
M = 0
# print "Buy: " + str(bstock)
hs[j] = 1
buytime.append(timeStamp[j])
buylist.append(j)
else:
hs[j] = hs[j-1]
# print "Keep: " + str(M + bstock*s[j])
valueOfPortfolio.append(M + bstock*s[j])
# print valueOfPortfolio
# print hs
# print buytime
# print selltime
if hs[N-1] is 1:
nsell = nsell+1
M = s[N-1]*(1-Ks)*bstock
hs[N-1] = 0
ntrade = nbuy + nsell
return [buylist, selllist, valueOfPortfolio]
def solveHJB(mu1, mu2, sigma, lambda1, lambda2, Kb, Ks, rho, T):
Ny = 1000
Nt = 200
beta = 1e7
tol = 1e-8
psell = []
pbuy = []
h = 1.0/Ny
dt = T*1.0/Nt
y = np.array([h*i for i in range(Ny+1)])
v = np.array([np.log(1-Ks) for i in range(Ny+1)])
vnew = [1 for i in range(Ny+1)]
c2 = 0.5*np.array([((mu1-mu2)*x*(1-x)/sigma)**2 for x in y])/h/h
c1 = (-(lambda1+lambda2)*y+lambda2)/h
c0 = 0
f0= (mu1-mu2)*y+(mu2-rho-sigma**2/2)
left = -c2+c1*(c1<0)
middle = 1/dt+2*c2+np.abs(c1)-c0;
right = -c2-c1*(c1>0)
for t in range(int(T/dt)):
counter = 0
vn = v
while True:
counter = counter+1
Indi1 = beta*(v-(np.log(1+Kb))>0);
Indi2 = beta*((np.log(1-Ks))-v>0);
b = vn/dt+(np.log(1+Kb))*Indi1+(np.log(1-Ks))*Indi2+f0;
A = scipy.sparse.spdiags(np.array([right, middle+Indi1+Indi2, left]),[-1,0,1],Ny+1,Ny+1).T
b[0] = A[0,0]*np.log(1-Ks)
A[0,1] = 0
b[Ny] = A[Ny,Ny]*np.log(1+Kb)
A[Ny,Ny-1]=0
vnew = dsolve.spsolve(A,b,use_umfpack=False)
if (np.linalg.norm(vnew-v)*1.0/np.linalg.norm(v))<tol:
break
v = vnew
uu = vnew
temp = np.where(uu-(np.log(1+Kb))>=0)[0]
pbuy.append(y[temp[0]])
temp = np.where(uu-(np.log(1-Ks))<=0)[0]
psell.append(y[temp[len(temp)-1]])
return [pbuy[Nt-1], psell[Nt-1]]
def main():
app = QApplication(sys.argv)
ex = GUI()
sys.exit(app.exec_())
if __name__ == '__main__':
main()