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How to use barrier method in autotest
Best Python code snippet using autotest_python
equity_barrier_option.py
Source:equity_barrier_option.py 
1###############################################################################2# Copyright (C) 2018, 2019, 2020 Dominic O'Kane3###############################################################################45import numpy as np6from enum import Enum78from ...utils.error import FinError9from ...utils.global_vars import gDaysInYear10from ...products.equity.equity_option import EquityOption11from ...models.process_simulator import FinProcessSimulator12from ...market.curves.discount_curve import DiscountCurve13from ...utils.helpers import label_to_string, check_argument_types14from ...utils.date import Date151617from ...utils.math import N1819# TODO: SOME REDESIGN ON THE MONTE CARLO PROCESS IS PROBABLY NEEDED2021###############################################################################222324class EquityBarrierTypes(Enum):25 DOWN_AND_OUT_CALL = 126 DOWN_AND_IN_CALL = 227 UP_AND_OUT_CALL = 328 UP_AND_IN_CALL = 429 UP_AND_OUT_PUT = 530 UP_AND_IN_PUT = 631 DOWN_AND_OUT_PUT = 732 DOWN_AND_IN_PUT = 83334###############################################################################353637class EquityBarrierOption(EquityOption):38 """ Class to hold details of an Equity Barrier Option. It also39 calculates the option price using Black Scholes for 8 different40 variants on the Barrier structure in enum EquityBarrierTypes. """4142 def __init__(self,43 expiry_date: Date,44 strike_price: float,45 option_type: EquityBarrierTypes,46 barrier_level: float,47 num_observations_per_year: (int, float) = 252,48 notional: float = 1.0):49 """ Create the EquityBarrierOption by specifying the expiry date,50 strike price, option type, barrier level, the number of observations51 per year and the notional. """5253 check_argument_types(self.__init__, locals())5455 self._expiry_date = expiry_date56 self._strike_price = float(strike_price)57 self._barrier_level = float(barrier_level)58 self._num_observations_per_year = int(num_observations_per_year)5960 if option_type not in EquityBarrierTypes:61 raise FinError("Option Type " + str(option_type) + " unknown.")6263 self._option_type = option_type64 self._notional = notional6566###############################################################################6768 def value(self,69 valuation_date: Date,70 stock_price: (float, np.ndarray),71 discount_curve: DiscountCurve,72 dividend_curve: DiscountCurve,73 model):74 """ This prices an Equity Barrier option using the formulae given in75 the paper by Clewlow, Llanos and Strickland December 1994 which can be76 found at7778 https://warwick.ac.uk/fac/soc/wbs/subjects/finance/research/wpaperseries/1994/94-54.pdf79 """8081 if isinstance(valuation_date, Date) == False:82 raise FinError("Valuation date is not a Date")8384 if valuation_date > self._expiry_date:85 raise FinError("Valuation date after expiry date.")8687 if discount_curve._valuation_date != valuation_date:88 raise FinError(89 "Discount Curve valuation date not same as option valuation date")9091 if dividend_curve._valuation_date != valuation_date:92 raise FinError(93 "Dividend Curve valuation date not same as option valuation date")9495 if isinstance(stock_price, int):96 stock_price = float(stock_price)9798 if isinstance(stock_price, float):99 stock_prices = [stock_price]100 else:101 stock_prices = stock_price102103 values = []104 for s in stock_prices:105 v = self._value_one(valuation_date, s, discount_curve,106 dividend_curve, model)107 values.append(v)108109 if isinstance(stock_price, float):110 return values[0]111 else:112 return np.array(values)113114###############################################################################115116 def _value_one(self,117 valuation_date: Date,118 stock_price: (float, np.ndarray),119 discount_curve: DiscountCurve,120 dividend_curve: DiscountCurve,121 model):122 """ This values a single option. Because of its structure it cannot123 easily be vectorised which is why it has been wrapped. """124125 texp = (self._expiry_date - valuation_date) / gDaysInYear126127 if texp < 0:128 raise FinError("Option expires before value date.")129130 texp = max(texp, 1e-6)131132 lnS0k = np.log(stock_price / self._strike_price)133 sqrtT = np.sqrt(texp)134135 r = discount_curve.cc_rate(self._expiry_date)136 q = dividend_curve.cc_rate(self._expiry_date)137138 k = self._strike_price139 s = stock_price140 h = self._barrier_level141142 volatility = model._volatility143 sigmaRootT = volatility * sqrtT144 v2 = volatility * volatility145 mu = r - q146 d1 = (lnS0k + (mu + v2 / 2.0) * texp) / sigmaRootT147 d2 = (lnS0k + (mu - v2 / 2.0) * texp) / sigmaRootT148 df = np.exp(-r * texp)149 dq = np.exp(-q * texp)150151 c = s * dq * N(d1) - k * df * N(d2)152 p = k * df * N(-d2) - s * dq * N(-d1)153# print("CALL:",c,"PUT:",p)154155 if self._option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL and s <= h:156 return 0.0157 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_CALL and s >= h:158 return 0.0159 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_PUT and s >= h:160 return 0.0161 elif self._option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT and s <= h:162 return 0.0163 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_CALL and s <= h:164 return c165 elif self._option_type == EquityBarrierTypes.UP_AND_IN_CALL and s >= h:166 return c167 elif self._option_type == EquityBarrierTypes.UP_AND_IN_PUT and s >= h:168 return p169 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_PUT and s <= h:170 return p171172 num_observations = 1 + texp * self._num_observations_per_year173174 # Correction by Broadie, Glasserman and Kou, Mathematical Finance, 1997175 # Adjusts the barrier for discrete and not continuous observations176 h_adj = h177 t = texp / num_observations178179 if self._option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL:180 h_adj = h * np.exp(-0.5826 * volatility * np.sqrt(t))181 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_CALL:182 h_adj = h * np.exp(-0.5826 * volatility * np.sqrt(t))183 elif self._option_type == EquityBarrierTypes.UP_AND_IN_CALL:184 h_adj = h * np.exp(0.5826 * volatility * np.sqrt(t))185 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_CALL:186 h_adj = h * np.exp(0.5826 * volatility * np.sqrt(t))187 elif self._option_type == EquityBarrierTypes.UP_AND_IN_PUT:188 h_adj = h * np.exp(0.5826 * volatility * np.sqrt(t))189 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_PUT:190 h_adj = h * np.exp(0.5826 * volatility * np.sqrt(t))191 elif self._option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT:192 h_adj = h * np.exp(-0.5826 * volatility * np.sqrt(t))193 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_PUT:194 h_adj = h * np.exp(-0.5826 * volatility * np.sqrt(t))195 else:196 raise FinError("Unknown barrier option type." +197 str(self._option_type))198199 h = h_adj200201 if abs(volatility) < 1e-5:202 volatility = 1e-5203204 l = (mu + v2 / 2.0) / v2205 y = np.log(h * h / (s * k)) / sigmaRootT + l * sigmaRootT206 x1 = np.log(s / h) / sigmaRootT + l * sigmaRootT207 y1 = np.log(h / s) / sigmaRootT + l * sigmaRootT208 hOverS = h / s209210 if self._option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL:211 if h >= k:212 c_do = s * dq * N(x1) - k * df * N(x1 - sigmaRootT) \213 - s * dq * pow(hOverS, 2.0 * l) * N(y1) \214 + k * df * pow(hOverS, 2.0 * l - 2.0) * N(y1 - sigmaRootT)215 price = c_do216 else:217 c_di = s * dq * pow(hOverS, 2.0 * l) * N(y) \218 - k * df * pow(hOverS, 2.0 * l - 2.0) * N(y - sigmaRootT)219 price = c - c_di220 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_CALL:221 if h <= k:222 c_di = s * dq * pow(hOverS, 2.0 * l) * N(y) \223 - k * df * pow(hOverS, 2.0 * l - 2.0) * N(y - sigmaRootT)224 price = c_di225 else:226 c_do = s * dq * N(x1) \227 - k * df * N(x1 - sigmaRootT) \228 - s * dq * pow(hOverS, 2.0 * l) * N(y1) \229 + k * df * pow(hOverS, 2.0 * l - 2.0) * N(y1 - sigmaRootT)230 price = c - c_do231 elif self._option_type == EquityBarrierTypes.UP_AND_IN_CALL:232 if h >= k:233 c_ui = s * dq * N(x1) - k * df * N(x1 - sigmaRootT) \234 - s * dq * pow(hOverS, 2.0 * l) * (N(-y) - N(-y1)) \235 + k * df * pow(hOverS, 2.0 * l - 2.0) * \236 (N(-y + sigmaRootT) - N(-y1 + sigmaRootT))237 price = c_ui238 else:239 price = c240 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_CALL:241 if h > k:242 c_ui = s * dq * N(x1) - k * df * N(x1 - sigmaRootT) \243 - s * dq * pow(hOverS, 2.0 * l) * (N(-y) - N(-y1)) \244 + k * df * pow(hOverS, 2.0 * l - 2.0) * \245 (N(-y + sigmaRootT) - N(-y1 + sigmaRootT))246 price = c - c_ui247 else:248 price = 0.0249 elif self._option_type == EquityBarrierTypes.UP_AND_IN_PUT:250 if h > k:251 p_ui = -s * dq * pow(hOverS, 2.0 * l) * N(-y) \252 + k * df * pow(hOverS, 2.0 * l - 2.0) * N(-y + sigmaRootT)253 price = p_ui254 else:255 p_uo = -s * dq * N(-x1) \256 + k * df * N(-x1 + sigmaRootT) \257 + s * dq * pow(hOverS, 2.0 * l) * N(-y1) \258 - k * df * pow(hOverS, 2.0 * l - 2.0) * \259 N(-y1 + sigmaRootT)260 price = p - p_uo261 elif self._option_type == EquityBarrierTypes.UP_AND_OUT_PUT:262 if h >= k:263 p_ui = -s * dq * pow(hOverS, 2.0 * l) * N(-y) \264 + k * df * pow(hOverS, 2.0 * l - 2.0) * N(-y + sigmaRootT)265 price = p - p_ui266 else:267 p_uo = -s * dq * N(-x1) \268 + k * df * N(-x1 + sigmaRootT) \269 + s * dq * pow(hOverS, 2.0 * l) * N(-y1) \270 - k * df * pow(hOverS, 2.0 * l - 2.0) * \271 N(-y1 + sigmaRootT)272 price = p_uo273 elif self._option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT:274 if h >= k:275 price = 0.0276 else:277 p_di = -s * dq * N(-x1) \278 + k * df * N(-x1 + sigmaRootT) \279 + s * dq * pow(hOverS, 2.0 * l) * (N(y) - N(y1)) \280 - k * df * pow(hOverS, 2.0 * l - 2.0) * \281 (N(y - sigmaRootT) - N(y1 - sigmaRootT))282 price = p - p_di283 elif self._option_type == EquityBarrierTypes.DOWN_AND_IN_PUT:284 if h >= k:285 price = p286 else:287 p_di = -s * dq * N(-x1) \288 + k * df * N(-x1 + sigmaRootT) \289 + s * dq * pow(hOverS, 2.0 * l) * (N(y) - N(y1)) \290 - k * df * pow(hOverS, 2.0 * l - 2.0) * \291 (N(y - sigmaRootT) - N(y1 - sigmaRootT))292 price = p_di293 else:294 raise FinError("Unknown barrier option type." +295 str(self._option_type))296297 v = price * self._notional298 return v299300###############################################################################301302 def value_mc(self,303 valuation_date: Date,304 stock_price: float,305 discount_curve: DiscountCurve,306 dividend_curve: DiscountCurve,307 process_type,308 model_params,309 numAnnObs: int = 252,310 num_paths: int = 10000,311 seed: int = 4242):312 """ A Monte-Carlo based valuation of the barrier option which simulates313 the evolution of the stock price of at a specified number of annual314 observation times until expiry to examine if the barrier has been315 crossed and the corresponding value of the final payoff, if any. It316 assumes a GBM model for the stock price. """317318 texp = (self._expiry_date - valuation_date) / gDaysInYear319 num_time_steps = int(texp * numAnnObs)320 K = self._strike_price321 B = self._barrier_level322 option_type = self._option_type323324 process = FinProcessSimulator()325326 r = discount_curve.zero_rate(self._expiry_date)327328 # TODO - NEED TO DECIDE IF THIS IS PART OF MODEL PARAMS OR NOT ??????????????329330 r = discount_curve.cc_rate(self._expiry_date)331 q = dividend_curve.cc_rate(self._expiry_date)332333 #######################################################################334335 if option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL and stock_price <= B:336 return 0.0337 elif option_type == EquityBarrierTypes.UP_AND_OUT_CALL and stock_price >= B:338 return 0.0339 elif option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT and stock_price <= B:340 return 0.0341 elif option_type == EquityBarrierTypes.UP_AND_OUT_PUT and stock_price >= B:342 return 0.0343344 #######################################################################345346 simple_call = False347 simple_put = False348349 if option_type == EquityBarrierTypes.DOWN_AND_IN_CALL and stock_price <= B:350 simple_call = True351 elif option_type == EquityBarrierTypes.UP_AND_IN_CALL and stock_price >= B:352 simple_call = True353 elif option_type == EquityBarrierTypes.UP_AND_IN_PUT and stock_price >= B:354 simple_put = True355 elif option_type == EquityBarrierTypes.DOWN_AND_IN_PUT and stock_price <= B:356 simple_put = True357358 if simple_put or simple_call:359 Sall = process.get_process(360 process_type, texp, model_params, 1, num_paths, seed)361362 if simple_call:363 c = (np.maximum(Sall[:, -1] - K, 0.0)).mean()364 c = c * np.exp(-r * texp)365 return c366367 if simple_put:368 p = (np.maximum(K - Sall[:, -1], 0.0)).mean()369 p = p * np.exp(-r * texp)370 return p371372 # Get full set of paths373 Sall = process.get_process(process_type, texp, model_params, num_time_steps,374 num_paths, seed)375376 (num_paths, num_time_steps) = Sall.shape377378 if option_type == EquityBarrierTypes.DOWN_AND_IN_CALL or \379 option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL or \380 option_type == EquityBarrierTypes.DOWN_AND_IN_PUT or \381 option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT:382383 barrierCrossedFromAbove = [False] * num_paths384385 for p in range(0, num_paths):386 barrierCrossedFromAbove[p] = np.any(Sall[p] <= B)387388 if option_type == EquityBarrierTypes.UP_AND_IN_CALL or \389 option_type == EquityBarrierTypes.UP_AND_OUT_CALL or \390 option_type == EquityBarrierTypes.UP_AND_IN_PUT or \391 option_type == EquityBarrierTypes.UP_AND_OUT_PUT:392393 barrierCrossedFromBelow = [False] * num_paths394 for p in range(0, num_paths):395 barrierCrossedFromBelow[p] = np.any(Sall[p] >= B)396397 payoff = np.zeros(num_paths)398 ones = np.ones(num_paths)399400 if option_type == EquityBarrierTypes.DOWN_AND_OUT_CALL:401 payoff = np.maximum(Sall[:, -1] - K, 0.0) * \402 (ones - barrierCrossedFromAbove)403 elif option_type == EquityBarrierTypes.DOWN_AND_IN_CALL:404 payoff = np.maximum(Sall[:, -1] - K, 0.0) * barrierCrossedFromAbove405 elif option_type == EquityBarrierTypes.UP_AND_IN_CALL:406 payoff = np.maximum(Sall[:, -1] - K, 0.0) * barrierCrossedFromBelow407 elif option_type == EquityBarrierTypes.UP_AND_OUT_CALL:408 payoff = np.maximum(Sall[:, -1] - K, 0.0) * \409 (ones - barrierCrossedFromBelow)410 elif option_type == EquityBarrierTypes.UP_AND_IN_PUT:411 payoff = np.maximum(K - Sall[:, -1], 0.0) * barrierCrossedFromBelow412 elif option_type == EquityBarrierTypes.UP_AND_OUT_PUT:413 payoff = np.maximum(K - Sall[:, -1], 0.0) * \414 (ones - barrierCrossedFromBelow)415 elif option_type == EquityBarrierTypes.DOWN_AND_OUT_PUT:416 payoff = np.maximum(K - Sall[:, -1], 0.0) * \417 (ones - barrierCrossedFromAbove)418 elif option_type == EquityBarrierTypes.DOWN_AND_IN_PUT:419 payoff = np.maximum(K - Sall[:, -1], 0.0) * barrierCrossedFromAbove420 else:421 raise FinError("Unknown barrier option type." +422 str(self._option_type))423424 v = payoff.mean() * np.exp(- r * texp)425426 return v * self._notional427428###############################################################################429430 def __repr__(self):431 s = label_to_string("OBJECT TYPE", type(self).__name__)432 s += label_to_string("EXPIRY DATE", self._expiry_date)433 s += label_to_string("STRIKE PRICE", self._strike_price)434 s += label_to_string("OPTION TYPE", self._option_type)435 s += label_to_string("BARRIER LEVEL", self._barrier_level)436 s += label_to_string("NUM OBSERVATIONS",437 self._num_observations_per_year)438 s += label_to_string("NOTIONAL", self._notional, "")439 return s440441###############################################################################442443 def _print(self):444 """ Simple print function for backward compatibility. """445 print(self)446
...
barrier.py
Source:barrier.py
1import re2from grblogtools.parsers.util import typeconvert_groupdict3class BarrierParser:4 # The pattern indicating the initialization of the parser5 barrier_start_pattern = re.compile(6 r"Iter(\s+)Primal(\s+)Dual(\s+)Primal(\s+)Dual(\s+)Compl(\s+)Time"7 )8 barrier_ordering_pattern = re.compile(r"Ordering time: (?P<OrderingTime>[\d\.]+)s")9 # The pattern indicating the barrier progress10 barrier_progress_pattern = re.compile(11 r"\s*(?P<Iteration>\d+)(?P<Indicator>\s|\*)\s+(?P<PObj>[^\s]+)\s+(?P<DObj>[^\s]+)\s+(?P<PRes>[^\s]+)\s+(?P<DRes>[^\s]+)\s+(?P<Compl>[^\s]+)\s+(?P<Time>\d+)s"12 )13 # The pattern indicating the crossover14 barrier_crossover_pattern = re.compile(15 r"\s*Push phase complete: Pinf (?P<PushPhasePInf>[^\s]+), Dinf (?P<PushPhaseDInf>[^\s]+)\s+(?P<PushPhaseEndTime>\d+)s"16 )17 # The pattern indicating the termination of the barrier algorithm18 barrier_termination_patterns = [19 re.compile(20 r"Barrier solved model in (?P<BarIterCount>[^\s]+) iterations and (?P<Runtime>[^\s]+) seconds"21 ),22 re.compile(23 r"Barrier performed (?P<BarIterCount>\d+) iterations in (?P<Runtime>[^\s]+) seconds"24 ),25 ]26 def __init__(self):27 """Initialize the Barrier parser."""28 self._summary = {}29 self._progress = []30 self._started = False31 def parse(self, line: str) -> bool:32 """Parse the given log line to populate summary and progress data.33 Args:34 line (str): A line in the log file.35 Returns:36 bool: Return True if the given line is matched by some pattern.37 """38 barrier_ordering_match = BarrierParser.barrier_ordering_pattern.match(line)39 if barrier_ordering_match:40 self._summary.update(typeconvert_groupdict(barrier_ordering_match))41 return True42 if not self._started:43 match = BarrierParser.barrier_start_pattern.match(line)44 if match:45 self._started = True46 return True47 return False48 progress_match = BarrierParser.barrier_progress_pattern.match(line)49 if progress_match:50 entry = {"Type": "barrier"}51 entry.update(typeconvert_groupdict(progress_match))52 self._progress.append(entry)53 return True54 for barrier_termination_pattern in BarrierParser.barrier_termination_patterns:55 barrier_termination_match = barrier_termination_pattern.match(line)56 if barrier_termination_match:57 self._summary.update(typeconvert_groupdict(barrier_termination_match))58 return True59 crossover_match = BarrierParser.barrier_crossover_pattern.match(line)60 if crossover_match:61 self._summary.update(typeconvert_groupdict(crossover_match))62 return True63 return False64 def get_summary(self) -> dict:65 """Return the current parsed summary."""66 return self._summary67 def get_progress(self) -> list:68 """Return the detailed progress in the barrier method."""...
test_barrier.py
Source:test_barrier.py
1from unittest import TestCase, main2from grblogtools.parsers.barrier import BarrierParser3from grblogtools.parsers.util import parse_block4example_log_barrier = """5Iter Primal Dual Primal Dual Compl Time6 0 4.56435085e+07 1.53061018e+04 1.69e+05 8.58e+00 1.59e+03 2s7 1 3.76722276e+07 -5.41297282e+05 8.07e+04 9.12e+00 8.17e+02 2s8 17 2.17403572e+02 2.17403571e+02 3.93e-14 7.11e-15 7.71e-13 5s9Barrier solved model in 17 iterations and 4.83 seconds (6.45 work units)10Optimal objective 2.17403572e+0211Crossover log...12 57249 DPushes remaining with DInf 0.0000000e+00 8s13 0 DPushes remaining with DInf 0.0000000e+00 9s14 9342 PPushes remaining with PInf 1.2118572e-05 9s15 0 PPushes remaining with PInf 0.0000000e+00 9s16 Push phase complete: Pinf 0.0000000e+00, Dinf 1.8540725e-14 9s17"""18expected_summary_barrier = {19 "BarIterCount": 17,20 "Runtime": 4.83,21 "PushPhasePInf": 0.0000000e00,22 "PushPhaseDInf": 1.8540725e-14,23 "PushPhaseEndTime": 9,24}25expected_progress_barrier = [26 {27 "Type": "barrier",28 "Iteration": 0,29 "Indicator": " ",30 "PObj": 45643508.5,31 "DObj": 15306.1018,32 "PRes": 169000.0,33 "DRes": 8.58,34 "Compl": 1590.0,35 "Time": 2,36 },37 {38 "Type": "barrier",39 "Iteration": 1,40 "Indicator": " ",41 "PObj": 37672227.6,42 "DObj": -541297.282,43 "PRes": 80700.0,44 "DRes": 9.12,45 "Compl": 817.0,46 "Time": 2,47 },48 {49 "Type": "barrier",50 "Iteration": 17,51 "Indicator": " ",52 "PObj": 217.403572,53 "DObj": 217.403571,54 "PRes": 3.93e-14,55 "DRes": 7.11e-15,56 "Compl": 7.71e-13,57 "Time": 5,58 },59]60class TestBarrier(TestCase):61 def setUp(self):62 self.barrier_parser = BarrierParser()63 def test_get_summary_progress(self):64 parse_block(self.barrier_parser, example_log_barrier)65 self.assertEqual(self.barrier_parser.get_summary(), expected_summary_barrier)66 self.assertEqual(self.barrier_parser.get_progress(), expected_progress_barrier)67if __name__ == "__main__":...
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