primo commit
This commit is contained in:
fredmaloggia
560
pricer/payoffs.py
Normal file
560
pricer/payoffs.py
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import math
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from dataclasses import dataclass
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from typing import List
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import numpy as np
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class PayoffEvaluator:
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def evaluate(self, path: List[List[float]], context: "PayoffContext") -> float:
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raise NotImplementedError
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@dataclass
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class PayoffContext:
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days_to_maturity: int
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r: float
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capital_value: float
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prot_min_val: float
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pdi_barrier: float
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pdi_style: str
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coupon_in_memory: float
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days_to_obs_y_fract: List[float]
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coupon_triggers: List[float]
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coupon_values: List[float]
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autocall_triggers: List[float]
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autocall_values: List[float]
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memory_flags: List[int]
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caso: str
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pdi_strike: float
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fattore_airbag: float
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trigger_one_star: float
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cap: float
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days_to_obs: List[int]
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volatility: List[float]
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airbag: int
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sigma: int
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relief: int
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twin_win: int
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one_star: int
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leva: float
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nominal_amount: float
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prices_ul: List[float]
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class PayoffFactory:
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@staticmethod
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def get_evaluator(caso: str) -> PayoffEvaluator:
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mapping = {
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"Standard": PayoffStandard(),
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"Airbag": PayoffAirbag(),
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"Sigma": PayoffSigma(),
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"Relief": PayoffRelief(),
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"TwinWin": PayoffTwinWin(),
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"OneStar": PayoffOneStar(),
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"Airbag + OneStar": PayoffAirbagOneStar(),
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"Sigma + OneStar": PayoffSigmaOneStar(),
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"Relief + OneStar": PayoffReliefOneStar(),
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"TwinWin + OneStar": PayoffTwinWinOneStar(),
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"Airbag + TwinWin": PayoffAirbagTwinWin(),
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}
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if caso not in mapping:
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raise ValueError(f"Payoff '{caso}' non gestito.")
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return mapping[caso]
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def _row_min(path, index: int) -> float:
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if isinstance(path, np.ndarray):
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return float(path[index].min())
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return min(path[index])
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def _row_max(path, index: int) -> float:
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if isinstance(path, np.ndarray):
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return float(path[index].max())
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return max(path[index])
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def _overall_min(path) -> float:
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if isinstance(path, np.ndarray):
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return float(path.min())
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return _overall_min(path)
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class PayoffStandard(PayoffEvaluator):
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def __init__(self):
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self.last_label = ""
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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payoff = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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payoff = (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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self.last_label = (
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f"Autocall ? Trigger={c.autocall_triggers[k]:.2f}, Min={min_val:.2f} ? Payoff={payoff:.2f}"
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)
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return payoff
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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barrier_check = min_mat if c.pdi_style == "European" else min_total
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if barrier_check >= c.pdi_barrier:
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payoff = (c.capital_value + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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self.last_label = (
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f"RIMBORSO standard ? minMat={min_mat:.4f}, minTotal={min_total:.4f} ? Payoff={payoff:.2f}"
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)
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else:
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payoff = max(c.prot_min_val, min_mat / 100.0) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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self.last_label = (
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f"PERDITA standard ? minMat={min_mat:.4f}, minTotal={min_total:.4f} ? Payoff={payoff:.2f}"
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)
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return payoff
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self.last_label = "UNKNOWN"
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return 0.0
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class PayoffAirbag(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_barriera:
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payoff = c.capital_value + memory
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else:
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payoff = max(c.prot_min_val, (min_mat * c.fattore_airbag / 100.0))
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return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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return 0.0
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class PayoffTwinWin(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_strike = min_mat >= c.pdi_strike and c.autocall_triggers[k] == 999
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_strike:
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payoff = memory + min(min_mat / 100.0, c.cap)
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elif sopra_barriera:
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payoff = (200.0 - min_mat) * c.capital_value / 100.0 + memory
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else:
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payoff = max(c.prot_min_val, min_mat / 100.0) + memory
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return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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return 0.0
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class PayoffOneStar(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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max_mat = _row_max(path, c.days_to_obs[k])
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_trigger = max_mat >= c.trigger_one_star
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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capitale = c.capital_value if (sopra_trigger or sopra_barriera) else max(c.prot_min_val, min_mat / 100.0)
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cedola_finale = 0.0
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if min_mat >= c.coupon_triggers[k]:
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cedola_finale = memory + c.coupon_values[k]
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return (
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capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ coupons
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)
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return 0.0
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class PayoffSigma(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_barriera:
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payoff = c.capital_value + memory
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else:
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payoff = max(c.prot_min_val, (min_mat + c.pdi_strike - c.pdi_barrier) / 100.0)
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return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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return 0.0
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class PayoffRelief(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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prices_at_mat = path[c.days_to_maturity]
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sorted_prices = sorted(prices_at_mat)
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second_min = sorted_prices[1] if len(sorted_prices) >= 2 else sorted_prices[0]
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min_mat = min(prices_at_mat)
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min_total = _overall_min(path)
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_barriera:
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payoff = c.capital_value + memory
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else:
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payoff = max(c.prot_min_val, second_min / 100.0)
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return payoff * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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return 0.0
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class PayoffAirbagOneStar(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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max_mat = _row_max(path, c.days_to_obs[k])
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_trigger = max_mat >= c.trigger_one_star
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_trigger or sopra_barriera:
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capitale = c.capital_value
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else:
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perc = max(c.prot_min_val, min_mat / 100.0) * c.fattore_airbag
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capitale = min(c.capital_value, perc)
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cedola_finale = 0.0
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if min_mat >= c.coupon_triggers[k]:
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cedola_finale = memory + c.coupon_values[k]
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return (
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capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ coupons
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)
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return 0.0
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class PayoffSigmaOneStar(PayoffEvaluator):
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def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
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memory = c.coupon_in_memory
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coupons = 0.0
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for k in range(len(c.days_to_obs)):
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min_val = _row_min(path, c.days_to_obs[k])
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if min_val >= c.autocall_triggers[k]:
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return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
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if min_val >= c.coupon_triggers[k]:
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coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
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memory = 0.0
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else:
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memory += c.memory_flags[k] * c.coupon_values[k]
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if k == len(c.days_to_obs) - 1:
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max_mat = _row_max(path, c.days_to_obs[k])
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min_mat = _row_min(path, c.days_to_maturity)
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min_total = _overall_min(path)
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sopra_trigger = max_mat >= c.trigger_one_star
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sopra_barriera = (
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(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
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or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
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)
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if sopra_trigger or sopra_barriera:
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capitale = c.capital_value
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else:
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capitale = max(c.prot_min_val, (min_mat + c.pdi_strike - c.pdi_barrier) / 100.0)
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cedola_finale = 0.0
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if min_mat >= c.coupon_triggers[k]:
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cedola_finale = memory + c.coupon_values[k]
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return (
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capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
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+ coupons
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)
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||||
|
||||
return 0.0
|
||||
|
||||
|
||||
class PayoffReliefOneStar(PayoffEvaluator):
|
||||
def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
|
||||
memory = c.coupon_in_memory
|
||||
coupons = 0.0
|
||||
|
||||
for k in range(len(c.days_to_obs)):
|
||||
min_val = _row_min(path, c.days_to_obs[k])
|
||||
|
||||
if min_val >= c.autocall_triggers[k]:
|
||||
return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
|
||||
|
||||
if min_val >= c.coupon_triggers[k]:
|
||||
coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
memory = 0.0
|
||||
else:
|
||||
memory += c.memory_flags[k] * c.coupon_values[k]
|
||||
|
||||
if k == len(c.days_to_obs) - 1:
|
||||
prices_at_mat = path[c.days_to_maturity]
|
||||
sorted_prices = sorted(prices_at_mat)
|
||||
second_min = sorted_prices[1] if len(sorted_prices) >= 2 else sorted_prices[0]
|
||||
min_mat = min(prices_at_mat)
|
||||
max_mat = _row_max(path, c.days_to_obs[k])
|
||||
min_total = _overall_min(path)
|
||||
|
||||
sopra_trigger = max_mat >= c.trigger_one_star
|
||||
sopra_barriera = (
|
||||
(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
|
||||
or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
|
||||
)
|
||||
|
||||
if sopra_trigger or sopra_barriera:
|
||||
capitale = c.capital_value
|
||||
else:
|
||||
capitale = max(c.prot_min_val, second_min / 100.0)
|
||||
|
||||
cedola_finale = 0.0
|
||||
if min_mat >= c.coupon_triggers[k]:
|
||||
cedola_finale = memory + c.coupon_values[k]
|
||||
|
||||
return (
|
||||
capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
+ cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
+ coupons
|
||||
)
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
class PayoffTwinWinOneStar(PayoffEvaluator):
|
||||
def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
|
||||
memory = c.coupon_in_memory
|
||||
coupons = 0.0
|
||||
|
||||
for k in range(len(c.days_to_obs)):
|
||||
min_val = _row_min(path, c.days_to_obs[k])
|
||||
|
||||
if min_val >= c.autocall_triggers[k]:
|
||||
return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
|
||||
|
||||
if min_val >= c.coupon_triggers[k]:
|
||||
coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
memory = 0.0
|
||||
else:
|
||||
memory += c.memory_flags[k] * c.coupon_values[k]
|
||||
|
||||
if k == len(c.days_to_obs) - 1:
|
||||
max_mat = _row_max(path, c.days_to_obs[k])
|
||||
min_mat = _row_min(path, c.days_to_maturity)
|
||||
min_total = _overall_min(path)
|
||||
|
||||
sopra_strike = min_mat >= c.pdi_strike
|
||||
sopra_barriera = (
|
||||
(c.pdi_style == "European" and min_mat >= c.pdi_barrier)
|
||||
or (c.pdi_style == "American" and min_total >= c.pdi_barrier)
|
||||
)
|
||||
|
||||
if sopra_strike:
|
||||
capitale = min(min_mat / 100.0, c.cap)
|
||||
elif sopra_barriera:
|
||||
capitale = (2 * c.capital_value) - (min_mat / 100.0)
|
||||
else:
|
||||
if max_mat >= c.trigger_one_star:
|
||||
capitale = c.capital_value
|
||||
else:
|
||||
capitale = max(c.prot_min_val, min_mat / 100.0)
|
||||
|
||||
cedola_finale = 0.0
|
||||
if min_mat >= c.coupon_triggers[k]:
|
||||
cedola_finale = memory + c.coupon_values[k]
|
||||
|
||||
return (
|
||||
capitale * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
+ cedola_finale * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
+ coupons
|
||||
)
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
class PayoffAirbagTwinWin(PayoffEvaluator):
|
||||
def evaluate(self, path: List[List[float]], c: PayoffContext) -> float:
|
||||
memory = c.coupon_in_memory
|
||||
coupons = 0.0
|
||||
|
||||
for k in range(len(c.days_to_obs)):
|
||||
min_val = _row_min(path, c.days_to_obs[k])
|
||||
|
||||
if min_val >= c.autocall_triggers[k]:
|
||||
return (c.autocall_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k]) + coupons
|
||||
|
||||
if min_val >= c.coupon_triggers[k]:
|
||||
coupons += (c.coupon_values[k] + memory) * math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
memory = 0.0
|
||||
else:
|
||||
memory += c.memory_flags[k] * c.coupon_values[k]
|
||||
|
||||
if k == len(c.days_to_obs) - 1:
|
||||
min_mat = _row_min(path, c.days_to_maturity)
|
||||
|
||||
if min_mat >= c.pdi_barrier:
|
||||
if c.autocall_triggers[k] == 999 and min_mat >= c.pdi_strike:
|
||||
return (
|
||||
math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
* (memory + min(min_mat / 100.0, c.cap))
|
||||
+ coupons
|
||||
)
|
||||
return (
|
||||
math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
* ((2 * c.capital_value) + memory - (min_mat / 100.0))
|
||||
+ coupons
|
||||
)
|
||||
|
||||
return (
|
||||
max(c.prot_min_val, min_mat / 100.0)
|
||||
* math.exp(-c.r * c.days_to_obs_y_fract[k])
|
||||
* c.fattore_airbag
|
||||
+ coupons
|
||||
)
|
||||
|
||||
return 0.0
|
||||
|
||||
Reference in New Issue
Block a user