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+# author : S. Mandalia
+#          shivesh.mandalia@outlook.com
+#
+# date   : March 19, 2020
+
+"""
+Pricing engine for exotic options.
+"""
+
+import math
+from statistics import mean, stdev
+from dataclasses import dataclass
+from typing import List
+
+from utils.path import PathGenerator
+from utils.payoff import BasePayoff
+
+
+__all__ = ['MCResult', 'PricingEngine']
+
+
+@dataclass
+class MCResult:
+    """
+    Price of option along with its MC error.
+    """
+    price: float
+    stderr: float
+
+
+@dataclass
+class PricingEngine:
+    """
+    Class for generating underlying prices using MC techniques.
+
+    Attributes
+    ----------
+    payoff : Payoff object for calculating the options payoff.
+    path : PathGenerator object for generating the evolution of the underlying.
+
+    Methods
+    ----------
+    price()
+
+    Examples
+    ----------
+    >>> from utils.engine import PricingEngine
+    >>> from utils.path import PathGenerator
+    >>> from utils.payoff import AsianArithmeticPayOff
+    >>> path = PathGenerator(S=100., r=0.1, div=0.01, vol=0.3)
+    >>> payoff = AsianArithmeticPayOff(option_right='Call', K=110)
+    >>> engine = PricingEngine(payoff=payoff, path=path)
+    >>> print(engine.price(T=range(4)))
+    2.1462567745518335
+
+    """
+    payoff: BasePayoff
+    path: PathGenerator
+
+    def price(self, T: List[float], ntrials: int = 1E4,
+              antithetic: bool = True) -> MCResult:
+        """
+        Price the option using MC techniques.
+
+        Parameters
+        ----------
+        T : Set of times {t1, t2, ..., tn} in years.
+        ntrials : Number of trials to simulate.
+        antithetic : Use antithetic variates technique.
+
+        Returns
+        ----------
+        MCResult : Price of the option.
+
+        """
+        if ntrials < len(T):
+            raise AssertionError('Number of trials cannot be less than the '
+                                 'number of setting dates!')
+
+        # Generation start
+        ntrials = int(ntrials // len(T))
+        payoffs = [0] * ntrials
+        for idx in range(ntrials):
+            # Generate a random path
+            if not antithetic:
+                spot_prices = self.path.generate(T)
+            else:
+                prices_tuple = self.path.generate_antithetic(T)
+                spot_prices, a_spot_prices = prices_tuple
+
+            # Calculate the payoff
+            payoff = self.payoff.calculate(spot_prices)
+            if antithetic:
+                a_po = self.payoff.calculate(a_spot_prices)
+                payoff = (payoff + a_po) / 2
+            payoffs[idx] = payoff
+
+        # Discount to current time
+        df = math.exp(-self.path.net_r * (T[-1] - T[0]))
+        dis_payoffs = [x * df for x in payoffs]
+
+        # Payoff expectation and standard error
+        exp_payoff = mean(dis_payoffs)
+        stderr = stdev(dis_payoffs, exp_payoff) / math.sqrt(ntrials)
+
+        return MCResult(exp_payoff, stderr)