Monte-carlo risk analysis implementation

monte-carlo.py

import random

class Project:
    risks = []

    def add(self, risk):
        self.risks.append(risk)
    
    def run_simulation(self, num_runs=1000):
        result = ProjectResult()
        for i in range(num_runs):
            acc = 0
            for i in self.risks:
                acc += i.calculate_value_from_probability(random.random())
            result.addResult(acc)
        return result
            

class Risk:
    probability = None
    value = None
    
    def __init__(self, prob, val):
        self.probability = prob
        self.value = val
    
    def calculate_value_from_probability(self, probEntered):
        if probEntered < self.probability:
            return 0
        else:
            return self.value

class ProjectResult:
    results = []

    def addResult(self, val):
        self.results.append(val)
    
    def get_probability_enough_contingency(self, contingency):
        amount_enough = 0
        for i in self.results:
            if i <= contingency:
                amount_enough += 1
        print("amount enough = {}, len = {}".format(amount_enough, len(self.results)))
        return (amount_enough/len(self.results))

if __name__ == '__main__':
    project = Project()
    project.add(Risk(0.5, 100))
    project.add(Risk(0.25, 300))
    project.add(Risk(0.3, 200))

    result = project.run_simulation()
    prob =result.get_probability_enough_contingency(500)
    print("There is a {}% chance that £500 is enough".format(round(prob*100)))