Monte Carlo(2).txt

import random

# Parameters
num_simulations = 5000
expected_return = 122.4
threshold = 117
min_threshold = 62.4
intermediate_min = 92.4
max_threshold = 182.4
intermediate_max = 152.4

# Counters for various thresholds
below_min_count = 0
below_threshold_count = 0
above_intermediate_min_count = 0
above_intermediate_max_count = 0
below_max_count = 0

# Perform simulations
for _ in range(num_simulations):
    simulated_return = random.gauss(expected_return, 30)
    
    if simulated_return <= min_threshold:
        below_min_count += 1
    
    if simulated_return >= intermediate_min:
        above_intermediate_min_count += 1
    
    if simulated_return <= threshold:
        below_threshold_count += 1
        
    if simulated_return >= intermediate_max:
        above_intermediate_max_count += 1
    
    if simulated_return <= max_threshold:
        below_max_count += 1

# Calculate probabilities
probability_below_min = below_min_count / num_simulations * 100
probability_above_intermediate_min = above_intermediate_min_count / num_simulations * 100
probability_below_threshold = below_threshold_count / num_simulations * 100
probability_above_intermediate_max = above_intermediate_max_count / num_simulations * 100
probability_below_max = below_max_count / num_simulations * 100

# Print results
print(f"Probability of return below {min_threshold}: {probability_below_min:.2f}%")
print(f"Probability of return above {intermediate_min}: {probability_above_intermediate_min:.2f}%")
print(f"Probability of return below {threshold}: {100 - probability_below_threshold:.2f}%")
print(f"Probability of return above {intermediate_max}: {probability_above_intermediate_max:.2f}%")
print(f"Probability of return below {max_threshold}: {probability_below_max:.2f}%")