Investment Portfolio Simulation

import random

# Initial portfolio distribution
initial_value = 100.0
equity = initial_value / 3
cash = initial_value / 3
property = initial_value / 3

# Number of trials and steps
num_trials = 40
num_steps = 30

# Lists to store results
values_list = []
low_count = 0

for trial in range(num_trials):  # 40 trials
    for step in range(num_steps):  # 30 steps per trial
        equity *= 1.13 * (random.randint(1, 200) / 100)  # Introducing randomness
        cash *= 0.95
        property *= 1.064
        
        value = equity + cash + property
        
        # Rebalancing
        equity = cash = property = value / 3
        
        print(f"Step {step + 1}: Total Value = {value:.2f}, Cash = {cash:.2f}, Equity = {equity:.2f}, Property = {property:.2f}")
    
    if value < 50:  # Tracking trials that more than halve in value
        low_count += 1
    
    values_list.append(value)
    
    # Resetting global starting points for the next trial
    equity = cash = property = initial_value / 3

# Results
mean_value = sum(values_list) / num_trials
range_value = max(values_list) - min(values_list)

print(f"Values List: {values_list}")
print(f"Number of trials with value below 50: {low_count}")
print(f"Mean Value: {mean_value:.2f}")
print(f"Range: {range_value:.2f}")
print(f"Max Value: {max(values_list):.2f}")
print(f"Min Value: {min(values_list):.2f}")