Monte Carlo simulation is a powerful technique for assessing the probability of passing a prop firm evaluation. Let's build one in Python.
The Problem
Before starting a prop firm evaluation, you want to know: "Given my win rate and average risk-reward, what are my chances of hitting the profit target without exceeding the drawdown limit?"
Building the Simulation
import numpy as np
import matplotlib.pyplot as plt
def monte_carlo_prop_sim(
initial_balance=50000,
profit_target=3000,
max_drawdown=2500,
win_rate=0.55,
avg_win=200,
avg_loss=150,
num_trades=100,
num_simulations=10000
):
results = {'pass': 0, 'fail_drawdown': 0, 'fail_target': 0}
for _ in range(num_simulations):
balance = initial_balance
peak = balance
for _ in range(num_trades):
if np.random.random() < win_rate:
balance += avg_win
else:
balance -= avg_loss
peak = max(peak, balance)
drawdown = peak - balance
if drawdown >= max_drawdown:
results['fail_drawdown'] += 1
break
if balance >= initial_balance + profit_target:
results['pass'] += 1
break
else:
results['fail_target'] += 1
return {k: v/num_simulations*100 for k, v in results.items()}
Interpreting Results
With typical prop firm parameters:
- Win rate: 55%
- Average win: $200
- Average loss: $150
- The simulation shows approximately 60-70% pass probability
Optimizing Your Approach
Key insights from Monte Carlo analysis:
- Win rate matters less than you think - A 50% win rate with 2:1 R:R beats 70% with 1:1
- Position sizing is crucial - Smaller positions = higher pass probability
- Drawdown type matters - EOD trailing gives more room than intraday
Finding the Right Firm
Different prop firms have different rules that affect your Monte Carlo outcomes. PropFirmKey lets you compare drawdown rules, profit targets, and other parameters across all major futures prop firms.
Use their comparison tool to find firms with rules that match your trading statistics.
Top comments (0)