How PropFlux's FluxEngine Works

When you type your win rate into FluxEngine and click "Analyse My Probability," something interesting happens in the background: your browser runs 10,000 complete simulated evaluation attempts for every prop firm in our database — all within about a second. This post explains exactly how that works, what assumptions we make, and what the results actually mean for your trading decisions.

What Is a Monte Carlo Simulation?

Monte Carlo simulation is a technique for modelling uncertainty by running thousands of randomised scenarios. The name comes from the famous casino district in Monaco — the idea being that randomness and probability are central to the method.

In practice, a Monte Carlo simulation works like this:

Define the rules of your system (your trading parameters + the firm's evaluation rules)
Run a large number of random trials, each following those rules
Observe the aggregate outcomes

The more trials you run, the more accurate your probability estimates become. At 10,000 simulations per firm, our results stabilise — running 100,000 would barely move the numbers.

Note:

Monte Carlo simulation is used everywhere from nuclear physics to financial risk management. Warren Buffett's insurance subsidiary Berkshire Hathaway uses similar probabilistic modelling for catastrophe risk. We're applying the same logic to prop firm evaluations.

How We Model Each Trade

Each simulation starts with a fresh evaluation account at the firm's starting balance (e.g., $50,000). For every trading day, we simulate a fixed number of trades based on your input.

For each individual trade, we generate a random number between 0 and 1:

If that number falls below your win rate (e.g., below 0.50 for a 50% win rate), the trade is a winner
If it falls at or above your win rate, the trade is a loser

Winners earn: account_balance × risk_per_trade% × reward_risk_ratio

Losers lose: account_balance × risk_per_trade%

We risk a percentage of the current account balance (not a fixed dollar amount), which means your position sizes grow as your account grows — a realistic model for professional futures trading.

The daily P&L is the sum of all trades taken that day. We also track the worst intraday point — the lowest cumulative P&L at any moment during the session — because daily drawdown limits are checked against intraday moves, not just closing values.

How We Apply Firm-Specific Rules

This is where FluxEngine does work that a simple expected-value calculation cannot. Each simulation applies the specific rules of each firm:

Profit Target Check

Each day, after updating the balance, we check whether the total profit has reached the firm's profit target (e.g., 6% for Apex on a $50K account = $3,000). If it has, we check whether any additional rules are met before declaring a pass.

Drawdown Rules

Drawdown rules are applied every day, and the implementation differs by firm type:

EOD Trailing Drawdown (most common in futures prop firms): The drawdown floor only updates at the close of each trading day based on the closing balance. Intraday equity peaks — even significant ones — do not move the floor. This is the most trader-friendly structure because a strong intraday gain that you partially give back doesn't permanently raise your minimum balance requirement.

Intraday Trailing Drawdown: The floor updates continuously with every new equity high, even intraday. This is more dangerous — a run-up during the day raises your floor, and a subsequent pullback can hit it even if you close the day in profit.

Static Drawdown: The floor never moves. It's set at starting_balance - max_drawdown_amount and stays there for the entire evaluation. Straightforward but doesn't reward profitable trading with more runway.

Daily Loss Limits

If a firm has a daily loss limit (a separate cap on how much you can lose in a single session), we check this against the worst intraday point of each simulated day. If the worst intraday drawdown exceeds the daily limit, the evaluation fails — even if the day ends within bounds.

Consistency Rules

Some firms (notably Apex) require that no single trading day accounts for more than 30% of your total accumulated profit. Our simulation models this correctly: when a trader reaches the profit target, we check the consistency ratio. If it's not met, the trader must continue trading — they're not failed, but they can't pass yet. Trading continues until either the consistency requirement is satisfied, or a drawdown rule is breached.

This is why the consistency rule is more insidious than it first appears: it forces you to keep trading after you've already hit your target, exposing you to additional drawdown risk.

What the Results Mean

After 10,000 simulations, we aggregate the outcomes:

Pass Rate is simply the percentage of simulations where the trader hit the profit target without breaching any rules. If 6,200 of 10,000 simulations passed, your pass rate is 62%. This is the single most important number.

Average Days to Pass is the mean number of trading days across all successful simulations. If you typically pass in 18 days but occasionally take 45, this average reflects that distribution.

Estimated Cost to Fund is calculated as fee × (100 / pass_rate). This is the expected number of attempts you'll need to multiply by the monthly fee. With a 50% pass rate at $147/month, your expected cost to get funded is $294. With a 25% pass rate, it's $588. This is why pass rate matters more than fee alone.

Risk of Ruin is the percentage of simulations that hit the maximum drawdown and were completely eliminated. A high risk of ruin means your position sizing is too aggressive relative to the firm's drawdown allowance.

Match Score is a composite 0–100 ranking: pass rate (50% weight), cost to fund (25% weight), average days to pass (15% weight), and risk of ruin (10% weight). It's designed to surface the firm that gives your specific trading style the best overall outcome — not just the highest individual pass rate.

Tip:

A firm with an 80% pass rate but a low Match Score usually has high fees or a large average days-to-pass. The Match Score balances all four factors so you see the full picture.

Limitations and What This Calculator Can't Tell You

We believe strongly in being honest about what our tool doesn't do.

It assumes your stated win rate is accurate. If your real win rate is 45% but you enter 55%, the results will be meaningfully wrong. We recommend tracking at least 100 live trades before using those stats.

It doesn't model correlated losing streaks. In real trading, losers sometimes come in clusters (news events, poor market conditions). Our simulation treats each trade as statistically independent. Traders with strategies sensitive to market regimes may experience worse real-world drawdowns than the simulator suggests.

It doesn't model slippage or commission. In live prop firm accounts, execution costs are real. Particularly for scalpers, these can meaningfully erode the expected value we calculate.

It doesn't replace firm-specific due diligence. Rules, fees, and policies change. The data in our system includes // VERIFY comments on figures we haven't personally confirmed. Always check the firm's current website before purchasing an evaluation.

With those caveats in place, FluxEngine provides something that previously didn't exist in this space: a data-driven, firm-specific probability estimate based on your actual trading parameters. Use it as one input into a broader decision, not as a guarantee.