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ROI simulator for binary options strategies

One backtest is an anecdote. This runs your strategy hundreds of times so you see the whole distribution of outcomes, not the one lucky path.

ROI Simulator

300 simulated runs of your exact strategy — not one lucky chart.

Starting balance
$
Trades per day
Win rate
%
Payout
%
Risk per trade
%
Days
Typical outcome
$1,202.26
+140.5% ROI
Chance of profit
96.7%
of 300 runs
Chance of blow-up
0%
Account to zero
Avg max drawdown
28.1%
Worst dip per run
Outcome range
Median Lucky / unlucky 10%
$2,231
$292
Day 0Day 30
Where you could land
Worst run
$353.5
Unlucky (10%)
$616.64
Lucky (90%)
$2,097.19
Best run
$3,658.28
Positive edge — but respect the range
At 85% payout you need 54.1% wins to break even. Notice how far apart the best and worst runs are with identical settings. That gap is variance — it is why risking 3% per trade matters more than picking a magic strategy.

What the roi simulator does

The mistake in most strategy evaluation is running a single sequence, seeing a profit, and calling the strategy good. With randomness involved, a losing strategy shows a profit on a decent share of runs, and a winning strategy shows a loss on plenty of them.

The simulator above runs many independent sessions using your win rate, payout, stake rule and trade count, then shows you the spread: the median result, the good runs, the bad runs, and how often the account ends underwater.

What you want to see is a median that is comfortably positive and a worst-decile that you could psychologically survive. If the worst 10% of runs would make you quit, the plan is too aggressive regardless of the average.

The maths

The formula, explained

Formula

EV per trade = (WinRate × Payout × Stake) − ((1 − WinRate) × Stake)

WinRate
your realistic hit rate as a decimal
Payout
broker payout as a decimal, e.g. 0.85
Stake
amount risked per trade
ROI
total profit ÷ starting balance, over the simulated trade count

Positive EV is necessary but not sufficient — variance decides whether you survive long enough to collect it. That is what the distribution in the simulator is for.

Worked example

A real set of numbers

300 simulated runs of 100 trades, $500 start, $10 flat stake, 85% payout:
Win rateEV per tradeMedian ROIWorst 10% of runsRuns ending in profit
50%−$0.75−15%−32%about 18%
54%−$0.010%−20%about 50%
57%+$0.55+11%−8%about 79%
62%+$1.47+29%+6%about 96%
68%+$2.58+52%+25%over 99%

Below roughly 54% at an 85% payout, nothing else you do matters. Above 60%, even the unlucky runs stay near break-even. The whole game is moving that first column.

Judgement

When to use it — and when not to

Use it when

  • You are evaluating a new strategy or signal source before committing real money.
  • You want to know how bad a normal bad month looks.
  • You are choosing between a flat stake and a percentage stake plan.
  • You need to sanity-check a claimed ROI from a signal seller.

Don't rely on it when

  • You feed it an aspirational win rate. Garbage in, confident garbage out.
  • Your strategy has correlated trades (several positions on the same pair at once) — the model assumes independence.
FAQ

ROI simulator questions, answered

+What win rate do I need to be profitable on binary options?

At an 85% payout, breakeven is about 54.1%. At 80% it is 55.6%; at 92% it is 52.1%. Anything below the breakeven number produces a negative expected value no matter how you stake.

+Why does the simulator show losses at a winning win rate?

Because variance is real. Even a 60% strategy loses money over 100 trades a meaningful share of the time. Longer horizons shrink that share.

+Is Monte Carlo better than backtesting?

They answer different questions. A backtest tells you what happened once; Monte Carlo tells you what range of things could plausibly happen. Use the backtest to estimate the win rate, then simulate.

+How many trades before I know a strategy works?

Rarely fewer than 200, and 500 is safer. Below 100 trades the confidence interval on your win rate is so wide it barely constrains anything.

Put the numbers to work

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