Phase 1 target
+10%
Close positions once the target is reached
FTMO 2-Step · Decision Dashboard
A practical answer to one question: which risk allocation gives this strategy the best chance of passing, without taking unnecessary risk?
Historical replay: €80,000 account · 2020-04-01 → 2026-07-31
Rule set
Phase 1 target
+10%
Close positions once the target is reached
Phase 2 target
+5%
New account; close positions at the target
Maximum loss
10%
Static floor below each phase’s initial capital
Maximum daily loss
5%
Previous daily close less a fixed initial-capital amount
Minimum activity
4 days
Four active days required in each phase
Each sampled date starts a fresh €80,000 account. Positions are treated as closed when a target is reached. Verification then restarts with a new account and a +5% target. Results are the share of completed historical attempts that passed or failed.
Engine A
0.1% risk per trade
Pass rate
100%
Fail rate
0%
252 pass · 0 fail · 98 unresolved of 350 windows
Engine B
0.1% risk per trade
Pass rate
100%
Fail rate
0%
226 pass · 0 fail · 123 unresolved of 349 windows
Engine C
0.5% risk per trade
Pass rate
96%
Fail rate
4%
349 pass · 16 fail · 31 unresolved of 396 windows
Combined (A+B+C)
A 0.1% + B 0.1% + C 0.5% per trade
Pass rate
86%
Fail rate
14%
273 pass · 44 fail · 23 unresolved of 340 windows
Your permitted range: A/B at 0.1–0.2% and C at 0.1–0.5% per trade. Results are ranked by pass probability, then speed. Confirm a preferred allocation with an exact-size backtest before using it live.
Under FTMO 2-Step, A 0.1% · B 0.1% · C 0.1% has the highest historical pass probability among resolved windows (98%). “Balanced” stays within 3 percentage points of that result; “Faster” is the shortest median pass among allocations at or above 90%.
| Allocation | Pass* | Fail* | Median pass | Resolved / total |
|---|---|---|---|---|
| A 0.1% · B 0.1% · C 0.1% · Safest | 98% | 2% | 173d | 283 / 340 |
| A 0.1% · B 0.1% · C 0.2% · Balanced | 96% | 4% | 162d | 292 / 340 |
| A 0.1% · B 0.1% · C 0.3% | 93% | 7% | 141d | 300 / 340 |
| A 0.2% · B 0.1% · C 0.1% · Faster | 91% | 9% | 128d | 287 / 340 |
| A 0.2% · B 0.1% · C 0.2% | 89% | 11% | 118d | 294 / 340 |
| A 0.1% · B 0.2% · C 0.1% | 89% | 11% | 124d | 285 / 340 |
| A 0.1% · B 0.2% · C 0.2% | 88% | 12% | 113d | 294 / 340 |
| A 0.1% · B 0.1% · C 0.4% | 88% | 12% | 120d | 309 / 340 |
| A 0.1% · B 0.2% · C 0.3% | 87% | 13% | 105d | 300 / 340 |
| A 0.1% · B 0.2% · C 0.4% | 86% | 14% | 95d | 310 / 340 |
| A 0.1% · B 0.1% · C 0.5% | 86% | 14% | 107d | 317 / 340 |
| A 0.2% · B 0.1% · C 0.3% | 86% | 14% | 108d | 300 / 340 |
| A 0.2% · B 0.1% · C 0.5% | 85% | 15% | 84d | 318 / 340 |
| A 0.2% · B 0.2% · C 0.1% | 85% | 15% | 91d | 289 / 340 |
| A 0.2% · B 0.2% · C 0.2% | 84% | 16% | 85d | 299 / 340 |
| A 0.2% · B 0.1% · C 0.4% | 84% | 16% | 97d | 309 / 340 |
| A 0.1% · B 0.2% · C 0.5% | 83% | 17% | 85d | 321 / 340 |
| A 0.2% · B 0.2% · C 0.3% | 82% | 18% | 74d | 305 / 340 |
| A 0.2% · B 0.2% · C 0.5% | 80% | 20% | 64d | 322 / 340 |
| A 0.2% · B 0.2% · C 0.4% | 80% | 20% | 69d | 313 / 340 |
* Pass and fail rates use resolved attempts only.
The optimizer's central trade-off, shown directly: faster allocations tend to exchange historical pass probability for shorter time to pass.
Each point is a constant allocation. The dashed line connects allocations not dominated by another allocation on both speed and historical pass probability. Rates use resolved windows only.
These plans size the +10% Challenge and the fresh +5% Verification account separately. They directly test the idea of pursuing the first phase faster, then reducing risk before Verification. Funded-account sizing is deliberately excluded: it has no profit target and needs a separate survival-and-payout study.
| Plan | Challenge | Verification | Pass* | Fail* | Failure phase | Median pass | Expected 2-Step completion† | Resolved / total |
|---|---|---|---|---|---|---|---|---|
| Aggressive throughout | A 0.1% · B 0.1% · C 0.5% | A 0.1% · B 0.1% · C 0.5% | 86% | 14% | 31 challenge · 12 verification | 107d | 138d | 317 / 340 |
| Aggressive → balanced | A 0.1% · B 0.1% · C 0.5% | A 0.1% · B 0.1% · C 0.2% | 88% | 12% | 31 challenge · 6 verification | 124d | 163d | 307 / 340 |
| Aggressive → conservative | A 0.1% · B 0.1% · C 0.5% | A 0.1% · B 0.1% · C 0.1% | 88% | 12% | 31 challenge · 5 verification | 126d | 170d | 290 / 340 |
| Balanced throughout | A 0.1% · B 0.1% · C 0.2% | A 0.1% · B 0.1% · C 0.2% | 96% | 4% | 11 challenge · 0 verification | 162d | 180d | 292 / 340 |
| 0.5% nominal sensitivity check | A 0.1% · B 0.1% · C 0.3% | A 0.1% · B 0.1% · C 0.3% | 93% | 7% | 20 challenge · 2 verification | 141d | 166d | 300 / 340 |
* Pass and fail rates use resolved attempts only. Failure phase counts show where the failed attempt ended. † Expected 2-Step completion models repeated, immediate retries after the observed failure duration, sampling from resolved historical windows; it assumes independent attempts even though the underlying windows overlap. The highlighted row is the proposed aggressive-to-balanced policy, not a recommendation or forecast.
Three deployment philosophies supported by the historical simulations. They are historical comparisons, not forecasts or individual recommendations.
Best for: Growing allocation while reducing late Verification failures.
Trade-off: Takes longer than staying aggressive, but lowers risk once Phase 1 has passed.
Best for: Prioritising faster account acquisition when failed challenges can be replaced immediately.
Trade-off: The fastest route in the immediate-retry model can also expose Verification to the most risk.
Best for: Maximising historical pass probability for each purchased challenge.
Trade-off: It reduces failed attempts at the cost of a slower median completion time.
The primary comparison is the real deployment choice: C at 0.5% per trade versus A 0.1% · B 0.1% · C 0.2% in both phases. It is not a matched-exposure comparison.
Engine C
0.5% risk per trade
Pass rate
96%
Fail rate
4%
Balanced Three-Engine Book
A 0.1% · B 0.1% · C 0.2% · both phases
Pass rate
96%
Fail rate
4%
C alone and the balanced three-engine book both passed in 96% of resolved historical starting windows. The balanced book reached the target sooner (162d versus 215d median), while using a lower 0.4% nominal maximum allocation when all three engines overlap, versus 0.5% for C alone.
For a speed-focused reference, the aggressive three-engine policy (A 0.1% · B 0.1% · C 0.5%) reached a 107d median pass, with a 86% historical pass rate among resolved windows.
Assumptions & Limitations
What is modelled, what is estimated, and how to use the results.
Rules. Phase 1 (challenge) passes at +10%, then Verification restarts at the initial capital and passes at +5%. Both phases enforce a static 10% maximum-loss floor from their initial capital and a 5% maximum daily-loss amount. The first violation before verification passes is a fail. Switch the rules tabs to compare the standard and stricter stress-test scenarios.
Rolling windows. Start dates are sampled every few trading days across the backtest. Each window is rebased to a fresh account at that date. Once Phase 1 passes, the next return sequence is rebased again to a fresh Verification account. Windows that reach the end of the backtest unresolved are counted as incomplete rather than fail.
Source data. The backtests use FTMO's historical backtest data. Its available history begins on 1 April 2020, so the study cannot test earlier market regimes.
Daily drawdown. Daily loss is measured from the previous daily close less a fixed percentage of initial capital. Days are grouped in UTC as a close approximation of the FTMO CE(S)T reset. The report's minimum-equity points are used when available, but timestamp-level account data cannot fully reproduce FTMO’s real-time equity, swap, and commission checks.
Minimum trading days.FTMO requires four days with a position opened in each phase. The source reports do not expose position-open events, so this model uses four distinct days with recorded account activity as a clearly labelled proxy.
Combined book. The combined series reuses the book simulation convention: per-trade return fractions from each engine are merged by timestamp and compounded onto one shared €80,000 account. It answers “what happens when all three run on the same account.”
Interpreting rates. Rolling windows overlap, so they are not independent trials. These rates describe this historical return sequence, not hundreds of unrelated future outcomes.
Advanced Diagnostics
Supporting evidence showing the long-run equity behaviour behind the simulations.
The default view focuses on the combined account, its peak-drawdown context, and daily-loss proxy breaches. Individual engine curves are available when needed.
Historical simulations cannot predict future outcomes, but they can show how risk allocations behaved across the same market history. This study is designed to make allocation decisions more informed and transparent—not to forecast returns.