Why the month you start your challenge — and the risk you choose — might matter more than your strategy.
This guide uses real backtested data from a 23-year ORB strategy to show how starting month and risk level interact to determine whether a prop firm challenge passes or fails — even when the underlying edge is identical.
There is a frustrating experience that many systematic traders eventually hit. You have a profitable strategy. You know it works. The backtests are solid, the drawdown is controlled, the profit factor is real. And yet you keep failing your prop firm challenge.
Most traders assume the problem is execution — slippage, emotion, overtrading. Sometimes it is. But there is another explanation that almost nobody discusses, and it has nothing to do with how you trade. It has to do with when you started and how much you risked.
This guide uses real backtested data from one of the strategies documented in The Anatomy of a 5-Minute ORB Trading System — tested across 23 years of trades on OANDA NAS100USD — to show exactly how much both of those variables matter. The results are striking enough that they deserve to be looked at carefully before you fund your next challenge.
Before anything else, one point needs to be stated clearly. Everything in this guide assumes your strategy is genuinely profitable. Not backtest-curve-fitted, not cherry-picked — actually profitable across a meaningful data set with honest reporting of both wins and losses.
If your strategy does not have verified edge, nothing here will help you. Timing and risk sizing are multipliers, not magic. They amplify what is already there, or they expose what was never there in the first place.
The guide that covers how to validate that edge before risking real money is Why Your Best Trading Strategy Might Fail a Prop Firm. Read that first if you are not certain your strategy is genuinely robust.
Assuming you are confident in your edge, here is what actually determines your challenge outcome.
When a trader fails a prop firm challenge, they almost always ask the same question: "What did I do wrong in my trading?"
That is often the wrong question. The right questions are: "How much was I risking per trade?" and "What was the market doing during the specific period I was trading?"
These two variables interact in a way that is not intuitive. The same strategy, risking different amounts per trade, can have dramatically different challenge outcomes depending on when the challenge started — even if the long-run performance of the strategy is identical.
The only way to see this clearly is with real data.
The following analysis uses ORB_NAS_5M, one of the strategies built from scratch and documented in full in the book. It has been run against a standard two-phase challenge structure matching FTMO's published objectives: Phase 1 requires a 10% profit target, Phase 2 requires a 5% profit target, maximum daily loss is 5%, and maximum total drawdown is 10%. There is no time limit on completing either phase.
The simulation runs a $100,000 challenge from every calendar month between January 2023 and December 2025 — 36 different starting points — at four different risk levels: 0.5%, 1.0%, 1.5%, and 2.0% of account per trade.
Each cell in the table below shows one of four outcomes. A funded profit means both phases were passed and the trader was live on a funded account for the remainder of the data — the figure shown is 80% of the funded account profit, reflecting the standard FTMO profit split. A disqualification means the 10% drawdown limit was actually breached during the challenge — the challenge is over and the fee is lost. In progress means the challenge was still running without breach when the data ended in March 2026, which is not a failure, just an incomplete result. A net loss means both phases passed but the funded account ended in a small loss over the remaining trades.
The full interactive version of this table — covering January 2016 through December 2025 at six different risk levels — is available at buildtradingstrategies.com/prop-firm-optimiser (login required).
| Start month | 0.5% · $500/trade | 1.0% · $1,000/trade | 1.5% · $1,500/trade | 2.0% · $2,000/trade | |---|---|---|---|---| | Jan 2023 | +$11,926 | +$35,829 | +$60,938 | +$86,033 | | Feb 2023 | +$9,782 | +$30,245 | +$53,743 | +$74,859 | | Mar 2023 | +$8,586 | +$28,650 | +$47,760 | +$68,458 | | Apr 2023 | +$7,786 | +$25,451 | +$45,367 | +$63,680 | | May 2023 | +$7,786 | +$25,451 | +$45,367 | +$63,680 | | Jun 2023 | +$1,407 | +$15,572 | ❌ Disqualified (Ph1) | ❌ Disqualified (Ph2) | | Jul 2023 | +$2,205 | +$17,171 | ❌ Disqualified (Ph2) | ❌ Disqualified (Ph2) | | Aug 2023 | +$1,407 | +$13,981 | ❌ Disqualified (Ph1) | ❌ Disqualified (Ph1) | | Sep 2023 | +$255 | +$11,596 | +$25,757 | ❌ Disqualified (Ph1) | | Oct 2023 | +$255 | +$13,981 | +$28,152 | ❌ Disqualified (Ph1) | | Nov 2023 | +$255 | +$13,189 | +$25,757 | ❌ Disqualified (Ph1) | | Dec 2023 | +$1,407 | +$15,572 | +$30,542 | ❌ Disqualified (Ph2) | | Jan 2024 | +$1,407 | +$15,572 | +$28,152 | ❌ Disqualified (Ph2) | | Feb 2024 | −$542 | +$11,596 | +$23,358 | ❌ Disqualified (Ph2) | | Mar 2024 | +$255 | +$13,981 | +$25,757 | +$39,130 | | Apr 2024 | +$255 | +$13,981 | +$25,757 | +$39,130 | | May 2024 | −$542 | +$10,005 | +$20,971 | ❌ Disqualified (Ph2) | | Jun 2024 | −$542 | +$11,596 | +$23,358 | ❌ Disqualified (Ph2) | | Jul 2024 | +$255 | +$11,596 | +$24,558 | ❌ Disqualified (Ph2) | | Aug 2024 | −$1,196 | +$9,202 | +$20,971 | ❌ Disqualified (Ph2) | | Sep 2024 | ⏳ In progress (Ph2) | +$4,411 | +$15,008 | ❌ Disqualified (Ph1) | | Oct 2024 | ⏳ In progress (Ph2) | +$6,008 | +$17,394 | +$27,962 | | Nov 2024 | −$1,196 | +$9,202 | +$20,971 | +$32,744 | | Dec 2024 | ⏳ In progress (Ph2) | +$6,008 | +$17,394 | +$27,962 | | Jan 2025 | ⏳ In progress (Ph2) | +$4,411 | +$11,408 | +$20,010 | | Feb 2025 | ⏳ In progress (Ph2) | +$4,411 | +$15,008 | +$23,193 | | Mar 2025 | ⏳ In progress (Ph2) | +$2,815 | +$11,408 | +$18,405 | | Apr 2025 | ⏳ In progress (Ph2) | +$4,411 | +$11,408 | +$20,010 | | May 2025 | ⏳ In progress (Ph2) | +$510 | +$9,012 | +$15,210 | | Jun 2025 | ⏳ In progress (Ph2) | +$510 | +$9,012 | +$15,210 | | Jul 2025 | ⏳ In progress (Ph2) | +$510 | +$9,012 | +$15,210 | | Aug 2025 | ⏳ In progress (Ph2) | +$510 | +$9,012 | +$15,210 | | Sep 2025 | ⏳ In progress (Ph1) | −$2,391 | +$764 | +$5,630 | | Oct 2025 | ⏳ In progress (Ph1) | ⏳ In progress (Ph1) | ⏳ In progress (Ph2) | ⏳ In progress (Ph2) | | Nov 2025 | ⏳ In progress (Ph1) | ⏳ In progress (Ph1) | ⏳ In progress (Ph2) | ⏳ In progress (Ph2) | | Dec 2025 | ⏳ In progress (Ph1) | ⏳ In progress (Ph1) | ⏳ In progress (Ph2) | ⏳ In progress (Ph2) |
Funded profit = 80% share of funded account P&L. All figures USD. Challenge parameters: Phase 1 target 10%, Phase 2 target 5%, max daily loss 5%, max total drawdown 10%, no time limit — consistent with FTMO's standard 2-step challenge objectives. In progress = challenge active with no rule breach when data ended March 2026.
The first thing that jumps out is the 2.0% column. Between June 2023 and September 2024, thirteen of sixteen starting months result in disqualification — the 10% drawdown limit was genuinely breached, the challenge is over, and the fee is lost. The strategy did not change across that period. The underlying trades are identical in every column. The only variable is how much was risked per trade, and at 2.0%, a sequence of losing trades that occurs across those months is large enough to breach the limit before the profit target is reached.
At 1.5%, the same effect appears but is considerably narrower: only the three-month window of June to August 2023 produces disqualifications. From September 2023 onwards, the challenge passes cleanly at 1.5% almost every month. At 1.0%, disqualifications disappear entirely — every starting month produces either a funded profit, a very small funded loss in the data's final months, or an in-progress challenge that had not breached any rule when the data ended. At 0.5%, there are no disqualifications either, but the funded profits are small and several months in 2024–2025 fail to generate enough profit to complete Phase 2 before the data ends.
The practical message is that 0.5% is too conservative for this strategy to generate meaningful returns from a challenge, and 2.0% is too aggressive for it to pass consistently. The viable range sits between those extremes, and the best level within that range depends on your own risk tolerance and how you weight the chance of disqualification against the size of the upside.
The second pattern worth examining is the variance within a single risk level. At 1.0% risk, a trader starting in January 2023 earns $35,829 on their funded account. A trader starting in September 2025 loses $2,391. The strategy is identical. The risk level is identical. What changed is that the January 2023 starter completes both challenge phases quickly and has 172 remaining funded trades to profit from. The September 2025 starter completes both phases but only has 3 remaining funded trades before the data ends in March 2026, and those three fall in a losing period.
Some of this is a data boundary effect — challenges started late in 2025 simply have fewer remaining trades in the dataset before March 2026. But even well within the same calendar year the variance is meaningful. Starting in January 2024 at 1.0% produces $15,572. Starting in August 2024 produces $9,202. Starting in September 2024 produces $4,411. The strategy's underlying annual performance was broadly similar across all of those periods. What differed was how many of the year's profitable trades fell before the challenge phases were completed versus after.
This is the timing effect in its clearest form. The challenge phases themselves consume a portion of the strategy's annual trade output as they build toward the profit targets. The more trades it takes to complete the phases — either because the strategy hits a rough stretch or because the phase targets require more ground to cover at a given risk level — the fewer remain to generate funded profits.
The disqualifications in this dataset do not happen randomly. At 1.5%, they cluster tightly around June to August 2023 — a specific three-month stretch where the strategy experienced a below-average run. At 2.0%, the same stretch causes the same result, but the amplification is strong enough that the disqualification zone extends across a much wider range of starting months either side of it.
This is the direct illustration of the interaction between timing and risk sizing. The underlying market difficulty was the same across all four columns in those months. What differed was whether the scaled losses exceeded the 10% disqualification threshold. A strategy with a Profit Factor above 1.5 and a 23-year track record can still be disqualified in the middle of its normal statistical variation — not because the edge has failed, but because the risk level turned a rough patch into a rule breach.
The Jun–Aug 2023 period at 1.5% and the extended 2023–2024 period at 2.0% are worth understanding before choosing a risk level. They represent what happens when a genuinely profitable strategy encounters a below-average stretch at an elevated risk level. At 1.0%, the same stretch passes without touching the disqualification limit. The edge is still expressed. The challenge completes. The only difference is how much was risked per trade.
The data above is specific to one strategy on one instrument. But the principle applies to any systematic approach.
Every strategy has periods where it underperforms its long-run average. That is not a flaw — it is a statistical property of any edge. A strategy with a 45% win rate will occasionally produce a run of five, six, or seven consecutive losses. Over thousands of trades that is unremarkable. But if that run happens to coincide with the first weeks of a prop firm challenge, and the risk level is elevated, that normal statistical variation becomes a disqualification.
The question worth asking before starting a challenge is not simply "is my strategy profitable?" You should already know the answer to that. The question is: "At the risk level I am planning to use, can my strategy survive its historical worst-case losing sequences without breaching the daily or total drawdown limits?"
If the answer is yes, the challenge outcome depends primarily on timing — and over enough starting points, a genuinely profitable strategy will pass. If the answer is no, the strategy is structurally overleveraged regardless of how good the underlying edge is.
The practical approach is to run this analysis on your own trade data before committing money to a challenge.
Take your complete trade history. Simulate the challenge starting from every month in your dataset at several different risk levels. At each level, count the disqualifications. The risk level at which disqualifications disappear is your ceiling. Anything below that level is structurally safe given your strategy's historical behaviour. Anything above it is a bet that the difficult periods in your strategy's history will not coincide with your challenge window.
That is a legitimate bet to make with clear eyes and data behind it. It is a very different thing to make by instinct or by copying a risk size that works for someone else's strategy on a different instrument.
The full simulation for ORB_NAS_5M, covering January 2016 through December 2025 at six different risk levels, is available at buildtradingstrategies.com/prop-firm-optimiser. It shows exactly what would have happened from any starting month at any risk level across a decade of data. Access is included with the book.
Most guides about passing prop firms focus on entries, exits, and trade management. Those things matter. But they matter downstream of a more fundamental question: is the relationship between your risk level and your strategy's historical volatility actually compatible with the challenge rules?
The table above shows that a strategy with 23 years of profitable data, a Profit Factor above 1.5, and a maximum drawdown under 0.2% can still be disqualified across more than a year of consecutive starting months — not because the strategy stopped working, but because 2.0% risk amplified normal statistical variation into a rule breach.
The solution is not a better strategy. It is the right risk level for the strategy you already have, determined by data rather than instinct.
If you want to understand how ORB_NAS_5M was built, what was tested and discarded along the way, and why each filter was added or removed across twelve versions of the system, that entire process is documented at buildtradingstrategies.com/anatomy-5m-orb-trading-system.
Challenge parameters used in this simulation: Phase 1 target 10%, Phase 2 target 5%, maximum daily drawdown 5%, maximum total drawdown 10%, no time limit — consistent with FTMO's standard 2-step challenge objectives. Funded profits represent an 80% trader share. Data source: OANDA NAS100USD, 5-minute chart, March 2003 – March 2026.