How to Pass a Prop Firm Challenge With an Algo Strategy

To pass a prop firm challenge with an algo, test the strategy against the firm's rules, not just for profit: check its worst day against the daily loss limit, its worst peak-to-trough against the maximum or trailing drawdown, its best day's share of profit against any consistency rule, and its longest losing streak against both. Then size each trade so a realistic losing streak cannot breach a limit, and confirm in writing that the firm allows automated trading at all.

Most algos that fail challenges are not losing strategies. They are profitable strategies sized for a personal account, where a bad week is survivable, run under rules where one bad day ends the account. This guide shows how to turn a challenge's rules into numbers you can check in your backtest.

Prop firm rules vary between firms and between account types, and they change often. This guide uses an example rule set, not any firm's actual rules. Always read your firm's current rule page and terms before you start, and re-read them before each new challenge.

Step 1: Check that the firm allows your automation

Do this first, because a passed challenge is worthless if the payout is refused for a rule you never read. In the firm's terms and FAQ, look for:

• Whether expert advisors, bots or API trading are allowed at all, and whether that differs between the evaluation and the funded account.
• Whether third-party or purchased EAs are treated differently from your own code.
• Banned strategy types, often described as high-frequency trading, latency or tick arbitrage, exploiting simulated fills, or hedging across accounts.
• Whether a VPS or a specific platform connection is required or forbidden.
• Whether the same strategy may run on several accounts at once, at the same firm or across firms.

If anything is unclear, ask support and keep the written answer. Wording like "we reserve the right to refuse strategies that do not reflect real market conditions" is common, so a strategy that relies on fills it would never get live is a risk even where bots are allowed. For two common firms, see our dated summaries of FTMO's algo trading rules and Topstep's algo trading rules.

Step 2: Write down the rules as numbers

Turn every rule into a number and a measurement method. Here is an example rule set on a $100,000 account. It is illustrative, not taken from any firm.

The details in the right-hand column decide whether your strategy passes. A daily limit that includes floating losses is much stricter for an EA that holds through drawdowns than one measured on closed trades.

Step 3: Map each rule to a backtest metric

Export your backtest's trade list and calculate these. Your platform's summary will give you some of them; the rest take a spreadsheet.

Two cautions. First, use net P&L after commission, swap and slippage, because the firm's account will charge them. Second, if your backtest's worst day only just passes the limit, it will fail live, since live fills are rarely better than the backtest's.

The trailing drawdown deserves a worked example, because it catches algos that let winners run and give some back. Say your equity rises intraday from $100,000 to $103,000, then the trade reverses and the day closes at $100,500. With a $5,000 drawdown that trails intraday equity, your floor has moved to $98,000 ($103,000 minus $5,000). If it trails end-of-day balance instead, the floor is $95,500 ($100,500 minus $5,000). The same day leaves you with $2,500 of room in the first case and $5,000 in the second. If your firm trails intraday, how far each trade runs in your favour before it closes matters as much as where it closes.

Step 4: Size for the losing streak, not the average

The single biggest decision is risk per trade. Work it out from the streak your strategy will plausibly hit, not from its average.

Say your strategy wins 45% of the time and you expect about 200 trades over the period you will be trading the account. If trades are independent, a quick simulation of that win rate over 200 trades gives a median longest losing streak of 8, and about 1 run in 10 has a streak of 11 or more. A streak of 8 is not bad luck; it is what to expect.

Now apply the example rules ($10,000 maximum loss, $5,000 daily limit):

These are fixed dollar amounts based on the starting balance. Losses can exceed the planned risk when stops slip or price gaps, so leave a margin.

Check the daily limit the same way. If the EA can open up to 4 trades a day, the worst plausible day is 4 losses. At 1% risk that is $4,000, which leaves only $1,000 for slippage, commission and floating losses against a $5,000 limit. At 0.5% it is $2,000. If the EA has no cap on trades per day, add one, or add a daily stop that halts trading well before the limit.

Then check the other side: can you still reach the target? Say your strategy's expectancy is 0.2R per trade after costs. At 0.5% risk, 1R is $500, so the average trade makes $100, and the $10,000 target takes about 100 trades on average. If the EA trades 5 times a week, that is around 20 weeks. If your challenge has a time limit, check that this fits, and do not respond to a slow start by raising risk mid-challenge, which is how most sizing plans fail.

The simulated streak figures assume independent trades with a constant win rate. Real strategies can cluster losses in particular conditions, so also check the actual longest streak and worst week in your own backtest, and use whichever is worse.

Step 5: Check the consistency rule and minimum days

Consistency rules punish exactly what a good algo sometimes does: one big trending day that makes half the target.

Using the example rule (best day no more than 40% of total profit): if your best day makes $4,500 and you reach the $10,000 target, that day is 45% of profit and you fail the rule even though you hit the target. To bring it to 40%, total profit has to reach $4,500 divided by 0.40, which is $11,250. Some firms make you keep trading until the ratio is met; others define the rule differently, so check yours.

In your backtest, take the largest day's share of profit in each challenge-length window. If it often exceeds the limit, the usual fixes are a daily profit cap in the EA, or lower size on days after a large win, rather than changing the entry logic.

For minimum trading days, look at how often the strategy trades. An EA that only trades a few times a month can satisfy every other rule and still not reach the minimum days in time.

Step 6: Handle news restrictions in code

If the firm restricts trading around high-impact news, the EA needs a news filter, and the rule has to hold for exits too: a stop or target that fills inside the window may count as trading in it. To estimate the cost of complying, take your backtest, remove every trade that would have been opened or closed inside a news window, and compare the results. If the strategy's edge comes mostly from news spikes, it is the wrong strategy for that firm.

Tessera's simulator does this kind of check on trades you have already taken: remove trades matching a condition, such as a time window, or change the stop, target, breakeven or trailing rules and replay them on real candles, then compare the runs side by side.

Step 7: Replay the backtest as many challenges

One backtest is one path. A challenge is a short window that starts on whatever day you begin. To see how often you would pass:

1. Split your backtest into overlapping windows the length of a challenge, for example starting a new window every week.
2. For each window, apply every rule in order: did it breach the daily limit, the maximum or trailing drawdown, or the consistency rule before hitting the target?
3. Count the share of windows that passed, failed, or neither passed nor failed by the end.

Then shuffle the trade order a few thousand times and repeat, to see how sensitive the result is to the sequence of wins and losses. If the pass rate depends heavily on a few windows in one market regime, check that the backtest is not overfit, and that it has enough trades to judge at all (see backtest sample size).

Simulate the challenge first

Step 7 by hand takes an afternoon in a spreadsheet. If you build the strategy in Tessera's Strategy Lab, the prop firm challenge simulator does it as part of the account test. It replays the strategy's trades through the account rules, starting a fresh run every week or month of the data, at up to six account sizes. Limits are checked inside every candle on open equity, so a trade that dips through a limit and recovers before it closes still counts as a breach.

It reports the pass rate, the days to pass, the challenges per pass, the deepest dip, the payout and how often the account ended below its start. Presets cover Personal, FTMO 1-Step, FTMO 2-Step, The5ers 1-Step and Topstep 50K, based on published rules as of October 2026. Firms change their rules, so check each preset against the firm's current page. An optional "never breach" guard sizes trades down to stay inside the limits.

If you are choosing between FTMO's two challenges, FTMO 1-Step vs 2-Step for algo traders works through which rule set suits which kind of bot. A pass rate from past trades is a measure, not a promise: live fills and future markets will differ.

Find out where your strategy loses first

Most breaches come from a few bad conditions: one session, one weekday, high-volatility days. Upload your strategy tester export or trade history to the free Entry Analysis report to see results by session, weekday, time of day and volatility, with the trade count behind each, before you pay for a challenge.

To compare the tools that test a strategy against challenge rules, from free odds calculators to full backtest replays, see our guide to the best prop firm challenge simulators.