prop firm evaluations for algo traders - honest guide 2025

been asked about prop firms a lot lately.

here’s my honest take for algo traders specifically.

my situation

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why i don’t use prop firms:

inherited $2M.

don’t need their capital.

don’t want their rules.

but if i didn’t have capital…

prop firms would be worth considering.

especially for algo trading.

the prop firm area 2025

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major players:

• apex trader funding
• topstep
• oneup trader
• leeloo trading
• bulenox

what they offer:

pass evaluation → get funded account

keep 80-90% of profits

their capital, your strategy

algo trading compatibility

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the challenge:

most prop firms designed for discretionary traders.

rules often conflict with algo execution.

key rule issues:

1. news trading restrictions

many ban trading around major releases.

algos often run 24/7.

conflict.

2. daily loss limits

typical: 2-3% daily max

algos can hit this in volatile sessions.

3. position limits

many cap contracts/shares.

limits scaling.

4. trailing drawdown

some use trailing max drawdown.

algos hate this (compounds risk).

which firms work for algos

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apex trader funding:

evaluation: $147/month for $50k account

profit split: 90%

algo-friendly: YES (explicitly allows)

daily loss limit: $2,500 on $50k

pros:

• allows automated trading
• reasonable rules
• fast payouts

cons:

• trailing drawdown
• need to pass evaluation first

topstep:

evaluation: $165/month for $50k account

profit split: 90%

algo-friendly: CONDITIONAL (must notify)

daily loss limit: $1,000 on $50k

pros:

• established reputation
• good support

cons:

• tighter daily limits
• must inform about algo use
• may reject certain strategies

oneup trader:

evaluation: $125/month for $50k account

profit split: 90%

algo-friendly: YES

daily loss limit: $1,100 on $50k

pros:

• allows full automation
• competitive pricing
• no trailing drawdown

cons:

• newer than others
• less track record

my recommendation framework

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use prop firm if:

• capital under $50k
• profitable backtest (1+ year)
• live track record (3+ months)
• can handle rule constraints

don’t use prop firm if:

• capital over $100k (use your own)
• strategies require high take advantage of
• can’t handle daily loss limits
• need full automation freedom

evaluation strategy for algos

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approach i’d use:

1. reduce position size 50% during evaluation

   evaluation is about passing, not maximizing.

   conservative > aggressive.

2. add circuit breakers matching firm rules

   stop trading if approaching daily limit.

   resume next day.

3. avoid news events if restricted

   pause algo during scheduled releases.

   resume after.

4. track metrics firm cares about

   max drawdown.

   daily P&L.

   winning percentage.

the math

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example: $50k apex evaluation

evaluation cost: $147/month

typical pass rate: ~15% (industry estimate)

expected cost to pass: $147 × 6 months = $882

funded account profit potential: $5k-$15k/year (conservative)

ROI: positive if you’re actually profitable

reality check:

most people fail.

but most people aren’t running backtested algos.

algo traders have edge in evaluations.

code for prop firm rules

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class PropFirmRiskManager:
    def __init__(
        self,
        daily_loss_limit: float,
        max_drawdown: float,
        trailing_drawdown: bool = False
    ):
        self.daily_loss_limit = daily_loss_limit
        self.max_drawdown = max_drawdown
        self.trailing_drawdown = trailing_drawdown
        self.daily_pnl = 0.0
        self.peak_balance = 0.0
        self.current_balance = 0.0

    def update(self, pnl: float, balance: float):
        self.daily_pnl += pnl
        self.current_balance = balance

        if self.trailing_drawdown:
            self.peak_balance = max(self.peak_balance, balance)

    def can_trade(self) -> tuple[bool, str]:
        # daily loss check
        if self.daily_pnl <= -self.daily_loss_limit:
            return False, "daily_limit_hit"

        # drawdown check
        if self.trailing_drawdown:
            current_dd = (self.peak_balance - self.current_balance) / self.peak_balance
            if current_dd >= self.max_drawdown:
                return False, "max_drawdown_hit"

        return True, "ok"

    def reset_daily(self):
        self.daily_pnl = 0.0

community insights

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the NexusFi funded trader discussion has good real-world experiences from traders who’ve passed evaluations and gotten funded. worth reading before committing money.

also check the funded trader platforms thread for current 2024-2025 comparisons.

my verdict

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prop firms for algo traders:

rating: 7/10

good for:

• undercapitalized algo traders
• strategy validation with real risk
• scaling without personal capital

bad for:

• strategies needing full flexibility
• high-frequency approaches
• anyone with sufficient capital

if i were starting over with $10k:

i’d run my algo on paper for 6 months.

then take apex or oneup evaluation.

use prop capital to scale to $50k+.

then transition to own capital as profits allow.

tonight (june 20, 2:38am)

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prop firm guide for algo traders. major players: apex (most algo-friendly), topstep (conditional), oneup (good alternative). key challenges: daily loss limits, trailing drawdown, news restrictions. recommendation: use if under $50k capital, profitable backtest, can handle rules. avoid if over $100k or need full flexibility. evaluation strategy: 50% position size, add circuit breakers, avoid restricted events. math works if actually profitable. algo traders have edge in evaluations vs discretionary.

2:38am friday. prop firm evaluation guide for algo traders. apex most algo-friendly (90% split, allows automation). topstep conditional (must notify). oneup good alternative (no trailing DD). key: reduce size 50% during eval, add circuit breakers matching rules, avoid news if restricted. math: $882 avg cost to pass, $5k-15k/year potential. use if undercapitalized, avoid if over $100k or need flexibility. algo traders have edge.

-AK