Video summary

The formula that beats prop trading firms

Main summary

Key takeaways

Finance

Finance-focused summary

The video lays out a math/expected-value (EV) framework to improve odds of profiting from prop trading firm challenges by treating the process like a repeatable bet, then adjusting assumptions for trading costs and prop rules.

Instruments / entities / tickers mentioned

Prop trading firms & accounts

  • Prop trading firms & accounts
    • FTMO (“$200,000 FTMO two-step account”)
    • “Fivers” (mentioned as an example of a firm with a specific “profitable day” definition)
    • “Prop trading firms” generally

Assets

  • Gold (used as an example for intraday stop/target sizing)

Notes

  • No public market tickers/ETFs were mentioned.

Methodology / step-by-step framework (EV model)

EV definition

Expected Value (EV) is defined as:

  • EV = (P(payout) × payout amount) − account fee

Step-by-step probability model for a prop challenge

1) Step 1 pass probability

  • Trade direction is modeled as a random (coin flip).

  • When target and loss allowance are equal distance from starting balance:

    • P(reach target) = loss allowance / (loss allowance + profit target)

  • Example inputs:

    • Loss allowance = $20,000 (from 10% of $200,000)
    • Profit target = $20,000
    • P(step 1 pass) = 0.5 (50%)

2) Step 2 pass probability

  • Max loss still 10%, but profit target set to 5%.
  • Profit target = $10,000 (implied by 5% of $200,000)

  • Loss allowance = $20,000

  • P(step 2 pass) = 20,000 / (20,000 + 10,000) = 66.67%

3) Payout generation on funded step

  • After funding, the target profit is set equal to the max loss allowance:
    • target = 10% (or 8% if the max loss is 8%)

  • Again modeled as coin-flip direction with symmetric distance:

    • P(payout hit) = 50%

4) Overall probability

  • Multiply step probabilities:

    • 0.50 × 0.6667 × 0.50 ≈ 0.1667 (16.67%)

Incorporate fees and payout sharing

  • Example payout share: 80% of profits
  • Account fee: 1,080 euros (~$1,257)
  • Fee refund detail: when the first payout is received, fee is refunded
  • Total payout used in EV math:
    • Base payout: $16,000
    • Plus fee refund: +$1,257
    • Total payout counted = $17,257

Final EV (before trading-cost haircut)

  • EV ≈ $1,619.17 per account purchase (for the $200,000 FTMO account)

Risk management / trading conduct recommendations (explicit)

To reduce rule-breaking risk and keep expected process “clean”:

  • Risk per trade: keep to 2% of initial balance
  • Risk-to-reward: 1:2 (implies potential reward ~4% if needed)
  • Trades per day: no more than one trade a day
  • Trading style: intraday, close same day (avoid overnight issues / daily loss limit resets)
  • Gold example (intraday sizing):
    • Average daily movement cited as $80
    • Example: $20 stop loss and $40 take profit
  • If you fail an account quickly (e.g., within 10 days):
    • consider a cool-down period before buying another challenge elsewhere (avoid “suspicious behavior” patterns)
  • Ballpark rule safety:
    • Daily loss limits (example 3% or 5%) are argued to be manageable under the “2% risk, one trade/day” approach.

Key prop-rule cautions that can materially change EV

Trailing max loss (major warning)

  • Static example: with $200,000 and 10% max loss, threshold = $180,000
  • Trailing example: after a win to $208,000, threshold becomes $188,000
    • i.e., $20,000 below the peak
  • Recommendation: skip accounts with trailing max loss; choose static.

Minimum profitable days

  • Distinct from minimum trading days.
  • Example: minimum 5 profitable days may require additional profitable days even after hitting the target early.
  • Workaround: lower risk per trade / risk-reward ratio.

Consistency rule / best day rule

  • Example: “best profitable day” can’t exceed 50% of total profit.
  • If Step 2 target is structured with asymmetric reward potential, “best day” could violate it.
  • Workaround: lower risk or risk-to-reward so one day doesn’t dominate profits.

News trading restrictions

  • Avoid trading around restricted high-impact news; keep an economic calendar open.
  • Adds another reason to treat costs carefully.

Trading costs & revised EV (explicit numbers)

Commission/spread impact example

  • Trade outcome: - $4,000 or + $8,000
  • Add $100 commission → outcomes become:
    • - $4,100 or + $7,900

Cost “haircut” assumption

  • Costs reduce payout probability by ~5% to 15%
  • Video uses a pessimistic end: 15% reduction

Updated payout probability

  • From 16.67% down to ~14.17%

Updated EV with costs

  • Expected profit ≈ $1,188.23 per $200,000 FTMO account

Mitigation recommendation

  • Trade assets with low spreads and low commissions.

Bankroll / variance and explicit bankroll rule

Variance concept

  • Payout timing can cluster; losing streaks are possible.
  • “1 payout every 7 accounts on average” does not mean you are guaranteed after 6 losses.

Bankroll rule

  • Spend no more than 5% of bankroll per single account
  • Example: with $1,000 bankroll, buy accounts costing ≤ $50
  • Rationale: keep enough runway so EV can “play out” despite losing streaks.

Disclosures / cautionary statements

  • The video does not explicitly include typical “no financial advice” language in the provided subtitles, but it includes strong cautions/disclosures:
    • Prop firms may refuse to pay; payouts are not guaranteed even if firms look trustworthy.
  • The presenter states they:
    • received sponsorship offers from prop firms,
    • turned them all down,
    • has no affiliate links for prop firms,
    • does not trust prop firms due to incentives aligned with challenge failures.
  • Even with an AI research agent, the viewer should still do their own due diligence.

AI agent usage (process recommendation)

Purpose

Build an AI agent to:

  • scan the web for best discounted deals (lower fee → higher EV),
  • research specific firm/account rules to detect constraints that could reduce EV.

Example tool mentioned

  • ChatGPT (or any AI that can run web research on a schedule)

Positioning

  • The agent is positioned as time-saving, not a substitute for personal verification.

Final effectiveness / claim framing

  • The presenter emphasizes EV can remain positive under their assumptions, but it depends heavily on:
    • static rules vs trailing rules
    • cost drag
    • variance management
  • Random entry (“coin flip”) is justified because:
    • the presenter can’t assume a profitable strategy exists (sample size issues),
    • randomness reduces emotion-driven trading variance (behavioral risk).

Presenters / sources

  • Presenter: Unnamed speaker in the video (no specific person named in the subtitles).
  • Source mentioned: ChatGPT as an AI tool (not a publisher/source for financial claims).

Original video