A matemática das MESAS PROPRIETÁRIAS (25-30% aprovação)

Finance-focused summary (proprietary trading desk “approval” math)

Core claims / thesis

• You don’t need a “winning” market strategy to profit on a proprietary trading (prop) firm test; you need the right statistical setup to clear prop-firm approval barriers.
• The prop test is treated as a structured product with fixed rules (not an open-ended real-money market). You’re essentially competing on whether the price/profit path hits the approval barrier before the loss/downside barrier.

Disclaimers / promotional framing

• The speaker claims this is proven with “math, data, statistics, results,” and advertises social links (Instagram) and closed classes/mentorship.
• Mentions having withdrawn payouts and claims “complete transparency,” but no verified external audit is shown in the subtitles.
• No explicit legal “not financial advice” disclaimer appears in the subtitles.

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Instruments / firms / tickers mentioned

• Apex Trader Funding (speaker claims they were banned/terminated; mentions rule dispute)
• TopStep (speaker cites a published stat: customers approving at 12.4% in 2024)
• CFD Forex / Forex (referenced via a collaborator)
• Brazil (context: “national level in Brazil”)
• Cassiano Lago (named as a reference; YouTube/Instagram to be linked)
• No stocks/ETFs/crypto/bonds/commodities tickers mentioned.

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Key concepts and step-by-step framework (barrier / risk-geometry model)

“Barrier problem” framing (approval vs blow-up)

• Approval is modeled as crossing the top barrier before the bottom barrier.
• The “second stage” (getting past the test to funded status) is what pays.

Risk geometry (two-parameter simplification)

The speaker reduces strategy behavior to:

• Win probability: how often the trader “gets it right”
• Reward relative to risk (gain vs loss size): described via risk-reward ratio (“RR”)

Then he introduces a setup:

• Set strategies to EV0 (expected value = 0):
  • Meaning strategies are mathematically neutral in expectation in the real-market sense.
  • Even if EV is zero, approval rates differ due to variance/standard deviation and barrier mechanics.

EV0 break-even examples (given explicitly)

For EV0 (break-even), the win rate must satisfy:

• RR 4:1 ⇒ must be right 20% of the time to break even
• RR 3:1 ⇒ must be right 40% of the time (as stated)
• RR 2:1 ⇒ must be right 50%
• RR 1:4 (also referenced as “one for every four at risk”) ⇒ must be right 80%

Why approval rates differ (despite EV0 = 0)

• He claims approval probability depends on standard deviation / outcome distribution compression, not just expected value.
• He gives example approval-rate ranges (from his simulations):
  • RR 4:1: about 37% approval with 20% success rate (EV0)
  • RR 3:1: about 40% success → ~36–40% approval (wording varies)
  • RR 2:1: success → ~36% approval (as stated)
  • RR 1:1: improves to around 45% peak in later comparisons

“Compressed standard deviation” conclusion

• Lower standard deviation (“compressed distribution”) ⇒ higher probability of approval.
• He notes this changes by stage (test vs funded) and mentions additional dimensions later:
  • “active status, time, position size, practical executables”

Simulation / path explanation

• He describes a toy model:
  • Start from scratch; each “account line” moves up/down.
  • If it hits green (approval) first ⇒ approved.
  • If it hits red (bankruptcy) first ⇒ rejected.
• Key point: the order of barrier contact matters more than the total amount earned prior to terminal outcome.

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Key numbers and metrics cited

Prop test approval benchmark

• TopStep stat (2024, cited): 12.4% of customers approve of the prop desk test.

Speaker’s simulation outcomes (approval rates)

He runs multiple simulations (examples explicitly stated):

• “0.25 to 1 ratio” with 80% success rate:
  • 76 approved out of 224 failed
  • Implies ~25% approval (consistent with wording)
• With 25% success rate (same ratio context referenced):
  • approval discussed as low (framed as “risk of negative return,” then reframed)
• “0.5 to 1” case:
  • approval improves to about 36% (as stated)
• “1 to 1 comparison”:
  • approval improves to about 45%
  • he claims repetition has limited variation: roughly 36% to 45%
• Later (from a different run / “one of my strategies”):
  • peak around 46%, valley around 36%, and “35” mentioned as a low stability number

Performance margin claim (scaling context)

• Speaker says his “model currently runs at performance margins between 25 and 30%” (source not specified).

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Explicit recommendations / cautions

Recommendation (implied)

• Stop thinking “which trades are winning?”
• Instead solve probability distribution + barrier hit order to maximize approval.

Caution about copy-trading / aggregation mechanics

• Linking multiple purchased test accounts to a copy trade collapses outcomes into a single “master” line.
• He claims this destroys the outcome distribution:
  • Instead of many individual accounts approving/failing (e.g., “120 approved and 180 failed”),
  • you end up with either all approved or all failed (no middle ground).
• He warns this can reduce the chance that enough individual tests pay to cover costs.

Caution about adopting “negative RR” blindly

• He says “negative RR is acceptable up to a certain point” and mentions an “ideal ratio,” though he declines to state it explicitly (“I’m not going to give it to you”).
• He also frames it as potentially difficult to “defend” originality if viewers adopt his specific approach after watching.

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Interpretation of prop-firm payoff (convexity / capped loss)

• He emphasizes prop firms offer a convex payoff structure:
  • Loss is capped to the test fee (illustrative fictional example: drawdown -4,000 leads to loss of 499, and loss doesn’t increase with further breaking of the account).
  • Profit becomes real after funding, with “no ceiling” on earnings.
• He argues this is viable only if the trader can statistically clear:
  • the test account stage, and
  • the approval “buffer/cushion zone”
  • to reach withdrawable funded profits.

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Performance / risk management framing at the end

• He compares prop trading to a casino with rules:
  • The rules are written and mathematically solvable.
• He outlines conceptual phases:
  • Phase 1: approval barrier problem (geometry / distribution)
  • Phase 2: “mattress” (consistency / construction) — referenced as “rule of consistency, mattress construction”
  • Phase 3: scaling / extraction

Scaling involves

• risk allocation among accounts
• management of account groups
• division between trading desks

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Presenters / sources mentioned

• Primary presenter (speaker): unnamed in the subtitles (Brazil-based prop trading educator; uses Instagram/YouTube and closed WhatsApp groups)
• Cassiano Lago: referenced as a major reference for Brazil, mainly CFD Forex; source links promised in description
• Apex Trader Funding: prop firm referenced; ban claim
• TopStep: prop firm referenced; 12.4% approval rate statistic attributed to TopStep (2024)