Video summary

Why I Can't Show You My Risk Management (It Would Kill Prop Firms)

Main summary

Key takeaways

Finance

Finance-focused summary (prop trading risk management)

Core premise: risk management is an account-state–dependent system

  • The creator argues that risk management isn’t a single universal set of rules (e.g., fixed stop-loss/profit-target/position-sizing).
  • Instead, risk management depends on:
    • the prop firm
    • the plan
    • the account type/state (e.g., evaluation vs funded, and funded states such as new, in profit, in drawdown, after payout, live)
  • They claim the number of possible combinations is very large, citing an example multiplier like: 25 × 3 × 3 (firms × plans × sizes), with additional multipliers for funded account states (e.g., profit/drawdown outcomes).

Why they won’t publish their full dashboard

They say publishing their exact dashboard would reveal an “entire system” that:

  • simulates millions of TP/SL combinations across:
    • each firm / plan / account type / balance / drawdown
  • selects the statistically optimal TP/SL to maximize expected value (EV) based on the account’s current status.

They also warn that:

  • widespread copying could force prop firms to change rules and harm their edge
  • applying one firm’s optimized settings to other firms/accounts is unlikely to work and can reduce EV

Methodology framework they recommend (optimization & testing)

Don’t backtest your live equity curve

Instead:

  • Backtest the prop-firm environment (using the prop firm’s specific rules)
  • specifically measure eval pass rate

Simulate/optimize across three environments with different objectives

They recommend treating each environment as a separate optimization problem:

  • Live account
    • optimize for net trading returns, including fees
    • once profitable, the focus is largely on time/efficiency
  • Evaluation (eval) accounts
    • optimize for the probability of reaching the target before breaching max loss
    • time may be secondary; fees are less important
  • Funded accounts
    • optimize for expected cash withdrawal / payouts
    • not win rate
    • not survival time

Track key state variables over time for funded accounts

For funded accounts, they emphasize monitoring:

  • current balance
  • distance to max loss (drawdown cushion)
  • top balance (because drawdown can trail)
  • payout eligibility status
  • potential effects of:
    • trailing drawdown
    • rule changes

Run large-sample testing

  • Pass rate requires a large sample size (not just a few trades).
  • If pass rate is low (they give an example around ~20%), switch strategy/risk management.

Risk optimization trade-offs

  • Lower risk can increase pass rate but may slow payout progression
  • Higher risk (with more edge) might increase pass rate
  • There is no universal “optimal” risk %—optimal sizing changes with:
    • account state
    • rule set

Strategy concept they mention (bias-based trading)

  • They describe using a bias-based strategy:
    • assume a slight linear drift in the direction of their bias
  • They claim this reduces the need for extremely rigid entry criteria because they focus on fitting TP/SL/size to the prop environment.
  • Example TP/SL numbers mentioned:
    • $832 profit target and $516 stop loss
  • Important note: these are illustrative, not claimed as universally optimal.
    • Hard-coded targets/SLs may stop being optimal when applied to different account rules/states.

Key numbers and quantitative examples (expected value / payout economics)

Example 1: Cost to acquire funded account vs expected payouts

Assumptions

  • $400 acquisition cost of a funded account
    • e.g., pay $100 per eval
    • pass 1 out of 4 → 25% pass rate
    • so: $100 × 4 = $400
  • probability of making a $2,000 payout = 50%

Expected payout value

  • 0.5 × $2,000 = $1,000

Expected profit after acquisition cost

  • $1,000 − $400 = $600

They also describe it via an equivalent netting approach:

  • 0.5 net $2,000 − 0.5 net $400 = $600

Example 2: Same $ risk, different account states → different value impact

They contrast:

  • fresh funded valued around $410 (based on acquisition cost)
  • funded in profit (“$2K in profit”) valued around $1,000 (because it’s close to a payout)

Common assumptions in the example

  • max loss limit is $2,000 away from both states (e.g., at -2K for both)
  • they risk $1,000 in each case

Result emphasized

  • If you lose from the profitable state, you lose more value
    • they estimate remaining value drops to ~$500 (about a $500 value loss)
  • If you lose from the fresh funded state, the value loss is smaller
    • they estimate a value drop of ~$200

Takeaway

  • Even with identical trade risk, account state changes the expected cashflow impact.

Simulation example: 100 trades with account rule constraints

They describe a simulation with:

  • 100 trades
  • random walk behavior (plus/minus “50 steps”)
  • break-even win rate around 1:1
  • profit/loss tolerance like ±$500
  • eval structure such as:
    • eval +3K before -2K
    • monitor end-of-day max loss as it trails

Observed in the example

  • about 47 wins / 53 losses
  • overall result about - $3K
  • “some accounts open,” with “attempt failures”
    • they mention two attempts fail, rest open

Point made

  • Passing prop evals can be slow/rare under realistic constraints and high risk—so backtest rather than rely on intuition.

Target ratio variation (1:1 vs 1:1.5 vs 1:2)

They vary only the target assumptions while holding other logic constant:

  • 1:1
  • 1:1.5
  • 1:2

Core message

  • account rules dominate outcomes; passing and getting paid are different objectives than single-trade profitability.

What to measure in prop trading (explicit performance metrics)

They rank metrics by importance:

  1. Eval spend (most important)
  2. Number of payouts in dollars (second most important)

They argue:

  • ultimately, what matters is whether money hitting your bank account exceeds money leaving your account
  • win rate and raw pass rate alone are treated as secondary diagnostics
  • so long as cashflow is positive, the primary goal is cash extraction

They also mention simple diagnostic checks:

  • Probability of payout × payout size (basic expected cashflow check)
  • Eval cost per funded account:
    • eval cost divided by pass rate (%)

Risk management cautions / disclosures

  • No explicit “not financial advice” disclaimer is included in the subtitles provided.
  • They repeatedly caution that:
    • copying their exact risk numbers to other firms/accounts is likely to reduce EV
    • optimized TP/SL/position sizing is specific to:
      • firm + plan + account state + balance + drawdown/payout status

Instruments / tickers / sectors mentioned

  • None.
  • The subtitles do not reference specific stocks, ETFs, bonds, commodities, FX pairs, or crypto tickers.

Presenters / sources

  • Presenter/source: “JJ” (the speaker; referenced as “JJ, can’t you just show me…”)
  • No other named presenters or external sources are cited in the provided subtitles.

Original video