Capital and risk framework

#Trading your own capital: the PDT wall

In the US, FINRA's Pattern Day Trader rule requires a minimum $25,000 equity in a margin account for anyone who executes four or more day trades within five business days, and restricts trading below that threshold. [Established] — FINRA Rule 4210, a primary regulatory source. That figure is specific to US equities day trading in a margin account; it does not apply the same way to futures (which use exchange- and broker-set margin per contract, not the equity-based PDT rule) or to non-US brokers, which is part of why futures — especially CME's smaller "micro" contracts (Micro E-mini S&P 500 and similar) — are the more common entry point for someone practicing order-flow reading without $25,000 sitting idle. [Directional] — standard industry description of why retail order-flow/DOM trading skews toward futures rather than equities; exact current micro-futures margin requirements vary by broker and by CME's own periodic adjustments, and should be checked directly before committing capital.

#The prop-firm alternative, and what it actually is

A funded-account "prop firm" — FTMO, Topstep, and similar are the most commonly referenced — sells an evaluation: you pay a fee for a simulated account with a stated starting balance, and if you hit a profit target within stated risk limits, you're offered a funded account trading (in most structures) simulated capital, with the firm paying you a share of the simulated profits. This is a real, if commonly misunderstood, business model — you are not depositing and trading your own capital in most of these arrangements; you are paying for an evaluation and, if you pass, a revenue-share agreement.

FTMO's own published rules, checked directly (2026): the two-step evaluation requires a 10% profit target in the first phase and 5% in the second, with a 5% maximum daily loss and a 10% maximum total loss that's static — meaning the floor is fixed at the account's starting balance minus 10% and never moves up as the account grows. A newer one-step format (launched February 2026) compresses this to a single 10%-target phase with a tighter 3% daily loss limit and a 10% trailing maximum loss instead. [Established] — ftmo.com's own published trading objectives, a primary source for that firm's own rules specifically; re-verify directly before paying for an evaluation, since these terms have changed materially even within 2026 and will keep changing. Other firms (Topstep is the most-cited futures-specific example) use different structures — Topstep's trailing drawdown, for instance, moves up as the account's high-water mark rises, which behaves very differently from FTMO's static floor even at a similar headline percentage. [Directional] — general structure is well-documented across prop-firm comparison content; treat any specific current numbers as needing direct verification at the source firm.

The mechanism worth understanding, independent of any one firm's current numbers: a daily loss limit and a trailing (versus static) drawdown floor are the two features that most determine whether a given trading style survives an evaluation, because they punish both a single bad day and a slow accumulation of smaller losing days differently. A strategy that's fine on a static-floor evaluation can fail a trailing-drawdown one at an identical win rate, purely because of how the floor moves.

#Monte Carlo stress-testing, as a concept worth adopting regardless of tool

A single backtest shows you one path through history — the exact sequence of wins and losses that happened to occur. Monte Carlo simulation addresses a real, specific weakness in that: it takes your actual trade results and re-runs them in thousands of randomized orders (trade shuffling), or resamples them with replacement (bootstrap), to show the range of outcomes consistent with your edge, not just the one sequence that happened to occur historically. If your worst realistic 5% outcome — not your average outcome — still stays inside a prop firm's drawdown limit (or inside what you can personally tolerate, trading your own capital), that's a meaningfully stronger claim than "my backtest passed." [Established] as a standard, legitimate quantitative risk-management technique, independent of any specific vendor — it's taught in quantitative finance generally, and it's the same method underlying the local research material's own Monte Carlo/prop-firm-validation script referenced in The tools and data landscape. The specific pass-rate percentages that script or any vendor's dashboard produces for a given strategy are only as good as the trade data fed into them — the method is sound; any specific output number is not something this course can verify without the underlying strategy and data, and shouldn't be trusted from a dashboard alone without knowing what was actually simulated.

#The realistic framework

Whether trading personal capital above the PDT threshold or a prop-firm evaluation, the same three numbers govern survival: a maximum loss per trade small enough that a losing streak realistic for your actual win rate (not your best week) doesn't approach your daily or total drawdown limit; a daily loss cap you respect mechanically, before discretion re-enters; and — the number retail content skips most often — the actual cost of being wrong while learning. An evaluation fee is a sunk cost the first several times through; personal capital below the PDT threshold in equities, or undersized futures positions while still building the DOM-reading skill in Order flow imbalance and absorption, is the honest on-ramp. Neither the local research tooling nor quantpad.ai's own Monte Carlo and prop-firm-evaluation features (see The tools and data landscape) can substitute for that — they tell you whether a strategy would have survived a given risk framework, not whether you personally will execute it under real conditions, which is a different and harder problem this course does not claim to solve.