Research & Explainers
36 articles · each dated, each independently sourced
Every article, grouped in one place
First-party studies, mechanism explainers and per-firm rule guides. No firm ranking, no pass-rate claim about any named company — every figure here is either derived by arithmetic or computed from a stated, seeded simulation. Search engines and LLMs are pointed at every one of these through sitemap.xml and llms.txt; this page is the same index for a person. Those studies feed two calculators: personal rules simulator and prop firm simulator.
Research original data
Reproducible, seeded studies with a published methodology and dataset — the subject is the mechanic, never a named firm's pass rate.
- Static vs trailing drawdown: how the floor mechanic changes pass probability One fixed trader profile run against 8 real rule sets, then against a static-floor counterfactual of each — 20,000 seeded paths per cell.
- Why prop firm evaluations fail — interactive tour A scroll-driven walkthrough of the mechanics that end evaluations: how a trailing floor ratchets, why the binding rule is often not the one traders watch.
- How much to risk per trade: the size that maximizes the probability of passing A seeded Monte Carlo sweep of risk-per-trade against the probability of passing — the optimal band, and why the failure cause flips from running out of time to breaching the drawdown, under a trailing versus a static floor.
- What a daily loss limit really costs A seeded study: beyond the max drawdown, what a daily loss limit costs the probability of passing — a loose cap only trims over-sizing, a tight one becomes a second binding wall.
- What a consistency rule costs A seeded study of a consistency / best-day rule's cost — a steep, non-linear tax that falls hardest on the trader whose edge is concentrated in a few big days.
- The minimum-trading-days drag A seeded study: what a "trade at least N days" rule costs a fast finisher — near-free while small, a real drawdown-exposure drag once it forces trading well past the target.
- Target-to-drawdown ratio: the evaluation difficulty knob A seeded study sweeping only the profit-target ÷ max-drawdown ratio on one fixed trader — static vs intraday trailing. The common 8/6 design sits at 34.2% trailing / 44.4% static, and raising the ratio costs more points than the static-vs-trailing floor gap does.
- Calculation receipt — corpus-bound, dollar levels + expectancy A free, citable analytical receipt binding a disclosed Topstep $50k firm-rules corpus id to trader inputs and honest public outputs — the dollar levels and the after-cost net expectancy.
- MC calculation receipt — corpus-bound sample + variance The Monte Carlo sibling under the same corpus id: sample mean 0.03834R ± 0.010999 SE over 10,000 paths against an analytical E_net of 0.05R — publishing the sample noise, not only the point estimate.
- Prop Firm Rules Atlas: every verified rule set, every account size, one dated table Drawdown type and floor basis, daily-loss cap, consistency limit and payout terms for every configuration in the corpus, each with its source and last-checked date — the whole rule set on one table instead of one page per firm.
- Consistency rule: how the percentage becomes a required total, with a calculator A best-day cap does not cap the day; it floors the total — required total = best day ÷ limit. The identity worked for every consistency limit on record, with a calculator for your own best day and running total.
- Prop firm payout rules: split, first-payout window, cycle and minimum across every rule set on record The published payout clauses per rule set, side by side — what each clause does to the payout calendar, with each value's source and recheck date.
- Pass Probability Atlas: one trader profile, every rule configuration, modeled pass odds One disclosed trader profile run through every verified rule configuration with the same seeded engine — the rule set moves the odds. A model of the mechanic, not a prediction and not a measured pass rate for any firm.
The Mechanism series 8 of 8 published
An eight-part series on the arithmetic underneath simulation, sizing, cost and capital — each one an independent, interactive explainer rather than a restatement of the others. The series is complete.
- 01 · What a trading simulation tells you before you risk money — and what it cannot A Monte Carlo laboratory run beside the exact answer it is estimating: watch a simulated probability converge onto a known truth.
- 02 · Why your backtest lied: fitting a trading rule to pure noise A backtest reports the best of everything you tried. On a price series with no edge in it at all, "the best of everything" still looks like a strategy.
- 03 · When five positions are really one position: correlation and portfolio heat Six positions at 1% of capital each do not carry six independent risks — the effective number of bets is what actually sizes the exposure.
- 04 · What trading costs per trade: spread, commission and slippage, computed Three different mechanisms with three different behaviours, together setting a toll charged on every position — and the frequency where it exceeds the edge.
- 05 · What trading capital keeps: dispersion, inflation, tax and the order of returns Four operations spend a trader's capital. Only one of them sends an invoice — the other three are just as real and much easier to miss.
- 06 · Position sizing: four methods, four assumptions, and the rounding problem Fixed dollar risk, fixed fractional, volatility-normalised and Kelly sizing compared on the same account, trade by trade.
- 07 · What a bad trading decision actually costs, in currency Run the same series of trades twice — once to plan, once with one deviation — and price the difference instead of just naming the bias.
- 08 · Trading journal intelligence: which of your statistics are real? A trade journal measures your behaviour exactly and estimates your edge badly — the sample size a journal statistic needs before it means anything.
Further mechanism guides 7 of 7 published
A second, independently produced batch — rule mechanics, cost and funded-phase arithmetic not covered above. The batch is complete. No firm ranking, no pass rate; each derives what it claims.
- Monte Carlo pass probability: the two errors, and which one you can fix Six trades, arranged 720 ways, add to $1,400 every time — and in 144 of those arrangements the account is closed before it collects. Only one of the two errors in that count falls when you add paths.
- What a daily loss limit actually measures What a daily loss limit measures, when the trading day starts, and whether breaching it closes the session or the account — with a live instrument that runs one session under each published convention.
- Which rule breaks first: ranking an account's clauses by expected trades to breach A method for ranking an account's clauses by expected trades to breach, with a live instrument that computes all four from a trader's own win rate, average win, average loss, risk per trade and trading day.
- What commissions and slippage actually decide in an evaluation Priced in R, a round turn is fee ÷ stop. Below a gross edge of 0.084R execution cost decides the evaluation; above it, almost nothing. Exact model + a sensitivity audit of this site’s 0.050R assumption.
- The evaluation stops pricing size long before the funded account does Among profiles one evaluation grades within 2%, funded death rates run 14.2% to 75.2% across 531,441 enumerated years — the two phases price a trader's size completely differently.
- Two-step pass probability: the 64.9% product is a floor, not the answer Multiplying phase pass rates (78.1% × 83.1% = 64.9%) is a floor — conditioning lifts composed odds to 70.5% (+5.6). Square-one understates at 61.0%; reset costs 5.4 points.
- A file of fills is not a list of trades Worked example: 91 fill rows · 51.5% when grouped as trades vs 65.2% as rows — one realised total fixed by the fills. Matching conventions change how that total is split, not the trade total.
Concepts explained 3 articles
The definitional explainers behind the terms used everywhere else on the site.
- Trailing drawdown, explained: why profitable accounts are eliminated The floor is F = M − A, derived rather than measured: five results follow from that one definition by arithmetic alone.
- Risk of ruin, explained for prop traders The probability of hitting an unrecoverable floor before a positive edge compounds — and why position size, not edge, is the dominant lever.
- Expectancy vs win rate: why a 90% win rate can still lose money Win rate does not determine profitability — expectancy does. Six profiles compared; the three highest win rates are the net losers.
Personal rule sets 6 articles
Modeling your own trading rules, free at full accuracy — the same engine pointed at your own account instead of a firm's.
- Write your own rulebook A firm's evaluation is five numbers and a simulation. A live, seeded calculator runs the same math against your own stated rule.
- Your own futures account has a floor A self-funded futures book is the same mathematical object as a prop evaluation — starting balance, drawdown floor, optional daily stop, horizon. The engine names which number hits first.
- A personal daily stop is a lockout. A firm's daily loss is often an account funeral The same percentage is not the same rule. On your own account a daily stop usually ends the day; at many firms it ends the evaluation.
- Nine numbers sit between a broker statement and a computed sentence How to fill the nine personal dials from a brokerage statement without inventing a win rate — education only, no broker API.
- Your last 100 trades What a broker export contains, what today's CSV import keeps and discards, and a live comparison of two resampling methods on the same trades.
- The Personal Case File: the document for your own rulebook A one-time, $49 document that runs against rules you set yourself, not a firm's — eight sections, priced once, with no renewal. Live now, built from the free personal rule editor, also live.
Rule guides 2 articles
How one specific, named firm's published rule works, in full — no pass rate, no ranking, just the mechanic and the firm's own worked example.
- Apex trailing drawdown, explained The intraday trailing floor with freeze-at-initial: the ratchet on unrealized peaks, the room formula, and the firm's own worked example.
- Topstep consistency rule, explained The published 50% consistency requirement rewritten as max(target, 2 × best day), and how it interacts with the 2% daily loss limit.
Firm-rule freshness dated snapshot
When each modeled rule set was last checked against the firm's own documentation, and when its rules last changed — the whole corpus, dated. No firm ranking, no pass rate.