Rule snapshot
Store product, phase, account type, official URL or document, date checked, timezone, loss formulas, restrictions, fees, and eligibility.
Editorially reviewed 24 August 2026
Optimize the path, not the headline
DESK 34Evaluation accounts are path-dependent: the same final profit can pass or breach depending on intraday equity, reset time, open positions, fees, and the order of wins and losses. Save a dated copy of the official rules for the exact product, then translate every material term into deterministic simulation logic.
Do not optimize only for reaching a profit target. Record hard breaches, near-breaches, daily buffers, time under drawdown, and dependence on one large trade. Run the unchanged strategy through several unseen start periods so one favourable historical sequence cannot masquerade as readiness.
Store product, phase, account type, official URL or document, date checked, timezone, loss formulas, restrictions, fees, and eligibility.
Recalculate limits at every relevant event—entry, mark-to-market update, close, fee, financing, and reset—using the saved rule definition.
A personal stop should sit inside the hard boundary by an amount justified by slippage, spread expansion, and platform uncertainty.
A passed historical path is process evidence, never a promise that an evaluation or payout will succeed.
Save the official product page or terms document for the exact programme, phase, account type, and jurisdiction. Record the source URL, access date, timezone, and version before copying a number. A brand-level summary is not enough: two products from the same provider can calculate loss and eligibility differently.
| Field to record | Operational question | Failure if omitted |
|---|---|---|
| Loss basis | Balance, equity, initial balance, high-water mark, or a combination? | A run can appear valid while floating loss already breaches. |
| Reset clock | Which timezone starts a new trading day, and how are open positions treated? | Loss is allocated to the wrong day. |
| Target and minimums | What counts toward the objective, and are minimum days or consistency rules used? | The simulator stops at the wrong event. |
| Restrictions | Are holding periods, news, instruments, automation, or inactivity constrained? | A profitable but ineligible path is counted as a pass. |
| Costs and fees | Which commissions, spread, swap, data, tax, and evaluation costs apply? | Net expectancy and usable buffer are overstated. |
Update the simulation after every fill, fee, swap charge, partial close, and mark-to-market event. At minimum store starting balance, current balance, current equity, start-of-day reference, any high-water reference, realised P&L, floating P&L, and cumulative costs. The breach engine should evaluate the provider’s stated sequence, not a convenient end-of-candle approximation.
The formula is intentionally generic because the applicable floor may be static, daily, trailing, balance-based, equity-based, or hybrid. Write that definition directly from the dated rule sheet.
Define entries, exits, sizing, and invalidations. Changes are allowed but versioned.
Run the frozen rule on untouched dates. Do not repair it while viewing the result.
Increase costs, delay entries, use conservative same-bar ordering, and include difficult regimes.
Replay sequentially with the product clock and restrictions, logging every deviation and breach.
Predeclare the minimum observation count, maximum acceptable breach frequency, buffer policy, permissible deviations, and rollback rule. Review path metrics as well as the ending balance: worst equity excursion, daily loss distribution, longest losing sequence, cost share, time under water, rule violations, and dependence on one month or one setup.
Practical conclusion: a simulation can reject a fragile plan. It cannot guarantee the next market path, the provider’s future terms, or the trader’s live execution.
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Beginner exploration
Open each answer for a plain-language way to read Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation, test it carefully and decide what to explore next.
This page focuses on “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation”.Build a prop-firm backtest from dated official terms, exact loss formulas, floating equity, reset clocks, costs, chronological paths, and safety buffers.For “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation”, a beginner should identify what the practice guide measures, assumes or teaches before acting on its conclusion.Treat this page's account of “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” as a learning reference rather than a prediction, signal or promise of future performance.
For “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation”, copy the current official loss, target and eligibility rules before building a practice scenario.While exploring “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation”, model the exact drawdown formula and reset clock rather than relying on a remembered headline limit.Keep your “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” record honest: judge the process by rule compliance and risk consistency as well as simulated profit.Before leaving “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation”, re-verify the provider terms before paying because commercial conditions can change.
Turn one idea from “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” into a rule with explicit inputs, dates, costs and pass-or-fail conditions.Ask AI to expose missing assumptions in that “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” test, not to guess the next market move.Use the FXAbsolute AI Backtesting Lab to inspect calculations connected to “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” and the assumptions behind them.Reproduce any important “Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation” result and reserve unseen data before deciding that an apparent pattern is useful.
Continue your exploration of Prop-Firm Backtesting Strategy: Build a Rule-Accurate Simulation with the beginner AI prompt guide, or inspect public calculations in the AI Backtesting Lab.