Simulate a trader through a challenge
propfirms_simulateMonte Carlo-simulate a trader with the given statistics through a prop-firm challenge and (by default) a funded horizon. Answers: "What is my chance of passing per attempt, and of ever getting funded? How many attempts and how much total money should I expect? Is this challenge positive expected value for me, and which rule actually kills my attempts?" Identify the challenge EITHER by directory reference (firmId + challengeId, discovered via propfirms_list_simulatable; firmId accepts the directory id or the firm's name) OR by a full inline spec object - the exact shape propfirms_challenge_rules returns, so you can fetch a directory entry, change one rule, and re-simulate to model rule variations. Provide exactly one of the two forms; providing both or neither is an error. Directory references need network access; inline specs are fully offline. The trader is described by flattened parametric fields (one clean design used across all tools): winRate (a FRACTION 0-1), avgWinR/avgLossR and optional winStdR/lossStdR in R-multiples (sizes relative to the amount risked per trade), tradesPerDay with a 'fixed' or 'poisson' day model, and risk sizing via riskMode + riskValue (percent units for percent modes). If you have the user's raw trade series rather than summary stats, prefer propfirms_simulate_trades - it preserves streaks. Returns structuredContent with the full SimResult: perAttempt.passProbability with a Wilson 95% CI and per-step pass rates plus a failure breakdown by rule (daily-loss vs max-loss vs time-limit - which tells the user WHAT to fix); journey.fundedProbability, attempts and cost distributions (cost includes prices, resets, monthly billing, activation, minus refunds), costGivenFunded and daysToFunded; perAttempt.avgDaysWhenPassed/avgDaysWhenFailed and perAttempt.stagnationDays (the longest run of days without a new equity high per attempt - the dead time between progress, which grows sharply as risk per trade shrinks); funded-stage payout distributions plus funded.payoutProbability (P(at least one payout | funded)) and funded.daysToFirstPayout - with payout gating these can be the deciding numbers, since getting funded is not the same as getting paid; ev.evTotal (mean payouts minus costs) with evStandardError and pPositive; drawdown stats; and assumptions (the fully-resolved spec/profile/options the engine actually ran, plus flags and disclaimer). Histogram arrays are omitted unless includeHistograms=true. A compact human summary is returned as text alongside. SIMULATED RULES (engine v1): consistency rules (steps[].consistency) and funded payout gating (funded.payoutRules) are actually SIMULATED, not merely flagged - a distinguishing feature of this engine. Consistency uses a rational stop rule (the trader stops a day once more profit cannot help and keeps trading until the best-day share complies - flag 'consistency-stop-rule'); payouts follow a maximum-withdrawal model (withdraw everything the rules allow above buffer/caps, never below the loss floor; balances and floors carry across payouts - flag 'funded-withdrawal-model'); a funded consistency gate is checked per payout window (flag 'funded-consistency-window-approximated'). The pre-1.0 flag id 'funded-payout-resets-account' no longer exists. UNITS: every *Pct rule field and every percent-mode risk value is in PERCENT UNITS (5 = 5%, 0.5 = 0.5%). The one exception is winRate, which is a FRACTION in [0, 1] (0.55 = 55% winners). Probabilities in results are fractions in [0, 1]. DETERMINISM: identical inputs including seed reproduce byte-identical results on any platform. Include the seed and path count when reporting numbers so users can reproduce them exactly; re-run with a few different seeds to gauge Monte Carlo spread. ASSUMPTIONS: every result carries assumptions.flags - dataset-declared rules the engine does NOT simulate (e.g. scaling plans or soft daily lockouts, which make real odds worse than simulated) plus engine simplifications - and assumptions.disclaimer. These are material: always surface the flags and the disclaimer to the user alongside the numbers, never just the headline probability. Results are distributions under stated assumptions, not promises. DATA SOURCE & PROVENANCE: firm data comes live from LuxAlgo's public, keyless prop-firm directory API - the data behind luxalgo.com/prop-firms (origin overridable via the LUXALGO_APP_ORIGIN env var). Rule semantics are used verbatim where the directory serves structured rule columns; where it serves only free text, semantics are inferred ONLY when one reasonable reading exists, and every inferred field is disclosed in inferredFields (provenance 'directory+inferred') - relay those to the user next to any numbers. Challenges whose loss rules cannot be established are refused as not simulatable rather than guessed. Firms change rules; each firm's own page is always authoritative. Composes with any broker-statistics tool: if another MCP server exposes round-trip statistics (winRate, avgWin, avgLoss) or a raw R-multiple series from the user's real trades, feed them here to answer "given my actual trading, what are my odds on this challenge and what risk should I use?". Convert currency statistics to R-multiples by dividing by the average amount risked per trade: winRate stays a fraction, avgWinR = avgWin / avgRisk, avgLossR = |avgLoss| / avgRisk.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| seed | No | RNG seed (integer or string). Default 42. Same inputs + seed reproduce byte-identical results - include the seed when reporting so users can reproduce the numbers. | |
| spec | No | Inline challenge ruleset, for challenges not in the directory or for what-if rule edits. Mutually exclusive with firmId/challengeId. Identify the challenge EITHER by directory reference (firmId + challengeId, discovered via propfirms_list_simulatable; firmId accepts the directory id or the firm's name) OR by a full inline `spec` object - the exact shape propfirms_challenge_rules returns, so you can fetch a directory entry, change one rule, and re-simulate to model rule variations. Provide exactly one of the two forms; providing both or neither is an error. Directory references need network access; inline specs are fully offline. | |
| paths | No | Monte Carlo paths (independent simulated trader journeys). Default 10,000 (well under a second); capped at 100,000 per tool call. Confidence intervals shrink roughly with the square root of paths. | |
| firmId | No | Directory firm id or firm name (e.g. 'ftmo' or 'FTMO'); discover with propfirms_list_simulatable. Must be paired with challengeId. Mutually exclusive with `spec`. | |
| avgWinR | Yes | Average winning trade in R-multiples, i.e. multiples of the amount risked per trade (1.5 = winners average 1.5x the risk). | |
| context | Yes | Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution." | |
| winRate | Yes | Probability a trade is a winner, as a FRACTION in [0, 1] (0.55 = 55% winners) - NOT percent units. The most impactful input: traders routinely overestimate it by a few points, which can flip EV negative, so prefer measured stats over self-reported ones. | |
| winStdR | No | Standard deviation of winner sizes in R (0 = every winner is exactly avgWinR). Default 0. Adding spread makes streak damage more realistic. | |
| avgLossR | No | Average losing trade in R, as a POSITIVE number. Default 1 (losers lose exactly the risked amount, i.e. stops are honored). Raise above 1 to model slippage or blown stops. | |
| lossStdR | No | Standard deviation of loser sizes in R (0 = every loser is exactly avgLossR). Default 0. | |
| riskMode | No | How riskValue is interpreted. 'percent-of-balance' (default): risk compounds with the current balance. 'percent-of-initial': constant currency risk derived from the initial account size - how most prop traders size, since loss limits are fixed in currency. 'fixed-amount': explicit currency risked per 1R. | |
| riskValue | Yes | Risk per trade - the value of 1R. PERCENT UNITS for percent modes (0.5 = 0.5% risked per trade; a typical prop range is 0.25-2), or a currency amount for 'fixed-amount'. NOT a fraction. | |
| attemptCap | No | Maximum challenge attempts per path before that path gives up. Default 25. Journey statistics (expected attempts/cost, P(funded)) are censored at this cap. | |
| challengeId | No | Directory challenge id; discover with propfirms_list_simulatable. Must be paired with firmId. Mutually exclusive with `spec`. | |
| tradesPerDay | Yes | Average trades per simulated trading day. More trades per day means more ways to hit the daily loss limit within a single day. | |
| simulateFunded | No | Whether to simulate the funded stage (payouts, blowup risk) after passing. Default true - EV is only meaningful with it on; set false to study the evaluation alone. | |
| fundedHorizonDays | No | Funded-stage horizon in trading days for the payout/EV simulation. Default 90 (about 4 calendar months). EV scales with this choice - state it when reporting EV. | |
| includeHistograms | No | Include histogram arrays (attempts, cost, net, drawdown) in the result. Default FALSE for this tool to keep responses compact; summary quantiles (p05...p95) are always included. | |
| tradesPerDayModel | No | 'fixed' (default): the same count every day. 'poisson': daily count drawn Poisson(tradesPerDay); days can then have zero trades, which do not count as trading days. |