propfirms_simulate

io.github.LuxAlgo/luxalgo-mcp-server · Verify this server

Monte 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.