Find pass- and EV-optimal risk per trade
propfirms_optimal_riskSweep risk-per-trade across a grid, simulate full prop firm journeys at each point, and compare two optima: the risk maximizing pass probability versus the risk maximizing expected value.
Instructions
Sweep risk-per-trade over a grid, run the full journey simulation at every point, and report two optima separately: bestByPassProbability (the risk that maximizes a single attempt's chance of passing) and bestByEv (the risk that maximizes expected value across attempts, fees and funded payouts). They usually differ (diverges=true) - and that divergence is the insight: lower risk survives loss limits more often, but EV also weighs the cost of extra attempts and the size of funded payouts, which can favor a different risk. Never present one number as THE optimal risk; report both optima and the trade-off, and let the user choose. The sweep uses common random numbers (the same seed at every grid point), so curves are smooth and the argmax is signal, not Monte Carlo noise. Grid units follow riskMode: percent units for percent modes (default grid 0.1 to 3 in steps of 0.1, i.e. 0.1%-3% per trade), currency per trade for 'fixed-amount' (set min/max/step explicitly). Parametric trader only (riskValue is not a parameter here - the grid supplies it). Cost scales with grid size: one full simulation per point, so ~30 points at the default 10,000 paths takes roughly 10 seconds; use fewer paths or a coarser grid for a first pass, then refine around the optima. 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.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| max | No | Grid end, same units as min. Default 3 (= 3% per trade for percent modes). | |
| min | No | Grid start, in the risk units of riskMode (percent units for percent modes, currency for 'fixed-amount'). Default 0.1 (= 0.1% per trade for percent modes). | |
| 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. | |
| step | No | Grid step, same units. Default 0.1. The sweep runs one full simulation per grid point, so (max - min) / step + 1 simulations in total - keep the grid coarse or paths low for a first pass. | |
| 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). | |
| 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. | |
| 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. | |
| 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. |