LuxAlgo

A single trading day: its stats (null when nothing traded), its trades (closed that day, or opened that day and still open) as summaries, and the day's notes with their ids. `date` is a YYYY-MM-DD day key in the journal timezone. Use it for 'how did Tuesday go', and to find note ids for journal_update_note. Requires signing in with a LuxAlgo account (OAuth).

journal_get_day

One trade in full: the summary fields plus its fills (each with the effective values, what the source reported, the user's corrections and whether it is hidden), per-exit gross P&L, hidden fills inside the trade's span, and every annotation — notes, tags, mistakes, playbook id, stop loss, profit target, review time. Use after journal_list_trades or journal_get_day when the user asks about a specific trade or before annotating it. Requires signing in with a LuxAlgo account (OAuth).

journal_get_trade

What the hosted Edge Stats store covers: the symbols, their session calendars, coverage windows, session counts, and when the nightly build last ran. Session statistics (how often a setup actually worked, with sample sizes and confidence intervals) come from the open-source edge-stats engine over free market data. Start here, then edge_presets for the questions you can ask, then edge_report for a result.

edge_symbols

Where the P&L actually comes from: closed trades in the window grouped nine ways — weekday, time of day, hold time, symbol, side, position size, tag, rating and asset class — each group with trade count, wins, losses, net P&L, average net P&L and win rate (breakevens excluded). Defaults to all time, since groups need sample size; narrow with `range` or `from`/`to`. The tool for 'what am I good or bad at' questions; journal_overview has the headline numbers. Requires signing in with a LuxAlgo account (OAuth).

journal_breakdown

One month of the P&L calendar: week rows of day cells (net and gross P&L, fees, trade/win/loss/breakeven counts, volume; null for days with no trades), each week's net P&L and trade count, and the month's net P&L, trade count, trading days and winning days. Days are in the journal timezone. Omit `month` for the current month. Drill into one day with journal_get_day. Requires signing in with a LuxAlgo account (OAuth).

journal_calendar

The journal dashboard in one call for a window: performance metrics (net/gross P&L, fees, win rate, day win rate, profit factor, expectancy, average win/loss and their ratio, largest win/loss, streaks, max drawdown, recovery factor, profit concentration, average realized R), the Edge Score with its six components, per-day P&L stats, the cumulative equity curve, open positions, and the accounts and settings the numbers cover. Closed trades bucket by close day in the journal timezone; open positions count toward any window that reaches today. `compare: true` adds `previous` — the equal-length window just before. Defaults to the last 30 days; pass `range: 'all'` or explicit `from`/`to` for more. Null metrics are not computable yet (e.g. no losses → `profitFactorIsInfinite`). Use journal_breakdown for where the P&L comes from and journal_list_trades for the trades themselves. Requires signing in with a LuxAlgo account (OAuth).

journal_overview

The catalog of session-statistics questions the hosted store precomputes nightly — gap fills, opening-range breakouts, day-of-week effects, event-day behavior, and more. Each preset states in plain language what its number means. Returns preset ids for edge_report.

edge_presets

Browse every trading and technical-analysis concept in the Library — paginated, optionally one family. Use to enumerate a topic area or find slugs for library_get_concept and the concept filter of library_list_indicators; for keyword lookup prefer library_search.

library_list_concepts

Browse the indicator catalog with filters and server-side sorting (newest first by default). The Library is organized by family and concept: filter by family (keys from library_list_families) or by concept slug (that concept's implementations; slugs from library_list_concepts or library_search), and optionally by trading platform or plan tier. Use for structured browsing — 'latest indicators', 'everything in the volatility family', 'indicators implementing liquidity sweeps'; for keyword discovery prefer library_search.

library_list_indicators

The Market Trackers catalog of US stock-market data from public filings: insider buying and selling (SEC Forms 3/4/5), institutional holdings (13F), congressional stock trades, short-sale volume, federal contracts and grants, lobbying filings, patents, clinical trials, FDA drug events, futures positioning (CFTC), Federal Reserve communications and Wikipedia pageviews. Returns each dataset's size, freshness, the years covered, and whether it can be searched by ticker. Pass a dataset for its fields, filters and caveats; trackers_query searches it.

trackers_datasets

The Library's top-level taxonomy: 17 families of trading concepts (trend, momentum, SMC/ICT, statistics, …) with concept counts and hub links. The natural first call for orientation.

library_list_families

The signed-in user's trade-journal accounts — id, name, broker, kind (`sync` mirrors a live broker connection, `import` came from statements, `manual` is hand-entered), currency, initial balance, P&L lot method, last broker sync, archived state — plus `timeZone`, the journal timezone every date in the journal tools is expressed in. Call this first: every other journal tool's `accounts` filter takes these ids and rejects unknown ones, and journal_add_trade needs a `manual` or `import` account. An empty list means no journal yet. Requires signing in with a LuxAlgo account (OAuth).

journal_list_accounts

The user's annotation vocabulary: every tag, mistake and playbook id they have put on any trade (open or closed), most-used first with the number of trades carrying each. Check it before journal_update_trade so new annotations reuse the user's own words instead of minting near-duplicates. Per-trade tags are on each trade summary, not here. Requires signing in with a LuxAlgo account (OAuth).

journal_list_tags

Trade summaries — `key`, account, symbol, asset class, direction, status (open/win/loss/breakeven), open and close times, quantity and open quantity, average entry/exit, gross and net P&L, fees, fill count, duration, realized R, tags, rating, reviewed flag, hasNotes — newest-opened first by default. `sort` orders by any of openedAt, closedAt, netPnl, grossPnl, durationMs, quantity, symbol or rating (names match the response fields); `order` is desc unless set, except symbol which defaults to asc. Trades lacking the sort value (open trades for closedAt/durationMs, unrated for rating) come last in either order; netPnl is after fees, grossPnl before. Filter by account ids, open-day window (`from`/`to` are inclusive YYYY-MM-DD day keys in the journal timezone, applied to the trade's open day; open positions are always listed), symbol, direction, status or one exact tag. Keyset-paginated: pass `nextCursor` back as `cursor` with the same sort, order and filters. Examples: biggest winners this month = from/to + sort netPnl; worst by gross = sort grossPnl, order asc, status loss; longest holds = sort durationMs. Summaries carry no fills or note text: journal_get_trade with the `key` has those. Requires signing in with a LuxAlgo account (OAuth).

journal_list_trades

List the prop firms in the live LuxAlgo directory together with every simulatable challenge (challengeId, display name, account size, currency, price, and its rule-semantics provenance). Call this first to discover the firmId + challengeId pairs accepted by propfirms_challenge_rules, propfirms_simulate, propfirms_optimal_risk, propfirms_compare and propfirms_simulate_trades. Challenges whose loss-rule semantics cannot be established are listed under notSimulatable instead of being guessed. The listing is data, not endorsement: firms are alphabetical - no recommendation or ranking is implied. DATA SOURCE & PROVENANCE: firm data comes live from LuxAlgo's public, keyless prop-firm directory API - the data behind luxalgo.com/prop-firms. 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'). 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. NOTE: this lists only the firms and challenges whose rules the engine can encode honestly. The full directory — every visible firm with platforms, prices, and payout terms — is served by propfirms_search and propfirms_search_challenges.

propfirms_list_simulatable

Log a trade by adding its fills to a `manual` or `import` journal account (never a broker-synced one — the sync owns those). The journal derives trades from fills: a long round trip is a buy fill then a sell fill, a short is sell then buy, scale-ins and partial exits are just more fills, and a lone fill opens a position. Times are ISO 8601 instants with offset; fees are per fill. Fills identical to existing ones are skipped as duplicates. Returns the insert counts and the trade(s) the fills now belong to, with `key`s for journal_update_trade. Correcting or removing an existing fill is done in the app, not here. Requires signing in with a LuxAlgo account (OAuth).

journal_add_trade

The signed-in user's LuxAlgo account: plan tier, entitlements (limits such as alerts, historical bars, AI credits) and profile basics. Use it to tailor answers to what the user's plan actually allows, or when the user asks what plan they are on. Requires signing in with a LuxAlgo account (OAuth).

luxalgo_account

The newest rows added to one Market Trackers dataset in the last daily update, optionally narrowed by ticker or field values: the latest insider filings, congressional trade disclosures or lobbying registrations. Patents are updated in bulk, not daily; use trackers_query for them.

trackers_latest

Search one Market Trackers dataset by ticker, exact field values and date range, newest or oldest first, with paging. Reads one year by default (the newest with data); pass years or since/until for others, one or two older years at a time. Every row links to the public filing or record it came from (provenance.sourceUrl). Examples: insider purchases at NVDA in 2024 → dataset insider-transactions, ticker NVDA, years [2024], where {code: 'P'}; congressional purchases of NVDA → congress-trades, ticker NVDA, where {side: 'buy'}; short volume for TSLA → short-volume, ticker TSLA.

trackers_query

Reference challenge pass rates computed live from the directory's encoded rules with the same engine, seed (42), path count (10,000) and reference archetypes luxalgo.com/prop-firms uses — per challenge and per archetype (developing 45% win rate / consistent 48% / proven edge 52%, all risking conservatively). Returns per-attempt pass probability with 95% CI, P(funded), expected attempts and total cost, EV, payout probability, funded-blowup probability, each cell's assumption flag ids, and the ruleset's provenance (structured directory columns vs fields inferred from listing text). Deterministic per ruleset and cached — cheap to call. These are REFERENCE odds for orientation and comparison, not the user's personal odds: for their own statistics use propfirms_simulate (summary stats) or propfirms_simulate_trades (their real trade series). Not a ranking; a firm's page is authoritative for current rules (check lastVerified). Expected costs use the directory's listed challenge prices; full firm profiles are directory data (propfirms_get).

propfirms_pass_rates

Answer 'which challenges would MY strategy actually pass?' in one call: simulate the given strategy through every simulatable challenge in the live directory (optionally scoped by productType, account-size range, priceMax, or firm) and split the results by an explicit, caller-stated bar. Describe the strategy EITHER as real trades (rSeries/rSeriesText R-multiples, preferred: the stationary block bootstrap preserves streaks, which is what breaches loss limits) OR as summary stats (winRate + avgWinR, optional spreads), plus tradesPerDay and risk sizing (riskMode + riskValue). The bar is minPassPerAttempt (a fraction, default 0.5) with optional requirePositiveEv. Returns per challenge: pass probability per attempt with 95% CI, P(funded), expected attempts and total cost, EV over the funded horizon, P(EV>0), assumption flag ids, and which rule semantics were inferred from listing text. HONESTY FRAME: this is a screen of distributions for the caller's inputs and bar, NOT a ranking or endorsement; challenges whose rules cannot be encoded honestly are excluded and counted, never guessed; flagged (unsimulated) rules make numbers optimistic. One full simulation runs per challenge (default 5,000 paths each; results are deterministic per seed), and scopes above 40 challenges are refused rather than silently truncated: narrow the scope instead. Numbers move with risk sizing; sweep one challenge with propfirms_optimal_risk afterwards. Fees and expected costs use the directory's listed prices (live discounts are NOT applied); prices and firm profiles are directory data (propfirms_search_challenges, propfirms_get).

propfirms_validate_strategy

Search the notes feed — day notes and trades that carry notes — newest first as one stream split by kind: `notes` (day notes, with ids for journal_update_note) and `tradeNotes` (the trade summary with its note text; annotate via journal_update_trade). Filter by case-insensitive text (`q`), day-key window, symbol (trade notes only — day notes have no symbol and drop out) and account ids; keyset-paginated via `cursor`/`nextCursor`. Omit every filter for the latest notes. Requires signing in with a LuxAlgo account (OAuth).

journal_search_notes

Search LuxAlgo's prop-firm catalog (proprietary trading firms offering funded accounts). Combine firm filters (platforms, markets, payment/payout methods, country availability, Trustpilot, year founded) with nested challenge filters (account size, price, steps, profit split, trading rules) — a firm matches when at least one of its challenges matches all of them. Omit every filter to list all firms. Use include to nest the matching challenges and the written overview; for one firm's full dossier prefer propfirms_get. Uncaptured (null) fields are omitted from results. This tool returns directory data (what exists and on what terms), not outcomes: for simulated pass odds on a challenge found here, use propfirms_pass_rates or propfirms_simulate with its firm and challenge ids.

propfirms_search

Search funded-account challenges across all visible prop firms. Filter by challenge rules (account size, fee, steps, profit split, drawdown mode, news/copy/auto trading, weekend holding, …) and by parent-firm properties. Pass propfirmId to list one firm's challenges, or challengeId to fetch specific ones. Uncaptured (null) rule fields are omitted from results and never match filters. This returns each challenge's listed rules and terms, not outcomes: to simulate a challenge found here pass its ids to propfirms_simulate or propfirms_pass_rates, and to screen one strategy across many challenges at once use propfirms_validate_strategy.

propfirms_search_challenges

Search the LuxAlgo Library — the encyclopedia of trading and technical analysis. One query over 800+ concepts (alias-aware: 'stochastics' finds Stochastic Oscillator) and 800+ ready-to-use indicators. Start here whenever you have a name, informal term, or topic; results carry slugs for the get tools plus canonical URLs for citation.

library_search

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. 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'). 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. Results carry the seed and path count, so they reproduce exactly; different seeds show the 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 to the numbers. 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. 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'). 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.

propfirms_simulate

Simulate a challenge by resampling the trader's OWN R-multiple trade series with a stationary block bootstrap instead of a win-rate model. Why resampling matters: challenge rules are breached by streaks, not by averages - a daily-loss limit dies to a cluster of losses inside one day, and a trailing drawdown dies to a losing streak right after an equity peak. Real trade series are streaky (autocorrelation, volatility clustering, edge that comes and goes), and the stationary bootstrap resamples contiguous blocks of the actual series (geometric length, mean blockMeanLength, default 5 trades), so the trader's real streak structure survives into every simulated day. A parametric model with identical summary statistics shuffles trades independently and therefore understates breach risk for streaky traders. Use propfirms_simulate when only summary stats are available; use this whenever the actual trades are. Provide the series as rSeries (array of R-multiples: each trade's P&L divided by the amount risked on it), rSeriesText (pasted JSON/CSV/whitespace text, optional 'R' suffix per value), or one of the timestamped-log inputs below; exactly one of the four, at least 10 trades, 100+ strongly recommended. Returns the same full SimResult as propfirms_simulate (structuredContent, histograms off by default) plus a text summary that also reports the sample's win rate and mean R. TIMESTAMPED LOGS: tradeLogText accepts a pasted CSV/TSV trade log with a header row (open time and R required; close time and direction optional; loose header names are matched; timestamps without an offset are read as UTC). The R-series and, unless tradesPerDay is passed, the trades-per-day rate are derived from the log, and parse warnings are surfaced in the text output. NEWS WINDOWS: with a timestamped input, newsFilter runs the simulation TWICE on the same seed and options, once on the full history and once without the trades opened inside configurable windows around scheduled releases (a built-in recurring-template calendar of high- and medium-impact events across USD, EUR, GBP, JPY, AUD, CAD, CHF, NZD, plus optional custom event times). The returned SimResult is the news-avoided scenario; structuredContent.newsComparison carries both scenarios' pass probability, funded probability and EV, the excluded-trade count, and a calendar caveat. PORTFOLIO MODE: tradeLogTexts (2 to 5 logs) merges several timestamped histories into one chronological series and simulates the combined account, so cross-strategy loss clustering survives. Overlap across the histories is ALWAYS analyzed and attached as structuredContent.portfolioOverlap; the text summary carries the audit-risk verdict, and a 'high' verdict is an explicit warning that a prop firm may audit or refuse payouts for correlated accounts. SIMULATED RULES (engine v1): consistency rules (steps[].consistency) and funded payout gating (funded.payoutRules) are actually SIMULATED, not merely flagged. 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'). 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. Results carry the seed and path count, so they reproduce exactly; different seeds show the 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 to the numbers. Results are distributions under stated assumptions, not promises.

propfirms_simulate_trades

One ticker across every ticker-bearing Market Trackers dataset for one year (default: the current year): insider transactions, congressional trades, 13F holdings, federal contracts and grants, lobbying filings by the company, short-sale volume, clinical trials, FDA events, patents, Wikipedia pageviews. Returns per-dataset match counts with the newest rows of each — a public-record dossier from primary sources. Deep-history archive years too large for one fan-out are listed under skipped with the trackers_query call that reads them.

trackers_ticker

Replace a day note's text and/or move it to another day, by note id (from journal_get_day or journal_search_notes). The body is replaced whole — to append, read the current text first and send the full new version. Trade notes are edited with journal_update_trade, not here. Returns the updated note. Day notes in the LuxAlgo journal: https://docs.luxalgo.com/platform/workspaces/journal/notes Requires signing in with a LuxAlgo account (OAuth).

journal_update_note

Add a new note to a trading day — any day, traded or not; `date` is YYYY-MM-DD in the journal timezone. Days hold any number of notes, so this never overwrites: to change an existing note use journal_update_note, and for a note about one specific trade use journal_update_trade's `notes`. Returns the note with its id. Day notes in the LuxAlgo journal: https://docs.luxalgo.com/platform/workspaces/journal/notes Requires signing in with a LuxAlgo account (OAuth).

journal_write_note