LuxAlgo MCP

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.

Explain a trading concept: the Library's full write-up as markdown — definition, formula, how traders read it, and its indicator implementations. Use for any 'what is X / how does X work' question. Needs the exact slug — find it with library_search or library_list_concepts.

Details for one indicator: what it does, how to read it, family, concept links, preview image — plus whether its source code is available (fetch the code itself with library_get_source_code). Use when the user asks about a specific indicator.

The full, working source code of a Library indicator (works on TradingView). Kept separate from library_get_indicator because sources are long — call it only when the user wants the code itself.

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; for keyword lookup prefer library_search.

Browse the indicator catalog with filters and server-side sorting (newest first by default). Filter by family, concept slug (implementations of one concept), tags (ids from library_list_tags, AND-combined), trading platform, or plan tier. Use for structured browsing — 'latest indicators', 'everything in the volatility family', 'indicators implementing liquidity sweeps'; for keyword discovery pre

The Library's indicator tag vocabulary (behavioral traits like 'Volatility', 'Trailing-Stop', 'Repainting Functionality'). Returns ids to pass as the tags filter of library_list_indicators — tags are orthogonal to the concept-family taxonomy.

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.

A family's hub page as markdown — the written overview of that school of analysis plus its complete concept roster. Use after library_list_families, or when the user asks about a whole area like 'SMC' or 'Wyckoff'.

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) and offer filters — a firm matches when at least one of its challenges/offers matches all of them. Omit eve

One prop firm's full dossier by slug: general profile (platforms, markets, payments, Trustpilot, restricted countries), every challenge with its rules, live offers with promo codes and affiliate links, and the written overview (about, rules, payout policy, FAQ). Find slugs with propfirms_search. Uncaptured (null) fields are omitted; challenges reference applicable offers via offerIds into the firm

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. include=['offers'] returns a deduplicated top-level offers list, with each challenge refere

Search promotional offers (discounts and promo codes) across prop firms — defaults to live (active, unexpired) offers only. propfirmId narrows to one firm; challengeId resolves the offers that apply to a challenge (firm-wide offers included). Every offer carries the promo code, discount, end date, and affiliate link.

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-

Fetch one directory challenge's complete ruleset (ChallengeSpec), adapted from the live LuxAlgo directory: evaluation steps (profit targets in percent units of the initial account, minimum trading days, time limits); the daily-loss rule with its exact semantics (basis = measured from prior-day balance vs prior-day equity; limitBasis = whether a pct limit is a fixed allowance of the initial balance

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 re

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 limi

Simulate the SAME trader across several challenges (directory references and/or inline specs, up to 12) under identical options and seed, and return one row per challenge sorted by expected value. THIS IS NOT A RANKING: rows are ordered by EV for the caller's specific inputs - trader stats, risk sizing, and options - and a different trader profile reorders them. The tool computes data for the user

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 (autocorr

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

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 b

The Market Trackers catalog: every dataset of US public-record market data the LuxAlgo pipeline publishes as CC0 dumps — congressional trades, insider (Forms 3/4/5) transactions, 13F holdings, federal contracts and grants, lobbying filings, FINRA short-sale volume, granted patents, clinical trials, FDA drug events, CFTC positioning, federal bills, FEC campaign finance, hearing transcripts, Feder

Search one Market Trackers dataset by ticker, free text, exact field values, and event-date range, with paging and newest/oldest ordering. Data is read from year-sharded CC0 dumps: pass years (or since/until) to choose which years to read — default is the newest year with data. Deep-history years (see archiveYears in trackers_datasets) can be tens of MB compressed each, so read them one or two a

What the last daily publish added to one dataset — the newest ingestion day's rows (the dumps' latest.json), optionally narrowed by ticker or text. The cheapest way to see what is new: today's insider filings, this week's congressional disclosures, the latest lobbying registrations. Not available for snapshot-only bulk datasets (patents); use trackers_query there.

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 p

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

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.

One precomputed session-statistics result: P(outcome | conditions) for a preset on a hosted symbol, in the engine's full honesty envelope — the estimate with N and a Wilson 95% confidence interval, minimum-sample guards, a first-half vs second-half stability split, per-year counts, the value distribution where the outcome is continuous, and the disclaimer. Historical conditional frequencies, not

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).

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

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

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 LuxA

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

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 r

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 spec

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).

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).

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; keys

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

Annotate a trade — the user-owned fields only: `notes` (free text about this trade), `tags`, `mistakes`, `playbookId`, `rating` 1–5, `stopLoss` and `profitTarget` (price levels; the stop is what realized R is measured against) and `reviewed`. `tags`/`mistakes` replace the whole list; use `addTags`/`removeTags`/`addMistakes`/`removeMistakes` to change a few entries without clobbering the rest (

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/

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 Requ