"Features of Grid Trading Strategies" matching MCP tools:

• get_feature_governance_status_toolA

  OneQAZ Trading IntelligenceA

  Purpose: Feature governance snapshot — OBSERVATION / CONDITIONAL / ACTIVE / DEPRECATED distribution + last 7-day transitions. Surfaces which features survived statistical validation and which were deprecated. Triggers (casual questions too): "which features are actually used?", "어떤 피처가 살아있어?", "any features promoted recently?", "피처 검증 현황 어때?", "did anything get deprecated?". When to call: trust evaluation, "which features are live right now?". Prerequisites: none. Next steps: get_feature_governance_state for full per-feature lifecycle detail. Caveats: promoter cycle runs hourly.

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• calculate_irr_sensitivityA

  valuation-apiA

  Compute an IRR sensitivity grid across a range of exit multiples and hold periods for a single lump-sum investment. WHEN TO USE: to stress-test how the annualised return varies with exit multiple and holding period before committing to an investment. Complements calculate_irr. WHEN NOT TO USE: when you need one precise IRR for a known exit value (use calculate_irr), or a full valuation (use calculate_dcf). BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive. NOTE ON GRID GEOMETRY: the byMultiple grid is computed at the SECOND hold period in hold_periods (default 5 years); the byHoldPeriod grid is computed at a 2.5x exit multiple. RETURNS: JSON object { byMultiple: { "2.0x": 14.9, ... } with IRR values as percentage numbers rounded to 1dp, byHoldPeriod: { "5y": 18.4, ... } }. PARAMETERS: initial_investment (number > 0), exit_multiples (optional array of numbers to test, default [1.5, 2.0, 2.5, 3.0, 3.5]), hold_periods (optional array of positive integers (years) to test, default [3, 5, 7, 10]).

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• calculate_irr_sensitivityA

  valuation-apiA

  Compute an IRR sensitivity grid across a range of exit multiples and hold periods for a single lump-sum investment. WHEN TO USE: to stress-test how the annualised return varies with exit multiple and holding period before committing to an investment. Complements calculate_irr. WHEN NOT TO USE: when you need one precise IRR for a known exit value (use calculate_irr), or a full valuation (use calculate_dcf). BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive. NOTE ON GRID GEOMETRY: the byMultiple grid is computed at the SECOND hold period in hold_periods (default 5 years); the byHoldPeriod grid is computed at a 2.5x exit multiple. RETURNS: JSON object { byMultiple: { "2.0x": 14.9, ... } with IRR values as percentage numbers rounded to 1dp, byHoldPeriod: { "5y": 18.4, ... } }. PARAMETERS: initial_investment (number > 0), exit_multiples (optional array of numbers to test, default [1.5, 2.0, 2.5, 3.0, 3.5]), hold_periods (optional array of positive integers (years) to test, default [3, 5, 7, 10]).

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• calculate_irr_sensitivityA

  Valuation APIA

  Compute an IRR sensitivity grid across a range of exit multiples and hold periods for a single lump-sum investment. WHEN TO USE: to stress-test how the annualised return varies with exit multiple and holding period before committing to an investment. Complements calculate_irr. WHEN NOT TO USE: when you need one precise IRR for a known exit value (use calculate_irr), or a full valuation (use calculate_dcf). BEHAVIOUR: pure deterministic calculation — no side effects, no network or storage access; idempotent and non-destructive. NOTE ON GRID GEOMETRY: the byMultiple grid is computed at the SECOND hold period in hold_periods (default 5 years); the byHoldPeriod grid is computed at a 2.5x exit multiple. RETURNS: JSON object { byMultiple: { "2.0x": 14.9, ... } with IRR values as percentage numbers rounded to 1dp, byHoldPeriod: { "5y": 18.4, ... } }. PARAMETERS: initial_investment (number > 0), exit_multiples (optional array of numbers to test, default [1.5, 2.0, 2.5, 3.0, 3.5]), hold_periods (optional array of positive integers (years) to test, default [3, 5, 7, 10]).

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• get_my_strategiesA

  Sentinel AlephA

  Lists the saved strategies on the API key's account, newest first, archived ones included (active false): id, mode, engines, minimum confidence, direction and symbols. Use it to pick the strategy_id that run_backtest, update_strategy and delete_strategy take; for live bot activity use get_my_bots. Archived strategies count toward limit, and there is no paging: a strategy beyond the newest limit is read by its id with get_strategy; an account without strategies gets an empty list. An empty symbols list means any pair, so run_backtest then needs symbol. Needs an API key (without one the call is refused with 401) and spends 1 read unit of the daily quota; read only.

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• compare_backtestsA

  Backtest360A

  Run several strategies on the same data and compare side by side. One quota-counted call, but compute scales with the number of strategies. If the wall-clock compute budget is exceeded, the call fails with a tool error (504) instead of returning partial results — narrow the request (fewer strategies, shorter date range, coarser frequency) and retry. Args: data_source: Shared data source (same shape as run_backtest). strategies: List of {"label": str, "strategy": {...}, "execution": {...}?} entries. Labels need not be unique or id-safe — they are echoed back verbatim in the result. include_benchmark: Add a buy-and-hold benchmark to the comparison. response_detail: Shaping level applied to each strategy's result. trades_limit: Max trades per strategy when detail is 'full'. Returns: {"strategies": [{"label", "result"}, ...], "equity_curves": {...}, "alignment"?}, each result shaped at the requested detail. When a benchmark is included, non-benchmark entries also carry "relative" (beta, alpha, information ratio, etc.). A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error.

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• arena_compare_strategiesA

  Backtesting ArenaA

  Which of these strategies performed best on the same data? Run 2–5 strategies against the SAME pair, interval and date range and return per-strategy metrics plus a comparison summary (best by CAGR, best by win-rate, worst by drawdown). Use this when the user asks which of several strategies fits a market — it holds the pair, interval and requested date range fixed, which a series of separate arena_run_backtest calls does not guarantee. What it does NOT equalize is the EVALUATION window: a strategy with a long warmup starts trading later, so compare actual_date_from across the runs and check result.benchmark before ranking by CAGR. For one strategy across many pairs use arena_run_universe_backtest instead. Caveat worth passing on: comparing N strategies and reporting the winner IS multiple testing — the winner’s edge is upward-biased. arena_get_robustness_field puts a counted N on that. Sequential, expect 10–50s. Per-day quota: Pro=20, Power=200. [API Pro tier]

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• arena_run_grid_backtestA

  Backtesting ArenaA

  Would a grid bot have made money here? Simulate a GRID BOT (buy-low / sell-high ladder inside a fixed price range) on historical candles. Returns final value, return %, CAGR, trade count, fees paid and a Buy & Hold comparison — plus `zerlegung` (spot runs): `decomposition` splits the result into ladder P&L from completed buy→sell cycles, allocation P&L of the starting coins, open grid buys and fees (identity_check_usd must be ~0); `benchmarks` anchors buy-and-hold, the never-touched starting split (static_allocation, with coin_share_start) and an arithmetic 50/50 at the entry price — grid_vs_static_pp is the number that says whether the ladder added anything over just holding the split; `fee_economics` gives the break-even spacing (2 × fee) and flags below_breakeven. Note: buyhold_return/outperformance keep their legacy anchor (first→last candle close); benchmarks anchor at entry. This is a different machine from the strategy backtester: grid bots earn from oscillation inside a range, not from trend — for signal-based strategies use arena_run_backtest instead. The result depends heavily on the range you choose (low_price / high_price); a range the price left early makes the bot idle (by default the grid pauses outside the range and resumes when price returns; set stop_on_range_exit to end the run at the first close outside it instead, selling all coins there), so treat range choice as part of the hypothesis, not a detail — arena_suggest_grid_range proposes a defensible range. Each run is saved to your account (the returned id is the run_id); publish a public snapshot page with arena_share_grid_backtest. grid_mode picks neutral (default) or long. Optional leverage (2/3/5, grid_mode long only, Pro) with funding_mode (conservative default / historical BTCUSDT / none): simulates an isolated-margin futures long grid — margin = total_investment, the grid trades margin × leverage, funding accrues daily on the open position, liquidation is checked per candle at the low. It simulates, it does not recommend: the result can be a total loss of the margin. Free tier limited to BTCUSDT/ETHUSDT. Per-day quota: Free=5, Pro=50, Power=500. [Free / Pro / Power tier]

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• get_strategyA

  HeistaA

  Read a creative strategy in full by its powersource_id. Returns the same brand-merged bundle shape as get_powersource(data) — buyer profile, 12 behavioral tensions, angles, narrative direction, tone of voice, selling points, CTAs, proof, brand story, homepage data, offering — projected through the public PowerSource API serializer. Use this when you already have a powersource_id (from list_strategies) and want the full strategy payload in one call, without the job_id round-trip that get_powersource needs. Archived strategies are excluded by default (parity with list_strategies). Pass include_archived=true to read archived strategies. Read-only, free, account-scoped.

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• get_grid_selected_photosA

  PexafyA

  Returns the photographs the person liked (hearted) in the Pexafy results grid. Use it when the person refers to that selection rather than asking for new photos — for example “the images I selected”, “the ones I chose”, “my selection”, “the photos I hearted”, “the photos I liked”, “use the ones I picked”, “put my selection in the document”, “download the ones I chose”, or “write the article around my photos”. The selection is held by the grid and the server, not by the conversation: this tool is how it is read, and search results are not the selection. It takes no arguments and returns every liked photograph with its rank, photo_id, photographer, source library, licence, pixel size, orientation, description, page URL and credit line, and `selection_count`, the number of them. To get one of them as an image file, pass its photo_id to get_photo_file_by_photo_id. `rank` is the person's order, #1 first: the order the photos were liked in, unless the person rearranged them by dragging in the grid. Where the grid is rendered, that number is drawn on each liked photograph, so it is the number the person sees and names them by (“#2”). The `rank` of a search result is only its position in that answer and is not drawn on the grid. An empty selection means nothing is liked in the grid currently on screen; a photograph is liked with the heart on it. A selection expires an hour after its last change, and a new search replaces the grid it belongs to.

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• get_featuresA

  FactorWeaveA

  Get factor row for a ticker. With no date arg, returns the most recent row. With date / start_date / end_date, returns the historical row(s) — useful for honest analogue-backtests (querying a setup as it was on a specific historical date, not as it looks today). History is the last 252 trading days. Stock/ETF = FREE+; futures = PRO+ (adds Open Interest features). PRO+ subscribers automatically get intraday-derived columns (overnight_ret, intraday_ret, or_high_30, or_low_30, or_breakout_pct, vwap, vwap_dev_close, intraday_rv, late_drift) on the stock row.

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• get_schemaA

  studioA

  Returns the base ReoGridJsonDocument schema and an index of advanced features. The base schema already covers cell values, formulas, styling, borders and merges — those need no follow-up call. Call get_feature_spec(feature) only for a name listed in the returned features[] array, passing features[].name verbatim.

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• get_tracking_settingsA

  Statable AnalyticsA

  What the installed script actually does: which tracking features are on, which exist, and what each costs in bytes. Response: {"site_id":123,"version":N,"bundle":"...","enabled":["hash","outbound"],"features":[{"id":"outbound","label":"...","enabled":true,"locked":false,"default":false,"requires":[...],"size_br":123}]}. Locked features cannot be changed; `requires` lists features that must be on for this one to work. Needs the same access as changing them (see update_tracking_settings).

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• get_tracking_settingsA

  StatableA

  What the installed script actually does: which tracking features are on, which exist, and what each costs in bytes. Response: {"site_id":123,"version":N,"bundle":"...","enabled":["hash","outbound"],"features":[{"id":"outbound","label":"...","enabled":true,"locked":false,"default":false,"requires":[...],"size_br":123}]}. Locked features cannot be changed; `requires` lists features that must be on for this one to work. Needs the same access as changing them (see update_tracking_settings).

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• list_strategiesA

  StratifyA

  Strategies from THIS account's history that held up under out-of-sample and walk-forward checks, not merely ones that made money. Ranked by worst walk-forward fold — consistency, not size. Answers what has worked on this account so far without re-running anything.

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• cotationA

  Gold & Silver CompanyA

  Today's public buying grid of Gold & Silver Company, timestamped: gold by fineness (9K, 14K, 18K, 22K in EUR per gram; 24K/999 refers to the live rate) or by coin/bar (napoleon_20f, pesos_50, argent_lingot_1kg in EUR per piece). Optional weight in grams or number of pieces gives an indicative amount; `objets` quotes several items in one call with a total. The firm price is set at the office after weighing and fineness check. Source: the public grid on the website, never internal tools.

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• traceable_trace_id_renameA

  TraceableA

  Rename a row's visible TraceID in place (a traceable row or an ID-grid row). The row keeps its database identity, so every trace link into or out of it survives; an ID-grid row's id cell is kept in sync and cached link labels that reference the old id are refreshed project-wide. Use this instead of delete-and-reinsert, which would break links. To renumber a whole grid, call this once per row (re-read afterwards is not needed — anchor by TraceID).

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  Destructive

  OAuth

• get_strategy_leaderboardA

  OneQAZ Trading IntelligenceA

  Purpose: Top RL-learned research strategies — GLOBAL pool + per-symbol partition. Layer E evidence (Layer E = strategy-performance tier of the 5-layer trust pyramid). The GLOBAL pool may include synthesized win_rate values, so per_symbol_leaderboard is the primary measured-edge surface for trust auditing. Triggers (casual questions too): "what are the best strategies?", "제일 잘 버는 전략 뭐야?", "top strategies?", "전략 순위 보여줘", "which strategy has the best win rate?". When to call: final trust-validation step. Prerequisites: none. Next steps: market://{market_id}/signals/summary for live signals. Caveats: `min_trades` filter enforces statistical validity. Strategies are paper-tested, not real-money executed.

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• get_top_trading_strategiesA

  Helium MCP Server - News, Markets & AIA

  Get the top-ranked short volatility and long volatility option trading strategies. Returns two ranked lists — short_volatility (sell premium / theta strategies) and long_volatility (buy premium / gamma strategies) — each containing up to `limit` tickers. Each entry has the same fields as get_ticker: - ticker, name, latest_price, page_url - bullish_case, bearish_case, potential_outcomes, takeaway, analysis_date (AI-generated, when available) - price_forecast_days, price_forecast_percent, price_forecast_lower/upper_bound_percent (when available) - iv_rank_percentile (0-100, IV rank over past year, when available) - short_vol_call, short_vol_put: best short volatility option packs (when available) - long_vol_call, long_vol_put: best long volatility option packs (when available) Sort options: - "helium_rank" (default): Helium AI edge score — best overall expected value - "odds_of_profit": Highest probability of profit - "historical_performance": Best annualized historical P&L across backtested trades - "reward_to_risk": Best reward-to-risk ratio - "smallest_max_loss": Strategies with the smallest maximum possible loss Args: sort: Ranking method (default "helium_rank"). One of: 'helium_rank', 'odds_of_profit', 'historical_performance', 'reward_to_risk', 'smallest_max_loss'. limit: Number of results per strategy type (1-20, default 5).

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• export_backtestA

  Backtest360A

  Export a multi-strategy comparison as an Excel workbook. Quota-counted; needs a key whose plan includes full-metrics export (a 403 means the configured key's plan does not — do not retry). Returns the workbook base64-encoded — decode and write it to a ``.xlsx`` file. Args: data_source: Shared data source (same shape as run_backtest). strategies: Same shape as compare_backtests' ``strategies``. include_benchmark: Add a buy-and-hold benchmark to the export. Returns: {"filename", "content_type", "size_bytes", "content_base64"}. A 400/422 rejection returns {"accepted": false, "error": ...}; capacity/timeout/permission failures raise a tool error. If the encoded workbook would exceed the output size limit, raises a tool error — narrow the request (shorter date range, fewer strategies, coarser frequency) and retry.

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• calculate_electricityA

  apiA

  GHG Protocol Scope 2 for purchased electricity, both methods. Always returns location-based (grid-average) emissions; also returns market-based when you supply a contractual supplier_factor (e.g. a green tariff / REC = 0) or a market_factor_key (residual mix). Find grid keys via search_factors (section "grid").

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