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392,572 tools. Last updated 2026-08-04 22:24

"Features of Grid Trading Strategies" matching MCP tools:

  • 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. Disclaimer: Information only, not investment advice.
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  • Use when a human asks about gas-fired or behind-the-meter power economics for a data center in a US state — "is gas power cheaper than the grid in Texas?", "what is the gas access + pipeline situation in Virginia?". The GAS analogue of get_grid_intelligence: fuses the DC Hub Gas Index (DCGI), live Henry Hub, gas-to-grid $/MWh across heat-rate scenarios, pipeline-operator presence, and the live grid gas share into one per-STATE brief. Params: region (US state code or name, e.g. "TX" | "Texas" | "Virginia"). Returns: {region, region_name, dcgi_score (0-100), dcgi_verdict (GAS-ADVANTAGED/ADEQUATE/GAS-CONSTRAINED), gas_access (pipeline counts + operators — PRESENCE not firm capacity), henry_hub_usd_mmbtu (live), basis_usd_mmbtu (synthetic-labeled), delivered_price_usd_mmbtu (null where the tariff table is sparse — surfaced honestly, never fabricated), gas_to_grid_usd_per_mwh (5 heat-rate scenarios), live_grid_gas_share_pct, headline_behind_meter_vs_grid_delta_usd_mwh (the punchline: gas vs grid $/MWh), pipeline_presence (operators + parent midstreams), data_basis (per-field provenance/confidence), omitted_no_fabrication}. Every field carries a data_basis label; gas storage / LNG / firm pipeline capacity are deliberately OMITTED (no feed). Do NOT use for electricity grid headroom (use get_grid_intelligence) or the DCGI score alone (use get_gas_index).
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  • 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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  • 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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  • 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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  • OVATION model aurora forecast for the next ~30–60 min: global grid of aurora probability percentages by latitude/longitude (1° resolution). With optional coordinates, returns the local aurora probability at the nearest grid point, the minimum Kp needed for aurora at that latitude, and a plain-language go/no-go verdict. Without coordinates, returns only global metadata. Data updates every ~5 minutes. Coordinates are geographic (WGS84), not geomagnetic.
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Matching MCP Servers

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    Provides Dev Container Features that install code intelligence (LSP) and repository knowledge search (Orama) MCP servers into any dev container, enabling coding agents to perform go-to-definition, find references, and hybrid search over project files.
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    MIT

Matching MCP Connectors

  • US ISO Grid MCP — real-time electricity generation, fuel mix, demand,

  • AI-powered trading strategy development: backtesting, market data, and portfolio analysis

  • Use when a human asks about gas-fired or behind-the-meter power economics for a data center in a US state — "is gas power cheaper than the grid in Texas?", "what is the gas access + pipeline situation in Virginia?". The GAS analogue of get_grid_intelligence: fuses the DC Hub Gas Index (DCGI), live Henry Hub, gas-to-grid $/MWh across heat-rate scenarios, pipeline-operator presence, and the live grid gas share into one per-STATE brief. Params: region (US state code or name, e.g. "TX" | "Texas" | "Virginia"). Returns: {region, region_name, dcgi_score (0-100), dcgi_verdict (GAS-ADVANTAGED/ADEQUATE/GAS-CONSTRAINED), gas_access (pipeline counts + operators — PRESENCE not firm capacity), henry_hub_usd_mmbtu (live), basis_usd_mmbtu (synthetic-labeled), delivered_price_usd_mmbtu (null where the tariff table is sparse — surfaced honestly, never fabricated), gas_to_grid_usd_per_mwh (5 heat-rate scenarios), live_grid_gas_share_pct, headline_behind_meter_vs_grid_delta_usd_mwh (the punchline: gas vs grid $/MWh), pipeline_presence (operators + parent midstreams), data_basis (per-field provenance/confidence), omitted_no_fabrication}. Every field carries a data_basis label; gas storage / LNG / firm pipeline capacity are deliberately OMITTED (no feed). Do NOT use for electricity grid headroom (use get_grid_intelligence) or the DCGI score alone (use get_gas_index).
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  • Submit a trading-edge idea to the governed edge-idea bounty. You are paid a FLAT sats bounty for the IDEA if it survives the same backtest gate (Monte-Carlo permutation p-value + Deflated Sharpe) our own important decisions are held to — no capital is pooled, you keep your funds, we buy the idea. Tiers auto-detected from `spec`: parameter (a search grid on an existing strategy family), code (a novel signal function — run only in a hardened, network-off Docker sandbox), or concept (a free-text idea). A code-tier signal_code must define generate_signals(candles).
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  • Get Arcadia LP strategies. Use featured_only=true for curated top strategies (recommended first call). Returns a paginated list with 7d avg APY for each strategy's default range. Increase limit or use offset for pagination. All APY values are decimal fractions (1.0 = 100%, 0.05 = 5%). For full detail on a specific strategy (APY per range width), use read_strategy_info.
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  • ERCOT (Texas) real-time grid fuel mix: coal/lignite, natural gas, nuclear, hydro, wind, solar, power storage, other (MW). Returns the most recent 5-minute snapshot plus monthly installed capacity per fuel type, so percent-of-installed can be computed. Use for "Texas grid mix right now", "ERCOT wind output", "is Texas burning coal today".
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  • Which market SECTORS politicians have been trading in over a trailing window. Aggregates congressional + executive trades by sector and returns, per sector: trade count, total dollar volume, number of distinct politicians, and the top tickers. Use it to see where political trading activity is concentrating (e.g. "politicians piled into Energy this month"). Sort by count or dollar volume.
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  • Point-in-time market-regime context for a scan date: VIX close, VIX3M, SPY trend state, and the 5-day VIX delta — all as-of <= scan_date (the selection point, leakage-safe), plus the engine's regime safety rail evaluated on those values. The rail: the engine fail-closes (no trade) when spot VIX > VIX3M (backwardation — the market pricing imminent volatility is an adverse regime for short-dated directional longs). Served from the labeled substrate, which lags the live pool by ~1-2 trading days. Values are constant per scan_date. Args: scan_date: YYYY-MM-DD. Defaults to the latest scan date carrying regime features. Returns: {scan_date, vix_at_scan, vix3m_at_enrich, spy_trend_at_scan, vix_5d_delta_at_scan, regime_rail_pass, rail_definition}
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  • 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 every variable but the strategy fixed, which a series of separate arena_run_backtest calls does not guarantee. 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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  • 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 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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  • 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. Args: market_id: Market identifier (crypto, kr_stock, us_stock) target_market: Alias for market_id (backward compat) top_n: Top N strategies to return (default 20) limit: Alias for top_n (client-compat) min_trades: Minimum trades count for inclusion (default 10) include_per_symbol: Include per-symbol PG partition results (default True) Disclaimer: Information only, not investment advice.
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  • Monte Carlo price simulation for the next ~10 trading days: thousands of random price paths estimate a likely price range and the odds of finishing higher. Args: ticker: Stock symbol, e.g. "AAPL".
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  • Check current UK electricity grid composition by source percentage (gas, coal, wind, solar, nuclear, hydro, biomass, imports). Use to understand real-time grid energy mix.
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