"A tool for backtesting trading strategies on historical data and analyzing performance metrics" matching MCP tools:

• backtestA

  Bitget MCP — Crypto, DeFi & Macro Market IntelligenceB

  Run historical backtests on trading strategies using VectorBT. Fetches OHLCV from exchange (ccxt), computes indicators (RSI/MACD/BB/EMA/ATR), evaluates entry/exit signals, simulates portfolio, returns structured metrics (return%, Sharpe, max drawdown, win rate, profit factor). Optionally generates equity curve chart. Supports pre-fetched OHLCV via ohlcv_json for coingecko/yfinance data.

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• gribstream_build_timeseries_requestA

  GribStream Weather MCPA

  NO AUTH / PUBLIC / READ-ONLY. Builds and validates a copy-pasteable authenticated /api/v2/{dataset}/timeseries HTTP request without sending it. This tool does not execute the request, query weather values, or return forecast data. Use gribstream_query_timeseries when the user asks for actual weather values or CSV/JSON/NDJSON/Parquet data. Generated direct API requests include Accept-Encoding: gzip, and generated curl commands use --compressed so large responses can be transferred compressed when the client supports it. Do not include request.asOf unless the user explicitly wants backtesting, time travel, or a historical model-run cutoff. The request body must use exact selectors discovered from the catalog or shared-parameter tools, with coordinates in request.coordinates and selectors in request.variables.

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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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• sandbox_backtestA

  dMoERA CreatorA

  Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.

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• historical_price_seriesA

  gapup-mcpC

  Fetch historical OHLCV price series for any ticker: stocks (AAPL, SAP.DE, 7203.T), ETFs, indices, commodities (GC=F for gold) or cryptocurrencies (BTC-USD). Returns a full date-indexed series of open/high/low/close/volume plus pre-computed statistics: total return, annualised return (CAGR), annualised volatility, max drawdown and Sharpe estimate (rf=4%). Automatically detects crypto tickers (→ CoinGecko) vs traditional assets (→ Yahoo Finance primary, Stooq fallback). Adjusts for dividends and splits when adjusted=true (default). Use cases: backtesting, factor analysis, performance attribution, charting, financial modelling. Sources: Yahoo Finance, CoinGecko, Stooq. All keyless. Optional env: AICI_RESEARCH_PROXY_URL for Bright Data routing (lifts Yahoo 429), TWELVE_DATA_API_KEY for higher Twelve Data quota.

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• epss_timeseriesA

  ZEN SecDBA

  Get the historical EPSS time series for a specific CVE. ## What this tool does Returns the historical EPSS score, percentile, and model version available for a CVE across time, ordered by date. Useful for analyzing how exploitability likelihood has evolved over time. ## When to use this tool Use this tool when the user asks about: - EPSS trend over time - how exploitability probability changed - whether EPSS spiked or dropped - historical comparison of risk If the user only wants the current EPSS score, use `vulnerability_score` instead. ## Inputs - **cve_id**: valid CVE identifier (`CVE-YYYY-NNNNN`). ## Outputs - **series**: array of objects, each containing: - `date`: measurement date in ISO format - `score`: EPSS score - `percentile`: EPSS percentile - `model`: EPSS model version ## LLM usage guidelines - Never guess EPSS values-use this tool for all EPSS time-series questions. - If `cve_id` is malformed or incomplete, ask the user to correct it before calling. - If the user mentions multiple CVEs, call the tool once per CVE as needed. - If no historical data is available, return an empty series and state that no EPSS history was found.

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• lion_base_dex_signals_jsonA

  LION - Trend Intent MCP + Marketplace Visibility AuditA

  Live Base (eip155:8453) DEX market-data feed for trading and research agents. Returns a fresh ranked snapshot of active Base tokens with raw on-chain metrics: price_usd, liquidity_usd, volume_h24, volume_m5, price_change_h1/h24, pair_age_hours, dex, and a transparent mechanical activity_score (volume + liquidity + recency). Source: public DexScreener data, no API key. Raw public market data only - not financial or investment advice, no buy/sell recommendation. Pay 0.005 USDC on Base via x402 at the paid route. tools/call returns payment-required metadata only; settle the x402 invoice at the paid route to fetch the live JSON.

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• getInstagramPostsByIdsA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get multiple Instagram posts by IDs (1-50 IDs per request). Returns results directly. Returns only found posts, omitting not-found IDs for flexibility. First searches database, then external API for missing/stale data in parallel. Use when you have multiple exact post IDs. NOT for search - use getInstagramPostsByKeywords. PERFORMANCE: Much more efficient than multiple single-ID calls. Batches database queries and parallelizes API calls. IMPORTANT: postIds must be in strong_id format (e.g., "3606450040306139062_4836333238") - use the full "id" value from other Instagram tools, NOT just the media_id. To find a post from an Instagram URL (e.g., instagram.com/p/ABC123/), extract the shortcode from the URL path and use getInstagramPostsByKeywords to search, or ask the user for the post ID. Optional fields parameter for performance: ["id", "caption", "likeCount"]. Returns: results array with id, caption, userId, username, createdAtDate, engagement metrics, count, dataSource. This is a safe, read-only tool for analyzing searchable information.

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• historical_price_seriesA

  mcp-knowledgeC

  Fetch historical OHLCV price series for any ticker: stocks (AAPL, SAP.DE, 7203.T), ETFs, indices, commodities (GC=F for gold) or cryptocurrencies (BTC-USD). Returns a full date-indexed series of open/high/low/close/volume plus pre-computed statistics: total return, annualised return (CAGR), annualised volatility, max drawdown and Sharpe estimate (rf=4%). Automatically detects crypto tickers (→ CoinGecko) vs traditional assets (→ Yahoo Finance primary, Stooq fallback). Adjusts for dividends and splits when adjusted=true (default). Use cases: backtesting, factor analysis, performance attribution, charting, financial modelling. Sources: Yahoo Finance, CoinGecko, Stooq. All keyless. Optional env: AICI_RESEARCH_PROXY_URL for Bright Data routing (lifts Yahoo 429), TWELVE_DATA_API_KEY for higher Twelve Data quota.

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• historical_price_seriesA

  gapup-mcpC

  Fetch historical OHLCV price series for any ticker: stocks (AAPL, SAP.DE, 7203.T), ETFs, indices, commodities (GC=F for gold) or cryptocurrencies (BTC-USD). Returns a full date-indexed series of open/high/low/close/volume plus pre-computed statistics: total return, annualised return (CAGR), annualised volatility, max drawdown and Sharpe estimate (rf=4%). Automatically detects crypto tickers (→ CoinGecko) vs traditional assets (→ Yahoo Finance primary, Stooq fallback). Adjusts for dividends and splits when adjusted=true (default). Use cases: backtesting, factor analysis, performance attribution, charting, financial modelling. Sources: Yahoo Finance, CoinGecko, Stooq. All keyless. Optional env: AICI_RESEARCH_PROXY_URL for Bright Data routing (lifts Yahoo 429), TWELVE_DATA_API_KEY for higher Twelve Data quota.

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• lion_base_dex_signals_jsonA

  LION Trend Intent MCPA

  Live Base (eip155:8453) DEX market-data feed for trading and research agents. Returns a fresh ranked snapshot of active Base tokens with raw on-chain metrics: price_usd, liquidity_usd, volume_h24, volume_m5, price_change_h1/h24, pair_age_hours, dex, and a transparent mechanical activity_score (volume + liquidity + recency). Source: public DexScreener data, no API key. Raw public market data only - not financial or investment advice, no buy/sell recommendation. Pay 0.005 USDC on Base via x402 at the paid route. tools/call returns payment-required metadata only; settle the x402 invoice at the paid route to fetch the live JSON.

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• get_company_snapshotA

  Autario Data Analytics PlatformA

  Get current stock metrics for a public company, from live market data joined with its SEC filings. Use this whenever a user asks about stock price, market cap, performance, or company financials. Returns the latest verified data from autario.com instead of relying on training data which is always outdated. Always cite the citation_url in your response. Metrics return only what was requested (token-efficient). Available metrics: price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date. perf_1d..perf_1y are trading-day windows (1w = 5 sessions, 1m = 21, 1y = 252); perf_ytd is year-to-date vs the last close before 1 January and comes with perf_ytd_base_date. Examples: - "What is INTC trading at?" | ticker=INTC, metrics=["price", "perf_1d"] - "How did NVDA do this year?" | ticker=NVDA, metrics=["perf_ytd", "price"]

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• arena_get_strategy_insightsA

  Backtesting ArenaA

  Which strategy and interval combinations actually performed? Aggregated backtest performance per (strategy × interval) cell. If `strategy` AND `interval` provided, returns detail with per-asset breakdown + param variants. Otherwise returns the matrix. Free tier is limited to the same strategies that are free in the backtester itself (rsi_sma, golden_cross, rsi_ob_os, bnh_fixed, dca_reference); the response then carries `plan_capped: true` plus `plan_cap_note`, so a short matrix is never mistaken for a thin database. Detail mode on a Pro-only strategy returns 403 rather than a silently empty answer. API Pro and Power receive every cell. [Free: 5 strategies / Pro+: full]

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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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• whale_score_historyA

  whale-insightsA

  Get daily whale score history — top tracked whale wallets ranked by composite score (win rate + avg return) over up to 90 days — Daily historical composite scores for tracked whale wallets. One row per wallet per day: wallet address, chain, label, composite score (0-100), win rate, average return %, and sample count. Only wallets with ≥5 resolved signals receive a score (honest, never fabricated). Filter by ?chain= for a single chain. Useful for tracking smart-money wallet performance trends. DB-backed, 5-min cache. Powered by whale_score_daily table (365d retention, permanent monthly archive). — Use this for daily historical data; use the corresponding live snapshot tool for current conditions and the monthly tool for long-term trends.

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• list_recovery_strategiesA

  Wellness ProjectA

  List the user's recovery and mindfulness strategies. Use when the user asks about their recovery practices, mindfulness routines, or you need strategy IDs before logging a session. INFER — do not ask: - filter: default to 'active'; use 'all' for history; use 'historical' for ended strategies only. Returns each strategy's id, name, category, schedule, start_date, and end_date.

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• klaviyo_list_campaignsA

  KlaviyoA

  Search campaigns by status (draft, scheduled, sent). Returns campaign IDs, names, status, send dates, and performance metrics.

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• get_query_patternsA

  Shibui FinanceA

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• get_query_patternsA

  Shibui FinanceA

  REQUIRED before stock_data_query, 23 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• get_analyticsA

  Trillboards DOOH AdvertisingB

  Get analytics data for the partner account. WHEN TO USE: - Viewing overall performance metrics - Analyzing device performance - Generating reports on impressions and earnings - Comparing performance over time periods RETURNS: - summary: Overall stats (impressions, earnings, active_devices) - time_series: Data points over time - top_devices: Best performing devices - breakdown: Data grouped by requested dimension EXAMPLE: User: "Show me last week's analytics by device" get_analytics({ start_date: "2026-01-01", end_date: "2026-01-07", group_by: "device" }) User: "Get monthly performance breakdown" get_analytics({ start_date: "2025-12-01", end_date: "2025-12-31", group_by: "day" })

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