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607,235 tools. Updated 2026-09-24 14:09

"A guide to backtesting for financial analysis and strategy development" matching MCP tools:

  • Call cc.openclaw_chat — Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Purpose: Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Behavior: conversational AI that CAN place/cancel orders and manage positions when the linked account allows it. Treat as potentially destructive. Confirm intent before asking it to trade live. Auth: X-Api-Key required (and linked exchange credentials for execution actions). Cost: $0.025 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 10/min (per API key). Tier: enterprise. Returns: Structured AI analysis with computed indicators, detected patterns, strategy recommendations, and task management for autonomous execution. Guidelines: Prefer paper/simulation paths. For live money require explicit human confirmation (confirm_live / action=execute). Report real HTTP errors; never invent proxy failures. Tags: ai, strategy, autonomous, backtesting, patterns, indicators.
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  • Call cc.openclaw_chat — Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Purpose: Autonomous AI agent specialized in strategy development, backtesting, and continuous market monitoring. Uses indicator libraries, pattern recognition, and instrument specifications. Behavior: conversational AI that CAN place/cancel orders and manage positions when the linked account allows it. Treat as potentially destructive. Confirm intent before asking it to trade live. Auth: X-Api-Key required (and linked exchange credentials for execution actions). Cost: $0.025 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 10/min (per API key). Tier: enterprise. Returns: Structured AI analysis with computed indicators, detected patterns, strategy recommendations, and task management for autonomous execution. Guidelines: Prefer paper/simulation paths. For live money require explicit human confirmation (confirm_live / action=execute). Report real HTTP errors; never invent proxy failures. Tags: ai, strategy, autonomous, backtesting, patterns, indicators.
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  • Search Flevy's marketplace of consulting frameworks, PowerPoint templates, Excel financial models, business toolkits, and management case studies. Use this whenever a user needs a best-practice framework, methodology, template, financial model, or real-world case example on any business or management topic (strategy, digital transformation, supply chain, pricing, operational excellence, M&A, etc.). Returns up to 10 relevance-ranked recommendations across two content types: "document" (premium documents authored by management consultants) and "case_study" (management case studies). ALWAYS include each recommended item's url as a clickable link when you mention it in your reply — never reference a document without its link, because the link is the only way the user can open it. Each result carries a content_id for get_content_details. Filters: topic (single, or "topics" for documents covering ALL of several topics), author (list more documents from an author seen in results), filetype (including tier1_consulting_deck for McKinsey-style strategy decks), content_type. Topic-filtered responses also list related_topics to pivot to. Provide at least one of query, topic(s), or author; use list_topics to map user phrasing to a canonical topic.
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  • Get a debt paydown analysis with payoff projections. Call with NO arguments to get the user's SAVED plan — their chosen strategy plus their saved extra monthly payment, the exact numbers the Debt Paydown page shows them. When the user asks "when will I be debt-free?" / "what's my payoff date?", use the no-argument call and report the returned `debt_free_date`, `projection`, and `strategy` VERBATIM — never re-derive a date from the month count and never substitute a different strategy. Only pass `strategy`/`extra_monthly` for explicit what-if questions ("what if I paid $200 extra?"). Args: extra_monthly: Extra monthly payment beyond minimums. Omit to use the user's saved extra payment (their plan). strategy: Payoff strategy - highest_rate, snowball, variable_snowball, minimum_payments, highest_balance, highest_payment, cashflow_index, npv, max_interest_savings. Omit to use the user's saved plan strategy. respect_goal_priorities: When True (default), active debt-payoff goals override strategy ordering for the primary projection. Pass False to see what the chosen strategy would do without goal interference. The strategies_comparison table always shows pure strategy ordering regardless of this flag. For "what should I pay this month?" / "which debt gets my extra?", read `this_month`: `this_month.target` is the debt the plan attacks first (`target_basis`: extra_this_month, or first_rollover when no extra is set and freed-up payments start rolling to it in `first_extra_month`). Each row has payment (what to send = minimum_payment + extra), minimum_payment (the account's stated minimum), and plan_minimum_payment / plan_payment (the projection's amortized figures, which can differ by a few dollars). Quote payment and minimum_payment as-is. Returns: Debt payoff timeline (incl. debt_free_date — quote it as-is), this_month (per-debt payments + the target debt), interest savings, strategy comparison, and is_user_saved_plan (True when the projection is the user's own saved plan).
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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
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  • How did this exact strategy, asset and interval perform? Aggregated backtest performance for ONE specific (strategy, asset, interval) combination. Returns run_count, avg_cagr, avg_win_rate, avg_drawdown, effective_years, vs_buy_hold comparison (beats_buy_hold, cagr_delta) and an `evidence` block declaring the gate machine-readably (gate_applies_to: stats.run_count, threshold 5 runs, benchmark value, aggregation data window). For multi-strategy overview use arena_get_strategy_insights. Use this to answer 'How does strategy X perform on asset Y?'. [Free tier]
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  • 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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  • List or search your portfolios with lightweight metadata (strategy id + name). Matches portfolio name, strategy names, and tickers — same workspace search as the dashboard / GET /api/chat-portfolio. include_chat_portfolios returns workspace DRAFTS only (excludes deployedMirror rows). Recover a past chat draft: include_paper=false, include_live=false, include_chat_portfolios=true, search="Delta 0.07" (then get_portfolio for full strategy JSON). Returns an object: { portfolios, page, limit, total, totalPages, scopes }. Deployed and draft rows page as one list (all deployed, then all drafts), so every row is reachable by walking pages. When search is set, include_positions defaults to false. Use analyze_portfolios only when you need LLM-written analysis.
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  • Run the Central Command agent console (strategy lifecycle + account actions). Purpose: guide/whoami, create/update strategies, backtest, deploy STOPPED, paper execute, and (explicitly) live orders. Behavior: READ + WRITE. Deploy without execute does NOT move money. Live place_order/close_position/cancel_order require confirm_live=true. Default force_paper=true. Auth: X-Api-Key (linked Connect keys preferred — free). Do not spoof X-Linked-User-Id. Cost: linked Connect keys free; otherwise prepaid / x402 per catalog price for agent-strategy. Rate limit: plan default. Returns: JSON envelope { ok, endpoint, status, data: { ok, guide|whoami|created|deployed|... } }. Guidelines: Start with action=guide then whoami. Prefer paper. Never invent outbound-proxy failures — report real HTTP status/body.
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  • 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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  • 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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  • 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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  • Run the Central Command agent console (strategy lifecycle + account actions). Purpose: guide/whoami, create/update strategies, backtest, deploy STOPPED, paper execute, and (explicitly) live orders. Behavior: READ + WRITE. Deploy without execute does NOT move money. Live place_order/close_position/cancel_order require confirm_live=true. Default force_paper=true. Auth: X-Api-Key (linked Connect keys preferred — free). Do not spoof X-Linked-User-Id. Cost: linked Connect keys free; otherwise prepaid / x402 per catalog price for agent-strategy. Rate limit: plan default. Returns: JSON envelope { ok, endpoint, status, data: { ok, guide|whoami|created|deployed|... } }. Guidelines: Start with action=guide then whoami. Prefer paper. Never invent outbound-proxy failures — report real HTTP status/body.
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  • Mamanida's editorial buying guides for one storefront, in that storefront's own language. Called without `guide` it lists the published guides (metadata only: title, deck, meta description, pillar, topics, dates and the canonical Mamanida URL), optionally filtered by `topic`. Called with `guide` (the guide slug from the listing) it returns that one localized edition plus its structured body: paragraphs, headings, lists, comparison tables, callouts, links to other guides and category calls-to-action. A `category_cta` gives a `category_slug` you can pass straight to search_products, which is the intended guide → category → product path. Retailer and affiliate URLs are never returned.
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  • ⚠ COSTS LLM CREDITS on the NexusTrade account — spins up an Aurora agent via Router V5 classification + ReAct execution loops, billed per token. **Manual approval required**: do NOT call unless the user explicitly asked to launch an Aurora agent. For strategy creation/backtesting/analysis prefer no-LLM tools: structured create_portfolio (pass full IPortfolio JSON), backtest_portfolio, query_backtest_history, query_*, fetch_portfolios. Create a new autonomous Aurora agent using the same body shape as POST /api/agent. When maxIterations or automationMode are omitted, applies the user's saved ChatSettings. Agent models are product-locked (openai/gpt-5.6-luna planner, xiaomi/mimo-v2.6-flash executor, and the platform tool-role defaults) and cannot be overridden. Pass attachment_ids from upload_chat_attachment (READY) to bind files onto the last user message — same as the web FILES tab. Use this for a method-brief PDF plus a short analyze/report request.
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  • Call cc.strategy_data_parser — Parses natural language strategy descriptions into structured executable specifications: timeframes, indicators, patterns, risk parameters, and entry/exit rules. Purpose: Parses natural language strategy descriptions into structured executable specifications: timeframes, indicators, patterns, risk parameters, and entry/exit rules. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Live / near-real-time data. Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.01 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 20/min (per API key). Tier: standard. Returns: Structured strategy object: TimeframeReq[], IndicatorReq[], PatternReq[], RiskParams, entry_conditions, exit_conditions — ready for backtesting or live execution. Guidelines: Compute / parse / backtest only — no live orders. Feed outputs into cc.agent_strategy with force_paper=true to paper-trade. Tags: parser, nlp, strategy, structured-data, automation.
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  • Wait for a `one_shot` deploy to finish and return its final result. `one_shot` returns a job_token immediately and the LIVE CARD already streams progress and renders the interactive backtest chart itself. Call this ONCE with the token to get the final numbers as TEXT so you can summarize them — it does NOT render another card (no need for get_model_chart). It BLOCKS until the deploy finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it again with the same token. IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by `@author` — tell the user that, share the `live_url` as the Live dashboard link, and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the `note` field as your guide. Args: job_token: the token returned by `one_shot`. Returns: dict with: ok, stem, model, live_url, symbol, timeframe, channels (list), stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE), source ("community" | "generated"), author (community username if any), author_url + strategy_url (render @author and "pre-existing strategy" as those Markdown links), community_id, suggest_custom (bool), and note (a ready instruction — follow it). On failure: {ok:false, error} (or {pending:true}).
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  • Call cc.strategy_data_parser — Parses natural language strategy descriptions into structured executable specifications: timeframes, indicators, patterns, risk parameters, and entry/exit rules. Purpose: Parses natural language strategy descriptions into structured executable specifications: timeframes, indicators, patterns, risk parameters, and entry/exit rules. Behavior: READ-ONLY. Does not place orders, move funds, or mutate your exchange account. Live / near-real-time data. Auth: X-Api-Key or x402 payment proof (X-PAYMENT / __x_payment). Anonymous unauthenticated calls receive HTTP 402 with payment accepts. Cost: $0.01 USDC per successful call (x402 Base USDC pay-per-use or prepaid X-Api-Key balance). Linked Connect keys are free. This is billing, not a side effect. Rate limit: 20/min (per API key). Tier: standard. Returns: Structured strategy object: TimeframeReq[], IndicatorReq[], PatternReq[], RiskParams, entry_conditions, exit_conditions — ready for backtesting or live execution. Guidelines: Compute / parse / backtest only — no live orders. Feed outputs into cc.agent_strategy with force_paper=true to paper-trade. Tags: parser, nlp, strategy, structured-data, automation.
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  • Signal-to-action suggestions from the synthesis engine (free read, G4). Per-asset directional observations aggregated hourly from the accuracy- weighted intelligence consensus (get_consensus math) plus the Fear & Greed context, each carrying a ready-to-use strategy config. PROPOSE flow: this tool never executes anything -- act on a suggestion by creating the strategy through the normal strategy tools (your wallet policy and the engine's risk / conflict / guardrail gates still apply), or approve it in the app. ``status`` filters proposed|approved|dismissed|executed|expired|all. Informational descriptions of observed data only. Not financial advice. Workflow: INTELLIGENCE -> DECIDE step -- review suggestions, then pair with backtest_strategy + get_risk_assessment before any create call.
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  • Get the versioning strategy configured for an API specification, including version aliases. Use this to read the strategy; to change it use set_versioning_strategy. Requires project context.
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  • Read a template in full: its blueprint plus its `description` — the markdown usage guide (what it is, how to use it, which business rules to fill). The guide IS the underlying model's FINANCE.md (a template's id equals its model id): to edit a template's guide, edit that model's FINANCE.md via layerz_set_finance_md — it propagates live, not as a snapshot. After forking or applying a template, follow the guide and update the new model's FINANCE.md so the conventions and objective match the project. Account-level, read-only.
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