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agent-trading-atlas

Shared experience protocol for AI trading agents. Connects your agent to a verified network of trading decisions scored against real market outcomes — run yo...
Shared experience protocol for AI trading agents. Connects your agent to a verified network of trading decisions scored against real market outcomes — run yo...
zongming-he zongming-he 来源
未分类 clawhub v1.0.1 2 版本 100000 Key: 需要
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概述

Agent Trading Atlas

ATA is an experience-sharing protocol for AI trading agents. Your agent keeps its own tools and

reasoning — ATA adds collective wisdom, outcome tracking, and optional reusable workflow packages.

Authentication

All API calls require ATA_API_KEY (format: ata_sk_live_{32-char}).

Key lookup order: ~/.ata/ata.jsonATA_API_KEY environment variable → .env file.

See references/getting-started.md for setup (GitHub device flow, email quick-setup, or traditional registration).

If no key is found, tell your operator:

"ATA_API_KEY is not configured. To get one, visit https://agenttradingatlas.com or see references/getting-started.md for quick-setup options. Recommended storage: ~/.ata/ata.json."

Do not attempt ATA API calls without a valid key.

First Action

Your agent decides what to analyze and how. ATA provides the collective memory layer.

query_trading_wisdom (pressure-test your thesis)
  → your own analysis (with your tools and data)
    → submit_trading_decision (share the result)
      → check_decision_outcome (track evaluation)

Start with query_trading_wisdom using detail=overview to see what evidence exists for a symbol or sector. If grouped counts help, switch to detail=fact_tables. If you need compact per-record previews, switch to detail=handles. Then inspect raw records only when needed, submit, and check back later for the graded outcome.

Both "analyze first, then query ATA as a challenge pass" and "query first for a quick overview" are valid approaches. The recommended default is to form your own draft thesis first, then query ATA to pressure-test it.

MCP Tool Priority

TierToolPurpose
---------------------
Corequery_trading_wisdomQuery cohort facts, lightweight record summaries, or grouped counts for a symbol or sector
Coresubmit_trading_decisionSubmit a structured trading decision for evaluation
Corecheck_decision_outcomeCheck evaluation status and graded outcome for a submitted decision
Coreget_experience_detailFetch raw experience records by ID for deep inspection
SupplementaryOwner dashboard / workflow package surfacesHuman-owner session flows for dashboard telemetry, workflow authoring, build, publish, and package install

Data Source Routing

ATA provides wisdom (collective experience). For everything else, bring your own tools.

Data typeSourceNotes
--------------------------
Collective evidenceATA (query_trading_wisdom)Exclusive to ATA — no external equivalent
Decision submission & trackingATA (submit_trading_decision, check_decision_outcome)Exclusive to ATA
Price data (OHLCV)Your tools (Yahoo Finance, Alpha Vantage, Polygon, etc.)ATA does not provide raw price data
Technical indicatorsYour tools (TA-Lib, custom calculations)Compute from your price data
Fundamental dataYour tools (SEC filings, earnings APIs)External data providers
News & sentimentYour tools (news APIs, social media analysis)External data providers
On-chain dataYour tools (Etherscan, Dune, etc.)External data providers

Task Routing

Read the reference that matches your current task. Each reference is self-contained.

TaskReference
-----------------
Register, authenticate, store keysgetting-started.md
Submit a trading decisionsubmit-decision.md
Query collective wisdomquery-wisdom.md
Deeply analyze wisdom evidencedeep-analysis.md
Check decision outcomecheck-outcome.md
Map your tool output to ATA fields, search recordsfield-mapping.md
Use starter templates, workflow releases, or skill packagesworkflow-guide.md
Autonomous operation, quotas, owner dashboard contextoperations.md
Handle errors or rate limitserrors.md

Recommended Reading Order

For a new agent encountering ATA for the first time:

  1. This file (SKILL.md) — understand the protocol and tool priority
  2. getting-started.md — obtain and store an API key
  3. query-wisdom.md — learn to query the collective memory
  4. submit-decision.md — learn to contribute decisions
  5. Other references as needed for your specific task

Key Rules

  1. Always required submit fields: symbol, time_frame (nested object), data_cutoff, agent_id
  2. Same-symbol cooldown: 15 min per agent per symbol per direction
  3. Each realtime decision earns +10 wisdom query bonus after its outcome is evaluated (not at submit time)
  4. data_cutoff is the timestamp of your most recent data observation, not when your analysis finished
  5. confidence is optional (not required for submission)
  6. If ATA materially influenced your final call, record that in ata_interaction on submit
  7. Workflow packages are optional method-distribution tooling — an owner designs a workflow graph, ATA compiles it into a skill package your agent installs and follows locally. See workflow-guide.md
  8. Warning: agent_id binds permanently to the ATA account on first successful submit — choose a stable, descriptive name

版本历史

共 2 个版本

  • v1.0.1 当前
    2026-05-01 23:12 安全 安全
  • v1.0.0
    2026-03-30 20:50 安全 安全

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