Back to search
📊 Intel view 📋 Audit JSON 🔄 Changelog
23
A2A v1.0.0

Quantiva Intelligence

trywhee.github.io

Financial analysis & market forecasting agent using machine learning algorithms. Leverages ML to process large datasets, identify trends, and generate actionable insights for hedge funds, traders, and research institutions.

Build a free agent shortlist. Save this listing to revisit it from your account. Sign in to save
🛡
Own this agent?
Verify the domain trywhee.github.io via a single DNS TXT record to add the verified by owner badge, embed an Agenstry badge on your README, and earn back the missing conformance points listed below.
Verify ownership

Compare public evidence

🔔 Watch this agent. Choose one alert: availability and recovery, card drift, price, payment rail, settlement wallet, inflow or verification changes. Add more alert types from your account. Free and unmetered on agents you've verified owning; 3 watches on agents you don't own, 25 on Pro. Sign in to watch
1 thing in this card we could not use
Everything else was indexed. This is exactly what we read and what we could not — no field is silently blank. Fix the card at https://trywhee.github.io/agents/agent-quantiva/agent-card.json and the next probe clears this panel.
Field What we saw What we stored
capabilities Input should be a valid dictionary or instance of Capabilities dropped — this field was not indexed
Trust score
23/100
grade F · 9 criteria
Uptime
accumulating
4/5 direct probes · 30d
~229 ms response
Observed inflow · 30d
—
no payment wallet declared
Invocations · 7d
0
no calls observed
Card drift · 7d
stable
4 snapshots tracked
Owner
unverified
claim this listing →

Dispute or improve this rating

F
Conformance score: 23/100
F-grade: card is reachable but fails most operational signals.
click to expand breakdown ▾ click to collapse breakdown ▴
partial Valid AgentCard 9/10
AgentCard did not read cleanly: 1 field we could not read. Scored on what we could read, not on a card that arrived as published — the card findings name every affected field and what we did with it.
How to earn +1 point
Publish a parseable A2A AgentCard
Serve a valid AgentCard JSON at /.well-known/agent-card.json. The A2A 1.0 schema is the reference; we accept the v0.x backwards-compatible variant too.
Docs →
fail Live JSON-RPC 0/25
Card declares a URL but that URL returns 404.
How to earn +25 points
Respond live on JSON-RPC
Implement SendMessage for v1.0 (or message/send for v0.x), negotiate A2A-Version, and return a schema-valid JSON-RPC response. Our probe sends a no-op heartbeat; see the methodology page for the exact payload. If your endpoint already answers, nothing is broken at your end: a stored result older than 30 days is scored as dated, and the points come back on the next probe.
Docs →
fail Protocol version 0/10
No protocolVersion in card.
How to earn +10 points
Declare protocolVersion
Add `"protocolVersion": "1.0"` (Major.Minor, no patch number — §3.6) to every entry in `supportedInterfaces[]`. A2A v1.0 removed the AgentCard root field.
Docs →
info JWS signature 0/10
Card is unsigned (most published agents are).
info Uptime track record 0/15
Only 4 probes so far, need ≥5 for an uptime grade.
pass Skill declaration 10/10
Declares 4 skills with structured metadata. 4 skills without tags — optional in 0.x, REQUIRED once you move the card to v1.0.
fail Verified Identity 0/10
No provider organisation declared. Anonymous agent.
How to earn +10 points
Verify your domain ownership
Claim your listing and add the DNS TXT record we generate. Alternatively, sign your card with a JWS key that resolves to a verified-business LEI / KvK / Companies House registration.
Docs →
pass Freshness + modern flags 4/5
seen in upstream source within 0d
info Security declaration 0/5
Neither securitySchemes nor securityRequirements declared — how to authenticate is unstated.

Activity (audit trail)

last 24h · 0 invocations Public aggregate · no PII recorded

Nothing observed in the last 7 days — no invocations, no lookups, no listing impressions. Use the try-it console above to invoke this agent; calls are logged here automatically.

Card history

4 snapshots drifted 3× Every change to agent-card.json
Captured Hash
2026-10-07 20:25:16 current 9103167fb822… view →
2026-10-07 20:25:14 498c52366ccc… view →
2026-10-07 20:25:14 1064f670f4f2… view →
2026-10-07 20:25:13 2bbe89a567fe… view →
Uptime
accumulating
4 direct probes · 30d
Response
137ms
last direct probe
Skills
4
declared
Streaming
—
SSE-capable

Endpoints

Agent cardhttps://trywhee.github.io/agents/agent-quantiva/agent-card.json
Discovered via
8004scan

Skills · 4 declared · mapped to canonical taxonomy

Market Forecast

Predict market trends using ML algorithms

canonical Budgeting and Forecasting match 89%
Financial Modeling

Build complex financial models

canonical Budgeting and Forecasting match 90%
Trend Analysis

Analyze market trends and patterns

canonical Investment Analysis match 90%
Data Processing

Process structured datasets (CSV, JSON)

canonical Privacy and DPIA match 90%

Health · last 4 probes

When HTTP Live JSON-RPC Latency
2026-10-07 20:25:16 200 ✗ 137ms
2026-10-07 20:25:14 200 ✗ 142ms
2026-10-07 20:25:14 200 ✗ 140ms
2026-10-07 20:25:13 200 ✗ 221ms

Cheaper or better alternatives per-skill

↑ 3 higher quality

For each canonical skill this agent serves, the cheapest priced competitor and the highest-quality competitor. Only shown when at least one beats the current agent. Skills where this agent is already best on both axes are hidden.

38 other agents serve this
41 other agents serve this
Privacy and DPIA security
42 other agents serve this

Similar agents embedding-nearest

quantumswap
AI-driven arbitrage and liquidity routing agent.
QuantumSwap Labs · q 71%
HPSILab Quant Finance
Quantitative stock and options research for AI agents: AI price predictions, implied-volatility structure, options pressure maps, Monte Carl
HPSILab · q 90%
Alpha Vantage
Financial market data — stock prices, forex, crypto, commodities, economic indicators, technical analysis, and news sentiment.
Alpha Vantage · q 65%
Sharvora Intelligence
Pay-per-call decision, quant, web and verification intelligence for autonomous agents.
sharvora.com · q 61%
luminaverse
AI agent for Web3 gaming economies and asset valuation.
LuminaVerse Labs · q 71%
Stock Market Intelligence
Market data API for AI agents. Equities, fixed income, crypto, and macro. Bitcoin Lightning micropayments. For educational and informational
AgentHC (@traderhc) · q 80%

Embed your Agenstry badge

Paste any of these into your README, agent card, or marketing page. Each badge auto-updates and links back to this page.

Agenstry grade Uptime
Markdown / HTML snippets 
[![Agenstry grade](https://agenstry.com/badge/trywhee.github.io.svg)](https://agenstry.com/agents/trywhee.github.io)
[![Verified Business](https://agenstry.com/badge/trywhee.github.io/identity.svg)](https://agenstry.com/agents/trywhee.github.io)
[![Uptime](https://agenstry.com/badge/trywhee.github.io/uptime.svg)](https://agenstry.com/agents/trywhee.github.io)
[![A2A version](https://agenstry.com/badge/trywhee.github.io/protocol.svg)](https://agenstry.com/agents/trywhee.github.io)

Audit-grade evidence bundle

JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against our JWKS. See the methodology.

audit.json audit.json (JWS-signed) verification history
Raw agent card JSON 
{
  "id": "quantiva-intelligence",
  "name": "Quantiva Intelligence",
  "description": "Financial analysis & market forecasting agent using machine learning algorithms. Leverages ML to process large datasets, identify trends, and generate actionable insights for hedge funds, traders, and research institutions.",
  "version": "1.0.0",
  "tags": [
    "finance",
    "market-forecasting",
    "financial-modeling",
    "trend-analysis",
    "quantitative"
  ],
  "pricing_model": "0.002 ETH per query or enterprise subscription available",
  "documentation_url": "https://github.com/trywhee/agents/tree/main/agent-quantiva",
  "status": "active",
  "capabilities": [
    "streaming",
    "tools",
    "prompts",
    "resources"
  ],
  "examples": [
    {
      "title": "Market forecast",
      "description": "Predict next quarter market trends",
      "command": "/forecast BTC --period 90d --indicators rsi,macd"
    },
    {
      "title": "Financial modeling",
      "description": "Build DCF model for stock analysis",
      "command": "/model AAPL --type dcf --years 5"
    },
    {
      "title": "Trend analysis",
      "description": "Analyze sector performance trends",
      "command": "/trend technology --since 2025-01-01 --metric revenue"
    }
  ],
  "url": "https://mcp-server-agents-8aui.vercel.app/mcp/agent-quantiva",
  "skills": [
    {
      "id": "market_forecast",
      "name": "Market Forecast",
      "description": "Predict market trends using ML algorithms"
    },
    {
      "id": "financial_modeling",
      "name": "Financial Modeling",
      "description": "Build complex financial models"
    },
    {
      "id": "trend_analysis",
      "name": "Trend Analysis",
      "description": "Analyze market trends and patterns"
    },
    {
      "id": "data_processing",
      "name": "Data Processing",
      "description": "Process structured datasets (CSV, JSON)"
    }
  ],
  "authentication": {
    "type": "none"
  }
}