Fully autonomous analysis
The agent independently queries market data, interprets the summary, and generates trade recommendations every 4 hours.
Illustrative scenario · Starter Plan — $29/mo
Building an Autonomous AI Trading Agent with Claude + TickAtlas
A developer connected Claude to real-time market data via the /v1/summary endpoint, creating a fully autonomous analysis pipeline that requires zero manual data parsing.
AI & agentsPython · Claude · Slack2 endpoints used
100%Autonomous
0Manual Parsing
$29/month
StarterPlan used
The challenge
What the workflow looked like before the API.
LLMs like Claude are powerful reasoning engines but they cannot access real-time market data. Feeding raw OHLCV data or indicator numbers to an LLM requires complex prompt engineering to make the data interpretable. The developer needed a way to give Claude "market vision" without building a custom data interpretation layer.
The solution
Move the calculation behind one request.
The /v1/summary endpoint was the key. It returns natural language market analysis that aggregates all 42 indicators into a human-readable (and LLM-readable) summary. Claude can directly interpret this output without any data transformation or custom parsing logic.
python AI Trading Agent
# The AI agent's data source
import requests
response = requests.get(
"https://tickatlas.com/v1/summary",
headers={"X-API-Key": API_KEY},
params={"symbol": "EURUSD", "timeframe": "H4"}
)
# Feed directly to Claude
analysis = response.json()["data"]["summary"]
claude_response = client.messages.create(
model="claude-sonnet-4-20250514",
messages=[{
"role": "user",
"content": f"Based on this analysis, should I trade? {analysis}"
}]
) The results
What changed in the scenario.
The figures below describe this worked example as written, not a measured outcome from an identified account.
No manual parsing required
The /v1/summary output is already formatted for LLM consumption, eliminating the need for custom indicator interpretation.
Low cost
At only 2-3 summary calls per day across 5 pairs, the Starter plan provides more than enough capacity.
Reasoning transparency
Claude explains its reasoning step by step, citing specific indicator readings from the summary.
About these figures. These case studies are illustrative implementation scenarios, not audited customer references. The people named in them are composite personas, no company is identified, and every figure describes the scenario as written rather than a measured result from an identified account. The endpoints, parameters and architectures are real and documented; the outcomes you would see depend on your own workflow, configuration, market coverage and surrounding application.
Endpoint toolkit
The primitives this workflow is built from.
Every scenario combines a small set of endpoints. The implementation changes; the contract stays predictable — HTTP requests, JSON responses and an API key.
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Give Your AI Agent Market Vision
The /v1/summary endpoint produces LLM-ready analysis from 42 indicators. Every account starts pay-as-you-go with $2.50 of credit.