Analysis Pipeline

Rekko’s analysis pipeline runs deep multi-stage research on a market. This takes 30-90 seconds, so it uses an async pattern: trigger, poll, retrieve.

The three-step pattern

Trigger

POST /v1/markets/{platform}/{market_id}/analyzeReturns 202 Accepted immediately with an analysis_id and poll_url.

{
  "analysis_id": "rk-abc123",
  "status": "running",
  "poll_url": "/v1/markets/kalshi/KXFED-26MAR19/analyze/rk-abc123/status",
  "estimated_seconds": 60
}

Poll

GET /v1/markets/{platform}/{market_id}/analyze/{analysis_id}/statusPoll every 5 seconds until status changes to "complete" or "error".

{
  "analysis_id": "rk-abc123",
  "status": "complete"
}

Retrieve

GET /v1/markets/{platform}/{market_id}/analysisReturns the full Analysis object with probability, confidence, edge, recommendation, and optional expansions.

Complete example

import time
import httpx

client = httpx.Client(
    base_url="https://api.rekko.ai/v1",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
)

# 1. Trigger
resp = client.post("/markets/kalshi/KXFED-26MAR19/analyze")
analysis_id = resp.json()["analysis_id"]

# 2. Poll
while True:
    status = client.get(
        f"/markets/kalshi/KXFED-26MAR19/analyze/{analysis_id}/status"
    ).json()
    if status["status"] == "complete":
        break
    if status["status"] == "error":
        raise Exception(status.get("error_message", "Analysis failed"))
    time.sleep(5)

# 3. Retrieve (with causal decomposition)
analysis = client.get(
    "/markets/kalshi/KXFED-26MAR19/analysis",
    params={"expand": "causal,scenarios"},
).json()

print(f"Probability: {analysis['probability']}")
print(f"Edge: {analysis['edge']}")
print(f"Recommendation: {analysis['recommendation']}")

The Analysis object

Field	Type	Description
`probability`	float (0-1)	Rekko’s estimated true probability
`confidence`	float (0-1)	Confidence in the estimate
`edge`	float	`probability - market_price` (positive = underpriced YES)
`recommendation`	string	`"BUY_YES"`, `"BUY_NO"`, or `"NO_TRADE"`
`risk_rating`	string	`"low"`, `"medium"`, or `"high"`
`summary`	string	Executive summary
`key_factors`	string[]	Top probability drivers
`risks`	string[]	Ways the analysis could be wrong
`source_count`	integer	Number of sources analyzed
`freshness`	string	`"fresh"` (<24h), `"stale"` (24-72h), `"expired"` (>72h)

Caching and freshness

Analyses are cached. The freshness field tells you how stale the result is:

Freshness	Age	Meaning
`fresh`	< 24 hours	Recent, likely still accurate
`stale`	24-72 hours	May have drifted, consider re-triggering
`expired`	> 72 hours	Likely outdated, re-trigger recommended

To force a fresh analysis even when a cached one exists, use ?force=true on the trigger endpoint.

Expandable fields

Add ?expand=scenarios,causal,meta to the retrieve step for deeper data:

Expansion	What it adds
`scenarios`	Bull/base/bear scenario assessments with probabilities and triggers
`causal`	Causal factor decomposition — Rekko’s key differentiator
`meta`	Pipeline metadata: what would change our mind, edge assessment, duration

See Expand Parameter for the full reference.

Getting Started

Core Concepts

Tutorials

Account

Analysis Pipeline

The three-step pattern

Complete example

The Analysis object

Caching and freshness

Expandable fields

​The three-step pattern

​Complete example

​The Analysis object

​Caching and freshness

​Expandable fields

The three-step pattern

Complete example

The Analysis object

Caching and freshness

Expandable fields