Deploy a prediction market consensus microservice to Railway — Python FastAPI, one-click deploy, with your Meridian Edge key as an environment variable.
Connect Railway to Meridian Edge to deploy a production prediction market data service in minutes. Railway handles the infrastructure; your service proxies consensus data to your application.
pip install fastapi uvicorn requests (in requirements.txt)from fastapi import FastAPI
import requests, os
app = FastAPI()
API_KEY = os.environ["MERIDIAN_API_KEY"] # set in Railway dashboard
HEADERS = {"X-API-Key": API_KEY}
@app.get("/")
def health():
return {"status": "ok", "service": "prediction-market-proxy"}
@app.get("/consensus/{sport}")
def consensus(sport: str, limit: int = 10):
r = requests.get(
"https://meridianedge.io/api/v1/consensus",
headers=HEADERS,
params={"sport": sport.upper(), "limit": limit}
)
return r.json()
@app.get("/divergence/{sport}")
def divergence(sport: str, min_div: float = 0.04):
r = requests.get(
"https://meridianedge.io/api/v1/consensus",
headers=HEADERS,
params={"sport": sport.upper(), "limit": 50}
)
events = r.json().get("events", [])
return {"events": [e for e in events if e.get("divergence_pct",0) >= min_div]}# Procfile web: uvicorn main:app --host 0.0.0.0 --port $PORT # requirements.txt fastapi uvicorn requests # Railway setup: # 1. Push to GitHub # 2. New project → Deploy from GitHub # 3. Add MERIDIAN_API_KEY env variable
Push to GitHub, connect Railway, add your MERIDIAN_API_KEY environment variable, and deploy in 2 minutes.
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