Real-time consensus probabilities via API, MCP, or Python SDK. Built for autonomous systems that need a single, reliable probability per event — aggregated from multiple regulated markets.
A single market price is noisy. Consensus across regulated markets is signal.
Any system comparing prices across markets needs a fair-value anchor. Consensus probability — averaged across regulated sources — is that anchor. Without it, your bot is comparing noise to noise.
When a news event moves a market, the signal isn't the new price — it's the gap between the new price and where consensus was before. Meridian Edge gives you both, timestamped.
For binary outcome markets, fair value is the consensus probability across all informed participants. Aggregating multiple regulated markets gives you a more stable estimate than any single source.
Divergence signals require knowing what "normal" looks like. Consensus is that reference. When a single market moves away from consensus, that's the signal your bot should act on.
Single aggregated probability per event with confidence rating (LOW / MEDIUM / HIGH) and spread across sources.
Events where individual markets deviate from consensus, ranked by divergence score. Available on Starter+.
Movement signals (up/down/stable) with price velocity over the last 30 minutes. Available on Starter+.
Verified outcomes for resolved markets. Use for backtesting and ground-truth labeling.
Full historical consensus data for backtesting autonomous strategies. Enterprise tier.
No exchange names in responses. Derived analytics, not raw data redistribution. See compliance page.
Connect in under five minutes via REST, Python SDK, or MCP.
Any language, any platform
pip install meridianedge
5 tools, 30-second install
Custom GPT in GPT Store
# REST API — works from any language or system curl -H "X-API-Key: me_free_demo000000000000" \ "https://meridianedge.io/api/v1/consensus?sport=NBA&limit=3" # Python SDK from meridianedge import MeridianEdge me = MeridianEdge(api_key="me_free_demo000000000000") events = me.consensus(sport="NBA", limit=3) for e in events: print(f"{e['event_name']}: {e['consensus_prob']:.0%}") # Claude MCP — add to claude_desktop_config.json { "mcpServers": { "meridian-edge": { "command": "uvx", "args": ["meridian-edge-mcp"], "env": {"MERIDIAN_API_KEY": "YOUR_KEY"} } } }
Copy and run this now required for the demo key.
# Working curl example — prediction market consensus API curl -H "X-API-Key: me_free_demo000000000000" \ "https://meridianedge.io/api/v1/consensus?sport=NBA&limit=3" # Response includes: event_name, consensus_prob, confidence, # spread, movement, n_platforms, sparkline, ts # Get divergence opportunities (Starter+) curl -H "X-API-Key: YOUR_KEY" \ "https://meridianedge.io/api/v1/opportunities?min_score=5" # Get recent signals (Starter+) curl -H "X-API-Key: YOUR_KEY" \ "https://meridianedge.io/api/v1/signals/recent"
pip install meridianedge. Instantiate with your API key. Call me.consensus(), me.opportunities(), me.signals(), me.settlements(). Full documentation at meridianedge.io/docs.html.100 calls/day on the starter tier — instant activation friction. Enterprise options for production systems.