DEV Community: MeridianEdge

How to Get Prediction Market Data in Python in 60 Seconds

MeridianEdge — Fri, 03 Apr 2026 00:05:49 +0000

Ever wanted to know what prediction markets think about the World Cup, the midterm elections, or tomorrow's NBA games?

Here's how to get that data in 60 seconds.

Install

pip install meridianedge

Get consensus data

from meridianedge import MeridianEdge

me = MeridianEdge(api_key="your-key")

# What do markets think about upcoming events?
markets = me.markets(category="sports", limit=5)
for m in markets:
    print(f"{m['name']}: {m['consensus']}%")

# What shifted the most today?
movers = me.movers(hours=24)
for m in movers[:3]:
    print(f"{m['name']}: {m['shift']}% shift")

What you get

Real-time consensus probabilities aggregated from multiple regulated prediction markets. One API, one number per event.

27,000+ active markets tracked
Politics, sports, economics, global events
14-second data pipeline updates
Cross-platform divergence detection

Also available as

Node.js: npm install meridian-edge
MCP Server: pip install meridian-edge-mcp (works with Claude Desktop, Cursor, VS Code)
REST API: meridianedge.io/docs.html

Why consensus beats single-source

A single prediction market can be wrong. Consensus across multiple platforms is more reliable — just like how aggregating polls beats any single poll.

When platforms disagree (divergence), it often signals new information entering the market. That's where the interesting data lives.

MCP server for AI agents

If you're building AI agents that need to understand probabilities of real-world events, the MCP server plugs directly into Claude Desktop, Cursor, or VS Code:

pip install meridian-edge-mcp

Add to your Claude Desktop config:

{
  "mcpServers": {
    "meridian-edge": {
      "command": "uvx",
      "args": ["meridian-edge-mcp"],
      "env": {
        "MERIDIAN_EDGE_API_KEY": "your-key"
      }
    }
  }
}

Now your AI assistant can answer "What do prediction markets say about the World Cup?" with real data.

Get started

API docs: meridianedge.io/docs.html
Plans start at $29/mo: meridianedge.io/#pricing
MCP server in the Official MCP Registry
npm: npmjs.com/package/meridian-edge
PyPI: pypi.org/project/meridianedge

Not investment advice. Prediction market data is for informational purposes only.

Building a Real-Time Consensus Engine for Prediction Markets — Architecture Deep Dive

MeridianEdge — Mon, 30 Mar 2026 00:44:08 +0000

Prediction markets are now a $1.3 trillion industry. But if you want to know the consensus probability on an event — a basketball game, a Fed rate decision, an election — you have to manually check a dozen different platforms.

I built Meridian Edge to solve this. It's a REST API that aggregates prediction market data from regulated sources and computes a real-time consensus probability for each event.

This post walks through the architecture.

The problem

Prediction market data is fragmented. Each platform has its own API (if it has one at all), its own data format, and its own pricing. There's no unified view.

If you're a researcher studying probability calibration, a developer building an AI agent that needs real-time event probabilities, or an analyst covering multiple categories — you're stuck doing manual work.

The data pipeline

The system processes data in three stages:

Stage 1: Collection
Snapshot jobs run every 14 seconds, pulling current state from each tracked source. We normalize everything into a common schema: event, outcome, probability, source, timestamp.

As of today, we're tracking 27,000+ active markets across five categories: sports (NBA, NHL, NFL, MLS, FIFA World Cup), politics (US Midterms, state races), economics (Fed rates, CPI, GDP), crypto, and weather.

Stage 2: Consensus computation
Raw probabilities from individual sources go through an ML-based consensus engine. The models retrain every 2 hours on fresh data. We use ensemble methods to weight sources based on historical accuracy, liquidity, and recency.

The output is a single consensus probability per event, plus divergence metrics showing where sources agree and disagree.

Stage 3: Storage and serving
Everything lands in PostgreSQL (currently 20GB and growing). The API serves requests through a Python backend running on EC2 with 110+ cron jobs keeping the pipeline running.

Over 100 million data points are stored with full historical coverage.

The API

Straightforward REST API. JSON responses. Example:

{
  "event": "NBA: BOS vs CHA",
  "consensus_probability": 0.59,
  "sources": 2,
  "spread": 0.18,
  "updated_at": "2026-03-29T15:30:00Z",
  "category": "sports"
}

Endpoints cover:

Current consensus by category or event
Historical time series
Cross-source divergence
Settlement outcomes

What I learned building this

Normalization is 80% of the work. Every source structures their data differently. Event naming is inconsistent. Timestamps are in different formats. Building the normalization layer took longer than the ML engine.

Real-time is relative. 14-second snapshots with 10-minute consensus updates hits a good balance for prediction markets. These aren't high-frequency price feeds — probability estimates move on news cycles (minutes to hours), not microseconds.

110 cron jobs is a lot of cron jobs. Monitoring and alerting became critical once the pipeline grew beyond ~30 jobs. I built a watchdog system that checks every subsystem and reports twice daily.

Who's using it

Early adopters fall into three buckets:

AI agent developers — using prediction market probabilities as real-time context for LLM-based systems via MCP
Researchers — studying probability calibration and market efficiency across platforms
Data teams — building dashboards and analysis tools on top of the consensus data

Try it

Live dashboard: meridianedge.io/dashboard.html
API docs: meridianedge.io/api.html
Researcher page: meridianedge.io/researchers.html

Plans start at $29/mo for 1,000 API calls/day. Built for research teams, developers, and anyone who needs structured prediction market data.

I'm a solo founder in California. Happy to answer questions about the architecture, data pipeline, or the prediction market data landscape.

For informational purposes only. Not investment advice. Participation in prediction markets involves risk of loss.

What Is Prediction Market Consensus and Why Does It Matter

MeridianEdge — Fri, 27 Mar 2026 22:56:19 +0000

What Is Prediction Market Consensus and Why Does It Matter

How aggregated prediction market data produces a single, more reliable consensus probability than any individual market can provide.

Key Findings:

Prediction market consensus is an aggregated probability derived from multiple regulated markets simultaneously.
No single market's price is used in isolation — consensus reflects the collective view across sources.
Meridian Edge currently tracks 39 active events with live consensus data updated every 10 minutes.
Consensus near 50% indicates genuine market uncertainty. Consensus above 70% indicates strong directional agreement.

The Problem: One Market Is Never the Whole Picture

A prediction market assigns a probability to a specific future outcome. When that outcome resolves, contracts pay out accordingly. This basic structure makes prediction markets useful tools for probability estimation.

The complication: the same event is often listed on multiple regulated markets simultaneously. Each market operates its own independent liquidity pool. Each attracts a different participant base. Each produces a slightly — sometimes significantly — different price.

Which one is correct? The answer is that no single market has a privileged claim to correctness. Each reflects the aggregated bets of its own participants. The most accurate probability estimate comes from combining them.

What Consensus Aggregation Produces

Consensus aggregation takes the prices from multiple regulated markets and combines them into a single probability figure. The result is more stable than any individual price and less susceptible to the idiosyncratic effects of one platform's user base or temporary liquidity conditions.

Consider a concrete example. For a typical NBA game, one regulated market might show 58% while another shows 63%. Consensus aggregation produces a single figure — say 61% — that reflects the weighted view across all available regulated sources.

"The value of aggregation is not that it eliminates uncertainty. It is that it distributes the error more evenly across a larger information base."

How to Read a Consensus Probability

A consensus of 65% means regulated markets collectively assign a 65% probability to that outcome based on currently available information. It does not mean the outcome will occur 65% of the time. It means that, given what the market knows right now, 65% is the best collective estimate.

Three ranges are useful for interpretation:

Consensus Range	Market Signal	Interpretation
45% – 55%	Uncertain	Genuine market uncertainty. Small information changes shift consensus materially.
55% – 70%	Directional	Moderate consensus. One side is favored but not decisively.
>70% or <30%	Strong	Strong market consensus. One outcome is substantially more likely.

Where Meridian Edge Fits

Meridian Edge is a data aggregation platform. The system collects publicly available pricing from multiple regulated prediction markets, computes consensus probabilities, and makes that data available via dashboard and API.

The data is available to developers, analysts, and researchers who want to build on top of aggregated prediction market intelligence. The API covers all available endpoints, data formats, and rate limits.

For developers:

from meridianedge import MeridianEdge

me = MeridianEdge()  # Free — 100 calls/day, no credit card

for event in me.consensus(sport="NBA", limit=3):
    print(f"{event['event_name']}: {event['consensus_prob']:.0%}")

Install: pip install meridianedge

What This Data Is Not

Prediction market consensus is not financial advice. It is not a recommendation to take any position. It is an aggregated representation of what regulated market participants collectively believe the probability of an outcome to be, at a specific point in time.

Events resolve in ways that contradict market consensus regularly. A 30% consensus event occurs approximately 30% of the time. Consensus accuracy is a property of calibration at scale, not of individual events.

FAQ

What is prediction market consensus?
An aggregated probability that combines pricing data from multiple regulated prediction markets into a single figure, representing the collective view across all available regulated sources.

How often is the data updated?
Every 10 minutes. Live data is available via the free dashboard and API.

How do I access the data?
Free dashboard at meridianedge.io — no signup required. API: 100 calls/day free. Python SDK: pip install meridianedge. MCP server for Claude/Cursor: GitHub.

For informational purposes only. Not investment advice. Data aggregated from publicly available prediction market sources.