Meridian Edge + Jupyter Notebook

import requests
import pandas as pd
import matplotlib.pyplot as plt

API_KEY = "me_live_YOUR_KEY"
HEADERS = {"X-API-Key": API_KEY}
BASE    = "https://meridianedge.io/api/v1"

# Fetch NBA consensus
r = requests.get(f"{BASE}/consensus",
                 headers=HEADERS, params={"sport": "NBA", "limit": 20})
df = pd.DataFrame(r.json()["events"])

# Display divergence summary
print(df[["title","consensus_prob","divergence_pct","market_count"]].head(10))

# Plot probability distribution
fig, ax = plt.subplots(figsize=(10, 4))
ax.barh(df["title"].str[:40], df["consensus_prob"],
        color=["#22c55e" if p > 0.65 else "#f59e0b" if p > 0.45 else "#ef4444"
               for p in df["consensus_prob"]])
ax.set_xlabel("Consensus Probability")
ax.set_title(f"NBA Prediction Market Consensus ({pd.Timestamp.now().strftime('%Y-%m-%d')})")
plt.tight_layout()
plt.show()

# Divergence analysis
print("\nHigh-divergence events:")
print(df[df["divergence_pct"] > 0.04][["title","consensus_prob","divergence_pct"]])