Big Idea in One Line

Pyth Network delivers live market prices straight from the original sources—like exchanges and trading firms—directly on-chain, so your app can access them quickly, cheaply, and transparently. No middlemen, no delays.


Why Pyth Was Created


Smart contracts often need real-world prices (BTC/USD, ETH/USD, FX rates, stocks, commodities). Traditional oracles push prices on a fixed schedule, even when no one is using them. This wastes gas and can give stale data during fast market moves.


Pyth flips this problem: your app pulls the latest price only when it actually needs it. This keeps costs down, reduces delays, and ensures your contracts act on fresh data.


What Makes Pyth “First-Party”?


All Pyth prices come straight from the source—big exchanges, market makers, and trading firms. Current contributors include Coinbase, Cboe, Revolut, Virtu, and 120+ institutions. Fewer hops mean faster, more reliable data.


What You Can Track with Pyth


Pyth supports hundreds of live price feeds, including:



  • Crypto assets

  • FX pairs


  • Commodities


  • ETFs and stocks


The platform is designed to handle multiple asset types across markets, all updated in real time.


How Pyth Works: Simple Overview



  1. Publishers (exchanges/trading firms) sign their latest prices.


  2. Pythnet appchain aggregates all updates into a single price with a confidence interval. Pythnet is a Solana-based chain optimized for fast aggregation.


  3. Wormhole moves the aggregated updates to other chains using a verifiable, signed message format (VAA).


  4. Your app pulls the latest update on-demand, writes it into the on-chain Pyth contract, and reads fresh prices instantly.


Think of it as: Publishers → Pythnet → Wormhole → Your chain


Pull vs. Push: Why It Matters


  • Pull (Pyth): Only updates when needed—saves gas, reduces congestion, keeps data fresh.


  • Push (traditional): Updates happen on a fixed schedule, even when unused—higher costs, risk of stale data.


Confidence Matters


Each Pyth price comes with a confidence interval, showing how uncertain the market is.



  • Helps your app widen spreads, pause risky trades, or switch to safer modes during volatility.


  • EMA (Exponential Moving Average) prices are also available to smooth fluctuations.


The Pythnet Appchain



  • Purpose: Aggregate prices from many sources into a single, reliable feed.


  • Tech: Fork of Solana, optimized for low-latency computation.


  • Why separate?: Fast aggregation without making every chain do heavy computation.


Cross-Chain Delivery



  • Pythnet emits updates → Wormhole signs them → Any supported chain can verify and use them.


  • Works across EVM chains (Arbitrum, Base, Gnosis), Solana, Move chains (Aptos, Sui), and more.


  • One standard message format for all chains, so your logic stays uniform everywhere.


Cost Model



  • Fees are optional and only paid when you pull updates.


  • Fee depends on your chain and number of feeds updated.


  • You can query the fee before calling your update transaction.


Step-by-Step Developer Flow



  1. Pick a feed from Pyth’s catalog (e.g., ETH/USD).


  2. Fetch update data via Pyth’s price service, Hermes endpoints, or SDKs.


  3. Estimate the update fee on your chain.


  4. Call updatePriceFeeds(updateData, fee) on your chain’s Pyth contract.


  5. Read price + confidence in the same transaction for atomicity.


Best Practices:



  • Never use stale prices.


  • Respect confidence intervals during sharp moves.


  • Use EMA for smoothing when needed.


Security Highlights



  • First-party signatures reduce risk of fake data.


  • Aggregation on Pythnet blends multiple sources; outliers have less effect.


  • Cross-chain verification via Wormhole ensures cryptographic proof.


  • Consumer safety: dApps should enforce staleness limits and confidence checks.


Token, Governance, and Airdrops



  • PYTH is the governance token.


  • Retrospective airdrops began Nov 2023 for early users and contributors.


  • Governance helps shape future development and ecosystem rules.


Use Cases



  • Perpetuals, DEXs, Money Markets: Liquidations, margin checks, pricing with confidence.


  • Structured Products / Automated Agents: Trigger logic only when confidence is high; widen spreads during volatility.


  • Cross-chain apps: Same feed logic across multiple chains through Wormhole.


Risks and How to Reduce Them



  • Ignoring confidence → wrong pricing during volatile markets. Always apply minimum confidence rules.


  • Stale data → set max age for feeds.


  • Gas spikes → pull feeds on-demand and batch updates if needed.


Quick Integration Checklist



  1. Pick feeds and get their IDs.


  2. Fetch updateData via Hermes or SDK.


  3. Query update fee and budget gas.


  4. Call updatePriceFeeds(updateData, fee) → read price + confidence.


  5. Enforce max staleness and minimum confidence.


  6. Use EMA optionally for smoothing.


Works across all major chains: EVMs, Solana, Aptos, TON, Ronin, and more.


Key Takeaways



  • Direct from source: First-party data ensures reliability.


  • On-demand updates: Save gas and reduce congestion.


  • More than a number: Confidence and EMA add safety.


  • Built for all chains: Single pipeline to multiple ecosystems via Wormhole.




  • Pyth Docs – Getting started, best practices, fees, EMA


  • Cross-Chain Overview – Pythnet + Wormhole delivery


  • Aptos/Base/Arbitrum/Ronin/TON Guides – Concrete endpoints and code


    Publishers List – See current data providers


    Messari Reports – Neutral adoption snapshots and metrics


#PythRoadmap @Pyth Network

$PYTH