The exponential moving average price published by Pyth alongside the spot price for each feed. EMA price smooths out short-term price spikes and is more resistant to manipulation than the instantaneous price. Lending protocols often use EMA prices for liquidation calculations to prevent unnecessary liquidations from brief price wicks. The EMA window is typically configured for several minutes of price history.
Start with the shortest useful explanation before going deeper.
The exponential moving average price published by Pyth alongside the spot price for each feed. EMA price smooths out short-term price spikes and is more resistant to manipulation than the instantaneous price. Lending protocols often use EMA prices for liquidation calculations to prevent unnecessary liquidations from brief price wicks. The EMA window is typically configured for several minutes of price history.
Use the quick analogy first so the term is easier to reason about when you meet it in code, docs, or prompts.
Think of it as a market mechanic used to price, route, or move capital through liquidity apps.
Place the term inside its Solana layer so the definition is easier to reason about.
AMMs, routing, liquidity, lending, and trading infrastructure.
Turn the term from vocabulary into something operational for product and engineering work.
This term unlocks adjacent concepts quickly, so it works best when you treat it as a junction instead of an isolated definition.
Use this compact block when you want to give an agent or assistant grounded context without dumping the entire page.
EMA Price (Oracle) (ema-price) Category: DeFi Definition: The exponential moving average price published by Pyth alongside the spot price for each feed. EMA price smooths out short-term price spikes and is more resistant to manipulation than the instantaneous price. Lending protocols often use EMA prices for liquidation calculations to prevent unnecessary liquidations from brief price wicks. The EMA window is typically configured for several minutes of price history. Aliases: Exponential Moving Average Price Related: Pyth Network, Price Feed, TWAP (Time-Weighted Average Price)
Use glossary context, relationships, mental models, and builder paths to get structured answers instead of generic chat output.
Optional: paste Anchor, Solana, or Rust code so the Copilot can map primitives back to glossary terms.
The Copilot will answer using the current term, related concepts, mental models, and the surrounding glossary graph.
These branches show which concepts this term touches directly and what sits one layer beyond them.
A high-frequency oracle network publishing price feeds for crypto, equities, commodities, and FX. Pyth aggregates data from 90+ first-party publishers (market makers, exchanges) and updates prices every 400ms on Solana. Each price feed includes a price, confidence interval, and exponential moving average. Pyth uses a pull model with Hermes for cross-chain delivery.
An on-chain account maintained by an oracle (Pyth or Switchboard) containing the current price, confidence interval, and timestamp for an asset pair (e.g., SOL/USD). DeFi programs read price feeds to calculate collateral values, trigger liquidations, and determine swap rates. Feed staleness must be checked to prevent using outdated prices.
Time-Weighted Average Price—an average price calculated over a specific time period, weighting each price equally by time. TWAP oracles reduce manipulation risk vs. spot prices. Some DEXs (e.g., Jupiter) offer TWAP execution that splits large orders over time. Pyth provides EMA (exponential moving average) prices as a similar anti-manipulation measure.
These are the next concepts worth opening if you want this term to make more sense inside a real Solana workflow.
These entries are easy to mix up when you are reading quickly, prompting an LLM, or onboarding into a new layer of Solana.
The reference spot price sourced from oracle feeds (Pyth, Switchboard) used as the benchmark in derivatives trading. Index price represents the aggregate market price across major exchanges and is used to calculate funding rates and as a component of mark price. Accurate index prices are critical for fair liquidation and funding rate calculations in perps protocols.
The fair value price used by perpetual contract platforms to calculate unrealized PnL and trigger liquidations. Mark price is typically derived from a combination of the oracle index price and the order book or AMM mid-price, weighted to resist manipulation. Using mark price instead of last traded price prevents liquidation hunting through short-lived price spikes on thin markets.
A service that provides external data (prices, randomness) to on-chain programs. DeFi protocols rely on oracles for accurate price feeds to calculate collateral ratios, liquidation thresholds, and swap rates. Solana's primary oracles are Pyth (high-frequency price feeds) and Switchboard (general-purpose data feeds and VRF).
Glossary entries become useful when they are connected. These links are the shortest path to adjacent ideas.
A high-frequency oracle network publishing price feeds for crypto, equities, commodities, and FX. Pyth aggregates data from 90+ first-party publishers (market makers, exchanges) and updates prices every 400ms on Solana. Each price feed includes a price, confidence interval, and exponential moving average. Pyth uses a pull model with Hermes for cross-chain delivery.
An on-chain account maintained by an oracle (Pyth or Switchboard) containing the current price, confidence interval, and timestamp for an asset pair (e.g., SOL/USD). DeFi programs read price feeds to calculate collateral values, trigger liquidations, and determine swap rates. Feed staleness must be checked to prevent using outdated prices.
Time-Weighted Average Price—an average price calculated over a specific time period, weighting each price equally by time. TWAP oracles reduce manipulation risk vs. spot prices. Some DEXs (e.g., Jupiter) offer TWAP execution that splits large orders over time. Pyth provides EMA (exponential moving average) prices as a similar anti-manipulation measure.
These entries live beside the current term and help the page feel like part of a larger knowledge graph instead of a dead end.
A protocol that enables token swaps using algorithmic pricing against pooled liquidity instead of matching individual buyers and sellers. AMMs use mathematical formulas (typically constant product x*y=k) to determine prices based on the ratio of tokens in a liquidity pool. On Solana, major AMMs include Raydium, Orca, and Meteora.
An AMM design where liquidity providers concentrate their capital within specific price ranges instead of across the full 0-to-infinity range. CLMMs dramatically improve capital efficiency—LPs earn more fees per dollar deposited within their active range. If the price moves outside the range, the position becomes inactive. Orca Whirlpools and Raydium CLMM are leading implementations on Solana.
A smart-contract-held reserve of two or more tokens that enables trading via an AMM. Users deposit token pairs in specified ratios to become liquidity providers and earn trading fees. Pools are identified by their token pair and fee tier. Pool depth (total value locked) determines price impact for trades.
A token issued to liquidity providers representing their proportional share of a pool's reserves and accrued fees. LP tokens can be burned to withdraw the underlying assets. The value of LP tokens changes as the pool's token ratios shift and fees accumulate. LP tokens are often stakeable in yield farming programs for additional rewards.