OHLCV (Open, High, Low, Close, and Volume) is an aggregated dataset consisting of five core data points. The Open and Close represent the first and last price levels within a given time interval, respectively. The High and Low reflect the maximum and minimum price levels observed during that same interval. Volume indicates the total quantity of assets traded within the specified period. This data is commonly visualized through candlestick charts to support technical analysis on intraday values. OHLCV data is available with minute, hourly, or daily granularity.Amberdata pioneered DeFi OHLCV by establishing a consistent methodology for aggregating decentralized exchange data. Due to the absence of a standardized “end of trading day” in crypto markets, comparing trading pairs across centralized and decentralized venues can be challenging. Amberdata standardizes volume normalization using 12:00 AM UTC as the end-of-day (EOD) for decentralized exchanges and lending protocols. This enables consistent cross-exchange comparison and supports arbitrage strategies.
OHLCV price values are expressed in the quote asset, while the volume is expressed in the base asset. For instance, in a BTC-USD pair, price values are denominated in USD and volume in BTC.
OHLCV provides a normalized view of trading activity across crypto ecosystems. It is commonly used to evaluate market structure, momentum, and to support arbitrage strategies between centralized and decentralized exchanges.
Why is DEX OHLCV unique?
Decentralized trading occurs across multiple liquidity pools and exchanges, each with its own pricing dynamics. DEX OHLCV aggregates prices across all relevant liquidity pools, incorporating volume-weighted and time-weighted average price calculations (VWAP and TWAP). This results in more accurate pricing reflective of decentralized market conditions.
How are OHLCV values generated?
OHLCV values are derived from underlying DEX trade data. On top of this data, additional calculations such as Price, TWAP, and VWAP are produced, following methodologies consistent with those used in centralized spot market data pipelines.