What open, high, low and close tell you about spot power price data

Open, high, low and close (OHLC) are the first, highest, lowest and last prices recorded in a given period, and they appear in almost every traded market. In power markets, however, what those four figures tell you depends on how the price for that period was set. A delivery hour cleared in a single day-ahead auction produces very different figures from a period traded continuously through the day. Reading the two in the same way is one of the more common ways power price data is misinterpreted.

For traders, for analysts whose models use OHLC inputs, and for anyone presenting power market data, this distinction separates a useful volatility signal from a number that only looks meaningful.

What each value means for power prices

• Open: the first traded or cleared price in the period

• High: the highest price in the period

• Low: the lowest price in the period

• Close: the last traded or cleared price in the period

In auction sessions, a single clearing price usually applies to the whole delivery period, so there is no true range. The four figures are then usually the same, because only one price was recorded.

"Period" can also mean two different things. For day-ahead data, it is usually the delivery period itself: an hour or, increasingly, a 15-minute block, with one price set by that period's auction. For continuous intraday trading, it is often a reporting window, such as a trading day or session, in which many trades for the same delivery product are matched. Check which meaning applies before reading any OHLC figure.

Why power OHLC data differs from other markets

Day-ahead prices are usually set once per delivery period by auction, not through continuous trading. A true OHLC range, one that builds up over a session, therefore applies mainly to continuous intraday trading, where buy and sell orders are matched throughout the session rather than cleared all at once.

Even in continuous trading, volume tends to cluster near gate closure as traders finalise their positions. This can affect where the session high and low fall relative to when most volume trades.

Power prices can also go negative. A low below zero reflects a real, if usually short-lived, market condition, not a data error.

Reading candlestick charts of power prices

Candlestick charts, borrowed from equity and FX markets, are sometimes used to display power OHLC data. Each candle has a body spanning the open and close, with thin lines (wicks) extending to the high and low. Where the data reflects continuous trading, this is a quick way to scan volatility and direction across many delivery periods at once.

The limitation comes from the data itself. A candle built from a single auction price is a flat line, because there is no movement from open to close and no range between high and low. Mixing auction and continuous periods on the same chart can suggest activity or volatility in the auction periods that the data does not support.

What the range between high and low tells you

Analysts using OHLC inputs generally treat the range, not the open or close, as the main volatility signal, because it captures the full extent of price movement in the period wherever it started or ended. This makes it a fairly robust input for comparing volatility across many periods, provided auction and continuous periods are treated consistently.

A wide range can point to real uncertainty or rapidly changing supply and demand during the session. A narrow range suggests a settled market with little new information moving prices. Comparing ranges across delivery hours on the same day shows which periods carried the most trading uncertainty, rather than reducing volatility to a single daily figure.

The range shows how much the price moved, not in which direction. A wide range that closes near the open describes a different market from a wide range that closes far from it, even though the spread between high and low is the same.

What open and close can suggest

A close well above the open may reflect information or conditions that pushed expectations up during the session; a close well below it may reflect the reverse. A gap between one period's close and the next period's open can reflect news or changes between sessions, rather than anything that happened within either one.

Neither signal is reliable on its own. Read movement between open and close alongside traded volume and the wider market context for that day.

Liquidity matters here too, and it is rarely constant across periods. A wide range built from a handful of trades in a thinly traded period means something very different from the same range built from thousands of trades in a liquid one, even though the OHLC figures look identical. Checking volume alongside the range is usually the quickest way to tell them apart.

Practical uses and limits of OHLC power data

Where it applies, OHLC data gives a quick read of volatility and trading activity across many delivery periods, and in risk management it can flag periods with unusually wide ranges for closer attention. It adds much less for auction-only markets, where a single clearing price usually applies and the four figures have little extra to describe.

Combined with volume, weather and generation data, OHLC figures contribute to a fuller picture of a delivery period. On their own, particularly for auction prices, they can imply more structure than the data contains.

Data providers and platform developers face a particular version of this problem, because the same interface often has to show auction and continuously traded products side by side. Marking clearly which periods have a true OHLC range and which reflect a single auction price, rather than displaying every period the same way, helps stop users reading structure into data that was never there.

OHLC data is most informative for continuous trading, where prices move as trades are matched through the session. For auction prices, which usually settle at a single clearing price, it adds little. Conventions also vary by exchange and session, so check how your data source defines each figure before drawing conclusions.