Market data is the live and historical information that trading venues publish about their markets. It includes current quotes, the last traded prices, volumes and the depth of outstanding orders. Without it, you cannot price an asset, place an informed order or assess risk.
In practice, market data covers everything from a simple price tick to a full order book feed, plus reference details that help you interpret those numbers. It powers charts, analytics, risk systems and most conversations on a trading desk.
What counts as market data
Core elements you will see in equity, crypto, futures and FX markets are broadly similar, even if naming conventions differ:
- Last price and size. The most recent trade and its quantity.
- Bid and ask. The highest price a buyer is willing to pay and the lowest price a seller will accept. The gap is the spread, a simple read on liquidity.
- Order book depth. Outstanding buy and sell orders at different prices, often called Level 2.
- Volume and turnover. How many units traded over a period, and sometimes the cash value.
- OHLCV bars. Open, high, low, close and volume for a set interval, used to build candlestick charts.
- Reference data. Identifiers, trading hours, tick sizes, corporate action flags and similar context.
Many vendors also distribute derived measures built from the raw stream. Examples include the bid-ask spread, mid price, moving averages, VWAP and market breadth indicators. Index levels and sector averages are also market data, as are derivatives metrics such as implied volatility. On crypto venues, you will encounter funding rates and open interest for perpetual futures, which are part of the data picture too.
Real-time, delayed and historical data
Timing defines how useful the feed is for a given task:
- Real-time data is delivered as it happens, subject to network and processing delays known as latency. Traders rely on it to manage entries and exits.
- Delayed data is typically held back by a set number of minutes. It suits research or casual monitoring but not precise execution.
- End-of-day and historical data aggregate trades and quotes into files or bars for backtesting, valuations and performance analysis. Histories may be adjusted for stock splits or dividends, which changes past prices versus unadjusted series.
Delivery methods vary. A streaming feed pushes every change. Polling APIs return snapshots at intervals. The choice affects how closely your screen tracks fast markets and how heavy the data load is for your systems.
Level 1 vs Level 2: top of book and depth
Feeds are often grouped by the amount of detail they provide:
- Level 1 shows the best bid and best ask with their sizes, plus the last trade and daily totals like volume and high-low. It answers the question: what is the current top-of-book price?
- Level 2 adds multiple price levels from the order book, so you can see where larger interest sits above and below the market. Some feeds also include order-by-order detail with timestamps and trade conditions.
Depth helps you anticipate slippage. If you plan to send a large limit order, Level 2 shows whether there is enough opposing interest near your price or if you are likely to move the market.
Why feeds differ across venues and providers
Prices are set where trades occur, which means market data is venue-specific. A stock can trade on more than one exchange. A cryptocurrency can trade across many spot and derivatives venues. The last price on one venue may not match another, and the best bid-ask can differ with local supply and demand. Some providers build a consolidated view, while others deliver a single venue in raw form.
Other reasons feeds vary:
- Latency. Direct connections to an exchange are usually faster than aggregated or redisplayed feeds.
- Coverage and symbology. Not every vendor carries every instrument, and ticker symbols can differ. Mapping errors create confusion if you compare mismatched series.
- Trade and quote flags. Off-book prints, auctions or special conditions may be included, excluded or labelled differently, which changes your totals and highs-lows.
- Licensing. Exchanges and venues license their data. Access, redistribution and permissible use can depend on your user type and the agreements in place. Details vary by provider and can change.
For longer term valuation work, you might blend or clean data from multiple sources. For short term execution, traders often prefer the specific venue they plan to hit, to minimise surprises between what they see and what they can trade.
How market data shapes trading decisions
Market data is not just for charts. It underpins practical decisions throughout the trade lifecycle:
- Idea generation. Screeners use prices, volumes and fundamentals to surface candidates. A common cut is by market capitalisation.
- Entry and exit. Traders watch spreads, depth and recent trade flow to judge timing and order type. A thin book and wide spread raises execution risk.
- Risk and P&L. Positions are marked to market with current prices. Volatility estimates and correlations often come from historical data.
- Post-trade analysis. Benchmarks like VWAP or opening and closing auction prices provide context for fills and costs.
For systematic strategies, the dataset is the strategy. Cleaning, timestamp alignment and survivorship checks matter as much as the model itself. For discretionary traders, a clear view of the tape and the book helps interpret momentum, absorption and liquidity pockets.
Example: reading a live quote and order book
Imagine a stock with this snapshot:
- Last trade 250.18, size 200
- Bid 250.10 x 500 and ask 250.20 x 400
- Day high 252.40, low 248.90, volume 1.2 million
The spread is 10 cents, so an immediate market buy would likely fill at 250.20. If you place a buy limit at 250.12 for 600 shares, the first 500 might match the existing ask if sellers step down or if your price becomes the best bid and is lifted. If you can see Level 2 and it shows several thousand shares stacked at 250.00 and very little above 250.20, you might infer stronger interest on the buy side near 250.00 but a fragile offer. That shapes your choice between being patient or paying the spread.
Translate the same idea to a crypto order book and the logic holds. The numbers shift faster and across more venues, but you are still reading bids, asks, sizes and recent trades to judge the path of least resistance.
Keep in mind that the last trade is descriptive rather than executable. It tells you where someone just traded, not where you can trade now. For that you look to the current bid-ask and the depth behind it.