Types of Market Data: Overview

Market data is the set of price, volume and order information produced by financial markets that traders and analysts use to make decisions; the most useful quick answer: market data breaks into (1) real‑time feeds for live pricing and orderbook depth (Level 1 and Level 2), (2) historical/reference data for backtesting and research, and (3) alternative/derived data layers that augment those streams. For vendor and product examples, see major exchange and vendor pages such as the NYSE data products list NYSE Data Products.

Educational note: This article is for educational purposes only and does not constitute financial, investment, legal, or tax advice. Finelo does not recommend any security, strategy, platform, or transaction. Investing and trading involve risk, including possible loss of principal. Verify current rules, fees, product terms, and suitability with official sources or a qualified professional.

Introduction to Market Data

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Market data is the stream of information that originates from markets: quotes (bids and asks), trade prints (executed prices), volumes, and orderbook activity, plus time‑stamped reference fields (symbols, corporate actions). Traders, quants, portfolio managers and analysts rely on market data to price instruments, execute orders, measure risk and test strategies. Exchanges and third‑party vendors collect, normalize and sell this information as feeds, APIs or terminal services — examples of vendors and exchange data product offerings are listed by major exchanges and vendors NYSE Data Products. Use market data to power live execution decisions (low latency), validate models with historical context (backtesting), and add signal layers (alternative data).

Types of Market Data

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Market data is commonly grouped by content and delivery frequency. Below are the practical categories to know:

• Real‑time feeds (live tick-level data)
  • What they include: best bid/ask, last trade, trade size and timestamp; often sold as Level 1 or Level 2 feeds (see the Level 1/2 section below).
  • Typical users: active traders, market‑making desks, execution engines.
• Market depth / orderbook data (Level 2 and beyond)
  • What they include: multiple price levels on both sides of the orderbook, order sizes and sometimes anonymized order IDs.
  • Typical users: algos that infer liquidity, institutional execution desks.
• Historical (time series / tick / bar) data
  • What they include: consolidated past prices, volumes, OHLC bars and corporate action–adjusted price series for backtesting and attribution.
  • Typical users: quantitative researchers, risk teams, model validation.
• Reference and static data
  • What they include: instrument identifiers (ISIN/CUSIP), exchange codes, corporate actions, instrument hierarchies.
  • Typical users: data engineers, compliance, portfolio accounting.
• Derived and alternative data
  • What they include: indicators created from raw feeds (VWAP, implied volatility surfaces), plus non‑market signals (satellite imagery, credit card spend) that augment market behavior.
  • Typical users: alpha researchers, hedge funds, macro strategists.

Examples of channel and vendor types you’ll encounter: exchange data feeds and consolidated feeds, market data vendors and terminals, and cloud/API providers — major exchanges and vendors publish product lists and delivery options NYSE Data Products. When evaluating types, match the data’s granularity and latency to your use case (example: a market‑making algo needs deeper, lower‑latency feeds than end‑of‑day statistical analysis).

Real-Time vs. Historical Market Data

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Real‑time market data and historical market data serve different decision horizons; choose the one that matches your workflow.

Real‑time data (live feeds)

• Purpose: immediate decisioning — order routing, smart order routers, market‑making, risk checks at execution time.
• Characteristics: tick‑level updates, sub‑second timestamps in low‑latency setups, sometimes exchange‑specific formatting and feed protocols.
• Practical impact: latency matters. Milliseconds can change whether an order executes at the intended price; low‑latency provisioning (co‑located servers, fast network interfaces) reduces slippage for high‑frequency strategies.

Historical data (backfills, aggregated bars)

• Purpose: model training, backtesting, research, reporting and compliance.
• Characteristics: stored, curated time series (ticks, second/minute bars, daily OHLC), adjusted for splits/dividends in reference datasets.
• Practical impact: clean historical data reduces look‑ahead bias and false strategy performance. Researchers should prefer tick or sub‑second historical data only when their strategy logic requires that granularity.

How to choose between them (decision checklist)

• Is your strategy latency‑sensitive? If yes → real‑time low‑latency feed.
• Is your goal model evaluation or statistical research? If yes → historical, high‑quality, cleaned series.
• Do you need both? Many setups ingest real‑time data for execution while archiving it into a historical store for analysis.

Example scenario

• A quant develops a mean‑reversion strategy on 1‑second bars. During backtesting they use historical 1‑second bars to validate signals; for live trading they subscribe to a real‑time 1‑second or tick feed to generate the same bars on the execution engine. Matching timestamp handling and corporate action adjustments between the two sources is critical to avoid discrepancies.

Note: exchanges and vendors list their live and historical products; for a sampling of market and vendor offerings see major exchange product pages and vendor lists NYSE Data Products.

Understanding Level 1 and Level 2 Market Data

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Level 1 and Level 2 are industry shorthand for the depth of market visibility:

• Level 1 (top‑of‑book)

  • Typical content: best bid and best ask (the inside market), last trade price and last trade size, and basic volume metrics.
  • Best for: retail or basic algorithmic traders who need current price and last trade for execution signals.

• Level 2 (market depth / orderbook)

  • Typical content: multiple price levels on both bid and ask sides (often several levels deep), and sometimes order sizes at each level or aggregate depth.
  • Best for: execution algorithms, liquidity takers, and market‑making strategies that infer supply/demand beyond the inside market.

Application examples

• Retail investor using a charting platform: Level 1 data is usually sufficient to place market or limit orders and display last price and bid/ask spread.
• Algorithmic execution manager: uses Level 2 to estimate available liquidity and to slice large orders to minimize market impact.

Many exchanges and vendors publish distinct Level 1 and Level 2 products and delivery methods; you can compare offerings on exchange product pages and vendor lists NYSE Data Products.

Choosing the Right Market Data Provider

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Selecting a provider balances data content, latency, coverage, licensing and cost. Below is a practical decision framework and checklist.