market-data

Market Data

Purpose

Guide the design and management of market data infrastructure for financial services firms. Covers real-time and delayed market data, depth of book levels, consolidated tape and direct feeds, data vendor selection and management, market data licensing and entitlements, data distribution architecture, and market data quality management. Enables building or evaluating market data infrastructure that delivers accurate, timely data to trading, portfolio management, and client-facing systems.

Layer

13 — Data Integration (Reference Data & Integration)

Direction

both

When to Use

• A firm is designing or upgrading its market data infrastructure
• Questions arise about real-time vs delayed data or Level 1/2/3 requirements
• A firm is evaluating market data vendors (Bloomberg, Refinitiv, ICE, FactSet)
• Questions involve consolidated tape (SIP) vs direct exchange feeds
• A firm needs to manage exchange data licensing, entitlements, or usage reporting
• A technology team is designing ticker plants, fan-out, or distribution architecture
• Data quality issues arise: stale quotes, missing ticks, erroneous prints
• Trigger phrases: "market data," "Level 1/2/3," "depth of book," "consolidated tape," "SIP," "direct feed," "NBBO," "ticker plant," "B-PIPE," "data license," "non-display use," "market data entitlements," "conflation," "tick data," "real-time feed"

Core Concepts

1. Market Data Types

• Trade data: Last sale price, quantity, timestamp, condition codes (regular, odd lot, opening/closing), cumulative volume, VWAP.
• Quote data: NBBO (best bid/offer across all exchanges), bid/ask sizes. Quote updates vastly outnumber trade updates in liquid instruments.
• Depth of book: Multiple price levels beyond the NBBO with resting order quantities. Aggregated depth (size per level) or order-by-order (individual orders visible).
• Index data: Real-time index values, composition, weightings, total return values. Sources include exchange-calculated (S&P 500 via Cboe) and third-party (MSCI, FTSE Russell).
• Fixed income pricing: Dealer quotes, evaluated pricing (ICE, Bloomberg BVAL), TRACE trade reports for corporates, EMMA for municipals. Inherently less standardized than equities.
• Options data: Chains (strikes/expirations), Greeks, implied volatility, volume, open interest. OPRA provides the consolidated options feed.
• Fundamental data: Earnings, financial statements, corporate actions, analyst estimates. Sourced from vendors (Bloomberg, FactSet, S&P Capital IQ) rather than exchange feeds.
• News and events: Headlines, economic calendar (FOMC, employment), corporate events (earnings dates, ex-dates), sentiment scores.

2. Data Levels

Level 1 — Top of Book: NBBO, last sale, volume, daily OHLC. Sufficient for portfolio management, client reporting, and order entry. Lowest cost and bandwidth.

Level 2 — Market Depth: Multiple price levels with aggregate size (top 5-20 levels per side). Reveals liquidity beyond the NBBO. Essential for active trading, market impact assessment, and algorithmic execution (TWAP, VWAP). Higher cost and bandwidth.

Level 3 — Full Order Book: Individual order detail (price, size, order ID) enabling complete book reconstruction and order lifecycle tracking. Provided by direct feeds (Nasdaq ITCH, NYSE Arca). Required for market making, HFT, and queue position modeling. Highest cost — hundreds of thousands of messages per second per exchange.

Use Case	Level	Rationale
Portfolio management / reporting	Level 1	NBBO and last sale sufficient for valuation
Active equity trading desk	Level 2	Traders assess depth before large orders
Algorithmic execution	Level 2	Algorithms adapt pace based on available liquidity
Market making / HFT	Level 3	Requires queue position and order flow modeling
Client-facing app (delayed)	Level 1 (delayed)	Display only, 15-minute delay acceptable

3. Consolidated Tape vs Direct Feeds

Securities Information Processors (SIPs): CTA/CQS for NYSE-listed (Tape A/B), UTP for Nasdaq-listed (Tape C), OPRA for options. SIPs collect data from all exchanges, compute the NBBO, and disseminate a consolidated stream. Under Reg NMS, the SIP NBBO is the regulatory benchmark for best execution.

Direct exchange feeds: Proprietary feeds from individual exchanges (NYSE Arca, Nasdaq TotalView/ITCH, Cboe PITCH, IEX DEEP) delivering order-by-order data with lower latency than the SIP. A firm must subscribe to multiple feeds and compute NBBO internally. Each exchange uses different protocols requiring per-exchange parsers.

Dimension	SIP (Consolidated)	Direct Feeds
Latency	Higher (~10-50 microseconds SIP processing)	Lower (bypasses SIP)
NBBO	Provided directly	Must compute from multiple feeds
Data depth	Level 1 (NBBO + last sale)	Level 2/3 (full depth, order-by-order)
Cost	Lower, predictable	Higher, scales with exchange count
Normalization	Pre-normalized	Requires per-exchange parsers
Typical consumer	Buy-side, advisory, retail	Prop trading, market making, HFT

4. Market Data Vendors

Bloomberg: Terminal ($20K-$25K/user/year), B-PIPE (enterprise real-time feed), Data License (bulk EOD/reference data), BEAP (cloud API).

Refinitiv (LSEG): Eikon (desktop, lower cost than Bloomberg, strong FX/FI), Elektron/ LSEG Real-Time (enterprise feed), DataScope (bulk EOD), Tick History (historical ticks).

ICE Data Services: Consolidated feeds, evaluated fixed income pricing (widely used for NAV and regulatory reporting), ICE Benchmark Administration.

FactSet: Research-oriented, flexible API delivery, competitive pricing for smaller buy-side, strong Excel/portfolio management integration.

S&P Capital IQ / Market Intelligence: Comprehensive fundamentals, credit ratings, company filings. Morningstar: Fund/ETF data, ratings, Morningstar Direct for research.

Free/open sources: Exchange websites and financial portals provide delayed (15-min) quotes. Useful for non-time-sensitive display but limited reliability and coverage.

Vendor selection criteria: Asset class coverage, latency, reliability/uptime SLA, API quality, total licensing cost (including exchange fees), historical data depth, support, data quality handling.

5. Market Data Licensing and Entitlements

License categories: Non-professional (retail, personal use, lower fees) vs professional (business use, significantly higher). Display (human views on screen) vs non-display (automated systems: algorithms, risk engines, pricing — fees based on application type, not per-user). Derived data (substantially transformed; redistribution may be permitted if original data cannot be reverse-engineered; policies vary by exchange).

Licensing models: Per-user/per-device (exact monthly count required), enterprise (flat fee covering a defined entity), usage-based non-display (fees by application category: trading, risk, valuation).

Reporting obligations: Monthly/quarterly subscriber counts submitted to each exchange or via data vendor. Under-reporting triggers back-billing, penalties, and contract termination.

Redistribution: Raw exchange data requires explicit redistribution agreements and additional fees for client-facing display. Vendors typically handle redistribution for data consumed through their platforms.

Cost management: Audit usage periodically to eliminate unused subscriptions. Use delayed data where real-time is unnecessary. Track non-display use — many firms discover unreported non-display obligations only during exchange audits.

6. Market Data Distribution Architecture

Ticker plant: Central ingestion and normalization layer. Parses exchange protocols (ITCH, PITCH, FIX), normalizes to unified schema, maps symbology, caches latest values, applies conflation, and monitors feed health.

Fan-out patterns: Topic-based pub-sub (dominant pattern; middleware: Solace, TIBCO, 29West, Kafka for lower-latency needs), request-reply (REST for on-demand lookups), multicast (network-level fan-out for ultra-low-latency co-located environments).

Conflation: Throttles update rates for slower consumers. Time-based (deliver latest value every N ms), change-based (suppress duplicates), priority-based (never conflate trades; conflate quotes for slower consumers).

APIs: REST for historical/reference data, WebSocket for real-time streaming to web/mobile applications, proprietary binary APIs for ultra-low-latency consumers.

Cloud services: AWS Data Exchange, Google Cloud Marketplace, Azure Data Share. Adds network latency (unsuitable for latency-sensitive trading) but appropriate for analytics, portfolio management, and client-facing applications.

7. Historical Market Data

EOD databases: Daily OHLCV. Sufficient for portfolio analytics and long-horizon backtesting. Tick-level data: Every trade/quote with microsecond timestamps. Required for intraday backtesting and microstructure research. A single day of U.S. equity ticks may exceed 10-20 TB. Providers: Refinitiv Tick History, NYSE TAQ, LOBSTER.

Adjusted vs unadjusted prices: Unadjusted for trade-level analysis and regulatory records. Split-adjusted and fully adjusted (splits + dividends) for return calculations.

Survivorship bias: Databases including only current listings inflate backtested returns. Point-in-time databases (showing the universe as it existed historically) are required for unbiased research. Point-in-time data also applies to fundamentals: initial earnings reports may be restated; using restated data introduces look-ahead bias.

8. Market Data Quality

Stale data detection: Flag quotes not updated within expected timeframes during market hours. Suppress stale data from trading and valuation decisions.

Gap detection: Feed-level (sequence number gaps in ITCH/PITCH) and application-level (expected vs actual data frequency).

Erroneous tick filtering: Process exchange trade-bust messages. Filter outlier prints (prices far from NBBO, adjusted for spread and volatility). Distinguish legitimate unusual trades (blocks, auctions, after-hours) from errors.

Monitoring and alerting: Feed health dashboards, latency tracking (exchange-to-receipt), volume monitoring against baselines, automated alerts for disconnections, latency spikes, staleness, and gaps.

Failover: Primary/secondary feed architecture with automatic failover on disconnection, excessive latency, or quality breach. Downstream systems must handle graceful degradation (e.g., losing Level 3 depth when failing from direct feed to SIP).

Metric	Target
Feed uptime (trading hours)	> 99.95%
Median latency	< 1ms (direct), < 50ms (SIP)
99th percentile latency	< 10ms (direct), < 100ms (SIP)
Staleness rate	< 0.1% of instruments
Gap rate	< 0.01% of expected messages

Worked Examples

Example 1: Market Data Infrastructure for a Mid-Size RIA

Scenario: A $2B RIA with 3,000 client accounts needs: real-time quotes for 15 portfolio managers/traders, delayed data for 40 client service associates, EOD data for portfolio accounting and performance, historical data for research, and a client portal with current market values.

Data level assessment: Level 1 is sufficient. The firm places client orders, not market making or HFT. This significantly reduces cost and infrastructure complexity.

Vendor evaluation:

Option	Est. Annual Cost	Key Trade-off
Bloomberg (15 Terminals + Data License)	$375K-$425K	Deep analytics but expensive per-terminal model
Refinitiv Eikon (15 seats) + DataScope	$200K-$275K	Lower cost but smaller user community
FactSet (15 seats) + EOD package	$150K-$225K	Flexible pricing, strong API, less real-time trading depth

FactSet offers the best balance for this firm: real-time quotes and screening for portfolio managers, historical data and factor tools for research, and API access for internal systems.

Client portal data strategy: Real-time redistribution would add $100K-$200K/year in exchange fees for 3,000 non-professional users. The firm selects 15-minute delayed data, eliminating redistribution fees and clearly labeling prices as delayed.

Exchange licensing: 15 professional users for real-time Level 1. 40 associates on delayed data (no exchange license). Client portal on delayed data (no redistribution fees). One administrator handles monthly subscriber reporting through the vendor.

Analysis: Total cost of approximately $175K-$250K vs $400K+ for Bloomberg-centric. The architecture separates real-time (licensed professionals) from delayed (everyone else), minimizing licensing complexity. Annual vendor reviews and usage audits ensure compliance.

Example 2: Market Data for an Electronic Trading Platform

Scenario: A broker-dealer building an institutional equity platform with real-time market data display, smart order routing, execution algorithms (TWAP, VWAP), and post-trade TCA. Must balance latency, completeness, cost, and Reg NMS compliance.

The firm needs both SIP and direct feeds: SIP provides the authoritative NBBO for best execution compliance. Direct feeds from major exchanges provide the per-exchange depth that smart order routing and algorithms require.

Feed selection: Direct feeds from NYSE Arca, Nasdaq TotalView (ITCH), NYSE (Pillar), Cboe BZX/EDGX (PITCH), and IEX DEEP — covering the majority of volume. Lower-volume exchanges added later if routing analysis indicates missed liquidity.

Ticker plant design: (1) Feed handlers per exchange with kernel bypass networking, (2) NBBO calculator comparing internal NBBO against SIP for validation, (3) Book builder maintaining per-exchange and consolidated order books, (4) Pub-sub publishing layer with full-rate feeds for algorithms and conflated feeds for client displays, (5) Historical capture for TCA, regulatory records, and strategy research.

Redistribution licensing: Displaying real-time data to institutional clients requires redistribution agreements with each exchange, monthly professional user reporting, and per-user fees — or enterprise redistribution pricing if economical.

Analysis: Total market data cost is substantial: direct feeds ($300K-$500K/year), SIP ($50K-$100K/year), ticker plant build ($200K-$400K initial), redistribution fees ($100K-$500K/year). Market data is one of the largest operating costs for an electronic platform. Budget for annual exchange fee increases.

Example 3: Entitlement Management and Exchange Licensing Compliance

Scenario: A 200-employee multi-strategy hedge fund (New York, London, Hong Kong) receives an NYSE audit notification. Subscriber counts have been estimated rather than tracked, and the fund is uncertain whether its risk system's use of NYSE pricing constitutes non-display use.

Data consumption inventory: The fund catalogs all NYSE data consumers: (1) Display users — every Bloomberg Terminal, Eikon desktop, and internal dashboard showing NYSE real-time data. Result: 120 professional display users found vs 95 previously reported. (2) Non-display applications — algorithmic trading, risk (VaR/Greeks), portfolio valuation, OMS, pricing engines. Result: 8 unreported non-display applications identified. (3) Derived data — a daily position file with NYSE closing prices sent to the prime broker requires review against NYSE's derived data policy.

Remediation: File amended subscriber reports (expect back-billing). Register non-display applications by category (A: trading, B: internal non-trading, C: derived/redistribution). Deploy an entitlement management platform (Bloomberg SSEOMS, Refinitiv DACS, or dedicated tools like TRG Screen). Establish provisioning/deprovisioning policies. Automate monthly subscriber count generation and reconciliation.

Financial exposure:

Gap	Estimated Back-Billing
Display under-reporting (25 users x 12 months)	$75K-$150K
Non-display applications (8, some Category A)	$200K-$500K
Potential redistribution (1 flow under review)	$0-$100K
Total exposure	$275K-$750K

Analysis: Remediation cost ($100K-$200K for entitlement system + ongoing administration) is modest vs audit exposure. Market data entitlement management must be a formal compliance function. Conduct internal audits annually before exchanges audit externally.

Common Pitfalls

1. Conflating SIP NBBO with direct feed best prices. The SIP NBBO is the Reg NMS regulatory benchmark. A firm's internally computed NBBO from direct feeds may differ due to latency. For best execution compliance, the SIP NBBO is authoritative.

2. Under-reporting exchange subscribers. Estimating rather than counting professional users and non-display applications risks material back-billing during exchange audits.

3. Ignoring non-display use fees. Any system consuming exchange data for automated purposes (algorithms, risk, pricing) typically requires a separate non-display license.

4. Treating delayed data as free. Vendor delivery costs and professional-user fees for delayed data through certain platforms still apply. Verify terms per use case.

5. Over-subscribing to market data. Firms accumulate unused subscriptions over time. Periodic usage audits identify significant cost savings.

6. Neglecting data quality monitoring. Consuming data without staleness, gap, and erroneous tick monitoring exposes the firm to silent failures. VaR computed on stale prices is dangerously misleading.

7. Failing to plan for peak data rates. Volumes spike during market events. Size infrastructure for 2-3x typical peak volumes to avoid failures when data matters most.

8. Ignoring survivorship bias in historical data. Use point-in-time, survivorship-free databases for strategy research to avoid inflated backtest returns.

9. Distributing raw exchange data without redistribution licenses. Client-facing real-time quotes require explicit redistribution agreements. Violations risk license termination and legal liability.

Cross-References

• reference-data (Layer 13) — Security master and symbology underpin market data infrastructure; market data systems rely on reference data for symbol mapping and corporate action processing.
• exchange-connectivity (Layer 13) — Physical and logical exchange connections over which market data feeds travel; covers co-location and protocol handling.
• trade-execution (Layer 12) — Smart order routers and execution algorithms consume Level 2/3 market data for routing decisions and execution pacing.
• portfolio-management-systems (Layer 10) — PMS platforms consume market data for position valuation, drift monitoring, and rebalancing triggers.
• performance-metrics (Layer 1a) — EOD pricing feeds provide closing prices for daily return calculations; data quality directly affects computed metrics.
• volatility-modeling (Layer 1b) — Implied volatility derived from OPRA options data; GARCH/EWMA models calibrated on historical price series from market data infrastructure.
• equities (Layer 2) — Equity market structure and instruments; this skill covers the data infrastructure delivering equity market information to consuming systems.