Market data feed comparison matrix · free AI skill

Builds a structured comparison matrix evaluating market data feeds across asset classes on coverage, latency, delivery protocols, licensing, cost, and reference data quality — with weighted scoring and a ranked…

Purpose

Produce a side-by-side comparison matrix of market data feeds — scored across the dimensions that actually drive a buy/build/switch decision — so product and data teams can defend a vendor selection with structured evidence instead of anecdote. Solves the problem of comparing feeds that describe themselves in incompatible terms.

When to Use

Invoke this skill when the user asks to:

Compare market data vendors or feeds (e.g., Bloomberg B-PIPE, Refinitiv Real-Time, ICE, Polygon, databento, exchange direct feeds)
Select a price or reference data provider for a new product or trading system
Consolidate or rationalize redundant data feeds across desks or asset classes
Prepare a vendor scorecard for a contract renewal or renegotiation
Evaluate whether to switch feeds to reduce latency or cost
Build the analysis backing a make-vs-buy or direct-vs-consolidated feed decision

Inputs Required

Before starting, collect:

Asset classes in scope: Which of equities, options, bonds, FX, futures (and sub-types, e.g., US listed vs. global, corporate vs. sovereign)
Feeds/vendors to compare: 2–6 named feeds or vendors
Use case: Trading (low-latency), analytics/research, risk/valuation, or display — this changes which dimensions matter most
Data types needed: Real-time / delayed / EOD / historical; Level 1 quotes, Level 2 depth, trades, reference/static data
Delivery/infra constraints: Cloud vs. on-prem, colocation, protocols already supported (FIX/FAST, WebSocket, REST, binary)
Budget context: Rough annual budget or the cost sensitivity level
Dimension weights (optional): Which dimensions matter most (e.g., latency 30%, cost 20%)

If weights are not provided, infer them from the use case and state the assumption. If a specific feed's public detail is thin, mark cells as "Vendor confirmation required" rather than guessing.

Instructions

Step 1: Establish the Comparison Dimensions

Score each feed on these dimensions. Adjust weighting by use case (trading weights latency/depth; analytics weights coverage/history; valuation weights reference data quality).

Asset class & instrument coverage — Breadth and depth per asset class in scope; venue coverage (exchanges, ECNs, dark pools, OTC contributors); international vs. domestic
Data granularity — Level 1 (BBO), Level 2 (depth-of-book), full order book (Level 3); trade prints, imbalances, auctions
Latency & timeliness — Wire latency profile, jitter, whether normalized or raw; real-time vs. conflated/snapshot; delayed/EOD availability
Delivery protocols & integration — Supported protocols (FIX/FAST, ITCH/OUCH, WebSocket, REST, binary), cloud regions, colocation options, SDK/language support
Reference & static data quality — Corporate actions, symbology/cross-referencing (FIGI, ISIN, RIC, CUSIP), instrument master accuracy, corrections handling
Historical data & backfill — Depth of history, tick-level vs. bars, point-in-time correctness, ease of replay
Licensing & entitlements — Display vs. non-display, derived data rights, redistribution, per-user vs. enterprise, exchange fee pass-through
Reliability & SLA — Uptime SLA, redundancy, failover, gap-fill/recovery, historical outage record
Cost structure — Base fee, exchange fees, per-message/bandwidth, per-user seats, hidden overage costs; total cost of ownership signal
Support & operations — Onboarding time, documentation quality, monitoring tools, incident response

Step 2: Build the Comparison Matrix

Construct a table: rows = dimensions, columns = feeds. Each cell contains a concise factual assessment (not a rating alone) plus a 1–5 score.

Fill cells with specifics per asset class where feeds differ materially (e.g., strong equities coverage but thin OTC bonds)
Where a feed's detail is not publicly confirmable, write the assessment and flag "Vendor confirmation required"
Keep cell text tight — one to two sentences maximum

Step 3: Apply Weighted Scoring

Assign each dimension a weight (from input, or inferred from use case — state the weights explicitly)
Compute a weighted score per feed (sum of dimension score × weight)
Present a scoring summary table: feed, per-dimension scores, weighted total, rank

Step 4: Per-Asset-Class Read

For each asset class in scope, name the best-fit feed and why. Feeds rarely win uniformly across equities, options, bonds, FX, and futures — surface where each feed is strongest.

Step 5: Trade-offs & Deal-Breakers

Call out the decisive factors:

Deal-breakers: Any missing capability that disqualifies a feed for this use case
Cost vs. capability trade-off: Where paying more buys meaningfully more, and where it doesn't
Hidden cost risks: Exchange fees, redistribution licensing, bandwidth overages
Integration effort: Which feed is fastest to onboard given existing infrastructure

Step 6: Recommendation

Deliver a ranked recommendation — primary feed, and where relevant a secondary/split-feed strategy (e.g., one vendor for equities/options, another for FX/bonds). Be opinionated and tie the recommendation to the weighted scores and use case.

Output Format

Structure: Dimension matrix table → weighted scoring summary table → per-asset-class read → trade-offs & deal-breakers → ranked recommendation
Length: 1,000–1,500 words including tables
Format: Primary comparison as a table (dimensions × feeds); scoring as a summary table; analysis as tight prose with bullets
Sections: Dimension definitions (brief) → Comparison Matrix → Weighted Scoring → Per-Asset-Class Fit → Trade-offs & Deal-Breakers → Recommendation
Tone: Data product manager — evidence-first, precise with market data terminology, decisive on the recommendation

Quality Checklist

Before delivering output, verify:

[ ] Every feed provided is scored on all 10 dimensions (or the reduced set explicitly agreed with the user)
[ ] Dimension weights are stated explicitly and sum to 100%
[ ] Weighted totals are computed and feeds are ranked — not left as an unscored table
[ ] Coverage differences are broken out per asset class, not collapsed into one "coverage" verdict
[ ] Unconfirmable cells are flagged "Vendor confirmation required" rather than fabricated
[ ] The recommendation names a specific primary feed (and split strategy if applicable) tied to the scores

Notes

For low-latency trading use cases, direct exchange feeds usually beat consolidated feeds on latency but lose on integration cost and breadth — weight this deliberately
Reference data quality and symbology cross-referencing are the most commonly underweighted dimensions and the most expensive to fix post-selection
Exchange fees and redistribution licensing frequently dwarf the vendor's base fee; treat cost as total cost of ownership, not sticker price
A split-feed strategy (best-of-breed per asset class) is often the honest answer — don't force a single winner if the scores don't support it

How to use this skill

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