--- name: market-data-feed-comparison-matrix description: "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 recommendation. Use when selecting, renegotiating, or consolidating price and reference data vendors for equities, options, bonds, FX, or futures." argument-hint: "[asset-classes] [feed-vendors]" allowed-tools: ["Read"] --- # Market Data Feed Comparison Matrix ## 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). 1. **Asset class & instrument coverage** — Breadth and depth per asset class in scope; venue coverage (exchanges, ECNs, dark pools, OTC contributors); international vs. domestic 2. **Data granularity** — Level 1 (BBO), Level 2 (depth-of-book), full order book (Level 3); trade prints, imbalances, auctions 3. **Latency & timeliness** — Wire latency profile, jitter, whether normalized or raw; real-time vs. conflated/snapshot; delayed/EOD availability 4. **Delivery protocols & integration** — Supported protocols (FIX/FAST, ITCH/OUCH, WebSocket, REST, binary), cloud regions, colocation options, SDK/language support 5. **Reference & static data quality** — Corporate actions, symbology/cross-referencing (FIGI, ISIN, RIC, CUSIP), instrument master accuracy, corrections handling 6. **Historical data & backfill** — Depth of history, tick-level vs. bars, point-in-time correctness, ease of replay 7. **Licensing & entitlements** — Display vs. non-display, derived data rights, redistribution, per-user vs. enterprise, exchange fee pass-through 8. **Reliability & SLA** — Uptime SLA, redundancy, failover, gap-fill/recovery, historical outage record 9. **Cost structure** — Base fee, exchange fees, per-message/bandwidth, per-user seats, hidden overage costs; total cost of ownership signal 10. **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 This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top. **Installing in Claude Code:** - `~/.claude/skills//SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects) - `/.claude/skills//SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills) Restart Claude Code or open a new session to register. **Using in Claude.ai, ChatGPT, Gemini, or other AI tools:** 1. 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