How do I choose between different streaming quotes APIs for algorithmic trading and portfolio valuation?

When choosing between streaming quotes APIs for algorithmic trading and portfolio valuation, think in terms of market data quality, latency, coverage, reliability, and licensing rather than just price. The “best” API depends heavily on what you’re trying to do.

1) Start with your use case

For algorithmic trading

Prioritize:

• Low latency
• Stable real-time streaming via WebSocket/FIX/etc.
• Tick-level or quote-level data
• High uptime and fast reconnects
• Deterministic timestamps
• Depth-of-book / Level 2 if your strategy needs it
• Exchange-grade data quality and clear handling of odd lots, auctions, halted symbols, etc.

If you’re trading short-term or intraday, small delays, missing quotes, or inconsistent update ordering can materially affect results.

For portfolio valuation

Prioritize:

• Broad asset coverage
• Accurate end-of-day and real-time last price
• Corporate actions support: splits, dividends, symbol changes
• Cross-asset support: equities, ETFs, options, FX, rates, crypto, futures
• Convenient normalization for accounting and reporting
• Historical data quality more than microsecond latency

For valuation, you usually care more about correctness and coverage than nanosecond performance.

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2) Key criteria to compare

A. Data quality

Ask:

• Are quotes direct from exchange, from a consolidated feed, or aggregated by the vendor?
• Are there stale quote filters?
• How are missing symbols, halts, crossed/locked markets, and outliers handled?
• Is data adjusted for corporate actions?

High data quality matters a lot if you use quotes to mark positions or trigger trades.

B. Latency and update frequency

Ask:

• What is the end-to-end latency from exchange to your app?
• Are updates pushed on every change or batched?
• Does the API provide market-by-quote or just last trade?
• Is there a published SLA?

For trading, “real-time” can mean very different things across vendors.

C. Coverage

Check:

• Asset classes: equities, options, futures, FX, crypto, mutual funds, ADRs
• Exchanges and geographies
• Pre-market / after-hours support
• Delisted symbols and survivorship bias in historical data

If your portfolio spans multiple asset types, a single vendor may not cover everything well.

D. Historical depth

For backtesting and analytics:

• How far back does data go?
• Is it tick, minute, or daily?
• Are quotes and trades both available?
• Is data survivorship-free?
• Can you query by symbol, date, and event type efficiently?

Many APIs are good live but weak historically.

E. Reliability and operational features

Look for:

• Uptime history
• Rate limits
• Reconnect semantics
• Sequence numbers / gap detection
• Retry behavior
• Data snapshots vs incremental updates
• Support responsiveness

Streaming infrastructure matters as much as the raw feed.

F. Licensing and usage rights

This is often overlooked.

• Can you use data for internal trading, client reporting, or redistribution?
• Are there per-user, per-seat, or per-machine restrictions?
• Are there exchange pass-through fees?
• Are there obligations to display exchange attribution?

Market data licensing can be a hidden cost.

G. Cost structure

Compare:

• Subscription fees
• Per-symbol or per-feed charges
• Historical data costs
• Exchange fees
• Overages / API call limits
• Enterprise support costs

Cheapest is not always cheapest once you include infrastructure and compliance.

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3) Streaming vs snapshot APIs

Streaming API

Best for:

• Intraday trading
• Live P&L
• Real-time risk
• Market monitoring

Pros:

• Push-based
• Lower latency
• Efficient for continuously updated symbols

Cons:

• More complex handling of disconnects and sequencing
• Requires robust state management

Snapshot API

Best for:

• Portfolio valuation
• Rebalancing
• End-of-day reporting
• Low-frequency apps

Pros:

• Simpler
• Easier to reason about
• Less operational complexity

Cons:

• Can be stale between requests
• Higher API usage if polling many symbols

A common pattern is to use streaming for live trading and snapshots or periodic refreshes for valuation.

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4) Ask for a sample and test it

Before committing, benchmark the API with your own symbols and use cases:

• Measure latency distribution: median, p95, p99
• Check data completeness
• Compare quotes against another source
• Test during volatile market hours
• Simulate disconnects and market open/close transitions
• Verify corporate action handling on known events

A vendor that looks good in a demo may behave very differently under load.

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5) Questions to ask vendors

Use these questions in evaluation:

1. Is the feed exchange-direct or consolidated?
2. What is the latency SLA?
3. Do you provide Level 1 or Level 2?
4. What asset classes and exchanges are covered?
5. How are splits/dividends/symbol changes handled?
6. Do you provide historical tick data?
7. What are the rate limits and reconnect rules?
8. Is there sequence numbering for gap recovery?
9. What are the licensing restrictions?
10. What support do you offer during market hours?

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6) Practical rule of thumb

• High-frequency / intraday trading: choose the vendor with the best latency, reliability, and depth for your specific venue(s), even if it costs more.
• Mid-frequency systematic trading: choose a balanced vendor with dependable streaming and decent historical quality.
• Portfolio valuation / reporting: choose the vendor with the best coverage, corporate action handling, and clean snapshots.

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7) A simple selection framework

Score each vendor 1–5 on:

• Latency
• Data accuracy
• Coverage
• Historical depth
• Reliability
• Ease of integration
• Licensing fit
• Cost

Then weight the scores by your use case:

• Trading: latency and reliability get the highest weight
• Valuation: accuracy, coverage, and corporate actions get the highest weight

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If you want, I can also give you:

1. a vendor comparison checklist,
2. a decision matrix template, or
3. recommendations based on your asset class and trading frequency.