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Infrastructure
Last updated: August 2025

Real-Time Market Data Engineering for Arbitrage

Ultra‑low latency real-time market data pipelines underpin profitable arbitrage strategies, risk dashboards and execution engines. A misordered tick, duplicate trade, or 300ms latency spike can flip a positive expectancy edge into negative slippage. This engineering guide provides a production blueprint: multi‑venue ingestion, sequencing & deduplication, adaptive latency SLOs, backpressure & flow control, replay & recovery, data quality validation, and monitoring KPIs driving reliable arbitrage signal generation.

Multi-Venue Ingestion & Fanout Architecture

WebSocket Core + REST Gap Filler

Primary live streams from CEX (trades, best bid/ask) + DEX subgraph snapshots; REST midpoints fill sequence holes and rehydrate on disconnect.

Fanout via Kafka / NATS

Partition by symbol ensuring ordering per instrument while enabling horizontal consumer scaling.

Canonical Tick Normalizer

Normalize into {ts_exchange, ts_received, venue, symbol, bid, ask, last, seq, volume, latency_ms} and append reliability metadata.

Sequencing, Deduplication & Gap Handling

1

Monotonic Sequence Tracking

Per venue/instrument rolling window of last seq; drop duplicates; flag forward gaps & schedule REST snapshot reconciliation.

2

Gap Replay Strategy

If missing_seq_count ≤ 5 retry via low-latency REST; above threshold trigger full depth snapshot + rebase snapshot_version.

3

Late / Out-of-Order Handling

If late tick timestamp drift < 150ms, apply; else store in side buffer for audit (avoid regressions in derived indicators).

Latency SLAs, Backpressure & Flow Control

  1. Target SLA: P95 ingest latency < 110ms; P99 < 180ms for Tier-1 instruments.
  2. Adaptive Batching: Micro-batch probability scaling when queue depth > threshold to smooth GC / context switches.
  3. Pressure Signal: Measure consumer lag; if lag growth slope positive for 3 intervals, reduce upstream subscription granularity.
  4. Prioritized Queues: Critical symbols (BTC, ETH) isolated from long tail alt streams to protect SLA.
  5. Fast Fail: Circuit break non-essential derived metrics when CPU saturation > 85% for 30s.

Fault Tolerance, Persistence & Replay

Immutable Tick Storage

Append Parquet segments (5min roll) with schema evolution version tags; ensures deterministic backtests & post-mortems.

Checkpoint Offsets

Store consumer group offsets + snapshot_version enabling idempotent replay & state reconstruction after incident.

Warm vs Cold Replay

Warm replay uses compressed memory queue (last 2 minutes). Cold replay spins loader to stream archived Parquet into Kafka with reduced partition concurrency.

Data Quality Assertions & Integrity Guards

  1. Schema Validation: Reject tick if required numeric fields null or bid > ask.
  2. Volatility Bounds: Price delta > 8 * rolling σ triggers quarantine & manual review.
  3. Cross-Venue Divergence: If mid deviates > 25 bps from median of Tier-1 set, mark low confidence.
  4. Clock Skew: Track server vs NTP drift; if > 250ms tag latency_suspect=1 for downstream risk discounting.
  5. Completeness: Coverage ratio (#active venues / #expected) published with reference price.

Monitoring, KPIs & Alerting Strategy

Latency Dashboard

P50 / P95 ingest, processing, publish; color-coded burn-down for SLA breaches & correlation with CPU / GC metrics.

Quality Metrics

Outlier rejection rate, duplicate drop count, gap frequency, divergence bps vs median reference line.

Impact Analytics

Link signal PnL attribution to data anomalies; escalate if anomaly-induced PnL drag > threshold (e.g. 5 bps / day).

Deployment & Reliability Checklist

  1. 1. Ordering: Sequence gap test suite passes (simulated packet loss scenario).
  2. 2. Latency: P95 ingest verified below SLA under stress load test.
  3. 3. Replay: Cold replay reproduces reference price bit‑exact for sampled intervals.
  4. 4. Quality: Outlier filter false positive rate < 0.3% on validation set.
  5. 5. Backpressure: Automatic throttling engages & clears lag within 60s in load spike simulation.
  6. 6. Observability: All critical KPIs exported (Prometheus) + alert routes triaged.
  7. 7. Change Control: Schema version & consumer compatibility matrix updated.

Core Tools & Technology Stack

  • Kafka / NATS (stream backbone)
  • Faust / asyncio (Python stream processing)
  • Redis (hot cache & TTL staging)
  • ClickHouse (OLAP tick analytics)
  • Prometheus + Grafana (metrics & dashboards)
  • Great Expectations (data quality tests)
  • S3 + Parquet (immutable archive)
  • OpenTelemetry (distributed tracing)

Accelerate Your Arbitrage Stack

Integrate this pipeline with our Price Feed Aggregation Architecture, refine execution using Private Transaction Pools, and validate strategies in the Perpetual Arbitrage Guide.

Conclusion

Arbitrage performance is a leveraged function of data correctness and latency predictability. Building a disciplined real-time pipeline—sequencing, dedup, adaptive backpressure, deterministic replay, quality gating and actionable monitoring—shrinks error surfaces that silently eat edge. Treat data like trading capital: version schemas, test failure modes, measure precision/recall of anomaly filters, and continuously tune cost vs performance trade-offs. The result is a resilient, auditable market data substrate enabling confident automation and scalable strategy deployment.

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Real-Time Data Data Pipeline Sequencing Deduplication Latency Monitoring Arbitrage Infrastructure Data Quality

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