Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining a production-grade market data platform, comparable to the internal capability programs run by electronic trading firms and financial data providers.

Module 1: Defining Data Requirements and Market Data Sources

Selecting between exchange-direct feeds, commercial data vendors, and consolidated data providers based on latency, cost, and coverage needs.
Mapping required financial instruments (equities, options, futures) to available data source APIs and feed formats (e.g., ITCH, FIX/FAST, binary vs. JSON).
Evaluating real-time vs. delayed data licensing agreements and compliance with redistribution restrictions.
Designing schema for ticker universes that supports dynamic additions, delistings, and corporate actions.
Assessing time zone handling strategies for global market data ingestion across NYSE, NASDAQ, LSE, and TSE.
Implementing fallback mechanisms for primary data feed outages using secondary vendors or historical replay systems.
Documenting metadata standards for ticker attributes (ISIN, CUSIP, SEDOL) and exchange mappings.
Integrating reference data (e.g., symbology crosswalks, corporate actions) from third parties like Bloomberg or Refinitiv.

Module 2: Ingestion Architecture for High-Velocity Market Data

Choosing between push-based (WebSocket, multicast UDP) and pull-based (REST polling) ingestion models based on throughput and jitter tolerance.
Configuring Kafka topics with appropriate partitioning schemes (by exchange, symbol, or asset class) to balance parallelism and ordering.
Implementing protocol decoders for binary market data formats (e.g., NASDAQ ITCH 5.0) with zero-copy parsing for low latency.
Designing ingestion pipelines with backpressure handling to prevent data loss during downstream system congestion.
Setting up monitoring for message sequence gaps and heartbeat timeouts in real-time feeds.
Deploying edge collectors in co-location facilities to minimize network round-trip time for time-sensitive strategies.
Validating payload integrity using checksums and message sequence numbers at ingestion points.
Normalizing timestamp sources (exchange timestamp vs. system timestamp) with precision to microsecond or nanosecond level.

Module 3: Data Storage and Schema Design for Time Series

Selecting columnar storage formats (Parquet, ORC) vs. time-series databases (InfluxDB, QuestDB) based on query patterns and retention policies.
Partitioning historical data by date and clustering by symbol to optimize range scans for technical analysis.
Implementing tiered storage strategies with hot (SSD), warm (HDD), and cold (object store) layers for cost-performance balance.
Designing schemas that support tick-level, minute-bar, and daily aggregates with schema evolution capabilities.
Choosing between row-level and batch-level compression for tick data based on access frequency and I/O patterns.
Enforcing data retention and purging policies in compliance with regulatory and audit requirements.
Indexing high-cardinality symbol dimensions without degrading write performance in distributed stores.
Implementing point-in-time snapshots for backtesting consistency across mutable corporate actions.

Module 4: Stream Processing and Real-Time Analytics

Developing Flink or Spark Streaming jobs to compute real-time VWAP, bid-ask spread, and volume surges.
Configuring watermarking strategies to handle late-arriving market data messages within acceptable tolerances.
Implementing sliding and tumbling windows for calculating moving averages and volatility metrics.
Designing stateful processing to track order book depth changes and detect spoofing patterns.
Optimizing serialization (e.g., Avro, Protobuf) for low-latency stream processing pipelines.
Integrating CEP (Complex Event Processing) rules to flag unusual trading activity or circuit breaker conditions.
Scaling stream processors horizontally while maintaining exactly-once semantics across failures.
Validating output accuracy by replaying test datasets with known expected results.

Module 5: Data Quality, Validation, and Anomaly Detection

Establishing data quality SLAs (e.g., max latency, completeness, accuracy) for each data product.
Implementing automated checks for out-of-range prices, zero-volume ticks, and duplicate sequence IDs.
Designing reconciliation processes between primary and backup data sources to detect silent failures.
Using statistical process control to identify anomalies in volume or volatility distributions.
Flagging stale instruments that have not reported trades for configurable time thresholds.
Correlating feed health metrics with external market events (e.g., exchange maintenance, news spikes).
Logging and routing data quality violations to alerting systems and data stewards.
Creating synthetic test data to simulate edge cases (e.g., flash crash, halts) for pipeline validation.

Module 6: Governance, Compliance, and Auditability

Implementing data lineage tracking from source feed to derived analytics using metadata tags and provenance logs.
Classifying data sensitivity levels (e.g., real-time quotes vs. historical closes) for access control.
Enforcing role-based access controls (RBAC) on data stores and APIs based on regulatory domains.
Logging all data access and modification events for audit trail compliance (e.g., MiFID II, Reg SCI).
Managing data retention and deletion workflows in alignment with legal hold policies.
Documenting data transformations and business logic for regulatory review and model validation.
Integrating with enterprise data catalogs to expose metadata to compliance officers and data owners.
Conducting periodic data governance reviews to assess vendor contract adherence and data integrity.

Module 7: Scalable Compute for Backtesting and Analytics

Architecting distributed backtesting frameworks using Dask or Spark to evaluate strategies across thousands of symbols.
Managing state consistency when replaying tick data with corporate action adjustments (splits, dividends).
Optimizing I/O patterns for random access to historical bars across large universes using predicate pushdown.
Versioning datasets and code to ensure reproducibility of backtest results over time.
Implementing walk-forward analysis pipelines with automated parameter re-optimization schedules.
Isolating compute environments to prevent production data contamination during research experimentation.
Estimating resource requirements for large-scale simulations based on historical data volume and concurrency.
Validating backtest results against known benchmarks and avoiding look-ahead bias in pipeline design.

Module 8: System Monitoring, Alerting, and Operational Resilience

Deploying end-to-end latency probes to measure data path delays from exchange to analytics layer.
Setting up real-time dashboards for feed health, ingestion rates, and processing lag metrics.
Configuring alert thresholds for abnormal conditions (e.g., missing heartbeats, spike in error rates).
Implementing automated failover between primary and backup data centers for high availability.
Conducting regular disaster recovery drills using data replay from persistent message queues.
Rotating and archiving logs from ingestion, processing, and storage layers for forensic analysis.
Integrating with incident management systems (e.g., PagerDuty) for on-call escalation workflows.
Performing capacity planning based on historical growth trends in data volume and query load.

Module 9: Integration with Downstream Applications and APIs

Designing REST and gRPC APIs to serve real-time quotes and historical data with rate limiting and quotas.
Implementing caching layers (Redis, Memcached) for frequently accessed reference and snapshot data.
Securing data endpoints using OAuth2, API keys, and mutual TLS based on consumer type.
Supporting batch data exports in standard formats (CSV, Parquet) for offline analysis and regulatory reporting.
Integrating with risk systems by streaming position and exposure updates derived from market data.
Providing websocket streams for front-end dashboards requiring live price updates.
Versioning APIs and managing deprecation cycles to support long-running client applications.
Monitoring API usage patterns to identify performance bottlenecks and optimize query plans.