How should you store a tick for analysis? - briefly
Store each tick as a time‑stamped record in a compact binary or columnar format, ideally within a database or immutable file system. Include symbol, exchange, and quality‑flag metadata, and protect integrity with checksums or version control.
How should you store a tick for analysis? - in detail
Tick data consists of individual price updates, timestamps, and trade identifiers. Each record must retain millisecond or nanosecond precision to preserve sequencing and enable accurate reconstruction of market events.
Choosing a storage format determines retrieval speed, compression efficiency, and compatibility with analysis tools. Common options include:
- Binary columnar formats (Parquet, ORC) – high compression, fast column‑wise scans, native support in Spark, Pandas, and Dask.
- Flat files (CSV, JSONL) – simple to create, easy to inspect, limited compression, slower random access.
- Serialized objects (Protocol Buffers, Avro) – flexible schema evolution, moderate compression, require custom deserialization code.
Compression should be applied at the file level (e.g., gzip, Zstandard) or within the columnar format itself. Retaining an index on the timestamp field allows O(log n) range queries and reduces I/O for time‑bounded analyses.
Database solutions provide additional capabilities:
- Time‑series databases (InfluxDB, TimescaleDB) – built‑in downsampling, retention policies, and efficient time‑range queries.
- Relational databases with partitioned tables – reliable ACID guarantees, familiar SQL interface, but may require sharding for high‑volume streams.
- NoSQL stores (MongoDB, Cassandra) – horizontal scalability, flexible document schema, suitable for heterogeneous tick attributes.
Best‑practice workflow:
- Ingest raw ticks through a low‑latency message broker (Kafka, RabbitMQ) to decouple producers from storage.
- Validate each record against a schema that enforces data types, non‑null fields, and monotonic timestamps.
- Batch records into time‑aligned blocks (e.g., 1‑minute intervals) before writing to the chosen format.
- Apply deterministic naming conventions that encode date, instrument, and interval for easy discovery.
- Store immutable copies in cold‑storage (object buckets, tape) for compliance and disaster recovery.
- Maintain a metadata catalog that tracks schema versions, compression settings, and retention schedules.
Implementing this pipeline preserves data fidelity, supports high‑performance queries, and ensures long‑term accessibility for quantitative research and regulatory reporting.