How to process ticks? - briefly
To handle tick data, normalize timestamps, discard invalid records, and store or aggregate them at the desired resolution. Then apply statistical or algorithmic techniques—such as moving averages, volatility calculations, or event detection—to extract actionable information.
How to process ticks? - in detail
Processing tick data efficiently requires a clear workflow that transforms raw events into usable information. The process can be divided into acquisition, normalization, filtering, aggregation, and storage.
Acquisition involves capturing each market event as it occurs. Use a low‑latency feed handler that reads binary or JSON messages directly from the exchange gateway. Ensure the handler runs on a dedicated thread to avoid contention with other application components.
Normalization converts heterogeneous messages into a uniform internal representation. Map fields such as timestamp, price, volume, and instrument identifier to a common schema. Apply time‑zone conversion and adjust timestamps to a monotonic clock if required for later sequencing.
Filtering removes irrelevant or erroneous ticks. Implement rule‑based checks that discard:
- events with zero or negative volume,
- prices outside predefined bounds for the instrument,
- duplicate timestamps for the same instrument.
Aggregation prepares data for analysis by summarizing tick streams over fixed intervals. Typical aggregations include:
- Bar construction – compute open, high, low, close, and volume for each interval.
- VWAP calculation – weight price by volume to obtain a volume‑weighted average price.
- Trade count – tally the number of ticks per interval.
Store the processed output in a high‑performance database or time‑series store. Choose a schema that supports fast range queries on timestamps and efficient retrieval by instrument. Index the primary key on (instrument_id, timestamp) and enable compression to reduce storage footprint.
Finally, expose the processed data through an API or messaging bus. Provide endpoints for real‑time subscription and historical query. Include versioning to accommodate schema evolution without breaking downstream consumers.
By following this sequence—capture, standardize, cleanse, summarize, persist, and distribute—developers can handle tick streams with minimal latency and maximal reliability.