Where is a tick analysis done? - briefly
Tick analysis is performed on the data‑processing side of a trading system, usually within the exchange’s data‑feed server or a specialized analytics platform. The computation may run in real time or on stored historical tick archives in databases or cloud environments.
Where is a tick analysis done? - in detail
Tick data analysis is performed wherever high‑resolution market information can be accessed, processed, and stored. The primary environments include:
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On‑premises data centers: Firms host servers in their own facilities or in colocation racks near exchange matching engines. Proximity reduces latency for real‑time strategies. Hardware typically consists of multi‑core CPUs, large RAM pools, and sometimes FPGA accelerators for ultra‑low‑delay computation.
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Cloud platforms: Providers such as AWS, Azure, and Google Cloud offer scalable compute instances, managed storage, and specialized services (e.g., Amazon Kinesis, Azure Stream Analytics). Cloud environments enable rapid provisioning of resources for back‑testing, machine‑learning pipelines, and distributed processing across clusters.
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Hybrid setups: Organizations combine local servers for latency‑critical components with cloud resources for batch analytics, historical data mining, and model training.
Data sources feeding the analysis are:
- Direct exchange feeds (e.g., NYSE OpenBook, CME FIX/FAST) delivering raw tick streams.
- Broker‑provided APIs (e.g., Interactive Brokers, CQG) offering aggregated tick data.
- Third‑party vendors (e.g., Bloomberg, Refinitiv) supplying curated tick histories.
Storage systems designed for tick granularity include:
- Columnar time‑series databases (kdb+/q, InfluxDB, TimescaleDB) that compress and index billions of records efficiently.
- Distributed file systems (HDFS, S3) used for archival and large‑scale batch jobs.
Processing frameworks commonly employed:
- Real‑time engines written in C++ or Java that ingest streams, calculate order‑book metrics, and trigger alerts.
- Batch analytics using Python (pandas, NumPy), R, or Scala/Spark for statistical studies, pattern detection, and model validation.
- GPU‑accelerated libraries (CUDA, RAPIDS) for deep‑learning inference on massive tick datasets.
Regulatory and compliance considerations dictate that certain analyses remain within jurisdiction‑specific data centers, while others may be off‑shored under data‑privacy agreements.
In summary, tick analysis occurs across a spectrum of infrastructures—local high‑performance servers, cloud‑based clusters, and hybrid configurations—leveraging specialized data feeds, storage solutions, and processing tools to handle the volume and velocity of tick‑level information.