Real-time trading requires an infrastructure capable of processing large volumes of data quickly. Any latency or delay can be costly for traders. To guarantee speed and accuracy in this ultra-competitive environment, it's imperative to optimize data processing. In this article, we'll look at best practices for optimizing data processing for real-time trading.
Use of suitable databases
- In-memory databases: For real-time trading, databases such as Redis or MemSQL, which store data in memory rather than on disk, offer much faster access times.
- Column-oriented databases: Databases like Apache Cassandra are designed for fast read and write operations, essential for real-time trading.
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Minimize network latency: Geographic location
- Geographical location: Position your servers as close as possible to the exchange servers to reduce transmission latency.
- Dedicated networks: Invest in dedicated, high-frequency network connections to ensure fast data transmission.
- Parallel programming**: Use multithreading and multiprocessing techniques to enable simultaneous processing of multiple data streams.
- Low-level languages**: Languages such as C++ or Rust are faster than high-level languages for real-time processing.
Process only essential data. Use filters to eliminate irrelevant data and reduce the volume of data to be processed in real time.
If certain analyses can be performed before real-time trading, do so. This reduces the processing load during live trading.
Use caching systems to temporarily store frequently used data, reducing the need for repeated database queries.
Use FPGA or GPU boards
FPGA (Field-Programmable Gate Array) or GPU boards can considerably speed up certain data processing operations by parallelizing tasks.
Optimizing data processing for real-time trading is essential to ensure fast, efficient operations in the financial markets. By adopting the best practices and technologies available, traders can maximize their chances of success and remain competitive in this demanding environment.
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