Direct Answer Market data ingestion is the process of receiving, parsing, and normalizing millions of messages per second from financial exchanges. Successful ingestion at scale requires a pipeline that can handle massive data bursts during market volatility without dropping packets or increasing latency, typically achieved through lock-free concurrency and efficient [...]
Direct Answer A low-latency trading stack is a specialized infrastructure designed to minimize the time between receiving market data and executing a trade. It requires a vertically integrated approach where every component—from the physical network interface to the application logic—is optimized for speed, typically utilizing binary streaming, in-memory processing, and [...]
Direct Answer Modernizing a market data pipeline involves replacing legacy "request-response" models with event-driven, streaming architectures. By utilizing in-memory processing and high-speed binary feeds, firms can ingest millions of messages per second and respond to market movements in real-time. Feature Legacy Middleware Modern Low-Latency Pipeline Data Source Aggregated API Polling [...]
Direct Answer The choice of API protocol—REST, WebSocket, or Streaming—determines how quickly and reliably you receive market data. While REST is best for historical lookups and WebSockets are suitable for basic real-time updates, high-performance trading requires a dedicated streaming protocol (TCP/UDP) to handle high message volumes with minimal overhead. [...]
Direct Answer Market data latency is the time delay between a trade or quote occurring at the exchange and that information being processed by a trading system. To eliminate this lag, firms must optimize every "hop" in the data journey, moving from slow API polling and heavy middleware to high-performance [...]
Direct Answer Real-time financial data streaming works by establishing a persistent connection between a provider and a client, allowing market updates to be delivered continuously as they occur. Unlike request-based APIs, streaming eliminates the overhead of repeated polling, ensuring that every tick, trade, and quote flows in a high-throughput [...]
Direct Answer The amount of historical data you need depends on strategy timeframe, trade frequency, and sensitivity to market conditions. HFT / Scalping: 6 - 24 months of tick data Intraday quantitative: 2 - 5 years of tick + intraday bars [...]
Direct Answer Historical market data is a record of past market activity - prices, trades, quotes, and order book changes - archived for analysis. It can be stored at various granularities: tick-level data capturing every event, intraday bars aggregating activity, or daily OHLC summarizing each session. NxCore provides 20+ years [...]
Direct Answer Algorithmic traders use a combination of real-time tick data, historical datasets, order book depth, and execution-quality data to build, test, and run strategies. The specific mix depends on strategy type. High-frequency systems need microsecond-precision tick data and full depth of book. Longer-horizon strategies rely on cleaned historical data [...]
Direct Answer Exchange-level market data is raw, real-time data delivered directly from an exchange's matching engine - every trade, quote update, and order book change as it occurs. Unlike consolidated feeds that aggregate data from multiple venues, exchange-level data provides the highest fidelity, lowest latency, and most complete view of [...]
