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Real Time Math
Real Time Math supplies zero-garbage numerical libraries for real-time quantitative trading, wrapping Intel MKL and IPP for .NET and Java.
Real Time Math
Real Time Math produces FinMath and FinAnalysis, two numerical libraries for the .NET and Java runtimes. The firm states its components integrate with Intel’s Math Kernel Library and Integrated Performance Primitives to deliver native-performance linear algebra, optimization, random-number generation, and time-series calculations — all marketed on the promise of zero-garbage interfaces that let quant teams re-estimate model parameters on the latest tick without pausing for memory cleanup. The website describes a footprint inside high-frequency stat-arb and algorithmic trading organizations active in global equity, futures, forex, and options markets. The two named libraries cover distinct ground: FinMath handles vector/matrix operations, solvers, statistics, and option valuation; FinAnalysis adds more than 40 technical indicators and 15 logical predicates for expressing relationships across multiple time-series data sets, designed for both real-time and historical processing with built-in correlation tools. No founder, employee count, headquarters, or corporate structure is disclosed. The firm runs a time-limited 30-day free trial and publishes a benchmark comparison against unnamed competitors, centering its sales pitch on avoiding the integration cost of stitching together separate math libraries. The website does not list any venture backing, adjacent vehicles, or philanthropic entities. Real Time Math’s architecture is defined by what it is not — it is neither a broker, a trading firm, nor a platform. It sells a performance-critical component that sits inside a client’s own execution stack, with language-identical APIs in C# and Java aimed at reducing the friction of prototyping and production deployment for latency-sensitive quant teams.
General information
Firm type
other
Year founded
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AUM
Undisclosed
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Frequently asked questions
What does Real Time Math actually sell?
It sells two on-premise numerical libraries — FinMath and FinAnalysis — for the .NET and Java runtimes. FinMath covers linear algebra, optimization, random-number generation, statistics, and option valuation; FinAnalysis layers on more than 40 technical indicators and 15 logical predicates for multi-series time-series analysis. Both are pitched as low-latency tools that avoid garbage-collection pauses during real-time model re-estimation.
Who uses Real Time Math’s libraries?
The firm states its components are in production at high-frequency stat-arb and algorithmic trading organizations operating across global equities, futures, forex, and options. The website does not name specific clients, but the focus on zero-garbage .NET/Java interfaces suggests quant teams that need to co-locate numerical work inside an existing execution stack rather than calling a separate analytics server.
How does Real Time Math achieve low latency?
It wraps Intel’s Math Kernel Library and Integrated Performance Primitives, then adds engineering to minimize or eliminate garbage collection in the managed runtime. The same object-oriented API — identical in C# and Java — exposes tuning parameters for precision control while the underlying native code runs on extensively threaded, optimized Intel routines.
Is Real Time Math a trading firm or a software vendor?
It is exclusively a software vendor. The company does not trade, manage capital, or run a platform. Its business model is licensing numerical libraries to trading organizations, and it offers a 30-day free trial on its website for evaluation.
What differentiates Real Time Math from open-source numerical libraries?
The commercial wrapper focuses on two things that generic open-source libraries often sacrifice: deterministic low latency inside a managed runtime, and a unified API across .NET and Java that handles threading and memory layout. The firm also bundles a broad set of functions — from linear algebra to technical indicators to solvers — under a single license, arguing this reduces the integration and procurement overhead of assembling separate libraries.
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