VectorTA ALMA Indicator Calculation Performance
VectorAlpha
High-Performance Quantitative Tools
Production‑grade open source quantitative finance tools. Published by VectorAlpha LLC for traders, researchers, and developers. GPU-accelerated with CUDA and SIMD optimizations.
Key Benefits
VectorAlpha LLC develops and maintains professional open source tools for the quantitative finance community.
Accelerated Performance
GPU-accelerated computations deliver speedups on parallel workloads for indicators and backtesting.
Production-Grade Quality
Thoroughly tested libraries with clear documentation; used in real trading setups.
Developer-First Design
Clean APIs, extensive documentation, and straightforward integration with existing workflows.
Start using VectorAlpha's open source tools in your projects
Why VectorAlpha?
We’re building modern quantitative finance tools. Our GPU‑accelerated libraries pair performance with the transparency and flexibility of open source.
Lightning Fast
Process large datasets quickly with GPU acceleration
Production Ready
Production-grade tools with 97% coverage and clear documentation
Open Source
Apache 2.0 licensed with transparent development and active community
Featured Project
Technical Analysis Library
Our flagship open‑source library implements 300+ technical indicators with GPU acceleration. Production‑ready and used in real trading setups.
const prices = new Float64Array([100.0, 102.0, 101.5, 103.0]);
const result = sma_js(prices, 20); // returns Float64Array
console.log('SMA[0]:', result[0]);
console.log('SMA(10)[0]:', fast[0]);
try { return sma_js(p, 20); }
catch (error) { console.error('SMA failed:', error); throw error; }
}
Performance Fundamentals
See how VectorAlpha's technical analysis library achieves blazing-fast performance through SIMD instructions and GPU acceleration.
Scalar vs SIMD
2-4x performance improvement
CPU vs GPU
10-30x performance for parallel workloads
SIMD Advantage
SIMD instructions process 8 data elements simultaneously, providing up to 8x speedup for vectorizable operations like SMA calculations. Modern CPUs achieve this through AVX-512 instructions.
GPU Parallelism
The RTX 4090's 16,384 CUDA cores organized in 128 SMs can process entire datasets in parallel, making it ideal for financial computations, ML inference, and real-time analytics.
CPU 0 of 128. GPU 0 of 128. Scalar 0 of 8. SIMD 0 of 8.
Real-World Performance Gains
These optimizations enable processing of millions of data points in real-time, making VectorAlpha ideal for high-frequency trading and large-scale backtesting.
*Performance gains vary based on workload characteristics, data size, memory bandwidth, and hardware configuration. Actual results may differ. ALMA indicator shows measured 1.5-2x improvement with SIMD.
Built with Modern Technology
Using modern technologies to deliver measurable performance
Rust
Memory-safe systems programming
CUDA
GPU parallel computing
SIMD
Vectorized CPU instructions
WebAssembly
Browser-based deployment
Professional Services
VectorAlpha offers specialized consulting in high-performance computing and quantitative finance systems optimization.
GPU Acceleration
CUDA optimization for financial computations
Trading Systems
Low-latency algorithmic trading infrastructure
Performance
System optimization and bottleneck analysis
Architecture
Scalable system design and implementation
Leveraging expertise in Rust, CUDA, and modern optimization techniques to deliver exceptional performance improvements.
For project inquiries, email us at consulting@vectoralpha.dev
Learn more about our consulting servicesOpen‑Source Quantitative Finance Tools
VectorAlpha publishes open‑source quantitative finance libraries. We focus on Rust, CUDA, and solid implementations to deliver reliable performance for trading systems.
GPU‑Accelerated Technical Analysis Library
Our technical analysis library implements 300+ indicators with CUDA acceleration and AVX‑512 SIMD optimizations. From simple moving averages to market microstructure analytics, the focus is on throughput and predictable latency for quantitative workloads.
Low‑Latency Backtesting Engine
Test strategies with our event‑driven backtesting framework. Built in Rust, it targets low‑microsecond compute paths on suitable hardware and workloads, with realistic order book simulation, latency modeling, and risk analytics.
For Quantitative Researchers
- Pre-built technical indicators and market microstructure metrics
- GPU acceleration without CUDA programming knowledge
- Python and JavaScript bindings for rapid prototyping
- Comprehensive documentation and examples
For High-Frequency Trading Firms
- Low‑microsecond computation on appropriate hardware
- Lock-free data structures and zero-copy operations
- SIMD vectorization with AVX-512 support
- Production‑ready with 97% coverage
Complete Transparency, Maximum Performance
Unlike proprietary trading platforms, VectorAlpha LLC provides complete transparency with open-source code. Our Apache 2.0 license allows unrestricted commercial use. Whether you're building a quantitative hedge fund or researching market inefficiencies, our tools provide the computational foundation for your success.
Start Building with VectorAlpha
Explore our open source libraries for quantitative finance. Built and maintained by professionals for traders, researchers, and developers who need reliable, high-performance tools.
Frequently Asked Questions
VectorAlpha is an open‑source effort focused on quantitative finance libraries. We build Rust and CUDA‑powered technical analysis tools and low‑latency backtesting components for trading systems.
Have more questions about our quantitative finance tools?
Contact Our Team