VectorTA ALMA Indicator Performance
VectorAlpha
High-Performance Quantitative Tools
Open source quantitative finance tools running 20x faster than traditional implementations. GPU accelerated with CUDA and SIMD optimizations. Published by VectorAlpha.
VectorTA ALMA Indicator Calculation Performance
VectorAlpha
High-Performance Quantitative Tools
Open source quantitative finance tools running 20x faster than traditional implementations. GPU accelerated with CUDA and SIMD optimizations. Published by VectorAlpha.
Key Benefits
Free, fast, and transparent. Our libraries process millions of data points per second on commodity hardware.
Accelerated Performance
GPU-accelerated computations deliver speedups on parallel workloads for indicators and backtesting.
Tested & Reliable
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?
340 technical indicators running at 1B+ calculations per second. Battle tested in live trading environments since 2025.
Lightning Fast
Process large datasets quickly with GPU acceleration
Proven in Production
Relied on in live trading with a rigorous automated test suite.
Open Source
Apache 2.0 licensed with transparent development and active community
Featured Project
Technical Analysis Library
Our flagship open source library implements 340 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 exceptional performance through SIMD instructions and GPU acceleration.
Scalar vs SIMD
~3x SIMD uplift is common for AVX-512 indicators at 10k candles
CPU vs GPU
6.57x overall CUDA on latest 1M x 250 benchmarks
Performance notesTap to expand
SIMD Advantage
SIMD instructions process multiple data elements per instruction. AVX-512 indicators are often around ~3x faster at 10k candles.
GPU Parallelism
GPUs can batch thousands of symbols and time windows in one kernel launch instead of nested CPU loops.
SIMD Advantage
SIMD instructions process multiple data elements per instruction. AVX-512 indicators are often around ~3x faster at 10k candles, though gains still depend on kernel type and memory access pattern.
GPU Parallelism
GPUs can evaluate technical indicators for thousands of symbols and time windows in a single batch, turning nested CPU loops into one parallel kernel launch.
CPU 0 of 128. GPU 0 of 128. Scalar 0 of 8. SIMD 0 of 8.
Real-World Performance GainsTap to expand
These optimizations make it possible to process millions of data points in real time, which makes VectorAlpha a good fit for high frequency trading and large scale backtesting.
*Latest 1M-candle x 250-parameter benchmarks (RTX 4090 + Ryzen 9 9950X): 123 indicators are faster on CUDA vs Rust, median CUDA speedup is 12.0x, 64 indicators are above 10x, and overall speedup across all CUDA-kernel indicators is 5.16x.
Real-World Performance Gains
These optimizations make it possible to process millions of data points in real time, which makes VectorAlpha a good fit for high frequency trading and large scale backtesting.
*Latest 1M-candle x 250-parameter benchmarks (RTX 4090 + Ryzen 9 9950X): 123 indicators are faster on CUDA vs Rust, median CUDA speedup is 12.0x, 64 indicators are above 10x, and overall speedup across all CUDA-kernel indicators is 5.16x.
Performance stack
Rust, CUDA, SIMD, and WebAssembly in one workflow
The same product surface spans systems programming, GPU acceleration, vectorized CPU execution, and browser delivery. That combination is what makes the libraries useful beyond a benchmark chart.
Core systemsRust
Memory-safe engines for analytics, indicators, and trading infrastructure.
Predictable performance
Batch accelerationCUDA
GPU compute paths for large indicator sweeps and parameter-heavy workloads.
Throughput where scale matters
CPU hot pathsSIMD
AVX-512 vectorization for low-latency CPU execution on suitable hardware.
Latency-sensitive execution
WebAssembly
JavaScript bindings for demos, dashboards, and interactive browser tooling.
Shipping performance to the web
Open source
Quant finance tools you can inspect, benchmark, and ship
VectorAlpha publishes open source Rust libraries for technical analysis and low-latency backtesting. The emphasis is not just speed in isolation, but transparent implementations that can move from research workflows into production systems.
From core trend and volatility studies to market microstructure analytics.
On the latest 1M-candle x 250-parameter benchmark for CUDA-faster indicators.
Use, modify, and deploy the codebase without licensing friction.
Product focus
What ships today
Tap to expand
Product focus
What ships today
GPU accelerated technical analysis library
340 indicators with CUDA, AVX-512, and JavaScript or Python bindings.
Low latency backtesting engine
Rust backtesting with realistic market simulation, latency modeling, and risk analysis.
Best fit
Research vs production
Tap to expand
Best fit
Research vs production
Research workflows
For quantitative researchers
Indicator coverage, market microstructure tooling, and GPU-backed experimentation without rebuilding the performance layer.
Fast indicator exploration and parameter sweeps without rebuilding the performance layer.
Production systems
For trading infrastructure teams
Rust components for latency budgets, throughput ceilings, and production infrastructure.
Use this when implementation details and low-latency execution matter.
GPU accelerated technical analysis library
340 indicators with CUDA, AVX-512, and JavaScript or Python bindings.
The flagship library implements 340 indicators with CUDA acceleration, AVX-512 SIMD optimization, and bindings for Python and JavaScript. It is designed for researchers who need throughput and for production systems that care about predictable latency.
Low latency backtesting engine
Rust backtesting with realistic market simulation, latency modeling, and risk analysis.
The event-driven backtesting engine targets realistic market simulation, latency modeling, and risk analysis in Rust. On suitable workloads and hardware, the architecture is built around low microsecond to millisecond compute paths rather than generic notebook-only experimentation.
Research workflows
For quantitative researchers
Indicator coverage, market microstructure tooling, and GPU-backed experimentation without rebuilding the performance layer.
Best when you need broad indicator coverage, market microstructure tooling, and GPU-backed experimentation without dropping into low-level implementation work.
Fast indicator exploration and parameter sweeps without rebuilding the performance layer.
Use it when
Fast indicator exploration, parameter sweeps, and transparent research workflows without rebuilding the performance layer yourself.
Production systems
For trading infrastructure teams
Rust components for latency budgets, throughput ceilings, and production infrastructure.
Best when you care about low-latency compute, SIMD-aware implementation details, and Rust components that can live inside production trading infrastructure.
Use this when implementation details and low-latency execution matter.
Use it when
Latency budgets, throughput ceilings, and implementation details matter enough that you need production-grade Rust components, not just wrappers.
Built in public
Start building with VectorAlpha
Explore the libraries, inspect the implementation details, and benchmark them in your own environment. The code is designed to be usable by traders, researchers, and developers who need transparent high-performance tools.
Professional services
Need custom acceleration beyond the library?
We help teams benchmark, vectorize, parallelize, and harden quantitative workloads when off-the-shelf components are not enough. The emphasis is measurable throughput, lower latency, and production-safe implementation work.
Or write directly to consulting@vectoralpha.dev
Engagement areasTap to expand
CUDA and SIMD acceleration
Profile hot loops and move the right workloads onto GPU or AVX-512 paths.
Low-latency trading systems
Reduce jitter in data pipelines, execution paths, and market data handling.
Rust architecture and hardening
Safer systems through ownership boundaries, FFI review, and performance-aware abstractions.
Benchmarking and regression guards
Set baselines and repeatable checks so performance gains survive after launch.
CUDA and SIMD acceleration
Profile hot loops and move the right workloads onto GPU or AVX-512 paths.
Profile hot loops, redesign kernels, and move the right workloads onto GPU or AVX-512 CPU paths.
Low-latency trading systems
Reduce jitter in data pipelines, execution paths, and market data handling.
Reduce jitter in data pipelines, execution paths, and market data handling for latency-sensitive workflows.
Rust architecture and hardening
Safer systems through ownership boundaries, FFI review, and performance-aware abstractions.
Build safer systems with disciplined ownership boundaries, FFI review, and performance-aware abstractions.
Benchmarking and regression guards
Set baselines and repeatable checks so performance gains survive after launch.
Set up measurements, baselines, and repeatable performance checks so gains survive after launch.
FAQ
Frequently Asked Questions
Quick answers on performance, licensing, deployment, and production suitability.
Have a workflow question that is not covered here?
Contact Our Team