Performance Benchmarks
See how VectorTA's Rust implementation compares to other popular technical analysis libraries in terms of speed and efficiency
VectorTA Performance Advantages
SIMD Optimizations
Leverages modern CPU SIMD instructions for parallel data processing, achieving up to 8x speedup on vectorizable operations.
Zero-Copy Architecture
Minimizes memory allocations and data copying, reducing overhead and improving cache efficiency.
Memory Safety
Rust's ownership system ensures memory safety without garbage collection overhead, preventing crashes and data races.
Cross-Platform
Compiles to native code on all platforms and WebAssembly for browser-based applications with near-native performance.
Technical Implementation Comparison
Detailed comparison of implementation approaches across popular technical analysis libraries
| Feature | VectorTA | TA-Lib | TulipIndicators | Pandas TA | |
|---|---|---|---|---|---|
| Performance | |||||
| AVX2/AVX-512 Intrinsics | Manual | — | — | — | |
| SIMD Auto-vectorization | Compiler | — | Limited | NumPy | |
| Multi-threading | Rayon | — | — | Limited | |
| Zero-copy Operations | ✓ | — | — | — | |
| Compile-time Optimization | LLVM | — | GCC | — | |
| Implementation | |||||
| Language | Rust | C | C | Python/C | |
| Memory Safety | Partial* | — | — | GC | |
| Type Safety | Strong | Weak | Weak | Dynamic | |
| Error Handling | Result<T> | Codes | NaN | Exceptions | |
| Features | |||||
| Indicators Count | 300+ | 150+ | 100+ | Varies | |
| Streaming Support | Planned | — | ✓ | — | |
| NaN Handling | Explicit | Skip | Propagate | Flexible | |
| Distribution | |||||
| WASM Support | ✓ | Unofficial | — | — | |
| Binary Size | < 2MB | < 1MB | < 500KB | > 100MB | |
| Dependencies | None | None | None | Many | |
* VectorTA uses unsafe Rust for manual AVX2/AVX-512 intrinsics in performance-critical paths
✓ = Full support, — = Not supported, Other values indicate partial or specific implementation
Benchmark Methodology
All benchmarks are conducted under controlled conditions to ensure fair comparison:
- Single-threaded execution to compare raw algorithm performance
- Warm-up phase to eliminate JIT compilation effects
- Multiple iterations with median timing to reduce variance
- Same input data (OHLCV) across all libraries
- Release/optimized builds for all implementations
When Performance Matters
Real-time Trading
Process streaming market data with minimal latency
Backtesting
Test strategies on millions of data points quickly
Portfolio Analysis
Calculate indicators for thousands of assets simultaneously
High-Frequency Trading
Sub-millisecond indicator calculations for HFT systems
Mobile Applications
Efficient battery usage and responsive UI on mobile devices
Web Applications
WebAssembly performance for browser-based trading platforms