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Desktop App Available

100x Faster Backtesting

GPU-accelerated desktop app with VRAM-resident optimization. Monte Carlo validation and intelligent parameter search for robust strategies.

2M+
Backtests/sec
100x
GPU Speedup
10K
Monte Carlo

Why VectorAlpha Backtester?

GPU Optimized

VRAM-resident execution with zero-copy optimization. Process millions of parameter combinations in seconds.

Robust Validation

Monte Carlo analysis and cross-fit validation ensure your strategies aren't overfitted to historical data.

QA

Desktop Native

Rust + Tauri desktop app with native performance. Your data never leaves your machine.

Technical Architecture

How It Works

  • VRAM-resident execution keeps data on GPU for zero-copy operations
  • Parallel kernels test thousands of parameter combinations simultaneously
  • Monte Carlo engine runs 10K+ simulations for robustness testing
  • Genetic optimizer intelligently searches parameter space

System Requirements

GPU (Recommended): NVIDIA GPU with 8GB+ VRAM for best performance
CPU Fallback: Multi-threaded CPU mode with SIMD optimizations
Platform: Desktop app for Windows, macOS, Linux (Rust + Tauri)
Privacy: 100% local - your data never leaves your machine

Performance Benchmarks

Industry-leading backtesting performance at any scale

Test Environment

Hardware
NVIDIA RTX 4090
24GB VRAM
Ryzen 9 5950X
Dataset
525K candles
1-min bars
1 year data
Strategy
MA Cross
10/20 period
100 params
Versions
VectorBT 0.24
VA 0.1.0
rustc 1.75
400x
GPU Speedup
2M/sec
Backtests
10K
Monte Carlo/s
0.5μs
Latency

Batch Backtesting Performance

10,000 Backtests
MA crossover strategy on 1 year data
VectorBT
2000s
5 tests/sec
VA CPU
200s
10x faster
50 tests/sec
VA GPU
5s
400x faster
2K tests/sec Best
Time Saved: 33 minutes → 5 seconds
Parameters Tested: 100 combinations per symbol
Total Trades: ~24M trade signals
Memory Usage: 12GB → 8GB → 6GB
Metrics Calculated: Sharpe, Sortino, Max DD
GPU Utilization: 95% parallel efficiency

Performance Scaling

Dataset Size VectorBT VA CPU VA GPU GPU Speedup
10K candles 15ms 1.5ms 0.5ms 30x
100K candles 150ms 15ms 1.5ms 100x
1M candles 1500ms 150ms 3ms 500x
10M candles 15s 1.5s 21ms 714x

GPU advantage increases with dataset size due to parallelization efficiency

Benchmark Methodology

Run These Benchmarks
git clone https://github.com/VectorAlpha/backtest-engine
cargo bench --features gpu
View benchmark source code →
Fair Comparison Notes
  • VectorBT with numba compilation enabled
  • Median of 100 runs, excluding compilation
  • GPU times include memory transfer
  • CPU tests use all available cores

Key Features

Monte Carlo Analysis

Test strategy robustness with 10K+ simulations

Random parameter variations reveal strategy weaknesses and edge cases

Cross Validation

K-fold validation prevents overfitting

Walk-forward analysis ensures strategies work on unseen data

VRAM Resident

Zero-copy GPU memory execution

Keep entire datasets in GPU memory for instant access

Smart Optimization

Genetic algorithms for parameter search

Intelligently explore parameter space without exhaustive testing

Risk Metrics

Sharpe ratio, max drawdown, more

Comprehensive risk analysis with 20+ performance metrics

Desktop Native

Private, secure, your machine only

No cloud uploads, no subscriptions, complete data privacy

Built For

Strategy Development

Test and optimize trading strategies with instant feedback

Risk Management

Validate strategies across different market conditions

Portfolio Optimization

Find optimal parameters for multi-asset strategies

Quick Start

# Install via cargo
cargo install vectoralpha-backtester
# Run your first backtest
backtester --data ./BTCUSD.csv --strategy ma-cross --gpu
# Output
✓ GPU acceleration enabled
✓ Completed in 5ms
Annual Return: 24.3% | Sharpe: 1.82 | Max DD: -8.2%

Ready to Backtest at Light Speed?

Join thousands of traders using GPU acceleration for robust strategy development and validation