Professional algorithmic trading backtester built in Rust. Institutional-grade metrics, reinforcement learning integration, and intrabar analysis — all at blazing speed.
Everything you need for professional algorithmic trading research and backtesting.
4.5M bars/sec with full event simulation. Zero-copy data transfer via PyO3.
Model price behavior within each bar. Accurate stop-loss and take-profit execution.
Gym-compatible environment for training PPO, A2C, DQN agents with Exit Rules.
Sharpe, Sortino, Calmar, VaR, CVaR, Ulcer Index, Burke Ratio and more.
Time-based exits, night sessions, max drawdown, min profit targets.
Declarative strategy definitions. No code required. Version control friendly.
See why professionals choose RLX over alternatives.
| Feature | RLX | VectorBT | Backtrader |
|---|---|---|---|
| Speed | 4.5M bars/sec | 40M+ (vectorized) | 10-30K |
| Full Event Simulation | ✅ | ❌ | ✅ |
| Intrabar Analysis | ✅ | ❌ | ❌ |
| RL Environment | ✅ | ❌ | ❌ |
| Institutional Metrics | 30+ | ~15 | ~10 |
| Exit Controller | ✅ | ❌ | Partial |
Get started in minutes with our intuitive Python interface. Full access to the Rust engine's power with Pythonic convenience.
from rlxbt import TradingEngine, load_data
# Load your data
data = load_data("BTCUSDT_1h.csv")
# Create engine
engine = TradingEngine(
initial_capital=100000,
license_key="rlx_pro_xxx"
)
# Run backtest — 130K bars in 30ms!
result = engine.run_with_signals(data)
print(f"Return: {result.total_return:.2%}")
print(f"Sharpe: {result.metrics['sharpe_ratio']:.2f}")Join hundreds of quantitative traders using RLX for their research.