🚀 RLXBT: Institutional-Grade Rust Backtesting SDK

RLXBT is an ultra-high-performance algorithmic trading backtesting engine. Built in Rust with a zero-copy Python API, it offers the speed of a vectorized engine with the precision of a full event-driven simulation.

🔥 Why RLXBT?

• Insane Performance: Process over 4.5 Million bars per second. Built for professional quant researchers who can't afford to wait hours for a single optimization.
• Event-Driven Precision: Unlike vectorized-only tools, RLXBT performs full per-bar simulation, supporting complex order types, intrabar resolution, and realistic slippage.
• RL-Native: The only backtester with built-in Reinforcement Learning (OpenAI Gym) support, including graph-based market observations and advanced reward shaping.
• Institutional Analytics: Over 30 metrics including VaR, CVaR, Sharpe, Sortino, Ulcer Index, and comprehensive drawdown analysis.
• Declarative JSON Strategies: Define complex trading logic using simple JSON rules (Simple or Graph formats), perfect for rapid prototyping without writing a single line of Python.
• Rust Native CLI: A standalone, ultra-fast binary to run backtests, optimize data, and convert CSVs to binary chunks without Python overhead.

💎 Features at a Glance

⚡ Core Engine (Rust)

• Zero-Copy Memory: Direct access to Numpy buffers for zero overhead.
• Intrabar Resolution: Simulate price action inside a candle (High-first vs Low-first) for high-precision TP/SL execution.
• Multi-Strategy Portfolios: Combine and optimize multiple strategies with unified capital management and rebalancing.

�️ Standalone Rust CLI

• rlx-cli rules-backtest: Run backtests directly from JSON strategy files and CSV data.
• rlx-cli convert: Optimize CSV data into binary chunk formats for instant loading.
• rlx-cli dashboard: Generate interactive web dashboards directly from the terminal.

�📊 Professional Analytics

• Stability Analysis: Built-in Monte Carlo simulations and Walk-Forward Analysis (WFA).
• Graph Analyzer: Identify winning streaks, loss recovery patterns, and correlation clusters via graph theory.
• Parameter Sensitivity: Automatically identify which parameters actually drive your performance.

🧠 AI & Reinforcement Learning

• Gymnasium Compatible: Seamless integration with Stable-Baselines3 and other RL frameworks.
• GNN-Ready: Graph-based observations for Graph Neural Networks.
• Separation of Concerns: RL agent learns Entry Timing, while dedicated Exit Controllers handle Risk Management.

📝 Declarative JSON Strategies

Define your strategy logic without writing boilerplate code. Supports multi-indicator confirmation and portfolio-aware conditions.

{
  "entry_rules": [{
    "condition": "RSI_14 < 30 && close > SMA_200 && current_drawdown < 0.10",
    "signal": "InstitutionalLong",
    "direction": 1
  }],
  "exit_rules": [{
    "condition": "RSI_14 > 70",
    "reason": "TakeProfit_Overbought"
  }],
  "stop_loss_pct": 0.02,
  "max_hold_bars": 48
}

💰 Pricing & Plans

RLXBT requires a valid license key. Sign up at rlxbt.com/pricing.

🚀 Quick Start

Installation

pip install rlxbt

Basic Strategy

import pandas as pd
from rlxbt import Strategy, Backtester

class SmaCross(Strategy):
    def generate_signals(self, data):
        sma = data['close'].rolling(20).mean()
        signals = pd.DataFrame(index=data.index)
        signals['signal'] = (data['close'] > sma).astype(int)
        return signals

# Initialize with your license key
bt = Backtester(license_key="your_key_here")

# Run backtest
data = pd.read_csv("market_data.csv")
results = bt.run(SmaCross(), data)

print(f"Total Return: {results['total_return']:.2%}")
bt.plot(data) # Launch interactive dashboard

🔗 Links

• Official Website: rlxbt.com
• Full Documentation: rlxbt.com/docs
• Pricing: rlxbt.com/pricing

🏛️ Commercial Use

RLXBT is a commercial product. While you can download the package freely, a paid license is required for live strategy research and production execution.

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