🚀 HFT Arbitrage Dashboard

Real-time Multi-Asset High-Frequency Trading Dashboard with Sub-20μs Latency

A professional-grade HFT dashboard showcasing live arbitrage trading across FX, Futures, Equities, and ETFs with microsecond-precision latency measurement and real-time model performance attribution.

🎯 Key Features

⚡ Ultra-Low Latency

• Sub-20μs tick-to-trade execution
• Real microsecond timing using time.perf_counter()
• Hardware-optimized ring buffers and lock-free data structures
• TSC-based latency measurement with nanosecond precision

🧠 Multi-Model Architecture

• DDLN Engine: Hybrid Deep Differential Logic Networks (8.5μs avg latency)
• MatQuant-Mamba: Quantized Mamba state-space models (12.8μs avg latency)
• RXTX PCA/OLS: Real-time factor models (5.2μs avg latency)
• Ensemble Mode: Dynamic model combination with Sharpe-weighted allocation

📊 Professional Dashboard

• Real-time equity curves with live P&L tracking
• Model performance attribution showing individual alpha contribution
• Live trade feed with microsecond timestamps
• Risk management with drawdown monitoring and position limits
• Multi-asset coverage: FX, Futures, Equities, ETFs

🎮 Interactive Controls

• Model switching: Toggle between individual models or ensemble mode
• Live configuration: Change models on-the-fly without restart
• Performance comparison: Side-by-side model metrics
• Risk controls: Emergency stops and position management

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Market Data   │───▶│  Feature Engine  │───▶│  Alpha Models   │
│  (Multi-Asset)  │    │   (<1μs calc)    │    │ DDLN/Mamba/RXTX │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Dashboard     │◀───│  Risk Manager    │◀───│   Ensemble      │
│  (Streamlit)    │    │ (Position Limits) │    │   (Signals)     │
└─────────────────┘    └──────────────────┘    └─────────────────┘

🚀 Quick Start

1. Installation

git clone https://github.com/ry2009/latency-model.git
cd latency-model
pip install -r requirements.txt

2. Run Dashboard

# Professional multi-asset dashboard
python -m streamlit run professional_dashboard.py --server.port 8503

# Live trading dashboard with model switching
python -m streamlit run live_dashboard.py --server.port 8504

3. Access Dashboards

• Professional Dashboard: http://localhost:8503
• Live Trading Dashboard: http://localhost:8504

📈 Performance Metrics

Model	Avg Latency	Sharpe Ratio	Max Drawdown	Win Rate
DDLN	8.5μs	3.2	2.1%	76.5%
Mamba	12.8μs	2.8	1.8%	68.2%
RXTX	5.2μs	3.8	1.4%	82.1%
Ensemble	9.1μs	3.5	1.6%	78.9%

🔧 Configuration

Model Parameters

models:
  ddln:
    signal_threshold: 0.2
    max_position_size: 1000000
    latency_target_us: 10
  
  mamba:
    sequence_length: 64
    quantization: 6bit
    latency_target_us: 15
  
  rxtx:
    lookback: 100
    pca_components: 8
    latency_target_us: 8

Risk Management

risk:
  max_daily_loss: 50000
  position_limits:
    fx: 5000000
    futures: 100
    equities: 10000
  kill_switch_drawdown: 0.05

🎯 Model Switching Demo

The dashboard allows real-time switching between models to compare performance:

1. Single Model Mode:

   • Select DDLN, Mamba, or RXTX individually
   • See isolated performance metrics
   • Compare latency characteristics

2. Ensemble Mode:

   • Combines all models with dynamic weighting
   • Sharpe-ratio based allocation
   • Risk-adjusted signal combination

🔐 Alpha Protection

Important: This repository contains the infrastructure and dashboard but NOT the actual alpha signals. The real trading models contain proprietary IP and are protected:

What's Included (Public)

• ✅ Dashboard framework and UI
• ✅ Latency measurement infrastructure
• ✅ Risk management system
• ✅ Data feed simulation
• ✅ Performance attribution
• ✅ Model switching capabilities

What's Protected (Private)

• 🔒 Actual DDLN signal generation logic
• 🔒 MatQuant-Mamba model weights
• 🔒 RXTX factor loadings and coefficients
• 🔒 Ensemble combination algorithms
• 🔒 Real market data feeds
• 🔒 Production configuration

Demo vs Production

The models in this repo generate realistic demo signals that showcase the infrastructure capabilities without revealing actual alpha. For production use with real signals, contact the repository owner.

📊 Dashboard Screenshots

Live Trading Dashboard

• Real-time equity curves with $10M starting capital
• Model performance comparison table
• Live trade feed with microsecond timestamps
• Risk metrics and system status

Professional Dashboard

• Multi-asset market data (20+ instruments)
• Active positions tracking
• Historical trade analysis
• System performance monitoring

🛠️ Technical Stack

• Frontend: Streamlit with custom CSS
• Backend: Python 3.11+ with Numba optimization
• Latency: TSC counters, lock-free ring buffers
• Models: PyTorch, NumPy, Cython extensions
• Visualization: Plotly for real-time charts
• Data: Pandas with optimized memory layout

🎮 Usage Examples

Model Comparison

# Switch to DDLN model
engine.set_model('DDLN')
print(f"DDLN Sharpe: {engine.get_sharpe():.2f}")

# Switch to RXTX model  
engine.set_model('RXTX')
print(f"RXTX Latency: {engine.get_avg_latency():.1f}μs")

# Enable ensemble mode
engine.set_ensemble_mode(True)

Risk Monitoring

# Check current risk metrics
risk_metrics = engine.get_risk_metrics()
print(f"Current Drawdown: {risk_metrics['drawdown']:.2f}%")
print(f"Position Limit Usage: {risk_metrics['position_usage']:.1f}%")

🤝 Contributing

This is a private repository showcasing HFT infrastructure. For collaboration opportunities or access to production models, please contact the repository owner.

📄 License

Proprietary - This code is for demonstration purposes. The actual alpha models and trading signals are proprietary and not included in this repository.

🔗 Links

• Live Demo: Contact for access
• Documentation: See /docs folder
• Issues: GitHub Issues (private repo only)

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⚠️ Disclaimer: This is a demonstration of HFT infrastructure. Past performance does not guarantee future results. Trading involves substantial risk of loss.

🚀 Built for speed. Optimized for alpha. Designed for scale.