A high-performance trading simulator that implements advanced market impact models, regression-based slippage estimation, and maker/taker proportion prediction.

• Overview
• Architecture
• Models Implementation
• Optimization Techniques
• Performance Metrics
• Installation
• Usage
• Contributing

This project implements a real-time trading simulator with advanced market impact analysis capabilities. It uses the Almgren-Chriss model for market impact estimation, logistic regression for maker/taker proportion prediction, and quantile regression-inspired logic for slippage estimation.

• WebSocket Client: Real-time market data processing
• Calculation Engine: Market impact and fee calculations
• UI Layer: wxWidgets-based graphical interface
• Thread Management: Asynchronous data processing

graph LR
    A[WebSocket] --> B[Order Book]
    B --> C[Calculation Engine]
    C --> D[UI Updates]
    E[User Input] --> C

double calculateMarketImpact(
    double quantity_usd,
    double volatility,
    const std::vector<OrderBookLevel>& asks,
    const std::vector<OrderBookLevel>& bids,
    double slippage_pct,
    double permanent_impact = 2.5e-6,
    double temporary_impact = 2.5e-6
)

• Temporary Impact: η * sqrt(quantity) * midPrice
• Permanent Impact: θ * quantity * midPrice
• Volatility Adjustment: Based on market conditions
• Depth Adjustment: Based on order book liquidity

double estimateExpectedSlippage(
    double quantity_usd,
    double volatility,
    double feeTier,
    const std::vector<OrderBookLevel>& asks,
    const std::vector<OrderBookLevel>& bids,
    double k_vol = 0.1
)

Features:

• Order book depth analysis
• Volatility adjustment
• Fee tier consideration
• Quantile-based estimation

double calculateMakerTakerProportion(
    const std::vector<OrderBookLevel>& asks,
    const std::vector<OrderBookLevel>& bids
)

Features:

• Volume-based analysis
• Price spread patterns
• Market depth consideration
• Logistic regression implementation

• Smart Pointers: For WebSocket and thread management
• Vector Pre-allocation: For order book data structures
• Move Semantics: For efficient data transfer
• RAII: For resource management

• WebSocket Optimization:
  • Binary message format
  • Compression enabled
  • Connection pooling
  • Automatic reconnection

• Order Book: std::vector<OrderBookLevel> for efficient iteration
• Fee Tiers: Static map for O(1) lookups
• Metrics: Vector for contiguous memory access

class WebSocketThread : public wxThread {
    // Thread-safe parameter handling
    std::mutex mutex;
    std::atomic<bool> needs_reconnect{false};
}

Features:

• Thread-safe parameter updates
• Atomic operations for flags
• Mutex-protected shared resources
• Event-based UI updates

• Feature Selection: Optimized for real-time processing
• Matrix Operations: Efficient vector operations
• Caching: Frequently used calculations
• Batch Processing: For historical data analysis

• Calculation Time: Market impact and fee calculations
• UI Update Time: Display refresh latency
• Network Latency: WebSocket communication

• Order Book: ~100KB per snapshot
• Calculation Engine: ~1MB peak
• UI Layer: ~10MB total

• C++17 or later
• wxWidgets 3.1+
• IXWebSocket library

This project is licensed under the MIT License - see the LICENSE file for details.

• Almgren-Chriss model implementation
• wxWidgets for UI framework
• IXWebSocket for network communication