Algorithmic Trading Bot Architecture with .NET 10

This guide outlines the architectural design and implementation strategy for a high-performance algorithmic trading bot utilizing the modern features of .NET 10, specifically focusing on its advanced scripting capabilities for dynamic strategy execution.

1. System Architecture Overview

An algorithmic trading bot requires a low-latency, modular architecture to handle real-time data ingestion, complex decision-making, and high-speed execution. The system is divided into four primary layers:

1.1. Data Ingestion Layer

Responsible for connecting to exchange APIs (via WebSockets or REST) to consume market data, including order books, trade history, and ticker updates.

1.2. Strategy Engine

This is the core logic where the bot analyzes market data and determines whether to buy, sell, or hold. By leveraging .NET 10 scripting, we can modify these strategies on the fly without restarting the entire service.

1.3. Execution Engine

Handles the submission, cancellation, and monitoring of orders. It ensures that trades are executed according to the parameters set by the Strategy Engine.

1.4. Persistence & Monitoring Layer

Logs all transactions, market data, and performance metrics for backtesting and auditing using time-series databases.

2. Leveraging .NET 10 Scripting Features

.NET 10 introduces enhanced support for C# scripting as a first-class citizen, allowing developers to load and compile code snippets at runtime with near-native performance. This is ideal for trading bots where strategies need to be tuned frequently based on market volatility.

2.1. Dynamic Strategy Loading

Instead of hard-coding logic, the bot can monitor a directory for .csx or the new .nps (NET PowerScript) files.

// Example: Dynamic strategy execution in .NET 10
using Microsoft.CodeAnalysis.CSharp.Scripting;
using Microsoft.CodeAnalysis.Scripting;

public async Task RunDynamicStrategy(MarketData data)
{
    string scriptContent = await File.ReadAllTextAsync("strategies/RsiMomentum.csx");
    var script = CSharpScript.Create<TradeSignal>(scriptContent, 
        ScriptOptions.Default.WithReferences(typeof(MarketData).Assembly));
    
    var result = await script.RunAsync(new { Market = data });
    ExecuteSignal(result.ReturnValue);
}

3. Technology Stack Recommendations

To build a robust bot, the following technologies are recommended alongside .NET 10:

3.1. Messaging & State

• Redis: Used as a high-speed message broker (Pub/Sub) for inter-service communication and for caching real-time order book states.
• RabbitMQ: Suitable for reliable task queuing, such as handling trade execution confirmations.

3.2. Storage

• TimescaleDB or InfluxDB: Specialized time-series databases for storing high-frequency tick data.
• PostgreSQL: For managing user accounts, trade history, and configuration data.

3.3. Infrastructure

• Docker & Kubernetes: For containerization and scaling components like data scrapers independently.
• Grafana: For real-time visualization of bot performance and PnL (Profit and Loss).

4. Practical Implementation Example

4.1. Project Structure

/TradingBot
├── /src
│   ├── TradingBot.Core          # Shared models and interfaces
│   ├── TradingBot.Ingestion     # WebSocket clients for exchanges
│   ├── TradingBot.Execution     # API wrappers for trade placement
│   └── TradingBot.StrategyHost  # The .NET 10 host that runs scripts
├── /strategies
│   └── ScalpingAlpha.csx        # Dynamic C# script
└── docker-compose.yaml

4.2. Strategy Script Example

Below is a sample script demonstrating a simple moving average crossover strategy using .NET 10 top-level scripting constructs:

// ScalpingAlpha.csx
if (Market.ShortTermMa > Market.LongTermMa && !Position.IsActive)
{
    return TradeSignal.Buy;
}
else if (Market.ShortTermMa < Market.LongTermMa && Position.IsActive)
{
    return TradeSignal.Sell;
}

return TradeSignal.Hold;

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Source: GitHub Issue #38 | Contributor: @coltonchrane