Backtest.Net 4.1.14

High-Performance Backtest.Net

A high-performance backtesting engine for algorithmic trading strategies in .NET.

Introduction

High-Performance Backtest.Net is a specialized backtesting library designed for quantitative trading applications. It processes multi-timeframe candlestick data across multiple symbols, simulating tick-by-tick strategy execution with proper warmup periods and OHLC handling.

Key Characteristics

• Multi-Symbol Support: Process multiple trading symbols in parallel
• Multi-Timeframe: Handle multiple timeframes per symbol with automatic synchronization
• Performance Optimized: 10 iteratively optimized engine versions with zero-allocation patterns
• Data Splitting: Intelligent data partitioning for memory-efficient large-scale backtests
• Cancellation Support: Graceful async cancellation with progress tracking

Features

Performance Optimizations

The library implements sophisticated performance techniques:

• Zero-Allocation Hot Paths: Uses Span<T> and CollectionsMarshal.AsSpan
• Parallel Processing: Parallel.ForEach for symbol-level parallelism
• Binary Search: O(log n) candlestick lookups
• Aggressive Inlining: [MethodImpl(MethodImplOptions.AggressiveInlining)]
• Sealed Classes: JIT optimization hints

Installation

Package Manager

dotnet add package Backtest.Net

PackageReference

<PackageReference Include="Backtest.Net" Version="4.1.11" />

Quickstart

Basic Engine Usage

using Backtest.Net.Engines;
using Backtest.Net.SymbolsData;
using Backtest.Net.SymbolDataSplitters;
using Backtest.Net.Timeframes;
using Models.Net.Enums;

// 1. Prepare your symbol data (candlesticks per timeframe)
var symbolsData = new List<SymbolDataV2>
{
    new SymbolDataV2
    {
        Symbol = "BTCUSDT",
        Timeframes = new List<TimeframeV2>
        {
            new TimeframeV2
            {
                Timeframe = CandlestickInterval.OneMinute,
                Candlesticks = yourOneMinuteCandles
            },
            new TimeframeV2
            {
                Timeframe = CandlestickInterval.OneHour,
                Candlesticks = yourOneHourCandles
            }
        }
    }
};

// 2. Split data for efficient processing
var splitter = new SymbolDataSplitterV2(
    daysPerSplit: 30,
    warmupCandlesCount: 100,
    backtestingStartDateTime: new DateTime(2024, 1, 1)
);

var splitData = await splitter.SplitAsyncV2(symbolsData);

// 3. Create and configure the engine
var engine = new EngineV10(
    warmupCandlesCount: 100,
    sortCandlesInDescOrder: false,
    useFullCandleForCurrent: false
);

// 4. Set up your strategy callback
engine.OnTick = async (symbolData) =>
{
    foreach (var symbol in symbolData)
    {
        var latestCandle = symbol.Timeframes[0].Candlesticks[^1];
        // Your strategy logic here
        Console.WriteLine($"{symbol.Symbol}: Close = {latestCandle.Close}");
    }
};

// 5. Run the backtest
await engine.RunAsync(splitData);

Console.WriteLine($"Progress: {engine.GetProgress()}%");

With Cancellation Support

using var cts = new CancellationTokenSource();

engine.OnCancellationFinishedDelegate = () =>
{
    Console.WriteLine("Backtest cancelled gracefully");
};

// Cancel after 30 seconds
cts.CancelAfter(TimeSpan.FromSeconds(30));

await engine.RunAsync(splitData, cts.Token);

Engine Versions

Development

Prerequisites

• .NET 9.0 SDK or later
• Git

Build

git clone https://github.com/islero/High-Performance-Backtest.Net.git
cd High-Performance-Backtest.Net
dotnet build

Test

dotnet test

Benchmark

cd benchmarks/Backtest.Net.Benchmarks
dotnet run -c Release

Format

dotnet format

Versioning & Releases

This project follows Semantic Versioning.

Release Process

1. Update version in src/Backtest.Net/Backtest.Net.csproj
2. Create GitHub Release with tag vX.Y.Z
3. CI automatically publishes to NuGet

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines.

License

See the LICENSE file for details.

Acknowledgments

• SimdLinq - SIMD-accelerated LINQ operations
• BenchmarkDotNet - Performance benchmarking

<p align="center"> Built for the algorithmic trading community </p>