How to use Go language for quantitative trading development?

With the popularity of quantitative trading, more and more developers choose to use Go language for quantitative trading development. The Go language has the advantages of high efficiency, concurrency, and ease of use, and can also bring many advantages in quantitative trading scenarios. This article will introduce how to use Go language for quantitative trading development.

1. Basic knowledge of Go language

Before using Go language for quantitative trading development, we need to master some basic knowledge of Go language.

1.1 Data types

Go language has rich data types, including integers, floating point types, strings, arrays, structures, etc.

For example, integer type can represent all integers, such as int8, int16, int32, int64, etc. Floating point types can represent numbers with decimal points, such as float32, float64, etc. The string type is used to represent text, and string processing can be achieved through string concatenation and other operations.

1.2 Function

Function is an important concept in Go language. Function declaration in Go language requires specifying function name, parameter list and return type. For example:

func add(x, y int) int {
    return x + y
}

The above code defines a function named add, which accepts two integer parameters x, y, and Return their sum.

1.3 Concurrent programming

The Go language inherently supports concurrent programming. Goroutine is a lightweight thread in the Go language that can run multiple tasks simultaneously in a program, thereby improving the concurrency performance of the program. Channels are used in conjunction with Goroutines, and channels can be used for communication and synchronization between Goroutines.

For example, we can use the following code to start a Goroutine and communicate with the main thread through the channel:

func count(c chan int) {
    for i := 1; ; i++ {
        c <- i
    }
}

func main() {
    c := make(chan int)
    go count(c)
    for i := 1; i <= 10; i++ {
        fmt.Println(<-c)
    }
}

The above code defines a function named count , this function generates consecutive integers and sends them to the main thread through channel c. In the main thread, we start a Goroutine to execute the count function, receive messages through the channel, and finally output the first 10 integers.

2. Application scenarios of Go language in quantitative trading

The application scenarios of Go language in quantitative trading are very diverse, including but not limited to the following aspects.

2.1 Data acquisition and processing

In quantitative trading, data acquisition and processing are a very important part, and the Go language can achieve efficient data acquisition through features such as network programming and concurrent programming. and processing. For example, we can use Go language to write a web crawler to obtain market data such as stocks and futures.

At the same time, the Go language also has better memory management and garbage collection mechanisms, which can help us process massive data efficiently.

2.2 Strategy development and backtesting

Quantitative trading strategies are the core of quantitative trading, and the high efficiency and concurrency features of the Go language can help us develop and backtest strategies faster. We can use Go language to write algorithm models, data processing, trading interfaces and other modules, as well as perform strategy backtesting and statistical analysis.

2.3 Trading interface development

Go language can also be used to implement key modules such as trading interfaces and trading engines. Since transaction processing requires high concurrency and high performance, and the Go language has exactly these characteristics, it can bring great advantages to quantitative trading systems.

3. Introduction to the Go language quantitative trading framework

The Go language quantitative trading framework has been recognized by many developers, and commonly used frameworks such as GoExchange, GoTrade, and GoQuant provide a series of Quantitative trading components and tools, including key modules such as data acquisition, strategy backtesting, and trading interfaces.

Let’s take GoExchange and GoQuant as examples to briefly introduce their basic usage and features.

3.1 GoExchange

GoExchange is a digital currency trading framework based on the Go language. It integrates the APIs of multiple exchanges and can help developers quickly access exchange data and perform trading operations. The main features of GoExchange include the following aspects:

• Supports multiple digital currency exchanges;
• Supports the public API and private API of exchanges;
• Use The channel achieves high concurrency in acquiring data and executing transactions;
• supports custom strategies and provides statistical analysis tools.

For example, we can use the following code to obtain the Trade History data of the OKEx exchange:

okex := exchange.NewOKEx(exchange.APIConfig{})
trades, err := okex.GetTrades("btc-usdt", nil)
if err != nil {
    log.Fatal(err)
}
for _, trade := range trades {
    fmt.Printf("ID: %d, Price: %f, Volume: %f
", trade.ID, trade.Price, trade.Volume)
}

In the above code, we first create an OKEx object and pass GetTrades The method obtains the transaction record of the btc-usdt trading pair. Finally, we traversed the transaction records and output the price, transaction volume and other information.

3.2 GoQuant

GoQuant is a quantitative trading framework based on the Go language. It provides simple and easy-to-use APIs and components, and supports multiple markets such as stocks, futures, and digital currencies. The main features of GoQuant include the following aspects:

• Provides various data processing and indicator calculation tools, such as K-line, moving average, Bollinger Bands, etc.;
• Supports a variety of Strategy development framework, and provides backtesting and statistical analysis;
• Supports interaction with multiple digital currency exchanges and provides common trading components;
• Uses Goroutine and channels to achieve high concurrency, And provide distributed task scheduling.

The following is an example of using GoQuant to backtest a simple trend strategy:

import (
    "github.com/Go-Quant/goquant/backtest"
    "github.com/Go-Quant/goquant/feeder"
    "github.com/Go-Quant/goquant/market"
    "github.com/Go-Quant/goquant/trader"
)

func main() {
    ticker := market.GetTicker("btcusdt")
    feed := feeder.NewBacktestFeed(ticker, 300)
    bt := backtest.NewBacktest(feed)
    signal := backtest.NewSignal("trend", "lr", []interface{}{120, 30})
    strat := trader.NewStrategy("trend", signal)
    bt.Run(strat)
    results := strat.Results()
    backtest.AnalyzeResults(results)
}

上述代码中，我们定义了一个名为 ticker 的市场行情对象，该行情对象对应了某个期货合约的实时行情。接着，我们创建了一个名为 feed 的回测数据源，并创建名为 bt 的回测对象，并调用 bt.Run 方法运行回测。最后，我们可以通过 backtest.AnalyzeResults 方法分析回测结果。

四、结语

本文介绍了如何使用 Go 语言进行量化交易开发，并介绍了 Go 语言在量化交易中的应用场景和常用的量化交易框架。在使用 Go 语言进行量化交易开发时，我们需要掌握 Go 语言的基本语法和并发编程特性，并借助现有的量化交易框架实现数据获取、策略开发、交易接口等关键模块。

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