Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide

C++ Scalper Bot High-Frequency Trading Architecture: A visualization of a C++ algorithmic bot digesting high-speed FIX/FAST network data streams.

Hello, friends! Today, I'll show you how to build your first trading scalper bot in C++ using the FAST/FIX protocols. Many believe that algorithmic trading is something extremely complex, accessible only to elite programmers with hedge fund experience. But let's be honest: you can start small, especially if you already have access to an exchange via FAST/FIX.

What Do We Need?

Before we dive into the code, let's prepare our toolkit:

C++ compiler (GCC, Clang, or MSVC)
Library for working with FIX/FAST (e.g., OnixS C++ FIX Engine)
A convenient IDE (Visual Studio, CLion, or your favorite)
A cup of strong coffee (optional, but highly recommended)

Understanding FIX/FAST Protocols

If you already have access to an exchange via FAST/FIX, you probably know that:

FIX (Financial Information eXchange) is a standard protocol for exchanging financial information. Imagine telegrams like "BUY BTCUSD QTY=1 PRICE=50000", but in a more formalized format.

FAST (FIX Adapted for Streaming) is a "turbo version" of FIX, optimized for high-speed data transfer. If FIX is regular mail, FAST is pneumatic mail on steroids.

High-frequency scalper five-module architecture

Our Scalper's Architecture

Any self-respecting scalper should have the following components:

Exchange connection module – our bridge to the world of big money
Market data processing module – our "eyes and ears"
Decision-making module – our "brain"
Order management module – our "hands"
Risk management module – our "self-preservation instinct"

Let's sketch the skeleton of our future bot:

#include <iostream>
#include <string>
#include <onixs/fix/engine.h>   // Presumed library for FIX

class SimpleScalper {
private:
    // Connection parameters
    std::string senderCompID;
    std::string targetCompID;
    std::string fixVersion;
    
    // Trading parameters
    double stopLossPercentage = 0.05;  // 5% stop-loss
    double profitTargetPercentage = 0.1;  // 10% profit target
    double tradeAmount = 0.1;  // Position size
    
    // Trading state
    bool inPosition = false;
    double entryPrice = 0.0;
    
    // FIX connection
    onixs::fix::Session* session;

public:
    SimpleScalper(const std::string& sender, const std::string& target, const std::string& version) 
        : senderCompID(sender), targetCompID(target), fixVersion(version) {
        // Initialize FIX connection
        initializeFixConnection();
    }
    
    // Other methods...
};

Connecting to the Exchange

First, we need to establish a connection to the exchange. This is like a first handshake – it's important not to mess it up:

void SimpleScalper::initializeFixConnection() {
    // Create session configuration
    onixs::fix::SessionConfig config;
    config.setSenderCompID(senderCompID);
    config.setTargetCompID(targetCompID);
    config.setFixVersion(fixVersion);
    
    // Set connection parameters
    config.setHost("exchange.hostname.com");
    config.setPort(9823);  // Exchange FIX port
    
    // Create and initialize session
    session = new onixs::fix::Session(config);
    
    // Register event handlers
    session->setMessageHandler(std::bind(&SimpleScalper::onMessage, this, std::placeholders::_1));
    
    // Establish connection
    if (!session->connect()) {
        std::cerr << "Failed to connect to the exchange!" << std::endl;
    } else {
        std::cout << "Connection established!" << std::endl;
    }
}

Market Data Processing and Decision Making

For simplicity, let's implement an RSI indicator and basic entry/exit logic:

double calculateRSI(const std::vector<double>& prices, int period = 14) {
    if (prices.size() < period + 1) return 50.0;
    double sumGain = 0.0, sumLoss = 0.0;
    for (size_t i = prices.size() - period; i < prices.size() - 1; ++i) {
        double change = prices[i + 1] - prices[i];
        if (change > 0) sumGain += change;
        else sumLoss -= change;
    }
    if (sumLoss == 0) return 100.0;
    double rs = sumGain / sumLoss;
    return 100.0 - (100.0 / (1.0 + rs));
}

FIX protocol order lifecycle sequence

Sending Orders to the Exchange

When our strategy says "buy" or "sell", we need to send the corresponding order to the exchange. It's like placing an order at a restaurant – you need to be precise:

void SimpleScalper::sendBuyOrder(double amount, double price) {
    std::cout << "Sending BUY order: " << amount << " @ " << price << std::endl;
    // Here we form and send a FIX NewOrderSingle
    // ...
}

Handling Exchange Responses

void SimpleScalper::onMessage(const onixs::fix::Message& message) {
    std::string msgType = message.getField(onixs::fix::Tag::MsgType);
    if (msgType == "8") {  // ExecutionReport
        // Handle execution report
    } else if (msgType == "W") {  // MarketDataSnapshotFullRefresh
        // Handle market data
    } else if (msgType == "3") {  // Reject
        std::string reason = message.getField(onixs::fix::Tag::Text);
        std::cout << "Order rejected: " << reason << std::endl;
    }
}

Testing and Debugging

Before running our bot with real money, let's test it on historical data or in simulation mode. This is like a pilot training on a simulator before a real flight:

void testScalperOnHistoricalData() {
    SimpleScalper scalper("TESTER", "EXCHANGE", "FIX.4.4");
    std::vector<double> historicalPrices = loadHistoricalData("BTCUSD_1min_2023.csv");
    for (const auto& price : historicalPrices) {
        // emulate market data
        // scalper.processMarketData(...);
    }
    // scalper.printPerformanceStats();
}

Conclusion

That's it! We've built a simple yet functional C++ scalper using FAST/FIX protocols. Of course, this is just the beginning. Real HFT systems are much more complex and optimized, but even such a simple bot can be useful for a beginner algorithmic trader.

Remember, trading on financial markets involves risks, and algorithmic trading adds technical risks on top. Always test your strategies on historical data and in simulation mode before going live.

And most importantly – keep learning and improving your bot. Markets change, and your algorithms must adapt with them.

Happy trading, and may your scalper always be profitable!

Citation

@software{soloviov2024scalpercppfastfix,
  author = {Soloviov, Eugen},
  title = {Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide},
  year = {2025},
  url = {https://marketmaker.cc/en/blog/post/scalper-cpp-fast-fix},
  version = {0.1.0},
  description = {A step-by-step guide to building a C++ trading scalper bot using FAST/FIX protocols.}
}

C++ Scalper Bot High-Frequency Trading Architecture: A visualization of a C++ algorithmic bot digesting high-speed FIX/FAST network data streams.

What Do We Need?

Before we dive into the code, let's prepare our toolkit:

C++ compiler (GCC, Clang, or MSVC)
Library for working with FIX/FAST (e.g., OnixS C++ FIX Engine)
A convenient IDE (Visual Studio, CLion, or your favorite)
A cup of strong coffee (optional, but highly recommended)

Understanding FIX/FAST Protocols

If you already have access to an exchange via FAST/FIX, you probably know that:

FIX (Financial Information eXchange) is a standard protocol for exchanging financial information. Imagine telegrams like "BUY BTCUSD QTY=1 PRICE=50000", but in a more formalized format.

FAST (FIX Adapted for Streaming) is a "turbo version" of FIX, optimized for high-speed data transfer. If FIX is regular mail, FAST is pneumatic mail on steroids.

High-frequency scalper five-module architecture

Our Scalper's Architecture

Any self-respecting scalper should have the following components:

Exchange connection module – our bridge to the world of big money
Market data processing module – our "eyes and ears"
Decision-making module – our "brain"
Order management module – our "hands"
Risk management module – our "self-preservation instinct"

Let's sketch the skeleton of our future bot:

#include <iostream>
#include <string>
#include <onixs/fix/engine.h>   // Presumed library for FIX

class SimpleScalper {
private:
    // Connection parameters
    std::string senderCompID;
    std::string targetCompID;
    std::string fixVersion;
    
    // Trading parameters
    double stopLossPercentage = 0.05;  // 5% stop-loss
    double profitTargetPercentage = 0.1;  // 10% profit target
    double tradeAmount = 0.1;  // Position size
    
    // Trading state
    bool inPosition = false;
    double entryPrice = 0.0;
    
    // FIX connection
    onixs::fix::Session* session;

public:
    SimpleScalper(const std::string& sender, const std::string& target, const std::string& version) 
        : senderCompID(sender), targetCompID(target), fixVersion(version) {
        // Initialize FIX connection
        initializeFixConnection();
    }
    
    // Other methods...
};

Connecting to the Exchange

First, we need to establish a connection to the exchange. This is like a first handshake – it's important not to mess it up:

void SimpleScalper::initializeFixConnection() {
    // Create session configuration
    onixs::fix::SessionConfig config;
    config.setSenderCompID(senderCompID);
    config.setTargetCompID(targetCompID);
    config.setFixVersion(fixVersion);
    
    // Set connection parameters
    config.setHost("exchange.hostname.com");
    config.setPort(9823);  // Exchange FIX port
    
    // Create and initialize session
    session = new onixs::fix::Session(config);
    
    // Register event handlers
    session->setMessageHandler(std::bind(&SimpleScalper::onMessage, this, std::placeholders::_1));
    
    // Establish connection
    if (!session->connect()) {
        std::cerr << "Failed to connect to the exchange!" << std::endl;
    } else {
        std::cout << "Connection established!" << std::endl;
    }
}

Market Data Processing and Decision Making

For simplicity, let's implement an RSI indicator and basic entry/exit logic:

double calculateRSI(const std::vector<double>& prices, int period = 14) {
    if (prices.size() < period + 1) return 50.0;
    double sumGain = 0.0, sumLoss = 0.0;
    for (size_t i = prices.size() - period; i < prices.size() - 1; ++i) {
        double change = prices[i + 1] - prices[i];
        if (change > 0) sumGain += change;
        else sumLoss -= change;
    }
    if (sumLoss == 0) return 100.0;
    double rs = sumGain / sumLoss;
    return 100.0 - (100.0 / (1.0 + rs));
}

FIX protocol order lifecycle sequence

Sending Orders to the Exchange

When our strategy says "buy" or "sell", we need to send the corresponding order to the exchange. It's like placing an order at a restaurant – you need to be precise:

void SimpleScalper::sendBuyOrder(double amount, double price) {
    std::cout << "Sending BUY order: " << amount << " @ " << price << std::endl;
    // Here we form and send a FIX NewOrderSingle
    // ...
}

Handling Exchange Responses

void SimpleScalper::onMessage(const onixs::fix::Message& message) {
    std::string msgType = message.getField(onixs::fix::Tag::MsgType);
    if (msgType == "8") {  // ExecutionReport
        // Handle execution report
    } else if (msgType == "W") {  // MarketDataSnapshotFullRefresh
        // Handle market data
    } else if (msgType == "3") {  // Reject
        std::string reason = message.getField(onixs::fix::Tag::Text);
        std::cout << "Order rejected: " << reason << std::endl;
    }
}

Testing and Debugging

Before running our bot with real money, let's test it on historical data or in simulation mode. This is like a pilot training on a simulator before a real flight:

void testScalperOnHistoricalData() {
    SimpleScalper scalper("TESTER", "EXCHANGE", "FIX.4.4");
    std::vector<double> historicalPrices = loadHistoricalData("BTCUSD_1min_2023.csv");
    for (const auto& price : historicalPrices) {
        // emulate market data
        // scalper.processMarketData(...);
    }
    // scalper.printPerformanceStats();
}

Conclusion

Remember, trading on financial markets involves risks, and algorithmic trading adds technical risks on top. Always test your strategies on historical data and in simulation mode before going live.

And most importantly – keep learning and improving your bot. Markets change, and your algorithms must adapt with them.

Happy trading, and may your scalper always be profitable!

Citation

@software{soloviov2024scalpercppfastfix,
  author = {Soloviov, Eugen},
  title = {Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide},
  year = {2025},
  url = {https://marketmaker.cc/en/blog/post/scalper-cpp-fast-fix},
  version = {0.1.0},
  description = {A step-by-step guide to building a C++ trading scalper bot using FAST/FIX protocols.}
}

Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide

What Do We Need?

Understanding FIX/FAST Protocols

Our Scalper's Architecture

Connecting to the Exchange

Market Data Processing and Decision Making

Sending Orders to the Exchange

Handling Exchange Responses

Testing and Debugging

Conclusion

Citation

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Queue Inside the Wall: Analyzing Order Position in Order Book Density

QuestDB for Algorithmic Trading: SQL Extensions That Change the Game

QuestDB for Algorithmic Trading: From Order Books to Production Architecture

What Do We Need?

Understanding FIX/FAST Protocols

Our Scalper's Architecture

Connecting to the Exchange

Market Data Processing and Decision Making

Sending Orders to the Exchange

Handling Exchange Responses

Testing and Debugging

Conclusion

Citation

What Do We Need?

Understanding FIX/FAST Protocols

Our Scalper's Architecture

Connecting to the Exchange

Market Data Processing and Decision Making

Sending Orders to the Exchange

Handling Exchange Responses

Testing and Debugging

Conclusion

Citation

Read More

Queue Inside the Wall: Analyzing Order Position in Order Book Density

QuestDB for Algorithmic Trading: SQL Extensions That Change the Game

QuestDB for Algorithmic Trading: From Order Books to Production Architecture

Gardez une longueur d'avance sur le marché

What Do We Need?

Understanding FIX/FAST Protocols

Our Scalper's Architecture

Connecting to the Exchange

Market Data Processing and Decision Making

Sending Orders to the Exchange

Handling Exchange Responses

Testing and Debugging

Conclusion

Citation