Machine learning in C++ for high-frequency trading

21 Sep 2023

In the world of high-frequency trading (HFT), speed and accuracy are critical factors for success. With the rise of machine learning (ML) algorithms, many HFT firms are turning to ML to gain an edge in the market. While Python has emerged as the dominant language for ML, C++ remains a popular choice for HFT due to its low-level control and performance. In this blog post, we will explore how to implement machine learning in C++ for high-frequency trading.

Why C++ for High-Frequency Trading?

C++ is known for its efficiency and low-level control, making it a preferred language for HFT applications. By writing algorithms in C++, traders can take advantage of the language’s ability to execute code with minimal overhead and achieve the ultra-low latencies required in HFT.

Machine Learning Libraries for C++

Although C++ may not have a vast selection of ML libraries compared to Python, there are still some excellent options available:

TensorFlow: TensorFlow is a popular ML library that offers a C++ API to build and deploy ML models efficiently. It provides a wide range of functionalities for training and inference, making it suitable for HFT applications.
CNTK: The Microsoft Cognitive Toolkit (CNTK) is another powerful ML library with a C++ API. CNTK offers various ML algorithms and supports distributed training, making it well-suited for high-performance computing environments.
MLpack: MLpack is a fast, scalable C++ library that provides various ML algorithms. It offers an easy-to-use API and allows for seamless integration with existing C++ codebases.

Implementing Machine Learning in C++ for HFT

To demonstrate how to implement machine learning in C++ for HFT, let’s consider the scenario of predicting stock price movements using a simple MLP (multi-layer perceptron) model.

First, we need to install the ML library of our choice (e.g., TensorFlow):

#include <tensorflow/c/c_api.h>

Next, we can define the MLP model architecture and prepare the training data:

// Define the MLP model architecture
tensorflow::Scope scope;
tensorflow::Output input = tensorflow::Placeholder(scope.WithOpName("input"), tensorflow::DT_FLOAT);

// Prepare training data
std::vector<float> features, labels;
// ... load and preprocess the data

// Define the MLP layers and loss function
tensorflow::Output hidden = tensorflow::layers::Dense(scope.WithOpName("hidden"), input, 64);
tensorflow::Output output = tensorflow::layers::Dense(scope.WithOpName("output"), hidden, 1);
tensorflow::Output loss = tensorflow::ops::MeanSquaredError(scope.WithOpName("loss"), output, labels);

We can then proceed to train the model using the training data:

// Create a session and initialize variables
tensorflow::ClientSession session(scope);
TF_CHECK_OK(session.Run({}, {}, {"init_all_vars_op"}, nullptr));

// Train the model
tensorflow::Tensor x(tensorflow::DT_FLOAT, tensorflow::TensorShape({batch_size, num_features}));
tensorflow::Tensor y(tensorflow::DT_FLOAT, tensorflow::TensorShape({batch_size, num_labels}));
// ... fill tensors with training data

tensorflow::run(session, {{"input", x}, {"output", y}}, {"train_op", "loss"}, &outputs);

Finally, we can use the trained model for making predictions:

// Create inference data
tensorflow::Tensor inference_data(tensorflow::DT_FLOAT, tensorflow::TensorShape({num_samples, num_features}));
// ... fill inference data with test data

// Get predictions
tensorflow::run(session, {{"input", inference_data}}, {"output"}, &outputs);

Conclusion

Implementing machine learning in C++ for high-frequency trading can provide the performance and low-level control needed to gain an edge in the market. By leveraging ML libraries such as TensorFlow, CNTK, or MLpack, traders can develop and deploy powerful ML models to predict stock price movements with high accuracy. With the right implementation, C++ can be a valuable tool in the arsenal of HFT firms.

#machinelearning #cpp #hft #highfrequencytrading