FPGA-based Quantitative Trading with Artificial Data

Introduction

This is a report outlining a project that integrates FPGAs with quantitative trading strategies using artificial data. The project aims to leverage the high-speed processing capabilities of FPGAs to implement and optimize trading algorithms, while utilizing artificial data generated through machine learning techniques to simulate market conditions.

Objectives

  • Utilize FPGA technology to accelerate the execution of quantitative trading algorithms.
  • Develop artificial data generation models to simulate various market scenarios.
  • Implement and test trading strategies using the FPGA-based system with artificial data.
  • Evaluate the performance and efficiency of the FPGA-based solution compared to traditional trading platforms.

    Methodology

    FPGA Implementation

  • Select an FPGA development board suitable for high-speed trading applications, maybe even a virtual simulator.
  • Design and implement trading algorithms using hardware description languages (HDL) such as Verilog or VHDL.
  • Utilize the parallel processing capabilities of FPGAs to execute multiple trading strategies simultaneously.
  • Implement risk management and order execution logic within the FPGA design. Artificial Data Generation
  • Utilize machine learning techniques such as generative adversarial networks (GANs) or recurrent neural networks (RNNs) to generate synthetic market data.
  • Train the artificial data generation models using historical market data to capture realistic market dynamics.
  • Generate artificial data streams representing various market conditions, including volatility, trends, and noise.

    Integration and Testing

  • Integrate the FPGA-based trading system with the artificial data generator.
  • Develop a simulation environment to test trading strategies using the generated artificial data.
  • Evaluate the performance of different trading strategies under various market conditions.
  • Optimize FPGA configurations and trading algorithms based on performance metrics such as latency, throughput, and profit.

Links

FPGA Design and Implementation Resources:

  • Verilog Tutorial: https://www.asic-world.com/verilog/verilog_tutorial.html
  • VHDL Tutorial: https://www.csee.umbc.edu/~tino/vhdl_tutorial/
  • FPGA Trading System Design: https://www.researchgate.net/publication/324753633_FPGA_Based_High_Frequency_Trading_System_Design

    Artificial Data Generation using Machine Learning:

  • Generative Adversarial Networks (GANs) Tutorial: https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-dataset/
  • Recurrent Neural Networks (RNNs) Tutorial: https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
  • Synthetic Market Data Generation using GANs: https://arxiv.org/abs/1903.09057