Efficient Trading with Price Impact: Replication Code (Under Construction)

This repository replicates key components of the paper "Efficient Trading with Price Impact" by Xavier Brokmann, Lukas Gonon, Guangyi He, David Itkin, and Johannes Muhle-Karbe (2024). The paper investigates optimal trading strategies that balance expected returns, risk, and trading costs due to price impact, focusing on both linear feedback policies and neural network-based methods for trading under nonlinear impact dynamics.

Reference

Brokmann, X., Gonon, L., He, G., Itkin, D., & Muhle-Karbe, J. (2024). Efficient Trading with Price Impact. SSRN. Link to paper.

Contents

The repository includes:

• Python implementations of the linear feedback strategy under the paper's proposed framework.
• Code for performance comparison between linear policies and neural network-based strategies.
• Replication of numerical case studies including parameter calibration, Sharpe ratio evaluation, and robustness checks with alternative decay kernels (e.g., exponential, power-law).

Key Features

• Price Impact Models: Simulates trading scenarios incorporating nonlinear price impact and multi-timescale decay kernels.
• Optimization Framework: Demonstrates both analytical solutions (linear models) and numerical approaches (neural networks).
• Performance Analysis: Evaluates strategies using metrics like net Sharpe ratio and trading cost sensitivity.

Requirements

• Python 3.8+
• Libraries: NumPy, SciPy, Matplotlib, TensorFlow/PyTorch (for neural networks)

Usage

1. Clone the repository: git clone <repository_url>
2. Follow the step-by-step instructions in the provided Jupyter notebooks to replicate the results.

For further details, refer to the comments and documentation in the code.