deepdow (read as “wow”) is a Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass.

Installation

pip install deepdow

Resources

• Getting started
• Detailed documentation
• More examples

Description

deepdow attempts to merge two very common steps in portfolio optimization

1. Forecasting of future evolution of the market (LSTM, GARCH,…)
2. Optimization problem design and solution (convex optimization, …)

It does so by constructing a pipeline of layers. The last layer performs the allocation and all the previous ones serve as feature extractors. The overall network is fully differentiable and one can optimize its parameters by gradient descent algorithms.

deepdow is not …

• focused on active trading strategies, it only finds allocations to be held over some horizon (buy and hold)
  • one implication is that transaction costs associated with frequent, short-term trades, will not be a primary concern
• a reinforcement learning framework, however, one might easily reuse deepdow layers in other deep learning applications
• a single algorithm, instead, it is a framework that allows for easy experimentation with powerful building blocks

Some features

• all layers built on torch and fully differentiable
• integrates differentiable convex optimization (cvxpylayers)
• implements clustering based portfolio allocation algorithms
• multiple dataloading strategies (RigidDataLoader, FlexibleDataLoader)
• integration with mlflow and tensorboard via callbacks
• provides variety of losses like sharpe ratio, maximum drawdown, …
• simple to extend and customize
• CPU and GPU support

Citing

If you use deepdow (including ideas proposed in the documentation, examples and tests) in your research please make sure to cite it. To obtain all the necessary citing information, click on the DOI badge at the beginning of this README and you will be automatically redirected to an external website. Note that we are currently using Zenodo.