# DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data

arXiv: https://arxiv.org/abs/2008.12152 · HTML: https://mlsanity.com/paper/2008.12152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units](https://mlsanity.com/paper/2105.10430/) (2021)
- [UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting](https://mlsanity.com/paper/2609.31491/) (2026)

## Abstract (arXiv)

This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB data to predict price movements. Our evaluation consists of two scenarios using a large dataset of millions of time series. The improvements deliver superior results both in cases of abundant as well as scarce data. The experiments show that DeepFolio outperforms the state-of-the-art on the benchmark FI-2010 LOB. Further, we use DeepFolio for optimal portfolio allocation of crypto-assets with rebalancing. For this purpose, we use two loss-functions - Sharpe ratio loss and minimum volatility risk. We show that DeepFolio outperforms widely used portfolio allocation techniques in the literature.