# Cryptocurrency Portfolio Management with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1612.01277 · HTML: https://mlsanity.com/paper/1612.01277/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 30 of 32)

- [FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance](https://mlsanity.com/paper/2111.09395/) (2021)
- [Universal Trading for Order Execution with Oracle Policy Distillation](https://mlsanity.com/paper/2103.10860/) (2021)
- [Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey](https://mlsanity.com/paper/2110.01411/) (2021)
- [Reinforcement Learning for Quantitative Trading](https://mlsanity.com/paper/2109.13851/) (2021)
- [Deep Reinforcement Learning for Active High Frequency Trading](https://mlsanity.com/paper/2101.07107/) (2021)
- [Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy](https://mlsanity.com/paper/2511.12120/) (2025)
- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting](https://mlsanity.com/paper/2209.05559/) (2022)
- [The Recurrent Reinforcement Learning Crypto Agent](https://mlsanity.com/paper/2201.04699/) (2022)
- [Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View](https://mlsanity.com/paper/2207.12217/) (2022)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [Asset Allocation: From Markowitz to Deep Reinforcement Learning](https://mlsanity.com/paper/2208.07158/) (2022)
- [Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent](https://mlsanity.com/paper/2407.14486/) (2024)
- [A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules](https://mlsanity.com/paper/2101.03867/) (2021)
- [Reinforcement Learning Applied to Trading Systems: A Survey](https://mlsanity.com/paper/2212.06064/) (2022)
- [CNN-DRL for Scalable Actions in Finance](https://mlsanity.com/paper/2401.06179/) (2024)
- [Review of deep learning models for crypto price prediction: implementation and evaluation](https://mlsanity.com/paper/2405.11431/) (2024)
- [Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization](https://mlsanity.com/paper/2602.17098/) (2026)
- [Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting](https://mlsanity.com/paper/2104.10483/) (2021)
- [Interpretability in Safety-Critical FinancialTrading Systems](https://mlsanity.com/paper/2109.15112/) (2021)
- [Financial Vision Based Differential Privacy Applications](https://mlsanity.com/paper/2112.14075/) (2021)
- [Model-Free Reinforcement Learning for Asset Allocation](https://mlsanity.com/paper/2209.10458/) (2022)
- [Cryptocurrency Price Prediction using Twitter Sentiment Analysis](https://mlsanity.com/paper/2303.09397/) (2023)
- [An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading](https://mlsanity.com/paper/2309.00626/) (2023)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management](https://mlsanity.com/paper/2502.07280/) (2025)
- [A Novel Loss Function for Deep Learning Based Daily Stock Trading System](https://mlsanity.com/paper/2502.17493/) (2025)
- [Deep Reinforcement Learning in Factor Investment](https://mlsanity.com/paper/2509.16206/) (2025)
- [Long-only cryptocurrency portfolio management by ranking the assets: a neural network approach](https://mlsanity.com/paper/2512.08124/) (2025)
- [The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithms](https://mlsanity.com/paper/2602.01388/) (2026)

## Abstract (arXiv)

Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.