Cryptocurrency Portfolio Optimization by Neural Networks (2310.01148v1)

Abstract: Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. The proposed algorithm constructs a portfolio that contains a pair of negatively correlated assets. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy. Extensive experiments were conducted using data collected from Binance spanning 19 months to evaluate the effectiveness of our approach. The backtest results show that the proposed algorithm can produce neural networks that are able to make profits in different market situations.

• The paper presents a deep learning algorithm that optimizes cryptocurrency portfolios by maximizing the Sharpe ratio.
• It utilizes a novel loss term to enforce a neutral asset position between BTCUP and BTCDOWN, effectively managing market volatility.
• Experimental results demonstrate superior risk-adjusted returns compared to traditional methods, paving the way for future AI-driven financial strategies.

Enhancing Cryptocurrency Portfolio Performance with Neural Networks

Introduction to Portfolio Optimization Challenges in Cryptocurrencies

Portfolio optimization remains a cornerstone of modern financial engineering, aiming to maximize returns for a given risk level or minimize risk for a given level of expected return. While conventional portfolio theories like the Modern Portfolio Theory (MPT) have laid the groundwork, the volatile nature of cryptocurrency markets demands more sophisticated, adaptable approaches. The cryptocurrency market, characterized by its significant price fluctuations, presents both challenges and opportunities for portfolio optimization.

In this context, the deployment of machine learning, especially deep learning techniques, has opened new avenues for developing dynamic, data-driven portfolio management strategies. These methods leverage market data to make informed decisions on asset allocation, adapting to market changes in real-time. This paper focuses on utilizing an end-to-end machine learning framework to optimize portfolios consisting of Binance Leverage Tokens (BLVTs), namely BTCUP and BTCDOWN, leveraging their inherent negative correlation to navigate the cryptocurrency market's volatility effectively.

Methodological Approaches to Portfolio Optimization

Baseline Model and Neural Network Utilization

The paper proposes an algorithm that employs deep learning to optimize the allocation within a portfolio containing a pair of negatively correlated assets, specifically focusing on BTCUP and BTCDOWN. The deep neural network used in this approach outputs allocation weights for each asset at designated time intervals, trained to maximize the Sharpe ratio - a measure of adjusting return for risk. This method innovates by introducing a novel loss term to regulate the network's inclination toward a certain asset, encouraging the network to learn an allocation strategy that closely aligns with a minimum variance strategy.

Handling Cryptocurrency Volatility and Asset Correlation

The methodology section dissects the intricacies of managing a portfolio comprising only two volatile assets - a scenario emblematic of the cryptocurrency market. It elaborates on constructing a mathematical model to optimize the Sharpe ratio while accounting for transaction and management fees inherent in real-world trading. Special emphasis is placed on ensuring the portfolio attains a 'neutral' position concerning asset price changes, conceptualized through the formulation of a neutral-position constraint. This constraint significantly mitigates the portfolio's exposure to directionality in asset price movements, thereby emphasizing risk-adjusted returns.

Implications and Future Directions

Enhanced Portfolio Performance in Volatile Markets

Experimental results underscore the efficacy of the proposed algorithm in navigating the cryptocurrency market's volatility, showcasing its ability to generate profit across various market conditions. The findings highlight the algorithm's superior performance compared to conventional non-learning methods and even the baseline learning approach, especially when facing market downturns. This enhanced performance is attributed to the algorithm's dynamic allocation strategy, informed by real-time market data and its ability to maintain a near-neutral portfolio position.

Speculations on Future Developments

While this paper presents significant advancements in utilizing neural networks for cryptocurrency portfolio optimization, it also paves the way for future research. Potential directions include extending this framework to a broader array of assets beyond BLVTs, incorporating additional market indicators for more nuanced allocation strategies, and exploring the integration of reinforcement learning techniques for real-time portfolio adjustment. Moreover, the exploration of different neural network architectures could yield further improvements in portfolio performance, adapting to the cryptocurrency market's evolving nature.

Conclusion

This work positions itself at the confluence of deep learning and financial engineering, offering a robust strategy for cryptocurrency portfolio optimization that leverages the power of neural networks. By focusing on a pair of negatively correlated assets and employing a novel loss term to ensure a near-neutral portfolio position, the proposed algorithm demonstrates notable potential in maximizing risk-adjusted returns. As the cryptocurrency market continues to mature, such data-driven, adaptive approaches will be instrumental in harnessing its opportunities, pointing to a promising horizon for AI-driven financial technologies.