Trading Crypto with AI: A New Approach to Portfolio Management

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Author: Denis Avetisyan

Deep reinforcement learning algorithms are proving to be powerful tools for navigating the volatile world of cryptocurrency investments.

Across Bitcoin, Ethereum, Litecoin, and Dogecoin, the performance of three reinforcement learning agents-SAC, DDPG, and MPT-demonstrates a nuanced decay, their normalized portfolio values charting a course relative to Bitcoin’s trajectory during a defined test period, suggesting that even within a volatile landscape, relative performance inevitably shifts over time.
Across Bitcoin, Ethereum, Litecoin, and Dogecoin, the performance of three reinforcement learning agents-SAC, DDPG, and MPT-demonstrates a nuanced decay, their normalized portfolio values charting a course relative to Bitcoin’s trajectory during a defined test period, suggesting that even within a volatile landscape, relative performance inevitably shifts over time.

This review demonstrates that Soft Actor-Critic and Deep Deterministic Policy Gradient algorithms outperform traditional methods in optimizing cryptocurrency portfolios for risk-adjusted returns.

Traditional portfolio optimization struggles with the inherent volatility and non-stationarity of cryptocurrency markets. This paper, ‘Cryptocurrency Portfolio Management with Reinforcement Learning: Soft Actor–Critic and Deep Deterministic Policy Gradient Algorithms’, introduces a deep reinforcement learning framework employing Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms to directly learn optimal trading strategies from historical data. Experimental results demonstrate that both algorithms outperform conventional approaches, with SAC exhibiting enhanced stability in noisy conditions and delivering superior risk-adjusted returns. Could these findings pave the way for more adaptive and robust automated investment strategies in increasingly dynamic financial landscapes?

The Inevitable Decay of Traditional Models

Conventional portfolio optimization techniques, such as the widely used Markowitz Mean-Variance Model, face significant challenges when applied to the cryptocurrency market. These models rely on historical data to estimate expected returns and correlations, assuming a degree of stability over time. However, cryptocurrency markets are characterized by non-stationarity – meaning statistical properties like mean and variance change unpredictably – and exceptionally high volatility. This inherent instability renders historical data a poor predictor of future performance, leading to suboptimal portfolio allocations and inaccurate risk assessments. The frequent and substantial price swings observed in cryptocurrencies drastically increase the uncertainty surrounding return predictions, effectively undermining the core assumptions upon which traditional portfolio theory is built and highlighting the need for more adaptive and robust approaches to asset allocation in this unique financial landscape.

Conventional portfolio optimization techniques frequently underestimate the erosive effect of transaction costs within cryptocurrency markets. While mathematical models may demonstrate appealing theoretical returns, the frequent trading required to maintain optimal allocations – coupled with exchange fees, slippage, and spread – can substantially diminish profitability. Studies reveal that these costs, often disregarded in initial calculations, can reduce annual returns by several percentage points, particularly for high-frequency strategies. Consequently, the development of more robust solutions is crucial; these must incorporate realistic cost assessments and explore strategies – such as reduced turnover or cost-aware rebalancing – to bridge the gap between modeled performance and actual investor outcomes. The pursuit of genuinely effective portfolio management, therefore, necessitates acknowledging and actively mitigating the practical realities of trading within these volatile digital asset environments.

Conventional portfolio strategies relying on fixed asset allocations falter when confronted with the inherent dynamism of modern financial landscapes. These static approaches, designed for relatively stable markets, struggle to respond effectively to sudden shifts in volatility, correlation breakdowns, and unforeseen events – characteristics increasingly prevalent in cryptocurrency and other rapidly evolving asset classes. Research indicates that maintaining a consistent portfolio weight, regardless of market conditions, often leads to suboptimal risk-adjusted returns and increased drawdown potential. Consequently, adaptive techniques – such as time-varying allocations, trend-following strategies, and machine learning-driven rebalancing – are gaining prominence as essential tools for navigating uncertainty and optimizing portfolio performance in these complex environments. These methods aim to dynamically adjust asset weights based on real-time market signals, thereby enhancing resilience and improving the likelihood of achieving long-term financial goals.

SAC and DDPG consistently outperformed the Markowitz MPT benchmark across average portfolio performance metrics.
SAC and DDPG consistently outperformed the Markowitz MPT benchmark across average portfolio performance metrics.

Reinforcement Learning: A System Adapts

Reinforcement Learning (RL) provides a framework for portfolio management by treating investment decisions as a Markov Decision Process. In this paradigm, an agent – the portfolio algorithm – iteratively selects actions (buy, sell, hold) within a defined state space representing market conditions and portfolio holdings. Each action generates a reward, typically based on portfolio return, and transitions the system to a new state. The agent learns an optimal policy – a mapping from states to actions – through trial and error, maximizing cumulative reward over time. Unlike traditional methods relying on pre-defined rules or statistical models, RL algorithms adapt to changing market dynamics by continuously learning from observed outcomes, enabling automated portfolio rebalancing based on direct feedback from market interactions. This sequential decision-making process allows for the consideration of long-term investment horizons and the optimization of portfolio performance under uncertainty.

The Actor-Critic method, utilized in reinforcement learning for portfolio control, employs two distinct components: an actor and a critic. The actor is responsible for selecting actions – in this context, portfolio allocations – based on the current market state. Simultaneously, the critic evaluates these actions, providing a value estimate that indicates the long-term reward expected from that allocation. This evaluation signal is then used to refine the actor’s policy, encouraging actions that lead to higher returns and discouraging those that do not. The interplay between these two components facilitates a balance between exploration – trying new allocations to discover potentially better strategies – and exploitation – leveraging known successful allocations to maximize immediate returns. This approach allows the agent to adapt to changing market conditions and optimize portfolio performance over time, addressing the inherent trade-off between seeking new opportunities and capitalizing on existing ones.

Deep Reinforcement Learning (DRL) addresses the limitations of traditional Reinforcement Learning when applied to financial portfolio management by utilizing deep neural networks as function approximators. These networks process high-dimensional input data, such as historical prices, trading volumes, and macroeconomic indicators, to learn complex, non-linear relationships within the market. This capability is crucial for modeling intricate market dynamics that are difficult to capture with simpler methods. Specifically, DRL algorithms employ various neural network architectures, including convolutional neural networks (CNNs) for pattern recognition in time series data and recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM) networks, to capture temporal dependencies. The resulting models can then learn optimal policies for portfolio allocation and trading strategies directly from raw data, without requiring extensive feature engineering or reliance on pre-defined models.

Entropy Regularization: Embracing Uncertainty

Deep Deterministic Policy Gradient (DDPG) represents an early reinforcement learning algorithm applied to continuous action spaces, functioning as a foundational method for algorithmic trading. However, DDPG’s performance is notably sensitive to the selection of hyperparameters such as the learning rate, replay buffer size, and the noise parameters used for exploration. Suboptimal hyperparameter configurations can lead to divergence during training or slow convergence. Furthermore, DDPG is susceptible to instability arising from the deterministic nature of its policy; small errors in value function estimation can be amplified, resulting in erratic policy updates and hindering reliable performance across different market conditions. This sensitivity necessitates extensive and often computationally expensive hyperparameter optimization to achieve robust results.

Entropy Regularization, a core component of Soft Actor-Critic (SAC), directly addresses the exploration-exploitation dilemma in reinforcement learning by adding a penalty to the policy based on its entropy. Policy entropy, calculated as $H(\pi) = -\sum_{\mathbf{a}} \pi(\mathbf{a}) \log \pi(\mathbf{a})$, quantifies the randomness of the policy; maximizing this value encourages the agent to sample a wider range of actions, even if those actions appear suboptimal based on current estimates. This increased exploration reduces the risk of converging to a local optimum and improves the robustness of the learned policy by preventing overconfidence in specific actions. By explicitly incentivizing exploration, SAC lessens the sensitivity to hyperparameter tuning often observed in algorithms like Deep Deterministic Policy Gradient (DDPG) and facilitates more consistent learning across diverse environments.

Entropy regularization, as implemented in Soft Actor-Critic (SAC), directly encourages the agent to select actions with higher stochasticity. This is achieved by adding an entropy term to the reward function, incentivizing the policy to maintain a probability distribution over actions rather than converging to a single, deterministic action. Empirical results demonstrate SAC’s superior performance; specifically, SAC consistently achieved higher cumulative returns compared to both the Deep Deterministic Policy Gradient (DDPG) algorithm and the Markowitz Mean-Variance Portfolio Theory (MPT) benchmark across multiple testing environments. This suggests that the increased exploration facilitated by entropy maximization enables SAC to discover and exploit more robust and potentially higher-rewarding trading strategies, while simultaneously avoiding premature convergence to local optima.

SAC consistently demonstrates superior risk-adjusted performance, as evidenced by its higher Sharpe and Sortino ratios, and lower maximum drawdown, Value at Risk, and Conditional Value at Risk compared to DDPG.
SAC consistently demonstrates superior risk-adjusted performance, as evidenced by its higher Sharpe and Sortino ratios, and lower maximum drawdown, Value at Risk, and Conditional Value at Risk compared to DDPG.

Feature Extraction and Performance Evaluation: Measuring Resilience

The efficacy of deep reinforcement learning in financial applications is heavily reliant on the quality of feature extraction, particularly when dealing with time-series data like Open-High-Low-Close-Volume (OHLCV). Traditional methods often struggle to capture the complex, non-linear relationships inherent in market dynamics; however, Long Short-Term Memory (LSTM) networks offer a compelling solution. These recurrent neural networks excel at identifying and leveraging temporal dependencies within sequential data, allowing the agent to learn patterns and predict future market behavior based on historical price movements and trading volume. By effectively processing OHLCV data, LSTMs enable reinforcement learning algorithms to make more informed trading decisions, ultimately improving portfolio performance and risk management. The ability to discern subtle, time-based relationships is crucial for navigating the complexities of financial markets and achieving consistent, profitable results.

Evaluating investment strategies solely on raw returns presents an incomplete picture of performance, as it disregards the level of risk undertaken to achieve those gains. A truly comprehensive assessment necessitates a suite of metrics that account for risk-adjusted returns and potential downside exposure. The Sharpe Ratio quantifies excess return per unit of total risk, while the Sortino Ratio focuses specifically on downside risk, offering a more refined measure for risk-averse investors. Beyond these, understanding Maximum Drawdown – the largest peak-to-trough decline – is crucial for gauging potential losses, and Value-at-Risk (VaR) estimates the maximum loss expected over a given time horizon with a specified confidence level. These metrics, considered collectively, provide a more nuanced and informative evaluation of an investment strategy’s overall effectiveness and its ability to deliver sustainable returns while managing risk.

Understanding the potential for substantial losses is paramount in financial modeling, and Conditional Value-at-Risk (CVaR) offers a critical tool for assessing this “tail risk” – the probability of extreme negative outcomes. Recent studies demonstrate the efficacy of algorithms like the Soft Actor-Critic (SAC) in navigating these complexities; in testing, SAC achieved a final portfolio value of $2.7627, surpassing other tested algorithms. This performance was further substantiated by a Sharpe Ratio of 0.0673 and a Sortino Ratio of 0.1093, indicating a strong risk-adjusted return profile. These metrics, particularly CVaR, allow investors to move beyond average performance and quantify the potential downside, providing a more comprehensive understanding of an investment strategy’s true risk exposure.

The data preprocessing workflow transforms raw OHLCV cryptocurrency data into a format suitable for analysis and modeling.
The data preprocessing workflow transforms raw OHLCV cryptocurrency data into a format suitable for analysis and modeling.

The study of cryptocurrency portfolio management, as presented, implicitly acknowledges the ephemeral nature of financial systems. Every fluctuation, every recalibration of an algorithm, signals the inevitable decay inherent in complex structures. As Aristotle observed, “The ultimate value of life depends upon awareness and the power of contemplation rather than upon mere survival.” This sentiment resonates with the core idea of adapting to market volatility; the system doesn’t strive for static perfection, but for a graceful aging process, constantly learning and adjusting to the passage of time, much like refactoring a system to engage with its past iterations. The algorithms detailed aren’t about preventing decay, but about navigating it.

What’s Next?

The demonstrated capacity of deep reinforcement learning to navigate the turbulent currents of cryptocurrency markets is not, in itself, surprising. What is noteworthy is the implicit acknowledgment that even these adaptive systems are built upon foundations destined for entropy. Each successful trade, each optimized portfolio, is merely a temporary deferral of inevitable decay. The algorithms do not prevent loss; they redistribute its arrival. Future work must confront this fundamental truth, shifting focus from maximizing returns to quantifying-and gracefully accepting-the rate of systemic aging.

Current approaches treat volatility as a challenge to overcome. A more honest assessment would recognize it as the system’s inherent character. The algorithms, therefore, should not strive for optimal portfolios, but for resilient ones-systems that maintain functionality, even as conditions erode. Exploring methods to incorporate explicit models of algorithmic decay-accounting for the inevitable drift in performance as the market evolves-represents a critical next step.

Ultimately, this research field will be defined not by its ability to predict market fluctuations, but by its willingness to acknowledge the temporal nature of all systems. Every bug in the code, every failed trade, is a moment of truth in the timeline, revealing the limits of any attempted permanence. Technical debt is the past’s mortgage paid by the present; future work must begin to calculate the interest on that debt, not simply accrue it.

Original article: https://arxiv.org/pdf/2511.20678.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-27 13:22