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A deep reinforcement learning approach for portfolio rebalancing with Dragon Pullback multi-stage candlestick pattern embedding
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DOI：10.1016/j.engappai.2026.114876.png)
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Abstract
En 中文
With the rapid advancement of artificial intelligence, deep reinforcement learning has emerged as a promising method for portfolio rebalancing. Existing deep reinforcement learning (DRL) methods for portfolio rebalancing typically rely on prices or price-based technical indicators as the state representation. However, such representation is sensitive to noise and tends to emphasize short-term and unstable price fluctuations, which often lead DRL agents to learn aggressive strategies that perform poorly in real markets with liquidity constraints, transaction costs and lot-size constraints. To address this issue, this paper proposes a deep reinforcement learning framework for portfolio rebalancing with Dragon Pullback multi-stage candlestick pattern embedding (DRL-DPMSC). The proposed approach consists of three modules. First, in the Dragon Pullback pattern capture module, Dragon Pullback patterns are efficiently captured online through a temporal-segment-based method. Then, captured patterns are modeled as a set of pattern-related features for price-trend characterization in the causal-discovery-guided feature selection module. Finally, these features are incorporated into the state representation and employed to generate portfolio rebalancing strategies via the Proximal Policy Optimization Clip model integrated with an asset-wise architecture. By introducing noise-robust Dragon Pullback multi-stage candlestick patterns that emphasize persistent price trends, DRL-DPMSC is able to make more reasonable rebalancing decisions, especially in constrained markets. To verify the effectiveness of the proposed DRL-DPMSC, an experiment is conducted on 5 test windows to compare DRL-DPMSC with six comparative methods under five different environment settings. The proposed method outperforms competitors in most cases on both the profit and risk-return metrics.
Keywords:
Deep reinforcement learning
Portfolio rebalancing
Dragon Pullback pattern
Candlestick pattern embedding
Proximal Policy Optimization
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