A deep reinforcement learning approach for portfolio rebalancing with Dragon Pullback multi-stage candlestick pattern embedding
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Y Bai, C Zhang, L Liu, B Sun, H Sun, S Wang…
… Applications of Artificial …, 2026
Elsevier
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 …
