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About Deep Reinforcement Learning For Automated Stock Trading - An Ensemble Strategy
The document proposes using deep reinforcement learning algorithms to develop an ensemble stock trading strategy. Specifically, it trains three actor-critic algorithms - PPO, A2C, and DDPG - on stock market data and combines them into an ensemble strategy. This strategy aims to maximize returns while managing risk by drawing on the strengths of the individual algorithms. The performance of the ensemble strategy is evaluated against benchmarks and shown to achieve higher risk-adjusted returns.
Detailed Information
| Author: | ['Sean Cheong'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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