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About Deep Reinforcement Learning in Agent Based Financial Market Simulation
This article proposes using deep reinforcement learning to develop trading strategies by training agents in an artificial financial market simulation. The simulation models realistic market dynamics like transaction costs and how an agent's actions impact the market. This approach can generate more diverse market conditions for training than are available in historical data alone. The authors developed a deep reinforcement learning framework for this purpose and confirmed it was able to learn robust investment strategies with attractive risk-return profiles under different market conditions.
Detailed Information
| Author: | ['Quant Trading'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 17 |
| Language: | English |
| Format: | |
| Price: | FREE |
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