Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting

– The paper proposes a practical approach to address backtest overfitting in cryptocurrency using deep reinforcement learning.
– The less overfitted deep reinforcement learning agents have a higher return than more overfitted agents, equal weight strategy, and market benchmark.

– Proposed a practical approach to address backtest overfitting in cryptocurrency trading.
– Showed that less overfitted deep reinforcement learning agents have higher returns.

The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning. It does not provide specific details about trading strategies or techniques.

– False positive issue due to overfitting in backtesting
– The crypto market crashed two times during the testing period

– Formulate detection of backtest overfitting as hypothesis
– Train DRL agents, estimate probability of overfitting, and reject overfitted agents

– The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading.
– The less overfitted deep reinforcement learning agents have a higher return.

– Less overfitted deep reinforcement learning agents have higher returns.
– The proposed approach offers confidence in possible deployment to a real market.

– Paper proposes a practical approach to address backtest overfitting in cryptocurrency trading using deep reinforcement learning.
– Less overfitted agents have higher returns than more overfitted agents.

– Paper addresses backtest overfitting in cryptocurrency trading using deep reinforcement learning.
– Proposes a practical approach to detect and reject overfitted agents.

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