DOI: 10.48550/arxiv.2209.05559
Thank you for reading this post, don't forget to subscribe!ABSTRACT: Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning. First, we formulate the detection of backtest overfitting as a hypothesis test. Then, we train the DRL agents, estimate the probability of overfitting, and reject the overfitted agents, increasing the chance of good trading performance. Finally, on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022 (during which the crypto market crashed two times), we show that the less overfitted deep reinforcement learning agents have a higher return than that of more overfitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market.
– The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading 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.
– The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading 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.
– The paper proposes a practical approach to address backtest overfitting in cryptocurrency trading 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.
– False positive issue due to overfitting in backtesting
– The crypto market crashed two times during the testing period
Methods used: – False positive issue due to overfitting in backtesting
– The crypto market crashed two times during the testing period
– 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.
In this article , the authors propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning, and show that less overfitted deep RL agents have a higher return than that of more overfitted agents, an equal weight strategy, and the S&P DBM Index.
