Abstract
Reinforcement Learning (RL) based machine trading attracts a rich profusion of interest. However, in the existing research, RL in the day-trade task suffers from the noisy financial movement in the short time scale, difficulty in order settlement, and expensive action search in a continuous-value space. This paper introduced an end-to-end RL intraday trading agent, namely QF-TraderNet, based on the quantum finance theory (QFT) and deep reinforcement learning. We proposed a novel design for the intraday RL trader’s action space, inspired by the Quantum Price Levels (QPLs). Our action space design also brings the model a learnable profit-and-loss control strategy. QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of actions. The profitability and robustness of QF-TraderNet have been verified in multi-type financial datasets, including FOREX, metals, crude oil, and financial indices. The experimental results demonstrate that QF-TraderNet outperforms other baselines in terms of cumulative price returns and Sharpe Ratio, and the robustness in the acceidential market shift.
Highlights
Financial trading is an online decision-making process (Deng et al, 2016)
Our work is in line with two sub-tasks: financial feature extraction and transactions based on deep reinforcement learning
Xiong et al (2018) employed the Deep Deterministic Policy Gradient (DDPG) baesd on the standard actor-critic framework to perform the stock trading. The experiments demonstrated their profitability over the baselines including the min-variance portfolio allocation method and the technical approach based on the Dow Jones Industrial Average (DJIA) index
Summary
Financial trading is an online decision-making process (Deng et al, 2016). Previous works (Moody and Saffell, 1998; Moody and Saffell, 2001; Dempster and Leemans, 2006) demonstrated the Reinforcement Learning (RL) agent’s promising profitability in trading activities. In the T + n strategy, RL agents are assigned a long, neutral, or short position in each trading day, including the Fuzzy Deep Recurrent Neural Networks (FDRNN) (Deng et al, 2016) and Direct Reinforcement Learning (DRL) (Moody and Saffell, 2001). Terminate the transaction and avoid a further loss if the price moved towards a loss direction (e.g., the price dropped down following a long position decision) These two hyperparameters are defined as a fixed shift relative to price to enter the market, as known as, points. Focusing on the mentioned challenges, we proposed a deep reinforcement learning-based end-to-end learning model, named QF-TraderNet. Our model directly generates the trading policy to control profit and loss instead of using fixed TP and SL. Under the same market information perception, we achieve better profitability and robustness than previous state-ofthe-art RL based models
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