Optimizing the Pairs‐Trading Strategy Using Deep Reinforcement Learning with Trading and Stop‐Loss Boundaries

Kim, Tae-Wook; Kim, Ha Young

doi:10.1155/2019/3582516

Cited by 25 publications

(40 citation statements)

References 27 publications

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“…For example, in intraday trading, the deadline is set to the closing time of the day. In general, once stop-loss or exit happens, they usually result in a negative return [ 50 ]. Overall, the behavior of pairs trading is to offset the systematic risk by the positions of two different assets.…”

Section: Introductionmentioning

confidence: 99%

“…To seize the arbitrage opportunities in intraday trading, our intuition is to design new pairs trading strategies in a fine-grained scale (e.g., minute scale) from the tick data. Note that most of the existing pairs trading strategies [ 35 , 50 , 66 ] were designed for daily data, in which the spreads are usually stable and long-term equilibrium. While the stock markets are easily influenced by some external factors (e.g., news and government policies) in real time [ 42 ], they may lose the arbitrage opportunities in intraday trading.…”

Section: Introductionmentioning

confidence: 99%

“…Pairs trading may suffer from a great loss if the strategy does not close the position when a breakpoint occurs. For instance, 16.2% of the trades executed by a state-of-the-art method PTDQN [ 50 ] in our Taiwan stock market dataset are forced to exit in the end of the trading period because PTDQN does not wisely close positions during structural breaks (detailed later). Therefore, the investments are put in risky conditions and 89.5% of these risky trades close with negative profit.…”

Section: Introductionmentioning

confidence: 99%

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Structural break-aware pairs trading strategy using deep reinforcement learning

Lai

Shih

et al. 2021

J Supercomput

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Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy ( SAPT ), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural break-aware pairs trading strategy using deep reinforcement learning

Lai

Shih

et al. 2021

J Supercomput

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“…For example, in the electricity market, RL is not only adopted to build the bidding strategy for power trading but also to perform real-time power management [5,6,7]. In the finance market, several studies use RL to develop RL agents to help decision making in trading [8,9,10,11]. Such success motivates to employ RL to build the bidding strategy which is able to adapt to the rapid change of the RTB market.…”

Section: Introductionmentioning

confidence: 99%

“…Google Ad Manager, https://reurl.cc/b5Y3Nl8 Google Ads, https://reurl.cc/3D46kX 9 Some ADXes will set the minimal acceptable bid price, and ask each DSP to submit an acceptable bid price for each bid request 10. Tenmax, https://www.tenmax.io/en/…”

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confidence: 99%

An Expected Win Rate-Based Real Time Bidding Strategy for Branding Campaign by the Model-Free Reinforcement Learning Model

Shih

Tsai

et al. 2020

IEEE Access

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The bidding strategy plays the most important role to help the Demand Side Platforms (DSPs) making bidding decisions on a large number of bid requests in Real Time Bidding (RTB) to satisfy the different objectives of campaigns under the lifetime and budget constraints. In this paper, we focus on branding campaign whose objective is to obtain as many impressions as possible under the lifetime and budget constraints. To achieve the objectives of branding campaigns, we propose a novel expected win rate-based bidding strategy for branding campaign under the lifetime and budget constraints by utilizing a model-free reinforcement learning model. Specifically, to prevent missing good opportunities resulting from submitting extremely low bid prices, the concept of the base winning price is introduced to determine the lower bound of expected winning price. In addition, to obtain more impressions, the concept of the DSP-specified budget spending plan is proposed to determine the proper winning prices. The base expected win rate is then calculated based on the base winning price and the winning price determined by the DSPspecified budget spending plan. Since RTB is a dynamic environment, we propose a novel expected win ratebased bidding strategy named EWDQN which utilizes Deep Q Network (DQN) to dynamically determine the expected win rate according to the base expected win rate and the current status of the RTB market, and then determines the bid price according to the expected win rate. To the best of our knowledge, this is the first research applying the reinforcement learning technique on the bidding strategies for branding campaign. To measure the performance of EWDQN, several experiments are conducted on two real datasets. Experimental results show that EWDQN outperforms the-state-of-the-art bidding strategies for branding campaign in terms of the number of obtained impressions and CPM (cost per thousand impressions).

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