Deep Reinforcement Learning based Multi-Objective Systems for Financial Trading

Bisht, Kiran; Kumar, Arun

doi:10.1109/icraie51050.2020.9358319

Cited by 10 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another -less investigatedapproach is to consider an intrinsic Multi-Objective approach, but without generalization (i.e., the reward weights are set a priori, and do not part in the learning process). This is the case for the two reference works [3,13], which we summarize in Section 2.…”

Section: Multi-objective Rl In Financementioning

confidence: 90%

“…1.2.2 Structure of the paper. We summarize the main contributions of the reference works [3,13] in Section 2. We provide the abstract setup of our proposed Algorithm in Section 3, and fill in the necessary quantitative details in Section 4.…”

Section: Our Contributionmentioning

confidence: 99%

“…The reference works [3,13] use multi-reward scalarization to improve on the following, well-established benchmark strategies in the context of price prediction of a single-asset: i) an actor-only, RL algorithm with total portfolio value as single reward, and ii) a standard Buy-and-Hold strategy. More specifically, the authors take as rewards the average and standard deviations from the classical definition of the Sharpe ratio, and combine them with pre-defined weights to favor risk-adjustment.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading

Cornalba¹,

Disselkamp²,

Scassola³

et al. 2022

Preprint

View full text Add to dashboard Cite

We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency trading. More specifically, we build on the generalized setting à la Fontaine and Friedman [6] (where the reward weighting mechanism is not specified a priori, but embedded in the learning process) by complementing it with computational speed-ups, and adding the cumulative reward's discount factor to the learning process.Firstly, we verify that the resulting Multi-Objective algorithm generalizes well, and we provide preliminary statistical evidence showing that its prediction is more stable than the corresponding Single-Objective strategy's. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties of the discount factor.The entirety of our code is provided in open source format. CCS CONCEPTS• Computing methodologies → Reinforcement learning; Neural networks; Multi-task learning; • Applied computing → Economics.

show abstract

Section: Multi-objective Rl In Financementioning

confidence: 90%

Section: Our Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading

Cornalba¹,

Disselkamp²,

Scassola³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…• Buy and Hold. This is the classical benchmark [38][39][40] when trading on the cryptocurrency markets since the markets are significantly increasing, thus simply buying an asset and holding it until the end of the testing period provides some informative baseline profit measure.…”

Section: Benchmark Strategiesmentioning

confidence: 99%

Deep Reinforcement Learning For Trading - A Critical Survey

Millea¹

2021

Preprint

View full text Add to dashboard Cite

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

show abstract

“…• Buy and Hold is the classical benchmark [38][39][40] when trading on cryptocurrency markets, since the markets are significantly increasing; thus, simply buying an asset and holding it until the end of the testing period provides some informative baseline profit measure.…”

Section: Benchmark Strategiesmentioning

confidence: 99%

Deep Reinforcement Learning for Trading—A Critical Survey

Millea

2021

Data

View full text Add to dashboard Cite

Deep reinforcement learning (DRL) has achieved significant results in many machine learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to trading on financial markets with the purpose of unravelling common structures used in the trading community using DRL, as well as discovering common issues and limitations of such approaches. We include also a short corpus summarization using Google Scholar. Moreover, we discuss how one can use hierarchy for dividing the problem space, as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market; the results of this survey are two-fold: firstly, to find the most promising directions for further research and secondly, to show how a lack of consistency in the community can significantly impede research and the development of DRL agents for trading.

show abstract

Deep Reinforcement Learning based Multi-Objective Systems for Financial Trading

Cited by 10 publications

References 19 publications

Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading

Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for selected applications in stock and cryptocurrency trading

Deep Reinforcement Learning For Trading - A Critical Survey

Deep Reinforcement Learning for Trading—A Critical Survey

Contact Info

Product

Resources

About