Profitable Strategy Design by Using Deep Reinforcement Learning for
Trades on Cryptocurrency Markets
- URL: http://arxiv.org/abs/2201.05906v1
- Date: Sat, 15 Jan 2022 18:45:03 GMT
- Title: Profitable Strategy Design by Using Deep Reinforcement Learning for
Trades on Cryptocurrency Markets
- Authors: Mohsen Asgari, Seyed Hossein Khasteh
- Abstract summary: We have applied Proximal Policy Optimization, Soft Actor-C Imitation and Generative Adversarialritic Learning to strategy design problem of three cryptocurrency markets.
Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit.
- Score: 2.741266294612776
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Deep Reinforcement Learning solutions have been applied to different control
problems with outperforming and promising results. In this research work we
have applied Proximal Policy Optimization, Soft Actor-Critic and Generative
Adversarial Imitation Learning to strategy design problem of three
cryptocurrency markets. Our input data includes price data and technical
indicators. We have implemented a Gym environment based on cryptocurrency
markets to be used with the algorithms. Our test results on unseen data shows a
great potential for this approach in helping investors with an expert system to
exploit the market and gain profit. Our highest gain for an unseen 66 day span
is 4850 US dollars per 10000 US dollars investment. We also discuss on how a
specific hyperparameter in the environment design can be used to adjust risk in
the generated strategies.
Related papers
- Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators [2.038893829552158]
This study introduces a machine learning approach to predict cryptocurrency prices.
We make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model.
We evaluate the model's performance through various simulations, showing promising results.
arXiv Detail & Related papers (2024-07-16T14:41:27Z) - A Framework for Empowering Reinforcement Learning Agents with Causal
Analysis: Enhancing Automated Cryptocurrency Trading [1.5683566370372715]
This study aims to develop a reinforcement learning-based automated trading system for five popular cryptocurrencies.
We present CausalReinforceNet, a framework framed as a decision support system.
We develop two agents using the CausalReinforceNet framework, each based on distinct reinforcement learning algorithms.
arXiv Detail & Related papers (2023-10-14T01:08:52Z) - Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
This paper proposes an effective algorithm based on neural networks to take advantage of these investment products.
A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio.
A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
arXiv Detail & Related papers (2023-10-02T12:33:28Z) - Dynamic Datasets and Market Environments for Financial Reinforcement
Learning [68.11692837240756]
FinRL-Meta is a library that processes dynamic datasets from real-world markets into gym-style market environments.
We provide examples and reproduce popular research papers as stepping stones for users to design new trading strategies.
We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance.
arXiv Detail & Related papers (2023-04-25T22:17:31Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective.
We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
arXiv Detail & Related papers (2021-12-08T14:55:21Z) - Application of Three Different Machine Learning Methods on Strategy
Creation for Profitable Trades on Cryptocurrency Markets [0.0]
We apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to direction detection problem of three cryptocurrency markets.
Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit.
arXiv Detail & Related papers (2021-05-14T13:42:46Z) - Taking Over the Stock Market: Adversarial Perturbations Against
Algorithmic Traders [47.32228513808444]
We present a realistic scenario in which an attacker influences algorithmic trading systems by using adversarial learning techniques.
We show that when added to the input stream, our perturbation can fool the trading algorithms at future unseen data points.
arXiv Detail & Related papers (2020-10-19T06:28:05Z) - GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method
for RoboTrading [0.4568777157687961]
Foreign exchange is the largest financial market in the world.
Most literature used historical price information and technical indicators for training.
To address this problem, we designed trading rule features that are derived from technical indicators and trading rules.
arXiv Detail & Related papers (2020-08-16T05:33:35Z) - Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency
Market Manipulations [63.732639864601914]
We perform an in-depth analysis of pump and dump schemes organized by communities over the Internet.
We observe how these communities are organized and how they carry out the fraud.
We introduce an approach to detect the fraud in real time that outperforms the current state of the art.
arXiv Detail & Related papers (2020-05-04T21:36:18Z) - Ascertaining price formation in cryptocurrency markets with DeepLearning [8.413339060443878]
This paper is inspired by the recent success of using deep learning for stock market prediction.
We analyze and present the characteristics of the cryptocurrency market in a high-frequency setting.
We achieve a consistent $78%$ accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs US dollars.
arXiv Detail & Related papers (2020-02-09T20:23:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.