Application of Three Different Machine Learning Methods on Strategy Creation for Profitable Trades on Cryptocurrency Markets

• URL: http://arxiv.org/abs/2105.06827v1
• Date: Fri, 14 May 2021 13:42:46 GMT
• Title: Application of Three Different Machine Learning Methods on Strategy Creation for Profitable Trades on Cryptocurrency Markets
• Authors: Mohsen Asgari, Hossein Khasteh
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: AI and data driven solutions have been applied to different fields with outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to direction detection problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We use these classifiers to design a strategy to trade in those 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. Our highest gain for an unseen 66 day span is 860 dollars per 1800 dollars investment. We also discuss limitations of these approaches and their potential impact to Efficient Market Hypothesis.

Related papers

• Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
  This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market. It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. The results show a buy/sell signal accuracy of over 92%.
  arXiv  Detail & Related papers  (2024-10-09T14:29:50Z)
• 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)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Beyond Trend Following: Deep Learning for Market Trend Prediction [49.89480853499917]
  We advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
  arXiv  Detail & Related papers  (2024-06-10T11:42:30Z)
• Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning [11.516147824168732]
  This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm.
  arXiv  Detail & Related papers  (2022-10-07T11:42:31Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets [2.741266294612776]
  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.
  arXiv  Detail & Related papers  (2022-01-15T18:45:03Z)
• 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)
• A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO [0.0]
  Convergence bidding, a.k.a., virtual bidding, has been widely adopted in wholesale electricity markets in recent years. It provides opportunities for market participants to arbitrage on the difference between the day-ahead market locational marginal prices and the realtime market locational marginal prices. We learn, characterize, and evaluate different types of convergence bidding strategies that are currently used by market participants.
  arXiv  Detail & Related papers  (2021-09-19T22:19:10Z)
• The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
  This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies. We report on three case studies related to pump and dump groups.
  arXiv  Detail & Related papers  (2021-05-03T10:20:47Z)
• 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)

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.