Is it a great Autonomous FX Trading Strategy or you are just fooling yourself

• URL: http://arxiv.org/abs/2101.07217v1
• Date: Fri, 15 Jan 2021 13:25:15 GMT
• Title: Is it a great Autonomous FX Trading Strategy or you are just fooling yourself
• Authors: Murilo Sibrao Bernardini and Paulo Andre Lima de Castro
• Abstract summary: We present the results of applying such method in several famous autonomous strategies in many different financial assets. The proposed method can be used to select among potential robots, establishes minimal periods and requirements for the test executions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: There are many practitioners that create software to buy and sell financial assets in an autonomous way. There are some digital platforms that allow the development, test and deployment of trading agents (or robots) in simulated or real markets. Some of these work focus on very short horizons of investment, while others deal with longer periods. The spectrum of used AI techniques in finance field is wide. There are many cases, where the developers are successful in creating robots with great performance in historical price series (so called backtesting). Furthermore, some platforms make available thousands of robots that [allegedly] are able to be profitable in real markets. These strategies may be created with some simple idea or using complex machine learning schemes. Nevertheless, when they are used in real markets or with data not used in their training or evaluation frequently they present very poor performance. In this paper, we propose a method for testing Foreign Exchange (FX) trading strategies that can provide realistic expectations about strategy's performance. This method addresses many pitfalls that can fool even experience practitioners and researchers. We present the results of applying such method in several famous autonomous strategies in many different financial assets. Analyzing these results, we can realize that it is very hard to build a reliable strategy and many published strategies are far from being reliable vehicles of investment. These facts can be maliciously used by those who try to sell such robots, by advertising such great (and non repetitive) results, while hiding the bad but meaningful results. The proposed method can be used to select among potential robots, establishes minimal periods and requirements for the test executions. In this way, the method helps to tell if you really have a great trading strategy or you are just fooling yourself.

Related papers

• 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)
• 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)
• Towards Realistic Market Simulations: a Generative Adversarial Networks Approach [2.381990157809543]
  We propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent.
  arXiv  Detail & Related papers  (2021-10-25T22:01:07Z)
• 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)
• mt5b3: A Framework for Building AutonomousTraders [0.0]
  Many AI techniques have been tested in finance field including recent approaches likeconvolutional neural networks and deep reinforcement learning. We present some fundamental aspects of modelling autonomoustraders and the complex environment that is the financialworld. We believe that mt5b3 may also contribute todevelopment of new autonomous traders.
  arXiv  Detail & Related papers  (2021-01-20T15:01:02Z)
• Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction [3.9189409002585562]
  There are several challenges in financial markets hindering practical applications of machine learning-based models. We propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders.
  arXiv  Detail & Related papers  (2020-12-18T13:19:27Z)
• 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)
• Efficient exploration of zero-sum stochastic games [83.28949556413717]
  We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay. During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well. Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly.
  arXiv  Detail & Related papers  (2020-02-24T20:30:38Z)
• Adversarial Attacks on Machine Learning Systems for High-Frequency Trading [55.30403936506338]
  We study valuation models for algorithmic trading from the perspective of adversarial machine learning. We introduce new attacks specific to this domain with size constraints that minimize attack costs. We discuss how these attacks can be used as an analysis tool to study and evaluate the robustness properties of financial models.
  arXiv  Detail & Related papers  (2020-02-21T22:04:35Z)

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.