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Best Forex Advisor : Rebellion Research has been using a Bayesian Machine Learning Ai Investing platform to successfully trade Forex markets for nearly 15 years.
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Below please see the abstract to our research on a type of Machine Learning, Deep Q Learning & Agriculture:
- Abstract/Objective of this Research Paper
The objective of this research paper is to examine the use of a Simple Moving Average trading strategy on Agricultural ETFs and explore whether Deep Q-Networks can improve trading performance.
The average sharpe ratio of a SMA strategy is 0.2951 with a standard deviation of 4.666463011, while the average sharpe ratios of Deep Q strategies trained on 2-month and 6-month periods are -0.3864 and -1.9062 with standard deviations of 2.895470252 and 3.02360978 respectively.
This indicates that although the volatility of performance of a Deep Q-Learner is less than that of a SMA strategy, the returns are very poor. By performing Dynamic Time Warping on the training and testing time series, it is clear to see that performance of a Deep Q Learner is extremely dependent on similarities between the training and testing data.
Hence, during periods of extreme uncertainty and volatility, such as during the 2020 SARS-CoV-2 pandemic, it is not a viable option. Steps for further research on this topic could include examining the performance of a Deep Q Learner that learns and trades on intraday prices (which might be more predictable than month-long data) or one that learns on long-term data and receives longer-term factors such as weather and GDP.
- ETFs and Price History
There are 7 Agricultural ETFs that are being examined in this paper: Teucrium Corn
Fund (CORN), Teucrium Soybean (SYB), iPath Series B Bloomberg Coffee Subindex (JO), iPath Bloomberg Cocoa Subindex (NIB), Teucrium Sugar (CANE), iPath Series B Bloomberg Livestock Subindex (COW), and iPath Series B Bloomberg Sugar (SGG). FULL PAPER : Policy Gradient Learners in Trading Agricultural ETFs by Alexander Fleiss, Vishal Dhileepan, Rohan Mehta, Zoe Wang, Corina Perez-Cobb :: SSRN