In attempting to the predict behavior of top 5 most liquid currency futures, using Java API with Interactive Brokers, I plan to incorporate machine-learning type of feature for managing the trade—specifically the closing of position aspect—either to take a maximum profit or minimize loss.
QUESTION: Given timeseries historical data containing examples of successful trading executions for the training, what is best machine-learning/deep-learning/neural-network technique/algorithm/approach for implementing a feature to an existing trading system, specifically to manage a trade after execution (position is opened) so as to take the maximum profit or incur the minimum loss in order to close position?
I would surmise, generally it would be something like classification—incorporating min-max. Furthermore, the classification approach would specifically call for a Reinforcement Learning type of structure/environment. However—for now— with the caveat, because of the complexity of java coding required to make the reinforcement learning in real-time, I do not presently wish to pursue a real-time reinforcement-learning approach. Instead I would manually update the ARFF file every week or on weekly basis. ARFF is the file (containing timeseries historical data of trades) that contains the list of instances sharing a set of attributes—it links with the JAR from Weka. Weka is the machine learning tool which I am using. Thank you.
QUESTION: Given timeseries historical data containing examples of successful trading executions for the training, what is best machine-learning/deep-learning/neural-network technique/algorithm/approach for implementing a feature to an existing trading system, specifically to manage a trade after execution (position is opened) so as to take the maximum profit or incur the minimum loss in order to close position?
I would surmise, generally it would be something like classification—incorporating min-max. Furthermore, the classification approach would specifically call for a Reinforcement Learning type of structure/environment. However—for now— with the caveat, because of the complexity of java coding required to make the reinforcement learning in real-time, I do not presently wish to pursue a real-time reinforcement-learning approach. Instead I would manually update the ARFF file every week or on weekly basis. ARFF is the file (containing timeseries historical data of trades) that contains the list of instances sharing a set of attributes—it links with the JAR from Weka. Weka is the machine learning tool which I am using. Thank you.
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