Zahra Pourahmadi; Dariush Farid; Hamid Reza Mirzaei
Abstract
Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in ...
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Stock trading is a significant decision-making problem in asset management. This study introduces a financial trading system (FTS) that leverages artificial intelligence (AI) techniques to automate buy and sell orders specifically in Iran's stock market. Due to limited availability of labeled data in financial markets, the FTS utilizes reinforcement learning (RL), a subset of AI, for training. The model incorporates technical analysis and a constrained policy to enhance decision-making capabilities. The proposed algorithm is applied to the Tehran Securities Exchange, evaluating its efficiency across 45 periods using three different stock market indices. Performance comparisons are made against common strategies such as buy and hold, randomly selected actions, and maintaining the initial stock portfolio, with and without transaction costs. The results indicate that the FTS outperforms these methods, exhibiting excellent performance metrics including Sharp ratio, PP, PF, and MDD. Consequently, the findings suggest that the FTS serves as a valuable asset management tool in the Iranian financial market.
emad koosha; Mohsen Seighaly; Ebrahim Abbasi
Abstract
The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to ...
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The purpose of the present research is to use machine learning models to predict the price of Bitcoin, representing the cryptocurrency market. The price prediction model can be considered as the most important component in algorithmic trading. The performance of machine learning and its models, due to the nature of price behavior in financial markets, have been reported to be well in studies. In this respect, measuring and comparing the accuracy and precision of random forest (RF), long-short-term memory (LSTM), and recurrent neural network (RNN) models in predicting the top and bottom of Bitcoin prices are the main objectives of the present study. The approach to predicting top and bottom prices using machine learning models can be considered as the innovative aspect of this research, while many studies seek to predict prices as time series, simple, or logarithmic price returns. Pricing top and bottom data as target variables and technical analysis indicators as feature variables in the 1-hour time frame from 1/1/2018 to 6/31/2022 served as input to the mentioned models for learning. Validation and testing are presented and used. 70% of the data are considered learning data, 20% as validation data, and the remaining 10% as test data. The result of this research shows over 80% accuracy in predicting the top and bottom Bitcoin price, and the random forest model’s prediction is more accurate than the LSTM and RNN models.
