Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing
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| Format: | Preprint |
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2025
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| author | Ramraj, Vishrut Nagaraj, Nithin B, Harikrishnan N |
| author_facet | Ramraj, Vishrut Nagaraj, Nithin B, Harikrishnan N |
| contents | In this paper, we explore the application of Permutation Decision Trees (PDT) and strategic trailing for predicting stock market movements and executing profitable trades in the Indian stock market. We focus on high-frequency data using 5-minute candlesticks for the top 50 stocks listed in the NIFTY 50 index and Forex pairs such as XAUUSD and EURUSD. We implement a trading strategy that aims to buy stocks at lower prices and sell them at higher prices, capitalizing on short-term market fluctuations. Due to regulatory constraints in India, short selling is not considered in our strategy. The model incorporates various technical indicators and employs hyperparameters such as the trailing stop-loss value and support thresholds to manage risk effectively. We trained and tested data on a 3 month dataset provided by Yahoo Finance. Our bot based on Permutation Decision Tree achieved a profit of 1.1802\% over the testing period, where as a bot based on LSTM gave a return of 0.557\% over the testing period and a bot based on RNN gave a return of 0.5896\% over the testing period. All of the bots outperform the buy-and-hold strategy, which resulted in a loss of 2.29\%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12828 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing Ramraj, Vishrut Nagaraj, Nithin B, Harikrishnan N Machine Learning In this paper, we explore the application of Permutation Decision Trees (PDT) and strategic trailing for predicting stock market movements and executing profitable trades in the Indian stock market. We focus on high-frequency data using 5-minute candlesticks for the top 50 stocks listed in the NIFTY 50 index and Forex pairs such as XAUUSD and EURUSD. We implement a trading strategy that aims to buy stocks at lower prices and sell them at higher prices, capitalizing on short-term market fluctuations. Due to regulatory constraints in India, short selling is not considered in our strategy. The model incorporates various technical indicators and employs hyperparameters such as the trailing stop-loss value and support thresholds to manage risk effectively. We trained and tested data on a 3 month dataset provided by Yahoo Finance. Our bot based on Permutation Decision Tree achieved a profit of 1.1802\% over the testing period, where as a bot based on LSTM gave a return of 0.557\% over the testing period and a bot based on RNN gave a return of 0.5896\% over the testing period. All of the bots outperform the buy-and-hold strategy, which resulted in a loss of 2.29\%. |
| title | Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.12828 |