用排列决策树和追踪策略预测印度股市，3个月赚1.18%。
Predicting Stock Prices using Permutation Decision Trees and Strategic Trailing
- 用排列决策树分析5分钟高频数据，结合追踪止损控制风险。
- 模型在测试期盈利1.18%，优于LSTM（0.56%）和RNN（0.59%）。
- 适合关注短线交易、量化策略的投资者参考。
本文探讨了排列决策树（PDT）与策略性追踪在预测印度股市走势及执行盈利交易中的应用。研究基于高频率数据，使用前50只成分股（NIFTY 50）及XAUUSD、EURUSD等外汇对的5分钟蜡烛图数据。交易策略旨在低买高卖，捕捉短期波动，由于印度监管限制，未包含做空机制。模型融合多种技术指标，并通过尾随止损值与支撑阈值等超参数进行风险控制。训练与测试基于雅虎财经提供的3个月数据集。基于排列决策树的交易机器人在测试期内实现1.1802%的收益，而基于LSTM的机器人回报为0.557%，基于RNN的为0.5896%。所有机器人均优于买入并持有策略（亏损2.29%）。
原文摘要 · Abstract (English)
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\%.
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