Prediction of US 30‐years‐treasury‐bonds movement and trading entry point using the robust 1DCNN‐BiLSTM‐XGBoost algorithm

Abstract

AbstractThis article presents a novel algorithm that accurately predicts market trends and identifies trading entry points for US 30‐year Treasury bonds. The proposed method employs a hybrid approach, integrating a 1‐dimensional convolutional neural network (1DCNN), long‐short term memory (LSTM), and XGBoost algorithms. The 1DCNN is used to learn local and short‐term patterns, while LSTM is employed to capture both short and long‐term dependencies. Furthermore, we have implemented an algorithm that utilizes hull moving average (HMA) and simple moving average (SMA) crossover data to detect trading entry points and major trends in the market. The combination of the SMA–HMA crossover algorithm and predictions provided by the 1DCNN‐BiLSTM‐XGBoost algorithm yields exceptional results in terms of prediction accuracy and profitability. Additionally, these integrated techniques effectively filter out noise and mitigate false breakouts, which are often observed with US 30‐year Treasury bonds. In the field of financial time series prediction, the effectiveness of 1DCNN and LSTM in identifying trading entry points and market perturbations has not been comprehensively studied. Therefore, our work fills this gap by demonstrating through experiments that the proposed 1DCNN‐BiLSTM‐XGBoost algorithm, in combination with moving average crossovers, effectively reduces noise and market perturbations. This leads to the precise identification of trading entry points and accurate recognition of trend signals for US 30‐year Treasury bonds. We demonstrate through experiments that our proposed approach achieves an average root mean squared error of 0.0001 and an R‐square value of 0.9999, highlighting its promise as a method for predicting market trends and trading entry points for US 30‐year Treasury bonds.