Revolutionize AI Trading Bots with AutoML-Based Multi-timeframe Bitcoin Price Prediction

Abstract

Multi-timeframe analysis/prediction provides essential information to traders. It gives a broader perspective of market trends and is used to identify significant levels of support and resistance. This will help traders/trading bots in making trading decisions. The majority of current studies focused on forecasting the closing price of daily candlesticks or high-frequency time frames, such as those of 1�min or 5�min. For artificially intelligent trading bots focusing on swing trading, price prediction related to other time frames is very significant. In this research, we present a study on developing a model to enable artificial intelligent-based trading bots to predict price components (open, high, low, and close prices) of the next 30-min, 1-h, and 4-h candlesticks of Bitcoin price. The study used two Auto-Machine Learning libraries: Tree-Based Pipeline Optimization Tool (TPOT) and AutoSklearn, to find the most suitable model for the task. The models are trained on historical price data of Bitcoin, and technical indicators are computed on these data. The performance of the trained models is evaluated in terms of R2 Score (Coefficient of Determination), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results showed that TPOT outperformed AutoSklearn library for all three time frames. It predicted all price components of 30-min candlestick with R2 Score of 0.999. � 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.