Optimizing transaction strategies with computational algorithms and time series analysis

This paper presents a computational strategy for optimizing transaction strategy through the fusion of time series analysis with advanced algorithmic strategies tailored for various frequencies of transactions. In the low-frequency mode of transaction, an ARIMA-based time series forecasting generates high-accuracy results backed by a simulated annealing algorithm for obtaining globally optimal solutions for maximizing the efficiency of decision-making. In the high-frequency mode, a Bollinger Bands algorithm generates actionable buy/sell signals rapidly while a risk evaluation strategy monitors success ratios and drawdowns for effective strategy design. The strategy developed in this work demonstrates significant improvements in transaction performance with the low-frequency strategy leading in accuracy while the high-frequency strategy balances speed with stability. The work demonstrates the strength of combining predictive analytics with optimization algorithms for addressing various transaction demands, offering a general technical framework for the design of computational strategy.