Statistical estimation of the probability of reaching a target price considering volatility and returns across different timeframes

The article proposes an original algorithm for statistical estimation of the probability of reaching a target price based on the analysis of returns and volatility using a drifted random walk model and integration of data from different timeframes. The relevance of the study stems from the need to make informed decisions in algorithmic trading under market uncertainty. The key feature of the approach is the aggregation of probabilities computed from different time intervals using Bayesian adjustment and weighted averaging, with weights dynamically determined based on volatility. The use of a universal fuzzy scale for qualitative interpretation of the evaluation results is also proposed. The algorithm includes the calculation of logarithmic returns, trend, and volatility, while stability is improved through data cleaning and anomaly filtering using a modified Hampel method. The article presents a calculation example using real OHLCV data and discusses possible approaches to validating the accuracy of the estimates when historical records of target price attainment are available. The results demonstrate the practical applicability of the proposed method for assessing the feasibility of reaching forecasted targets and for filtering trading signals. The developed algorithm can be used in risk management, trading strategy design, and expert decision support systems in financial markets.

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