Co-occurrence of candlestick patterns as a decision support tool
a computational model applied to stocks and cryptocurrencies
DOI:
https://doi.org/10.12662/2359-618xregea.v15i1.6534.pe6534.2026Keywords:
technical analysis, candlestick, co-occurrence, decision support, financial marketAbstract
This study analyzes the co-occurrence of candlestick patterns as a decision support tool for investments, considering the difficulty of interpreting chart signals in markets subject to volatility and uncertainty. Python algorithms were developed to automatically identify bullish and bearish patterns in historical series of stocks and cryptocurrencies, using daily and weekly datasets. The methodology involved pattern detection, verification of reinforcing factors, and performance evaluation through backtesting, using metrics such as hit rate, average return, Sharpe Ratio, and maximum drawdown. The results indicated overall hit rates above 79% for stocks and 84% for cryptocurrencies, although pattern effectiveness varied according to the asset analyzed, data frequency, and market conditions. As a contribution, the study presents a computational model applied to co-occurrence analysis, capable of organizing technical signals and quantitative metrics to support the decision-making process.
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Copyright (c) 2026 Rodrigo Fernando Galzerano Baldo, Alessandro Lucas da Silva, Luiz Fabiano Franzoni, Bruno Tezza Godoy, Rodrigo Rocha Meca
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