<![CDATA[ Research aim : This study aims to develop and test the effectiveness of a Quantitative Market Indicator (QMI)-based trading strategy to optimize Bitcoin investment returns while managing risk through controlled position allocation.. Design/Methode/Approach : The research follows five stages: data preprocessing, market indicator calculation, trading strategy design, performance analysis, and visualization. The QMI integrates three key indicators—Puell Multiple, Golden Fibonacci Index, and Pi Cycle—to identify market cycles and trend reversals. The systematic trading strategy applies buying rules when QMI < 20 and selling when QMI > 80, allocating 3% of capital for each transaction. Research Finding : The QMI strategy generated 1,101 transactions (479 buys, 622 sells), achieving a 12,670.71% return with an initial capital of USD 1,000 growing to USD 127,700.32. The maximum drawdown was 32.4%, the win rate 68.5%, and the Sharpe ratio 2.1, indicating strong performance with controlled risk. Theoretical contribution/Originality : This study introduces the integration of the Fibonacci sequence and the golden ratio in market cycle detection, enhancing prediction accuracy in volatile crypto markets. Practitioner/Policy implication : The QMI model provides a structured decision-making framework for crypto investors, emphasizing disciplined trading and adaptive risk management. Research limitation : Further development should integrate market sentiment and dynamic thresholds to refine model robustness.]]>
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