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Implementasi Strategi Trading Cryptocurrency Menggunakan Indikator QMI: Perspektif Sebelas Tahun pada Bitcoin (2013–2024).

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Title: Implementasi Strategi Trading Cryptocurrency Menggunakan Indikator QMI: Perspektif Sebelas Tahun pada Bitcoin (2013–2024).
Authors: Swastika, Windra1 windra.swastika@machung.ac.id, Suganda, Tarsisius Renald2, Cahyadi, Rino Tam2
Source: Jurnal Nusantara Aplikasi Manajemen Bisnis. okt2025, Vol. 10 Issue 2, p610-621. 12p.
Subject Terms: *BITCOIN, *CRYPTOCURRENCIES, *INVESTMENT policy, *PRICE fluctuations, *RATE of return, *RISK managers, FIBONACCI sequence
Abstract (English): 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. [ABSTRACT FROM AUTHOR]
Abstract (Indonesian): Tujuan Penelitian : Penelitian ini bertujuan untuk mengembangkan dan menguji efektivitas strategi perdagangan berbasis Quantitative Market Indicator (QMI) dalam mengoptimalkan imbal hasil investasi Bitcoin serta mengelola risiko melalui alokasi posisi yang terkendali. Desain/ Metode/ Pendekatan : Penelitian menggunakan pendekatan kuantitatif deskriptif melalui lima tahapan utama, yaitu data preprocessing, perhitungan indikator pasar, perancangan strategi perdagangan, analisis kinerja, serta visualisasi hasil. QMI dibangun dari kombinasi tiga indikator utama: Puell Multiple, Golden Fibonacci Index, dan Pi Cycle, yang berfungsi mendeteksi siklus pasar dan tren harga. Strategi diterapkan secara sistematis dengan aturan pembelian saat QMI < 20 dan penjualan saat QMI > 80, menggunakan alokasi modal 3% untuk tiap transaksi. Temuan Penelitian : Strategi menghasilkan 1.101 transaksi (479 pembelian dan 622 penjualan) dengan total imbal hasil 12.670,71% dan maximum drawdown 32,4%, win rate 68,5%, serta Sharpe ratio 2,1. Kontribusi Teoritis/ Originalitas: Penelitian ini mengintegrasikan deret Fibonacci dan rasio emas dalam perhitungan indikator pasar kripto, yang mampu meningkatkan akurasi identifikasi kondisi pasar. Implikasi Praktis : Model QMI dapat dijadikan kerangka kerja pengambilan keputusan investasi kripto dengan manajemen risiko terukur. Keterbatasan Penelitian : elum mengintegrasikan analisis sentimen dan metrik on-chain untuk memperkuat sinyal QMI. [ABSTRACT FROM AUTHOR]
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Database: Business Source Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:25495291
DOI:10.29407/nusamba.v10i2.25032