A comparative study between price-driven and mechanistic moving averages using causal analysis on bitcoin historical data

Ihor Tsapro

Abstract


The subject of this study is the comparative analysis of price-driven and mechanistic moving averages applied to Bitcoin volume and price data, using causal analysis to assess profitability and accuracy in historical records. This study aims to explore the effectiveness of mechanistic versus price-driven moving averages in predicting Bitcoin price trends. The objectives are as follows: 1) To evaluate the performance of the traditional price-driven simple moving average (SMA) against a mechanistic simple moving average (MSMA) that incorporates trading volume as an asset "mass"; 2) Perform backtesting with fast and slow moving average crossovers to determine each method’s profitability and trade accuracy across different parameter settings; 3) To calculate cause-and-effect relationships between moving average choice and observed trading outcomes, and further between Bitcoin price trend directions and returns using causal analysis; 4) To analyze the implications of these results on trading strategies within the volatile cryptocurrency market. The following results were obtained: 1) The price-driven SMA demonstrated higher profitability and higher volatility compared to MSMA which yielded more uniform but lower returns with significantly better trade accuracy; 2) Correlation analysis found stronger relationships between return and win rate for MSMA than for SMA, suggesting MSMA’s relative stability in volatile trading environments; 3) Causal analysis confirmed a statistically significant causal relationship between MSMA use and consistent returns; 4) MSMA returns were strongly affected by market trends with uptrends yielding higher returns than downtrends by 16%. Conclusions. This research contributes to the cryptocurrency technical analysis by demonstrating the advantages and limitations of price-driven and mechanistic moving averages. While SMA is better suited for researchers prioritizing higher potential returns despite volatility, MSMA offers a stable, volume-based approach. The study provides valuable insights for researchers aiming to refine investment strategies in the fast-evolving the cryptocurrency sector.

Keywords


cryptocurrency; moving average; statistical analysis; causal analysis; technical indicators; econophysics

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References


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DOI: https://doi.org/10.32620/reks.2025.1.03

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