A Volume-Price-Adjusted MACD Trading Strategy with Sensitivity Calibration for U.S. Equity Indices

Battle Count: 61

The paper is directly relevant to quantitative trading as it proposes a concrete, implementable trading strategy enhancement for major U.S. equity indices. The VP-MACD framework provides a structured approach to incorporating volume and price-structure information into a widely-used technical indicator. The backtesting methodology, evaluation metrics, and statistical testing framework are standard in quantitative finance. However, the strategy remains relatively simple compared to modern quantitative approaches, and the limited number of trades in some cases (e.g., 6 for SPY) raises practical concerns about robustness and statistical reliability.

The VP-MACD implementation is moderately straightforward. It requires computing EMA (standard), constructing the adjusted price P* using volume-weighted averages with volatility and candlestick body ratio components, applying the λ-adjusted crossover rule, and running a grid search for λ optimization. The backtesting engine with transaction costs, next-day execution, and long-only constraints adds some complexity. No ML training or complex optimization is required. A competent quantitative developer could implement this in Python within a few days using standard libraries (pandas, numpy, yfinance).

The paper provides detailed mathematical formulations for VP-MACD, explicit parameter ranges (λ ∈ [0.8, 1.0] with 0.02 increments), data source (Yahoo Finance), sample periods, and evaluation metrics. However, no code repository or implementation script is provided. The grid search procedure and multi-criteria λ selection involve some subjective judgment. Transaction cost assumptions (4 bps one-way) are clearly stated. Reproduction would require implementing the adjusted price formula, EMA calculations, and backtesting engine from scratch.