Comparing Moving Average Crossover Systems with Strategy Tester Metrics

Summary

The article builds a simple automated crossover system and uses it as a baseline for testing simple, adaptive, double exponential, triple exponential, and fractal adaptive moving averages. The signal rule buys when a completed closing price crosses above its moving average and sells when it crosses below; the program checks for a new bar before acting. It then compares test results using net profit, relative balance and equity drawdown, profit factor, expected payoff, recovery factor, and Sharpe ratio.

The stated conclusion is that the adaptive moving average and simple moving average produced the strongest results among the tested types, with the adaptive version ranked highest. The article frames this as an educational example and encourages readers to test other settings and strategies. It supplies no basis for treating the ranking as universal: results depend on the selected symbol, timeframe, period, sample, and optimization choices, and the document itself cautions that the systems may not fit a reader's trading style. Backtest comparisons are not evidence of live performance.

Key ideas

• The example strategy buys and sells on closing-price crossovers of a moving average.
• The EA evaluates signals after detecting a new bar and closes an existing position before reversing direction.
• The comparison includes SMA, AMA, DEMA, TEMA, and FrAMA variants.
• The author ranks AMA highest among the tested systems and reports SMA as another strong result.
• Performance rankings depend on test settings and require further evaluation before live use.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.