EMA Candle Close Strategy

Overview

This strategy generates trading signals based on the golden cross and dead cross of moving averages. It incorporates three moving averages with different parameter settings - short-term, medium-term and long-term ones. By comparing the height relationship among these three MAs, it determines the bullish/bearish state of the market and produces trading signals.

Strategy Principle

The strategy sets three moving average lines, which are a short-term simple moving average, a medium-term weighted moving average and a long-term exponential moving average. Specifically, it sets a 1-period SMA, a 20-period WMA and a 25-period EMA.

When the short-term SMA line crosses over the medium-term WMA line upwardly and the closing price is also higher than the WMA line, it indicates the market is reversing upward and forms a bullish signal. When the short-term SMA crosses below the medium-term WMA or the closing price is lower than the WMA, it gives a bearish signal. Therefore, this strategy judges the bullish/bearish state of the market by comparing the height and crossover among the three MAs.

Advantage Analysis

The strategy incorporates three MAs of short, medium and long terms, which can react to market changes in different cycles and improve the accuracy of capturing trends. Especially, the medium-term WMA has a better effect of filtering out market noise and avoids wrong signals effectively. In addition, the strategy only sends long signals when the bullish signals of SMA and closing price reach high consistency, which prevents whipsaws and ensures every entry efficient.

Risk Analysis

The strategy has the risk of false signals. When the short-term SMA produces wrong signals, unnecessary losses may be caused due to the strategy’s strict dependence on the SMA line. Also, the strategy is sensitive to parameters. When parameters are set improperly under range-bound markets, many wrong trades can be triggered.

To prevent such risks, it’s suggested to adjust the MA lengths, properly loosen trading conditions and set stop loss to limit single loss. When the market trend is unclear, the strategy can be stopped temporarily.

Optimization Directions

The strategy can be optimized from the following aspects:

1. Incorporate more types of MAs like KC to form an indicator portfolio to improve accuracy

2. Add volume factors like breakout with high volume

3. Combine volatility indicators to avoid failure in choppy markets

4. Employ machine learning to train and optimize parameters

Conclusion

The strategy determines market bullish/bearish status based on the crossover and height relationship among three MAs and closing prices. By combining MAs of different terms, it can effectively discover trends and the signals are of high quality. With proper parameter tuning and introducing more auxiliary indicators, the strategy can be further enhanced in pertinence and stability.