# Moving average based trading system

If the market remains without clear direction for a longer period, algorithmic trading based on moving averages can be devastating to the investment. Although hundreds of indicators are used, the reliable early detection of false signals is a topic of intense research.

The strategy in table 1 is simulated on a closing price basis. The trader is assumed to make the trading decision just before the close of every day. The SMA periods of 2 to are considered in the simulation. The returns are assumed to be equal to the change in the underlying spot price.

The futures premium and commissions are not taken into consideration. The net returns are displayed in Fig. The number of trade opportunities or crossovers for each period is displayed in Fig. The total net returns for the SMA period of 2 is the highest. Also the number of crossovers is also the highest for the period of 2. This indicates that at buying at every positive closing day and selling at every negative closing day has yielded the greatest net returns for the simulated period.

The Profit to loss ratio for the SMA period of 2 is 1. In every ten trades about six trades will yield positive results. This is due to the high sensitivity of the indicator.

The slowest indicators around a period of 97 yields a ratio of 2, i. For a good tradeoff between overall net returns and good Profit to loss ratio the period of 27 can be considered. The maximum drawdown of the account is an important factor in considering the trading strategy.

It is displayed in Fig. The weakness of fast moving indicators with low value of SMA periods is visible from the Fig. The period of 2 which delivers the highest net returns can bring a downfall of The drawdown occurs usually at the beginning of the simulation.

This can be attributed to the large number of small losses incurred due to false crossovers of the highly sensitive periods.

The drawdown has both a psychological effect on the trader as well as financial impact. This phenomenon of high sensitivity with the highest returns can be explained by the black swan theory proposed by Taleb [18].

The large number of false triggers of the strategy result in a number of small losses and few small profits. When the markets are trading within a range, the losses accrue much more than the profits. But during a few rare trending movements where the index moves consistently in the same direction for several days, the fast indicator captures most of the movement. These few profits usually around four to five per year are sufficient to restore any deficit in the account as well as leave with a good surplus.

The success of the trader is in applying the strategy day after day with persistence. If due to some reasons, the large movements are missed, then the entire trading system will yield negative results. This phenomenon is clearly illustrated in Fig.

The strength of technical indicators is in allowing most of these large black swan movements to be captured. The effect of few strong moves is further illustrated in Fig. It shows the evolution of account balance in index points for the cases of missing the top few best trades in the SMA 2 system.

The trading system would result in loss if a total of 25 best trades out of are missed in a period of 5 years. This demonstrates the fat tail distribution effect of trading returns. SMA is equal to the mean of all data points collected. Again, data points can be seconds, minutes, days, months or years.

The main thing to understand about the simple moving average is that each data point is weighted equally. The exponential moving average EMA is similar to the simple moving average, except that it applies more weight to the most recent data points and less weight to more historical data points. This weight distribution treats recent data as more relevant, thus more deserving of greater amounts of weight than less recent data.

Many stock and options trading strategists criticize the simple moving average for its equal weight distribution. The exponential moving average was a method created to portray the weight distribution of time periods more accurately. The exponential moving average is more complex than the simple moving average. Because of its complexity, it indicates earlier changes in stock price and direction. Because the exponential moving average gives faster indications of stock movement, Chuck Hughes uses the exponential moving average system.

Because the simple moving average takes a longer amount of time to register movement, it will indicate changes in price less quickly, potentially resulting in a loss of money. Buying and selling prices will not be at their lowest or highest when the SMA is able to indicate a change in the system. Are you interested in learning how to use exponential moving average strategies to invest in the stock market?

Call Chuck Hughes today at to find out more about how Chuck uses EMA to create profit form the stock and options trading system. The exponential moving average is able to be used as an indicator of price changes within the stock market when two exponential moving averages of different amounts of time are measured and compared.

Exponential moving averages look like bell shaped curves. When one EMA is measured against another EMA of a different length of time, they will collide and crossover each other at different points. These points of collision are the signal points for price changes within the stock market. EMA crossovers are an extremely complex stock market strategy.

This strategy is what Chuck Hughes uses to indicate buying and selling points. Using his exclusive exponential moving average crossover strategy, Chuck Hughes has been successful at creating revenue in the stock and options trading market. The indicator was triggered because the day exponential moving average crossed below the day exponential moving average. This weight distribution treats recent data as more relevant, thus more deserving of greater amounts of weight than less recent data.

Many stock and options trading strategists criticize the simple moving average for its equal weight distribution. The exponential moving average was a method created to portray the weight distribution of time periods more accurately.

The exponential moving average is more complex than the simple moving average. Because of its complexity, it indicates earlier changes in stock price and direction. Because the exponential moving average gives faster indications of stock movement, Chuck Hughes uses the exponential moving average system. Because the simple moving average takes a longer amount of time to register movement, it will indicate changes in price less quickly, potentially resulting in a loss of money.

Buying and selling prices will not be at their lowest or highest when the SMA is able to indicate a change in the system. Are you interested in learning how to use exponential moving average strategies to invest in the stock market?

Call Chuck Hughes today at to find out more about how Chuck uses EMA to create profit form the stock and options trading system. The exponential moving average is able to be used as an indicator of price changes within the stock market when two exponential moving averages of different amounts of time are measured and compared.

Exponential moving averages look like bell shaped curves. When one EMA is measured against another EMA of a different length of time, they will collide and crossover each other at different points. These points of collision are the signal points for price changes within the stock market. EMA crossovers are an extremely complex stock market strategy. This strategy is what Chuck Hughes uses to indicate buying and selling points. Using his exclusive exponential moving average crossover strategy, Chuck Hughes has been successful at creating revenue in the stock and options trading market.

The indicator was triggered because the day exponential moving average crossed below the day exponential moving average.

This indication was correct, which allowed Chuck to buy stock at extremely low prices. The stock Chuck bought at low prices would later be sold for much higher prices, creating a high return on investment ROI.

By following the sell signal given by the EMA crossover strategy, Chuck Hughes was able to avoid a loss, which many traders incurred. Again, Chuck Hughes was saved from this loss of securities because of strategy involving the exponential moving average trading system.