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Stock Trading Systems
An obvious failing of our first trading system was the high number of times it indicated you should buy and sell shares. In these circumstances, brokerage costs would be high. You can reduce the number of buy and sell decisions if you lower the volatility of daily prices.
Volatility
If you study a large number of stocks, you will find that some stocks are naturally more volatile than others. The chart on the right shows the price history of two stocks, both rising by the same amount in the same time. The prices of the stock shown in red are more volatile than the stock shown in green.
The problem caused by high volatility is known as the “whipsaw effect” – frequent buy and sell signals are triggered by relatively large rises and falls in price within a short timeframe. In the chart above, you would buy and sell the red stock more frequently than the green stock because of whipsawing on the red stock. Over the whole period covered by the chart, both stocks rise by the same amount but you would make a lower profit on the red stock because of the costs of buying and selling more frequently.
Reducing Volatility
To lessen volatility, you can use an average price – averaged over several days – rather than the actual price in your trading system.
Moving Averages
You can reduce the number of “buys” and “sells” by employing smoothed / averaged data. Smoothed data reduce the impact of daily price fluctuations.
The (dashed) chart on the right shows the effect of smoothing the original (solid) data by taking the average of the previous 7 days data. Notice how, compared with the unsmoothed data, there are fewer peaks and troughs in the smoothed data. This naturally reduces the number of buy and sell signals.
As an alternative to using a simple average, you might want to consider use a weighted, moving average of closing prices in your system. You could use a weighted, moving average to give higher relevance to the most recent data and less relevance to older data.