Ultimate Algorithmic Trading System

The manner in which we developed the MACD algorithm does not care about
absolute prices because it looks at the difference between the moving averages as
either positive or negative; it does not care about the magnitude of difference.
■ Price-Based Indicators
Price-based indicators reflect trend and volatility. As we stated earlier, applying a
moving average to an instrument’s historical data will smooth out the data and soften
wild market swings. In other words, a moving average eliminates a good portion of
the noise associated with daily price changes.
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STOCHASTICS AND AVERAGES AND RSI! OH, MY!
Simple, Exponential, and Weighted Moving Averages
The most often used moving average is the simple version. Here, you sum up
the closing prices over the past N days and then divide by N. The exponential
moving average calculation uses a smoothing factor to place a higher weight on
recent data points and is regarded as much more efficient than the linear weighted
average. Having an understanding of the calculations is not generally required
for most traders because most charting packages do the calculation for you. But if
you are interested in the calculations, here are the steps involved in calculating the
exponential and weighted moving averages:
1. Seed the exponential average initially with a simple moving average.
2. Calculate the smoothing factor: multiplier = 2 / (EMA length + 1).
3. EMA[0] = (close[0] − EMA[1]) * multiplier + EMA[1].
The key to the EMA calculation is the smoothing factor multiplier. If we were
calculating a 20-day EMA, the multiplier would be equal to 2 / 21, or 0.0952. In
this example, a weighting of nearly 10 percent is applied to the most recent data
point. A weighted moving average is slightly more complicated to calculate. Sticking
with 20 periods, here are the steps involved in the calculation:
1. Sum up the values of the length of moving average : 20 + 19 + 18 + 17 +16
+15 + 14 + 13 + 12 + 11 + 10 + 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 +1 = 210.
2. Multiply the oldest price by 1 /20 and second oldest by 2/20 and third oldest by
3/20 and so on.
3. Sum up the products and you will have a weighted moving average. As you can
see, the most recent data is weighted the most.
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