Pinpointing entries across time frames

Futures, Feb 2008 by Reynolds, Matt

By breaking a market down to its essential elements, we can identify what indicators can be used to properly gauge that essence and tip the market's hand. Here, we walk through this process for the E-mini Russell.

When trading highly leveraged auction markets, it's critical to stay on top of changes in the market environment with a strategic management approach that continuously reviews and enhances all aspects of a trading program. As you develop this process, you'll be able to assess, mitigate and reduce your risk on a tradeby-trade basis.

Strategic Management describes this practice. It is a methodical feedback process that takes current market information and system performance and uses it to adapt a viable trading strategy (see "Managing strategy," right). Formulation is the creation of the strategy. Implementing is the process the trader must undergo to accomplish the trading mission. Eighty percent of traders fail in the execution stage due to a failure to link the mission and strategy implementation.

One missing piece is often a wellconstructed argument that exemplifies legitimate market situations. Traders are best suited by identifying and implementing leading indicators that seek to increase the probability of making a winning trade prior to entry, while conforming to the natural flow of the market and trading in the same direction of the larger trend.

The process for identifying leading indicators begins by pinpointing the essence of the underlying instrument itself. According to dictionary.com, essence is the "intrinsic or indispensable properties that serve to characterize or identify something, the most important ingredient; the crucial element." The essence of the market is composed of money flow, volume, open interest, price, support/resistance and volatility. Now, all we need to do is determine how to measure that essence, and we should find ourselves with a high probability of entering a winning position.

MEASURING ESSENCE

The Herrick Payoff Index (HPI), developed by John Herrick, displays the money flowing into and out of the market through the fluctuation of volume, price changes and open interest. Money is considered to be flowing into the underlying instrument when HPI is above a zero line, creating a bullish indication. Money is considered to be flowing out of the underlying instrument when HPI is below the zero line, establishing a bearish sentiment.

Divergence between price and the HPI line is a key development. If HPI is decreasing and prices are increasing, analysis suggests the uptrend will reverse. If HPI is increasing and prices are decreasing, analysis suggests the downtrend should reverse.

The fluctuation of prices can be statistically assessed through the use of regression analysis, and standard deviation.

Regression analysis sounds complicated, but in our application, it is quite simple. Linear regression, sometimes called simple regression, can use time as the independent variable, or predictor variable, to forecast price as the dependent variable, or predicted variable. Linear regression is much more valid and less lagging than moving averages.

Standard deviation is a measure of the dispersion of a set of data from its mean. The more spread apart the data is, the higher the deviation. The volatility of the underlying instrument characterizes standard deviation. The first deviation away from the mean is accountable for approximately 68% of the data. The second deviation away from the mean represents relatively 95% of the data. Three standard deviations from the mean contain roughly 99% of the fluctuations from the mean.

In practice, a 55-period linear regression curve (LRC) with three standard deviation bands both above and below the curve should provide a leading indication of future price direction. It also can identify support/resistance. The greater the standard deviation of prices is away from the 55-period LRC, the higher the probability the underlying instrument will revert back to the mean. This tendency is known as mean reversion.

The forecast oscillator is derived from linear regression; it was popularized by Tushar Chande. The oscillating line is plotted by computing the percent difference between the forecast price and the actual price, hence "forecast oscillator." The forecast price is determined with an x-period linear regression. When the forecast price is greater than the actual price, the oscillator will be above zero. When the forecast price is less than the actual price, the oscillator will be below zero. By plotting an x-period moving average of the oscillator, Chande created a trigger line that determines trend - or, more important, changes in trend. If the forecast oscillator crosses below the signal line, expect lower prices. If the forecast oscillator crosses above the signal line, expect higher prices.

The Commodity Channel Index (CCI) is another indicator. It measures overbought/oversold levels. The CCI was created by Donald Lambert and measures the distance between the current price and the statistical mean. The statistical mean is derived by the standard deviation and serves to standardize the indicator's output. The CCI indicator is unlimited in regard to the distance between the current price of the market and the statistical mean; however, key extreme levels are considered overbought and oversold distances, suggesting price reversals when the CCI is above or below these extremes. The most popular settings for extreme levels are plus/minus 100 and 200.