Markets change, and change quickly. They cycle, trend, wander, and test extremes. Frequently, they simply test an analyst's patience.

The Dynamic Market Lab provides methods to measure these changes responsively. The methods process quickly in real-time, and integrate into Metastock as external formulas.

The Dynamic Market Lab offers two types of techniques to measure market behavior:

1. Adaptive Standard Indicators (ASI)
2. Adaptive Digital Signal Indicators (ADSI)

These methods use variable periods to filter data. This synchronizes their measurements to a market's current state - Traditional methods use fixed periods, and may be accurate if the period selected reflects current conditions.

Two powerful signal processing algorithms are used to generate period lengths:

1. Cybernetic Cycle Period
2. Homodyne Period

The period lengths generated change as markets change, and are used to adapt indicators to current conditions. The two algorithms are derived from the work of John Ehlers PhD., a respected engineer, and market and signal processing analyst.

The user selects the algorithm to use, or in many cases may interface one of his own design (e.g., a volatility based period generator). Refer to the Knowledge Base to find out more about the different market theories that can "drive" our adaptive indicators.

ASI includes 34 adaptive standard indicators. They are based on common technical analysis indicators, with a major difference! They use varying, not static periods in their calculations. This dramatically improves their responsiveness.

ADSI includes 33 adaptive digital signal indicators. These indicators are unique to or adapted from research found in Cybernetic Analysis for Stocks and Futures and Rocket Science for Traders. As a group, these indicators:

• minimize or eliminate lag
• identify trending vs. cycling markets
• identify noisy markets
• filter very short data arrays for extremely responsive, but smooth indicators
• transform price or indicators to approximately normal distributions. This permits measurement of statistical extremes.

These methods offer deep insight into market behavior. They have been developed with several other critical considerations:

• Modularity: there are innovative ways to apply Dr. Ehlers' techniques that are not in his books. The DML provides adaptive building blocks for these techniques, not just final indicators. A creative user may use these to build other indicators based on the underlying theory.
• Processing speed: Advanced programming methods ensure our formulations of these techniques process quickly in real-time. After all, a responsive measurement should not suffer from slow computational speed.