SmartChannel™ is a spatial probability prediction tool based on investor self-organization. Consider a snow metaphor. The first skiers on new snow move some of that snow. Later skiers must react to tracks of early skiers. Over time, the slope becomes a highly organized set of moguls, demanding different skiing skills.
SmartChannel maps out the spatial geometry of the moves of all investors. Early in a market, decisions are simple, but as more investors determine new decision points, structure appears—self-organized geometry. It takes cool math to ski the mogul structures of a complex market. This is a different manifestation of crowd effect, as decisions impact investment strategies in real time. In scientific terms, it is an emergent property of Complexity theory. The market has a long-term memory, and only certain points in investment space are meaningful. Wealth depends on leveraging that memory correctly.
The Purpose of Channels
Technicians have long observed the curious tendency of almost perfectly parallel price channels to form, often with numerous contact points. These channels are recognized by their geometry, and tend to nest inside of bigger scale channels.
The reasons for finding and using these channels are that it gives us a point of reference for judging the probability of future price movements, allows us some measure of trend, and confirms the presence of an active investor interest. We can make judgments about the levels to set targets and stops. We can also estimate the price levels reached if the channel continues unbroken for a period of time. There is also no built-in lag or drop-off effect. If today’s bar breaks the channel, it’s broken. We don’t have to wait a few days for moving averages to cross over. Once a channel is found, it just extends into the future. There are never any days dropped off the back.
The SmartChannel Edge
The Parallax channel-finding tools use a high-speed geometry algorithm to search for statistically significant parallel channels. The candidates are screened for contacts, compactness, and the degree to which they are parallel. A complete analysis of each channel is done which covers volatility, trend, and volume accumulation. These are fed into a neural net to give a further overall forecast.
Channels may also be used for geometrical price projections using a spatial application of log-periodicity. Price breaking a channel usually leads to a run at a price target which is one channel width away on whichever side the break occurred. One channel width is simply the point at which a convergent log-periodic cycle of wavelength 2 would converge, given the width of the starting cycle matches the channel width. Our channel forecast tool finds past channels that have been broken, where there is an offset target that has not yet been hit.