Consider an experiment. Instead of taking a single technical indicator and applying it across all stocks to determine buy and sell candidates, let's identify the dominant cycle for each stock and project it into the next time period to provide unique buy and sell signals. Imagine, moreover, that we do that for all stocks listed on the New York Stock Exchange. Each stock has its own cyclical patterns that change over time; sometimes the dominant cycles will be shorter-term, other times longer-term. We're not taking one "system" and applying it to all stocks. Rather, we're identifying significant cyclical patterns for each stock and then aggregating the buy and sell signals to infer general market direction.
(For an example of such an approach, please see the work of John Ehlers).
Going back to late 2013, what we find is that when we have a high number of buy signals across stocks (top octile of the distribution), the next ten days in SPY average a gain of +.81%. When we have a high number of sell signals across the stock universe, the next ten days in SPY average a loss of -.40%.
Fascinatingly, the number of buy signals is only modestly correlated with the number of sell signals (r = -.17).
When we have a high number of sell signals across stocks, the next ten days in SPY average a loss of -.26%. When we have a low number of sell signals, the next tend days in SPY average a gain of +.78%.
In other words, we have two potential sources of edge in the market. The first is when we see broad strengths; the second is when we see a broad absence of weakness. Think about why that might be the case...
Take your traditional charts and technical tools and throw them away. Look at new information or creatively think through new ways to view traditional information. Your edge lies in the rigorous testing and implementation of uniqueness.
Further Reading: Cultivating Emotional Creativity
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