# Improve Your Trading: Heat Zones Classification System Using Statistics

Every asset class has a unique “fingerprint” for how it behaves in different types of scenarios. While market behavior changes over time like everything else, these fingerprints generally remain stable for long periods of time. *The key is to understand how a given market behaves when it is in certain zones.* This allows you to clearly formulate what approach you should take at this point in time, rather than react and try to process new information as it arrives. Every financial instrument behaves in essentially 6 different “zones” over the longer term. This can be simplified by stating that the market is either trending up, trending down, or trendless, with either high or low volatility for each of these scenarios. The table below shows the historical magnitude of returns to the S&P500 (SPY) under each scenario, with the zones being color coded from green (best) to red (worst). Each zone can be delineated based on an intermediate r-square of high, low and close data, an intermediate slope of the regression line reading, and a 30-day historical volatility ranking using percentiles (>.5 is high, <.5 is low). For my own work I use a special indicator called the DVRAC which stands for R-squared and auto-correlation. Interestingly enough the performance of different indicators such as simple moving averages and oscillators is radically different in each of these zones. **Thus the static use of any indicator is quite silly. Optimal performance can be achieved by using the right indicator the right way, in the right zone.** Ironically for the S&P500, the use of a moving average is most effective in the trendless zone, while buying on small dips is more effective in uptrends, and shorting on small rallies is more effective in down trends except when volatility is very high (market returns are slightly positive with much more risk for a huge rally against shorts). For more research on zones and to obtain the DVRAC email us at dvindicators@gmail.com

Heat Zone |
Scenario |
r-square |
slope |

Green | Trend Up Low Volatility | >.2 | positive |

Bright Green | Trend Up High Volatility | >.2 | positive |

Dark Yellow | No Trend Low Volatility | <.2>-.2 | flat or slightly negative |

Yellow | No Trend High Volatility | <.2>-.2 | flat or slightly positive |

Red | Trend Down Low Volatility | <-.2 | negative |

Orange | Trend Down High Volatility | <-.2 | negative |

david,

any study on month end buying and selling after 5 or 6 days.

also give some example or spreadsheet of trades which you just talked..will help laymen like me to grasp what u speak..thanks

Bill

David,

This sounds similar to the idea of trading using market regimes (which seems to become quite popular..). I understood the idea was to:

1- Understand the current market conditions (ie as per your classification)

2- Define and use which strategy (ie mean reversion, trend following, etc.) works under which condition

ie you would not necessarily change the indicator parameters but instead adapt what trading strategy you use. You could for example trade the SPY differently in a low volatility uptrend than in a volatile trendless period.

I have also heard of adaptive indicators that apply some logic to vary based on current market dynamics (volatility, trend, etc). One that comes to mind is the Adapative Moving Average by Kaufman. The formula is “static” (ie you dont need to worry about changing parameters yourself) but its behavior/values are adaptive..

Jez, it is comparable to the concept of trading using market regimes, however this is a simpler and easier breakdown as we are not trying to predict what will work in the current environment and whether the market is trending/mean-reverting. This is more intuitive—ie the chart will appear to be trending up/down or consolidating, and either be volatile or quiet. The ability to objectively measure what zone the market is in, is much easier. Ascertaining market regimes is a complicated task. Nonetheless there is a relationship, and both are useful.

cheers,dv

David- could you elaborate on what you mean by “intermediate r square of high, low, close data”? Intermediate in terms of period length? Or do you calculate 3 r squares and take the median figure? Thanks

hi johnny i recommend using an r-squared of the last 20-40 days of the highs, do the same for the lows and the same for the close. then take the average r-squared from these 3 figures. if you want you can smooth this result over a couple days as well.

cheers

dv

How is it possible to have a negative R-squared ? By definition, this is always positive. Please clarify.

Thanks

eb