Discrete Market Timeframes

One of the most important building blocks for understanding market action is the idea of discrete market analysis. By this I am referring to analysis derived from discrete tools (as opposed to continuous tools.)

Discrete data can be counted.
Continuous data must be measured.

If you think about it, traders use discrete language when speaking of  markets. How is today’s price action compared to yesterday? Compared to last week? How are this quarter’s earnings compared to last quarter’s? Market Profile® traders define a market’s “value area” based on data from the previous session. Comparisons like this are ingrained in the market’s psyche.

If we want to understand how markets tick, we need to align our tools to the market’s (discrete) characteristics. Yet most traders never make this connection., continuing to apply continuous data indicators to markets.

A good comparison to understand the difference between discrete and continuous indicators is that of VWAP versus a moving average. A traditional VWAP resets at the start of every session, while a moving average does not.

Natural Trading Cycles

What natural trading cycles can you think of? Here are some obvious ones:

  • Day (pit session or 24 hour)
  • Week
  • Month
  • Quarter
  • Year

Now think about this: There are traders whose primary time frame is each of these periods. We all have heard of the infamous day traders, but there are weekly swing traders, monthly, etc. in every market. And the dominant trader group varies from market to market, and from time to time.

Enter Fractal analysis

The most common application of fractal analysis in trading is Elliott Wave Theory. But there are other applications, as well.

A fractal is an object or quantity that displays self-similarity, in a somewhat technical sense, on all scales. The object need not exhibit exactly the same structure at all scales, but the same “type” of structures must appear on all scales.
                                                                            source: mathworld.wolfram.com

How do we apply this “self-similarity” to trading?

  • Price’s relationship to VWAP, Value Area, etc. on the weekly or monthly scale works the same as it does on the day/session scale.
  • Price’s relationship to a geometric models work the same on multiple scales.
Steidlmayer’s Timeframes

In Markets and Market Logic, Peter Steidlmayer wrote extensively about traders’ timeframes.

Understanding the concepts of timeframes and their influence on the market is key because acknowledging them is necessary to be able to grasp the next concept: to the extent that one can isolate or separate out and read the activity of the various timeframe influences, one can gauge the strength or weakness of a market. In other words, in order to understand the market, one should endeavor to understand the current actions of the long versus short timeframe participants. This is why participants should be isolated and categorized by the length of their individual timeframe, their ability to remain patient in buying or selling, in order for one to gain a market understanding. For example, one should find out whether the long timeframers are predominantly buying aggressively from the short timeframers, whether they are liquidating, etc.

Steidlmayer was a pit trader. From his vantage point, the difference between long and short timeframe traders was obvious: Short timeframe trading was mostly guys in the pit trading among themselves, while long timeframe trading was when large, institutional “paper” orders entered the marketplace. Short timeframe traders traded within the value area, and long timeframe traders moved the value area. Timeframes are a core principle underlying Market Profile® analysis.

Technical analysis has come a long way since Markets and Market Logic was published in 1986. Today, we can apply a more refined — and more precise — characterization to trader timeframes.