Market Idenfitification: Theory-Crafting

The truth is that not all markets offer good opportunities to make money. A poor market will have: 1) low and sporadic liquidity 2) a high but unpredictable degree of noise 3) few if any discernible patters or anomalies.

– David Varadi in How Tradeable is a Market

For some data sets, there is simply no edge available.

– Jaffray Woodriff in Hedge Fund Market Wizards

It is a commonly accepted assumption that trading systems will perform differently in different markets, most likely due to the particular markets’ idiosyncratic behaviour. For example, as seen in this XIV trading strategy, even a simple trend-following system can produce extra-ordinary results within a highly trending market. It is for this reason that I believe that market selection is not only extremely important, but possibly more-so than regime detection or the trading system itself.

In order for us to select a group of markets to trade successfully, we should try to decide the characteristics of a market the particular trading system will excel in. For the two most popular trading systems, trend-following and mean-reversion, this seems to be straightforward. In theory, trend-following systems as a whole should perform better in markets with positive serial correlation and extremely few whipsaws, while mean-reversion trading systems should perform better in markets with negative serial correlation and extremely few trends. Here are a couple of indicator ideas I thought of that could measure market “tradeability”

The Y-Day rolling average of:

  • X-Day Auto-Correlation Function
  • X-Day Historical Standard Deviation
  • X-Day R-Squared
  • TSI
  • X-Day Signal-to-Noise Ratio (Not the engineering one. Will be defined in subsequent post).
  • Frequency of MA crossovers

For these indicators either a static numeric threshold can be used (ex: if Y-Day rolling average of TSI < 1.6, then market is tradeable for mean-reversion systems) or some kind of adaptive approach can be used (Ex: if Y-Day rolling averge of TSI < 15 day MA of Y-Day rolling average of TSI, then market is tradeable for mean-reversion systems).

There is another indicator I thought of, but it wouldn’t fit in with the rolling average category.

  • Equity curve of a daily follow through system

A static approach cannot be used with this indicator since there is no boundary that the indicator usually stays between. I can only think of an adaptive approach making this indicator useful (Ex: if equity curve < 15 day MA of equity curve, then market is tradeable for mean reversion systems).

I can only think of two potential problems with this:

1) Bad indicators of market tradeability

This is an obvious problem, which can be solved by finding better indicators. This is easier said than done.

2) Past performance isn’t indicative of future performance

Since technical indicators only measure historical tendencies, this may not work since the future differs from the past. Seen through MarketSci’s post The Simple Made Powerful with Adaptation, daily follow was an extremely successful strategy up until ~2000. If in 2000, we used the historical performance of daily follow through as an indicator, it would provide a signal for trend-following systems to succeed, which would be horribly incorrect for the next 12 years. This problem should be solved using a rolling screening approach rather than a “screen once and forget about it” approach.


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