The strength of a zone and retests.

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In another forum post on future EA developments I recently referred to the idea of using the area bounded between the price chart and a zone as a measure of how strong a zone might be. Having thought a bit more about this, I figured it was worth writing a few more ideas down and developing these thoughts further.

By way of background information, retests in this forum have been considered as a way in which institutions pull price back to fill residual orders left at a level (or for us a zone) This hypothesis works well when trading with the trend - entering long on pull backs or short on rallies. However, when we look to identify tops and bottoms to get low risk entries early on in a new trend, there is often a question over why institutions would have orders left from such a long time ago.

In practice, the orders are probably not left over in these scenarios, but in fact the zone is just being used as a way of easily limiting risk and therefore naturally attracts new limit orders to similar levels. Therefore, when it comes to identifying the strength of a zone in this scenario, it becomes less about how many orders were left over and more about how many new orders are being attracted to that particular zone.

To take this idea further, one of the key ideas I am introducing is that when analysing a zone, the number of previous retests on that zone is far less important than what happened with price between last leaving that zone and returning back to it.

We can understand this better by considering a real life example.

Back in November 2014, I left for a months holiday in the UK. Before leaving, petrol in Australia was hovering above $1.60 a litre. We didn't like paying that much for it, but it was accepted as the now expected price. After I came back in January, thanks to the crash in crude oil prices, the cost of petrol had dropped right down to under $1.05. My wife and I were ecstatic, we were filling up all the time and very quickly this became the norm. However, it wasn't long before it started creeping up again and is now at around $1.30. Suddenly, this felt expensive, simply because we had got used to cheaper price levels.

Obviously, there are some things people can't do with out, and petrol is one, so from a supply and demand perspective, price may have less influence. Nevertheless, we can still take a few ideas away from this example as follows (considering a supply zone):

a) The longer price is away from a given level, the more "value" is perceived to be a lower level
b) A brief spike away from a level may be seen as an anomaly, whilst a prolonged stay at a much cheaper price will have a much more profound effect on our perception of value.
c) The further price falls from a value, the more expensive that original value is going to start seeming in comparison.
d) The quicker price returns back to a level after a prolonged stay away, the harder it is going to be to adjust our perceptions of value.

Each of the above can easily be translated into a method for judging the strength of a supply zone (opposite applies to a demand zone).

- A good supply zone is where price has been a long way from that level for a prolonged period.
- The further price moved from that zone and the more time spent at greater distances from the level, the better the zone will be
- A rapid move away and then back to the zone is less likely to mean a good supply zone.
- If price has been some distance away from a zone for some time, the quicker it comes back to that level, the more strong the zone is likely to be.

Of course, to measure strength, we always need a range and a base line to compare against. For example, we may say that strength of a zone is measured on a scale of 0-10 and a value of 5 is considered to be the strength required for us to expect a move of at least 2R away from the level. A higher score means a bigger move away is expected, whilst a lower score means a smaller move is expected.

To quantify the zone strength in real terms, we can use the following basic principle:

The bigger the area bound by the zone and price , the stronger the zone is considered to be. If price only stays away from the zone for a couple of candles, the area will be much smaller and the zone strength weak. But if while price was away from the zone, most of the candles closed a long distance away from the zone, the area will be bigger and the zone therefore stronger.

Of course in the extreme circumstances where price either spikes briefly away from the zone, or never actually moves far away from the zone, this means our area of supply is likely to small and our zone much weaker.

Looking at the development of the EA, I will be doing the following to include these ideas:

The focus will be taken away from just counting retests and instead moved towards consideration of each areas between when price was last at the zone and when it came back to the zone.

The area bounded within these areas will be used to generate a score (total number of “candle Rs"). Historic values will be used to determine an approximate base line score where we expect at least 2R move away from the zone (this may vary by currency pair).

Scores and base line values will be normalised to a value between 1 and 10.

To account for where price was most of the time when away from the zone, a distribution could be used.
Along the lines of the suggestions Spud made, this effectively splits the total distance moved away from the zone into equal bands and measures the time spent in each band. The more time spent in the furthest quartile (for example) compared with time spent outside of that quartile, the strong the zone will be. This can be expressed as a simple ratio, again normalised to a value between 0 and 10.

One final thought in its infancy is that when we see successive retests of a zone
If the areas bound by the zone and price leading up to each retest are each getting smaller, then this can be directly associated with the amount of supply at that level become less and the zone weaker. Conversely, if the successive areas are increasing, the supply is increasing and we can expect more of a down move from the zone.

This summarises how real life supply and demand can show us how to determine the strength of a zone and how the EA is going to get developed to benefit from these ideas.