The indicators and maths weave their way into algorithmic offerings which naturally their producers are very secretive about. If you ask how and why any given robot works – you are most likely to be directed at the 100% and 1000% successful results of the programme and this fills you with enough confidence to part with money for the subscription and you forget about needing responses to the very searching questions you had devised originally.

One truth seems to surface repeatedly – nobody really knows how the market works and no single approach, algorithm or mathematical projection holds up under all circumstances. This ripples through from the individual retail trader right through to the billion dollar asset management and hedge funds.

I set about trying to work out the concrete reasons behind the moves in the market and it took me on a very revealing journey. What is the journey? In this case it is the journey to profit using trading as the vehicle. The trade sets off, meets a series of turning points and this gives birth to a series of questions. The driver of the trade needs rules to get to the profit, so how are the rules defined?

We look to historical data and price charts for evidence of the prospective routes to follow. It is rapidly apparent that the price charts are not maps. There are no fixed roads on the charts. It is best to consider the charts as a record of paths invented by the market at specific points.

Mathematical vs quantifiable approach

We have a choice here. To define the rules do we take a mathematical approach or a heuristic quantifiable approach? Using maths – we could take a Bayesian distribution approach using averaging of prices (TWAP or VWAP?). It is a very popular approach taken by algo traders in the cash equity firms.

It is said that the top ten funds in the world use systems which are 70% accurate and that their business models absorb the 30% losses because overall the systems are profitable. But it also appears that there are an increasing number of funds who are admitting to the fact that their systems deteriorate over time, and what was a 70/30 winning paradigm at inception becomes a dinosaur five years or so down the track and then they call in legions of consultants to try and resolve the predicament.

It would appear that predictive maths is not a useful tool for defining our rules. They do not explain the reasons driving the market moves nor how and why the turning points come about. In my mind a quantifiable approach was needed.

It is public knowledge that the market is an auction driven beast – so at the heart of it lies a Pricing Machine. In order to evaluate the Market Price the machine has to gather in orders and it is the filling of the orders which drives the price moves.

The filling of the orders is either a Buy function or a Sell function. In my mind this would account for the turning points on the chart. So how to prove it?

Price action categories

Looking at the price action, we know that there are various functions buried in that activity. One is Order Filling – the other is Order Gathering. Martin Coles does a series of youtube videos on this subject and to me his approach and vocabulary has merit – so I have adopted much of his point of view, but what I found was that there is more in the Price Actions than just Order Fill and Order Gathering.

After many months analyzing historical data I eventually identified 6 categories of Price Action in live price feeds. The next step was to match market functions to each category. Endless hours of Excel spreadsheet analysis ensued, but after another several months I was able to identify a Price Action type as being Order Fill and it made up the majority of the market – but I learned it has no informational value what so ever.

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