Tuesday, 15 February 2022

A Possible, New Positionbook Indicator?

In my previous post I ended with saying that I would post about some sort of "sentiment indicator" if, and only if, I had something positive to say about my progress on this work. This post is the first on this subject.

The indicator I'm working on is based on the open position ratios data that is available via the Oanda api. For the uninitiated, this data gives the percentage of traders holding long and short positions, and at what price levels, in 14 selected forex pairs and also gold and silver. The data is updated every 20 minutes. I have long felt that there must be some value hidden in this data but the problem is how to extract it.

What I've done is take the percentage values from the (usually) hundreds of separate price levels and sum and normalise them over three defined ranges - levels above/below the high/low of each 20 minute period and the level(s) that span the price range of this period. This is done separately for long and short positions to give a total of 6 percentage figures that sum to 100%. Conceptually, this can be thought of as attaching to the open and close of a 20 minute OHLC bar the 6 percentage position values that were in force at the open and close respectively. The problem is to try and infer the actual, net changes in positions that have taken place over the time period this 20 minute bar was forming. In this way I am trying, if you like, to create a sort of  "skin in the game" indicator as opposed to an indicator derived from order book data, which could be said to be based on traders' current (changeable) intentions as expressed by their open orders and which are subject to shenanigans such as spoofing.

The methodology I've decided on to realise the above is constrained optimization using Octave's fmincon function. The objective function is simply:

    denom = X' * old_pb_net_pos ;
    J = mean( ( new_pb_net_pos .- ( ( X .* old_pb_net_pos ) ./ denom ) ).^2 ) ;

for a multiplicative position value change model where:

  • X is a vector of constants that are to be optimised
  • old_pb_net_pos is a vector of the 6 percentage values at the open
  • new_pb_net_pos is a vector of the 6 percentage values at the close

This is a constrained model because percentage position values at price levels outside the bar range cannot actually increase as a result of trades that take place within the bar range, so the X values for these levels are necessarily constrained to a maximum value of 1 (implying no real, absolute change at these levels). Similarly, all X values must be greater than zero (a zero value would imply a mass exit of all positions at this level, which never actually happens).

The net result of the above is an optimised X vector consisting of multiplicative constants that are multiplied with old_pb_net_pos to achieve new_pb_net_pos according to the logic exemplified in the above objective function. It is these optimised X values from which the underlying, real changes in positions will be inferred and features created. More on this in my next post.


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