It is the processes that matter. Not the things. Look for the relationships between things to identify processes at work. Sometimes the market will be noisy…….and sometimes the market will be coordinated. Explicate versus Implicate. Don’t predict it, just follow it. The table with the metronomes holds the key to the connection between them. Observe the entire experimental setup, as without it, you will start trying to make sense of the things and lose sight of the process. Just having this understanding empowers you to see opportunity and risk and then simply make better decisions.
The reason that this toy seems mystical is the way you have approached the problem. A table, a metronome, many metronomes. All “nouns” of convenient human categorisation that take form in our language and then become permanent ‘objects’ of your thoughts leaving us blind to the underlying process . All based on a limited understanding of a system forgetting the importance of the relationships between different emergent aspects of the system. You need to approach this problem using verbs to find the processes at work and eradicate those nouns of perceived bias.
In classical systems there is order in every system state. The physicist David Bohm coined the term ‘the Explicate versus the Implicate Order to wrestle with this notion. What changes is the nature of the state. In complexity lies deep simplicity. Very simple things that are configured in different arrangements.
The grander system state moves to different states through gradual transition but the impact of this transition is not linear throughout. What makes the fat tails is that you are only observing a small sub section of the entire state.
Observation of the processes is the key as opposed to small sample prediction of the things. If you first observe the greater system then you can choose the optimal path by going with the ‘flow of it all’.
Methods of prediction without knowledge of the entire processes at work can be disruptive to the system state. When you think you know it all from your sample statistics, then backtrack and think again as markets are adaptive and constantly changing. Don’t ever deal in certainties. For example, don’t think you now need to understand the ‘Reynolds Number’ in this example to use as a weapon to obtain predictive certainty….as it will take you away from the whole point of this discussion and lead you into the abyss of uncertainty.
Knowledge is an adaptive process that allows you to ‘keep up’, not a cup that is either empty or full. There is great value in using statistics. The problem however lies in it’s naive application.
Observe first before letting your biases take over. Understanding complexity lies in expanding your knowledge and understanding how to overcome your biases formed from a very limited data set of narrow opinion. Be the all powerful critical thinker not the fractured, weak and hostile imbecile.
Convergent trading systems are based on the principle that price will converge to an estimated value whether that be an estimate of intrinsic value, or an estimated historic mean. They are predictive in nature given this assumptive stance and are couched in terms of market predictability or assumed stationery conditions. Given the predictive postulate, this style of system has a high win rate (during periods of market predictability) and relative high risk to reward relationship as profit targets are used given the predicted future value. As a result, this style of system tends to have high negative skew (many small winners when conditions are stationery (equilibrium) with occasional large losses during market transitions). Consecutive large losses during transitions can lead to account blow ups.
Divergent trading systems are based on the principle that price will diverge away from an estimated value. They are non-predictive in nature and relate to market transitions as opposed to periods of market stability. Given the non-predictive stance, these strategies ‘follow price’ as opposed to predict price and cut losses short and let profits run. As a result, this style of system has a low win rate (as transitions are unpredictable and less frequent) but relative low risk to reward relationship. As a result, this style of system tends to have high positive skew (many small losses when conditions are stationery with occasional large wins during market transitions to new equilibria).
Markets like any complex system tend to exhibit quasi stability (false equilibria) punctuated by unpredictable periods of non-stationarity. They therefore exhibit two types of arbitrage opportunity. That arbitrage associated with predictable stationery conditions (eg. convergent trading styles) and that arbitrage associated with unpredictable moves away from stationarity (eg. divergent trading styles).
The two pendulums below exhibit the two broad forms of trading style.
A single pendulum exhibits predictable oscillatory behaviour (eg. convergent trading styles such as mean reversion or value investing).
…..and the double pendulum which exhibits rich dynamic behaviour and suited to price following.
Trade well and prosper