Before reading this post the reader is advised to read the post 9, of all laws of the financial markets, and the post 10, of the lower law of volumes (law of attraction) and the law of action (Newtons laws)
There are 3 contexts of laws required in trading . The appropriate LAWS OF THINKING for trading, the appropriate LAWS OF FEELINGS for trading , and the appropriate LAWS OF ACTIONS for trading.
The Successful trading is based according to these three laws on
1) POWER OF COLLECTIVE SCIENTIFIC THINKING: A GREAT AND SIMPLE SCIENTIFIC PERCEPTION OF THE FUNCTION OF THE ECONOMY THROUGH SOME GLOBAL STATISTICAL LAW. E.g. The law of Universal attraction in economy: that big money attracts more big money in the capital markets, and this by the balance of demand and supply makes securities indexes of the companies , that are indeed the big money, to have mainly stable ascending trend, whenever one can observe such one. Valid statistical deductions can be obtained with simple statistical hypotheses tests about the existence or not of a trend, with sample size half the period of a dominating cycle). (STABLE GREAT SCIENTIFIC THOUGHT-FORM OR BELIEF FACTOR IN TRADING. )
2) POWER OF COLLECTIVE PSYCHOLOGY: A LINK WITH THE POSITIVE COLLECTIVE PSYCHOLOGY.(E.g. that the growth of security indexes also represent the optimism of the growth and success of real business of the involved companies. And we bet or trade only on the ascension of the index, whenever an ascending trend is observable). (STABLE GREAT POSITIVE COLLECTIVE EMOTIONAL OR PSYCHOLOGICAL FACTOR IN TRADING. )
3) POWER OF INDIVIDUALS SIMPLE , CONSISTENT AND EASY TO CONDUCT PRACTICE. (e.g. a trading system with about 80% success rate that utilizes essentially only one indicator in 3 time frames, simple risk management rules of stop loss, take profit, trailing and escalation, and time spent not more than 20 minutes per day. In this way there are not many opportunities of human errors in the conduction of the trading practice. Failed trades are attributed to the randomness and are not to blame the trader). (STABLE SIMPLE AND EASY PRACTICAL FACTOR IN TRADING)
We may make the metaphor that successful trading is the ability to have successful resonance with the activities of top minority of those who determine the markets.
In trading there are 3 components in the feelings that must be dealt with. 1) The feeling of MONEY itself, 2) The feeling of the UTILITY of the money 3) The feeling of the RISK of the money each time. What is called usually money management in trading is essentially RISK MANAGEMENT.
VALID STATISTICS AND PREDICTABILITY
We must make here some remarks about the robust application of statistical predictions in the capital markets.
1) The theory that the efficient markets and in particular that they follow a pure random walk is easy to refute with better statistical experiments and hypotheses tests. The random walk would fit to a market where the sizes of the economic organizations are uniformly random. But the reality is that they follow a Pareto or power distribution, therefore this is inherited in the distribution of the volumes of transactions and also in the emerging trends or drifts.
2) The statistical models of time series are more robust , when they apply to the entity MARKET as a whole and are better as non-parametric , and not when they apply to single stocks and are linear or parametric. The reasons is that a time series as a stochastic process , requires data of a sample of paths, and for a single stock is available only a single path. While for all the market the path of each stock or security is considered one path from the sample of all paths of all the stocks.
3) The less ambitious the statistical application the more valid the result. E.g. applying a statistical hypothesis test, or analysis of variance to test if there is an up or a down trend (drift) or none, is a more valid statistical deduction , than applying a linear model of a time series and requiring prediction of the next step price.
4) Multivariate statistics, like factor analysis, discriminant analysis , logistic regression, cluster analysis , goal programming e.t.c., are possible to utilize for a more detailed theory of predictability and of portfolio analysis, and sector analysis of the market and not only H. Markowitz theory.
5) In applying of the above applications of statistics, the researcher must have at first a very good "feeling" of the data, and should verify rather with statistics the result rather than discover it.
6) The "Pareto rule of complexity-results" also holds here. In other words with less than 20% of the complexity of the calculations is derived more than 80% of the deduction. The rest of the 20% requires more than 80% more complexity in the calculations.
The Successful trading is based according to these three laws on
1) POWER OF COLLECTIVE SCIENTIFIC THINKING: A GREAT AND SIMPLE SCIENTIFIC PERCEPTION OF THE FUNCTION OF THE ECONOMY THROUGH SOME GLOBAL STATISTICAL LAW. E.g. The law of Universal attraction in economy: that big money attracts more big money in the capital markets, and this by the balance of demand and supply makes securities indexes of the companies , that are indeed the big money, to have mainly stable ascending trend, whenever one can observe such one. Valid statistical deductions can be obtained with simple statistical hypotheses tests about the existence or not of a trend, with sample size half the period of a dominating cycle). (STABLE GREAT SCIENTIFIC THOUGHT-FORM OR BELIEF FACTOR IN TRADING. )
2) POWER OF COLLECTIVE PSYCHOLOGY: A LINK WITH THE POSITIVE COLLECTIVE PSYCHOLOGY.(E.g. that the growth of security indexes also represent the optimism of the growth and success of real business of the involved companies. And we bet or trade only on the ascension of the index, whenever an ascending trend is observable). (STABLE GREAT POSITIVE COLLECTIVE EMOTIONAL OR PSYCHOLOGICAL FACTOR IN TRADING. )
3) POWER OF INDIVIDUALS SIMPLE , CONSISTENT AND EASY TO CONDUCT PRACTICE. (e.g. a trading system with about 80% success rate that utilizes essentially only one indicator in 3 time frames, simple risk management rules of stop loss, take profit, trailing and escalation, and time spent not more than 20 minutes per day. In this way there are not many opportunities of human errors in the conduction of the trading practice. Failed trades are attributed to the randomness and are not to blame the trader). (STABLE SIMPLE AND EASY PRACTICAL FACTOR IN TRADING)
We may make the metaphor that successful trading is the ability to have successful resonance with the activities of top minority of those who determine the markets.
In trading there are 3 components in the feelings that must be dealt with. 1) The feeling of MONEY itself, 2) The feeling of the UTILITY of the money 3) The feeling of the RISK of the money each time. What is called usually money management in trading is essentially RISK MANAGEMENT.
VALID STATISTICS AND PREDICTABILITY
We must make here some remarks about the robust application of statistical predictions in the capital markets.
1) The theory that the efficient markets and in particular that they follow a pure random walk is easy to refute with better statistical experiments and hypotheses tests. The random walk would fit to a market where the sizes of the economic organizations are uniformly random. But the reality is that they follow a Pareto or power distribution, therefore this is inherited in the distribution of the volumes of transactions and also in the emerging trends or drifts.
2) The statistical models of time series are more robust , when they apply to the entity MARKET as a whole and are better as non-parametric , and not when they apply to single stocks and are linear or parametric. The reasons is that a time series as a stochastic process , requires data of a sample of paths, and for a single stock is available only a single path. While for all the market the path of each stock or security is considered one path from the sample of all paths of all the stocks.
3) The less ambitious the statistical application the more valid the result. E.g. applying a statistical hypothesis test, or analysis of variance to test if there is an up or a down trend (drift) or none, is a more valid statistical deduction , than applying a linear model of a time series and requiring prediction of the next step price.
4) Multivariate statistics, like factor analysis, discriminant analysis , logistic regression, cluster analysis , goal programming e.t.c., are possible to utilize for a more detailed theory of predictability and of portfolio analysis, and sector analysis of the market and not only H. Markowitz theory.
5) In applying of the above applications of statistics, the researcher must have at first a very good "feeling" of the data, and should verify rather with statistics the result rather than discover it.
6) The "Pareto rule of complexity-results" also holds here. In other words with less than 20% of the complexity of the calculations is derived more than 80% of the deduction. The rest of the 20% requires more than 80% more complexity in the calculations.
But during the last part of the 20th century the science of psychology ( Dr Gottman) has also applied with success (in helping couples to avoid divorce and resolve issues) the same equations as interplay in the couple of the underlying true emotions (as accumulated positiveness or negativity) during conversations.
And ,I , (as far as I know), apply for the first time the same equations to model the qualitative dynamics of Demand and Supply in the financial (and capital) markets. (a non-Marshalian approach to the demand supply, where the demand and supply are not considered independent).
We may classify 1) demand, or buyers as three types 1.1) Those that buy when the market prices increase following compelling behavior and mimicking other buyers. We call the buyer-bulls. Then 1.2) those buyers, that buy when the market prices fall, we call them buyer-bears, 1.3) Those that simply want to buy whatever the prices are doing. We call them buyer-horses
2) Supply or Sellers are also of three types 2.1) Those that sell when the market prices fall, following compelling behavior and mimicking other sellers. We call them seller-bull. Then 2.2) those sellers that sell when the prices rise. We call them seller-bears. 2.3) Those that simply want to sell whatever the prices are doing. We call them seller-horses
The combination of those 3 cases in any situation in the markets, as populations of demand and supply create 3*3=9 cases , that lead to 3 types of coupling Domination (buyer-horses/seller-horses or buyer-horses/seller-bears or seller-horses/buyer-bears ) Competition (buyer-bulls/seller-bulls or buyer-bears/seller-bears) , and Cooperation (buyer-bulls/seller-bears, or seller-bulls/buyer-bears), as below.
The qualitative analysis of their dynamics (as non-linear coupling systems of equations) give that
1) The Domination interplay creates waves , of stable, or diminishing or expanding amplitude, or flat stationary (ranging) fluctuations. More often produced by small populations of demand-supply and in smaller time scales.
2) The Competition interplay creates mainly Spikes and rarely constant trend. More often produced by medium size populations of demand-supply, and in mid-term time scales.
3) The Cooperation interplay creates constant trend.More often produced by large populations of demand-supply and in larger time scales.
As a result we have the 4 basic patterns of the price activity
a) Stationarity (ranging fluctuations), (in the average about 40% of the time)
b) Waves (e.g. special elliot waves usually called triagles in technical analysis),(in the average about 30% of the time)
c) Constant Trend (drift or momentum),(in the average about 20% of the time)
d) Spikes (extreme slope trend for a short time),(in the average about 10% of the time).
For example a currency pair (e.g. EURUSD) is already a coupling of two economies (US and EU) , and this coupling is mainly or is rotating among the above three modes: Domination, Competition, Cooperation. Different economies are inclined to dwell more on one mode than another, depending on the global fundamentals of the economies, their trade, and interaction.
And the most predictable effect modulated by cycles (see post 5) is the reaction to an super-exponential moves (a blow-up at the end of trend in the form of super-exponential move or terminal spike). (See e.g. https://www.ted.com/talks/didier_sornette_how_we_can_predict_the_next_financial_crisis and http://www.er.ethz.ch/ Such super-exponential terminal patterns of trend may occur usually as result of overgrowth of the one of the two populations in a demand-supply coupling rather that of domination and not so much of competition or cooperation. See also post 32. The frequency of emergence and the size of such super-exponential blow-ups follows the law of inequalities in other words the Pareto or Log-normal distribution and is thus by fat more often than pure randomness would predict!).
THUS THE ORDER OF BETTER PREDICTABILITY IS
1) LONG TERM PERMANENT TREND
2) A CYCLE IN THE ORDER OF PREDICTABILITY DESCRIBED ABOVE AND REALIZED AS REACTION TO SUPER-EXPONENTIAL TERMINAL MOVE (OR SPIKE).
1) LONG TERM PERMANENT TREND
2) A CYCLE IN THE ORDER OF PREDICTABILITY DESCRIBED ABOVE AND REALIZED AS REACTION TO SUPER-EXPONENTIAL TERMINAL MOVE (OR SPIKE).
Here in the chart below which is with the prices after taking the logarithm, we may watch deviations from the linear moves (super-exponential moves) and their highly predictable reaction lasting in the average 3.75 years. during 30 years!
The most usual pattern coming out from Domination is the waves. The most usual pattern coming out from competitions is spikes, and the most usual pattern coming out from cooperation is a trend. The full qualitative mathematical definitions of domination, competition, and cooperation as well as the proofs that only the above 4 price patterns are the possible solutions is outside the scope of a rather practical Blog like this.
The price waves created by the demand-supply follow of course the laws of periodicity and universal attraction or economic inequality as far as period and amplitude is concerned.
If we plot histograms of the High-Low of bars at various time-frames it becomes apparent through their multiple histogram peaks, how, trends, and spikes are distinguished (Fan principle).
In the next part of the post we display the equations and diagram of the 3 interplays of Demand Supply.
1) The equations of domination in non-linear demand-supply coupling (non-Marshalian demand-supply) are as the equations of pray-predator in ecology (Voltera's equations)
2) The equations of competition in non-linear demand-supply coupling (non-Marshalian demand-supply) are as the equations of competition in ecology.
3) The equations of cooperation in non-linear demand-supply coupling (non-Marshalian demand-supply) are as the equations of cooperationin ecology and social behaviour.
All the above 3-types of dynamic coupling have been applied also in the psychology of social behaviour, and even in the psychology of behaviour of couples in the marriage
(see "The mathematics of marriage" by Gottman, Murray, Swanson, Tyson, and Swanson, MIT press 2002. See also for the Voltera's equations "Models in Ecology" by J. Maynard Smith, Cambridge University press )
All of the above 3 types of non-linear coupling have been solved and their solutions have been classified.
From their solutions come the 4 patterns described at the beginning of the post and elsewhere in this Blog.
The support-resistance levels of the prices represent the poles of the dynamic system, that correspond to the carrying capacity of the subpopulations of demand-supply.
For a non-coupled single population the equation is the equation of logistic growth of a population, and the asymptotic upper bound is the resistance level
http://mathworld.wolfram.com/PopulationGrowth.html
http://mathworld.wolfram.com/LogisticEquation.html
The price waves created by the demand-supply follow of course the laws of periodicity and universal attraction or economic inequality as far as period and amplitude is concerned.
If we plot histograms of the High-Low of bars at various time-frames it becomes apparent through their multiple histogram peaks, how, trends, and spikes are distinguished (Fan principle).
In the next part of the post we display the equations and diagram of the 3 interplays of Demand Supply.
1) The equations of domination in non-linear demand-supply coupling (non-Marshalian demand-supply) are as the equations of pray-predator in ecology (Voltera's equations)
3) The equations of cooperation in non-linear demand-supply coupling (non-Marshalian demand-supply) are as the equations of cooperationin ecology and social behaviour.
All the above 3-types of dynamic coupling have been applied also in the psychology of social behaviour, and even in the psychology of behaviour of couples in the marriage
(see "The mathematics of marriage" by Gottman, Murray, Swanson, Tyson, and Swanson, MIT press 2002. See also for the Voltera's equations "Models in Ecology" by J. Maynard Smith, Cambridge University press )
All of the above 3 types of non-linear coupling have been solved and their solutions have been classified.
From their solutions come the 4 patterns described at the beginning of the post and elsewhere in this Blog.
The support-resistance levels of the prices represent the poles of the dynamic system, that correspond to the carrying capacity of the subpopulations of demand-supply.
For a non-coupled single population the equation is the equation of logistic growth of a population, and the asymptotic upper bound is the resistance level
http://mathworld.wolfram.com/PopulationGrowth.html
http://mathworld.wolfram.com/LogisticEquation.html
