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.
The scientific statistical and mathematical method can answer in a valid and reliable way the next questions.
1) Is there a trend (drift) in the market or in a particular instrument ?
2) Is the trend up, or down or neutral?
3) What is the expected and most probable duration of the trend?
4) What is the most probable slope or intensity of the trend?
5) What is the average volatility of this trend ?
6) What is the most dominant cyclic behavior in a particular time scale?
7) How predictability depends on time scales?
8) What are the betas per instrument in the market relative to the whole of the market?
9) What is an optimal according to probability of profit , portfolio of instruments in the market?
10) What is an optimal according to portfolio volatility , portfolio of instruments in the market?
11) What is an optimal trading in respect to escalation, stop loss and take profit or trailing based on the probability of crashing the account ?
12) What is the optimal risked funds in opening a position?
etc
The statistical quantities from the front-office in trading need to me measured are
1) the price position in the channel around the average, 2) the velocity (1st derivative) and
3) the acceleration-deceleration (2nd derivative), which is done as statistical quantities by a hypothesis test or confidence interval.
4) The support-resistance levels can be measured also by action-volume histograms. The measurements are done with convenient indicators, and can also define in a statistically valid way, not only , the channels , the trend, reversal, and
5) (Eliot) waves but also
6) the spikes.
7) It is required also an in advance in the past measurement and discovery of the basic stable cycles in the markets (see post 5)
8) An in advanced in the past measurement and discovery that trends duration and length, and volumes follow the Pareto distribution (see post 11,25 etc).
In addition for the back-office of trading we need to measure the
9) probability of success of trade based on the past history of trades, to apply the Kelly criterion, and also
10) the average rate of increase of the trading funds and
11) its variance again from the past history of trading.
The back-office statistical quantities in 9), 10), 11) are related , by simulation as e.g. in the simulator in post 43.
I have also created a simulator to experiment with different rules of withdrawals.
There are many who complain that the indicators have lag, and prefer not to use indicators at all, but only the prices. This is rather stupid! The indicators when are measuring a statistical quantity MUST have lag, because we are not interested for the price at the now only, but in a short-term past horizon too, which defines the statistical momentum which is statically conserved. It is not a race of speed, it is a challenge of successful perception. The science of statistics is the best for the moment one can have , from the collective scientific thinking, and collective consensus, and we must be honest and humble to admit is restricted abilities, but also trust , respect it and be confident for the success when applying it. When we are applying the statistical mode of thinking for the markets, we never run serious dangers of being "burned"and "busted" in our deductions, as statistics claims everything only up to some probability, or probability inequality and intervals.
There are some also that claim to have "intuitive guessing" about how the markets will move beyond the observable state of the markets. This of course cannot be included easily in the standard statistical inference. But it seems to me that sometimes, this is in certain human and social environmental conditions, is too much to ask from yourself, and it may fire-back to systematic opposite to the actual markets moves, guessing! I believe that fortunately pure statistical inference from the observable states of the market only, may be adequate for very profitable trading.
Traditionally the academic scientific world has been accused that the dominant public enforceable theory it supports is that of the efficient markets, in other words that e.g. in securities, only the very long term trend (more than 11.1 years) is real and constant, and anything else in the fluctuations is randomly neutral for up or down direction. It seems to me that there is a hidden moral reason behind it: To discourage people to trade, and encourage them only to invest long term which is the closest to real business. It is also true that it is hardly possible to find sufficient realistic and successful academic models for a time varying and variable (shorter term) trend in the markets, which is a common notion to practical traders. On the other side practical traders are quite proud of their successful practice, and that they do not make any scientific assumption whatsoever for the statistical behaviour of the markets. But what practical traders maybe ignore is that
1) For any successful trading system, there is always a minimal set of statistical assumptions about the (front office )behaviour of the market (mathematician would call it hypothesis about the market being particular stochastic process see http://en.wikipedia.org/wiki/Stochastic_process), that after these assumptions, the particular trading system is rather optimal mathematical solution according to some criteria or metrics, that leads to a growing account (backoffice statistical behaviour) .
2) Conversely of course it is known to econometricians, that from the moment you make a set of statistical assumptions about the (front office) behaviour of the markets, and set a system of criteria or metrics then there is an optimal (relative to the metrics) solution of it as trading, which makes a growing (backoffice) account.
So practical traders are one sided only: They just invent a trading protocol that creates a growing account (back-office statistical or stochastic process). Scientists are double sided: The assume a set of statistical assumptions for the behaviour of the market (front-office statistical or stochastic proces), and then rationally mathematically solve it (as we solve a system of equations) to derive an optimal protocol for trading, that creates a growing account (back-office statistical or stochastic process).
Once the scientists make the hypothesis of the statistical behaviour of the market, and once the trading protocol is defined, the back-office behaviour of the account can be derived with mathematical-statistical reasoning. While the practical non-scientific traders, just invent or discover the trading protocol, and then they must measure (back-tests) the statistical behaviour of the account (back-office statistics)
This scientist's approach we may call scientifically complete trading. It is obvious to me that the latter scientific approach has advantages:
a) It is an inner thinking imagery-temple and conceptual system of thinking which is based on the collective scientific thinking, so it is stable and rational for many decades
a) It is an inner thinking imagery-temple and conceptual system of thinking which is based on the collective scientific thinking, so it is stable and rational for many decades
b) The statistical assumptions about the market may be easier to define and test compared to trading system backtesting, while the optimal trading solutions maybe non-obvious but valid mathematical solution.
There are of course disadvantages too:
a) The scientific approach is more difficult, more complicated and a minority only of scientists are good in doing it.
b) Very often the correct statistical assumptions about the behaviour of the market, are obscured by a numerous of carrier-oriented econometricians, that are not at all interested in trading money or helping other people trade money, but just to publish an impressively complicated scientific paper.
a) The scientific approach is more difficult, more complicated and a minority only of scientists are good in doing it.
b) Very often the correct statistical assumptions about the behaviour of the market, are obscured by a numerous of carrier-oriented econometricians, that are not at all interested in trading money or helping other people trade money, but just to publish an impressively complicated scientific paper.
We notice here that from an econometric point of view there are two stochastic processes:
1) The front-office statistical behaviour of the market (a complicate stochastic process)
2) The back-office statistical behaviour of the account ( a simpler stochastic process).
It is ironic that the academic public enforceable theory of efficient markets, and long term only constant trend, fits rather well to the back-office statistical behaviour of the account of a successful trading system, rather than the real statistical behaviour of the markets.
From 1997, to 2003, I was following myself the traditional, approach of practical traders, not making any statistical assumptions about the markets. Since 2004, nevertheless, I programmed a quite realistic simulator of how the markets behave statistically, and solved it to find optimal trading systems ( I called it rainbow price generator or simulator) . By far the mental and emotional satisfaction was greater. Both intellect and emotions were satisfied in a scientific valid way. But it is difficult.
I tested that any known to me successful trading system (not invented by me) when applied to my artificially generated prices by my rainbow simulator, it gave approximately the same profitability performance as when on the real historic data, Conversely, any successful trading system, that I invented, on the artificial prices of the rainbow simulator, gave approximately the same profitability performance when applied to the real historic price data.
I tested that any known to me successful trading system (not invented by me) when applied to my artificially generated prices by my rainbow simulator, it gave approximately the same profitability performance as when on the real historic data, Conversely, any successful trading system, that I invented, on the artificial prices of the rainbow simulator, gave approximately the same profitability performance when applied to the real historic price data.
Somehow I did it, so as to exorcise a scientific mocking voice in me claiming that "there is no long term successful trading". In addition I did it to prove to me that in shorter time frames than daily bars, there are long term successful trading systems, with surprisingly adequate profitability performance.
THE TOP 6 FACTORS OF ATTENTION IN MANUAL TRADING
1) NEVER USE ALL YOUR FUNDS FOR TRADING. DIVIDE THEM TO TRADING AND NON-TRADING FUNDS BY THE RATIO f=R/a^2 RULE (see below for this ratio or in posts 3,13,33). THE DIVISION OF FUNDS AT EACH PERIOD IS ADJUSTED TO CONFORM WITH THIS PERCENTAGE RATIO. NEVER WITHDRAW PER PERIOD FROM THE NON-TRADING FUNDS MORE THAN HALF OF THE AVERAGE PROFITS OF THE TRADING FUNDS PER PERIOD. This division and adjustment of the funds has been applied for many years in buy and hold investments by professor Michael LeBoeuf.
2) THE ONLY CERTAINTY, WHILE TRADING IS ALSO OUR FIRST PRIORITY: WE MAY DETERMINE THAT OUR LOSSES AT EACH POSITION WILL NOT BE LARGER THAN A SPECIFIED PERCENTAGE DEFINED BY THE KELLY CRITERION (see below or posts 3, 13, 33)
3) FOCUS ON MACROSCOPIC INSTRUMENTS LIKE STOCK INDEXES WITH PERMANENT STRONG LONG TERM TREND, even if you want to trade at short time scales. (e.g. of the American Economy which is young and strong and indexes like Dow Jones, SnP500, Nasdaq etc)
4) FOR VERY LOW RISK AT OPENING POSITIONS ON THE PREVIOUS INDEXES WITH PERMANENT STRONG TREND, OPEN AT TERMINAL SPIKES AGAINST THE TREND. This is the Bill Williams technique.
5) THE ASSESSMENT OF THE PATTERNS OF THE MARKET REQUIRES THAT IT IS DONE IN MANY SUCCESSIVE TIME FRAMES CHARTS. This is a basic recommendation by Alexander Elder, which, by now, it is a common knowledge to traders
6) BE FLEXIBLE IN FORECASTING THE MARKET AND DO NOT HESITATE TO FOLLOW PROMPTLY ANY UNEXPECTED CHANGES OF THE TREND OF THE MARKET.
THE TOP 6 FACTORS OF ATTENTION IN MANUAL TRADING
1) NEVER USE ALL YOUR FUNDS FOR TRADING. DIVIDE THEM TO TRADING AND NON-TRADING FUNDS BY THE RATIO f=R/a^2 RULE (see below for this ratio or in posts 3,13,33). THE DIVISION OF FUNDS AT EACH PERIOD IS ADJUSTED TO CONFORM WITH THIS PERCENTAGE RATIO. NEVER WITHDRAW PER PERIOD FROM THE NON-TRADING FUNDS MORE THAN HALF OF THE AVERAGE PROFITS OF THE TRADING FUNDS PER PERIOD. This division and adjustment of the funds has been applied for many years in buy and hold investments by professor Michael LeBoeuf.
2) THE ONLY CERTAINTY, WHILE TRADING IS ALSO OUR FIRST PRIORITY: WE MAY DETERMINE THAT OUR LOSSES AT EACH POSITION WILL NOT BE LARGER THAN A SPECIFIED PERCENTAGE DEFINED BY THE KELLY CRITERION (see below or posts 3, 13, 33)
3) FOCUS ON MACROSCOPIC INSTRUMENTS LIKE STOCK INDEXES WITH PERMANENT STRONG LONG TERM TREND, even if you want to trade at short time scales. (e.g. of the American Economy which is young and strong and indexes like Dow Jones, SnP500, Nasdaq etc)
4) FOR VERY LOW RISK AT OPENING POSITIONS ON THE PREVIOUS INDEXES WITH PERMANENT STRONG TREND, OPEN AT TERMINAL SPIKES AGAINST THE TREND. This is the Bill Williams technique.
5) THE ASSESSMENT OF THE PATTERNS OF THE MARKET REQUIRES THAT IT IS DONE IN MANY SUCCESSIVE TIME FRAMES CHARTS. This is a basic recommendation by Alexander Elder, which, by now, it is a common knowledge to traders
6) BE FLEXIBLE IN FORECASTING THE MARKET AND DO NOT HESITATE TO FOLLOW PROMPTLY ANY UNEXPECTED CHANGES OF THE TREND OF THE MARKET.
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