ANYONE WHO WILL TRY TO MAKE MONEY SOLELY BY TRADING AND SUCH SYSTEMS OF TRANSACTIONS SHOULD BE AWARE THAT THERE IS A VERY POWERFUL AND ALMOST UNBEATABLE COLLECTIVE WILL SO AS NOT TO SUCCEED! NO-ONE WANTS PEOPLE TO QUITE THEIR JOBS AND MAKE MONEY THIS WAY AS IT IS SOMEHOW PARASITIC. IT IS IN SOME SENSE UNETHICAL AS A PRACTICE ENFORCEABLE TO THE MAJORITY. AND OF COURSE NEITHER THOSE WHO HAVE LARGE CAPITAL WANT THAT A MAJORITY WILL MAKE MONEY THIS WAY, AS THEY WOULD PREFER THAT THEY WORK IN THEIR COMPANIES FOR THEM. ONLY IN SPECIAL CONTINGENCIES AND SITUATIONS SOMETHING LIKE THIS WOULD BE ETHICAL. AND IN PARTICULAR A HIGHER MORALITY THAT WOULD SUPPORT SUCH A PRACTICE, WOULD BE PROVABLE WITH COLLECTIVELY BENEVOLENT DEEDS FROM A POSSIBLE SURPLUS OF SUCH MONEY!
The Successful trading is based according to these three laws on
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. )
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
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. Linear time series models or derived like ARMA, ARIMA, SARIMA etc are destined to fail for particular patterns like those described in the post 32, because the true equations are non-linear and in addition with random, time varying coefficients that derive the random emergence of the 4 basic observable patterns (see post 32 ). In addition the standard application of the time series by the researchers, focuses on stationary time series after they extract a stable exponential trend, while in the reality the main concern should be the random path of the average value of the prices that shapes the patterns and is neither constant exponential trend neither zero ! The "statistical momentum conservation" might then be nothing else than an hypothesis that the random and time varying 1st order in time steps , partial correlation of the prices , is always positive. This can be easily tested statistically. E.g. in the cross exchange rate EURUSD but also in the indexes, the partial correlation of the current to the previous time step bar is measured indeed positive, in almost all time frames, except at the daily time frame, where the cyclic behavior prevails. In the daily time frame the partial correlation is negative , which means if one day is up the next day it is more probable that it is down. In addition, the cyclic behavior is even stronger in pairs of two days with negative partial correlation (two days up two days down etc)
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 , conjoint analysis, correspondence analysis, multidimensional scaling etc , goal programming etc are possible to utilize for a more detailed theory of 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 less hypothesis we use in applying statistical hypotheses, the better. That is why non-parametric statistics is better. An exception is our knowledge of the application of the Pareto distribution in various aspects of the market which we is parametric.
That is why we avoid applying very complicated with many hypotheses and time consuming to estimate models to forecast the markets, but we prefer to respond to the market, by measuring only in a valid statistical way, the average position of the price, and the channel around it, the velocity (trend, 1st derivative) and acceleration-deceleration (2nd derivative) of the prices.
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.
THE OVER ALL STATISTICAL BEHAVIOR OF PRICE PATTERNS
We notice that although the price patterns are essentially 4 categories in details there are 6 distinct statistical patterns of the random or statistical position, velocity and acceleration of the prices (see post 32).
A class of stochastic processes can be defined as the behavior of the markets based on these 6 basic patterns P1, P2, P3, P4, P5, P5, P7. The Pi i=1,2,3,4,5,6,7 are essentially random statistical patterns with random and variable parameters of size duration, and relative analogies that define them. E.g. we may have a Pareto distribution of the duration and price height of the patterns because of the inequalities in the markets. The 7th pattern P7 of stationary behavior we may call intermittency pattern. We may then assume a class of Markov processes with random and variable transition probabilities, where each random type of pattern occurs and then a next one occurs. Bu the transition probabilities are not arbitrary! E.g. spikes occur usually at the begging (initial spikes) and the end (terminal spikes) of trends, up and down trends with stationary channels in between them, shape cycles, that in the average of stable period, and some times of fixed beginning and end.
The probability that a type of pattern occurs changes also according to the time scale. In 2, 5 or more years annual bars time frames, the non-waving trend pattern is dominating, while say in 5-minutes bars time-frame flat patterns are dominating.
This is the overall behavior of patterns of the markets, and there are some invariant properties like
1) Cyclic behavior as alternation with up and down trend patterns with flat channels at the bottoms and tops
2) Statistical momentum conservation (see post 10) where the 1st time-step partial correlation of a price is almost always positive,
3) A Pareto distribution of the duration and height of the patterns, due to the inequalities of the enterprises in the economic system (see post 10, 25, 57,63 )
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.
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.The statistical quantities from the front-office in trading need to me measured are the price position in the channel around the average, the velocity (1st derivative) and the acceleration-deceleration (2nd derivative), which is done as statistical quantities by a hypothesis test or confidence interval. 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 Eliot-waves but also the spikes. In addition for the back-office of trading we need to measure the probability of success of trade based on the past history of trades, to apply the Kelly criterion, and also the average rate of increase of the trading funds and its variance again from the past history of trading. (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. Linear time series models or derived like ARMA, ARIMA, SARIMA etc are destined to fail for particular patterns like those described in the post 32, because the true equations are non-linear and in addition with random, time varying coefficients that derive the random emergence of the 4 basic observable patterns (see post 32 ). In addition the standard application of the time series by the researchers, focuses on stationary time series after they extract a stable exponential trend, while in the reality the main concern should be the random path of the average value of the prices that shapes the patterns and is neither constant exponential trend neither zero ! The "statistical momentum conservation" might then be nothing else than an hypothesis that the random and time varying 1st order in time steps , partial correlation of the prices , is always positive. This can be easily tested statistically. E.g. in the cross exchange rate EURUSD but also in the indexes, the partial correlation of the current to the previous time step bar is measured indeed positive, in almost all time frames, except at the daily time frame, where the cyclic behavior prevails. In the daily time frame the partial correlation is negative , which means if one day is up the next day it is more probable that it is down. In addition, the cyclic behavior is even stronger in pairs of two days with negative partial correlation (two days up two days down etc)
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 , conjoint analysis, correspondence analysis, multidimensional scaling etc , goal programming etc are possible to utilize for a more detailed theory of 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 less hypothesis we use in applying statistical hypotheses, the better. That is why non-parametric statistics is better. An exception is our knowledge of the application of the Pareto distribution in various aspects of the market which we is parametric.
That is why we avoid applying very complicated with many hypotheses and time consuming to estimate models to forecast the markets, but we prefer to respond to the market, by measuring only in a valid statistical way, the average position of the price, and the channel around it, the velocity (trend, 1st derivative) and acceleration-deceleration (2nd derivative) of the prices.
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.
THE OVER ALL STATISTICAL BEHAVIOR OF PRICE PATTERNS
We notice that although the price patterns are essentially 4 categories in details there are 6 distinct statistical patterns of the random or statistical position, velocity and acceleration of the prices (see post 32).
A class of stochastic processes can be defined as the behavior of the markets based on these 6 basic patterns P1, P2, P3, P4, P5, P5, P7. The Pi i=1,2,3,4,5,6,7 are essentially random statistical patterns with random and variable parameters of size duration, and relative analogies that define them. E.g. we may have a Pareto distribution of the duration and price height of the patterns because of the inequalities in the markets. The 7th pattern P7 of stationary behavior we may call intermittency pattern. We may then assume a class of Markov processes with random and variable transition probabilities, where each random type of pattern occurs and then a next one occurs. Bu the transition probabilities are not arbitrary! E.g. spikes occur usually at the begging (initial spikes) and the end (terminal spikes) of trends, up and down trends with stationary channels in between them, shape cycles, that in the average of stable period, and some times of fixed beginning and end.
The probability that a type of pattern occurs changes also according to the time scale. In 2, 5 or more years annual bars time frames, the non-waving trend pattern is dominating, while say in 5-minutes bars time-frame flat patterns are dominating.
This is the overall behavior of patterns of the markets, and there are some invariant properties like
1) Cyclic behavior as alternation with up and down trend patterns with flat channels at the bottoms and tops
2) Statistical momentum conservation (see post 10) where the 1st time-step partial correlation of a price is almost always positive,
3) A Pareto distribution of the duration and height of the patterns, due to the inequalities of the enterprises in the economic system (see post 10, 25, 57,63 )
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.
After more than 10 years of studies, and trading practice, I came to the conclusion that the next is the most simple but also safe and successful trading, with good also profits.
As I said
in the previous post, all the three, fundamental analysis, news and Technical
Analysis should be combined in order to have maximum probability of success.
1)
We start, at first with fundamental analysis in discovering a trend at an horizon of about
2,77-2.75 years (=(11.1 years climate cycle)/4. The 22.2), cycle is the Kuznets cycle the discovery of which gave him the Nobel Prize. The 22.2/4=5.55 cycle is called Kitchin-cycle and is discussed in the post 61) which should be supported by news confirmation
of one of the commodities, currency pair, or securities index. If we conclude
that indeed there is such an on going trend, this should be also visible within
technical analysis, as a channel in weekly or monthly charts. Let us say for the
sake of simplicity in descriptions that such a trend is upward. Such trends have sub-cycles usually seasonal e.g. of 3-6 months, for example in the financial sector increasing from 5/Jan. to 1/June and decreasing from 1/June to 5/Jan. (See also posts 5, 61). A very stable trend of course is the artificial trend of indexes of securities (index funds). The reason is that although a single security of an enterprise has an initial growth, then maturity stability and finally decay [thus a cycle] , the index has only artificial constant trend. This is so because the organizations that define the indexes (e.g. the SnP500 etc) , when a security of an enterprise that participates, starts decaying in the fundamental analysis indicators, they eliminate it from the index and put another security of another enterprise which is growing. Thus although enterprises have cyclic life-cycle, the index fund has only artificial constant, in the long run trend, following the macro-fundamentals of the whole economy.
2)
Then we search for down spike, or wait for such a spike, and we
enter, when the spike starts reversing up again. Because of this the stop-loss
in our positions is very tight (e.g. 2%-6% of the funds) which means that we can have high leverage in
the trading (e.g. leverage at least 3). If we are lucky, the spike would be a final
down spike of a previous down trend, which means that we start our trading from
the beginning of the trend. Otherwise we are taking the trend from somewhere inside
it, and we should expect a total trading-excursion of seasonal duration that is
2-6 months. Based on that we should look to enter early enough that is not more than 40% of the expected duration of the trend. If there is no spike, and we want to take the trend at a down wave of it, , then we should
put a not very tight stop loss, based on the width of the channel , but in such
a case the leverage should be very low, or even =1. Obviously starting with a
spike is very much less risk, and the best is when the spike is a final of a previous descending channel trend.
3)
We apply the trading at daily bars, and we spend not more that
15-30 minutes per day (this is important so as not corrupt the usual other activities
of the days) We go one escalating the total position , in other words increasing the total position (e.g. based on a grid of levels) with a
decreasing arithmetic progression of new positions (e.g. 5,4,3,2,1), at the down waves of the ascending channel. We
put stop-losses and total trailing, so that at any unexpected reversing of the
trend the over all position will close still in profit.
4)
The trading-excursion (total position) will close either by the
trailing, or manually if there is a final upward spike, or if we have news that
cancel the fundamental analysis, and expectation of further trend.
Such a trading
is more or less identical with the trading that B. Williams was practicing at least or the
last 10 years after 2000 (B. Williams has been practicing profitably, trading for the last 40 years) , with great success, and claimed annual rate of return
of about 300%! See his book Trading Chaos , editions Wiley 2004 This includes searching for opportunities, among about one hundred of commodities and indexes. Of course there is no guarantee that two different persons applying the above system will have the same results because it is not a mechanical algorithm and requires the subjective discretion of the trader, at least in assessing the fundamental analysis and news, part of it. The technical analysis part of it, is more or less a mechanical algorithm.
Notice that the adjustments or moyen or inverse pyramiding technique (see post 3) is based on the assumption of a constant infinite time trend. While the direct pyramiding technique is based on the assumption of a constant finite time trend following a power or Pareto distribution of time duration (see post 25 ). As the finite duration trend get longer and longer, the conditional probability that it will last one more time step get higher, at least due to the relative size of the next step and the duration of the trend so far.
Notice that the adjustments or moyen or inverse pyramiding technique (see post 3) is based on the assumption of a constant infinite time trend. While the direct pyramiding technique is based on the assumption of a constant finite time trend following a power or Pareto distribution of time duration (see post 25 ). As the finite duration trend get longer and longer, the conditional probability that it will last one more time step get higher, at least due to the relative size of the next step and the duration of the trend so far.
And because the above trading method
a)
It is simple
b)
Combines fundamental analysis, news and technical analysis
c)
It is conducted on daily bars, that is 15-30 minutes time cost per
day
d)
It utilizes optimal channel trading and escalation.
e)
It has been practiced successfully by B. Williams and many others
It is one
of the most stable and successful systems of trading.
The trend of index funds of securities, changes by the quarter financial statements publications, therefore it is supposed to last one or more 3-months time intervals like 1) January-February-March, 2) April-May-June 3) July-August-September 4) October-November-December.
THE TOP 6 FACTORS OF ATTENTION IN MANUAL TRADING ARE
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 posts 3, 13, 33)
3) FOCUS ON MACROSCOPIC INSTRUMENTS LIKE STOCK INDEXES WITH PERMANENT STRONG LONG TERM TREND OR AT LEAST STRONG AND CLEAR SEASONAL 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).The statistical quantities from the front-office in trading need to me measured are the price position in the channel around the average, the velocity (1st derivative) and the acceleration-deceleration (2nd derivative), which is done as statistical quantities by a hypothesis test or confidence interval. 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 Eliot-waves but also the spikes. In addition for the back-office of trading we need to measure the probability of success of trade based on the past history of trades, to apply the Kelly criterion, and also the average rate of increase of the trading funds and its variance again from the past history of trading.
4) FOR VERY LOW RISK AT OPENING POSITIONS ON THE PREVIOUS INDEXES WITH PERMANENT STRONG TREND, OPEN BETTING UPWARDS, AT TERMINAL SPIKES AGAINST THE TREND. This is the Bill Williams technique.
5) READ THE NEWS AND FINANCIAL STATEMENTS BUT 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 RESPONDING TO THE MARKET AND DO NOT HESITATE TO FOLLOW PROMPTLY ANY UNEXPECTED CHANGES OF THE TREND OF THE MARKET. (This is the Bill Williams psychological "Holy Grail" of trading )
In order to conduct successfully an intra-day system of transactions , that is successful in the long run and easy to keep on applying it the next points must be met.
1) It must be relatively utterly simple! Only the "eye of simplicity"can put order and tame the chaos of intra-day price patterns! It must be manual and not automated!
2) Therefore it has to be one only pattern among the 4 price patterns (see post 32)
3) To deal with this one only pattern, we may apply simplifiers like , velocity or rate of change of prices, acceleration, support-resistance.
4) Celestial periodicity will give the long-run stability, but it need not be one only frequency or period but a few neighboring frequencies or periods in the spectrum of celestial frequencies or cycles.
5) But most of all the strongest simplifier is that , when measuring the velocity or rate of change , by a stratified sampling hypothesis test, then it has to be an extreme value , which will indicate a reaction or closing of the cycle.
6) It must be a phenomenon tested scientifically with valid quantitative procedures , with sufficient good (intermittent) predictability , for many years.
7) The financial result should be adequate (e.g. >= 1MDS).
8) The financial result, in my case, is to be used not only for economic freedom, but also for a worthy goal e.g. so as to finance my innovative research in the new millennium digital mathematics.
9) The solution to all of the above 8 points leads to one only system: The (solar spikes) super-bubbles system: (See end of the post 44) For non-intraday transactions system, see post 68.
