7.1.It is common that a system developer presents his recipe for trading in a particular time scale (e.g. daily bars charts) and then he adds proudly : "The system holds well too in all time frames".
It sounds encouraging, magical and convincing as the trading pattern can indeed be detected and the trading conducted, in different time frames. From the point of view of the entering the trades, it seems as if it does not matter what time duration are the bars! BUT alas! Only from the point of view of trades entry!
7.2. I am afraid that is a very slippery common fallacy, that requires careful analysis.
7.3. Trading robot makers, are well aware, at backtesting, that while their system can be very profitable, in the time frame that they designed it, it turns out to be losing or significantly less profitable in different time frames. E.g. say that the system developer makes a system at hourly bars. At backtests it is often less profitable at daily bars, more profitable at 30 minute bars, and suddenly entirely losing, at 5 minutes or 1 minutes bars! This is a common experience in backtesting. We should not confuse the similarity of an hourly bars chart at entering a trade, with the similarity say of 1 minutes bars chart at entering the trade. It is not only entering the trade, but how the market really behaves statistically at different time frames. To expect that a system will be profitable in all time frames as well, because it is profitable in a particular time frame from where it was discovered, is as if expecting that if you use to listen to nice jazz music in a particular radio station frequency say at Medium Wavelength Band, you will still listen to nice Jazz, if you try the same number-frequency say at Short Wavelength Band!
7.4. You may argue of course that there are systems, that are indeed profitable (even after programming them as robots, and backtesting them) in almost , or entirely all, time frames (of a particular trading platform). And indeed there are such systems either as robots or manual conduction. Still they are NOT EQUALLY PROFITABLE IN ALL TIME FRAMES! Usually their are best profitable in a characteristic time frame, and less profitable in the rest.
7.5 So what is really happening in the market at different time scales? Is the market behaving in an exact self-similar way, as the fractals of Mandelbrot or not? The answer at first is no! The market does not have an exact self-similarity in all time frames, as the Mandelbrot fractals. As I a perceive it, the market has characteristic frequencies, or scales, that behaves in a optimally predictable and profitable way, compared to all immediate neighboring scales. And these frequencies are the ones that I call Rainbow frequencies as in the tables or link, in the post no 5. On the other hand, the market behaves almost in a self-similar way from Rainbow frequency, to Rainbow frequency. I say almost, because there is also a systematic deviation from this self-similarity, as we shift to faster, higher frequencies, that we shall discuss in an another post.
7.6 For practical purposes, the conclusion is that all indicators, and technical analysis measurements of trading systems must be tuned to the characteristic optimal frequencies of the markets. So if say at a daily chart, you have a profitable system, that uses indicator A1 at period N1 bars, indicator A2 at period N2 bars etc, you should not expect that the same numbers N1, N2 etc would hold equally well say at 15 minutes bars. If they somehow still provide a profitable system at 15 minute bars, you are simply lucky, but not really optimal. It is only the convenience of leaving all parameters of the indicators unchanged when we shift to different time frames, that is misleading. We should not analyse the market based on chart-platform invariants (an egotistic-phenomenological attitude) but rather on real hidden market frequencies invariants.
7.7 The law of systematic progressive asymmetry among scales. Finally there is another source of "non-similarity" among the systems which does not come from the special characteristic frequencies. There is a major asymmetry among larger time frames and smaller time frames, that all experienced and successful traders, speculators and investors are well aware of. The relative size of the random fluctuations (usually measured by the standard deviation S of the drift or trend R ) compared to the size of the trend is in a systematic way increasing at smaller time scales. Usually the appropriate measure is the Sharpe ratio R/S, and this fundamental asymmetry simply says that the Sharpe ratio of larger scales is better. This means that in a portfolio of tradings of the same system in different time rames, larger percentages are to be allocated to the larger scale. It also means that if we are to base the trading in a trend-following principle, the then by far the larger timeframe trends are the better with less "noise" and easier detectable trend. So there is no hope to simply get better results in a blind way, from a system that was designed and is successful say at daily bars, and we just shift it to 5 minuts or 1 minutes bars by a similarity hypothesis. The basic asymmetry among time scales and that the Sharpe ratio is better at larger scales is relevant to the Pareto law of the size of the duration of trends, and the size of the volumes. The tail of the Pareto distribution and the asymmetry of its shape has as consequence this asymmetry relative to noise and Sharpe ratio among time sales.
7.8 If you are not aware of the above, and you naively believe that if a system holds in a time frame it also holds in all time frames with the same parameters of the same indicators, you are simply the victim of highly imperfect selling products.
7.9 Most of the traders think that intraday manual trading is radically more profitable than day-to-day trading where only once per day (e.g. for 15 minutes) a control and a decision is taken. They think so because they estimate that profits will increase in direct analogy to the shorter time scale they will use. But it is not so at least for two reasons. a) The shorter the scale the more the “noise” (=non profitable and non-tradable fluctuations of the markets due to unpredictability) b) In intraday manual trading, one has to spend many hours in front of the screen as if he was working at office while in day-to-day trading only 15-20 minutes per day, while he may have a normal work and normal day with other non-trading activities. c) Intraday and short time scales waves and patterns depend on a small number of people (mainly some packets of transactions by employees of the big banks) and therefore are subject to unpredicted changes, of the actions of these employees. But long term waves depend on a very larger volumes , and global populations involved in the economy, therefore are more stable.
The truth is that for manual trading the golden scale is the seasonal cycles (2-6 months) as sub-cycles of a 5.55 Kitchin cycle or 22.2 years global climate cycle (Kuznet cycle). Of course by programming automate trading it is possible to trade intra-day without spending human time. But the rate of return of such automated intraday trading is not higher than the seasonal manual trading if in the latter, the human pattern recognition is involved which is superior to automated trading pattern recognition. Some sellers of automated trading show the results of their programs for a limited time intervals (some months only) which appear very high so as to sell or rent them. But sooner or latter such automated trading had also significant failures so that in the long run (5 years or more) have less rate of return that the manual seasonal trading.
The way that unpredictability increases as the time scale becomes shorter, seems to be a result of the law of inequality. The very shape of the Pareto distribution could be used to present how unpredictability increases when the time sale decreases. In the next diagram of the Pareto didstribution, the x-axis is the time scale, and the y-axis is the unpredictability. A hint of why this is so is the next: A wave in shorter time scale takes less volumes of transactions to be shaped. And the volumes of transactions, follow the pareto or power distribution. The less the volumes the smaler the population (of transactions but also of traders) the less the predictability and stability.
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