Trading probability - page 12
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This, we must assume, is by the generated series? Since the graph remained clearly non-linear, and any degree dependence would have turned into a straight line.
As I copied the post from another thread, the old designations remain: TP and SL
.It is worth interpreting them as Pips1 and Pips2. The main conclusion is formulated in the Hypothesis.
Well, that, imho, is another objective argument in favour of distinguishing price series from random rambling.
The probability is 0.5 if the distances the price has to travel are equal. SL-Spread=TP+spread
That is the statement put forward : TP=SL, you gave the formula, I substituted there the tested values, and it is TP=SL, and got not 0.5. Where is the mistake? I put it in your formula.
Am I the only one who doesn't understand what Getch is doing? Maybe you should be clear about what is being done. What does zigzag have to do with it? What zigzag? What does it have to do with the zigzag?
То есть выдвинуто утверждение : TP=SL, Вы дали формулу, я туда подставил проверчные значения, и именно TP=SL, и получил не 0.5. Где ошибка? Я подставил в вашу формулу.
There is no mistake... Reaching the SL is more likely than the TP, with TP=SL. The higher the value of TP=SL, the closer the probability is to the value 0.5.
Ah, I didn't look at the notation. To exhibit a degree dependence, the logarithmic scale must be on both axes.
There is no mistake... Reaching the SL is more likely than the TP, with TP=SL. The higher the value of TP=SL, the closer the probability is to the value of 0.5.
Prove . Here is the data --Op -- opening price
TP --
SL --
Point -- point price
Spread -- spread at open and constant to close.
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Calculate for SELL and BUY.
Ah, I didn't look at the notation. The logarithmic scale must be on both axes to exhibit a degree dependence.
Power dependence for market prices:
Power dependence for generated prices with normal increment distribution and Deviation = 15:
Power dependence for generated prices with normal incremental distribution and Deviation = 30:
A linear power relationship is probably the simplest result of probability theory.
Another thing is that it is the quadratic dependence that persists in the market data for the majors.
Я один не понимаю что Getch делает? Может надо четко и понятно сформулировать - что делается.
Two ZigZags on a certain piece of data are considered:
The hypothesis arisen above.The codes and graphs are the results of research on this topic.
Скорее всего линейная степенная зависимость - простейший результат теории вероятности.
Другое дело, что на рыночных данных по мажорам сохраняется именно квадратная зависимость.
Visually, the generated slope is about the same and indeed noticeably different than the real one. The first thing that comes to mind is that this is due to a different distribution of increments (thick tails).
P.S. Ideally there should be paths to fractal characteristics from here, to the same Hearst.Sorry to interrupt - but, gentlemen, why are you generating something you do not know how to generate? I suggested (above) a simple approach - let me formulate it more precisely - 1) any TS is a change in order distribution (by time and type) from uniform to something else (it does not matter which one... it may be even to uniform). 2) If we can calculate (very accurately) profit (loss) from invested money, using rule (*) (or some other rules), in case of uniform distribution - in other words in working lots (fixed lot). Do the same rules apply for a different TP than for a fixed lot?
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To calculate the profit of TS with evenly distributed, we can denote it as eTS ("Reference TS"), we should take probability of profitable trades and multiply it by the average value in pips, and multiply probability of losing trades by their value in pips. Then we need to subtract the first from the second and multiply by the profit by one pip. That's it.
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I think this is the key question!
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I hope everyone understands what the average trade size is at ETS?