Topic: Can I predict the value of BTC/USD? - page 5. (Read 12889 times)
So apparently, I'm not able to make a reasoned guess that buy and hold has a much greater than average chance of working out for me. Once I retire next year, I'll have more time to ponder my errors.
this thread is well beyond my level of understanding, well done, you earned my respect.
That was not my intention; really. I really hoped to keep it simple for the masses. Thanks for the kind words though.
The analogy to that would be the market can't be static after going in one direction for a longer time, but it can on shorter timescales. This is why TA, well some TA works.
The attractors.
They really are.
The analogy to that would be the market can't be static after going in one direction for a longer time, but it can on shorter timescales. This is why TA, well some TA works.
The attractors.
Are they strange?
http://en.wikipedia.org/wiki/Attractor
The analogy to that would be the market can't be static after going in one direction for a longer time, but it can on shorter timescales. This is why TA, well some TA works.
The attractors.
Crazy mac is correct. There are FOREX traders that stare at fractals all the time to study. They do this because markets have chaotic dynamics. My favorite is @EdMatts, I use twitter only to speak with him. He is brilliant.
this thread is well beyond my level of understanding, well done, you earned my respect.
Markets aren't pendulums.
Of course they are not, but they share dynamical behavior. See large pictures above.
The lowest points the double pendulum can reach is the same as a single pendulum of the same length.
The highest point is at the origin of the pendulum plus the length of the lower part of the arm.
The upper part of the arm always swings back and forth and can not flip over.
So while the overall system may be chaotic it can be predicted with specific constraints.
The highest point is at the origin of the pendulum plus the length of the lower part of the arm.
The upper part of the arm always swings back and forth and can not flip over.
So while the overall system may be chaotic it can be predicted with specific constraints.
What you're saying can be simplified to this:
The lowest value of USD/BTC is zero
The highest value of USD/BTC is the total capitalization/BTCs produced
The value can only go up and down (well, it can also be static for a while)
Not quite, the lower arm of the pendulum can flip over.
The analogy to that would be the market can't be static after going in one direction for a longer time, but it can on shorter timescales. This is why TA, well some TA works.
A fellow Physicist!!! WE ARE MANY!!!
Thanks for the comments - you've wrote some of the crazy ideas I wanted to post, mentioned on the above comment... BTW: It's Poincare section.
http://www.mwit.ac.th/~physicslab/applet_04/fun@learning/JAVA/pendchao/pendchao.html
Coolness, I need help developing the bitcoin RF idea. :-P
I'm a sucker for these kind of games...
Excellent thread. My compliments Mac. 
Thanks KFR
Excellent thread. My compliments Mac.
A fellow Physicist!!! WE ARE MANY!!!
Thanks for the comments - you've wrote some of the crazy ideas I wanted to post, mentioned on the above comment... BTW: It's Poincare section.
http://www.mwit.ac.th/~physicslab/applet_04/fun@learning/JAVA/pendchao/pendchao.html
Coolness, I need help developing the bitcoin RF idea. :-P I sounded like a loon describing it but its real!
While chaotic systems are, well, chaotic, their trajectories often tend to evolve toward one of a number of attractors, at least temporarily.
While it is not in principle feasible to determine a trajectory of some dynamical variable p (say, price) as a function of time p(t) as you can for simpler systems, the case of chaos and attractors is unique in that the phase space is of a dimension that is non-integer.
A double pendulum for example: You might not be able to get the position q(t) or momentum q'(t) as a function of time, but plotting the set of points {q(t),q'(t)} measured when damped, driven a double pendulum is set going will give you fractals. The plot is known as a pioncare section. The 2-D plot may have dimension 1.578887 - the trajectory is drawn on the 2-D plane, but the curves with mathematical certainty do not live in a 2-D space. They have less room than all of R2 to move.
The fractal nature of the trajectories suggests that despite inability to reliably produce a trajectory, there are always pairs of values ( q(t), q'(t) } which are never allowed! That lends predictive power, albeit in a different sense than what we're used to.
While it is not in principle feasible to determine a trajectory of some dynamical variable p (say, price) as a function of time p(t) as you can for simpler systems, the case of chaos and attractors is unique in that the phase space is of a dimension that is non-integer.
A double pendulum for example: You might not be able to get the position q(t) or momentum q'(t) as a function of time, but plotting the set of points {q(t),q'(t)} measured when damped, driven a double pendulum is set going will give you fractals. The plot is known as a pioncare section. The 2-D plot may have dimension 1.578887 - the trajectory is drawn on the 2-D plane, but the curves with mathematical certainty do not live in a 2-D space. They have less room than all of R2 to move.
The fractal nature of the trajectories suggests that despite inability to reliably produce a trajectory, there are always pairs of values ( q(t), q'(t) } which are never allowed! That lends predictive power, albeit in a different sense than what we're used to.
A fellow Physicist!!! WE ARE MANY!!!
Thanks for the comments - you've wrote some of the crazy ideas I wanted to post, mentioned on the above comment... BTW: It's Poincare section.
http://www.mwit.ac.th/~physicslab/applet_04/fun@learning/JAVA/pendchao/pendchao.html
Great and erudite analysis of why all TA is utterly useless, unless you have inside knowledge of what the big movers are about to do or know news before others do.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
This thread is burning a straw man.
Having a p value of less than 100% is different from being useless.
TA is less about being always right, and more about how much confidence you put in being right at any particular point.
How much you make when you are right, vs how much you lose when you aren't is what makes it valuable or not.
For example:
RP gave himself an 80% p value for that prediction. He may be right or not, but either way it isn't going to be indicative of whether TA is useful of not, just whether that application of it worked well or didn't.
What you have here is an analogy and a theory. Science uses data, so from that point RP's TA is more scientific than your criticism of it.
Markets aren't pendulums.
In any efficient market TA should yield zero edge over random chance.
That said, Bitcoin markets have demonstrated, time and again, startling examples of inefficiency.
The scientific way to settle this argument is to create 30 TA algos that backtest well, simulate them them for a year, and find out if there's statistical significance.
Just the fact that other traders use TA makes it viable as a predictive indicator.
While chaotic systems are, well, chaotic, their trajectories often tend to evolve toward one of a number of attractors, at least temporarily.
While it is not in principle feasible to determine a trajectory of some dynamical variable p (say, price) as a function of time p(t) as you can for simpler systems, the case of chaos and attractors is unique in that the phase space is of a dimension that is non-integer.
A double pendulum for example: You might not be able to get the position q(t) or momentum q'(t) as a function of time, but plotting the set of points {q(t),q'(t)} measured when a damped, driven double pendulum is set in motion will yield fractals. The plot is known as a pioncare section. The 2-D plot may have dimension 1.578887 - the trajectory is drawn on the 2-D plane, but the curves with mathematical certainty do not live in a 2-D space. They have less room than all of R2 to move.
pioncare section pic:
The fractal nature of the trajectories suggests that despite inability to reliably produce a trajectory, there are always pairs of values ( q(t), q'(t) } which are never allowed! That lends predictive power, albeit in a different sense than what we're used to.
And we have a tulip fractal:
While it is not in principle feasible to determine a trajectory of some dynamical variable p (say, price) as a function of time p(t) as you can for simpler systems, the case of chaos and attractors is unique in that the phase space is of a dimension that is non-integer.
A double pendulum for example: You might not be able to get the position q(t) or momentum q'(t) as a function of time, but plotting the set of points {q(t),q'(t)} measured when a damped, driven double pendulum is set in motion will yield fractals. The plot is known as a pioncare section. The 2-D plot may have dimension 1.578887 - the trajectory is drawn on the 2-D plane, but the curves with mathematical certainty do not live in a 2-D space. They have less room than all of R2 to move.
pioncare section pic:
The fractal nature of the trajectories suggests that despite inability to reliably produce a trajectory, there are always pairs of values ( q(t), q'(t) } which are never allowed! That lends predictive power, albeit in a different sense than what we're used to.
And we have a tulip fractal:
In any efficient market TA should yield zero edge over random chance.
That said, Bitcoin markets have demonstrated, time and again, startling examples of inefficiency.
The scientific way to settle this argument is to create 30 TA algos that backtest well, simulate them them for a year, and find out if there's statistical significance.
Exactly! Statistically, this would offer a much better model, in order to make valid predictions. It's like narrowing down the specific movement factors of the pendulum and predicting timeframe by timeframe what will happen. But this NEEDS HISTORICAL DATA (with certain similarities to the current conditions)! I do have a couple of ideas about it, but are a bit too crazy to be honest for me to start posting them on a public forum on the net...
This thread is burning a straw man.
Having a p value of less than 100% is different from being useless.
TA is less about being always right, and more about how much confidence you put in being right at any particular point.
How much you make when you are right, vs how much you lose when you aren't is what makes it valuable or not.
For example:
RP gave himself an 80% p value for that prediction. He may be right or not, but either way it isn't going to be indicative of whether TA is useful of not, just whether that application of it worked well or didn't.
What you have here is an analogy and a theory. Science uses data, so from that point RP's TA is more scientific than your criticism of it.
Markets aren't pendulums.
Having a p value of less than 100% is different from being useless.
TA is less about being always right, and more about how much confidence you put in being right at any particular point.
How much you make when you are right, vs how much you lose when you aren't is what makes it valuable or not.
For example:
RP gave himself an 80% p value for that prediction. He may be right or not, but either way it isn't going to be indicative of whether TA is useful of not, just whether that application of it worked well or didn't.
What you have here is an analogy and a theory. Science uses data, so from that point RP's TA is more scientific than your criticism of it.
Markets aren't pendulums.
Thanks for your criticism. Let me please answer:
This is not a game of words and surely has no intention of being an informal fallacy based on misrepresentation of an opponent's position. If that's what you made out of it I'm truly very sorry. This was not my intention.
This article is based on a chaotic system modeling in order to define the possibilities to predict WITH CERTAINTY what will happen after a specific time frame. This thing cannot be happened and it's proven. It's a theory with scientific proof and thus (until someone presents something different that kills its validity) still valid.
Markets are looking a lot like pendulums. In fact, I'd add, a lot like triple or quadruple pendulums which in their very essence can be A LOT more chaotic than a double pendulum which I above mentioned. Therefore there cannot be a certain prediction about their next timeframe of existence.
If you can provide me with scientific data that defies what I wrote; I will be glad to rephrase and/or change my thesis. Until then... me and mr. Edward Lorenz here we stand.
I'd indulge you to take a look here:
http://en.wikipedia.org/wiki/Chaos_theory
Hint: You're aware of the urban myth that physicists are one of the most wanted jobs in Wall Street... aren't you?
Great and erudite analysis of why all TA is utterly useless, unless you have inside knowledge of what the big movers are about to do or know news before others do.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
This thread is burning a straw man.
Having a p value of less than 100% is different from being useless.
TA is less about being always right, and more about how much confidence you put in being right at any particular point.
How much you make when you are right, vs how much you lose when you aren't is what makes it valuable or not.
For example:
RP gave himself an 80% p value for that prediction. He may be right or not, but either way it isn't going to be indicative of whether TA is useful of not, just whether that application of it worked well or didn't.
What you have here is an analogy and a theory. Science uses data, so from that point RP's TA is more scientific than your criticism of it.
Markets aren't pendulums.
In any efficient market TA should yield zero edge over random chance.
That said, Bitcoin markets have demonstrated, time and again, startling examples of inefficiency.
The scientific way to settle this argument is to create 30 TA algos that backtest well, simulate them them for a year, and find out if there's statistical significance.
Great and erudite analysis of why all TA is utterly useless, unless you have inside knowledge of what the big movers are about to do or know news before others do.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
Which is why financial markets have a preponderance of insider knowledge cheats and market manipulators, it's the only thing that gives you an edge.
Perhaps it is possible to learn about sentiments, meaning sometimes it is possible to have a good idea of how others are likely to react - but this is again at best marginal and can of course go horribly wrong.
This thread is burning a straw man.
Having a p value of less than 100% is different from being useless.
TA is less about being always right, and more about how much confidence you put in being right at any particular point.
How much you make when you are right, vs how much you lose when you aren't is what makes it valuable or not.
For example:
RP gave himself an 80% p value for that prediction. He may be right or not, but either way it isn't going to be indicative of whether TA is useful of not, just whether that application of it worked well or didn't.
What you have here is an analogy and a theory. Science uses data, so from that point RP's TA is more scientific than your criticism of it.
Markets aren't pendulums.
Markets always bust after they boom.
The bust defines the boom.
tautology
As the pendulum has high and lows. The same thing.
Markets always bust after they boom.
The bust defines the boom.
tautology
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