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Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
no amount of historical data, whether from years ago and days ago determines the current price of bitcoin
You are going against your hypothesis. Also, tell me what would be the basis of prediction model if not the previous data? the superposition principle does not determine the price of bitcoin, don’t know where you got that impression.
Are you kidding me? You have mentioned it yourself in the paper, in fact, that is the core of your paper.
Code:
(i) Quantum Superposition
Thus, to effectively model the first challenge of the dualistic overlapping uncertainty is to utilize the
quantum principle of superposition. The first postulate of quantum mechanics is “the state of an isolated
physical system is represented, at a fixed time t, by a state vector |ψ⟩ belonging to a Hilbert space ℋ called
the state space [81].” Thus, when something is in a superposed state, all the possible states can be expressed by
a state |ψ⟩, which can be represented as a linear combination of the states of the observable as (5). Essentially
a superposed state is where all the possible states are simultaneously existent until it is observed [82], just like
how Schrodinger’s Cat can be dead and alive before the box is opened [83] or a qubit can be in a state of 0 and
1 until it is actually observed [84].
|ψ⟩ = c1
|ψ1
⟩+ c2
|ψ2
⟩ + ⋯ + cn|ψn
⟩ (5)
There are corresponding observed values of o1
, o2
, …, on, and once the measurement happens [85] only
one of these values on can be observed with a probability of |cn
|
2
, as in (6).
|ψ⟩ → |ψn
⟩ (6)
By superposing all the possible states of the market (either going up or down) and all the possible actions
that the traders can take (either buy or sell) together according to the principle of quantum superposition we
are able to postulate an effective model of both the potential states of the market and the collective possible
actions taken by all the traders as in (7) and (8).
|Q⟩ = c1
|q1
⟩+ c2
|q2
⟩ (7)
where |q1
⟩ denoting the market trending upwards; |q2
⟩ denoting the market trending downwards. ω1 =
|c1
|
2
is the objective frequency of the increase; ω2 = |c2
|
2
is the objective frequency of the decrease.
|A⟩ = μ1
|a1
⟩+ μ2
|a2
⟩ (8)
where |a1
⟩ denotes the buy action; |a2
⟩ denotes the sell action. p1 = |μ1
|
2 are the degree of beliefs to buy;
p2 = |μ2
|
2 are the degree of beliefs to sell.
Thus, to effectively model the first challenge of the dualistic overlapping uncertainty is to utilize the
quantum principle of superposition. The first postulate of quantum mechanics is “the state of an isolated
physical system is represented, at a fixed time t, by a state vector |ψ⟩ belonging to a Hilbert space ℋ called
the state space [81].” Thus, when something is in a superposed state, all the possible states can be expressed by
a state |ψ⟩, which can be represented as a linear combination of the states of the observable as (5). Essentially
a superposed state is where all the possible states are simultaneously existent until it is observed [82], just like
how Schrodinger’s Cat can be dead and alive before the box is opened [83] or a qubit can be in a state of 0 and
1 until it is actually observed [84].
|ψ⟩ = c1
|ψ1
⟩+ c2
|ψ2
⟩ + ⋯ + cn|ψn
⟩ (5)
There are corresponding observed values of o1
, o2
, …, on, and once the measurement happens [85] only
one of these values on can be observed with a probability of |cn
|
2
, as in (6).
|ψ⟩ → |ψn
⟩ (6)
By superposing all the possible states of the market (either going up or down) and all the possible actions
that the traders can take (either buy or sell) together according to the principle of quantum superposition we
are able to postulate an effective model of both the potential states of the market and the collective possible
actions taken by all the traders as in (7) and (8).
|Q⟩ = c1
|q1
⟩+ c2
|q2
⟩ (7)
where |q1
⟩ denoting the market trending upwards; |q2
⟩ denoting the market trending downwards. ω1 =
|c1
|
2
is the objective frequency of the increase; ω2 = |c2
|
2
is the objective frequency of the decrease.
|A⟩ = μ1
|a1
⟩+ μ2
|a2
⟩ (8)
where |a1
⟩ denotes the buy action; |a2
⟩ denotes the sell action. p1 = |μ1
|
2 are the degree of beliefs to buy;
p2 = |μ2
|
2 are the degree of beliefs to sell.
Code:
. In this paper, we
put forth a quantum-like evolutionary algorithm for time series forecasting – highlighting the dual uncertainty
challenge and the corresponding methodology used.
put forth a quantum-like evolutionary algorithm for time series forecasting – highlighting the dual uncertainty
challenge and the corresponding methodology used.
And lastly our methodology can be utilized to time series forecasting generically, stocks and bitcoin are just one case study,
But you jumped straight to price prediction, I wonder why? 
Our algorithm has been tested on many real-world datasets,
Proof? Let the community examine.Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
......
So price of bitcoin older than 4 weeks has no impact on current price but principle of superposition does.You are using the term superposition like some spiritual guru is using by giving the example of Schrödinger's cat.
Again I am not trying to be someone who is just passing some unnecessary sceptical comments. I have gone through your paper and this is my honest opinion. You started your paper with the absolute importance of data in order to make prediction models and when I pointed out that the data sample is too short for claiming 80% success rate, you started new argument about the relevance of previous data. I did not say 10 years, I said not enough sample size which will be pointed out by anyone who has given your paper a serious glance.
This is the reason I said this is more of a philosophical paper.
Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
I love this part of the conclusion,
Really! Four weeks, lol. Quite a large sample size hahaha.
This paper does nothing but highlight the importance of data which everyone already knows. There is no specific use case for the 'superposition principle' outlined explicitly in the paper, this should be a philosophical paper.
Of course, four weeks of data may not seem like much, but let’s be blunt, what does the price of Bitcoin or some stock from 10 years ago have to do with predicting the future price of Bitcoin for the following week or for the next 10 years? Looking back that long in the past or forward towards the future doesn’t really have much of an effect for forecasting what’s happening at hand now, it doesn’t mean the larger the dataset the more accurate the prediction you’ll get – quality not quantity. Yes, for some mathematical or statistical analysis it might be of some use, but if we’re just looking at the forecast horizon of the foreseeable future, studying the more recent past is more efficient, well at least in our opinion we believe that you don’t need a tremendously large dataset to forecast a short horizon, so we only used four weeks of data in our case study. As to the superposition principle, we clearly stated in the paper it is utilized to model the challenge of dual uncertainty; the uncertain market and the traders’ actions, where at any given time the market could go up or down and the traders’ can either buy or sell, and this is, to the best of our knowledge, an approach that has not yet been attempted.Quote
Finally, by using equity trading as a medium, we have
shown by training four weeks of stock market data and then producing two groups of 6 possible forecast
outcomes for the next week on the preceding weekend, the quantum-like evolutionary algorithm can produce
a forecast with odds of 80%.
shown by training four weeks of stock market data and then producing two groups of 6 possible forecast
outcomes for the next week on the preceding weekend, the quantum-like evolutionary algorithm can produce
a forecast with odds of 80%.
Really! Four weeks, lol. Quite a large sample size hahaha.This paper does nothing but highlight the importance of data which everyone already knows. There is no specific use case for the 'superposition principle' outlined explicitly in the paper, this should be a philosophical paper
.Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
Those trader should know when they can buy or sell so that is why they must analyze before they trade. That will eliminate them to buy or sell in a wrong time because they can determine the time they can do action. I don't rely on AI agents to let it analyze but I always analyze by myself so I can see what I can do related to the market situation.
If the market say that the situation is not good, I will skip to trade and just wait for more. I don't have any reason to force myself to keep trade because that can make me in a risk situation.
I never tested how much accurate prediction can give AI agents, so I don't want to trust with AI agents, I am trading with my own strategies, So for that need to research myself. Yes when we see market condition is very bad when definitely we will never entry in the market, but I don't know how AI agents decide it. And when we see good time to start trading then I will buy coins. If the market say that the situation is not good, I will skip to trade and just wait for more. I don't have any reason to force myself to keep trade because that can make me in a risk situation.
Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
All the important "meat and potatoes" information is in the current closing price at a given time, so basically no matter how much knowledge you gain about the current price of Bitcoin now you still won't be able to predict the future prices and that's exactly what the efficient market hypothesis states and that is also where the term the market is in a random walk all the time comes about, random meaning that there is no pattern that can be recognized easily. At the end of the day the best a trader can do is to guess and guess "emotionally"; but it's these emotions of greed and fear that will eventually cost traders' big time in the long run, so a "good" trader isn't the one who has a breadth of knowledge about the market's historical past, but one who can "control" their emotions effectively. Thus when it comes to AI, AI is non-emotional and only computes, and so AI is somehow able to find some form of trend from very complex data in ways which we humans don't seem to be very good at.
Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
The quantum superposition principle can be utilized to model the infinite possibilities of the market and of the traders’ minds, where the evolutionary algorithm then optimizes the most satisfactory trading strategy (action sequence of buy or sell).
You are not the first entity to introduce a new kind of methodology for trading to this community. We have seen 1000s of similar algo trading introductions in recent past but nothing had brought any differences in trader's final result. These days AI and quantum are real clickbaits, hence even you might have come up with a real thing, I am sorry, you are falling into same category just because of saving my time and energy. This is the reason, long back itself I have concluded that trading is not a way to make money but to lose. I know, people who are yet to make similar conclusion are your targets but there are people here to listen rather than rushing up which is what exactly I look for, out of my experience.resulting in odds consistently greater than 60%.
Still, only 60% ? Quantum principles are also not enough to beat these markets? Very sad, because there technical analysts who claim about more than 90% accuracy with their manual signals. Better improve your "evolutionary algorithms".Re: Trading strategies for dual uncertainty of the crypto market and traders’ minds
In the below case study, 3 AI agents cooperatively studied around a month of historical Bitcoin prices and then collectively produced an action sequence (buy or sell) for the following week, resulting in odds consistently greater than 60%. For more details, please read more here: www.xinvisionq.com/subpages/caseStudies.html.
Thats a huge result of 60% but can we really trust AI to compete with human in regards to predictive movement of the market? I mean yes trading is hard and has historical results and thats also our basis as trader, does it mean we can rely fully on them maybe if not now more than ever in the future. Trading strategies for dual uncertainty of the crypto market and traders’ minds
Essentially the biggest challenge of crypto trading is a dual uncertainty: the market itself is in a “random walk” all the time, and it is because of the uncertainty posed by the market that the traders’ can’t make up their minds decisively (they don’t know exactly when to buy or sell). The quantum superposition principle can be utilized to model the infinite possibilities of the market and of the traders’ minds, where the evolutionary algorithm then optimizes the most satisfactory trading strategy (action sequence of buy or sell).
In the below case study, 3 AI agents cooperatively studied around a month of historical Bitcoin prices and then collectively produced an action sequence (buy or sell) for the following week, resulting in odds consistently greater than 60%. For more details, please read more here: www.xinvisionq.com/subpages/caseStudies.html.
And if you’re interested in our algorithm, you can find more details in the paper here: www.xinvisionq.com/pdf/QxEAI.pdf.
In the below case study, 3 AI agents cooperatively studied around a month of historical Bitcoin prices and then collectively produced an action sequence (buy or sell) for the following week, resulting in odds consistently greater than 60%. For more details, please read more here: www.xinvisionq.com/subpages/caseStudies.html.
And if you’re interested in our algorithm, you can find more details in the paper here: www.xinvisionq.com/pdf/QxEAI.pdf.
market can be up/down (q1/q2) and traders can buy/sell (a1/a2), but before a decision is made, both exist in a quantum-like superposition of states. They use this to model how traders make decisions under uncertainty. Would be interesting to see how this performs against traditional forecasting methods. Will give it a try. Thanks
Thanks for interest in our work, glad that you found it interesting. Essentially the biggest challenge of crypto trading is a dual uncertainty: the market itself is in a “random walk” all the time, and it is because of the uncertainty posed by the market that the traders’ can’t make up their minds decisively (they don’t know exactly when to buy or sell). The quantum superposition principle can be utilized to model the infinite possibilities of the market and of the traders’ minds, where the evolutionary algorithm then optimizes the most satisfactory trading strategy (action sequence of buy or sell).Like someone already mentioned earlier AGI is still a long shot away, though AI is indeed helpful in many ways, particularly for assisting crypto trading. AI agents can learn from historical trading data of Bitcoin to forecast future trends, and we’ve just developed an AI agent tool (www.xinvisionq.com) of this sort, just by learning one month of historical trading data, our AI agent can produce a trading action sequence for the following week, with odds consistently greater than 60%.
Greetings Bitcoiners, we’d like to introduce www.xinvisionq.com, a data analysis and forecast tool (AI forecast agent on your PC) which can get you high quality forecasts in hours without any coding.
In the below case study, 3 AI agents cooperatively studied around a month of historical Bitcoin trading data and then collectively produced an action sequence (buy or sell) for the following week, resulting in odds consistently greater than 60%. For more details, please read more here: www.xinvisionq.com/subpages/caseStudies.html.
And if you’re interested in our algorithm (trading strategies), you can find more details in the paper here: www.xinvisionq.com/pdf/QxEAI.pdf.
We look forward to your feedback and suggestions to help us further improve xINvisionQ.
www.xinvisionq.com
In the below case study, 3 AI agents cooperatively studied around a month of historical Bitcoin trading data and then collectively produced an action sequence (buy or sell) for the following week, resulting in odds consistently greater than 60%. For more details, please read more here: www.xinvisionq.com/subpages/caseStudies.html.
And if you’re interested in our algorithm (trading strategies), you can find more details in the paper here: www.xinvisionq.com/pdf/QxEAI.pdf.
We look forward to your feedback and suggestions to help us further improve xINvisionQ.
www.xinvisionq.com