Machine learning in trading: theory, models, practice and algo-trading - page 2860
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Trying to understand how it works. He's got to get information from somewhere to show it to you. He's not gonna invent Ohm's Law, is he? He's just gonna show it from a textbook. Maybe he'll unique the text to be original.
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Judging by the conversation, am I right in thinking that you think that when he calls GPT he starts looking for the most plausible answer on the Internet?
Trying to understand how it works. He's got to get information from somewhere to show it to you.
There's only one answer, the best one the algorithm thinks is best. So he should only use verified information, not the internet. And not even wikipedia should not be used, which anyone can edit. Apparently only textbooks (there are different ones too), technical documentation and code samples from it, for the variant of writing code.
The minus of the fact that there is only 1 answer is the lack of alternatives. You can always find another variant in Google or compile the best one for your task.
One answer may well contain a list of answers. Again, the answer may well contain a brief reference on the topic and a link to the search result by the same google. Nothing prevents the bot from becoming just an intelligent interface for google. In any case, the potential flexibility is much higher than that of search engines. I can't say that this thing will be unambiguously useful, but the life of smm users may become much more complicated).
I would very much like to meet a person here who will say - thanks to MO I have made a shitload of money in trading, and you are just a loser.... Preferably with a screenshot of the broker's report and a chart under + 1000%.
But so far I haven't seen any of them...
I'd believe it if I saw one, most likely :)
First 30 days of real market test. Neural network opens and closes positions. +10% to deposit without leverage. 2-3 trades per day.
No need to evaluate good or bad, I show what I have today.
I wouldn't trust information from the internet, I wrote about the documentation ....
I don't think he came up with it himself. Most likely, he copied it from somewhere on the Internet. From github, StackOwerflow or somewhere else. I.e. search engine.
Google's gonna have a hard time, too. There's bound to be a proliferation of sites with AI-generated articles. Spam offers are already coming.... Full sites will start to go up, good sites will go down. And it will be harder for us as users when we get to such pages.
Users will also have to use more intelligent means of searching and filtering content. And if they will be connected to a wide range of reference books and textbooks, as you think, it will be quite good.
How to solve "Too many requests in 1 hour. Try again later. "with residential vpn?
If you use a VPN, because many countries are not open chatgpt, many people use VPN to log in, if your VPN IP is datacenter IP is easily identified, so it will show "Too many requests in 1 hour. Try again later.", if you use residential ip then it will be restored and can be used multiple times.
show me screenshot of clustering result bitcoin prices 1 year period
I'm sorry, but I don't have access to any external resources such as images or real-time data. However, I can provide some information about clustering and how it can be used to analyse bitcoin prices.
Clustering is a technique in data analysis that involves grouping a set of data points into clusters based on their similarity. In the context of bitcoin prices, clustering could be used to identify patterns or trends in the data over a certain period of time, such as a year. For example, a clustering algorithm might identify clusters of days or weeks where the price of bitcoin exhibited similar behaviour, such as periods of rapid growth or decline.
To perform clustering on bitcoin price data, you would need a dataset containing the daily or hourly prices for a period of time, and a clustering algorithm to process the data. There are many different clustering algorithms available, each with its own strengths and limitations. Commonly used clustering algorithms include k-means, hierarchical clustering, and density-based clustering.
Once the data has been processed by the clustering algorithm, you can visualise the results to help identify patterns and trends in the data. This can be done through a variety of methods, such as scatter plots, line graphs, or heat maps.
I hope this information helps! Let me know if you have any other questions.
The first 30 days of the test with the real market. Neural network opens and closes positions. +10% to deposit without leverage. 2-3 trades per day.
No need to evaluate good or bad, I show what I have today.
I will not give evaluations - I will just say that I do not have such a graph :)
Since the end of March I have +10% result to time-weighted average assets. True, practically without drawdowns. But it has nothing to do with MO.
Success in development! Respect for sharing. So there is life on Mars, I was just not dripping well.
I'm sorry, but I don't have access to any external resources such as images or real-time data. However, I can provide some information about clustering and how it can be used to analyse bitcoin prices.
Clustering is a technique in data analysis that involves grouping a set of data points into clusters based on their similarity. In the context of bitcoin prices, clustering could be used to identify patterns or trends in the data over a certain period of time, such as a year. For example, a clustering algorithm might identify clusters of days or weeks where the price of bitcoin exhibited similar behaviour, such as periods of rapid growth or decline.
To perform clustering on bitcoin price data, you would need a dataset containing the daily or hourly prices for a period of time, and a clustering algorithm to process the data. There are many different clustering algorithms available, each with its own strengths and limitations. Commonly used clustering algorithms include k-means, hierarchical clustering, and density-based clustering.
Once the data has been processed by the clustering algorithm, you can visualise the results to help identify patterns and trends in the data. This can be done through a variety of methods, such as scatter plots, line graphs, or heat maps.
I hope this information helps! Let me know if you have any other questions.
It won't generate anything interesting on the topic, so far this is the conclusion. Pieces of code, if only some, which can be easily found on Google in the same way.
It's just an unraveling on the topic of AI, they did it with StarCraft and Go. Well, with Go, let's say you're good, it's pure combinatorics. In StarCraft it won unfairly, when they put it in the same conditions with players, it started losing. I didn't see anything surprising here either. Well generates letters, well ok :)