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Future of trading still lucrative?
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Future of trading still lucrative?

  #11 (permalink)
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mattz View Post
I am trying to understand the logic of your question. AI tries to imitate humans: voice, thought, brain process and even consciousness. So you asking if an imitation of you is better than you? "machine mimics "cognitive" functions"

Wouldn't that depend on the time frame in question? Surely, on a micro level, it is becoming increasingly hard to compete against HFT algos, since they are faster than any human. In addition, expensive infrastructure provides an additional advantage to those who can afford it.

Higher time frames on the other hand are a different story. Here, I tend to agree with you. Much has been written about machine learning, deep learning and black boxes, and while such an approach can certainly help with developing alpha factor, I am not yet convinced that there is a benefit over an experienced trader with a good understanding of market structure, macro themes and crowd psychology.

Just adding my 2 cents to yours.

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  #12 (permalink)
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I am currently doing Andrew Ngs Coursera on machine learning to update my comp engineering skills and potentially find some trading ideas. Only half way through but a lot of the intelligence seems to be a misconception... Machine learning uses Algos and historical training data to make a best fit prediction... usually for much slower moving relationships (ie house prices). It currently struggles when taking in a large number of parameters with little data (think 5 minutes of order flow in the eminis what the parameters would be) to give meaningful predictions. It will probably be quite a while before they can figure out the intraday trading game which is a much more dynamic changing dataset.

Having followed the poker AI vs Human game. My intuition is that there was a lot of human input to the AI between sessions (they were able to update their Algos each night). That is where the battle will be in trading for purely discretionary traders. Discretionary traders tuning machine learning Algos intraday with large computing resources will most certainly be hard to compete with based on the poker match outcome. So... Why not learn a bit about it (ML) and employ some AI in your trading plan eh?

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The leap into freedom is the exchanging of risk for reward. This can be done only by shifting from tension to ease, and that can be done only when one perceives the reward and not the risk. That you won't win all the time has nothing to do with it - that's life, that's the [stock] market. The trying itself is freeing. And being free has its own reward - Justin Mamis, The Nature of Risk
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  #13 (permalink)
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grahamg View Post
It will probably be quite a while before they can figure out the intraday trading game which is a much more dynamic changing dataset.


grahamg View Post
Having followed the poker AI vs Human game. My intuition is that there was a lot of human input to the AI between sessions (they were able to update their Algos each night). That is where the battle will be in trading for purely discretionary traders. Discretionary traders tuning machine learning Algos intraday with large computing resources will most certainly be hard to compete with based on the poker match outcome. So... Why not learn a bit about it (ML) and employ some AI in your trading plan eh?

If you are not profitable as a trader, it is very unlikely that adding AI/ML will make you profitable.

If you are already profitable and you know what is your edge, then it is not that difficult to come
to an even more profitable system. The reason is that you will know what features to feed into
the system.

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  #14 (permalink)
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Naturally would make it easier for experienced traders to input features for their system. If you aren't profitable then that is not to say ML will help... Could be a million reasons why not profitable... But learning to be the best at using the plough (farming analogy again) in order to be profitable might not be good enough with the rapid growth of programming / trading automation, and still not produce any observable results either. Always need to maintain a broader view of what is at play in the markets (ML, discretionary etc.. market makers ). Thus reasoning for getting upto date on ML, learn to program a little etc.. or at least learn to use ADL which will no doubt include function blocks for the gradient descent / regression algos used in ML soon enough.

Definitely makes sense to learn to trade the order flow / price action or whatever starting out, but as the game changes, you have to learn to use modern tools as well E.g learn order flow or value spreads... whatever works for you.... And then figure out a way to employ automation in trading to compete.

until the point .. if ever.. general AI exists, will always be muppets behind the machine / coding part whole make mistakes... input incorrect bias etc.. so retail traders have a chance perhaps to be that unique consistently profitable trader.

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The leap into freedom is the exchanging of risk for reward. This can be done only by shifting from tension to ease, and that can be done only when one perceives the reward and not the risk. That you won't win all the time has nothing to do with it - that's life, that's the [stock] market. The trying itself is freeing. And being free has its own reward - Justin Mamis, The Nature of Risk
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  #15 (permalink)
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I had a similiar discussion in my previous life as a pilot. Could AI replace pilots? Planes can take off and land and navigate without pilots but they can not handle the nuances of which clouds you can fly through and wich ones that you can't along with a multitude of little decisions that humans must make. Trading is very similiar. A well trained human will always be able to benifit from small nuances in the market that as of now computers can not detect. Thats why in my opinion most automated systems fail. They simply can not read a change in market conditions.

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https://www.bloomberg.com/news/articles/2017-02-06/silicon-valley-hedge-fund-takes-on-wall-street-with-ai-trader

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  #17 (permalink)
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At the end of the day, AI trading is all about pattern recognition. They decide what to do based on past experience with the same of similar set of variables. Whatever AI trading does will be reflected in price like all the other market participants.

I predict that markets will become more predictable as the players (AI) will be more rational.

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  #18 (permalink)
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Not quite. Backwards propogation can be used on a test set as well as real-time data to adapt to changing conditions.

On a similar topic check out the automated vs manual volume in different markets in this publication:

https://t.co/O9H2XsnckT

So in 2014 88% of transactions in the eminis had an automated order on at least one side of the trade. This could be just an autospreader but interesting to ponder how much ML volume contributes. Definitely firms publicly doing well in this area so worth staying in the loop.

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The leap into freedom is the exchanging of risk for reward. This can be done only by shifting from tension to ease, and that can be done only when one perceives the reward and not the risk. That you won't win all the time has nothing to do with it - that's life, that's the [stock] market. The trying itself is freeing. And being free has its own reward - Justin Mamis, The Nature of Risk
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  #19 (permalink)
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Here is a podcast with Manoj Narang on quant trading that I found interesting: https://chatwithtraders.com/ep-118-manoj-narang/


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  #20 (permalink)
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Very interesting topic.

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