Best chess programs have long surpassed best human players. Top chess engines (Komodo, StockFish) have a rating of 3400. The top human player (Carlsen) has a rating of 2800. He does not stand a chance against the computer. In fact, he is known to say that he does not play against computers anymore, as he loses every single time.
A Relic Trading On Feeling
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Best chess programs have long surpassed best human players. Top chess engines (Komodo, StockFish) have a rating of 3400. The top human player (Carlsen) has a rating of 2800. He does not stand a chance against the computer. In fact, he is known to say that he does not play against computers anymore, as he loses every single time.
Most people find "true religion" and go on professing that that's the only way forward, be it some technical indicator or high frequency trading.
I flew to Asia recently and the dude sitting next to me was some sort of professional fighter (don't ask who he was since I have no idea, but people were asking him for autographs, so he must be OK at what he does). We chatted for a while and he said something interesting - "I learned judo as a kid and got good at it. But it teaches you to learn other things. You need to learn everything. If you wanna win, you have to kick, punch and grapple. You gonna be better at something, but you need to learn it all."
It sounded to me like a true recipe for success. In finance, you can get good at something specific (quant, fundamental etc), but you have to know that other ways exist and try to internalize/learn as much as you can. Eventually, you will come up with your own unique way that includes bits of everything. It struck me that very few people in any field are willing to adopt this synthetic approach since it pushes them out of their comfort zone.
How exactly do you think "quant guys" cheat?
There is a good reason why everyone is so tight lipped about their methods (btw that covers most market players, including fundamental long short or macro trades). The only real edge in the market is doing something that others aren't and the best way to ensure that is to keep information from spreading around. If you see someone giving away gold, either it's not real gold anymore or they are somehow trying to swindle you.
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A very good comment. the part though about giving away gold is inaccurate. The open source software development community is that. Other non-profit social benefit organizations would also be a part of this movement. There are philanthropies, private clubs, religious organizations doing the same.
A refugee's gold is very different than the 1%.
What if one were to engage with an open mind, like a traveler in a foreign land learning the culture and language just as you suggest. Collecting a diversity of viewpoints, values and lifestyles is a bit more productive than superficial judgment.
If what you say were true, the internet would have never come into being.
Google is gold for me. Anytime I have a question, I have a buffet of possible answers.
I remember a time when the local library was the way to find answers.
Dewey decimals and card catalogs. Oh such times we now live in where ruffians can say "google" to old ladies and get away with it! My name is Fred. I am a cataloger. I design, build and sell catalogs.
If interesting in delving into feel/impulse vs, considered action, Daniel Kahneman (Nobel prize winner) has several talks online about his life's work on systems of thinking. He wrote "Thinking fast and slow". He frames understanding of "feel" in practical application very well.
Of the many, I like this older one for bang for buck (time).
Of the many, I like this older one for bang for buck (time).
Nice results, and thanks for sharing the graphs.Hello.
Kind of tired reading of all the quant/AI/ML/coding hype everywhereand that if you're not doing it, you'll be out competed. I'm an old fashioned brain and eyes (along with bits of guts and groins) trader. So just putting it out there and let's see in a couple of years time if I get eaten up by the machines and codes.
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I'll post this year's results after it's updated for the close of this week.
Quick summary because most TLDR
- predominantly in futures & FOPs (ES & FX 6's with bits of GC, CL)
- day trading preferred
- favorite character gregor the mountain
- mostly chart reading
I wonder if you could make a comment about your stats. I realize that since you trade different instruments this might vary, but generally speaking, can you comment about your win rate and average win and loss? There have been so many discussions and arguments on the board about what is necessary and possible, and so I wonder if you would be able to provide a data point from your live trading experience. I'm mostly curious about ES, but if you also have numbers for GC and CL, that would be interesting.
Are you saying FT and London Guardian chess columnist Leonard Barton is wrong. Kasperov just published a book explaining the psychological reasons he lost to Deep Blue in 1996. Title is "Deep thinking Where Machine Intelligence Ends and Human Creativity Begins." Basically, Kasperov was not playing against the Deep Blue computer. He was playing against a team of talented chess players (not world champion) and talented programmers who expressed their knowledge by writing a computer program. Kasperov says he was effected by the psychological tactics of the team.
I am not sure if this thread is the right place to post this. My reason to say deep learning and neural networks are hype is that that those algorithms run into combinatorial explosion that human thinking does not. AI was first falsified in 1972 by Cambridge professor James Lighthill (search for "The Lighthill Report"). I have written a paper that shows Lighthill's computing limited by combinatorial explosion applies to current AI ("A Popperian Falsification of AI - Lighthill's Argument Defended" ArXiv:1704:08111).
It takes some web searching to see why deep learning does not work for automated trading. In order to make neural network searching efficient enough to run on physical computers (avoid the combinatorial explosion), deep learning searching uses something called regimes. AI researchers are somewhat disingenuous in discussing regimes. I think they are just ways of pre assuming the probability distribution of what is learned. Try searching for meta learning and regimes. Deep learning works by assuming no black swans. It easy to design a sure fire option selling algorithm if say stock price distribution can only be Gaussian.
The problems with deep learning and neural networks were understood in the 1950s by John von Neumann who invented neural network automata. Neumann wrote (quoted in "John von Neumann and the Origins of Modern Computing" by W. Aspray, p. 321):
"The insight that a formal neuron network can do anything which you can describe in words a very important insight and simplifies matters enormously at low complication levels. It is by no means certain that it is a simplification on high complication levels. It is perfectly possible that on high complication levels the value of the theorem is in the reverse direction, namely, that you can express logics in terms of these efforts and the converse may not be true."
It takes some web searching to see why deep learning does not work for automated trading. In order to make neural network searching efficient enough to run on physical computers (avoid the combinatorial explosion), deep learning searching uses something called regimes. AI researchers are somewhat disingenuous in discussing regimes. I think they are just ways of pre assuming the probability distribution of what is learned. Try searching for meta learning and regimes. Deep learning works by assuming no black swans. It easy to design a sure fire option selling algorithm if say stock price distribution can only be Gaussian.
The problems with deep learning and neural networks were understood in the 1950s by John von Neumann who invented neural network automata. Neumann wrote (quoted in "John von Neumann and the Origins of Modern Computing" by W. Aspray, p. 321):
"The insight that a formal neuron network can do anything which you can describe in words a very important insight and simplifies matters enormously at low complication levels. It is by no means certain that it is a simplification on high complication levels. It is perfectly possible that on high complication levels the value of the theorem is in the reverse direction, namely, that you can express logics in terms of these efforts and the converse may not be true."
What does Leonard Barton say?
With regards to Kasparov vs Deep Blue, chess engines made tremendous gains since 1996 in their play quality. Humans made very marginal gains. Back in 1996, we could have a debate about whether humans are better chess players than machines. This debate is now over: there is simply no contest.
These days, when grand masters play exhibition matches against the chess engines, they play with the handicap to "level the field". The engines start without a pawn or two, while humans have the entire 16 pieces on board.