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This is the most interesting bit for me: >Sentient's system is inspired by evolution. According to patents, Sentient has thousands of machines running simultan
by JTon 10y ago
This is the most interesting bit for me:
>Sentient's system is inspired by evolution. According to patents, Sentient has thousands of machines running simultaneously around the world, algorithmically creating what are essentially trillions of virtual traders that it calls "genes." These genes are tested by giving them hypothetical sums of money to trade in simulated situations created from historical data. The genes that are unsuccessful die off, while those that make money are spliced together with others to create the next generation. Thanks to increases in computing power, Sentient can squeeze 1,800 simulated trading days into a few minutes
- alva 10y agoThis is the most interesting bit for me : > It shares little about the data used for the AI's decision-making and isn't profitable
- rayuela 10y agoYup. This is a bullshit PR piece.
- rbinv 10y agoSo, genetic algorithms? Good luck with that.
- sixtypoundhound 10y agoI recall reading an early article about using genetic algorithms for stock trading in the early 90's. This isn't really that new....
- draugadrotten 10y agoOne of my school mates did it in mid-90s and he got his first yacht five years later. It worked for a while.
- gaius 10y agoThe second happiest day in your life is when you buy a yacht...
- vijucat 10y agoGenetic Programming, rather. See the papers authored by their Co-founder and Chief Scientist: https://scholar.google.com.hk/scholar?q=Babak+Hodjat+genetic&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwjR1JPfk_7RAhWCk5QKHVGsC8wQgQMIGDAA https://scholar.google.com.hk/scholar?q=Babak+Hodjat+genetic...
- jcfrei 10y agoI believe anyone who has considered applying machine learning tools for identifying trading signals came across the idea of using some kind of optimization algorithm to find the best parameters. Evolutionary algorithms seem like a fair approach because the parameter space is (for lack of a better word) very sparse and anything but continuous (lots of parameter combinations don't make sense / don't work). Using a genetic algorithm - like for example the differential evolution algorithm by Price, Storn and Lampinen - is really the most naive approach. I assume they must have some pretty clever, human made models in the back - because in a very general model the parameter space would be so vast, they would probably never find good parameters. And even if they did, they would have to run thousands of tests to make sure they are not overfitting the data - making this a very slow optimization. Not saying this will or will not work - I'm just thinking out loud here.
- uchish 10y agoIt's not slow if you have enough compute
- scottlegrand2 10y agoSo basically genetic programming? They didn't invent that, John Koza did...