3 ms·

I fixed the article's headline image: http://i.imgur.com/QVgcgNI.png http://i.imgur.com/QVgcgNI.png If everyone began using the paper's strategy, would the str

I fixed the article's headline image: http://i.imgur.com/QVgcgNI.png http://i.imgur.com/QVgcgNI.png If everyone began using the paper's strategy, would the strategy still work? Also, the strategy seems less effective than portrayed in the news article. If you look at the "results" section, it seems like the profit flatlined shortly after starting, then had success due to some major trading event, then eventually flatlined again: http://i.imgur.com/CBjEjgo.png http://i.imgur.com/CBjEjgo.png Wouldn't it be more accurate to say "this strategy is effective under some very specific circumstances"? Also, does anyone know how equation 4 was derived? http://i.imgur.com/vkx8ZEC.png http://i.imgur.com/vkx8ZEC.png It seems like the key insight of the paper, but there's no mention of where it originated from. Is it a common equation in statistical modeling? I'd like to learn more about it. Does anyone have any suggested reading or coursework I should study?

dash2 12y ago
I think equation 4 is just derived from equation 3. The top bit counts every time before that y_i has taken a particular value y, and multiplies it by the distance of the x value then, x_i, from the value of x now. (Squared and exponentiated because this is the pdf of the normal distribution.) Here's an interpretation: if there were no noise, you might just count the number of times x took on a particular value and y took on a particular value, and divide that by the total number of times x took on that value. This would give you an empirical estimate of the prob of y given x. Because there's noise, they weight the counts by the pdf of the normal distribution of x - x_i. So, whenever x_i was close to current x, and y_i was a given y, that increases the probability of y occuring now.

3 ms·

I fixed the article's headline image: http://i.imgur.com/QVgcgNI.png http://i.imgur.com/QVgcgNI.png If everyone began using the paper's strategy, would the str

by sillysaurus3 12y ago

dash2 12y ago
I think equation 4 is just derived from equation 3. The top bit counts every time before that y_i has taken a particular value y, and multiplies it by the distance of the x value then, x_i, from the value of x now. (Squared and exponentiated because this is the pdf of the normal distribution.) Here's an interpretation: if there were no noise, you might just count the number of times x took on a particular value and y took on a particular value, and divide that by the total number of times x took on that value. This would give you an empirical estimate of the prob of y given x. Because there's noise, they weight the counts by the pdf of the normal distribution of x - x_i. So, whenever x_i was close to current x, and y_i was a given y, that increases the probability of y occuring now.