Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
load AAPL

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
Creating AAPL Apple Inc....
load MSFT

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
load AMZN

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
Error parsing Market Cap (Real-time) , cannot convert N/A
Error parsing Last Trade Size , cannot convert 79,934
Price/EPS Estimate Current Year  : 10.59
Book Value  : 144.124
Last Trade (Price Only)  : 420.8
Earnings/Share  : 41.896
Creating AMZN Amazon.com...
Error parsing Market Cap (Real-time) , cannot convert N/A
Error parsing Last Trade Size , cannot convert 51,666
Price/EPS Estimate Current Year  : 218.25
Book Value  : 18.532
Last Trade (Price Only)  : 284.03
Earnings/Share  : -0.192
Creating MSFT Microsoft Corporation...
Error parsing Market Cap (Real-time) , cannot convert N/A
Error parsing Last Trade Size , cannot convert 587,566
Price/EPS Estimate Current Year  : 12.34
Book Value  : 9.185
Last Trade (Price Only)  : 34.01
Earnings/Share  : 1.938
measure_similarity MSFT AAPL
0.9487773944602984

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
measure_similarity AAPL AMZN
0.7766864280064972

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
measure_similarity MSFT AMZN
0.9341696778035647

Welcome to StockSimilarities.
Here are your options:

load <ticker> : loads all information about a stock into memory
load_everything : load all companies into memory (takes awhile)
print_vector <ticker> : print the formatted stock vector for a ticker which has been loaded into memory
print_attribute_map <ticker> : print all raw attributes of a stock which is in memory
measure_similarity <ticker> <ticker> : print the cosine similarity of two vectors
visualization : enter visualization mode
q : quit the system
visualization
Visualization options:

similarity_triangulation <ticker> <ticker> <ticker>: draw similarity triangle
similarity_triangulation AAPL MSFT AMZN
package visualization.SimilarityGraph.generated.preproc;

import processing.core.PApplet;
import processing.core.PFont;

public class SimilarityGraph extends PApplet {
	PFont f;
public void setup(){ 
	size(600,600); 
	f = createFont("Papyrus", 16, true);
} 
 
public void draw(){
	
	textFont(f);		
	
	int borderMin = 50;
	int borderMax = 500; 
	
	//length of lines (similarity)
	int xy = 263;
	int xz = 321;
	int yz = 267;
	
	// length and width of nodes
	int x = 10;
	int y = 12;
	int z = 218;


	fill(255);
	
	// Pythag
	int i = x / 2 + xz + z / 2;
	int j = borderMin + x / 2 + z / 2;
	int pythagXZ = (int)sqrt(i * i - j * j);
	
	int p1x = borderMin + x/2;
	int p1y = borderMin;
	int p2x = borderMin + x + xy;
	int p2y = borderMin;
	int p3x = borderMin;
	int p3y = borderMin + pythagXZ;
	
	ellipse(borderMin, borderMin, x, x);
	line(p1x, p1y, p2x, p2y);
	ellipse(borderMin + x + xy + y / 2, borderMin, y, y);
	ellipse(borderMin, borderMin + pythagXZ + z / 2, z, z);
	line(p1x, p1y, p3x, p3y);
	line(p2x, p2y, p3x, p3y);
	
	
	fill(0);
	text("AAPL",borderMin,borderMin);
	text("MSFT",borderMin + x / 2 + xy + y,borderMin);
	text("AMZN",borderMin,borderMin + pythagXZ);
	
} 
    static public void main(String args[]) {
        PApplet.main(new String[] { "--bgcolor=#ECE9D8", "SimilarityGraph" });
    }
}
/**
* Paste all preceding code into visualization.SimilarityGraph.generated.preproc
**/
Visualization options:

similarity_triangulation <ticker> <ticker> <ticker>: draw similarity triangle