Neha and Heyla -- good understanding of the code.
Good understanding of the techniques of decision tree learning.
Code that they actually built was minimal but fine.
Need to test by showing examples of trades done and
give profit/loss and sharpe ratio.

Sweta and Hari Om -- good understanding of the code.
Good understanding of decision tree learning.
Need to test by showing examples of trades done and
give profit/loss and sharpe ratio.
Code that they actually built was minimal but fine.

Abhay and Sandhya -- good understanding of sketches.
Code needs to have loops removed, but is otherwise basically ok.
And test is to use affinity propagation for clustering.

Prateek and Pranita -- good understanding of cointegration.
Coding was only ok.

Ramanpreet and Urvashi -- excellent understanding of affinity propagation.
code all written by themselves.
Really nice work.

Vinod and Meena -- data warehouse.
Nice use of partitioning.
End of day data from market.
Understood the data model very well.
Will do scale work.
Code is nice.

Tanay and Rohit -- understood risk metrics well.
Uses normal distribution.
Might include a notion of portfolio that is independent
of the total generated data.
Code is completely reasonable.

Heramb and Dilip -- high performance trading entry and exit.
Should do performance tests.
Reorganize the code for better performance -- pull out 
common functions.
Code is nice.

Rachit and Alind -- genetic algorithms.
Remarkably unrealistic assumptions.
Very small chromosomes.
More measures.
End of day prices instead of assuming can buy at low
and sell at high.
Coding very loopy.

Swati and Showvik -- genetic algorithm
Same strange assumptions.
More measures.
End of day prices instead of assuming can buy at low
and sell at high.
Coding very loopy.


