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import numpy as np import pandas as pd C=[ [ 1.0, 0.6 ,0.3], [ 0.6 ,1.0 ,0.5],[ 0.3, 0.5 ,1.0]] mC=np.mat(C) U=np.linalg.cholesky(mC) cU=np.random.randn(10,3) * U pd.DataFrame(cU).corr()

yes. some old ones were finding icebergs, pushing around poorly designed automarketmakers ,spoofs.

you can look for local minima or maxima ,google for a "peaks" lib, or calc slope. in all cases your answer will be from the past

No. This is for brokers/clearers exchange members. Maybe can be lucrative for some

what's interesting is that every winning algo from those contests is negative when running live

http://www.optionsclearing.com/market-data/open-interest/

Capm , efficient frontier or similar

so someone pays them ~$100 to paper trade with tight risk limits. Then maybe get funded with tight risk limits. Tom Vu would love it.

in limit up/down the options still trade so you can buy/sell a synthetic

Am I reading this correctly? You tested against data that you generated

collusion

It's a lot less cumbersome to get the data from a data provider than using ftp. instead of ftp.get you essentially use dataproviderAPI.get PM me i will write it for you

Its used to neutralize your delta

i wrote one a while ago http://bit.ly/TFFL7V

http://vlab.stern.nyu.edu/

I wrote a Python wrapper that uses the cme margin api. $

You're comparing H raw to K refined

thats not slippage. it's latency. It's clearer if you say to compensate for latency adjust prices. Using the word slippage just adds to confusion.

def updatePortfolio(self, contract, position, marketPrice, marketValue, averageCost, unrealizedPNL, realizedPNL, accountName): self.myvariable = position def cxlAccountUpdates(self): self.connection.reqAccountUpdates(False,"DU666") PM me. I will write it for you

i updated the github gist to display accountUpdates gist.github.com/audubon/b14eeda1c2cad704b333