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def percentile_to_probability (hits, percentile):
    """  hits 190
         percentile .99
         desc: given a 99 percentile what is the probability of seeing it in 190 attempts
    """
    return (1-(percentile**hits)) * 100

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group 'chronlogger'
version '1.0-SNAPSHOT'

apply plugin: 'java'
apply plugin: 'application'

sourceCompatibility = 1.8
version = '1.0' + new Date().format("MMddHHmm")

//remove javaagent when not monitoring with kamon

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"""
[DEFAULT]
ConnectionType=initiator
ReconnectInterval=60
FileStorePath=store
FileLogPath=log
StartTime=00:00:00
EndTime=00:00:00
UseDataDictionary=Y

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from time import time, sleep


_128k = 128 * 1024
_256k = 256 * 1024
_512k = 512 * 1024
_1024k = 1024 * 1024

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w='8=FIX.4.2
9=481
35=E
34=36
49=OCO2
52=20140115-07:51:41.088
56=DFIX202
66=INTERNALISEDOCO21234567.53485783
68=2
394=2
9382=2
73=2
11=QLR6546-20140124
67=1
109=ABC
76=CBXX:230
1=OCO2
21=1
55=AA
167=OPT
200=201312
205=21
201=1
202=1
54=1
38=500
40=1
59=0
47=C
77=C
440=OCO2
9465=CBXX:OCO2
386=1
336=SESS
11=QLR6556-20140124
67=2
109=ABC
76=CBOE:230
1=OCO2
21=1
55=AA
167=OPT
200=201312
205=21
201=1
202=1
54=2
38=500
40=4
44=1.2
99=1.35
59=0
47=M
77=C
440=OCO2
9465=CBXX:OCO2
386=1
336=SESS
10=112
'

import quickfix
import quickfix42
import quickfix50sp2

dd=quickfix.DataDictionary('c:/tmp/fix42.xml')
m=quickfix.Message()
m=quickfix.Message(w,dd,False)

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import numpy as np
from fractions import Fraction
import pandas as pd
from odo import odo


def dec2percent (dec):
    """dec2percent(2)
    """

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"""
microsoft TrueSkill algorithm for rankings
"""

from __future__ import print_function
import math
import sys

from scipy.stats.distributions import norm as scipy_norm

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import pandas as pd
import numpy as np

option_data_frame = pd.read_csv('C:/code/python/pyql/examples/data/df_SPX_24jan2011.csv',parse_dates=[0,2])
grouped = option_data_frame.groupby('dtExpiry')


for spec, group in grouped:
    print('processing group %s' % spec)
    indx = group.index

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"""
This was written using Python 2.7.2

This requires the attached gziped blist library and the numpy library
or you can install blist and numpy from commandline with
easy_install blist or pip install blist
easy_install numpy or pip install numpy

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************************************************************************************************************
*** Installation on Ubuntu 12.04 ***************************************************************************
************************************************************************************************************
# Dependencies
sudo apt-get install build-essential
sudo apt-get install libreadline-dev libncursesw5-dev libssl-dev libsqlite3-dev tk-dev libgdbm-dev libc6-dev libbz2-dev

# Download Python
cd ~/Downloads/
wget http://python.org/ftp/python/2.7.5/Python-2.7.5.tgz