raddy

def f_gradient(params,y,market_vols,tte,q):
    c,d,abar = params
    y = y[market_vols>0]
    vols = market_vols[market_vols>0]
    n = len(y)
    Y1 = np.sum(y)
    Y2 = np.sum(y**2)
    Y3 = np.sum(np.sqrt(y**2+1))
    Y4 = np.sum(y*np.sqrt(y**2+1))
    var_t = vols**2*tte

raddy

def optimize_weighted(y,max_vi,weights,market_vols,tte,s):
# {{{
    y    = y[market_vols>0]
    vols = market_vols[market_vols>0]
    w    = weights[market_vols>0]
    n = len(y)

    # R ends up n by 3
    R = np.concatenate([np.ones(n), np.sqrt(y**2+1), y]).reshape(3,n).T

raddy

import pandas as pd
import numpy as np
from bsm_implieds import implied_fut,implied_futs

def is_kospi(symbol_list):
    s = pd.Series(symbol_list)
    is_call = s.str[2:6]=='4201'
    is_put = s.str[2:6]=='4301'
    is_fut = s.str[2:6]=='4101'
    return np.logical_or(is_call,np.logical_or(is_put,is_fut))

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import pandas as pd
import numpy as np
cimport numpy as np
cimport cython
from libc.math cimport exp, sqrt, pow, log, erf, abs, M_PI

ctypedef np.double_t DTYPE_t

@cython.cdivision(True)
@cython.boundscheck(False)

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import bisect

def calc_pnl_futs(actions,observations):
    pnls=[]
    if observations.last_valid_index() == None:
        return np.zeros(len(observations))
    settle = np.mean(observations[observations.last_valid_index()])
    for a,o in zip(actions,observations):
        if a<0:
            pnls.append(o[0]-settle)

raddy

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np

def training_to_XY(hi,side='buy'):
    X = pd.DataFrame(hi.implied_size)
    if side == "buy":
        X['Through2'] = hi.implied_trd-hi.AskPrice2

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import pandas as pd
import os,sys
from tcp_pcap2h5 import open_pcap



def main(pcap1,pcap2):
    open_pcap(pcap1)
    open_pcap(pcap2)
    h5name1 = pcap1+'.h5'

raddy

import pandas as pd
import numpy as np
cimport numpy as np
cimport cython
from bsm_implieds import bs_tv,delta_py

ctypedef np.double_t DTYPE_t
cdef extern from "math.h":
    bint isnan(double x)

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%%cython
import numpy as np
import pandas as pd
import sys
cimport cython
cimport numpy as np

ctypedef np.double_t DTYPE_t
cdef extern from "math.h":
    bint isnan(double x)

raddy

import pykalman
import numpy as np

#model of the form -- I apologize for mathjax weirndess but too lazy to fix right now

#yt = β0,t +β1,txt +νt, νt ∼ N(0, σ2 ν)

#β0,t+1 = β0,t +ξt, ξt ∼ N(0, σ2 ξ)

#β1,t+1 = β1,t +ςt, ςt ∼ N(0, σ2 ς )