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def optimize(y,max_vi,market_vols,tte,s,weights):
    y = y[market_vols>0]
    vols = market_vols[market_vols>0]
    w = weights[market_vols>0]

    n = len(y)
        
    R = np.concatenate([np.ones(n), np.sqrt(y**2+1), y]).reshape(3,n).T
    W = np.diag(w)
    

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import numpy as np
import sympy as sy
from sympy.statistics import Normal as syNormal
sy.init_printing()

#spot, strike, vol, days till expiry, interest rate, call or put (1,-1)
S, K, vol, dte, r,cp = sy.symbols('S,K,vol,dte,r,cp')

T = dte / 260.
N = syNormal(0.0, 1.0)

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from cvxopt import lapack, solvers, matrix, mul
from cvxopt.modeling import op, variable, max, sum


#def const_spline(n=12):
   # """ Adapted from example on CVXOPT site -- Changing as little as possible """

#t = matrix(v.index.values[::4])
#y = matrix(v.values[::4])
t, y = data['t'], data['y']

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raddy

import seaborn
import pandas as pd
import numpy as np
import matplotlib.patches as mpatches

#create random trade pnls

n = 5000
sigma = .1
pnl1 = pd.DataFrame(np.random.randn(n,15)) * sigma

raddy

import seaborn
import pandas as pd
import numpy as np
import matplotlib.patches as mpatches

#create random trade pnls

n = 5000
sigma = .1
pnl1 = pd.DataFrame(np.random.randn(n,15)) * sigma

raddy

import numpy as np
import numpy.random as rnd
from cvxopt import matrix
from cvxopt import blas
from cvxopt import solvers
from cvxopt.modeling import op

class IndexReplication(object):   
    def __init__(self):
        self.G = None # number of groups undergoes mutation

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import pandas as pd
from scipy import stats, linalg
 
def partial_corr(C):
    """
    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling 
    for the remaining variables in C.


    Parameters

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def fit_ab_kf(single_cumulative_returns,dex_cumulative_returns):
    n_steps = len(dex_cumulative_returns)

    A = np.matrix([[1,0],[0,1]])
    # Transition matrix is dependent on previous B0/B1 with no inherent cross dependency
    
    C = map(lambda x: np.matrix(np.append(1,x[0])),np.reshape(dex_cumulative_returns,(n_steps,1,1)))
    # Observation matrix is tensor of [1,x_t] (of length equal to our observations)
    
    # Initial hidden state means are 0 for intercept and 1 for slope

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

#yt = a_tx_t + νt ∼ N(0, σ2 ν)

#at = a_t-1 + b_t-1 + 1/2 c_t-1
#bt = b_t-1 + c_t-1
#ct = c_t-1 + ut ~ N(0,..)