StuartGordonReid

def get_probability(self, d, y, x, n, c):
    """
    This gets the probability of drop / pickup for any given Datum, d
    :param d: the datum
    :param x: the x location of the datum / ant carrying datum
    :param y: the y location of the datum / ant carrying datum
    :param n: the size of the neighbourhood function
    :param c: constant for convergence control
    :return: the probability of
    """

StuartGordonReid

import os
import math
import time
import numpy
import pandas
import random
import matplotlib
import numpy.random as nrand
import matplotlib.pylab as plt
from sklearn.preprocessing import normalize

StuartGordonReid

import math
import numpy
import numpy.random as nrand

"""
Note - for some of the metrics the absolute value is returns. This is because if the risk (loss) is higher we want to
discount the expected excess return from the portfolio by a higher amount. Therefore risk should be positive.
"""

StuartGordonReid

import numpy
import numpy.random as nrand


def vol(returns):
    # Return the standard deviation of returns
    return numpy.std(returns)


def beta(returns, market):

StuartGordonReid

import numpy
import numpy.random as nrand


"""
Note - for some of the metrics the absolute value is returns. This is because if the risk (loss) is higher we want to
discount the expected excess return from the portfolio by a higher amount. Therefore risk should be positive.
"""

StuartGordonReid

import numpy
import numpy.random as nrand


def lpm(returns, threshold, order):
    # This method returns a lower partial moment of the returns
    # Create an array he same length as returns containing the minimum return threshold
    threshold_array = numpy.empty(len(returns))
    threshold_array.fill(threshold)
    # Calculate the difference between the threshold and the returns

StuartGordonReid

import numpy
import numpy.random as nrand


"""
Note - for some of the metrics the absolute value is returns. This is because if the risk (loss) is higher we want to
discount the expected excess return from the portfolio by a higher amount. Therefore risk should be positive.
"""

StuartGordonReid

"""
Note that this Gist uses functions made available in another Gist -
https://gist.github.com/StuartGordonReid/67a1ec4fbc8a84c0e856
"""


def treynor_ratio(er, returns, market, rf):
    return (er - rf) / beta(returns, market)
 
 

StuartGordonReid

"""
Note that this Gist uses functions made available in another Gist -
https://gist.github.com/StuartGordonReid/67a1ec4fbc8a84c0e856
"""


def excess_var(er, returns, rf, alpha):
    return (er - rf) / var(returns, alpha)
 
 

StuartGordonReid

"""
Note that this Gist uses functions made available in another Gist -
https://gist.github.com/StuartGordonReid/67a1ec4fbc8a84c0e856
"""


def omega_ratio(er, returns, rf, target=0):
    return (er - rf) / lpm(returns, target, 1)