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

def monobit(self, bin_data: str):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf
  
    The focus of this test is the proportion of zeros and ones for the entire sequence. The purpose of this test is
    to determine whether the number of ones and zeros in a sequence are approximately the same as would be expected
    for a truly random sequence. This test assesses the closeness of the fraction of ones to 1/2, that is the number
    of ones and zeros ina  sequence should be about the same. All subsequent tests depend on this test.
  

StuartGordonReid

def approximate_entropy(self, bin_data: str, pattern_length=10):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    As with the Serial test of Section 2.11, the focus of this test is the frequency of all possible overlapping
    m-bit patterns across the entire sequence. The purpose of the test is to compare the frequency of overlapping
    blocks of two consecutive/adjacent lengths (m and m+1) against the expected result for a random sequence.

    :param bin_data: a binary string

StuartGordonReid

def non_overlapping_patterns(self, bin_data: str, pattern="000000001", num_blocks=8):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    The focus of this test is the number of occurrences of pre-specified target strings. The purpose of this
    test is to detect generators that produce too many occurrences of a given non-periodic (aperiodic) pattern.
    For this test and for the Overlapping Template Matching test of Section 2.8, an m-bit window is used to
    search for a specific m-bit pattern. If the pattern is not found, the window slides one bit position. If the
    pattern is found, the window is reset to the bit after the found pattern, and the search resumes.

StuartGordonReid

class ClusteringQuality:
    """
    Instances of this class implement the two measures of clustering quality discussed in the article, namely the davies
    bouldin index and the silhouette index. It also implements a number of useful helper methods.
    :param solution: the clustering solution of type Clustering
    :param minimum: the minimum distance allowable
    """

    def __init__(self, solution, minimum):
        """

StuartGordonReid

# Portfolio optimization using particle swarm optimization article - PSO bare bones code
 
import random
 
w = 0.729844 # Inertia weight to prevent velocities becoming too large
c1 = 1.496180 # Scaling co-efficient on the social component
c2 = 1.496180 # Scaling co-efficient on the cognitive component
dimension = 20 # Size of the problem
iterations = 3000
swarmSize = 30

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

def linear_complexity(self, bin_data, block_size=500):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    The focus of this test is the length of a linear feedback shift register (LFSR). The purpose of this test is to
    determine whether or not the sequence is complex enough to be considered random. Random sequences are
    characterized by longer LFSRs. An LFSR that is too short implies non-randomness.

    :param bin_data: a binary string

StuartGordonReid

def random_excursions_variant(self, bin_data):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    The focus of this test is the total number of times that a particular state is visited (i.e., occurs) in a
    cumulative sum random walk. The purpose of this test is to detect deviations from the expected number of visits
    to various states in the random walk. This test is actually a series of eighteen tests (and conclusions), one
    test and conclusion for each of the states: -9, -8, …, -1 and +1, +2, …, +9.

StuartGordonReid

def matrix_rank(self, bin_data: str, q=32):
    """
    Note that this description is taken from the NIST documentation [1]
    [1] http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf

    The focus of the test is the rank of disjoint sub-matrices of the entire sequence. The purpose of this test is
    to check for linear dependence among fixed length sub strings of the original sequence. Note that this test
    also appears in the DIEHARD battery of tests.

    :param bin_data: a binary string