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

library(devtools)
devtools::install_github(repo="stuartgordonreid/emh")

StuartGordonReid

results <- emh::is_random(my.zoo.object)
emh::plot_results(results)
View(results)

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

bettingStrategyProof <- function(t = (252 * 7), w = 5, sd = 0.02, sims = 30) {
  # Store market and strategy returns.
  markets <- NULL
  strats <- NULL
  
  for (i in 1:sims) {
    # Generate an underlying signal.
    signal <- sin(seq(1, t)) / 50
    signal <- signal - mean(signal)
    

StuartGordonReid

testRobustness <- function(t = (252 * 7)) {
  # Generate an underlying signal.
  signal <- sin(seq(1, t)) / 50
  signal <- signal - mean(signal)
  
  # For different noise levels
  sds <- seq(0.0, 0.020, 0.0005)
  cratios <- c()
  for (s in sds) {
    # Generate a noisy signal

StuartGordonReid

compressionTest <- function(code, years = 7, algo = "g") {
  # The generic Quandl API key for TuringFinance.
  Quandl.api_key("t6Rn1d5N1W6Qt4jJq_zC")
  
  # Download the raw price data.
  data <- Quandl(code, rows = -1, type = "xts")
  
  # Extract the variable we are interested in.
  ix.ac <- which(colnames(data) == "Adjusted Close")
  if (length(ix.ac) == 0) 

StuartGordonReid

def random_excursions(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 number of cycles having exactly K visits in a cumulative sum random walk. The
    cumulative sum random walk is derived from partial sums after the (0,1) sequence is transferred to the
    appropriate (-1, +1) sequence. A cycle of a random walk consists of a sequence of steps of unit length taken at
    random that begin at and return to the origin. The purpose of this test is to determine if the number of visits
    to a particular state within a cycle deviates from what one would expect for a random sequence. This test is

StuartGordonReid

def cumulative_sums(self, bin_data: str, method="forward"):
    """
    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 maximal excursion (from zero) of the random walk defined by the cumulative sum of
    adjusted (-1, +1) digits in the sequence. The purpose of the test is to determine whether the cumulative sum of
    the partial sequences occurring in the tested sequence is too large or too small relative to the expected
    behavior of that cumulative sum for random sequences. This cumulative sum may be considered as a random walk.
    For a random sequence, the excursions of the random walk should be near zero. For certain types of non-random

StuartGordonReid

def universal(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 number of bits between matching patterns (a measure that is related to the
    length of a compressed sequence). The purpose of the test is to detect whether or not the sequence can be
    significantly compressed without loss of information. A significantly compressible sequence is considered
    to be non-random. **This test is always skipped because the requirements on the lengths of the binary
    strings are too high i.e. there have not been enough trading days to meet the requirements.