mtmoses

//The Greeks Calculator for European vanilla options
//by Todd Moses
#include <iostream>
#include <cmath>

//standard normal probability density function
double PDF(const double d) {
  return (1.0/(pow(2*M_PI,0.5)))*exp(-0.5*d*d);
}

mtmoses

// turing.go
// by Todd Moses

package main

import (
    "fmt"
)

type Tape struct {

mtmoses

symbol,year,quarter,assets,liabilities,earnings

11100,2018,1,8614953000,3480139000,3052336000
3030,2018,1,416657000,220331000,-1972000

mtmoses

symbol,date,open,high,low,pe

1130,20180531,43.40,44.71,42.97,7.84

mtmoses

def partition_function(SIGMA, DELTA, XT, Q):
    print("Calculating the partition function...\nThis step will take quite a while... so strap yourself in...\n")
    SIGMA=[[0 for x in range(len(DELTA))] for y in range(len(Q))]
    for k in range (0, len(Q)):
        if k%30==0:
            print("calculating i=" + str(k) + ' out of ' + str(len(Q)-1))
        for j in range (0,len(DELTA)):
            for i in range (0,len(XT)-1):
                if i < int(len(XT)/DELTA[j]):
                    SIGMA[k][j]=SIGMA[k][j] + abs(XT[i*DELTA[j]+DELTA[j]]-XT[i*DELTA[j]])**Q[k]

mtmoses

def estimate_multifractal_spectrum(TAU_Q, Q, MIN_Q, MAX_Q):
    TAU_Q_ESTIMATED = np.polyfit(Q[MIN_Q:MAX_Q], TAU_Q[MIN_Q:MAX_Q], 2)

    F_A = [0 for x in range(len(q)-10)]
    p = [0 for x in range(len(q)-10)]

    a = TAU_Q_ESTIMATED[0][0]
    b = TAU_Q_ESTIMATED[1][0]
    c = TAU_Q_ESTIMATED[2][0]

mtmoses

def lognormal_cascade(k, v,ln_lambda, ln_theta):

    k = k - 1

    m0 = np.random.lognormal(ln_lambda,ln_theta)
    m1 = np.random.lognormal(ln_lambda,ln_theta)
    M = [m0, m1]

    if (k >= 0):
        d=[0 for x in range(0,2)]

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new_cascade = list(np.array(lognormal_cascade(k=K, v=1, ln_lambda = simulated_lambda, ln_theta = simulated_sigma)).flat)
tradingtime = 2**K*np.cumsum(new_cascade)/sum(new_cascade)

mtmoses

from fbm import FBM
new_fbm_class = FBM(n = 10*2**K+1, hurst = simulated_H, length = magnitude_parameter, method='daviesharte')
new_fbm_simulation = new_fbm_class.fbm()

mtmoses

from fbm import FBM
from fbm import fbm, fgn, times
from fbm import MBM
from fbm import mbm, mgn, times

import math

def MMAR(K, simulated_H, simulated_lambda, simulated_sigma, original_price_history, magnitude_parameter, GRAPHS):

    # --- VARIABLES ---