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from __future__ import print_function, division

from timeit import default_timer as timer
import numpy as np
import matplotlib.pyplot as plt
from numba import njit

w = 400
h = 300

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from pandas import *
from datetime import *
import pdb as pdb

df = DataFrame.from_csv('aapl_1-2012_5min.csv')
dayCount=0
rangeHigh = -1
rangeLow = 9999
openDayRangeDict = {}
getRange = 1

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Risk Parity & Budgeting

With Python

Dr. Yves J. Hilpisch | The Python Quants & The AI Machine

Python for Quant Finance Meetup, London, 16. November 2022

(short link to this Gist: http://bit.ly/pqf_risk)

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#include <glm/matrix.hpp>

class Frustum
{
public:
	Frustum() {}

	// m = ProjectionMatrix * ViewMatrix 
	Frustum(glm::mat4 m);

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#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

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# -*- coding: utf-8 -*-
import array
import random
import json

import numpy as np
import matplotlib.pyplot as plt

from math import sqrt

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import numpy as np
from numba import jit
from numba import float64
from numba import int64

@jit((float64[:], int64), nopython=True, nogil=True)
def _ewma(arr_in, window):
    r"""Exponentialy weighted moving average specified by a decay ``window``
    to provide better adjustments for small windows via: