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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:

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import numpy as np
import pandas as pd
import datetime as dt

from sklearn.datasets import make_classification

def create_price_data(start_price: float = 1000.00, mu: float = .0, var: float = 1.0, n_samples: int = 1000000):
    
    i = np.random.normal(mu, var, n_samples)
    df0 = pd.date_range(periods=n_samples, freq=pd.tseries.offsets.Minute(), end=dt.datetime.today())

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Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide