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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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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import timedelta


def generate_stock_equity(start_date, end_date):
    num_days   = (end_date - start_date).days + 1
    changes    = np.random.randint(low=-150, high=150, size=num_days)

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#%%
import random
import numpy as np
from pymoo.core.problem            import ElementwiseProblem
from pymoo.algorithms.moo.nsga2    import NSGA2
from pymoo.algorithms.moo.nsga3    import NSGA3
from pymoo.optimize                import minimize
from pymoo.util.ref_dirs           import get_reference_directions

from pymoo.operators.sampling.rnd  import FloatRandomSampling

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#%%
import random
import numpy as np
from pymoo.core.problem            import ElementwiseProblem
from pymoo.algorithms.moo.nsga2    import NSGA2
from pymoo.optimize                import minimize

from pymoo.operators.sampling.rnd  import FloatRandomSampling
from pymoo.operators.crossover.sbx import SBX
from pymoo.operators.mutation.pm   import PM