random_weights.ipynb

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    "In order to find the constant z for our constraint equations, we need to derive several random datasets to get an idea of what the value-at-risk typically is for most portfolios. To do this, we need 2 things:\n",
    "To create a function that can generate random weights for the 32 assets, such that the sum of all the weights are equal to 1 and that all weights are positive:\n",
    "To take the results of this function and get the return by following the procedure in step 4:\n",
    "To then analyze several runs of this function and come up with a rough idea of what z should be."
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    "Function w random weights for 32 assets. "
   ]
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       "(array([0.02145949, 0.00456517, 0.00902196, 0.01646961, 0.02766029,\n",
       "        0.02103205, 0.0039472 , 0.01579856, 0.02687757, 0.06383004,\n",
       "        0.0754687 , 0.0664405 , 0.05007134, 0.01548443, 0.07385879,\n",
       "        0.01271008, 0.00995356, 0.06317959, 0.05776948, 0.01503632,\n",
       "        0.01587735, 0.0581431 , 0.0447636 , 0.00933955, 0.00324077,\n",
       "        0.03831576, 0.01376532, 0.05882531, 0.02058008, 0.01440161,\n",
       "        0.06448786, 0.00762494]),\n",
       " 0.9999999999999999)"
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   "source": [
    "import numpy as np\n",
    "\n",
    "def generate_random_weights(n_assets):\n",
    "    # Generate random numbers\n",
    "    random_numbers = np.random.random(n_assets)\n",
    "    # Normalize so that the sum is 1\n",
    "    weights = random_numbers / sum(random_numbers)\n",
    "    return weights\n",
    "\n",
    "# Test the function with 32 assets\n",
    "n_assets = 32\n",
    "random_weights = generate_random_weights(n_assets)\n",
    "random_weights, sum(random_weights)\n"
   ]
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