dgx-spark-playbooks/nvidia/portfolio-optimization/assets/cvar_basic.ipynb

{
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BIgIGUorAuFoBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUMJDid0ABBRRQQAEFFFBA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\npaM1TSx/Z6zY+WDsalubximfFrPH/F7qAFe4qyFd4yrKRqTJmS7Pfg5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GMl6W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\nw9ixY+NoEwIxnIsF7Y8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MAYKUY8DwNAIIIIAAAggggAACCCCAAAII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Ld1U4NP2vZPvsxtz1JexDopx8WKBXekZMry+M0rX49Lp/2wPavQqa5VVTI4xg8w6Njbh0KC\ng2TKwoULs/VHSHqwEPxLL70Uw4cPz5IanJORIA+kxeFJbrAva5OwMH1emJaLheF/97vfZYmRF154\nIWpra+PII4/MRrvw/w+Ovffee7PjGQXz8ssvB2u0kGzh/CR0OH7ZsmXZeihPPfVUVocxY8ZkU4Dl\n16r/b3l5eVRVVcXcuXPj/vvvz6YUmzx5ckycOLH+bv6ugAIKKKCAAgoo0M0F+hT1T4mIz8bU6nPS\n+ik/i6cWp/VTtja9fsrra16L19d8PqYMPjzOYP2UkuH73DltR11tSuCsiUcW/Dh+//r1LdLetQ5K\nRRw1/Lx49/ivRM80VbVFAQUUUEABBRTo6gK2aLrIE2T9kIumfDP++5mvtLjGO9Oe23duSwmJOS0+\nJt9x7eYF2RRbfYpatpZEftz+/ltVMjQ1uC+Jh1MiZduO7Wl4+qLdp9y+c0us2cLf7bsgPD2vOqIw\nDL6ieGAKPN4aIdER9eiK1yTp8P73vz8eeeSRePTRR7PkByNEpk2bFqecckqQxCC5ctJJJ8VDDz2U\nrZ8ybty4OOqoo4IRLPxQ8im3WByedVZY/+SMM87IzpO7kDBh/zvvvDPb//DDD4+zzz47W1uF9VUo\nLDB/zz33ZOulsLA856AOnHPw4MFB4qR+4Trnnntuds6ZM2dm1x09enT9XfxdAQUUUEABBRRQ4AAS\n6F82Jls/ZVTV9Lhrzney6b6aW0PzpRXPxUsrLotpQ46J8yb+XYotqtP6KWUtUqur25ESKGtj0frn\n4/FF18VzSx9r9jjWQalIC8lPG3qm66A0q+UOCiiggAIKKNDVBEykdJEnxtDuYZVTo09RcWrQbmtR\nremVv3jj0vjeoxe1aP/6O+1Mx5amodgHDzi+/uZ2+v2tdVLq6kgH7SpLN86Onz59ZUqwtOz+8+P2\n918sOqKM7Ds2Thn3+Y64dJe/JtNmnXbaadlPoZshgXHRRRdlP43ts2DBgmzzYYcdFscdd1w2DRhT\na1VUVGQ/fMj/x1iA/sILL4zLLrss259r11/MntEr/DRWjj766OCnscJ1+bEooIACCiiggAIKKLBL\noEccNvT8mFx9dkps3Br3vv5vKTZsfv2U55c9mTqoXRHvSlNsHTTgxLQe5faCoHWp09rmlEBZUfN6\nzFx0Yzy88I6C++Yf0AGMheQnDT4hrYPyubQOysH5R/6rgAIKKKCAAgp0GwETKV3oUZalYd0njL6k\nxUOquTW+6N28vXaf7nJA2cA4/aD/sU/H7s9BvXuWRr/Sqli1eV1sT0PJN25bnvWeKu1dmZJJ42P2\nylktPj0L15f03r8RHZyjT8/iKEnrt7RX6Z3W8ehfNiKqyw9pr0t6nQYC+bR2jFxhOrDGComVkpKS\nbEQJa6BYFFBAAQUUUEABBRRoawE62R01/ENpAfezUrLjhnhowXUpodL0+ilvrl8UM2Z9Mw4ZMCmO\nHv6BbNH4xurJNF4PzPu3uO+NGxr7eI9t+TooJ6QkzTjXQdnDxw0KKKCAAgoo0H0ETKR0oWdJIuGI\nYRfvVSKlC93e7qpWFA+IQwa+M1a9eXes2rQi7nrtf8fFU///GNTnoJTY+WJKpFy+e9/mfulbWhnv\nGHFxc7u16PMBaTh9e5Xq8uo4ctgH2utyXqcRAUaVkEBhBEqhwlooJFNYuN6igAIKKKCAAgoooEB7\nCrB+yklpBMihQ85t8fopr62eHa+t/oeC1Xx+2RPpM36aLmVp9oKK4ooUs5wb7z7oq66D0jSXnyqg\ngAIKKKBANxDw278u9RB7RHGv0hhQVpWGZq9v05qzxHzvnh3zelSlxRCPGfnheCwlUrbuqI0Vm97Y\n53vtV1qdGvZf3ufjO+rAfqVDY8LA0zrq8l43CTQ1JVcO1NTUXPk+/quAAgoooIACCiigQFsK5Oun\njKw6PH43519atH7KvtZn9zooQ86I08Z/MZg1waKAAgoooIACChwIAj0PhJvsTvfYNyUZ/mL6f7T5\nLRWleW4H9Wm/ERh73tCudVJYnaT+HL49evQK5uDtzoXgpG9KpPTo4f89u/Nz9t4UUEABBRRQQAEF\nuqpAx6yh2LRWj7TI+wXxpePvjXMnfDmtU9I/yotKmj5kLz4lBuufRvsfOez0uOLo6+LctHi9SZS9\nAHRXBRRQQAEFFOjyAh0z5KDLs3XcDZBIqCwelEal9E2jUta1WUVKU6N74qBT2+z8zZ2YeX8rSypi\n7ZYNsWMn66SsSEPHBwfD18f2nxCvrnq5uVN02c/HD5gS50/6dpetvxVXQAEFFFBAAQUUUKC7Cmzb\nUROrtyyMol7labaA9ltDsaWexFFHj7g0plS/J62fMiMtFv/z2LStJq2bua2lp9hjvz5FxWm0/FFx\nwphPpnVQjt/j886yoa5uR2yqXRUbtq3sLFWyHgoooIACCijQjQTs8t4FH2ZVyYj4yPTvt2nNy4sq\nUgP8sja9RlMnrygamBrrx2S7rK5ZkRY7/D/Z74P7HBxnHvzVpg7t0p+VptEo/UtHpGnVirv0fVh5\nBRRQQAEFFFBAAQW6o8Aba16PX8z6Ujy/9NZYs2VB1umrM97nrvVTroorj54Rx426IPqW7H3Sh3VQ\nqvsMiBNGXxKXTvtBJ06i1KVk0apYsH5m3PHKN+PB+b/ujI/EOimggAIKKKBAFxdwREoXfIA906iU\nqjQ6Y1Aarr2yZk2b3EFHTyvVNyUT3jHqo/HEoj9k66Qs3TC7Te6zM52U6dSmVB8XF0z+p85ULeui\ngAIKKKCAAgoooIAC9QSWbFweN77wrRjTd2ycmdZjHFI5MSUqhnfKqXkHlo2NcyZ8I0ZWTY+753w3\nWz9lfRqh0lShc1dFmh3g0OrT492dfB2U2h2bY/22pfHgvP+Mhxb8tqnbih7RMz2j7j1NdJMAfqiA\nAgoooIAC+yVgImW/+Dru4L6lI+Pyo66J7z92SRq+vKVVK9KrR480hVZVq55z307WIzV0e6Q1Uupi\n28637pEkD0mH2p07mj1t7Y4tsXzTa83uV9KrLK1LMjLbb8fObdmQ8C3bN2Z/l6Qh+yR2KHy2sXZl\nbN2+Kfu70H/WbF4QXHtvyuDywXH08A8FiTKLAgoooIACCiiggAIKdJxAca+KqEqjONZvLZx0mL9u\nXvzo6avjkAGTUrLif6X9h+yOGzqu5o1dedf6KUz39eySm+PeN76fRnBsStN91e6xM9N4TRtyapw8\n7qq0zsrBe3zeWTbkMdsbqx+KRxZeG4wUaq4U9y6PkjQlm0UBBRRQQAEFFNgXARMp+6LWCY5JKYY0\nKmVInDzm0rhzzn+3ao1K0xDuCQNObNVz7svJevcojsqistTDqCYbMs9w7fLigVHWu3+M7jsuXl8z\np9nTLtmwOP7ziQ81u9/YflPjY0dcm+23avP8uPu1/50a489nf4+oOjg+MPU70a90VJpvd1k8MPff\n47ml9zZzzrrYvnNnM/u89XFx6vU1smpqTOjAdWneqo2/KaCAAgoooIACCihwYAuM739cnDfhr+KO\n176T1m3c1cGqkMhrq2fHvz72kZg06NC4cMo/ZnFaUaddP+WyGNH3iLhvznfi2WWP7XFLhw85LS6a\n+t09tneWDXV1O1Ont5WxaP2seOzNa2LWsqearVqfFN+WF1ekUUNDU6c1vwJpFswdFFBAAQUUUKBR\ngV5/m0qjn7ix0wuwkGC/shHxwrLbY8t+LB7Y8EYH9RkYlx3+w4ab2/3vnXW1aSHH12PJhvnZSJAd\ndZvikIHvypIpg8vHpWm/mp/7dmek0Sw7tjf5w0JBo/tNzhZk5CYXb3ghJad+mE0pxrFrNq+IFZtm\nxRHDLorS3n3TwpKlMXPxb6Nm+9Ymz7u9BSNmctTR/cbFeyd+I8qLBuSb/FcBBRRQQAEFFFBAAQU6\nSKBXz6IYUjEprREyJsUHT0evFDRs3bHnCI761VtZszzeWP2HKCsqT52/+kZx7z6dcrT59h01sWjD\n87Fw/Z4d00alTmRTqs+qf1ud5Pdd66As2fRi/DF1bLv9le+nmQcWN1k3pijrW1oVx4x4X0oO/WOK\nJU9tcn8/VEABBRRQQAEFmhKwO0ZTOl3gs8rUq+bCyX8b//3Ml7tAbfeuiowAOX705Slp8acsUbRo\n/a4RIrvOsmvar7edMU0BVve2DW//oyQ1pAeU7ZmoGNl3ahpx8q/Zztt2bEqJk3lRl86VF6YWq6ld\nm3qiLcxGpfQrGxlHDj895q6Zme+y+9+taTqv1ZvX7/67Jb+U9iqKYRUTO/XQ+Zbch/sooIACCiig\ngAIKKNCdBJhyd3L12XHQwJNi1tLb4765/5FNidXU1MqLNyyNG2b9fVqYfXyckdZPqa44JH2Zn9ZP\nSetzWPZNoHZnWgdl67J4aN5/xYMLbm/RSSqKS9MaLyfHCaOviGGVU1t0jDspoIACCiiggAJNCZhI\naUqnC3zGqJThaUqowX0GxIqa1ftd4x7pDPS+6jzlrXVSNv95zRLqVtK7IkZXjXlbNbemnlVL08KP\nhcrIqrHx2WObbngvWDszfvnSnou9L1w3P259+f+NTxxxfQwsGxfvn/IvjV5m9orfx4+f/kKjnzW2\nsTit9XLokBPSAvPfbuxjtymggAIKKKCAAgoooEAHCxSndTWOGvHhNA3vu1Mnrxvi4QUzYs2WDU3W\nau7aN+KHM6+KCQMnZ+unVBYPTgmVkU0e44dvF9iRZijYtG1lzF39aDyapvGas7r5tS/Li0qiorgy\n3jnyg3Hi2M+awHo7qX8poIACCiigwH4ImEjZD7zOcmi/0tHxqaN/Hv/55Af3ejREw3soSqM2qtO0\nWZ2l9OpRlKa7KomN27bEzp21aWTI6uiTpr8aVjE1rn7nnW+r5ty1j8T3H7/ibdv25g9Go6zePPdt\no1Hy4xmVsmnbmt2jUvLt+/vvsKpR2UKOztW7v5Ier4ACCiiggAIKKKBA2wpUllTHKeO+kEY4HBq/\nfeXv02LtNc2un/Lqqpfj1UcvS+unHJbWT/l2Wj9laJoquKxtK9rFz56vg7J4/Qvx+JvXxXPLHm/2\njsrSOiiVJZVx9LAL4oQxn8463jV7kDsooIACCiiggAJ7IWAiZS+wOvOuVSXD4uLUML/++S+mxfe2\n7nNVWWh+Yida8Jw1Qw4ecEQ8u/TRFKSsTg3pa+LUcV/cp/vblqbdWrLxxYLHLiwwGiU/YOG6eXHT\nC38Z75v0d/mmPf5ds3nBHtsKbSjtXRQjK6dkSaFC+7hdAQUUUEABBRRQQAEFOpfAxDQyhbUbX15+\nd9z52j+nhMqmNPVUTZOVnL1yVlz37Kfi5DGfirH9j42qNN0XncYsbxdgBMqq1LntiTdnpMXk7377\nh438xfTNFcXlcdiQ0+PElEDp56ifRpTcpIACCiiggAKtIdAjrQXx1mIQrXFGz9FhAlu2r4+H5v9H\n/G7Otftch2EV1fHlEx7Y5+Pb4sB5ax+Pf3/8E1GUpsEa339SvHfS3zZ6mUXrnosbX/iHRj/rbBtp\n8B8x7N1pbZbvpqoxoZpFAQUUUEABBRRQQAEFuprAlu0b4vmlv4oH5v04jWDf2KJObeP7H5ytnzK4\nfHw23VeazLhdb3vN5vlx/9zvxSML79rjuseNPDstzE6M0r6ldueW2JjWQXlw/g/jT/NvbdHFWQdl\n8uDjs3VQRlZNb9Ex7qSAAgoooIACCuyrgCNS9lWuEx5X2rsqjhp+aTy95I5YvmnVPtWwNk2ftXjD\nC/t0bFsdtKpmbnbq2p074pVVL8YrD1/cVpdql/OyLsrkwcemuZL/Jl2vfYOmdrlBL6KAAgoooIAC\nCiigwAEiUNq7Mo4d+bGYNPiMePLN69MoiluaXT/ljTVz4gdPfTbNBDA1zpv4d9koCs5zIJbd66Cs\neSweW3hdvLZ6drMMTP1cWVIVxwy/KE4ae1X07NGr2WPcQQEFFFBAAQUU2F8BR6Tsr2AnPJ6h0D98\n8tI0JHpdJ6ydVRrdd3RarP7bYa8p3wUFFFBAAQUUUEABBbqTQF3MXnFv3PHqt1q0fgp3PrjPgDh+\n9KUxpfo9UVWS1k/p2fbrp3SGESn5OihLNrwYTyz6eTyz5JFmX4Rd66BUxBFD35sSKJ8LOhJaFFBA\nAQUUUECB9hJwREp7SbfjdfqXjopLp30vrn/u/2m2N1Q7VstLJQHWRRlVdahJFN8GBRRQQAEFFFBA\nAQW6nUCPbGTKIQNPjheX3xn3zPk/aaqvjbFh2+aCd7qiZnX8eva/x9OLfx2njP10jO53dJrua3ga\nZdF9Q/VN21Zl66A8teiGRqcXa4iVr4MytfrUOH7MlTGobFzDXfxbAQUUUEABBRRoc4Hu2zprc7rO\newEa3SOqDosLJ38jbn7pb5pd+LDz3kn3qhlJlKOGn5meyz93rxvzbhRQQAEFFFBAAQUUUGC3QK+e\nxTFt6AVpQfpT4tklN8eDC66JjWn9lJrarbv3afjLwvUL47rn/1ccPGBCnD7+SzGofNyfF07vPlMB\nb0/roGzYujyt6/mD+ONerIMycdCxcfyoy2NMv2Mbsvm3AgoooIACCijQbgImUtqNun0v1LtnaRyc\nekKdedBVcfecf2+yF1T71uzAvBpJlCOHnWES5cB8/N61AgoooIACCiigwAEoUFbUL44bfUVaH/HM\ntHbKdTFz8e3NzhgwZ/WrMWf1Z2LSoMPivEnfjH4lI6Kki6+fsrNue0okrYj5a56Ix9M0XrNXNr8m\nZ74OytHDL8zWQenVo+gAfIO8ZQUUUEABBRToTAImUjrT02jluhT3Ko/pwz4QW3dsjPvf+GlsrN3S\nylfwdC0R2JVEOT0unOJIlJZ4uY8CCiiggAIKKKCAAt1JoF/Z6DjrkK/F6L5Hxl2v/VMambIx1m3d\n1OQtzl45K9Y886m0fsplMTElYvqVDAs6y3WlUhc7g2m8lm54KZ5cNCNmLnmw2eoTO1UWV8bhQ98T\n7xr7mehTNLDZY9xBAQUUUEABBRRoDwETKe2h3IHXKE29l44d8RdRu31zPLTwxtQTyGRKez6O3UmU\nNJ1Xj/Q/iwIKKKCAAgoooIACChx4AsQCLCh/yKBT4oVlv4373vh+SjJsaHLmgGWbVsatL/9rjFl8\na5yckgqj+h6R1k8Z0SXWT9m0bWW2DsrMxb+Mhxf8tkUPvLK4NCVQTk/JoyujuvyQFh3jTgoooIAC\nCiigQHsJmEhpL+kOvA5Dyk9MCxf27NkrHpw/o8nGegdWs9tdelcS5d1xweR/ih49ena7+/OGFFBA\nAQUUUEABBRRQYO8EinqWxRHDLo4JA0+NZ5b8Ii22fkNs3LoharZvK3ii+esWxLXPfS0OGTApTj/o\nizGwz9hOu35Ktg7KtuXx8PwfxwPzbi54T/U/qCgqjarSfsE0Xu8ae3X9j/xdAQUUUEABBRToNAIm\nUjrNo2jbipT27pv17Ondozgt7Hdts0PJ27Y23f/sZX9eE+UCRqL0cCRK93/i3qECCiiggAIKKKCA\nAi0XKC8eFCeO+VxaP+XseGzhz+LZpXc3u37Ka6tnx2urPx1TBh8e5074RpZMKeld0fKLtuGeu9dB\nWftkPPHmjHh55fPNXq1PUXFUlfRLa0melxIoV3W5qcuavUF3UEABBRRQQIFuJWAipVs9zqZvprR3\nVbxz9OXRp3hA3PP6v8XqzeubPsBP90kgG4ky/EwXlt8nPQ9SQAEFFFBAAQUUUODAERjYZ3yck5Ii\nY/sfG3fP+W423Vdz66e8tOK5FMt9Jk4Y9ZE0VdhpKaEyvMOSEHVRl+q8Iq2D8nLMXHxTPLn4gWYf\nHvFSVVoH5dAhZ6SF5D+T1kQZ0uwx7qCAAgoooIACCnS0gImUjn4C7Xz9XQvQXxTlxQPj9tl/Hytq\n1rRzDbrv5YrS1GlVJRUxcdAJJlG672P2zhRQQAEFFFBAAQUUaFWBHj16xdTq98Yhabqv55felqbE\n+lFa23J9k+tbLt24PG55+bsxth/rp3w6RlYdntZPGZnWT+nVqnVr6mS71kGZF08vuTkemn97U7vu\n/qwirYNy2JBT0mwJn4xhFVN3b/cXBRRQQAEFFFCgswuYSOnsT6gN6te7Z2n6sv+M+Mj0YTHjuc/H\nsk0r2uAqB9YpS3r1jvEDpsQpaUj6QQNOOrBu3rtVQAEFFFBAAQUUUECB/Rag09vRIy6LiYNPj5mL\nbkrrp8xodhaBeWvnxrxn/zomDpwSp43/yxhQNjr6l41KdWm76YW379waG7YuS/X7Sdw/95ctuu98\nHZSjhl0QJ49jHZS2q1+LKuROCiiggAIKKKDAXgqYSNlLsO6yOz2VRlQeHp846mdx7TOfTA3hdS5C\nv48Pl6HpU6uPj/cc8rU0rJ6gxaKAAgoooIACCiiggAIK7JsAU12dMu7zUV0+IU339Z2oqd3Q7Pop\nr6x6KV5Z9amYOnh6nDMxrZ9SMiJae/2UfB2UBWtnxpOLbogXVzzT7A326Z3WQUkLyU8fek62DgrJ\nIosCCiiggAIKKNAVBUykdMWn1op1HlQ2Lj537G3x1Js/j4cWXh8rneqrxbr5VF4TBh0XF0z+x+jV\no6jFx7qjAgoooIACCiiggAIKKNCUwJTqs2NSGp3y4rI74vdv/GuL1k95ccWzKeny2Th+1F/EwQNP\nTh29RqT1U0qaukyzn+1aB2VlWgdldprG6xfxxKL7mj2mNI3YryypSh3OTo0Tx3wmq0ezB7mDAgoo\noIACCijQiQVMpHTih9NeVWMR+uPHXBGj+h8Zv3zhr2LpRqf6as6ewGD8gEPjxNFXxIS0wKNFAQUU\nUEABBRRQQAEFFGhtgZ49esdhQ8/PYo7nlv4qHpz/0zSTQNPrpyzesDRufumfY1z/W+LkMZ+OYVWH\nxYA0cp61WPa2bNuxKSVmFsXjC6+JP82/tUWHsw7K1MEnxnGjL09rt0xv0THupIACCiiggAIKdHYB\nEymd/Qm1U/16ptEUY/q+Iz5+xE/j+uc+HVu317gQfSP2jELpW1oZB/U/Jt478ZtRVtSvkb3cpIAC\nCiiggAIKKKCAAgq0nkBJ78o4duTH0lqXp8dTi2bE44tuaXb9lLlr3oi5a/5HTBp0WFo/5eroX8r6\nKaNbVCnWQdm4bXm8vvqheCzNXDA3rcXSXCkvKkmxUv84Yuh52dRk+5K4ae4afq6AAgoooIACCnSU\nQI+6VDrq4l63cwrsqKuNhetmxq9e+p9p+Pj6WLd1U+esaDvXirVQDh4wLY1C+VQaJu+C8u3M7+UU\nUEABBRRQQAEFFFDgzwIvLr8z7nn9u1Gzrfn1U3K0Q6uPinMmfD121u2IRxb8OC0Wf1f+0e5/3zHy\n9Djr4K+lePDpeGrxjTFr2VO7Pyv0S1laB6VvWgflsCFnxsljr47SlPSxKKCAAgoooIAC3U3AREp3\ne6KteD9bt28Mho//af5PDujpvkrSNF4D+wyKMf2mxwWTvh29eha3orKnUkABBRRQQAEFFFBAAQX2\nXmDHzm0p0fGb+MPc/0ijR9bF+q01zZ5kROXwLOGxZMNL8dyyJ/bYf+LAKWnUysh47M179vis4Qbi\npKq0DsrkwSfHCWOujIFlYxvu4t8KKKCAAgoooEC3ETCR0m0eZdvdyIqaOTHjuc/Fth1bYtmmlW13\noU52ZgKDAWUDYlTfQ1PPrW9ERfHgTlZDq6OAAgoooIACCiiggAIHusDm2rXxzJKb49E0Bdf6reti\nU+3WNidhHZRJg96Z1kH5eJoi+tg2v54XUEABBRRQQAEFOlrAREpHP4Eucv26qIvlm16NG5+/utsn\nVHYlUAbGiKpJcfYhX4t+aWFGiwIKKKCAAgoooIACCijQmQXWbF4QTyy6PmYuvr3Z9VP29T7ydVCm\nDz03WweFtTYtCiiggAIKKKDAgSBgIuVAeMqteo91sWxjSqjM+nxKrexIQ8jXx9otG1v1Ch11stJe\nRTGovDqGVRySEij/M83zO6KjquJ1FVBAAQUUUEABBRRQQIG9FqAD3IvLfhv3vvG9bL3LNVs27PU5\nGjsgXwfl0OrT411jr4o+Rf0b281tCiiggAIKKKBAtxUwkdJtH23b3xhrqMxadns8vOBnsXVHTRqx\nsrrtL9rKVyhO03f1TwsjFvcqi+FpBMr7Jn4rSnpXtPJVPJ0CCiiggAIKKKCAAgoo0H4C23duieeX\n/jr+OO8HsSF1fmvJ+imFaldZXBaHD313HD/6yqguP6TQbm5XQAEFFFBAAQW6tYCJlG79eNvn5nbW\nbY+F65+O2176eur/tDO2bK+JlTVr2ufi+3CV4p69ol9ZvyhJyZNB5aPjtPFfSqNQpuzDmTxEAQUU\nUEABBRRQQAEFFOi8Apu2rUrrp9yUFo//RWzYy/VTWAelX+nAOHzIOXHq+C923pu0ZgoooIACCiig\nQDsImEhpB+QD6RI7dm6LeWsfjzte/Va67brYvrM2DSnfEOu2bupQBtY9GdhnUPTuWZwWkB+R5vO9\nOkZUTosePXp2aL28uAIKKKCAAgoooIACCijQ1gIra96IJ968Lp5delez66ewDkq/0gExbchZcfK4\nL6QYqqStq+f5FVBAAQUUUECBTi9gIqXTP6KuW8G6up1pDZXl8dKK38Xjb96YbiSNV0nbtu3YEptr\na9IQ881tcnP5iJPiXqXRs0ev7BrV5ePjnAl/E1UlQ9vkmp5UAQUUUEABBRRQQAEFFOjMAsRnTM38\nh7nfT3Hauj3WuiztXRT9SvrF5OpT4+S0DkpF8eDOfDvWTQEFFFBAAQUUaFcBEyntyn1gX6yuri6t\npbIhlm16Jd5Y9VDMWn73bhAa9TvrdsQOfnZuz34n6bI9/Z4nXIrSlFwVxX1ScqRn9tOrZ1H0SokS\nkiX1R5b0LxueFkD8TAwpnxSlvat2X8NfFFBAAQUUUEABBRRQQIEDXWDbjk3x3NJb46H5P0ux1trU\n0a02+pb0jYMGHJ3WQbkihjrt8YH+inj/CiiggAIKKNCIgImURlDc1N4CdSl5UpvWVlkfG2tXRk3t\nmticGvT8vX7r4nh+2a6ES1XJoJg+9IK0MHx5lBZVpaRKdZQX9U+Lw1c63Ly9H5nXU0ABBRRQQAEF\nFFBAgS4tUFO7Op5Z/ItYuvGVeOfoT2RTH3fpG7LyCiiggAIKKKBAGwqYSGlDXE+tgAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACXVvAlba79vOz9goooIACCiiggAIKKKCAAgoooIACCiiggAIKKNCG\nAiZS2hDXUyuggAIKKKCAAgoooIACCiiggAIKKKCAAgoooEDXFjCR0rWfn7VXQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUECBNhQwkdKGuJ5aAQUUUEABBUgZ44wAACvBSURBVBRQQAEFFFBAAQUUUEAB\nBRRQQAEFuraAiZSu/fysvQIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCrShgImUNsT11AoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKNC1BUykdO3nZ+0VUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFCgDQVMpLQhrqdWQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUECBri1gIqVrPz9rr4ACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAm0oYCKlDXE9tQIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCnRt\nARMpXfv5WXsFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRoQwETKW2I66kVUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFCgawuYSOnaz8/aK6CAAgoooIACCiiggAIKKKCAAgoooIACCiigQBsKmEhp\nQ1xPrYACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAl1boHfXrr61bw+BnTt3xqZNm2Lr1q3Z5UpK\nSqKysrLgpbdv3x7r16+P3r17R1VVVcH9/EABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAgc4uYCKl\nsz+hevXbsWNHrF69OlauXBk9evTY/UlxcXGWsCBpwe/7Ujj3mjVrolevXtG/f//dp2D7smXL4qGH\nHsquzfmnTJkS73znO3fv0/AXkih33nlnDBo0KE4//fRYvnx5bNy4MUaPHh2lpaUNd2/y782bN8eC\nBQuy+xs2bNge+5K0wYRrDhw48G1132NnNyiggAIKKKCAAgoooECHC9BRa8uWLbFu3brsZ9u2bVl8\n06dPn6w9T1xTW1sbS5YsCbYNHTp0d51p/xO3EF9UV1dHz549s/04R17YRtzBecrLy6OoqCj/KPK4\nh2sPGDCgyfiBcxJr1NTUZLFG3759s/NQ/w0bNsSqVauyzmNDhgwJOpvlhU5oS5cuze4h30adysrK\ndteJTmfNFTqycR32bXgfzR2bf44jXmvXrn1bxzjuhbivufiR47jP5qzy63XEv8SMxMgYDx48+G33\nxL3zw73yw34rVqzYHU8TV/N+8K5gkr8rPHPuG3/eOc5dUVGRnYN/iZvrF96V3Dl/F3knuGa/fv2y\n8xIXc86GsTzn49r5O8Q7hzvxbf7O8S5Qb+6V9z7fXr8O/q6AAgoooIACbSvQfOutba/v2fdCgMbT\n73//+/jVr34VNNYpNMIIEsaNGxdnnHFGTJgwYS/O+NauNBB/85vfZA2y97///bs/oAF53333xa9/\n/evs3DSgm0uGEDjMnDkz258EB8e+8sor8YUvfCGr5+6Tt+CXhQsXxte//vU48sgj4+qrr84CiPqH\n0cC8+eab49lnn40LL7ww3vOe99T/2N8VUEABBRRQQAEFFFCgkwnwZfAzzzwT999/f9DeJ+bgi2q+\ncD7ssMPirLPOyr4w/sEPfhCTJk2KT33qU7vvgATKPffcE08++WRcfvnlWWzyX//1X1nCg2QDJT/X\nxIkT44QTTnhbDEIC5e67744HH3wwzjzzzDjvvPN2f3m++yJ//oVr/elPf4onnngi60h27rnnZskQ\ntv/xj3/M6kH9PvjBD2Zf4OfHv/766/HjH/84+xKeRFBdXV2WDOFL9cmTJ8dxxx0XY8aMyXcv+O+i\nRYuy6/OFOsfQUW1vCkmjefPmZfV84YUXMmeOp07U47TTTst8m0rqPP/881mcSAc5nktnLG+++Wbc\neOON2bvwkY98JOp3wOM9+d3vfpfVnfrz3G677bYsriQZksfTxNHvete74uCDDw4SIY899lj2fi5e\nvDhL+mGEPx0KTz755CyZkVvg/Nprr8W9994bL7/8cvbu8hnvI+8zzpyX95YftvOOUkiu0eGQ93Tq\n1KnZPTz88MPxwAMPZO/mKaecktXnpZdeit/+9rfZO3XJJZfE0UcfnR3vfxRQQAEFFFCg/QRMpLSf\ndatciUYagcc555yTNYDpHUNDnQQLCQ4agHkPKXof5Q3DPKhorBLsT6+XV199NeuJQ/IjL/SY4fwH\nHXRQfOITn8gCAHotcQwJk/q9behJ07DQQKThf+yxx+7+iHvgWOpHod6F6sf90fNm1qxZQSOeACIv\nnIeGLUkbfs8LvxOMcX7qyfnzunE+rp0X9qEBzT40YvPCfjg0PJ7POT/PgMQWvvRayoMjghIKx5Oc\nYl8a3Zyfn6YKdeGYxq7Z1HF+poACCiiggAIKKKBAVxGgrctoDZIZb7zxRtYZiqQC7fdHH300+/J6\nxIgRWfxR6J7ydnP+Oe1t4hW+jKZdTnufxAHXYF86oeXtdDpicd08mUOcMnLkyPxUb/uXTmR8cU48\nxBfb1Ovwww/PkhOPPPJI1r7ny3lGQTQsxEwkcjg/daKd/9xzz8Vdd92VtfcZVUD8Qf3y2IXYiW35\ndkY4zJkzJxtFMX369Cw+Ifbgc+IwEjqcmzijsXgKBxIJxEvUg85pFL6Uf/HFF7Nk1vDhw7PRJpyP\n/Xk+xEdcgxESJA+IMfN4iuOJg9iXa7Nfbss5cCVG4jzEQvXPxbGUQtfis9yDeIrzcO7m4iiOoz4c\n21yhzjyvd7zjHdl7QV3o9EeyjDqTzJs/f36WbOFc73vf+wIjEnB0MCQBN3bs2LclUni2vA+4cl6S\nJ5yL+JUEC7Es7yDX4t/jjz8+68BIfbnWU089lc0CgRXH1i95/UgGcR06T5K8syiggAIKKKBA+wuY\nSGl/8/26Ig0/fmi80WOFctRRRwW9lWgM0+CkMUYPGhrWNMYZCk9jjYQGjV4a4wxdJklC45j1TggM\n8gb8HXfcsbvRzL6ch0YdjXD2J9ChYfj4449nw5fzxihJjvrD7qkbx9EY5dw0hKkfU3XRUCVRwTaO\nIUBpLADhHAQFNDg5hgCCxjqF46kTiRbujcL5mQKAxijJIf6m8U9QxXUInJimjHpxX5yDIIfAi149\n/M4+BEu5J8dzb/Rs4nwkb+hBx5RnnIdr44Ip1yF4mD17djZKhsYx9eX81J3nwZBungOfYUfPNI7H\nmkY2AQd1JdDBpCWBQ3bz/kcBBRRQQAEFFFBAgU4uwJRefOE8d+7cOP/88+MDH/hAVmPaxiQXaFvT\nxt7bwnH0/M8LMdK1116bffFN8oO2NdcgFqE9Tvt8XhqtwU+hRArnok6MQLj11luzEQe01Z9++uns\nS21GohxyyCH5Jff4lximfp2IB6655pqsTnxhzrF80U6ChRiCWIE4j7iEJAoJEOIovkznC3ySGYxs\nID6hExwJAOpDgueII47YHZfkFSHmIDbCkzgl/5Kea3D/xCAUkhDEPvxwLZIXfFl/6KGHZrEIBsR0\n+BErcd382qNGjcqSS/gTkxLPEONwTuItYiGmhs7PRTKAJBfxEtci1uNzfih48Hkeg3K/uBFrtWYh\nps2nqybhxZRfvBv88HyJ+3g/GaWSx5/cI581HMHDs8M5jwc5HwUbYjzukbg3L9hPmzYt+5N4lPMy\nUoUYtv5IJXzwIInC+8C7RJ3rJ7Xyc/qvAgoooIACCrS9gImUtjdu1SvQAKvfCCOhQTBCI5hGKY3X\nn/zkJ8HwZhphNJ5JHBAsfPjDH84aiH/913+dNc5pfNO75tRTT80a6TSKOTf7kzSg0ciUWTTOacQx\nxJhggB+m66Khlw+b5hgaefWH3XPjNEBJSpAEobGYD7um4U3DkvNyLMmfj3/84402Cmkojk2NferC\ncTSkqRONVYZOM60ZDVAKBgy/Z8g29WQ/gg7q8LnPfW53IoXkBMeQRCIhQvDCNoIZGrGcgwAFX+qH\n1VVXXZUFOKz/Qm85EiAkXrhHjiXpccwxx2Q9j6677rrsWOqAK0EJ+9KrbcaMGdk1eTY0ygl6eH5M\nbcA5eAYkajiGwHJfp2vLQPyPAgoooIACCiiggAKdSIA2MDEBXz7XT0Iw0mL8+PG7kym0+/ensIYE\nX2jzhT3XI5HCl+UkJ0gskKzgy3pGDfB7oc5LbCcJQGcq4gSmVyL+YoRHnphoaT2JBUgMEPtwPhIW\nt99+eza9GXEZHd3oEIcRcQR14wv0PDHBF/DEKHRoIxYjHiG5wWgIkg8XX3xxZpjXhy/8cSAmwoHr\nEXdwPmIMvsDnM+K8X/7yl9nvefxCoov9iSOJrYhbqD9f6lNHPsOGenDtCy64IOvgRtxH/bkGx2BO\nnMh1ed7UlamqiXmIfbg/PDgfsRfTbjFiic8YBcLz4l9GYhR6Rtwv5yO2pL6cJy95nJj/3fBfYjnq\nyvuHBe8E081xLd7HPInCcfjz0zCpw77YcG3eW85F7Mf5SBDxnAolBzkXHSR5BsTyXD8vuBADk9xh\najU6RxK/WhRQQAEFFFCgYwRMpHSM+z5flQYiPZVoeNM4o5FKw5W/adTRqOUL+K985Stx0kknZckC\nej0x9Re9smjg0djmuK9+9au7e8fQC+uGG27IEhkf/ehHs4YoDe8rrrgim2+Whi2JDgoNPK5z9tln\nB3PQ0tuIIeo0vkkwEGg0LDRmCZZoWBIQXHbZZdmcrzSK6d3FKBjqV3/qrvwc3DPX54cRKMznS/3p\nDUSwQQ+xB9KIGgqNYBqyJIfe+973ZqNSODef0/ON++dYegWRWCHZRGP+lltuyRwJbHAlicR8yYxq\nIXFC8EDvKnpikXQh2CJYoG4EDySWaOwzOoZr8YyYs5lnQhKHOXs5jnpRRxrTXIPrY0AShbp98Ytf\nzJ4PdaUBT8PeooACCiiggAIKKKBAdxGgfUtcQGephl9I82U7P61RaNOTdKDdT7zCl9rETHxJzwgV\n2v10liKWYqovvvBmPzpZ5YVjSSxQTxIn7E+8w/HELbTp96Zwb8QMfDnPSBO+XCfZwzqPedxATEU8\nd0paG+NDH/pQlrggBrnooouykRDEDtgx1TPxHvFHviYlSQu+/M8L8ROdtojfiNdIcnAvnI/9uA+S\nD8Q6xFCf+cxnsvoQr5Gs4bP6hUQBBhzL+pQcQ+zD/iReqBfPdmzqBEcsxb98TnIEY+6fa3H9K6+8\nMnsGJEqI67gWiQPiO9bsJN6i3r/4xS+y2JPng1GhgiUJM2K3+qV+YiLfTjzMcybBQ6KFJBOOJHp4\nNnm8yPOvXxq+r/lnvAeM+OGcrDtKRzwSHmznnHQE5D0qVPL/L/CO1jfn3aR+jAjieLwtCiiggAIK\nKNBxAiZSOs5+n69M45TeUHyJTyOPBj+JkBNPPDFLCjDSg0Y1hYYgjec//OEPWQ8ZvqxnG0kLGnR5\nyQMGes9wfF64BoXt+ZB3rs/IjzxhQgOTuWAZxUFvmXx7fo78XxqsNFA5F3Wi0EOKetPYpKFcqNDY\nJoFCQ5ueOARGjEYh8GAoO8kJCo1bGt005uktRV0ZEUKjHSfunwQRI2nyuWVpkLOd/TieUSUkTfLj\n6SVFEEKww3VpjGOXO9EjjcQShX1o/NMYJsjghzpwzdwYBz6nVxGFZA3BEkkmkkrUgaCPXlf0ErMo\noIACCiiggAIKKNCdBIgx6HiUxxp7e29527+p42h/E/fwBTvxB19I88U0bXviGj7ny2m+RKcTE194\nz0vTfF1//fW7T0tcQwcwkj8kMGj382U27Xv+pl3PuVta8jrxb379PGFAAoDz82U6MQXxBzEKBSu+\nmCdGYApg/iXpwBow1IV7I97huPqFbYx24At9Eh3EW8Qk3DOdvIiPWOic6zJ7QD4lFZ29uA5f4NPh\nKy/ES1wjn+KK7VybOhPTEOPwTIm1mDWAQrzHcyB+5HjMGB1EIotCLEYdSDARExHfUkcSPxTOzTau\n01zh2sRr/OSFxARG9Qv3TfzJKBDOzefUl6mzODZ/P+sfw+/UhdLY6BLs6JCIMzMnkLDjfohLeX/y\n2C87QYP/cD3OyXPOz821eH4ku3hu+YwJJLHyfRqcxj8VUEABBRRQoI0FTKS0MXBrn56gIU9EkMCg\nEUXDeGzq7cPf9PChIZY3vvmchjaNMhqtFH5v2Ltmb+vJOeqXvLGZX6P+Z/nv1J1GJA3CPChgpAoN\nd45v6lga5TTmafDT4KXxTgP7lNRTq36hAc8IFBqsNNhJStBo5/x54ff6AQ9/559zPAkUptbieAIW\nEifUPf+pP1Sc32nk5h78jT2N87xwz/zkgQn7EjjlBQvug6COZApBAuchqGBxw3wtnHx//1VAAQUU\nUEABBRRQoKsK0A4mFuEL7IajBfjSm7YwyQra58QybOMn/3KcdjJxA23zpr5Qpk3OCBTa6sQExA6s\nV8ExJFT44Txci+mTGGFC2z/vbIUvHbY4ni/GibPy0fCc5957782+/B+b4rCWFmIE6sQ90FGM8/z8\n5z/PHIjpiFFINjQVq1EXphej/sQrnId7xaNhoe44sw+j+LEnaUAi5eabb85GqORxTP3RDiRx6v+d\nn5fzUT/OSdyUF5IleQcwfOvHOvVjLY7nb+LBvHCv9euOD7MQcG8UPiN5QJ2aKuzHs7j00kt3Tz/N\n/nRAJL6rX/AgSca/1Bd7Ejsk1vJpvXAiTq1fuHe2c695/ficWI93lMRTnujgWZOgIzHHSKCmpoFj\nX0yJC3N3zkfnRzpIMjqIxA8zKVBX4nuLAgoooIACCrS/wFvf5rb/tb3iPgjQQKTxSc+Uhl+wkzCg\n8c+Qaxpt9G6i8Zevc0IAUajkDVwagTTE878b25/PaPwzuoTCMfxOw5KGXaFCI5WECL23GPHBKJa8\ngUkQRUO2UKGxzTy+BDY0IGmgMiKEUSqMTMkLdSCo4N6ZegwP5h0m0GlJIZmBHSNmzj333Ky+BCp5\nTyzqQeOWHmD02iIY4HeuSy8qgh8awNTv05/+dBYEEXDRM4n6M9y+YeEZ4UAjmfvjfCSCCNaYFqzh\nc254vH8roIACCiiggAIKKNBVBGhLMwKBxAg99vmdbcQXtINZ35BpnNhO/MKI93lppAhtf2Ih4g56\n+/PlPfED7WgK7XLiGArxCdM80UmJL+D54pvkAdsYecC1KMQ1FHr8k9RgeqaPfexj2bb8P7Tz+SKb\n4xm5TgcoOnbdd9992b/EAIUSH3lsxbn4nfvgOsQojNpguitiBabU4ktz/ma9y7zkMRn3RtKHL/H5\nop/Ygv2JV3BgGmdmIGhYiLv4Ap57IJHCF/2ck2OwJW4k6UEMQz2IyYjX2M4PMU39wnMi2cA0Z0yj\nzN8kvohDiQOJO5sqJB+ICYmJeG64kRgiKcO1SOqQ0GCKZGIgkgvcA6WpWJHPMeKnpYVObvli8/WP\n4dmMTQkZ4jGeFUkL3lWeH+8A24g168do1J8RI9gS4/K+cS84cz5K/brxLPPkF8+U2Ji/iUHz/bHi\nnacuPCOeObMz4MQ18sRi/br7uwIKKKCAAgq0rYCJlLb1bfWz543pxk5MY4ov4mnUk2wgaKDhyZyz\nBAz5VFSNHZsnOWjIkjTgWIIXGtINC406GnQEEAQANPpovNNYJOhprFBv6sdxNChpaHJuGvWMMqEh\nnw/vbux4trEPCSTujWCHhdgb9sbhOtSDOhEI5MFK/R5Dhc7P9vx4GrQEBBxPQJP3gKIBzz0wlVd+\nT3yeB2PcG9Z8jgkNdAI2RrjQ+CZ4aVgIUuglRWBIooZnQX35vX5vrobH+bcCCiiggAIKKKCAAl1N\ngHYuvfNpHz+Q1s+gUxEjP/jSnC+qSabwZT2dlohtmOKJKayYeov2Mh2NaD+z3iDHMZUuX1LT5qb9\nzT605fnimYQL0wgTd7Af8QjrjtDBicI1Gf3A6AzqQyet+u11vvDmi3PWReHLcdY7ZNor2uj5dEvU\nIZ96uP6zoO3PveR1InFA3fnS/ZSUjMlHPxC7EIPROYw1OxiRQTxBob7EBNwv6zISi5DwoDBigU5g\nJKOoO0mNhgVbHPiint+pO9ej7lyHuAZXfidxwOgN4hcSUNSFetYvJI2Id4gvSZwQC9KJjePpxNZc\n4Z754Vr8S/xH/eelmIupr5iBgPqRMCDW4r45P9tJXvC8SL7kU07Xvx7787O/BWOSVDwr3juSPsSc\nxJZ5wiN/f/Jr8Sy4B36Ib4m7qQvPn+dNDEssSyHGJOHE+8o7wj1iwHvEfo0l5bjfs846Kxu9xP9n\ncG8uds7r5r8KKKCAAgoo0HoCvf42ldY7nWdqSwECBBpiNCDpPdOwVw4Nehq3BA98kU8DjSQFjdGL\nL744C1hoQNPAo5FGYy0vNPQ4Pw1tejmRgKERTY8YGvacNx+OzBf9/HB+emfRoCQwOP/887OAhoYk\nx9DgY3oqGvucn54zJGeoH8ObaYBzLIXFE2mwNiz0cCKgyhcXJPDifCQ2WJSRvxmmzw8NWoIOGq8E\nRDRICXwInAhACBIIjOjFRuOWvyl4so0GMj2LCEgIVGi0kyQhKOE63AvX4NoEC5yfxjHu7MN1mFcX\nKz4n0cQ5CERwZC5gGsbUP78W18eGYA9PgjSeGYEedSRAbGqUD8dbFFBAAQUUUEABBRToSgIkCPhC\nnjY8cQttdhIJxCAkKxgZQuKCtjvxC4kCPqeNTHzAeoV8yc+X03SgIn6hPU/bnR8SBbTfSaLka0ey\nD4kZjs2TDrTjiROIZYgJiDnyEQF48qU3bXM+ZwQGiRYK9addTxueDlXEOFwvL3zpTh34kjyvE/ER\n56Z9zzRixHLETcQTJA5ILuDBPfND4oA4jnpRd0bmkNjhWsRtmBBLsZ0RJZybWKh+5znqyXWoJ/eB\nIbEVdRmbkigkP4h/iDfYlscixITEcsRnxG4Y88V9nkTimsRbxC8kq4iTsGFfzDg3+1Kw4LlyPmJQ\nrsX95PEq98++jMYgRuL51o8V8SX2Jf780Y9+lMVZxJX1C+8IcTLHU4+8Exz7sJ37p/7Ev7wnJDB4\nF4jbGivEdfxQN2JW3jtmLaAOvJ/Utf7z5j1kVgLOzTPJnbHhOjhzbf7GjliR94K4HC+mGSOOxI1n\nmcf8xKskkSg8X94HfEik8GNRQAEFFFBAgfYV6JEaO3tOptq+dfBqrSxAY5sGMgkFGs/09mk4LHt/\nL8k18h5VnItr5ImJ5s5NQ5ceTiQsCF5oHBaqH412AisCFRro/E2jM09s5EkbGuf5PjTWuX/2pXFM\nMJEnVFrSc6fh8TT6uVcar/xOw5/AKO9VRIDEiBICryuvvDK7fRq4JFCoA41wGrrDhw8vSMN+NKpp\nNBMgcS/s39QxBU/mBwoooIACCiiggAIKdHIB2rz09ufLZdrOfIFM25cvk+sX2sl8sU9nJL5I5sty\n2tYkYrpDwYAv6inEABSSDSRMChXiKOIb4irM2J9Ygi/zGzsOX5ICxIcU7Phin2RVXohDiLPy+IW4\nh4ROY4WEAdcnIUGs1fCZNXZM/W3cM8+U+hMrcQ6eK4V7417yd4L74R6p30033ZTFt5/85Cfrn67N\nfuc+ccOE5BsxLyb1kyj1L04dGWlCbEpnOZJYODbsAFn/GH9XQAEFFFBAga4jYCKl6zwra9oJBOgl\nxporBHz0HCKYI2lDcoWpxk5pMPy9E1TZKiiggAIKKKCAAgoooIACXVqA0S/MGsAof34sCiiggAIK\nKKBAewuYSGlvca/XpQUYrcJQc9Z4ocdRPq0Xw/OZNsDeRl368Vp5BRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBgDwETKXuQuEEBBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU2CXQUwgFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQIHGBUykNO7iVgUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFAg\nTKT4EiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACBQRMpBSAcbMCCiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgooYCLFd0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAARMpvgMKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiig\nQAEBEykFYNysgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCphI8R1QQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBQoImEgpAONmBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKFBAwERKARg3K6CAAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAImUnwHFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIECAiZSCsC4WQEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRQwkeI7oIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoUEDCRUgDG\nzQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAiRTfAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFCggICJlAIwblZAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFTKT4DiiggAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACBQRMpBSAcbMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooYCLF\nd0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAARMpvgMKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQAEBEykFYNysgAIKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCphI8R1QQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQoI\nmEgpAONmBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKFBAwERKARg3K6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAImUnwHFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQIECAiZSCsC4WQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRQwkeI7oIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoUEDCRUgDGzQoooIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAiRTfAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCggICJlAIwblZA\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFTKT4DiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACBQRMpBSAcbMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooYCLFd0ABBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAARMpvgMK\nKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQAEBEykFYNysgAIKKKCAAgoooIACCiiggAIKKKCA\nAgoooIACCphI8R1QQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQoImEgpAONmBRRQQAEFFFBA\nAQUUUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKFBAwERK\nARg3K6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAImUnwHFFBAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQIECAiZSCsC4WQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQwkeI7oIACCiiggAIK\nKKCAAgoooIACCiiggAIKKKCAAgoUEDCRUgDGzQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCA\niRTfAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCggICJlAIwblZAAQUUUEABBRRQQAEFFFBA\nAQUUUEABBRRQQAEFTKT4DiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACBQRMpBSAcbMCCiig\ngAIKKKCAAgoooIACCiiggAIKKKCAAgooYCLFd0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUU\nKCBgIqUAjJsVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAARMpvgMKKKCAAgoooIACCiiggAIK\nKKCAAgoooIACCiigQAEBEykFYNysgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCphI8R1QQAEF\nFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQoImEgpAONmBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBA\nAQUUUMBEiu+AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKFBAwERKARg3K6CAAgoooIACCiig\ngAIKKKCAAgoooIACCiiggAImUnwHFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIECAiZSCsC4\nWQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQwkeI7oIACCiiggAIKKKCAAgoooIACCiiggAIK\nKKCAAgoUEDCRUgDGzQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAiRTfAQUUUEABBRRQQAEF\nFFBAAQUUUEABBRRQQAEFFFCggICJlAIwblZAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFTKT4\nDiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACBQRMpBSAcbMCCiiggAIKKKCAAgoooIACCiig\ngAIKKKCAAgooYCLFd0ABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEABBRRQ\nQAEFFFBAAQUUUEABBRRQQAEFFFBAARMpvgMKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQAEB\nEykFYNysgAIKKKCAAgoooIACCiiggAIKKKCAAgoooIACCphI8R1QQAEFFFBAAQUUUEABBRRQQAEF\nFFBAAQUUUEABBQoImEgpAONmBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoooIAC\nCiiggAIKKKCAAgoooIACCiiggAIKKFBAwERKARg3K6CAAgoooIACCiiggAIKKKCAAgoooIACCiig\ngAImUnwHFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQIECAiZSCsC4WQEFFFBAAQUUUEABBRRQ\nQAEFFFBAAQUUUEABBRQwkeI7oIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoUEDCRUgDGzQoo\noIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAiRTfAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEF\nFFCggICJlAIwblZAAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFTKT4DiiggAIKKKCAAgoooIAC\nCiiggAIKKKCAAgoooIACBQRMpBSAcbMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooYCLFd0AB\nBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEABBRRQQAEFFFBAAQUUUEABBRRQ\nQAEFFFBAARMpvgMKKKCAAgoooIACCiiggAIKKKCAAgoooIACCiigQAEBEykFYNysgAIKKKCAAgoo\noIACCiiggAIKKKCAAgoooIACCphI8R1QQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQoImEgp\nAONmBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoooIACCiiggAIKKKCAAgoooIAC\nCiiggAIKKFBAwERKARg3K6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAImUnwHFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUUEABBRRQQIECAiZSCsC4WQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQw\nkeI7oIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoUEDCRUgDGzQoooIACCiiggAIKKKCAAgoo\noIACCiiggAIKKKCAiRTfAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCggICJlAIwblZAAQUU\nUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFTKT4DiiggAIKKKCAAgoooIACCiiggAIKKKCAAgoooIAC\nBQRMpBSAcbMCCiiggAIKKKCAAgoooIACCiiggAIKKKCAAgooYCLFd0ABBRRQQAEFFFBAAQUUUEAB\nBRRQQAEFFFBAAQUUKCBgIqUAjJsVUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFBAARMpvgMKKKCA\nAgoooIACCiiggAIKKKCAAgoooIACCiigQAEBEykFYNysgAIKKKCAAgoooIACCiiggAIKKKCAAgoo\noIACCphI8R1QQAEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBQoImEgpAONmBRRQQAEFFFBAAQUU\nUEABBRRQQAEFFFBAAQUUUMBEiu+AAgoooIACCiiggAIKKKCAAgoooIACCiiggAIKKFBAwERKARg3\nK6CAAgoooIACCiiggAIKKKCAAgoooIACCiiggAImUnwHFFBAAQUUUEABBRRQQAEFFFBAAQUUUEAB\nBRRQQIECAiZSCsC4WQEFFFBAAQUUUEABBRRQQAEFFFBAAQUUUEABBRQwkeI7oIACCiiggAIKKKCA\nAgoooIACCiiggAIKKKCAAgoUEDCRUgDGzQoooIACCiiggAIKKKCAAgoooIACCiiggAIKKKCAiRTf\nAQUUUEABBRRQQAEFFFBAAQUUUEABBRRQQAEFFFCggICJlAIwblZAAQUUUEABBRRQQAEFFFBAAQUU\nUEABBRRQQAEF/i+bAdpnnW728gAAAABJRU5ErkJggg==\n"
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"
    }
   },
   "cell_type": "markdown",
   "id": "d7e32f85",
   "metadata": {},
   "source": [
    "# Quantitative Portfolio Optimization developer example\n",
    "\n",
    "This notebook introduces NVIDIA's Quantitative Portfolio Optimization developer example. We use the Mean-CVaR model to solve the problem of portfolio asset allocation. We show step-by-step how to pre-process input data, build the optimization model, solve the problem, and backtest the portfolio performance.\n",
    "\n",
    "![arch_diagram.png](attachment:7e12dc25-f387-4104-b0fb-25d73de45e5f.png)\n",
    "\n",
    "In particular, we will cover the following in this notebook: \n",
    "- **Data-preprocessing** - Calculate daily returns from input price data \n",
    "- **Scenario generation** - Use cuML KDE to model returns distribution and generate return scenarios \n",
    "- **Model Building** - Build the optimization problem with optional constraints\n",
    "- **Solving Optimization on GPU** – Call GPU/CPU solvers with customizable settings to solve the problem\n",
    "- **Backtesting** - Visualize and evaluate optimized portfolio\n",
    "- **Comparing CPU and GPU Performance** – Evaluate performance solving optimization problem on GPU vs CPU\n",
    "\n",
    "If you're new to concepts like **[Mean-CVaR (Conditional Value-at-Risk)](https://www.youtube.com/shorts/9u-VrCyneM4)** and would like a deeper dive into the notebook itself as you do it, **[check out the blog: Accelerating Real-Time Financial Decisions with Quantitative Portfolio Optimization](https://developer.nvidia.com/blog/accelerating-real-time-financial-decisions-with-quantitative-portfolio-optimization/)**\n",
    "\n",
    "Before diving into portfolio optimization...\n",
    "## ***PLEASE ENSURE YOU RUN THIS NOTEBOOK WITH THE \"Portfolio Optimization\" KERNEL***\n",
    "![image.png](attachment:d510574b-6b9e-4ba3-b4a7-7a3f40979945.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061ff854",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "0. [Portfolio Optimization Setup](#portfolio-optimization-setup)\n",
    "   - GPU Check\n",
    "   - Import Libraries\n",
    "\n",
    "2. [Introduction to Mean-CVaR Optimization](#build-a-mean-cvar-problem)\n",
    "   - Mathematical Formulation\n",
    "\n",
    "3. [Data Preprocessing and Model Setup](#data-preprocessing-and-model-setup)\n",
    "   - Data Preparation\n",
    "   - Define Problem Parameters\n",
    "   - Build Mean-CVaR Problem\n",
    "\n",
    "4. [Solve CVaR Optimization](#solve-cvar-optimization)\n",
    "   - cuOpt GPU LP Solver\n",
    "   - CPU Solver Comparison\n",
    "   - Portfolio Visualization\n",
    "   - cuOpt GPU MILP Solver\n",
    "\n",
    "5. [Backtest Portfolio](#backtest-portfolio)\n",
    "   - Testing Methods\n",
    "   - Performance Metrics\n",
    "   - Benchmark Comparison\n",
    "\n",
    "6. [GPU vs. CPU: Comparison over Different Regimes](#gpu-vs-cpu-comparison-over-different-regimes)\n",
    "   - Regime Analysis\n",
    "   - Performance Comparison\n",
    "\n",
    "7. [Appendix](#appendix)\n",
    "   - Optional Parameter Constraints\n",
    "   - cuOpt Python API\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69085f1c",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "## 0. Portfolio Optimization Setup\n",
    "<a id='portfolio-optimization-setup'></a>\n",
    "\n",
    "Before diving into portfolio optimization, we need to import the necessary libraries and perform initial setup if required.\n",
    "\n",
    "### 0.1 GPU Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9abe356-0892-4c66-9c37-df4375bbc436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Dec  6 13:53:03 2025       \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                        |               MIG M. |\n",
      "|=========================================+========================+======================|\n",
      "|   0  NVIDIA GB10                    On  |   0000000F:01:00.0 Off |                  N/A |\n",
      "| N/A   40C    P0             11W /  N/A  | Not Supported          |      0%      Default |\n",
      "|                                         |                        |                  N/A |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "\n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                              |\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n",
      "|        ID   ID                                                               Usage      |\n",
      "|=========================================================================================|\n",
      "|  No running processes found                                                             |\n",
      "+-----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e989cda",
   "metadata": {},
   "source": [
    "### 0.2 Import Libraries\n",
    "\n",
    "Import the portfolio optimization modules and other required packages.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "24968ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cvxpy as cp\n",
    "\n",
    "from cufolio import cvar_optimizer, cvar_utils, utils\n",
    "from cufolio.cvar_parameters import CvarParameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3c96f3",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='build-a-mean-cvar-problem'></a>\n",
    "## 1. Introduction to Mean-CVaR Optimization\n",
    "\n",
    "The Mean-CVaR model captures the risk-return trade-off, aiming to maximize expected return while minimizing Conditional Value-at-Risk. \n",
    "\n",
    "Traditionally, variance of portfolio returns is used as the measure of risk. Here, we use Conditional Value-at-Risk (CVaR) as an alternative risk measure because it provides a more robust assessment of potential tail losses, and allows a data-driven approach to portfolio optimization without making assumptions on the underlying returns distribution. CVaR measures the average worst-case loss of a return distribution. Formally, for a loss random variable L,\n",
    "$$\\text{CVaR} = {E}[L| L \\leq \\text{VaR}_\\alpha (L)]$$\n",
    "where  $\\text{VaR}(L) = \\inf\\{l: P(L \\leq l) \\geq \\alpha  \\}$ is the $\\alpha$-quantile of the loss distribution. CVaR is a more appropriate risk measure for portfolios that may contain assets with asymmetric return distribution - it has replaced Value-at-Risk(VaR) in Basel III market-risk rules; Mathematically, CVaR is a coherent risk measure—satisfying subadditivity, translation invariance, positive homogeneity, and monotonicity—which aligns with the diversification principles. Moreover, it has a computationally tractable transformation as a scenario-based optimization: for confidence level $\\alpha$, the CVaR of portfolio ${w}$ can be written in abstract form as \n",
    "$$\\text{CVaR}({w}) = \\min_t\\left\\{t + \\frac{1}{1-\\alpha}\\sum_{\\omega \\in \\Omega}p(\\omega)\\max \\{\\text{loss}({w},\\omega) - t, 0\\}\\right\\}, $$\n",
    "where $\\Omega$ is the probability space of returns scenarios and $p(\\omega)$ is the probability of a particular scenario $\\omega \\in \\Omega$. Intuitively, this expression represents the portfolio’s average loss below the $\\alpha$-quantile of all return scenarios. This scenario-based formulation makes CVaR more robust regardless whether the asset returns distribution is Gaussian or not. When we use linear loss, i.e. $\\text{loss}(w,\\omega) = -R(\\omega){w}$, with $R$ as the return scenarios matrix of size (num_scenarios, num_assets),  the minimization of the CVaR can be transformed into a linear program by replacing the $\\max\\{\\cdot,0\\}$ non-negative operator with an auxiliary variable ${u}$: \n",
    "\n",
    "\\begin{align*}\n",
    "\\text{minimize} \\; \\quad & t + \\frac{1}{1-\\alpha}{p}^\\top {u},\\\\\n",
    "\\text{subject to} \\quad &{u} +t \\geq -R^\\top {w},\\\\\n",
    "&u\\geq 0\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "\n",
    "Then, we add constraints to model real-world trading limitations:\n",
    "- Concentration limits on single assets\n",
    "- Amount invested in risk-free assets (cash)\n",
    "- Investment budgets\n",
    "- Leverage constraints\n",
    "- Turnover from an existing portfolio\n",
    "- Cardinality constraints (maximum number of assets allowed in the portfolio)\n",
    "\n",
    "\n",
    "The mathematical formulation of the problem is given as follows: \n",
    "\\begin{align*}\n",
    "\\text{maximize } \\quad &\\mu^\\top {w} - \\lambda_{\\text{ risk}}\\left(t + \\frac{1}{1-\\alpha}p^\\top {u}\\right)\\\\\n",
    "\\text{subject to} \\quad &{u} +t \\geq -R^\\top {w}, \\, u \\geq 0 \\quad \\text{ (CVaR)}\\\\\n",
    "& \\sum_i w_i + c = 1\\quad \\text{(self-financing)}\\\\\n",
    "& w_i^{\\text{ min}} \\leq w_i \\leq w_i^{\\text{ max}}, \\forall i \\quad \\text{ (concentration)}\\\\\n",
    "& c^{\\text{ min}} \\leq c \\leq c^{\\text{ max}} \\quad \\text{ (cash)}\\\\\n",
    "& L= \\Vert {w} \\Vert_1 \\leq L^{\\text{ limit}} \\quad\\text{ (leverage)}\\\\\n",
    "& T = \\Vert {w} - {w}_{\\text{ pre}}\\Vert_1 \\leq T^{\\text{ limit}}\\quad \\text{ (turnover)}\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948b8261",
   "metadata": {},
   "source": [
    "## 2. Data Preprocessing and Model Setup\n",
    "<a id='data-preprocessing-and-model-setup'></a>\n",
    "\n",
    "We start with an example using a 397-stock subset of the S&P 500 stocks. We aim to build a long-short portfolio that maximizes risk-adjusted returns while meeting custom trading constraints. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67635399",
   "metadata": {},
   "source": [
    "### 2.1 Data Preparation\n",
    "\n",
    "We load closing prices from `2021-01-01` to `2024-01-01` and compute daily log‐returns.\n",
    "\n",
    "Key Components:\n",
    "\n",
    "- Historical Dataset (`data_path`): CSV file with adjusted closing prices.\n",
    "- Regime Information (`regime_dict`): select the market regime \n",
    "- Computation Setting (`returns_compute_settings`): \n",
    "    - type of returns to compute \"LOG\", \"NORMAL\"\n",
    "    - frequency: dault to 1 - daily returns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ba44d4",
   "metadata": {},
   "source": [
    "#### Disclaimer\n",
    "*Each user is responsible for checking the content of datasets and the applicable licenses and determining if suitable for the intended use.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "31173d1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rapids/notebooks/setup/src/utils.py:530: FutureWarning: YF.download() has changed argument auto_adjust default to True\n",
      "  data = yf.download(tickers, start=start_date, end=end_date, timeout = 30)\n",
      "[*********************100%***********************]  392 of 392 completed\n"
     ]
    }
   ],
   "source": [
    "# User inputs for S&P 500 example\n",
    "dataset_name = \"sp500\"\n",
    "dataset_format = \"csv\"\n",
    "data_dir = \"./data/stock_data\"\n",
    "data_path = f\"{data_dir}/{dataset_name}.{dataset_format}\"\n",
    "\n",
    "# Create data folder structure if it doesn't exst \n",
    "if not os.path.exists(data_dir):\n",
    "    print('creating data directory')\n",
    "    os.system(f'mkdir -p {data_dir}')\n",
    "    \n",
    "# Download data if not exists\n",
    "if not os.path.exists(data_path):\n",
    "    utils.download_data(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbbf9697",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set date range\n",
    "regime_name = \"recent\" \n",
    "time_range = (\"2021-01-01\", \"2024-01-01\")\n",
    "\n",
    "\n",
    "# Define the regime for this example\n",
    "regime_dict = {\"name\": regime_name, \"range\": time_range}\n",
    "\n",
    "# Define the settings for returns computation\n",
    "returns_compute_settings = {'return_type': 'LOG', 'freq': 1}\n",
    "\n",
    "# Compute returns from price data\n",
    "returns_dict = utils.calculate_returns(\n",
    "    data_path,\n",
    "    regime_dict,\n",
    "    returns_compute_settings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6057226b",
   "metadata": {},
   "source": [
    "### 2.2 Scenario Generation\n",
    "Assume the return distribution is stationary over the optimization period and use historical returns to approximate future ones.\n",
    "In particular, we use `cuml.KDE` on GPU or `sklearn.KDE` on CPU to fit return distribution and sample scenarios. Also support fitting a Multivariate Gaussian distribution and sampling from it. One can configure the scenario generation step by setting `scenario_generation_settings`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "954e3c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the settings for scenario generation\n",
    "scenario_generation_settings = {'num_scen': 10000, # Number of return scenarios to simulate \n",
    "                                'fit_type': 'kde', \n",
    "                                'kde_settings': {'bandwidth': 0.01, \n",
    "                                                 'kernel': 'gaussian', \n",
    "                                                 'device': 'GPU'\n",
    "                                                 },\n",
    "                                'verbose': False\n",
    "                                }\n",
    "\n",
    "# Generate return scenarios from KDE\n",
    "returns_dict = cvar_utils.generate_cvar_data(\n",
    "    returns_dict,\n",
    "    scenario_generation_settings\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b0afab",
   "metadata": {},
   "source": [
    "### 2.3 Define Optimization Parameters\n",
    "We define a `CvarParameters` object to encapsulate key constraints and settings for our portfolio optimization pipeline. This object configures the Mean-CVaR model with both required and optional parameters:\n",
    "\n",
    "Required parameters: Settings like `w_min`, `w_max`, `risk_aversion`, `confidence`, `num_scen`, and `fit_type` must be specified to establish the basic optimization framework.\n",
    "\n",
    "Optional constraints: Parameters such as `T_tar` (turnover constraint), `cvar_limit` (maximum CVaR threshold), and `cardinality` (portfolio size limit) are optional. Setting any of these to a non-`None` value will add the corresponding constraint to the Mean-CVaR model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "69d91a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define CVaR optimization parameters for the S&P 500 example\n",
    "cvar_params = CvarParameters(\n",
    "    w_min={\"NVDA\":0.1, \"others\": -0.3}, w_max={\"NVDA\": 0.6, \"others\": 0.4},  # Asset weight allocation bounds\n",
    "    c_min=0.0, c_max=0.2,  # Cash holdings bounds\n",
    "    L_tar=1.6, T_tar=None,  # Leverage and turnover (None for this example)\n",
    "    cvar_limit=None,  # Max CVaR (None = unconstrained for this example)\n",
    "    cardinality=None,  # Cardinality constraints\n",
    "    risk_aversion=1,  # Risk aversion level\n",
    "    confidence=0.95  # CVaR confidence level (alpha)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f3cdbe",
   "metadata": {},
   "source": [
    "### 2.4 Build Mean-CVaR Problem\n",
    "We take the processed data `returns_dict` and the problem parameters `cvar_params` and formulates the problem `cvar_problem`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53e27d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate CVaR optimization problem for the S&P 500 example\n",
    "cvar_problem = cvar_optimizer.CVaR(\n",
    "    returns_dict=returns_dict,\n",
    "    cvar_params=cvar_params\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba37c6be",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='solve-cvar-optimization'></a>\n",
    "## 3. Solve CVaR Optimization\n",
    "\n",
    "### 3.1 cuOpt GPU LP Solver\n",
    "We then call the cuOpt LP solver and returns the optimized portfolio. One may also provide customized configurations to the cuOpt LP solver including solver mode, accuracy, etc. \n",
    "\n",
    "cuOpt LP solver supports four solve methods: choose one of the following by setting `solver_method`: \n",
    "1. PDLP algorithm has three solver modes: choose one of the following by setting `solver_mode`:\n",
    "    - \"Stable2\": balance speed and accuracy\n",
    "    - \"Methodical1\": prioritize accuracy\n",
    "    - \"Fast1\": prioritize speed\n",
    "\n",
    "2. Dual Simplex\n",
    "3. Barrier Method\n",
    "4. Concurrent: runs all three algorithms and return the fastest.\n",
    "\n",
    "For details, see cuOpt documentation: https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "898d3f8c-2649-4c4b-90ba-30e38ed4b7ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "Setting parameter time_limit to 1.500000e+01\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              recent\n",
      "Time Period:         2021-01-01 to 2024-01-01\n",
      "Scenarios:           10,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002553 (0.2553%)\n",
      "CVaR (95%):          0.025875 (2.5875%)\n",
      "Objective Value:     -0.001445\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.1277 seconds\n",
      "CVXPY API Overhead:  0.0652 seconds\n",
      "Solve Time:          2.0053 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2021-01-01 to 2024-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "LLY         0.289 ( 28.94%)\n",
      "NVDA        0.172 ( 17.18%)\n",
      "MCK         0.167 ( 16.72%)\n",
      "JBL         0.101 ( 10.14%)\n",
      "COP         0.097 (  9.68%)\n",
      "IRM         0.091 (  9.11%)\n",
      "PWR         0.088 (  8.84%)\n",
      "IT          0.063 (  6.32%)\n",
      "NUE         0.052 (  5.17%)\n",
      "FICO        0.042 (  4.22%)\n",
      "STLD        0.037 (  3.70%)\n",
      "Total Long    1.200 (120.01%)\n",
      "\n",
      "SHORT POSITIONS (2 assets)\n",
      "--------------------------\n",
      "MTCH       -0.279 (-27.90%)\n",
      "ILMN       -0.121 (-12.09%)\n",
      "Total Short   -0.400 (-39.99%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.000 ( -0.01%)\n",
      "\n",
      "Net Equity         0.800 ( 80.02%)\n",
      "Total Portfolio    1.000 (100.02%)\n",
      "Gross Exposure     1.600 (160.00%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# GPU solver settings\n",
    "gpu_solver_settings = {\"solver\": cp.CUOPT, \n",
    "                       \"verbose\": False, \n",
    "                       \"solver_method\": \"PDLP\", \n",
    "                       \"time_limit\":15, \n",
    "                       \"optimality\": 1e-4\n",
    "                       }\n",
    "\n",
    "# Solve on GPU\n",
    "gpu_results, gpu_portfolio = cvar_problem.solve_optimization_problem(solver_settings=gpu_solver_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2c151cb0-58f6-4cb7-8537-ab978cbf2cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regime          recent\n",
       "solver           CUOPT\n",
       "solve time    2.005276\n",
       "return        0.002553\n",
       "CVaR          0.025875\n",
       "obj          -0.001445\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpu_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1483b8",
   "metadata": {},
   "source": [
    "### 3.2 Solve on CPU solver\n",
    "Using CVXPY as the modeling API, we can easily change to any CPU solver supported on CVXPY to solve the same problem. We can compare the performance and verify that CPU and GPU solver results are the same up to the tolerance level. Just change the solver in the solver settings dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4c1fa1e-3e89-42ec-8abb-168f3d3059b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              recent\n",
      "Time Period:         2021-01-01 to 2024-01-01\n",
      "Scenarios:           10,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002553 (0.2553%)\n",
      "CVaR (95%):          0.025924 (2.5924%)\n",
      "Objective Value:     -0.001443\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.3141 seconds\n",
      "CVXPY API Overhead:  0.1857 seconds\n",
      "Solve Time:          16.4686 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2021-01-01 to 2024-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "LLY         0.289 ( 28.94%)\n",
      "NVDA        0.172 ( 17.17%)\n",
      "MCK         0.166 ( 16.58%)\n",
      "JBL         0.102 ( 10.20%)\n",
      "COP         0.097 (  9.67%)\n",
      "IRM         0.091 (  9.07%)\n",
      "PWR         0.088 (  8.82%)\n",
      "IT          0.066 (  6.58%)\n",
      "NUE         0.051 (  5.06%)\n",
      "FICO        0.041 (  4.14%)\n",
      "STLD        0.038 (  3.77%)\n",
      "Total Long    1.200 (119.99%)\n",
      "\n",
      "SHORT POSITIONS (2 assets)\n",
      "--------------------------\n",
      "MTCH       -0.279 (-27.94%)\n",
      "ILMN       -0.121 (-12.06%)\n",
      "Total Short   -0.400 (-39.99%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.800 ( 79.99%)\n",
      "Total Portfolio    1.000 (100.00%)\n",
      "Gross Exposure     1.600 (159.98%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cvar_problem = cvar_optimizer.CVaR(\n",
    "    returns_dict=returns_dict,\n",
    "    cvar_params=cvar_params,\n",
    "    api_settings={\"api\": \"cvxpy\"}\n",
    ")\n",
    "\n",
    "# CPU solver settings\n",
    "cpu_solver_settings = {\"solver\":cp.CLARABEL, \"verbose\": False, \"tol_gap_abs\": 1e-4, \"tol_gap_rel\": 1e-4, \"tol_feas\": 1e-4}\n",
    "\n",
    "# Solve on CPU\n",
    "cpu_results, cpu_portfolio = cvar_problem.solve_optimization_problem(solver_settings=cpu_solver_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b8f73f3b-e7fa-4cfd-8b54-0cbbfaed8c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regime           recent\n",
       "solver         CLARABEL\n",
       "solve time    16.468553\n",
       "return         0.002553\n",
       "CVaR           0.025924\n",
       "obj           -0.001443\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cpu_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e993daf-d209-44c3-ae15-e6ec3991a69b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "SOLVER COMPARISON\n",
      "============================================================\n",
      "Solver          Solve Time (s)  Objective    Return     CVaR      \n",
      "-----------------------------------------------------------------\n",
      "cuOpt (GPU)     2.0053          -0.001445    0.002553   0.025875  \n",
      "CLARABEL (CPU)  16.4686         -0.001443    0.002553   0.025924  \n",
      "\n",
      "Objective Differences:\n",
      "cuOpt (GPU) vs CLARABEL (CPU): 0.00000194\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compare results between GPU and CPU solvers\n",
    "utils.compare_results(gpu_results, cpu_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb878a29",
   "metadata": {},
   "source": [
    "### 3.3 Optional: Visualize Optimized Portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1cd3113-f64c-4955-9d39-9cd52ad0d84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot portfolio\n",
    "ax = gpu_portfolio.plot_portfolio(show_plot = True, min_percentage = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ded9844",
   "metadata": {},
   "source": [
    "### 3.4 Adding Cardinality Constraint: Mixed-Integer Linear Program \n",
    "\n",
    "A **cardinality constraint** limits the maximum number of assets held in the portfolio, promoting sparsity and reducing transaction costs. This constraint transforms the problem into a **Mixed-Integer Linear Program (MILP)** because it requires binary decision variables to track whether each asset is included.\n",
    "\n",
    "**Mathematical Formulation:**\n",
    "\n",
    "We introduce binary variables $y_i \\in \\{0,1\\}$ for each asset $i$, where:\n",
    "- $y_i = 1$ if asset $i$ is held in the portfolio (i.e., $w_i \\neq 0$)\n",
    "- $y_i = 0$ otherwise\n",
    "\n",
    "The cardinality constraint is then:\n",
    "$$\\sum_{i=1}^{N} y_i \\leq K$$\n",
    "where $K$ is the maximum number of assets allowed. To link the binary variables with the continuous weights, we add:\n",
    "$$w_i^{\\min} \\cdot y_i \\leq w_i \\leq w_i^{\\max} \\cdot y_i, \\quad \\forall i$$\n",
    "\n",
    "This ensures that $w_i = 0$ when $y_i = 0$, and $w_i$ can take non-zero values only when $y_i = 1$. The presence of integer decision variables $y_i$ makes this a MILP.\n",
    "\n",
    "The complete problem can be written as follows:\n",
    "\n",
    "\\begin{align*}\n",
    "\\text{maximize } \\quad & \\mu^\\top {w} - \\lambda_{risk}\\left(t + \\frac{1}{1-\\alpha}p^\\top {u}\\right),\\\\\n",
    "\\text{subject to } \\quad & {1}^\\top {w} = 1,\\\\\n",
    "&{u} \\geq -R^\\top {w} - t,\\\\\n",
    "& {w}^{\\min} \\circ {y} \\leq {w} \\leq {w}^{\\max} \\circ {y}, c^{\\min} \\leq c \\leq c^{\\max},\\\\\n",
    "& L= \\Vert {w} \\Vert_1 \\leq L^{tar},\\\\\n",
    "& T = \\Vert {w} - {w}_{b}\\Vert_1 \\leq T^{tar},\\\\\n",
    "& \\sum{{y}_i} \\leq K,\\\\\n",
    "&{y}_i \\in \\{0,1\\}^N.\n",
    "\\end{align*}\n",
    "\n",
    "**Model Set-Up:** \n",
    "\n",
    "To set up a MILP problem, we just need to set the `cardinality` to an integer number (max number of assets allowed in the portfolio) when instantiating the `CvarParameters` dataclass. cuOpt offers MILP solvers with a GPU-accelerated heuristics algorithm and a CPU branch-and-cut algorithm. Since MILPs are much challenging to solve in general, we should expect a longer solve time. After solving, one can verify that the number of assets in the portfolio is indeed less than or equal to the cardinality constraint. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f6fc040a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "MIXED-INTEGER LINEAR PROGRAMMING (MILP) SETUP\n",
      "==================================================\n",
      "Cardinality Constraint: K ≤ 10 assets\n",
      "==================================================\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter mip_absolute_tolerance to 1.000000e-04\n",
      "Setting parameter time_limit to 2.000000e+02\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              recent\n",
      "Time Period:         2021-01-01 to 2024-01-01\n",
      "Scenarios:           10,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "Cardinality Limit:   10 assets\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002576 (0.2576%)\n",
      "CVaR (95%):          0.026755 (2.6755%)\n",
      "Objective Value:     -0.001431\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.1288 seconds\n",
      "CVXPY API Overhead:  0.1748 seconds\n",
      "Solve Time:          45.3261 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2021-01-01 to 2024-01-01\n",
      "\n",
      "LONG POSITIONS (8 assets)\n",
      "-------------------------\n",
      "LLY         0.295 ( 29.54%)\n",
      "MCK         0.195 ( 19.54%)\n",
      "NVDA        0.190 ( 18.99%)\n",
      "PWR         0.121 ( 12.11%)\n",
      "COP         0.115 ( 11.53%)\n",
      "JBL         0.114 ( 11.38%)\n",
      "IRM         0.091 (  9.06%)\n",
      "NUE         0.078 (  7.85%)\n",
      "Total Long    1.200 (120.00%)\n",
      "\n",
      "SHORT POSITIONS (2 assets)\n",
      "--------------------------\n",
      "MTCH       -0.273 (-27.28%)\n",
      "ILMN       -0.127 (-12.72%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "\n",
      "Net Equity         0.800 ( 80.00%)\n",
      "Total Portfolio    1.000 (100.00%)\n",
      "Gross Exposure     1.600 (160.00%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rapids/notebooks/setup/.venv/lib/python3.13/site-packages/cvxpy/problems/problem.py:1539: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Define CVaR optimization parameters for the S&P 500 example\n",
    "milp_cvar_params = CvarParameters(\n",
    "    w_min={\"NVDA\":0.1, \"others\": -0.3}, w_max={\"NVDA\": 0.6, \"others\": 0.4},  # Asset weight allocation bounds\n",
    "    c_min=0.0, c_max=0.2,  # Cash holdings bounds\n",
    "    L_tar=1.6, T_tar=None,  # Leverage and turnover (None for this example)\n",
    "    cvar_limit=None,  # Max CVaR (None = unconstrained for this example)\n",
    "    cardinality=10,  # Cardinality constraints\n",
    "    risk_aversion=1,  # Risk aversion level\n",
    "    confidence=0.95,  # CVaR confidence level (alpha)\n",
    ")\n",
    "\n",
    "# Instantiate the MILP problem\n",
    "milp_cvar_problem = cvar_optimizer.CVaR(\n",
    "    returns_dict=returns_dict,\n",
    "    cvar_params=milp_cvar_params\n",
    ")\n",
    "\n",
    "# cuOpt MILP solver settings\n",
    "gpu_solver_settings = {\"solver\": cp.CUOPT, \n",
    "                       \"verbose\": False, \n",
    "                       \"time_limit\":200, \n",
    "                       \"mip_absolute_tolerance\": 1e-4\n",
    "                       }\n",
    "\n",
    "# Solve the MILP problem\n",
    "milp_results, milp_portfolio = milp_cvar_problem.solve_optimization_problem(solver_settings=gpu_solver_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dc4cf60",
   "metadata": {},
   "source": [
    "## 4. Backtest Portfolio\n",
    "<a id='backtest-portfolio'></a>\n",
    "\n",
    "The `portfolio_backtester` evaluates portfolio performance against historical data and benchmarks. It supports multiple testing methods and calculates comprehensive performance metrics:\n",
    "\n",
    "**Testing Methods:**\n",
    "- **Historical**: Uses actual historical return data\n",
    "- **KDE Simulation**: Generates scenarios using Kernel Density Estimation\n",
    "- **Gaussian Simulation**: Generates scenarios from multivariate normal distribution\n",
    "\n",
    "**Performance Metrics:**\n",
    "- **Sharpe Ratio**: Risk-adjusted return relative to volatility (annualized)\n",
    "- **Sortino Ratio**: Return relative to downside deviation (annualized)\n",
    "- **Maximum Drawdown**: Largest peak-to-trough decline\n",
    "\n",
    "**Benchmarking:** By default, compares against an equal-weight portfolio. Custom benchmarks can also be provided.\n",
    "\n",
    "The backtester visualizes cumulative returns over time and allows comparison between the optimized portfolio and benchmark strategies.\n",
    "\n",
    "The `cut_off_date` marks the boundary between training data used for optimization and test data used for out-of-sample evaluation. One can test whether the optimized portfolio is robust to potential regime shifts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "280ab80d-d3d2-4ab2-9564-29034333564c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define test regime and calculate test returns\n",
    "test_regime_dict = {\"name\": \"test_recent\", \"range\": (\"2023-09-01\", \"2024-07-01\")}\n",
    "test_returns_dict = utils.calculate_returns(data_path, test_regime_dict, returns_compute_settings)\n",
    "\n",
    "# Backtest settings\n",
    "test_method = \"historical\"\n",
    "risk_free = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "24e079b7-f0f2-4040-8a13-5b310b9277a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>returns</th>\n",
       "      <th>cumulative returns</th>\n",
       "      <th>mean portfolio return</th>\n",
       "      <th>sharpe</th>\n",
       "      <th>sortino</th>\n",
       "      <th>max drawdown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>portfolio name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CUOPT_optimal</th>\n",
       "      <td>[0.002920315102210369, 0.0013245199061181615, ...</td>\n",
       "      <td>[1.0029245833762488, 1.004253857081673, 1.0131...</td>\n",
       "      <td>0.00302</td>\n",
       "      <td>3.514806</td>\n",
       "      <td>6.410716</td>\n",
       "      <td>0.072152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMZN-JPM</th>\n",
       "      <td>[-0.006441757889020252, -0.010386305987946275,...</td>\n",
       "      <td>[0.9935789457535249, 0.9833127370811252, 0.994...</td>\n",
       "      <td>0.001548</td>\n",
       "      <td>1.966758</td>\n",
       "      <td>3.128698</td>\n",
       "      <td>0.136417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAPL-MSFT</th>\n",
       "      <td>[0.009376198001004993, -0.011796913326585681, ...</td>\n",
       "      <td>[1.0094202922498714, 0.9975822122430166, 0.983...</td>\n",
       "      <td>0.001181</td>\n",
       "      <td>1.867662</td>\n",
       "      <td>2.827687</td>\n",
       "      <td>0.073053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NKE-MCD</th>\n",
       "      <td>[-0.009164162734788928, -0.00906895491356785, ...</td>\n",
       "      <td>[0.9908777002272608, 0.981932099973256, 0.9830...</td>\n",
       "      <td>-0.000643</td>\n",
       "      <td>-1.054596</td>\n",
       "      <td>-1.269279</td>\n",
       "      <td>0.192486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          returns  \\\n",
       "portfolio name                                                      \n",
       "CUOPT_optimal   [0.002920315102210369, 0.0013245199061181615, ...   \n",
       "AMZN-JPM        [-0.006441757889020252, -0.010386305987946275,...   \n",
       "AAPL-MSFT       [0.009376198001004993, -0.011796913326585681, ...   \n",
       "NKE-MCD         [-0.009164162734788928, -0.00906895491356785, ...   \n",
       "\n",
       "                                               cumulative returns  \\\n",
       "portfolio name                                                      \n",
       "CUOPT_optimal   [1.0029245833762488, 1.004253857081673, 1.0131...   \n",
       "AMZN-JPM        [0.9935789457535249, 0.9833127370811252, 0.994...   \n",
       "AAPL-MSFT       [1.0094202922498714, 0.9975822122430166, 0.983...   \n",
       "NKE-MCD         [0.9908777002272608, 0.981932099973256, 0.9830...   \n",
       "\n",
       "               mean portfolio return    sharpe   sortino max drawdown  \n",
       "portfolio name                                                         \n",
       "CUOPT_optimal                0.00302  3.514806  6.410716     0.072152  \n",
       "AMZN-JPM                    0.001548  1.966758  3.128698     0.136417  \n",
       "AAPL-MSFT                   0.001181  1.867662  2.827687     0.073053  \n",
       "NKE-MCD                    -0.000643 -1.054596 -1.269279     0.192486  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<Figure size 3600x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cufolio import backtest\n",
    "\n",
    "# (Optional) Compare results between optimized portfolio and user-defined portfolios\n",
    "portfolios_dict = {'AMZN-JPM':({'AMZN': 0.72, 'JPM': 0.18}, 0.1),\\\n",
    "                   'AAPL-MSFT': ({'AAPL': 0.29, 'MSFT': 0.61}, 0.1),\\\n",
    "                   'NKE-MCD': ({'MCD': 0.65, 'NKE': 0.25}, 0.1)}\n",
    "\n",
    "benchmark_portfolios = cvar_utils.generate_user_input_portfolios(portfolios_dict, test_returns_dict)\n",
    "\n",
    "# Uncomment the following lineto use equal-weight benchmark portfolio\n",
    "# benchmark_portfolios = None \n",
    "\n",
    "# Set cut-off date for backtest visualization\n",
    "cut_off_date = regime_dict[\"range\"][1]\n",
    "\n",
    "# Create backtester and run backtest\n",
    "backtester = backtest.portfolio_backtester(gpu_portfolio, test_returns_dict, risk_free, test_method, benchmark_portfolios = benchmark_portfolios)\n",
    "\n",
    "backtest_result,_ = backtester.backtest_against_benchmarks(plot_returns=True, cut_off_date=cut_off_date)\n",
    "\n",
    "backtest_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d71d7cff-7f48-4d7b-a898-4f298445f4de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined plot saved: ../results/backtest/combined_cuopt_optimal_historical_analysis.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4800x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot portfolio and backtest results side by side\n",
    "utils.portfolio_plot_with_backtest(portfolio=gpu_portfolio, \\\n",
    "                                   backtester=backtester, \\\n",
    "                                   cut_off_date=cut_off_date, \\\n",
    "                                   backtest_plot_title=\"Backtest Results\", \\\n",
    "                                   save_plot = True, \\\n",
    "                                   results_dir = \"../results/backtest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04b193df",
   "metadata": {},
   "source": [
    "## 5. GPU vs. CPU: Comparison over Different Regimes\n",
    "<a id='gpu-vs-cpu-comparison-over-different-regimes'></a>\n",
    "\n",
    "To further compare the performance of cuOpt GPU solver vs. CPU solvers, we can compare CPU vs. GPU performance over solving the same problems over different market regimes. The function `cvar_utils.optimize_market_regimes` generates Mean-CVaR optimization problems iteratively over different regimes from `regime_comparison_selected_dict`, solve each problem using all the solvers specified in `solver_settings_list`, and returns a dataframe of results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbd039c9-347e-4576-b1c4-568a66c8be57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "Processing Regime: pre_crisis\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              pre_crisis\n",
      "Time Period:         2005-01-01 to 2007-10-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003253 (0.3253%)\n",
      "CVaR (95%):          0.027550 (2.7550%)\n",
      "Objective Value:     -0.001916\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2676 seconds\n",
      "CVXPY API Overhead:  0.3611 seconds\n",
      "Solve Time:          40.9632 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2005-01-01 to 2007-10-01\n",
      "\n",
      "LONG POSITIONS (14 assets)\n",
      "-------------------------\n",
      "DLR         0.227 ( 22.74%)\n",
      "AAPL        0.169 ( 16.85%)\n",
      "MNST        0.165 ( 16.55%)\n",
      "ILMN        0.110 ( 10.99%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "ISRG        0.094 (  9.36%)\n",
      "HOLX        0.074 (  7.44%)\n",
      "LKQ         0.063 (  6.34%)\n",
      "SBAC        0.060 (  5.95%)\n",
      "NDAQ        0.048 (  4.78%)\n",
      "VRTX        0.034 (  3.43%)\n",
      "SLB         0.025 (  2.46%)\n",
      "MOS         0.015 (  1.55%)\n",
      "BKNG        0.013 (  1.28%)\n",
      "Total Long    1.197 (119.72%)\n",
      "\n",
      "SHORT POSITIONS (7 assets)\n",
      "--------------------------\n",
      "JCI        -0.106 (-10.59%)\n",
      "LEN        -0.079 ( -7.93%)\n",
      "DHI        -0.071 ( -7.10%)\n",
      "AXON       -0.063 ( -6.29%)\n",
      "AMD        -0.033 ( -3.31%)\n",
      "PHM        -0.027 ( -2.67%)\n",
      "BSX        -0.021 ( -2.11%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.797 ( 79.72%)\n",
      "Total Portfolio    0.997 ( 99.73%)\n",
      "Gross Exposure     1.597 (159.72%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.001916, Time: 40.9632s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              pre_crisis\n",
      "Time Period:         2005-01-01 to 2007-10-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003253 (0.3253%)\n",
      "CVaR (95%):          0.027507 (2.7507%)\n",
      "Objective Value:     -0.001917\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2856 seconds\n",
      "CVXPY API Overhead:  0.0936 seconds\n",
      "Solve Time:          3.2496 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2005-01-01 to 2007-10-01\n",
      "\n",
      "LONG POSITIONS (14 assets)\n",
      "-------------------------\n",
      "DLR         0.228 ( 22.82%)\n",
      "AAPL        0.169 ( 16.86%)\n",
      "MNST        0.166 ( 16.60%)\n",
      "ILMN        0.109 ( 10.88%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "ISRG        0.093 (  9.28%)\n",
      "HOLX        0.075 (  7.49%)\n",
      "LKQ         0.063 (  6.33%)\n",
      "SBAC        0.060 (  5.98%)\n",
      "NDAQ        0.047 (  4.72%)\n",
      "VRTX        0.035 (  3.46%)\n",
      "SLB         0.026 (  2.62%)\n",
      "MOS         0.016 (  1.57%)\n",
      "BKNG        0.011 (  1.15%)\n",
      "Total Long    1.197 (119.74%)\n",
      "\n",
      "SHORT POSITIONS (7 assets)\n",
      "--------------------------\n",
      "JCI        -0.105 (-10.53%)\n",
      "LEN        -0.079 ( -7.86%)\n",
      "DHI        -0.072 ( -7.18%)\n",
      "AXON       -0.063 ( -6.32%)\n",
      "AMD        -0.033 ( -3.33%)\n",
      "PHM        -0.026 ( -2.63%)\n",
      "BSX        -0.022 ( -2.15%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.000 ( -0.00%)\n",
      "\n",
      "Net Equity         0.797 ( 79.75%)\n",
      "Total Portfolio    0.997 ( 99.74%)\n",
      "Gross Exposure     1.597 (159.74%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.001917, Time: 3.2496s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: crisis\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              crisis\n",
      "Time Period:         2007-10-01 to 2009-04-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.005043 (0.5043%)\n",
      "CVaR (95%):          0.061742 (6.1742%)\n",
      "Objective Value:     -0.003765\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4999 seconds\n",
      "CVXPY API Overhead:  0.3712 seconds\n",
      "Solve Time:          48.6149 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2007-10-01 to 2009-04-01\n",
      "\n",
      "LONG POSITIONS (6 assets)\n",
      "-------------------------\n",
      "NFLX        0.400 ( 40.00%)\n",
      "AZO         0.400 ( 40.00%)\n",
      "ILMN        0.164 ( 16.42%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "ROST        0.079 (  7.85%)\n",
      "EW          0.057 (  5.73%)\n",
      "Total Long    1.200 (119.99%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "MGM        -0.219 (-21.88%)\n",
      "AIG        -0.168 (-16.81%)\n",
      "LVS        -0.013 ( -1.30%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.800 ( 79.99%)\n",
      "Total Portfolio    1.000 (100.00%)\n",
      "Gross Exposure     1.600 (159.99%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.003765, Time: 48.6149s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              crisis\n",
      "Time Period:         2007-10-01 to 2009-04-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.005039 (0.5039%)\n",
      "CVaR (95%):          0.061607 (6.1607%)\n",
      "Objective Value:     -0.003764\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4636 seconds\n",
      "CVXPY API Overhead:  0.0920 seconds\n",
      "Solve Time:          8.7385 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2007-10-01 to 2009-04-01\n",
      "\n",
      "LONG POSITIONS (6 assets)\n",
      "-------------------------\n",
      "AZO         0.400 ( 40.00%)\n",
      "NFLX        0.400 ( 40.00%)\n",
      "ILMN        0.164 ( 16.41%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "ROST        0.078 (  7.76%)\n",
      "EW          0.058 (  5.83%)\n",
      "Total Long    1.200 (120.00%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "MGM        -0.218 (-21.84%)\n",
      "AIG        -0.168 (-16.82%)\n",
      "LVS        -0.013 ( -1.30%)\n",
      "Total Short   -0.400 (-39.96%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.000 ( -0.01%)\n",
      "\n",
      "Net Equity         0.800 ( 80.04%)\n",
      "Total Portfolio    1.000 (100.03%)\n",
      "Gross Exposure     1.600 (159.96%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.003764, Time: 8.7385s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: post_crisis\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              post_crisis\n",
      "Time Period:         2009-06-30 to 2014-06-30\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.001885 (0.1885%)\n",
      "CVaR (95%):          0.024646 (2.4646%)\n",
      "Objective Value:     -0.000929\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4658 seconds\n",
      "CVXPY API Overhead:  0.3662 seconds\n",
      "Solve Time:          38.8701 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2009-06-30 to 2014-06-30\n",
      "\n",
      "LONG POSITIONS (16 assets)\n",
      "-------------------------\n",
      "STZ         0.139 ( 13.89%)\n",
      "BKNG        0.138 ( 13.82%)\n",
      "INCY        0.106 ( 10.63%)\n",
      "REGN        0.106 ( 10.55%)\n",
      "BBWI        0.101 ( 10.06%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "DPZ         0.096 (  9.61%)\n",
      "OKE         0.084 (  8.38%)\n",
      "EXR         0.070 (  7.01%)\n",
      "COO         0.064 (  6.44%)\n",
      "BIIB        0.051 (  5.06%)\n",
      "TYL         0.043 (  4.32%)\n",
      "MKTX        0.033 (  3.29%)\n",
      "MCK         0.024 (  2.38%)\n",
      "AAPL        0.024 (  2.36%)\n",
      "NFLX        0.022 (  2.18%)\n",
      "Total Long    1.200 (119.99%)\n",
      "\n",
      "SHORT POSITIONS (6 assets)\n",
      "--------------------------\n",
      "BAC        -0.121 (-12.05%)\n",
      "AMD        -0.080 ( -7.98%)\n",
      "MS         -0.079 ( -7.93%)\n",
      "J          -0.073 ( -7.26%)\n",
      "HPQ        -0.022 ( -2.15%)\n",
      "TPR        -0.019 ( -1.91%)\n",
      "Total Short   -0.393 (-39.27%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.001 ( -0.09%)\n",
      "\n",
      "Net Equity         0.807 ( 80.71%)\n",
      "Total Portfolio    1.006 (100.63%)\n",
      "Gross Exposure     1.593 (159.26%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.000929, Time: 38.8701s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              post_crisis\n",
      "Time Period:         2009-06-30 to 2014-06-30\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.001886 (0.1886%)\n",
      "CVaR (95%):          0.024644 (2.4644%)\n",
      "Objective Value:     -0.000930\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2807 seconds\n",
      "CVXPY API Overhead:  0.0928 seconds\n",
      "Solve Time:          3.4689 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2009-06-30 to 2014-06-30\n",
      "\n",
      "LONG POSITIONS (16 assets)\n",
      "-------------------------\n",
      "STZ         0.140 ( 14.04%)\n",
      "BKNG        0.138 ( 13.78%)\n",
      "INCY        0.106 ( 10.65%)\n",
      "REGN        0.106 ( 10.58%)\n",
      "BBWI        0.102 ( 10.16%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "DPZ         0.097 (  9.70%)\n",
      "OKE         0.084 (  8.38%)\n",
      "EXR         0.068 (  6.84%)\n",
      "COO         0.064 (  6.35%)\n",
      "BIIB        0.051 (  5.11%)\n",
      "TYL         0.042 (  4.23%)\n",
      "MKTX        0.033 (  3.28%)\n",
      "MCK         0.025 (  2.47%)\n",
      "AAPL        0.022 (  2.23%)\n",
      "NFLX        0.022 (  2.20%)\n",
      "Total Long    1.200 (120.00%)\n",
      "\n",
      "SHORT POSITIONS (6 assets)\n",
      "--------------------------\n",
      "BAC        -0.122 (-12.18%)\n",
      "AMD        -0.079 ( -7.93%)\n",
      "MS         -0.079 ( -7.90%)\n",
      "J          -0.073 ( -7.32%)\n",
      "HPQ        -0.021 ( -2.12%)\n",
      "TPR        -0.019 ( -1.87%)\n",
      "Total Short   -0.393 (-39.31%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.001 ( -0.06%)\n",
      "\n",
      "Net Equity         0.807 ( 80.69%)\n",
      "Total Portfolio    1.006 (100.63%)\n",
      "Gross Exposure     1.593 (159.31%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.000930, Time: 3.4689s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: oil_price_crash\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              oil_price_crash\n",
      "Time Period:         2014-06-01 to 2016-03-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003050 (0.3050%)\n",
      "CVaR (95%):          0.026816 (2.6816%)\n",
      "Objective Value:     -0.001877\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4931 seconds\n",
      "CVXPY API Overhead:  0.3742 seconds\n",
      "Solve Time:          40.8305 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2014-06-01 to 2016-03-01\n",
      "\n",
      "LONG POSITIONS (12 assets)\n",
      "-------------------------\n",
      "EW          0.282 ( 28.19%)\n",
      "MKTX        0.243 ( 24.30%)\n",
      "HRL         0.135 ( 13.45%)\n",
      "TTWO        0.126 ( 12.64%)\n",
      "DPZ         0.121 ( 12.11%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "GPN         0.076 (  7.59%)\n",
      "AMZN        0.052 (  5.20%)\n",
      "EA          0.021 (  2.08%)\n",
      "NDAQ        0.018 (  1.76%)\n",
      "MNST        0.011 (  1.13%)\n",
      "VMC         0.011 (  1.07%)\n",
      "Total Long    1.195 (119.51%)\n",
      "\n",
      "SHORT POSITIONS (4 assets)\n",
      "--------------------------\n",
      "DVN        -0.228 (-22.82%)\n",
      "FCX        -0.101 (-10.06%)\n",
      "MU         -0.053 ( -5.31%)\n",
      "WMB        -0.018 ( -1.81%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.795 ( 79.51%)\n",
      "Total Portfolio    0.995 ( 99.52%)\n",
      "Gross Exposure     1.595 (159.51%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.001877, Time: 40.8305s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              oil_price_crash\n",
      "Time Period:         2014-06-01 to 2016-03-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003051 (0.3051%)\n",
      "CVaR (95%):          0.026809 (2.6809%)\n",
      "Objective Value:     -0.001878\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4925 seconds\n",
      "CVXPY API Overhead:  0.0876 seconds\n",
      "Solve Time:          5.6258 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2014-06-01 to 2016-03-01\n",
      "\n",
      "LONG POSITIONS (12 assets)\n",
      "-------------------------\n",
      "EW          0.283 ( 28.26%)\n",
      "MKTX        0.243 ( 24.27%)\n",
      "HRL         0.135 ( 13.54%)\n",
      "TTWO        0.125 ( 12.52%)\n",
      "DPZ         0.122 ( 12.15%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "GPN         0.076 (  7.59%)\n",
      "AMZN        0.052 (  5.24%)\n",
      "EA          0.021 (  2.14%)\n",
      "NDAQ        0.017 (  1.65%)\n",
      "MNST        0.011 (  1.10%)\n",
      "VMC         0.011 (  1.07%)\n",
      "Total Long    1.195 (119.53%)\n",
      "\n",
      "SHORT POSITIONS (4 assets)\n",
      "--------------------------\n",
      "DVN        -0.229 (-22.90%)\n",
      "FCX        -0.101 (-10.06%)\n",
      "MU         -0.053 ( -5.27%)\n",
      "WMB        -0.018 ( -1.79%)\n",
      "Total Short   -0.400 (-40.02%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.795 ( 79.51%)\n",
      "Total Portfolio    0.995 ( 99.51%)\n",
      "Gross Exposure     1.595 (159.55%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.001878, Time: 5.6258s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: FAANG_surge\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              FAANG_surge\n",
      "Time Period:         2015-01-01 to 2021-01-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002204 (0.2204%)\n",
      "CVaR (95%):          0.035408 (3.5408%)\n",
      "Objective Value:     -0.001038\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4637 seconds\n",
      "CVXPY API Overhead:  0.3668 seconds\n",
      "Solve Time:          44.2222 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2015-01-01 to 2021-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "MKTX        0.253 ( 25.35%)\n",
      "NVDA        0.231 ( 23.10%)\n",
      "AMZN        0.186 ( 18.63%)\n",
      "CPRT        0.150 ( 14.98%)\n",
      "DHR         0.101 ( 10.06%)\n",
      "AMD         0.099 (  9.87%)\n",
      "ALGN        0.070 (  6.97%)\n",
      "NEM         0.036 (  3.64%)\n",
      "NFLX        0.034 (  3.36%)\n",
      "WST         0.021 (  2.07%)\n",
      "PGR         0.012 (  1.19%)\n",
      "Total Long    1.192 (119.22%)\n",
      "\n",
      "SHORT POSITIONS (6 assets)\n",
      "--------------------------\n",
      "WDC        -0.090 ( -8.97%)\n",
      "DVN        -0.078 ( -7.76%)\n",
      "PCG        -0.075 ( -7.47%)\n",
      "APA        -0.071 ( -7.09%)\n",
      "VTRS       -0.067 ( -6.73%)\n",
      "BIIB       -0.018 ( -1.80%)\n",
      "Total Short   -0.398 (-39.82%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.00%)\n",
      "\n",
      "Net Equity         0.794 ( 79.41%)\n",
      "Total Portfolio    0.994 ( 99.41%)\n",
      "Gross Exposure     1.590 (159.04%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.001038, Time: 44.2222s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              FAANG_surge\n",
      "Time Period:         2015-01-01 to 2021-01-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002205 (0.2205%)\n",
      "CVaR (95%):          0.035420 (3.5420%)\n",
      "Objective Value:     -0.001039\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2639 seconds\n",
      "CVXPY API Overhead:  0.1018 seconds\n",
      "Solve Time:          4.4764 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2015-01-01 to 2021-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "MKTX        0.254 ( 25.37%)\n",
      "NVDA        0.232 ( 23.21%)\n",
      "AMZN        0.187 ( 18.69%)\n",
      "CPRT        0.151 ( 15.05%)\n",
      "DHR         0.101 ( 10.15%)\n",
      "AMD         0.099 (  9.91%)\n",
      "ALGN        0.069 (  6.87%)\n",
      "NEM         0.036 (  3.61%)\n",
      "NFLX        0.033 (  3.29%)\n",
      "WST         0.019 (  1.92%)\n",
      "PGR         0.011 (  1.14%)\n",
      "Total Long    1.192 (119.21%)\n",
      "\n",
      "SHORT POSITIONS (6 assets)\n",
      "--------------------------\n",
      "WDC        -0.090 ( -9.01%)\n",
      "DVN        -0.078 ( -7.81%)\n",
      "PCG        -0.075 ( -7.47%)\n",
      "APA        -0.071 ( -7.06%)\n",
      "VTRS       -0.067 ( -6.69%)\n",
      "BIIB       -0.018 ( -1.77%)\n",
      "Total Short   -0.398 (-39.82%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.000 ( -0.01%)\n",
      "\n",
      "Net Equity         0.794 ( 79.40%)\n",
      "Total Portfolio    0.994 ( 99.39%)\n",
      "Gross Exposure     1.590 (159.03%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.001039, Time: 4.4764s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: covid\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              covid\n",
      "Time Period:         2020-01-01 to 2023-01-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002585 (0.2585%)\n",
      "CVaR (95%):          0.038982 (3.8982%)\n",
      "Objective Value:     -0.001398\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.5059 seconds\n",
      "CVXPY API Overhead:  0.3704 seconds\n",
      "Solve Time:          48.0219 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2020-01-01 to 2023-01-01\n",
      "\n",
      "LONG POSITIONS (9 assets)\n",
      "-------------------------\n",
      "PWR         0.269 ( 26.94%)\n",
      "STLD        0.223 ( 22.30%)\n",
      "LLY         0.210 ( 20.99%)\n",
      "MCK         0.175 ( 17.52%)\n",
      "EQT         0.101 ( 10.14%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "BBWI        0.053 (  5.34%)\n",
      "ON          0.047 (  4.68%)\n",
      "DECK        0.019 (  1.86%)\n",
      "Total Long    1.198 (119.76%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "CCL        -0.300 (-30.00%)\n",
      "SWK        -0.078 ( -7.82%)\n",
      "INTC       -0.012 ( -1.22%)\n",
      "Total Short   -0.390 (-39.03%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.00%)\n",
      "\n",
      "Net Equity         0.807 ( 80.73%)\n",
      "Total Portfolio    1.007 (100.73%)\n",
      "Gross Exposure     1.588 (158.79%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.001398, Time: 48.0219s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              covid\n",
      "Time Period:         2020-01-01 to 2023-01-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002586 (0.2586%)\n",
      "CVaR (95%):          0.038959 (3.8959%)\n",
      "Objective Value:     -0.001400\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2707 seconds\n",
      "CVXPY API Overhead:  0.0916 seconds\n",
      "Solve Time:          5.1766 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2020-01-01 to 2023-01-01\n",
      "\n",
      "LONG POSITIONS (9 assets)\n",
      "-------------------------\n",
      "PWR         0.272 ( 27.18%)\n",
      "STLD        0.224 ( 22.41%)\n",
      "LLY         0.210 ( 20.96%)\n",
      "MCK         0.174 ( 17.37%)\n",
      "EQT         0.102 ( 10.24%)\n",
      "NVDA        0.100 ( 10.00%)\n",
      "BBWI        0.052 (  5.24%)\n",
      "ON          0.046 (  4.56%)\n",
      "DECK        0.019 (  1.86%)\n",
      "Total Long    1.198 (119.82%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "CCL        -0.300 (-30.00%)\n",
      "SWK        -0.079 ( -7.93%)\n",
      "MTCH       -0.011 ( -1.11%)\n",
      "Total Short   -0.390 (-39.04%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.808 ( 80.78%)\n",
      "Total Portfolio    1.008 (100.78%)\n",
      "Gross Exposure     1.589 (158.86%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.001400, Time: 5.1766s--------------------------------\n",
      "======================================================================\n",
      "Processing Regime: recent\n",
      "======================================================================\n",
      "\n",
      "--- Testing Solver: CLARABEL ---\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CLARABEL\n",
      "Regime:              recent\n",
      "Time Period:         2022-01-01 to 2024-07-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003073 (0.3073%)\n",
      "CVaR (95%):          0.026250 (2.6250%)\n",
      "Objective Value:     -0.001678\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.4617 seconds\n",
      "CVXPY API Overhead:  0.3704 seconds\n",
      "Solve Time:          40.2861 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CLARABEL_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2022-01-01 to 2024-07-01\n",
      "\n",
      "LONG POSITIONS (9 assets)\n",
      "-------------------------\n",
      "LLY         0.387 ( 38.74%)\n",
      "FICO        0.277 ( 27.73%)\n",
      "NVDA        0.164 ( 16.37%)\n",
      "MCK         0.123 ( 12.34%)\n",
      "GE          0.100 (  9.97%)\n",
      "XOM         0.049 (  4.93%)\n",
      "VLO         0.044 (  4.36%)\n",
      "PGR         0.041 (  4.12%)\n",
      "STLD        0.014 (  1.43%)\n",
      "Total Long    1.200 (119.99%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "MTCH       -0.207 (-20.66%)\n",
      "ILMN       -0.116 (-11.56%)\n",
      "EL         -0.078 ( -7.78%)\n",
      "Total Short   -0.400 (-40.00%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.01%)\n",
      "\n",
      "Net Equity         0.800 ( 79.99%)\n",
      "Total Portfolio    1.000 (100.00%)\n",
      "Gross Exposure     1.600 (159.99%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CLARABEL - Objective: -0.001678, Time: 40.2861s--------------------------------\n",
      "\n",
      "--- Testing Solver: CUOPT ---\n",
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "Setting parameter absolute_dual_tolerance to 1.000000e-04\n",
      "Setting parameter relative_dual_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_primal_tolerance to 1.000000e-04\n",
      "Setting parameter relative_primal_tolerance to 1.000000e-04\n",
      "Setting parameter absolute_gap_tolerance to 1.000000e-04\n",
      "Setting parameter relative_gap_tolerance to 1.000000e-04\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              recent\n",
      "Time Period:         2022-01-01 to 2024-07-01\n",
      "Scenarios:           20,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.003074 (0.3074%)\n",
      "CVaR (95%):          0.026240 (2.6240%)\n",
      "Objective Value:     -0.001680\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.3406 seconds\n",
      "CVXPY API Overhead:  0.0988 seconds\n",
      "Solve Time:          4.8934 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2022-01-01 to 2024-07-01\n",
      "\n",
      "LONG POSITIONS (9 assets)\n",
      "-------------------------\n",
      "LLY         0.388 ( 38.81%)\n",
      "FICO        0.277 ( 27.75%)\n",
      "NVDA        0.163 ( 16.35%)\n",
      "MCK         0.123 ( 12.34%)\n",
      "GE          0.100 (  9.99%)\n",
      "XOM         0.050 (  4.99%)\n",
      "VLO         0.043 (  4.28%)\n",
      "PGR         0.040 (  4.02%)\n",
      "STLD        0.015 (  1.47%)\n",
      "Total Long    1.200 (119.99%)\n",
      "\n",
      "SHORT POSITIONS (3 assets)\n",
      "--------------------------\n",
      "MTCH       -0.206 (-20.65%)\n",
      "ILMN       -0.116 (-11.58%)\n",
      "EL         -0.078 ( -7.80%)\n",
      "Total Short   -0.400 (-40.03%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.02%)\n",
      "\n",
      "Net Equity         0.800 ( 79.96%)\n",
      "Total Portfolio    1.000 ( 99.98%)\n",
      "Gross Exposure     1.600 (160.02%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n",
      "  ✓ CUOPT - Objective: -0.001680, Time: 4.8934s--------------------------------\n",
      "\n",
      "======================================================================\n",
      "Optimization Complete!\n",
      "======================================================================\n",
      "\n",
      "\n",
      "Results saved to: ./results/regime_results/both_results_sp500_20000.csv\n"
     ]
    }
   ],
   "source": [
    "# CVaR parameters for regime comparison\n",
    "regime_comparison_cvar_params = CvarParameters(\n",
    "    w_min={\"NVDA\":0.1, \"others\": -0.3}, w_max={\"NVDA\": 0.6, \"others\": 0.4},  # Asset weight allocation bounds\n",
    "    c_min=0.0, c_max=0.2,  # Cash holdings bounds\n",
    "    L_tar=1.6, T_tar=None,  # Leverage and turnover (None for this example)\n",
    "    cvar_limit=None,  # Max CVaR (None = unconstrained for this example)\n",
    "    cardinality = None,  # Cardinality constraints\n",
    "    risk_aversion=1,  # Risk aversion level\n",
    "    confidence=0.95,  # CVaR confidence level (alpha)\n",
    ")\n",
    "\n",
    "# User inputs for regime comparison\n",
    "regime_comparison_dataset_name = \"sp500\"\n",
    "regime_comparison_num_scen = 20000\n",
    "regime_comparison_returns_compute_settings = {'return_type': 'LOG', 'freq': 1}\n",
    "regime_comparison_scenario_generation_settings = {'num_scen': regime_comparison_num_scen, # Number of return scenarios to simulate \n",
    "                                'fit_type': 'kde', \n",
    "                                'kde_settings': {'bandwidth': 0.01, \n",
    "                                                 'kernel': 'gaussian', \n",
    "                                                 'device': 'GPU'\n",
    "                                                 },\n",
    "                                'verbose': False\n",
    "                                }\n",
    "\n",
    "# Prepare output directory and file name\n",
    "regime_comparison_output_folder = \"./results/regime_results\"\n",
    "os.makedirs(regime_comparison_output_folder, exist_ok=True)\n",
    "regime_comparison_results_csv_path = os.path.join(\n",
    "    regime_comparison_output_folder,\n",
    "    f\"both_results_{regime_comparison_dataset_name}_{regime_comparison_num_scen}.csv\"\n",
    ")\n",
    "\n",
    "# Regime settings (customize as needed)\n",
    "regime_comparison_selected_dict = {\n",
    "    \"pre_crisis\"                  : (\"2005-01-01\", \"2007-10-01\"),\n",
    "    \"crisis\"                      : (\"2007-10-01\", \"2009-04-01\"),\n",
    "    \"post_crisis\"                 : (\"2009-06-30\", \"2014-06-30\"),\n",
    "    \"oil_price_crash\"             : (\"2014-06-01\", \"2016-03-01\"),\n",
    "    \"FAANG_surge\"                 : (\"2015-01-01\", \"2021-01-01\"),\n",
    "    \"covid\"                       : (\"2020-01-01\", \"2023-01-01\"),\n",
    "    \"recent\"                      : (\"2022-01-01\", \"2024-07-01\")\n",
    "}\n",
    "\n",
    "# List of solvers to compare - any supported solver on CVXPY can be used.\n",
    "solver_settings_list = [\n",
    "    {\"solver\": cp.CLARABEL, \"verbose\": False, \"tol_gap_abs\": 1e-4, \"tol_gap_rel\": 1e-4, \"tol_feas\": 1e-4}, \n",
    "    {\"solver\":cp.CUOPT, \"verbose\": False, \"solver_method\": \"PDLP\", \"optimality\": 1e-4}\n",
    "]\n",
    "\n",
    "regime_comparison_dataset_path = f\"./data/stock_data/{regime_comparison_dataset_name}.csv\"\n",
    "\n",
    "# Run CPU vs. GPU comparison across selected regimes\n",
    "regime_comparison_results_df = cvar_utils.optimize_market_regimes(\n",
    "    input_file_name=regime_comparison_dataset_path,\n",
    "    returns_compute_settings=regime_comparison_returns_compute_settings,\n",
    "    scenario_generation_settings=regime_comparison_scenario_generation_settings,\n",
    "    all_regimes=regime_comparison_selected_dict,\n",
    "    cvar_params=regime_comparison_cvar_params,\n",
    "    solver_settings_list=solver_settings_list,\n",
    "    results_csv_file_name=regime_comparison_results_csv_path\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21b08082-b0b3-4564-b69d-a7973f384359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regime</th>\n",
       "      <th>CLARABEL-obj</th>\n",
       "      <th>CLARABEL-solve_time</th>\n",
       "      <th>CLARABEL-return</th>\n",
       "      <th>CLARABEL-CVaR</th>\n",
       "      <th>CLARABEL-optimal_portfolio</th>\n",
       "      <th>CUOPT-obj</th>\n",
       "      <th>CUOPT-solve_time</th>\n",
       "      <th>CUOPT-return</th>\n",
       "      <th>CUOPT-CVaR</th>\n",
       "      <th>CUOPT-optimal_portfolio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pre_crisis</td>\n",
       "      <td>-0.001916</td>\n",
       "      <td>40.963183</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>0.027550</td>\n",
       "      <td>({'AAPL': 0.1685073593576531, 'AMD': -0.033126...</td>\n",
       "      <td>-0.001917</td>\n",
       "      <td>3.249603</td>\n",
       "      <td>0.003253</td>\n",
       "      <td>0.027507</td>\n",
       "      <td>({'AAPL': 0.16860751658281875, 'AMD': -0.03328...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>crisis</td>\n",
       "      <td>-0.003765</td>\n",
       "      <td>48.614852</td>\n",
       "      <td>0.005043</td>\n",
       "      <td>0.061742</td>\n",
       "      <td>({'AIG': -0.16812210214261433, 'AZO': 0.399955...</td>\n",
       "      <td>-0.003764</td>\n",
       "      <td>8.738473</td>\n",
       "      <td>0.005039</td>\n",
       "      <td>0.061607</td>\n",
       "      <td>({'AIG': -0.16815012352088726, 'AZO': 0.4, 'EW...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>post_crisis</td>\n",
       "      <td>-0.000929</td>\n",
       "      <td>38.870057</td>\n",
       "      <td>0.001885</td>\n",
       "      <td>0.024646</td>\n",
       "      <td>({'AAPL': 0.02358144584078945, 'AMD': -0.07975...</td>\n",
       "      <td>-0.000930</td>\n",
       "      <td>3.468889</td>\n",
       "      <td>0.001886</td>\n",
       "      <td>0.024644</td>\n",
       "      <td>({'AAPL': 0.022262262417416197, 'AMD': -0.0793...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>oil_price_crash</td>\n",
       "      <td>-0.001877</td>\n",
       "      <td>40.830537</td>\n",
       "      <td>0.003050</td>\n",
       "      <td>0.026816</td>\n",
       "      <td>({'AMZN': 0.051964279112029, 'DPZ': 0.12105432...</td>\n",
       "      <td>-0.001878</td>\n",
       "      <td>5.625769</td>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.026809</td>\n",
       "      <td>({'AMZN': 0.05237064378401218, 'DPZ': 0.121522...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FAANG_surge</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>44.222174</td>\n",
       "      <td>0.002204</td>\n",
       "      <td>0.035408</td>\n",
       "      <td>({'ALGN': 0.06974858672725494, 'AMD': 0.098709...</td>\n",
       "      <td>-0.001039</td>\n",
       "      <td>4.476395</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.035420</td>\n",
       "      <td>({'ALGN': 0.06872191517563653, 'AMD': 0.099075...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>covid</td>\n",
       "      <td>-0.001398</td>\n",
       "      <td>48.021944</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.038982</td>\n",
       "      <td>({'BBWI': 0.05343440949863733, 'CCL': -0.29996...</td>\n",
       "      <td>-0.001400</td>\n",
       "      <td>5.176596</td>\n",
       "      <td>0.002586</td>\n",
       "      <td>0.038959</td>\n",
       "      <td>({'BBWI': 0.05235860238493561, 'CCL': -0.3, 'D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>recent</td>\n",
       "      <td>-0.001678</td>\n",
       "      <td>40.286131</td>\n",
       "      <td>0.003073</td>\n",
       "      <td>0.026250</td>\n",
       "      <td>({'EL': -0.07779108743969854, 'FICO': 0.277290...</td>\n",
       "      <td>-0.001680</td>\n",
       "      <td>4.893435</td>\n",
       "      <td>0.003074</td>\n",
       "      <td>0.026240</td>\n",
       "      <td>({'EL': -0.07802863183939351, 'FICO': 0.277490...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            regime  CLARABEL-obj  CLARABEL-solve_time  CLARABEL-return  \\\n",
       "0       pre_crisis     -0.001916            40.963183         0.003253   \n",
       "1           crisis     -0.003765            48.614852         0.005043   \n",
       "2      post_crisis     -0.000929            38.870057         0.001885   \n",
       "3  oil_price_crash     -0.001877            40.830537         0.003050   \n",
       "4      FAANG_surge     -0.001038            44.222174         0.002204   \n",
       "5            covid     -0.001398            48.021944         0.002585   \n",
       "6           recent     -0.001678            40.286131         0.003073   \n",
       "\n",
       "   CLARABEL-CVaR                         CLARABEL-optimal_portfolio  \\\n",
       "0       0.027550  ({'AAPL': 0.1685073593576531, 'AMD': -0.033126...   \n",
       "1       0.061742  ({'AIG': -0.16812210214261433, 'AZO': 0.399955...   \n",
       "2       0.024646  ({'AAPL': 0.02358144584078945, 'AMD': -0.07975...   \n",
       "3       0.026816  ({'AMZN': 0.051964279112029, 'DPZ': 0.12105432...   \n",
       "4       0.035408  ({'ALGN': 0.06974858672725494, 'AMD': 0.098709...   \n",
       "5       0.038982  ({'BBWI': 0.05343440949863733, 'CCL': -0.29996...   \n",
       "6       0.026250  ({'EL': -0.07779108743969854, 'FICO': 0.277290...   \n",
       "\n",
       "   CUOPT-obj  CUOPT-solve_time  CUOPT-return  CUOPT-CVaR  \\\n",
       "0  -0.001917          3.249603      0.003253    0.027507   \n",
       "1  -0.003764          8.738473      0.005039    0.061607   \n",
       "2  -0.000930          3.468889      0.001886    0.024644   \n",
       "3  -0.001878          5.625769      0.003051    0.026809   \n",
       "4  -0.001039          4.476395      0.002205    0.035420   \n",
       "5  -0.001400          5.176596      0.002586    0.038959   \n",
       "6  -0.001680          4.893435      0.003074    0.026240   \n",
       "\n",
       "                             CUOPT-optimal_portfolio  \n",
       "0  ({'AAPL': 0.16860751658281875, 'AMD': -0.03328...  \n",
       "1  ({'AIG': -0.16815012352088726, 'AZO': 0.4, 'EW...  \n",
       "2  ({'AAPL': 0.022262262417416197, 'AMD': -0.0793...  \n",
       "3  ({'AMZN': 0.05237064378401218, 'DPZ': 0.121522...  \n",
       "4  ({'ALGN': 0.06872191517563653, 'AMD': 0.099075...  \n",
       "5  ({'BBWI': 0.05235860238493561, 'CCL': -0.3, 'D...  \n",
       "6  ({'EL': -0.07802863183939351, 'FICO': 0.277490...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display regime comparison results\n",
    "regime_comparison_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c922c623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regime\n",
       "pre_crisis         12.605598\n",
       "crisis              5.563312\n",
       "post_crisis        11.205334\n",
       "oil_price_crash     7.257770\n",
       "FAANG_surge         9.878970\n",
       "covid               9.276743\n",
       "recent              8.232689\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the speed-up ratio of CPU solvers vs cuOpt GPU LP solver \n",
    "regime_comparison_results_df.index = regime_comparison_results_df['regime']\n",
    "speed_comparison_df = regime_comparison_results_df['CLARABEL-solve_time'] / regime_comparison_results_df['CUOPT-solve_time'] # CPU solve time / GPU solve time\n",
    "speed_comparison_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b48ae7b9",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='appendix'></a>\n",
    "## 6. Appendix\n",
    "\n",
    "### 6.1 Optional: Parameter Constraints\n",
    "Optional to define weight and cash constraints as CVXPY parameter for faster iteration. \n",
    "\n",
    "In some use cases, there is a need to update the weight and cash constraints while keeping the rest of the original problem (dataset and other constraints), this is the faster way without re-building the entire problem. \n",
    "\n",
    "Note that in `cvar_optimizer.py` the weight and cash constraints are defaulted to be variable bounds for the weight variable ${w}$ and cash variable $c$, i.e. \n",
    "```\n",
    "self.w = cp.Variable(num_assets, name=\"weights\", bounds=[self.params.w_min, self.params.w_max])\n",
    "```\n",
    "because empirically cuOpt LP solver is faster when constraints are set as variable bounds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e44a8167",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting parameter log_to_console to false\n",
      "Setting parameter method to 1\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              CUOPT\n",
      "Regime:              recent\n",
      "Time Period:         2021-01-01 to 2024-01-01\n",
      "Scenarios:           10,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002556 (0.2556%)\n",
      "CVaR (95%):          0.025915 (2.5915%)\n",
      "Objective Value:     -0.001447\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          0.2948 seconds\n",
      "CVXPY API Overhead:  0.0465 seconds\n",
      "Solve Time:          1.6466 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2021-01-01 to 2024-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "LLY         0.291 ( 29.06%)\n",
      "NVDA        0.173 ( 17.31%)\n",
      "MCK         0.168 ( 16.84%)\n",
      "JBL         0.101 ( 10.13%)\n",
      "COP         0.098 (  9.79%)\n",
      "IRM         0.090 (  9.00%)\n",
      "PWR         0.088 (  8.84%)\n",
      "IT          0.062 (  6.19%)\n",
      "NUE         0.051 (  5.11%)\n",
      "FICO        0.040 (  4.03%)\n",
      "STLD        0.038 (  3.76%)\n",
      "Total Long    1.200 (120.04%)\n",
      "\n",
      "SHORT POSITIONS (2 assets)\n",
      "--------------------------\n",
      "MTCH       -0.279 (-27.90%)\n",
      "ILMN       -0.122 (-12.15%)\n",
      "Total Short   -0.401 (-40.06%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual    0.000 (  0.04%)\n",
      "\n",
      "Net Equity         0.800 ( 79.99%)\n",
      "Total Portfolio    1.000 (100.03%)\n",
      "Gross Exposure     1.601 (160.10%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate CVaR optimization problem for the S&P 500 example\n",
    "api_settings = {\n",
    "                \"api\": \"cvxpy\",  # \"cvxpy\" or \"cuopt_python\"\n",
    "                \"weight_constraints_type\": \"parameter\",  # \"parameter\" or \"bounds\" (CVXPY only)\n",
    "                \"cash_constraints_type\": \"parameter\",  # \"parameter\" or \"bounds\" (CVXPY only)\n",
    "            }\n",
    "cvar_problem = cvar_optimizer.CVaR(\n",
    "    returns_dict=returns_dict,\n",
    "    cvar_params=cvar_params,\n",
    "    api_settings=api_settings\n",
    ")\n",
    "\n",
    "# Solve on GPU\n",
    "gpu_solver_settings = {\"solver\":cp.CUOPT, \"verbose\": False, \"solver_method\": \"PDLP\"} \n",
    "gpu_results, gpu_portfolio = cvar_problem.solve_optimization_problem(solver_settings=gpu_solver_settings)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4724a6",
   "metadata": {},
   "source": [
    "### 6.2 Optional: cuOpt Python API\n",
    "Instead of using CVXPY modeling framework, we can build the problem directly in the cuOpt native python API by choosing `api_settings`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3dd08270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "cuOpt PROBLEM SETUP COMPLETED\n",
      "==================================================\n",
      "Variables: 392 weights + 1 cash + 10000 auxiliary + 1 threshold\n",
      "           + 784 leverage decomposition\n",
      "Constraints: Budget + 10000 CVaR scenarios + additional constraints\n",
      "Problem Type: LP\n",
      "==================================================\n",
      "Setting parameter log_to_console to true\n",
      "Setting parameter presolve to false\n",
      "Setting parameter method to 1\n",
      "cuOpt version: 25.10.1, git hash: 876fcfc, host arch: aarch64, device archs: 75-real,80-real,86-real,90a-real,100f-real,120a-real,120\n",
      "CPU: Unknown, threads (physical/logical): 1/20, RAM: 46.66 GiB\n",
      "CUDA 13.0, device: NVIDIA GB10 (ID 0), VRAM: 119.70 GiB\n",
      "CUDA device UUID: 978712c9-013d-7aa8-c22e-fd237bb0e910\n",
      "\n",
      "Solving a problem with 10394 constraints, 11178 variables (0 integers), and 3942353 nonzeros\n",
      "Problem scaling:\n",
      "Objective coefficents range:          [6e-07, 4e-02]\n",
      "Constraint matrix coefficients range: [2e-08, 1e+00]\n",
      "Constraint rhs / bounds range:        [0e+00, 2e+00]\n",
      "Variable bounds range:                [1e-01, 6e-01]\n",
      "Warning: input problem contains a large range of coefficients: consider reformulating to avoid numerical difficulties.\n",
      "\n",
      "Third-party presolve is disabled, skipping\n",
      "Objective offset 0.000000 scaling_factor 1.000000\n",
      "   Iter    Primal Obj.      Dual Obj.    Gap        Primal Res.  Dual Res.   Time\n",
      "      0 +0.00000000e+00 +0.00000000e+00  0.00e+00   1.00e+00     4.53e-02   0.097s\n",
      "   1000 -1.52650212e-03 -1.38184241e-03  1.45e-04   7.00e-02     2.12e-04   0.809s\n",
      "   2000 -1.44908759e-03 -1.44508348e-03  4.00e-06   2.98e-03     1.38e-05   1.292s\n",
      "   2800 -1.44522073e-03 -1.44546814e-03  2.47e-07   2.42e-04     2.82e-06   1.671s\n",
      "LP Solver status:                Optimal\n",
      "Primal objective:                -1.44522073e-03\n",
      "Dual objective:                  -1.44546814e-03\n",
      "Duality gap (abs/rel):           +2.47e-07 / +2.47e-07\n",
      "Primal infeasibility (abs/rel):  +2.42e-04 / +8.37e-05\n",
      "Dual infeasibility (abs/rel):    +2.82e-06 / +2.70e-06\n",
      "PDLP finished\n",
      "Status: Optimal   Objective: -1.44522073e-03  Iterations: 2800  Time: 1.673s, Total time 1.688s\n",
      "cuOpt solution found in 1.69 seconds\n",
      "Status: Optimal\n",
      "Objective value: -0.001445\n",
      "\n",
      "============================================================\n",
      "CVaR OPTIMIZATION RESULTS\n",
      "============================================================\n",
      "PROBLEM CONFIGURATION\n",
      "------------------------------\n",
      "Solver:              cuOpt\n",
      "Regime:              recent\n",
      "Time Period:         2021-01-01 to 2024-01-01\n",
      "Scenarios:           10,000\n",
      "Assets:              392\n",
      "Confidence Level:    95.0%\n",
      "\n",
      "PERFORMANCE METRICS\n",
      "------------------------------\n",
      "Expected Return:     0.002552 (0.2552%)\n",
      "CVaR (95%):          0.025864 (2.5864%)\n",
      "Objective Value:     -0.001445\n",
      "\n",
      "SOLVING PERFORMANCE\n",
      "------------------------------\n",
      "Setup Time:          6.5339 seconds\n",
      "cuOpt API Overhead:  0.4624 seconds\n",
      "Solve Time:          1.6879 seconds\n",
      "\n",
      "OPTIMAL PORTFOLIO ALLOCATION\n",
      "------------------------------\n",
      "\n",
      "PORTFOLIO: CUOPT_OPTIMAL\n",
      "----------------------------------------\n",
      "Period: 2021-01-01 to 2024-01-01\n",
      "\n",
      "LONG POSITIONS (11 assets)\n",
      "-------------------------\n",
      "LLY         0.289 ( 28.89%)\n",
      "NVDA        0.172 ( 17.18%)\n",
      "MCK         0.167 ( 16.70%)\n",
      "JBL         0.102 ( 10.16%)\n",
      "COP         0.096 (  9.65%)\n",
      "IRM         0.091 (  9.12%)\n",
      "PWR         0.089 (  8.86%)\n",
      "IT          0.064 (  6.36%)\n",
      "NUE         0.051 (  5.07%)\n",
      "FICO        0.043 (  4.25%)\n",
      "STLD        0.038 (  3.76%)\n",
      "Total Long    1.200 (120.00%)\n",
      "\n",
      "SHORT POSITIONS (2 assets)\n",
      "--------------------------\n",
      "MTCH       -0.279 (-27.91%)\n",
      "ILMN       -0.121 (-12.07%)\n",
      "Total Short   -0.400 (-39.98%)\n",
      "\n",
      "CASH & SUMMARY\n",
      "--------------------\n",
      "Cash        0.200 ( 20.00%)\n",
      "Residual   -0.000 ( -0.02%)\n",
      "\n",
      "Net Equity         0.800 ( 80.02%)\n",
      "Total Portfolio    1.000 (100.00%)\n",
      "Gross Exposure     1.600 (159.99%)\n",
      "----------------------------------------\n",
      "============================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate CVaR optimization problem using cuOpt Python API\n",
    "cvar_problem = cvar_optimizer.CVaR(\n",
    "    returns_dict=returns_dict,\n",
    "    cvar_params=cvar_params,\n",
    "    api_settings={\"api\": \"cuopt_python\"}\n",
    ")\n",
    "\n",
    "# Solver settings for cuOpt Python API\n",
    "cuopt_settings = {\"log_to_console\":True, \"presolve\": False, \"method\": 1}\n",
    "\n",
    "# Solve using cuOpt Python API\n",
    "cuopt_gpu_results, cuopt_gpu_portfolio = cvar_problem.solve_optimization_problem(solver_settings=cuopt_settings)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b81e78",
   "metadata": {},
   "source": [
    "SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. \n",
    "\n",
    "SPDX-License-Identifier: Apache-2.0\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
   ]
  }
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