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Commit 38c1a6a9 authored by Dr.李's avatar Dr.李
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FEATURE: update notebooks

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notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
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......@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -32,7 +32,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -80,22 +80,14 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 976 µs\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
"Wall time: 1.98 ms\n"
]
}
],
......@@ -118,7 +110,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -142,17 +134,17 @@
" warm start: on, polish: on, time_limit: off\n",
"\n",
"iter objective pri res dua res rho time\n",
" 1 -7.8878e+03 4.61e+00 6.68e+04 1.00e-01 1.04e-03s\n",
" 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 3.81e-03s\n",
" 1 -7.8878e+03 4.61e+00 6.68e+04 1.00e-01 1.10e-03s\n",
" 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 3.80e-03s\n",
"\n",
"status: solved\n",
"solution polish: unsuccessful\n",
"number of iterations: 125\n",
"optimal objective: -248.2989\n",
"run time: 4.41e-03s\n",
"run time: 4.54e-03s\n",
"optimal rho estimate: 1.87e+00\n",
"\n",
"Wall time: 42 ms\n"
"Wall time: 48 ms\n"
]
},
{
......@@ -161,7 +153,7 @@
"-248.29894124530716"
]
},
"execution_count": 19,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -173,7 +165,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -182,7 +174,7 @@
"('optimal', -248.29894124530716)"
]
},
"execution_count": 20,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
......@@ -193,7 +185,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -219,9 +211,9 @@
"12 -2.483e+02 -2.483e+02 +3e-07 5e-13 2e-15 1e-10 8e-10 0.9775 1e-04 1 1 1 | 0 0\n",
"\n",
"OPTIMAL (within feastol=5.3e-13, reltol=1.3e-09, abstol=3.2e-07).\n",
"Runtime: 0.008978 seconds.\n",
"Runtime: 0.013942 seconds.\n",
"\n",
"Wall time: 40 ms\n"
"Wall time: 51.3 ms\n"
]
},
{
......@@ -230,7 +222,7 @@
"-248.2989614228091"
]
},
"execution_count": 21,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
......@@ -242,7 +234,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"outputs": [
{
......@@ -251,7 +243,7 @@
"('optimal', -248.2989614228091)"
]
},
"execution_count": 22,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
......@@ -262,7 +254,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -285,7 +277,7 @@
"12: -2.4829e+02 -2.4831e+02 1e-02 2e-16 4e-16\n",
"13: -2.4830e+02 -2.4830e+02 1e-04 2e-16 1e-15\n",
"Optimal solution found.\n",
"Wall time: 62 ms\n"
"Wall time: 108 ms\n"
]
}
],
......@@ -316,14 +308,14 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 18 ms\n"
"Wall time: 24 ms\n"
]
},
{
......@@ -332,7 +324,7 @@
"0"
]
},
"execution_count": 24,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
......@@ -369,7 +361,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
......@@ -378,7 +370,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -390,18 +382,9 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
" and should_run_async(code)\n"
]
}
],
"outputs": [],
"source": [
"def cvxpy(n):\n",
" w = Variable(n)\n",
......@@ -423,7 +406,7 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
......@@ -455,7 +438,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
......@@ -474,7 +457,7 @@
},
{
"cell_type": "code",
"execution_count": 39,
"execution_count": 16,
"metadata": {},
"outputs": [
{
......@@ -482,9 +465,9 @@
"output_type": "stream",
"text": [
"Scale(n) cvxpy cvxopt ipopt\n",
"100 0.0230 0.0200 0.0050\n",
"200 0.0290 0.0360 0.0110\n",
"300 0.0360 0.0500 0.0150\n"
"100 0.0527 0.0660 0.0142\n",
"200 0.0710 0.3040 0.0601\n",
"300 0.1614 0.0690 0.0155\n"
]
}
],
......
notebooks/Example 12 - Machine Learning Model Prediction.ipynb
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notebooks/Example 13 - Evaluation within Industry Groups.ipynb
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notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb
View file @ 38c1a6a9
......@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -46,7 +46,7 @@
" np.random.seed(1)\n",
" er = np.random.randn(n)\n",
"\n",
" objective = cvxpy.Minimize(-w.T * er)\n",
" objective = cvxpy.Minimize(-w.T @ er)\n",
" prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='ECOS')\n",
" return w, prob"
......@@ -54,7 +54,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -62,21 +62,21 @@
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 8.98 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n",
"400 10.97 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n",
"600 12.01 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n",
"800 11.93 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n",
"1000 12.00 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n",
"1200 13.97 -1.81 -0.000000 0.010000 1.0000000.014999999999661145\n",
"1400 15.92 -1.90 -0.000000 0.010000 1.0000000.014999999999617875\n",
"1600 18.97 -1.96 -0.000000 0.010000 1.0000000.01499999999998295\n",
"1800 19.99 -2.03 -0.000000 0.010000 1.0000000.014999999999785373\n",
"2000 22.93 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n",
"2200 21.92 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n",
"2400 25.90 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n",
"2600 29.93 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n",
"2800 28.87 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n",
"3000 32.96 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n"
"200 23.15 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n",
"400 29.00 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n",
"600 31.04 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n",
"800 37.00 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n",
"1000 42.99 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n",
"1200 83.97 -1.81 -0.000000 0.010000 1.0000000.014999999999661145\n",
"1400 121.77 -1.90 -0.000000 0.010000 1.0000000.014999999999617875\n",
"1600 125.93 -1.96 -0.000000 0.010000 1.0000000.01499999999998295\n",
"1800 75.05 -2.03 -0.000000 0.010000 1.0000000.014999999999785373\n",
"2000 45.95 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n",
"2200 68.05 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n",
"2400 144.36 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n",
"2600 140.00 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n",
"2800 145.48 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n",
"3000 125.97 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n"
]
}
],
......@@ -91,7 +91,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -122,21 +122,21 @@
"output_type": "stream",
"text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 2.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n",
"400 4.00 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n",
"600 4.02 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n",
"800 3.99 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n",
"1000 7.94 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n",
"1200 5.03 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n",
"1400 8.94 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n",
"1600 8.02 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n",
"1800 9.98 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n",
"2000 9.97 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n",
"2200 14.91 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n",
"2400 12.97 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n",
"2600 15.96 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n",
"2800 19.92 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n",
"3000 21.93 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n"
"200 4.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n",
"400 5.38 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n",
"600 5.02 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n",
"800 7.89 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n",
"1000 7.97 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n",
"1200 14.04 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n",
"1400 13.85 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n",
"1600 20.96 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n",
"1800 26.95 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n",
"2000 25.59 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n",
"2200 27.25 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n",
"2400 13.10 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n",
"2600 19.00 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n",
"2800 30.60 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n",
"3000 25.00 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n"
]
}
],
......@@ -159,9 +159,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python [conda env:alpha-mind]",
"language": "python",
"name": "python3"
"name": "conda-env-alpha-mind-py"
},
"language_info": {
"codemirror_mode": {
......@@ -173,7 +173,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.7.9"
},
"varInspector": {
"cols": {
......@@ -206,5 +206,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
notebooks/Quick Start 3 - Portfolio Builder.ipynb
View file @ 38c1a6a9
......@@ -2,9 +2,7 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"metadata": {},
"source": [
"alpha-mind的portfolio文件夹提供了构建组合的工具函数。\n",
"- 不同的构建方式实质上对应了不同的优化问题。\n",
......@@ -33,10 +31,24 @@
"output_type": "stream",
"text": [
"Optimization status - optimal\n",
"Optimal expect return - -0.18504428442588658\n",
"Optimial portfolio weights - [0.05 0.05 0.3 0.3 0.3 ]\n",
"Initial portfolio weights - [0. 0.1 0.3 0.3 0.3]\n",
"Turn over amount - 0.1000000000000593\n"
"Optimal expect return - 0.750529042208698\n",
"Optimial portfolio weights - [0.1 0.2 0.05 0.4 0.25]\n",
"Initial portfolio weights - [0.1 0.2 0. 0.4 0.3]\n",
"Turn over amount - 0.10000000000050759\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\cvxpy\\expressions\\expression.py:550: UserWarning: \n",
"This use of ``*`` has resulted in matrix multiplication.\n",
"Using ``*`` for matrix multiplication has been deprecated since CVXPY 1.1.\n",
" Use ``*`` for matrix-scalar and vector-scalar multiplication.\n",
" Use ``@`` for matrix-matrix and matrix-vector multiplication.\n",
" Use ``multiply`` for elementwise multiplication.\n",
"\n",
" warnings.warn(__STAR_MATMUL_WARNING__, UserWarning)\n"
]
}
],
......@@ -183,7 +195,16 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
......@@ -260,9 +281,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python [conda env:alpha-mind]",
"language": "python",
"name": "python3"
"name": "conda-env-alpha-mind-py"
},
"language_info": {
"codemirror_mode": {
......@@ -274,7 +295,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.7.9"
},
"varInspector": {
"cols": {
......@@ -307,5 +328,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
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