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Commit 515ec9c6 authored by Dr.李's avatar Dr.李
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update all the example

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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 @ 515ec9c6
...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -48,13 +48,13 @@ ...@@ -48,13 +48,13 @@
"\n", "\n",
" objective = cvxpy.Minimize(-w.T * er)\n", " objective = cvxpy.Minimize(-w.T * er)\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve()\n", " prob.solve(solver='ECOS')\n",
" return w, prob" " return w, prob"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -62,37 +62,21 @@ ...@@ -62,37 +62,21 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n", "Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 18.00 -0.82 -0.000021 0.011996 1.0000100.013991955470964355\n", "200 8.98 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n",
"400 21.40 -1.28 -0.000000 0.010000 1.000000 0.015\n", "400 10.97 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n",
"600 21.00 -1.64 -0.000435 0.009980 1.0001740.014845697577983343\n", "600 12.01 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n",
"800 24.01 -1.83 -0.000802 0.010028 1.0002310.01474166785085744\n", "800 11.93 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n",
"1000 44.88 -1.89 -0.000166 0.010000 1.0001490.014851819526468822\n", "1000 12.00 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n",
"1200 44.02 -2.07 -0.000571 0.009999 1.0001670.014833266833006432\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",
"name": "stderr", "2000 22.93 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n",
"output_type": "stream", "2200 21.92 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n",
"text": [ "2400 25.90 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\cvxpy-1.0.8-py3.6-win-amd64.egg\\cvxpy\\problems\\problem.py:614: RuntimeWarning: overflow encountered in long_scalars\n", "2600 29.93 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n",
" if self.max_big_small_squared < big*small**2:\n", "2800 28.87 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\cvxpy-1.0.8-py3.6-win-amd64.egg\\cvxpy\\problems\\problem.py:615: RuntimeWarning: overflow encountered in long_scalars\n", "3000 32.96 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n"
" self.max_big_small_squared = big*small**2\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1400 57.00 -2.31 -0.000504 0.009997 1.0002170.014785380519459417\n",
"1600 47.02 -2.90 -0.001650 0.009905 1.0004330.014667925787486116\n",
"1800 79.06 -2.67 -0.000885 0.009999 1.0002300.014771011591262167\n",
"2000 68.01 -2.81 -0.000333 0.010001 1.0002410.014758149340884413\n",
"2200 72.02 -3.71 -0.000849 0.009969 1.0004940.014536311105299406\n",
"2400 94.19 -2.46 -0.000536 0.010001 1.0000810.014917972574632015\n",
"2600 135.07 -2.54 -0.000105 0.010001 1.0000900.01490885499964943\n",
"2800 106.02 -3.40 -0.000983 0.010000 1.0002670.014733268551116162\n",
"3000 109.02 -3.77 -0.001612 0.010001 1.0003180.014680802417975643\n"
] ]
} }
], ],
...@@ -138,21 +122,21 @@ ...@@ -138,21 +122,21 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n", "Scale(n) time(ms) feval min(x) max(x) sum(x) x(0) + x(1)\n",
"200 2.99 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n", "200 2.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n",
"400 2.99 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n", "400 4.00 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n",
"600 4.00 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n", "600 4.02 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n",
"800 5.00 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n", "800 3.99 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n",
"1000 7.00 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n", "1000 7.94 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n",
"1200 6.00 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n", "1200 5.03 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n",
"1400 9.01 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n", "1400 8.94 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n",
"1600 10.00 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n", "1600 8.02 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n",
"1800 10.16 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n", "1800 9.98 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n",
"2000 12.05 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n", "2000 9.97 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n",
"2200 12.99 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n", "2200 14.91 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n",
"2400 12.00 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n", "2400 12.97 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n",
"2600 23.02 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n", "2600 15.96 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n",
"2800 20.99 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n", "2800 19.92 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n",
"3000 22.00 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n" "3000 21.93 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n"
] ]
} }
], ],
...@@ -189,7 +173,7 @@ ...@@ -189,7 +173,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.5" "version": "3.6.8"
}, },
"varInspector": { "varInspector": {
"cols": { "cols": {
......
notebooks/Quick Start 3 - Portfolio Builder.ipynb
View file @ 515ec9c6
...@@ -25,9 +25,21 @@ ...@@ -25,9 +25,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"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"
]
}
],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"from alphamind.portfolio.linearbuilder import linear_builder\n", "from alphamind.portfolio.linearbuilder import linear_builder\n",
...@@ -169,7 +181,7 @@ ...@@ -169,7 +181,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -221,9 +233,18 @@ ...@@ -221,9 +233,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimal weight is [0.09999998 0.4 0.5 ]\n",
"Risk exposure of optimal potfolio is [0.99999998 0.59999998]\n"
]
}
],
"source": [ "source": [
"print('Optimal weight is {0}'.format(x))\n", "print('Optimal weight is {0}'.format(x))\n",
"print('Risk exposure of optimal potfolio is {0}'.format(x @ risk_exposure))" "print('Risk exposure of optimal potfolio is {0}'.format(x @ risk_exposure))"
...@@ -253,7 +274,7 @@ ...@@ -253,7 +274,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.5" "version": "3.6.8"
}, },
"varInspector": { "varInspector": {
"cols": { "cols": {
......
notebooks/Quick Start 4 - Step By Step Alpha Modeling.ipynb
View file @ 515ec9c6
...@@ -87,7 +87,7 @@ ...@@ -87,7 +87,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Wall time: 459 ms\n" "Wall time: 454 ms\n"
] ]
} }
], ],
...@@ -346,7 +346,7 @@ ...@@ -346,7 +346,7 @@
"2 6 房地产\n", "2 6 房地产\n",
"3 8 机械设备\n", "3 8 机械设备\n",
"4 9 综合\n", "4 9 综合\n",
"Wall time: 185 ms\n" "Wall time: 198 ms\n"
] ]
} }
], ],
...@@ -387,7 +387,7 @@ ...@@ -387,7 +387,7 @@
"2 6 0.00039\n", "2 6 0.00039\n",
"3 8 0.00071\n", "3 8 0.00071\n",
"4 9 0.00075\n", "4 9 0.00075\n",
"Wall time: 121 ms\n" "Wall time: 150 ms\n"
] ]
} }
], ],
...@@ -461,7 +461,7 @@ ...@@ -461,7 +461,7 @@
"2 6 -0.043098\n", "2 6 -0.043098\n",
"3 8 -0.174035\n", "3 8 -0.174035\n",
"4 9 -0.138300\n", "4 9 -0.138300\n",
"Wall time: 98.7 ms\n" "Wall time: 118 ms\n"
] ]
} }
], ],
...@@ -525,7 +525,7 @@ ...@@ -525,7 +525,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Wall time: 6.98 ms\n" "Wall time: 8.97 ms\n"
] ]
} }
], ],
...@@ -574,7 +574,7 @@ ...@@ -574,7 +574,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x195c3192358>" "<matplotlib.axes._subplots.AxesSubplot at 0x2844b3c43c8>"
] ]
}, },
"execution_count": 15, "execution_count": 15,
...@@ -684,7 +684,7 @@ ...@@ -684,7 +684,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x195c36554a8>" "<matplotlib.axes._subplots.AxesSubplot at 0x284521258d0>"
] ]
}, },
"execution_count": 19, "execution_count": 19,
......
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