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Commit e97a8be4 authored by Dr.李's avatar Dr.李
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FIX: update all the examples

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Showing with 1281 additions and 840 deletions
alphamind/portfolio/meanvariancebuilder.py
View file @ e97a8be4
......@@ -69,10 +69,10 @@ def mean_variance_builder(er: np.ndarray,
risk_exposure = risk_model['factor_loading']
if cov is None:
risk = cvxpy.sum_squares(cvxpy.multiply(cvxpy.sqrt(special_risk), w)) \
+ cvxpy.quad_form((w.T * risk_exposure).T, risk_cov)
+ cvxpy.quad_form((w.T @ risk_exposure).T, risk_cov)
else:
risk = cvxpy.quad_form(w, cov)
objective = cvxpy.Minimize(-w.T * er + 0.5 * lam * risk)
objective = cvxpy.Minimize(-w.T @ er + 0.5 * lam * risk)
prob = cvxpy.Problem(objective)
prob.solve(solver='ECOS', feastol=1e-9, abstol=1e-9, reltol=1e-9)
......
notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ e97a8be4
......@@ -134,17 +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 1.40e-03s\n",
" 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 4.57e-03s\n",
" 1 -7.8878e+03 4.61e+00 6.68e+04 1.00e-01 1.82e-03s\n",
" 125 -2.4830e+02 3.58e-07 2.76e-05 5.82e-01 5.80e-03s\n",
"\n",
"status: solved\n",
"solution polish: unsuccessful\n",
"number of iterations: 125\n",
"optimal objective: -248.2989\n",
"run time: 5.42e-03s\n",
"run time: 6.87e-03s\n",
"optimal rho estimate: 1.87e+00\n",
"\n",
"Wall time: 49 ms\n"
"Wall time: 38 ms\n"
]
},
{
......@@ -211,9 +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: 0.015751 seconds.\n",
"Runtime: 0.014540 seconds.\n",
"\n",
"Wall time: 59 ms\n"
"Wall time: 49 ms\n"
]
},
{
......@@ -277,7 +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: 69 ms\n"
"Wall time: 74 ms\n"
]
}
],
......@@ -315,7 +315,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 26 ms\n"
"Wall time: 15 ms\n"
]
},
{
......@@ -465,9 +465,9 @@
"output_type": "stream",
"text": [
"Scale(n) cvxpy cvxopt ipopt\n",
"100 0.0320 0.0240 0.0060\n",
"200 0.0360 0.0420 0.0120\n",
"300 0.0440 0.0630 0.0180\n"
"100 0.0320 0.0270 0.0090\n",
"200 0.0370 0.0500 0.0160\n",
"300 0.0430 0.0630 0.0160\n"
]
}
],
......
notebooks/Example 11 - Long Short Strategy Model.ipynb
View file @ e97a8be4
......@@ -15,7 +15,7 @@
{
"data": {
"text/plain": [
"datetime.datetime(2020, 11, 22, 1, 6, 28, 118072)"
"datetime.datetime(2020, 11, 23, 0, 38, 28, 821914)"
]
},
"execution_count": 1,
......@@ -117,7 +117,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 5.93 s\n"
"Wall time: 6.09 s\n"
]
}
],
......@@ -135,14 +135,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2020-11-22 01:06:34,553 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-22 01:06:35,020 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-22 01:06:35,023 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-22 01:06:35,230 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-22 01:06:35,233 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-22 01:06:35,428 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-22 01:06:35,432 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-22 01:06:35,711 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
"2020-11-23 00:38:35,439 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-23 00:38:35,874 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-23 00:38:35,877 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-23 00:38:36,076 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-23 00:38:36,080 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-23 00:38:36,276 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-23 00:38:36,280 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-23 00:38:36,486 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
]
}
],
......
notebooks/Example 12 - Machine Learning Model Prediction.ipynb
View file @ e97a8be4
......@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -35,7 +35,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -57,16 +57,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"我们使用当期的`[ROE, EPS, ETOP]`因子，来尝试预测未来大概一个月以后的收益。\n",
"我们使用当期的`[\"EMA5D\", \"EMV6D\"]`因子，来尝试预测未来大概一个月以后的收益。\n",
"\n",
"* 训练的股票池为`zz800`;；\n",
"* 训练的股票池为`hs300`;；\n",
"* 因子都经过中性化以及标准化等预处理；\n",
"* 对于线性模型，我们以20个工作日为一个时间间隔，用过去8期的数据作为训练用特征。"
"* 对于线性模型，我们以10个工作日为一个时间间隔，用过去8期的数据作为训练用特征。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -100,7 +100,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
......@@ -111,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -144,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
......@@ -157,7 +157,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
......@@ -167,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -203,7 +203,7 @@
}
],
"source": [
"print(\"\\nConst. Testing IC: {0:.4f}\".format(const_composer.ic(ref_date=ref_date)[0]))\n",
"print(\"Const. Testing IC: {0:.4f}\".format(const_composer.ic(ref_date=ref_date)[0]))\n",
"print(\"Regression Testing IC: {0:.4f}\".format(regression_composer.ic(ref_date=ref_date)[0]))"
]
},
......@@ -227,7 +227,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"outputs": [
{
......@@ -324,7 +324,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -373,7 +373,7 @@
"std 0.140552 0.166313"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
......@@ -399,7 +399,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -416,7 +416,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -489,7 +489,7 @@
"16 2020-01-02 2010000012 -0.017889"
]
},
"execution_count": 12,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
......@@ -500,21 +500,21 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-11-22 01:31:22,196 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-22 01:31:22,210 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-22 01:31:22,220 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-22 01:31:22,239 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-22 01:31:22,252 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-22 01:31:22,269 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-22 01:31:22,280 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-22 01:31:22,297 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
"2020-11-23 00:39:52,840 - ALPHA_MIND - INFO - 2020-01-02 full re-balance: 300\n",
"2020-11-23 00:39:52,869 - ALPHA_MIND - INFO - 2020-01-02 is finished\n",
"2020-11-23 00:39:52,878 - ALPHA_MIND - INFO - 2020-01-16 full re-balance: 300\n",
"2020-11-23 00:39:52,888 - ALPHA_MIND - INFO - 2020-01-16 is finished\n",
"2020-11-23 00:39:52,897 - ALPHA_MIND - INFO - 2020-02-07 full re-balance: 300\n",
"2020-11-23 00:39:52,909 - ALPHA_MIND - INFO - 2020-02-07 is finished\n",
"2020-11-23 00:39:52,917 - ALPHA_MIND - INFO - 2020-02-21 full re-balance: 300\n",
"2020-11-23 00:39:52,931 - ALPHA_MIND - INFO - 2020-02-21 is finished\n"
]
}
],
......@@ -575,7 +575,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 15,
"metadata": {},
"outputs": [
{
......@@ -584,7 +584,7 @@
"<AxesSubplot:title={'center':'Fixed freq rebalanced: 10b'}>"
]
},
"execution_count": 18,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
......
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ e97a8be4
......@@ -131,19 +131,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2020-11-22 01:37:30,583 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2020-11-22 01:37:30,745 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2020-11-22 01:37:30,878 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2020-11-22 01:37:31,240 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2020-11-22 01:37:31,909 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"2020-11-22 01:37:32,006 - ALPHA_MIND - INFO - starting backting ...\n",
"2020-11-22 01:37:32,013 - ALPHA_MIND - INFO - alpha models training finished ...\n",
"2020-11-22 01:37:32,018 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 re-balance: 300 codes\n",
"2020-11-22 01:37:32,037 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 re-balance: 300 codes\n",
"2020-11-23 00:40:30,181 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2020-11-23 00:40:30,383 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2020-11-23 00:40:30,576 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2020-11-23 00:40:30,944 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2020-11-23 00:40:31,694 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"2020-11-23 00:40:31,795 - ALPHA_MIND - INFO - starting backting ...\n",
"2020-11-23 00:40:31,803 - ALPHA_MIND - INFO - alpha models training finished ...\n",
"2020-11-23 00:40:31,811 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 re-balance: 300 codes\n",
"2020-11-23 00:40:31,828 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 re-balance: 300 codes\n",
"D:\\ProgramData\\Anaconda3\\envs\\alpha-mind\\lib\\site-packages\\cvxpy\\problems\\problem.py:1061: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
" \"Solution may be inaccurate. Try another solver, \"\n",
"2020-11-22 01:37:32,130 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 re-balance: 300 codes\n",
"2020-11-22 01:37:32,218 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 re-balance: 300 codes\n"
"2020-11-23 00:40:31,910 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 re-balance: 300 codes\n",
"2020-11-23 00:40:31,986 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 re-balance: 300 codes\n"
]
}
],
......
notebooks/Example 5 - Style Factor Analysis.ipynb
View file @ e97a8be4
......@@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
......@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -51,7 +51,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -77,7 +77,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -86,44 +86,44 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2020-11-21 15:10:18,028 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:18,038 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:18,041 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 3.25829145513029e-31\n",
"2020-11-21 15:10:18,510 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:18,513 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:18,516 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 4.602789610027951e-31\n",
"2020-11-21 15:10:19,244 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:19,248 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:19,252 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 7.380374200927195e-31\n",
"2020-11-21 15:10:20,041 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:20,046 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-21 15:10:20,052 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 1.425987166389731e-31\n",
"2020-11-21 15:10:21,151 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:21,159 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:21,169 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 1.626600676017078e-31\n",
"2020-11-21 15:10:21,952 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:21,961 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:21,970 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 7.114752841729456e-31\n",
"2020-11-21 15:10:22,823 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:22,831 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:22,839 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 2.894999049336361e-31\n",
"2020-11-21 15:10:23,345 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-21 15:10:23,348 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-21 15:10:23,350 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 3.207077036087234e-31\n"
"2020-11-23 00:41:53,439 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:53,445 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-23 00:41:53,447 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 4.943553232055517e-31\n",
"2020-11-23 00:41:54,467 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:54,470 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-23 00:41:54,473 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 8.396812257213363e-31\n",
"2020-11-23 00:41:55,338 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:55,340 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-23 00:41:55,343 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 5.682726866635588e-31\n",
"2020-11-23 00:41:56,058 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:56,061 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for BETA\n",
"2020-11-23 00:41:56,063 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 9.795355575973521e-32\n",
"2020-11-23 00:41:57,012 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:57,014 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-23 00:41:57,016 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 risk_exposure: 1.484415398332342e-31\n",
"2020-11-23 00:41:57,794 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:57,796 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-23 00:41:57,799 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 risk_exposure: 4.379171870810272e-31\n",
"2020-11-23 00:41:58,810 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:41:58,813 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-23 00:41:58,815 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 risk_exposure: 2.2500853516126014e-31\n",
"2020-11-23 00:42:00,437 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2020-11-23 00:42:00,439 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 is finished with 300 stocks for SIZE\n",
"2020-11-23 00:42:00,442 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 risk_exposure: 1.4975497993984135e-31\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 6.91 s\n"
"Wall time: 8.23 s\n"
]
}
],
......@@ -134,7 +134,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
......@@ -147,7 +147,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 7,
"metadata": {},
"outputs": [
{
......@@ -198,7 +198,7 @@
"SIZE 0.941047"
]
},
"execution_count": 16,
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
......
notebooks/Example 9 - Linear Optimizer Comparison with CVXOPT.ipynb
View file @ e97a8be4
......@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
......@@ -54,7 +54,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"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 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"
"200 16.00 -0.82 -0.000000 0.010000 1.0000000.014999999999355636\n",
"400 14.00 -1.28 -0.000000 0.010000 1.0000000.014999999999977868\n",
"600 15.00 -1.54 -0.000000 0.010000 1.0000000.014999999999630973\n",
"800 16.00 -1.63 -0.000000 0.010000 1.0000000.014999999999937863\n",
"1000 20.00 -1.72 -0.000000 0.010000 1.0000000.014999999999985369\n",
"1200 24.00 -1.81 -0.000000 0.010000 1.0000000.014999999999661145\n",
"1400 27.00 -1.90 -0.000000 0.010000 1.0000000.014999999999617875\n",
"1600 36.00 -1.96 -0.000000 0.010000 1.0000000.01499999999998295\n",
"1800 35.03 -2.03 -0.000000 0.010000 1.0000000.014999999999785373\n",
"2000 43.00 -2.06 -0.000000 0.010000 1.0000000.014999999999994327\n",
"2200 36.97 -2.07 -0.000000 0.010000 1.0000000.014999999999979582\n",
"2400 45.00 -2.13 -0.000000 0.010000 1.0000000.014999999999836155\n",
"2600 49.00 -2.14 -0.000000 0.010000 1.0000000.01499999999985058\n",
"2800 59.00 -2.16 -0.000000 0.010000 1.0000000.014999999999853686\n",
"3000 61.00 -2.19 -0.000000 0.010000 1.0000000.014999999999981861\n"
]
}
],
......@@ -91,7 +91,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 5,
"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 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"
"200 2.00 -0.82 0.000000 0.010000 1.0000000.015000000005429394\n",
"400 3.00 -1.28 0.000000 0.010000 1.0000000.015000000000751215\n",
"600 3.00 -1.54 0.000000 0.010000 1.0000000.01500000000851949\n",
"800 5.00 -1.63 0.000000 0.010000 1.0000000.015000000002481837\n",
"1000 9.00 -1.72 0.000000 0.010000 1.0000000.015000000001100414\n",
"1200 7.00 -1.81 0.000000 0.010000 1.0000000.01500000000548405\n",
"1400 7.03 -1.90 0.000000 0.010000 1.0000000.015000000001956426\n",
"1600 7.97 -1.96 0.000000 0.010000 1.0000000.015000000000082848\n",
"1800 8.00 -2.03 0.000000 0.010000 1.0000000.01500000000204834\n",
"2000 10.03 -2.06 0.000000 0.010000 1.0000000.0150000000008303\n",
"2200 15.97 -2.07 0.000000 0.010000 1.0000000.01500000000729576\n",
"2400 16.03 -2.13 0.000000 0.010000 1.0000000.015000000004022507\n",
"2600 13.97 -2.14 0.000000 0.010000 1.0000000.015000000001118521\n",
"2800 17.02 -2.16 0.000000 0.010000 1.0000000.01500000000064263\n",
"3000 17.98 -2.19 0.000000 0.010000 1.0000000.015000000003030482\n"
]
}
],
......
notebooks/Quick Start 1 - Factor Preprocess.ipynb
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notebooks/Quick Start 3 - Portfolio Builder.ipynb
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......@@ -31,24 +31,10 @@
"output_type": "stream",
"text": [
"Optimization status - optimal\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"
"Optimal expect return - -0.5259220412385935\n",
"Optimial portfolio weights - [0.11111111 0.38333333 0.22222222 0.11111111 0.17222222]\n",
"Initial portfolio weights - [0.11111111 0.33333333 0.22222222 0.11111111 0.22222222]\n",
"Turn over amount - 0.10000000000058906\n"
]
}
],
......@@ -195,16 +181,7 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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"
]
}
],
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
......
notebooks/Quick Start 5 - Alpha Factor Quantile Analysis.ipynb
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notebooks/Quick Start 7 - Single Factor IC Analysis.ipynb
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notebooks/Quick Start 9 - Back Testing Stock Screening.ipynb
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requirements.txt
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......@@ -15,4 +15,5 @@ scikit-learn
scipy
simpleutils
sqlalchemy
statsmodels
xgboost
\ No newline at end of file
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