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Commit 61bfd381 authored by Dr.李's avatar Dr.李
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update all the examples

parent 8839d11c
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Showing with 468 additions and 3635 deletions
notebooks/Example 1 - Factor IC analysis.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -8,6 +15,7 @@
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
......@@ -47,7 +55,7 @@
"ref_dates = makeSchedule(start_date, end_date, frequency, 'china.sse')\n",
"\n",
"executor = NaiveExecutor()\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
"data_source = os.environ['DB_URI']\n",
"engine = SqlEngine(data_source)"
]
},
......@@ -278,13 +286,6 @@
" ic_res.to_excel(writer, sheet_name='ic_stat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -309,7 +310,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from cvxpy import *\n",
......@@ -31,7 +39,7 @@
"risk_penlty = 0.5\n",
"ref_date = '2018-02-08'\n",
"\n",
"engine = SqlEngine()\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['ashare_ex'])\n",
"codes = engine.fetch_codes(ref_date, universe)\n",
"\n",
......@@ -331,7 +339,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 11 - Long Short Strategy Model.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2018, 4, 16, 9, 33, 24, 663186)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import datetime as dt\n",
"import numpy as np\n",
"import pandas as pd\n",
......@@ -31,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -42,10 +39,10 @@
"horizon = map_freq(freq)\n",
"neutralized_risk = risk_styles + industry_styles\n",
"universe = Universe(\"custom\", ['zz500'])\n",
"data_source = None\n",
"data_source = os.environ['DB_URI']\n",
"offset = 1\n",
"method = 'ls'\n",
"industry_name = 'sw_adj'\n",
"industry_name = 'sw'\n",
"industry_level = 1\n",
"\n",
"risk_model = 'short'\n",
......@@ -56,23 +53,17 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"alpha_factors = {\n",
" 'f01': LAST('ep_q'),\n",
" 'f02': LAST('roe_q'),\n",
" 'f03': LAST('GREV'),\n",
" 'f04': LAST('SGRO'),\n",
" 'f05': LAST('ILLIQUIDITY'),\n",
" 'f01': LAST('EPS'),\n",
" 'f02': LAST('ROE')\n",
" }\n",
"\n",
"weights = dict(f01=0.5,\n",
" f02=0.5,\n",
" f03=0.5,\n",
" f04=0.5,\n",
" f05=0.5,\n",
"weights = dict(f01=1.0,\n",
" f02=1.0,\n",
" )\n",
"\n",
"alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
......@@ -80,7 +71,7 @@
"def predict_worker(params):\n",
" data_meta = DataMeta(freq=freq,\n",
" universe=universe,\n",
" batch=0,\n",
" batch=1,\n",
" neutralized_risk=neutralized_risk,\n",
" risk_model='short',\n",
" pre_process=[winsorize_normal, standardize],\n",
......@@ -94,17 +85,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 18.6 s\n"
]
}
],
"outputs": [],
"source": [
"%%time\n",
"predicts = [predict_worker((d.strftime('%Y-%m-%d'), alpha_model)) for d in ref_dates]"
......@@ -112,114 +95,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 09:33:44,891 - ALPHA_MIND - INFO - 2017-04-10 full re-balance: 500\n",
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]
}
],
"outputs": [],
"source": [
"industry_names = industry_list(industry_name, industry_level)\n",
"industry_total = engine.fetch_industry_matrix_range(universe, dates=ref_dates, category=industry_name, level=industry_level)\n",
......@@ -268,30 +146,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x157bcc7a4a8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=ref_dates)\n",
"ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], freq)] = 0.\n",
......@@ -328,7 +185,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 12 - Machine Learning Model Prediction.ipynb
View file @ 61bfd381
......@@ -4,7 +4,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"> 本例展示如何在alpha-mind中使用机器学习模型"
"* 本例展示如何在alpha-mind中使用机器学习模型\n",
"\n",
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
......@@ -15,6 +17,7 @@
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import datetime as dt\n",
"import numpy as np\n",
"import pandas as pd\n",
......@@ -44,7 +47,7 @@
"pre_process = [winsorize_normal, standardize]\n",
"post_process = [standardize]\n",
"warm_start = 3\n",
"data_source = None\n",
"data_source = os.environ['DB_URI']\n",
"horizon = map_freq(freq)\n",
"\n",
"engine = SqlEngine(data_source)"
......@@ -120,14 +123,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 19:44:49,889 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
"2018-04-16 19:44:50,436 - ALPHA_MIND - INFO - factor data loading finished\n",
"2018-04-16 19:44:51,753 - ALPHA_MIND - INFO - fit target data loading finished\n",
"2018-04-16 19:44:51,921 - ALPHA_MIND - INFO - industry data loading finished\n",
"2018-04-16 19:44:52,029 - ALPHA_MIND - INFO - benchmark data loading finished\n",
"2018-04-16 19:44:53,205 - ALPHA_MIND - INFO - data merging finished\n",
"2018-04-16 19:44:53,403 - ALPHA_MIND - INFO - Loading data is finished\n",
"2018-04-16 19:44:53,523 - ALPHA_MIND - INFO - Data processing is finished\n"
"2018-05-05 14:00:27,215 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
"2018-05-05 14:00:29,623 - ALPHA_MIND - INFO - factor data loading finished\n",
"2018-05-05 14:00:32,271 - ALPHA_MIND - INFO - fit target data loading finished\n",
"2018-05-05 14:00:36,819 - ALPHA_MIND - INFO - industry data loading finished\n",
"2018-05-05 14:00:38,193 - ALPHA_MIND - INFO - benchmark data loading finished\n",
"2018-05-05 14:00:44,173 - ALPHA_MIND - INFO - data merging finished\n",
"2018-05-05 14:00:44,449 - ALPHA_MIND - INFO - Loading data is finished\n",
"2018-05-05 14:00:44,624 - ALPHA_MIND - INFO - Data processing is finished\n"
]
}
],
......@@ -167,14 +170,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 19:44:53,628 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
"2018-04-16 19:44:54,124 - ALPHA_MIND - INFO - factor data loading finished\n",
"2018-04-16 19:46:01,930 - ALPHA_MIND - INFO - fit target data loading finished\n",
"2018-04-16 19:46:02,091 - ALPHA_MIND - INFO - industry data loading finished\n",
"2018-04-16 19:46:02,197 - ALPHA_MIND - INFO - benchmark data loading finished\n",
"2018-04-16 19:46:03,408 - ALPHA_MIND - INFO - data merging finished\n",
"2018-04-16 19:46:03,614 - ALPHA_MIND - INFO - Loading data is finished\n",
"2018-04-16 19:46:03,656 - ALPHA_MIND - INFO - Data processing is finished\n"
"2018-05-05 14:00:49,320 - ALPHA_MIND - INFO - Starting data package fetching ...\n",
"2018-05-05 14:00:50,281 - ALPHA_MIND - INFO - factor data loading finished\n",
"2018-05-05 14:02:09,314 - ALPHA_MIND - INFO - fit target data loading finished\n",
"2018-05-05 14:02:09,570 - ALPHA_MIND - INFO - industry data loading finished\n",
"2018-05-05 14:02:09,700 - ALPHA_MIND - INFO - benchmark data loading finished\n",
"2018-05-05 14:02:11,229 - ALPHA_MIND - INFO - data merging finished\n",
"2018-05-05 14:02:11,486 - ALPHA_MIND - INFO - Loading data is finished\n",
"2018-05-05 14:02:11,553 - ALPHA_MIND - INFO - Data processing is finished\n"
]
}
],
......@@ -233,9 +236,9 @@
"data": {
"text/plain": [
"dx 1.000000\n",
"T-M 0.053046\n",
"T 0.124049\n",
"Δ 0.076091\n",
"T-M 0.051985\n",
"T 0.122122\n",
"Δ 0.076031\n",
"dtype: float64"
]
},
......@@ -363,38 +366,32 @@
"output_type": "stream",
"text": [
"\n",
"2010-07-07 Const. Testing IC: 0.5775\n",
"2010-07-07 Regression Testing IC: 0.5750\n",
"\n",
"2010-10-11 Const. Testing IC: 0.6586\n",
"2010-10-11 Regression Testing IC: 0.6907\n",
"\n",
"2011-01-04 Const. Testing IC: 0.5799\n",
"2011-01-04 Regression Testing IC: 0.5460\n",
"2011-01-04 Const. Testing IC: 0.5800\n",
"2011-01-04 Regression Testing IC: 0.5802\n",
"\n",
"2011-04-07 Const. Testing IC: 0.4843\n",
"2011-04-07 Regression Testing IC: 0.6691\n",
"2011-04-07 Const. Testing IC: 0.4844\n",
"2011-04-07 Regression Testing IC: 0.6694\n",
"\n",
"2011-07-04 Const. Testing IC: 0.5862\n",
"2011-07-04 Regression Testing IC: 0.6395\n",
"2011-07-04 Regression Testing IC: 0.6396\n",
"\n",
"2011-09-27 Const. Testing IC: 0.6134\n",
"2011-09-27 Regression Testing IC: 0.6809\n",
"2011-09-27 Const. Testing IC: 0.6321\n",
"2011-09-27 Regression Testing IC: 0.6784\n",
"\n",
"2011-12-27 Const. Testing IC: 0.6155\n",
"2011-12-27 Regression Testing IC: 0.5721\n",
"2011-12-27 Const. Testing IC: 0.6823\n",
"2011-12-27 Regression Testing IC: 0.6613\n",
"\n",
"2012-03-29 Const. Testing IC: 0.3999\n",
"2012-03-29 Regression Testing IC: 0.5205\n",
"2012-03-29 Const. Testing IC: 0.3676\n",
"2012-03-29 Regression Testing IC: 0.4809\n",
"\n",
"2012-06-29 Const. Testing IC: 0.0054\n",
"2012-06-29 Regression Testing IC: 0.0579\n",
"2012-06-29 Const. Testing IC: 0.8619\n",
"2012-06-29 Regression Testing IC: 0.8502\n",
"\n",
"2012-09-21 Const. Testing IC: 0.6827\n",
"2012-09-21 Regression Testing IC: 0.6291\n",
"2012-09-21 Const. Testing IC: 0.6434\n",
"2012-09-21 Regression Testing IC: 0.5957\n",
"\n",
"2012-12-21 Const. Testing IC: 0.7544\n",
"2012-12-21 Regression Testing IC: 0.2699\n",
"2012-12-21 Regression Testing IC: 0.7612\n",
"\n",
"2013-03-27 Const. Testing IC: 0.4713\n",
"2013-03-27 Regression Testing IC: 0.6270\n",
......@@ -405,41 +402,41 @@
"2013-09-25 Const. Testing IC: 0.6586\n",
"2013-09-25 Regression Testing IC: 0.6992\n",
"\n",
"2013-12-25 Const. Testing IC: 0.2487\n",
"2013-12-25 Regression Testing IC: 0.2631\n",
"2013-12-25 Const. Testing IC: 0.2465\n",
"2013-12-25 Regression Testing IC: 0.2582\n",
"\n",
"2014-03-27 Const. Testing IC: 0.3904\n",
"2014-03-27 Regression Testing IC: 0.6418\n",
"2014-03-27 Const. Testing IC: 0.3836\n",
"2014-03-27 Regression Testing IC: 0.6062\n",
"\n",
"2014-06-25 Const. Testing IC: 0.5018\n",
"2014-06-25 Regression Testing IC: 0.6655\n",
"2014-06-25 Regression Testing IC: 0.6660\n",
"\n",
"2014-09-18 Const. Testing IC: 0.6088\n",
"2014-09-18 Regression Testing IC: 0.7215\n",
"2014-09-18 Regression Testing IC: 0.7217\n",
"\n",
"2014-12-18 Const. Testing IC: 0.7788\n",
"2014-12-18 Regression Testing IC: 0.6722\n",
"2014-12-18 Regression Testing IC: 0.6732\n",
"\n",
"2015-03-23 Const. Testing IC: 0.4714\n",
"2015-03-23 Regression Testing IC: 0.7190\n",
"2015-03-23 Regression Testing IC: 0.7187\n",
"\n",
"2015-06-17 Const. Testing IC: 0.6239\n",
"2015-06-17 Regression Testing IC: 0.6565\n",
"2015-06-17 Regression Testing IC: 0.6569\n",
"\n",
"2015-09-14 Const. Testing IC: 0.5984\n",
"2015-09-14 Regression Testing IC: 0.6728\n",
"2015-09-14 Const. Testing IC: 0.5672\n",
"2015-09-14 Regression Testing IC: 0.6353\n",
"\n",
"2015-12-14 Const. Testing IC: 0.9509\n",
"2015-12-14 Regression Testing IC: 0.8566\n",
"2015-12-14 Const. Testing IC: 0.9450\n",
"2015-12-14 Regression Testing IC: 0.8404\n",
"\n",
"2016-03-15 Const. Testing IC: 0.4935\n",
"2016-03-15 Regression Testing IC: 0.6239\n",
"2016-03-15 Regression Testing IC: 0.6238\n",
"\n",
"2016-06-13 Const. Testing IC: 0.5908\n",
"2016-06-13 Regression Testing IC: 0.5992\n",
"\n",
"2016-09-05 Const. Testing IC: 0.6832\n",
"2016-09-05 Regression Testing IC: 0.6782\n",
"2016-09-05 Const. Testing IC: 0.6523\n",
"2016-09-05 Regression Testing IC: 0.6519\n",
"\n",
"2016-12-07 Const. Testing IC: 0.9502\n",
"2016-12-07 Regression Testing IC: 0.9013\n",
......@@ -450,11 +447,11 @@
"2017-06-08 Const. Testing IC: 0.5680\n",
"2017-06-08 Regression Testing IC: 0.5823\n",
"\n",
"2017-08-31 Const. Testing IC: 0.6802\n",
"2017-08-31 Regression Testing IC: 0.6701\n",
"2017-08-31 Const. Testing IC: 0.6659\n",
"2017-08-31 Regression Testing IC: 0.6648\n",
"\n",
"2017-11-30 Const. Testing IC: 0.9940\n",
"2017-11-30 Regression Testing IC: 0.8682\n"
"2017-11-30 Regression Testing IC: 0.8681\n"
]
}
],
......@@ -508,13 +505,13 @@
" <tbody>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.592022</td>\n",
" <td>0.617903</td>\n",
" <td>0.617097</td>\n",
" <td>0.658524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.193060</td>\n",
" <td>0.168037</td>\n",
" <td>0.172611</td>\n",
" <td>0.123526</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -522,8 +519,8 @@
],
"text/plain": [
" naive ic. regress ic.\n",
"mean 0.592022 0.617903\n",
"std 0.193060 0.168037"
"mean 0.617097 0.658524\n",
"std 0.172611 0.123526"
]
},
"execution_count": 16,
......@@ -572,68 +569,64 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 19:48:10,968 - ALPHA_MIND - INFO - 2010-07-07 full re-balance: 799\n",
"2018-04-16 19:48:12,597 - ALPHA_MIND - INFO - 2010-07-07 is finished\n",
"2018-04-16 19:48:12,606 - ALPHA_MIND - INFO - 2010-10-11 full re-balance: 798\n",
"2018-04-16 19:48:14,369 - ALPHA_MIND - INFO - 2010-10-11 is finished\n",
"2018-04-16 19:48:14,379 - ALPHA_MIND - INFO - 2011-01-04 full re-balance: 799\n",
"2018-04-16 19:48:16,279 - ALPHA_MIND - INFO - 2011-01-04 is finished\n",
"2018-04-16 19:48:16,287 - ALPHA_MIND - INFO - 2011-04-07 full re-balance: 798\n",
"2018-04-16 19:48:18,041 - ALPHA_MIND - INFO - 2011-04-07 is finished\n",
"2018-04-16 19:48:18,050 - ALPHA_MIND - INFO - 2011-07-04 full re-balance: 798\n",
"2018-04-16 19:48:19,781 - ALPHA_MIND - INFO - 2011-07-04 is finished\n",
"2018-04-16 19:48:19,790 - ALPHA_MIND - INFO - 2011-09-27 full re-balance: 797\n",
"2018-04-16 19:48:21,799 - ALPHA_MIND - INFO - 2011-09-27 is finished\n",
"2018-04-16 19:48:21,807 - ALPHA_MIND - INFO - 2011-12-27 full re-balance: 798\n",
"2018-04-16 19:48:23,524 - ALPHA_MIND - INFO - 2011-12-27 is finished\n",
"2018-04-16 19:48:23,532 - ALPHA_MIND - INFO - 2012-03-29 full re-balance: 796\n",
"2018-04-16 19:48:25,323 - ALPHA_MIND - INFO - 2012-03-29 is finished\n",
"2018-04-16 19:48:25,331 - ALPHA_MIND - INFO - 2012-06-29 full re-balance: 798\n",
"2018-04-16 19:48:27,215 - ALPHA_MIND - INFO - 2012-06-29 is finished\n",
"2018-04-16 19:48:27,225 - ALPHA_MIND - INFO - 2012-09-21 full re-balance: 799\n",
"2018-04-16 19:48:28,993 - ALPHA_MIND - INFO - 2012-09-21 is finished\n",
"2018-04-16 19:48:29,001 - ALPHA_MIND - INFO - 2012-12-21 full re-balance: 799\n",
"2018-04-16 19:48:30,722 - ALPHA_MIND - INFO - 2012-12-21 is finished\n",
"2018-04-16 19:48:30,730 - ALPHA_MIND - INFO - 2013-03-27 full re-balance: 800\n",
"2018-04-16 19:48:32,704 - ALPHA_MIND - INFO - 2013-03-27 is finished\n",
"2018-04-16 19:48:32,712 - ALPHA_MIND - INFO - 2013-07-01 full re-balance: 800\n",
"2018-04-16 19:48:34,441 - ALPHA_MIND - INFO - 2013-07-01 is finished\n",
"2018-04-16 19:48:34,450 - ALPHA_MIND - INFO - 2013-09-25 full re-balance: 799\n",
"2018-04-16 19:48:36,183 - ALPHA_MIND - INFO - 2013-09-25 is finished\n",
"2018-04-16 19:48:36,191 - ALPHA_MIND - INFO - 2013-12-25 full re-balance: 800\n",
"2018-04-16 19:48:38,121 - ALPHA_MIND - INFO - 2013-12-25 is finished\n",
"2018-04-16 19:48:38,130 - ALPHA_MIND - INFO - 2014-03-27 full re-balance: 800\n",
"2018-04-16 19:48:39,910 - ALPHA_MIND - INFO - 2014-03-27 is finished\n",
"2018-04-16 19:48:39,920 - ALPHA_MIND - INFO - 2014-06-25 full re-balance: 800\n",
"2018-04-16 19:48:41,848 - ALPHA_MIND - INFO - 2014-06-25 is finished\n",
"2018-04-16 19:48:41,856 - ALPHA_MIND - INFO - 2014-09-18 full re-balance: 800\n",
"2018-04-16 19:48:43,598 - ALPHA_MIND - INFO - 2014-09-18 is finished\n",
"2018-04-16 19:48:43,606 - ALPHA_MIND - INFO - 2014-12-18 full re-balance: 800\n",
"2018-04-16 19:48:45,300 - ALPHA_MIND - INFO - 2014-12-18 is finished\n",
"2018-04-16 19:48:45,309 - ALPHA_MIND - INFO - 2015-03-23 full re-balance: 799\n",
"2018-04-16 19:48:47,258 - ALPHA_MIND - INFO - 2015-03-23 is finished\n",
"2018-04-16 19:48:47,267 - ALPHA_MIND - INFO - 2015-06-17 full re-balance: 800\n",
"2018-04-16 19:48:48,995 - ALPHA_MIND - INFO - 2015-06-17 is finished\n",
"2018-04-16 19:48:49,004 - ALPHA_MIND - INFO - 2015-09-14 full re-balance: 800\n",
"2018-04-16 19:48:50,814 - ALPHA_MIND - INFO - 2015-09-14 is finished\n",
"2018-04-16 19:48:50,822 - ALPHA_MIND - INFO - 2015-12-14 full re-balance: 800\n",
"2018-04-16 19:48:52,784 - ALPHA_MIND - INFO - 2015-12-14 is finished\n",
"2018-04-16 19:48:52,792 - ALPHA_MIND - INFO - 2016-03-15 full re-balance: 799\n",
"2018-04-16 19:48:54,587 - ALPHA_MIND - INFO - 2016-03-15 is finished\n",
"2018-04-16 19:48:54,597 - ALPHA_MIND - INFO - 2016-06-13 full re-balance: 800\n",
"2018-04-16 19:48:56,296 - ALPHA_MIND - INFO - 2016-06-13 is finished\n",
"2018-04-16 19:48:56,307 - ALPHA_MIND - INFO - 2016-09-05 full re-balance: 800\n",
"2018-04-16 19:48:58,297 - ALPHA_MIND - INFO - 2016-09-05 is finished\n",
"2018-04-16 19:48:58,306 - ALPHA_MIND - INFO - 2016-12-07 full re-balance: 800\n",
"2018-04-16 19:49:00,028 - ALPHA_MIND - INFO - 2016-12-07 is finished\n",
"2018-04-16 19:49:00,036 - ALPHA_MIND - INFO - 2017-03-09 full re-balance: 800\n",
"2018-04-16 19:49:01,747 - ALPHA_MIND - INFO - 2017-03-09 is finished\n",
"2018-04-16 19:49:01,754 - ALPHA_MIND - INFO - 2017-06-08 full re-balance: 800\n",
"2018-04-16 19:49:03,679 - ALPHA_MIND - INFO - 2017-06-08 is finished\n",
"2018-04-16 19:49:03,688 - ALPHA_MIND - INFO - 2017-08-31 full re-balance: 800\n",
"2018-04-16 19:49:05,486 - ALPHA_MIND - INFO - 2017-08-31 is finished\n",
"2018-04-16 19:49:05,494 - ALPHA_MIND - INFO - 2017-11-30 full re-balance: 800\n",
"2018-04-16 19:49:07,468 - ALPHA_MIND - INFO - 2017-11-30 is finished\n"
"2018-05-05 14:05:43,938 - ALPHA_MIND - INFO - 2011-01-04 full re-balance: 799\n",
"2018-05-05 14:05:46,929 - ALPHA_MIND - INFO - 2011-01-04 is finished\n",
"2018-05-05 14:05:46,941 - ALPHA_MIND - INFO - 2011-04-07 full re-balance: 798\n",
"2018-05-05 14:05:49,923 - ALPHA_MIND - INFO - 2011-04-07 is finished\n",
"2018-05-05 14:05:49,935 - ALPHA_MIND - INFO - 2011-07-04 full re-balance: 798\n",
"2018-05-05 14:05:53,094 - ALPHA_MIND - INFO - 2011-07-04 is finished\n",
"2018-05-05 14:05:53,106 - ALPHA_MIND - INFO - 2011-09-27 full re-balance: 797\n",
"2018-05-05 14:05:56,334 - ALPHA_MIND - INFO - 2011-09-27 is finished\n",
"2018-05-05 14:05:56,345 - ALPHA_MIND - INFO - 2011-12-27 full re-balance: 798\n",
"2018-05-05 14:05:59,460 - ALPHA_MIND - INFO - 2011-12-27 is finished\n",
"2018-05-05 14:05:59,469 - ALPHA_MIND - INFO - 2012-03-29 full re-balance: 796\n",
"2018-05-05 14:06:02,529 - ALPHA_MIND - INFO - 2012-03-29 is finished\n",
"2018-05-05 14:06:02,538 - ALPHA_MIND - INFO - 2012-06-29 full re-balance: 798\n",
"2018-05-05 14:06:05,431 - ALPHA_MIND - INFO - 2012-06-29 is finished\n",
"2018-05-05 14:06:05,441 - ALPHA_MIND - INFO - 2012-09-21 full re-balance: 799\n",
"2018-05-05 14:06:08,657 - ALPHA_MIND - INFO - 2012-09-21 is finished\n",
"2018-05-05 14:06:08,668 - ALPHA_MIND - INFO - 2012-12-21 full re-balance: 799\n",
"2018-05-05 14:06:11,593 - ALPHA_MIND - INFO - 2012-12-21 is finished\n",
"2018-05-05 14:06:11,602 - ALPHA_MIND - INFO - 2013-03-27 full re-balance: 800\n",
"2018-05-05 14:06:14,838 - ALPHA_MIND - INFO - 2013-03-27 is finished\n",
"2018-05-05 14:06:14,849 - ALPHA_MIND - INFO - 2013-07-01 full re-balance: 800\n",
"2018-05-05 14:06:17,748 - ALPHA_MIND - INFO - 2013-07-01 is finished\n",
"2018-05-05 14:06:17,759 - ALPHA_MIND - INFO - 2013-09-25 full re-balance: 799\n",
"2018-05-05 14:06:20,887 - ALPHA_MIND - INFO - 2013-09-25 is finished\n",
"2018-05-05 14:06:20,897 - ALPHA_MIND - INFO - 2013-12-25 full re-balance: 800\n",
"2018-05-05 14:06:24,106 - ALPHA_MIND - INFO - 2013-12-25 is finished\n",
"2018-05-05 14:06:24,117 - ALPHA_MIND - INFO - 2014-03-27 full re-balance: 800\n",
"2018-05-05 14:06:27,141 - ALPHA_MIND - INFO - 2014-03-27 is finished\n",
"2018-05-05 14:06:27,151 - ALPHA_MIND - INFO - 2014-06-25 full re-balance: 800\n",
"2018-05-05 14:06:30,239 - ALPHA_MIND - INFO - 2014-06-25 is finished\n",
"2018-05-05 14:06:30,249 - ALPHA_MIND - INFO - 2014-09-18 full re-balance: 800\n",
"2018-05-05 14:06:33,049 - ALPHA_MIND - INFO - 2014-09-18 is finished\n",
"2018-05-05 14:06:33,058 - ALPHA_MIND - INFO - 2014-12-18 full re-balance: 800\n",
"2018-05-05 14:06:35,926 - ALPHA_MIND - INFO - 2014-12-18 is finished\n",
"2018-05-05 14:06:35,936 - ALPHA_MIND - INFO - 2015-03-23 full re-balance: 799\n",
"2018-05-05 14:06:39,030 - ALPHA_MIND - INFO - 2015-03-23 is finished\n",
"2018-05-05 14:06:39,040 - ALPHA_MIND - INFO - 2015-06-17 full re-balance: 800\n",
"2018-05-05 14:06:41,833 - ALPHA_MIND - INFO - 2015-06-17 is finished\n",
"2018-05-05 14:06:41,844 - ALPHA_MIND - INFO - 2015-09-14 full re-balance: 800\n",
"2018-05-05 14:06:44,789 - ALPHA_MIND - INFO - 2015-09-14 is finished\n",
"2018-05-05 14:06:44,800 - ALPHA_MIND - INFO - 2015-12-14 full re-balance: 800\n",
"2018-05-05 14:06:48,028 - ALPHA_MIND - INFO - 2015-12-14 is finished\n",
"2018-05-05 14:06:48,039 - ALPHA_MIND - INFO - 2016-03-15 full re-balance: 799\n",
"2018-05-05 14:06:51,033 - ALPHA_MIND - INFO - 2016-03-15 is finished\n",
"2018-05-05 14:06:51,044 - ALPHA_MIND - INFO - 2016-06-13 full re-balance: 800\n",
"2018-05-05 14:06:54,095 - ALPHA_MIND - INFO - 2016-06-13 is finished\n",
"2018-05-05 14:06:54,107 - ALPHA_MIND - INFO - 2016-09-05 full re-balance: 800\n",
"2018-05-05 14:06:57,177 - ALPHA_MIND - INFO - 2016-09-05 is finished\n",
"2018-05-05 14:06:57,188 - ALPHA_MIND - INFO - 2016-12-07 full re-balance: 800\n",
"2018-05-05 14:07:00,012 - ALPHA_MIND - INFO - 2016-12-07 is finished\n",
"2018-05-05 14:07:00,023 - ALPHA_MIND - INFO - 2017-03-09 full re-balance: 800\n",
"2018-05-05 14:07:03,315 - ALPHA_MIND - INFO - 2017-03-09 is finished\n",
"2018-05-05 14:07:03,328 - ALPHA_MIND - INFO - 2017-06-08 full re-balance: 800\n",
"2018-05-05 14:07:06,336 - ALPHA_MIND - INFO - 2017-06-08 is finished\n",
"2018-05-05 14:07:06,348 - ALPHA_MIND - INFO - 2017-08-31 full re-balance: 800\n",
"2018-05-05 14:07:09,893 - ALPHA_MIND - INFO - 2017-08-31 is finished\n",
"2018-05-05 14:07:09,904 - ALPHA_MIND - INFO - 2017-11-30 full re-balance: 800\n",
"2018-05-05 14:07:13,134 - ALPHA_MIND - INFO - 2017-11-30 is finished\n"
]
}
],
......@@ -700,7 +693,7 @@
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1b6adc713c8>"
"<matplotlib.axes._subplots.AxesSubplot at 0x1fbc693ee48>"
]
},
"execution_count": 19,
......@@ -709,9 +702,9 @@
},
{
"data": {
"image/png": 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\n",
"image/png": 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U8PKkbvkiUlcshBAu5PDhw/Tq1YtVq1Yxbdo0Vq9ezc6dOwkPD2fs2LEkJyfz3HPPsWLF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\nREREJOD9P7YiQOMpZl+hAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
"<matplotlib.figure.Figure at 0x1fbc693e438>"
]
},
"metadata": {},
......@@ -752,7 +745,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 13 - Evaluation within Industry Groups.ipynb
View file @ 61bfd381
This source diff could not be displayed because it is too large. You can view the blob instead.
notebooks/Example 2 - Strategy Analysis.ipynb
View file @ 61bfd381
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notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
......@@ -23,7 +31,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -41,27 +49,13 @@
" neutralized_risk = None\n",
"\n",
" alpha_factors = {\n",
" 'f01': CSQuantiles(LAST('ep_q'), groups='sw1_adj'),\n",
" 'f02': CSQuantiles(LAST('roe_q'), groups='sw1_adj'),\n",
" 'f03': CSQuantiles(LAST('SGRO'), groups='sw1_adj'),\n",
" 'f04': CSQuantiles(LAST('GREV'), groups='sw1_adj'),\n",
" 'f05': CSQuantiles(LAST('con_peg_rolling'), groups='sw1_adj'),\n",
" 'f06': CSQuantiles(LAST('con_pe_rolling_order'), groups='sw1_adj'),\n",
" 'f07': CSQuantiles(LAST('IVR'), groups='sw1_adj'),\n",
" 'f08': CSQuantiles(LAST('ILLIQUIDITY'), groups='sw1_adj'),\n",
" 'f09': CSQuantiles(LAST('DividendPaidRatio'), groups='sw1_adj'),\n",
" 'f01': CSQuantiles(LAST('EPS'), groups='sw1'),\n",
" 'f02': CSQuantiles(LAST('ROE'), groups='sw1'),\n",
" }\n",
"\n",
" weights = dict(\n",
" f01=0.5,\n",
" f02=1.,\n",
" f03=1.,\n",
" f04=1.,\n",
" f05=-1.,\n",
" f06=-0.5,\n",
" f07=0.5,\n",
" f08=0.5,\n",
" f09=0.5\n",
" f01=1.,\n",
" f02=1.\n",
" )\n",
"\n",
" alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
......@@ -71,58 +65,32 @@
" batch=1,\n",
" neutralized_risk=None,\n",
" pre_process=None,\n",
" post_process=None)\n",
" post_process=None,\n",
" data_source=os.environ['DB_URI'])\n",
"\n",
" industries = industry_list('sw_adj', 1)\n",
" total_risk_names = ['benchmark', 'total'] + \\\n",
" ['EARNYILD', 'LIQUIDTY', 'GROWTH', 'SIZE', 'BETA', 'MOMENTUM'] + \\\n",
" industries\n",
" industry_names = industry_list('sw', 1)\n",
" constraint_risk = ['SIZE', 'SIZENL', 'BETA'] + industry_names\n",
" total_risk_names = constraint_risk + ['benchmark', 'total']\n",
" all_styles = risk_styles + industry_styles + macro_styles\n",
"\n",
" b_type = []\n",
" l_val = []\n",
" u_val = []\n",
"\n",
" previous_pos = pd.DataFrame()\n",
" rets = []\n",
" turn_overs = []\n",
" leverags = []\n",
"\n",
" for name in total_risk_names:\n",
" if name == 'benchmark':\n",
" b_type.append(BoundaryType.RELATIVE)\n",
" l_val.append(0.8)\n",
" u_val.append(1.0)\n",
" elif name == 'total':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(.0)\n",
" u_val.append(.0)\n",
" elif name == 'EARNYILD':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.00)\n",
" u_val.append(0.20)\n",
" elif name == 'GROWTH':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.00)\n",
" u_val.append(0.20)\n",
" elif name == 'MOMENTUM':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.20)\n",
" u_val.append(0.20)\n",
" elif name == 'SIZE':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(-0.2)\n",
" u_val.append(0.0)\n",
" elif name == 'LIQUIDTY':\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(-0.40)\n",
" u_val.append(-0.20)\n",
" elif name not in [\"计算机\", \"医药生物\", \"国防军工\", \"信息服务\", \"机械设备\"] and name in industries:\n",
" b_type.append(BoundaryType.RELATIVE)\n",
" l_val.append(0.8)\n",
" u_val.append(1.0)\n",
" elif name in [\"计算机\", \"医药生物\", \"国防军工\", \"信息服务\", \"机械设备\"]:\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.0)\n",
" u_val.append(0.05)\n",
" else:\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0)\n",
" u_val.append(0)\n",
" l_val.append(0.0)\n",
" u_val.append(0.0)\n",
"\n",
" bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)\n",
" running_setting = RunningSetting(universe,\n",
......@@ -143,7 +111,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -178,946 +146,9 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": null,
"metadata": {},
"outputs": [
{
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"text": [
"2018-05-04 22:01:52,871 - ALPHA_MIND - INFO - starting backting ...\n",
"2018-05-04 22:02:12,307 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2018-05-04 22:02:13,368 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2018-05-04 22:02:13,757 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2018-05-04 22:02:21,004 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2018-05-04 22:02:49,000 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:106: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" is_in_benchmark = (total_data.weight > 0.).astype(float).reshape((-1, 1))\n",
"2018-05-04 22:02:54,655 - ALPHA_MIND - INFO - 2010-01-04 00:00:00 re-balance: 798 codes\n",
"2018-05-04 22:02:54,708 - ALPHA_MIND - INFO - 2010-01-18 00:00:00 re-balance: 798 codes\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:142: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
" remained_pos = previous_pos.loc[codes]\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
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"2018-05-04 22:03:23,592 - ALPHA_MIND - INFO - 2017-02-21 00:00:00 re-balance: 852 codes\n"
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},
{
"name": "stderr",
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"text": [
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"2018-05-04 22:03:23,941 - ALPHA_MIND - INFO - 2017-03-21 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:03:24,104 - ALPHA_MIND - INFO - 2017-04-06 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:03:24,273 - ALPHA_MIND - INFO - 2017-04-20 00:00:00 re-balance: 853 codes\n",
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"2018-05-04 22:03:24,960 - ALPHA_MIND - INFO - 2017-06-20 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:03:25,127 - ALPHA_MIND - INFO - 2017-07-04 00:00:00 re-balance: 855 codes\n",
"2018-05-04 22:03:25,299 - ALPHA_MIND - INFO - 2017-07-18 00:00:00 re-balance: 855 codes\n",
"2018-05-04 22:03:25,466 - ALPHA_MIND - INFO - 2017-08-01 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:03:25,635 - ALPHA_MIND - INFO - 2017-08-15 00:00:00 re-balance: 853 codes\n",
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"2018-05-04 22:03:25,957 - ALPHA_MIND - INFO - 2017-09-12 00:00:00 re-balance: 853 codes\n",
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"2018-05-04 22:03:26,952 - ALPHA_MIND - INFO - 2017-12-12 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:03:27,129 - ALPHA_MIND - INFO - 2017-12-26 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:03:27,295 - ALPHA_MIND - INFO - 2018-01-10 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:03:27,476 - ALPHA_MIND - INFO - 2018-01-24 00:00:00 re-balance: 856 codes\n",
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"2018-05-04 22:03:27,970 - ALPHA_MIND - INFO - 2018-03-14 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:03:28,134 - ALPHA_MIND - INFO - 2018-03-28 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:03:28,297 - ALPHA_MIND - INFO - 2018-04-13 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:03:28,463 - ALPHA_MIND - INFO - 2018-04-27 00:00:00 re-balance: 859 codes\n",
"2018-05-04 22:03:29,098 - ALPHA_MIND - INFO - weight_gap: 0.005 finished\n",
"2018-05-04 22:03:29,105 - ALPHA_MIND - INFO - starting backting ...\n",
"2018-05-04 22:03:48,345 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2018-05-04 22:03:49,381 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2018-05-04 22:03:49,813 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2018-05-04 22:03:57,033 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2018-05-04 22:04:24,952 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:106: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" is_in_benchmark = (total_data.weight > 0.).astype(float).reshape((-1, 1))\n",
"2018-05-04 22:04:30,608 - ALPHA_MIND - INFO - 2010-01-04 00:00:00 re-balance: 798 codes\n",
"2018-05-04 22:04:30,659 - ALPHA_MIND - INFO - 2010-01-18 00:00:00 re-balance: 798 codes\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:142: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
" remained_pos = previous_pos.loc[codes]\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"2018-05-04 22:04:38,859 - ALPHA_MIND - INFO - 2012-02-14 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:04:54,230 - ALPHA_MIND - INFO - 2015-10-15 00:00:00 re-balance: 869 codes\n",
"2018-05-04 22:04:54,396 - ALPHA_MIND - INFO - 2015-10-29 00:00:00 re-balance: 869 codes\n",
"2018-05-04 22:04:54,563 - ALPHA_MIND - INFO - 2015-11-12 00:00:00 re-balance: 869 codes\n",
"2018-05-04 22:04:54,734 - ALPHA_MIND - INFO - 2015-11-26 00:00:00 re-balance: 869 codes\n"
]
},
{
"name": "stderr",
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"text": [
"2018-05-04 22:04:54,898 - ALPHA_MIND - INFO - 2015-12-10 00:00:00 re-balance: 869 codes\n",
"2018-05-04 22:04:55,070 - ALPHA_MIND - INFO - 2015-12-24 00:00:00 re-balance: 869 codes\n",
"2018-05-04 22:04:55,233 - ALPHA_MIND - INFO - 2016-01-08 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:55,409 - ALPHA_MIND - INFO - 2016-01-22 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:55,586 - ALPHA_MIND - INFO - 2016-02-05 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:55,761 - ALPHA_MIND - INFO - 2016-02-26 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:55,928 - ALPHA_MIND - INFO - 2016-03-11 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:56,092 - ALPHA_MIND - INFO - 2016-03-25 00:00:00 re-balance: 863 codes\n",
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"2018-05-04 22:04:56,605 - ALPHA_MIND - INFO - 2016-05-10 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:56,788 - ALPHA_MIND - INFO - 2016-05-24 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:56,956 - ALPHA_MIND - INFO - 2016-06-07 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:57,122 - ALPHA_MIND - INFO - 2016-06-23 00:00:00 re-balance: 863 codes\n",
"2018-05-04 22:04:57,289 - ALPHA_MIND - INFO - 2016-07-07 00:00:00 re-balance: 858 codes\n",
"2018-05-04 22:04:57,464 - ALPHA_MIND - INFO - 2016-07-21 00:00:00 re-balance: 858 codes\n",
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"2018-05-04 22:04:58,644 - ALPHA_MIND - INFO - 2016-11-07 00:00:00 re-balance: 859 codes\n",
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"2018-05-04 22:04:59,314 - ALPHA_MIND - INFO - 2017-01-03 00:00:00 re-balance: 851 codes\n",
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"2018-05-04 22:05:03,199 - ALPHA_MIND - INFO - 2017-12-12 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:05:03,372 - ALPHA_MIND - INFO - 2017-12-26 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:05:03,539 - ALPHA_MIND - INFO - 2018-01-10 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:05:03,716 - ALPHA_MIND - INFO - 2018-01-24 00:00:00 re-balance: 856 codes\n",
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"2018-05-04 22:05:04,041 - ALPHA_MIND - INFO - 2018-02-28 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:05:04,205 - ALPHA_MIND - INFO - 2018-03-14 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:05:04,371 - ALPHA_MIND - INFO - 2018-03-28 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:05:04,535 - ALPHA_MIND - INFO - 2018-04-13 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:05:04,706 - ALPHA_MIND - INFO - 2018-04-27 00:00:00 re-balance: 859 codes\n",
"2018-05-04 22:05:05,340 - ALPHA_MIND - INFO - weight_gap: 0.01 finished\n",
"2018-05-04 22:05:05,348 - ALPHA_MIND - INFO - starting backting ...\n",
"2018-05-04 22:05:24,568 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2018-05-04 22:05:25,638 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2018-05-04 22:05:26,039 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2018-05-04 22:05:33,113 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2018-05-04 22:06:01,127 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:106: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" is_in_benchmark = (total_data.weight > 0.).astype(float).reshape((-1, 1))\n",
"2018-05-04 22:06:06,749 - ALPHA_MIND - INFO - 2010-01-04 00:00:00 re-balance: 798 codes\n",
"2018-05-04 22:06:06,802 - ALPHA_MIND - INFO - 2010-01-18 00:00:00 re-balance: 798 codes\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:142: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
" remained_pos = previous_pos.loc[codes]\n",
"2018-05-04 22:06:06,943 - ALPHA_MIND - INFO - 2010-02-01 00:00:00 re-balance: 799 codes\n",
"2018-05-04 22:06:07,090 - ALPHA_MIND - INFO - 2010-02-22 00:00:00 re-balance: 798 codes\n",
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"2018-05-04 22:06:07,645 - ALPHA_MIND - INFO - 2010-04-20 00:00:00 re-balance: 799 codes\n",
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"2018-05-04 22:06:08,060 - ALPHA_MIND - INFO - 2010-06-02 00:00:00 re-balance: 799 codes\n",
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"2018-05-04 22:06:08,659 - ALPHA_MIND - INFO - 2010-08-02 00:00:00 re-balance: 892 codes\n",
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"2018-05-04 22:06:08,991 - ALPHA_MIND - INFO - 2010-08-30 00:00:00 re-balance: 892 codes\n",
"2018-05-04 22:06:09,154 - ALPHA_MIND - INFO - 2010-09-13 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:09,321 - ALPHA_MIND - INFO - 2010-09-30 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:09,488 - ALPHA_MIND - INFO - 2010-10-21 00:00:00 re-balance: 899 codes\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"2018-05-04 22:06:09,818 - ALPHA_MIND - INFO - 2010-11-18 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:09,980 - ALPHA_MIND - INFO - 2010-12-02 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:10,141 - ALPHA_MIND - INFO - 2010-12-16 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:10,306 - ALPHA_MIND - INFO - 2010-12-30 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:06:10,470 - ALPHA_MIND - INFO - 2011-01-14 00:00:00 re-balance: 899 codes\n",
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"2018-05-04 22:06:10,796 - ALPHA_MIND - INFO - 2011-02-18 00:00:00 re-balance: 897 codes\n",
"2018-05-04 22:06:10,974 - ALPHA_MIND - INFO - 2011-03-04 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:06:11,139 - ALPHA_MIND - INFO - 2011-03-18 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:06:11,819 - ALPHA_MIND - INFO - 2011-05-18 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:06:11,983 - ALPHA_MIND - INFO - 2011-06-01 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:06:24,696 - ALPHA_MIND - INFO - 2014-07-07 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:24,875 - ALPHA_MIND - INFO - 2014-07-21 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:25,048 - ALPHA_MIND - INFO - 2014-08-04 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:25,226 - ALPHA_MIND - INFO - 2014-08-18 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:25,395 - ALPHA_MIND - INFO - 2014-09-01 00:00:00 re-balance: 900 codes\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-05-04 22:06:25,564 - ALPHA_MIND - INFO - 2014-09-16 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:25,732 - ALPHA_MIND - INFO - 2014-09-30 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:25,901 - ALPHA_MIND - INFO - 2014-10-21 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,073 - ALPHA_MIND - INFO - 2014-11-04 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,252 - ALPHA_MIND - INFO - 2014-11-18 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,422 - ALPHA_MIND - INFO - 2014-12-02 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,593 - ALPHA_MIND - INFO - 2014-12-16 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,763 - ALPHA_MIND - INFO - 2014-12-30 00:00:00 re-balance: 900 codes\n",
"2018-05-04 22:06:26,935 - ALPHA_MIND - INFO - 2015-01-15 00:00:00 re-balance: 880 codes\n",
"2018-05-04 22:06:27,112 - ALPHA_MIND - INFO - 2015-01-29 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:27,286 - ALPHA_MIND - INFO - 2015-02-12 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:27,463 - ALPHA_MIND - INFO - 2015-03-05 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:27,629 - ALPHA_MIND - INFO - 2015-03-19 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:27,807 - ALPHA_MIND - INFO - 2015-04-02 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:27,978 - ALPHA_MIND - INFO - 2015-04-17 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:28,149 - ALPHA_MIND - INFO - 2015-05-04 00:00:00 re-balance: 879 codes\n",
"2018-05-04 22:06:28,329 - ALPHA_MIND - INFO - 2015-05-18 00:00:00 re-balance: 877 codes\n",
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"2018-05-04 22:06:28,848 - ALPHA_MIND - INFO - 2015-06-30 00:00:00 re-balance: 869 codes\n",
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"2018-05-04 22:06:29,358 - ALPHA_MIND - INFO - 2015-08-11 00:00:00 re-balance: 869 codes\n",
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"2018-05-04 22:06:31,064 - ALPHA_MIND - INFO - 2016-01-08 00:00:00 re-balance: 863 codes\n",
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"2018-05-04 22:06:34,780 - ALPHA_MIND - INFO - 2016-12-05 00:00:00 re-balance: 859 codes\n",
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"2018-05-04 22:06:35,115 - ALPHA_MIND - INFO - 2017-01-03 00:00:00 re-balance: 851 codes\n",
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"2018-05-04 22:06:35,535 - ALPHA_MIND - INFO - 2017-02-07 00:00:00 re-balance: 853 codes\n",
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"2018-05-04 22:06:36,217 - ALPHA_MIND - INFO - 2017-04-06 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:36,388 - ALPHA_MIND - INFO - 2017-04-20 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:36,558 - ALPHA_MIND - INFO - 2017-05-05 00:00:00 re-balance: 854 codes\n",
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"2018-05-04 22:06:36,905 - ALPHA_MIND - INFO - 2017-06-06 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:37,076 - ALPHA_MIND - INFO - 2017-06-20 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:37,246 - ALPHA_MIND - INFO - 2017-07-04 00:00:00 re-balance: 855 codes\n",
"2018-05-04 22:06:37,429 - ALPHA_MIND - INFO - 2017-07-18 00:00:00 re-balance: 855 codes\n",
"2018-05-04 22:06:37,601 - ALPHA_MIND - INFO - 2017-08-01 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:37,775 - ALPHA_MIND - INFO - 2017-08-15 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:37,941 - ALPHA_MIND - INFO - 2017-08-29 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:38,116 - ALPHA_MIND - INFO - 2017-09-12 00:00:00 re-balance: 853 codes\n",
"2018-05-04 22:06:38,303 - ALPHA_MIND - INFO - 2017-09-26 00:00:00 re-balance: 853 codes\n",
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"2018-05-04 22:06:38,648 - ALPHA_MIND - INFO - 2017-10-31 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:38,825 - ALPHA_MIND - INFO - 2017-11-14 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:38,992 - ALPHA_MIND - INFO - 2017-11-28 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:39,163 - ALPHA_MIND - INFO - 2017-12-12 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:39,341 - ALPHA_MIND - INFO - 2017-12-26 00:00:00 re-balance: 854 codes\n",
"2018-05-04 22:06:39,509 - ALPHA_MIND - INFO - 2018-01-10 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:39,698 - ALPHA_MIND - INFO - 2018-01-24 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:39,861 - ALPHA_MIND - INFO - 2018-02-07 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:40,031 - ALPHA_MIND - INFO - 2018-02-28 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:40,202 - ALPHA_MIND - INFO - 2018-03-14 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:40,368 - ALPHA_MIND - INFO - 2018-03-28 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:40,531 - ALPHA_MIND - INFO - 2018-04-13 00:00:00 re-balance: 856 codes\n",
"2018-05-04 22:06:40,699 - ALPHA_MIND - INFO - 2018-04-27 00:00:00 re-balance: 859 codes\n",
"2018-05-04 22:06:41,328 - ALPHA_MIND - INFO - weight_gap: 0.015 finished\n",
"2018-05-04 22:06:41,335 - ALPHA_MIND - INFO - starting backting ...\n",
"2018-05-04 22:07:00,554 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2018-05-04 22:07:01,610 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2018-05-04 22:07:02,023 - ALPHA_MIND - INFO - benchmark data loading finished ...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-05-04 22:07:09,293 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2018-05-04 22:07:37,234 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:106: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
" is_in_benchmark = (total_data.weight > 0.).astype(float).reshape((-1, 1))\n",
"2018-05-04 22:07:42,821 - ALPHA_MIND - INFO - 2010-01-04 00:00:00 re-balance: 798 codes\n",
"2018-05-04 22:07:42,872 - ALPHA_MIND - INFO - 2010-01-18 00:00:00 re-balance: 798 codes\n",
"d:\\ProgramData\\Anaconda3\\lib\\site-packages\\alpha_mind-0.1.1-py3.6-win-amd64.egg\\alphamind\\strategy\\strategy.py:142: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
" remained_pos = previous_pos.loc[codes]\n",
"2018-05-04 22:07:43,011 - ALPHA_MIND - INFO - 2010-02-01 00:00:00 re-balance: 799 codes\n",
"2018-05-04 22:07:43,158 - ALPHA_MIND - INFO - 2010-02-22 00:00:00 re-balance: 798 codes\n",
"2018-05-04 22:07:43,300 - ALPHA_MIND - INFO - 2010-03-08 00:00:00 re-balance: 799 codes\n",
"2018-05-04 22:07:43,443 - ALPHA_MIND - INFO - 2010-03-22 00:00:00 re-balance: 799 codes\n",
"2018-05-04 22:07:43,577 - ALPHA_MIND - INFO - 2010-04-06 00:00:00 re-balance: 799 codes\n",
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"2018-05-04 22:07:45,878 - ALPHA_MIND - INFO - 2010-11-18 00:00:00 re-balance: 899 codes\n",
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"2018-05-04 22:07:47,364 - ALPHA_MIND - INFO - 2011-04-01 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:47,529 - ALPHA_MIND - INFO - 2011-04-19 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:47,695 - ALPHA_MIND - INFO - 2011-05-04 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:47,938 - ALPHA_MIND - INFO - 2011-05-18 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:48,101 - ALPHA_MIND - INFO - 2011-06-01 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:48,262 - ALPHA_MIND - INFO - 2011-06-16 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:07:48,583 - ALPHA_MIND - INFO - 2011-07-14 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:07:48,909 - ALPHA_MIND - INFO - 2011-08-11 00:00:00 re-balance: 897 codes\n",
"2018-05-04 22:07:49,082 - ALPHA_MIND - INFO - 2011-08-25 00:00:00 re-balance: 896 codes\n",
"2018-05-04 22:07:49,251 - ALPHA_MIND - INFO - 2011-09-08 00:00:00 re-balance: 897 codes\n",
"2018-05-04 22:07:49,418 - ALPHA_MIND - INFO - 2011-09-23 00:00:00 re-balance: 897 codes\n",
"2018-05-04 22:07:49,583 - ALPHA_MIND - INFO - 2011-10-14 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:49,755 - ALPHA_MIND - INFO - 2011-10-28 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:49,920 - ALPHA_MIND - INFO - 2011-11-11 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:50,094 - ALPHA_MIND - INFO - 2011-11-25 00:00:00 re-balance: 898 codes\n",
"2018-05-04 22:07:50,255 - ALPHA_MIND - INFO - 2011-12-09 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:07:50,581 - ALPHA_MIND - INFO - 2012-01-10 00:00:00 re-balance: 898 codes\n",
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"2018-05-04 22:07:53,589 - ALPHA_MIND - INFO - 2012-10-10 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:07:53,755 - ALPHA_MIND - INFO - 2012-10-24 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:07:53,923 - ALPHA_MIND - INFO - 2012-11-07 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:07:54,098 - ALPHA_MIND - INFO - 2012-11-21 00:00:00 re-balance: 899 codes\n",
"2018-05-04 22:07:54,262 - ALPHA_MIND - INFO - 2012-12-05 00:00:00 re-balance: 899 codes\n",
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},
{
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"2018-05-04 22:08:17,199 - ALPHA_MIND - INFO - weight_gap: 0.02 finished\n"
]
}
],
"outputs": [],
"source": [
"weight_gaps = [0.005, 0.010, 0.015, 0.020]\n",
"\n",
......@@ -1145,13 +176,6 @@
" alpha_logger.info(f\"target_vol: {target_vol:.4f} finished\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......
notebooks/Example 4 - Single Factor Analysis.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
......@@ -18,7 +26,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -41,7 +49,7 @@
"batch = 1\n",
"horizon = map_freq(freq)\n",
"universe = Universe(\"custom\", ['zz800'])\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
"data_source = os.environ['DB_URI']\n",
"benchmark_code = 905\n",
"weight_gap = 0.01\n",
"\n",
......@@ -52,7 +60,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -73,7 +81,7 @@
" neutralized_risk=neutralized_risk,\n",
" risk_model='short',\n",
" pre_process=[winsorize_normal, standardize],\n",
" post_process=[winsorize_normal, standardize],\n",
" post_process=[standardize],\n",
" warm_start=0,\n",
" data_source=data_source)\n",
" ref_date, model = params\n",
......@@ -83,133 +91,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 18:41:19,906 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:19,908 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,265 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,291 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,608 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,610 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,956 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:20,958 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,320 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,323 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,667 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,669 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,997 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:21,999 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:22,395 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:22,397 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:22,744 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:22,747 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,072 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,080 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,444 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,446 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,768 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:23,770 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,126 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,127 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,460 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,463 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,840 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:24,842 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,193 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,195 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,557 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,559 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,885 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:25,887 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,194 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,195 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,527 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,529 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,847 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:26,849 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,185 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,187 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,554 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,557 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,907 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:27,910 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,249 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,263 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,619 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,622 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,982 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:28,984 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:29,346 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:29,348 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:29,753 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:29,755 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,126 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,128 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,476 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,479 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,816 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:30,818 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,164 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,167 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,502 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,504 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,842 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:31,859 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:32,196 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:32,198 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:32,639 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:32,642 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-04-16 18:41:32,990 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:32,992 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:33,343 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:33,345 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:33,678 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:33,680 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,037 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,054 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,403 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,405 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,787 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:34,789 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,137 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,140 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,482 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,484 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,842 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:35,844 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,181 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,183 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,552 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,554 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,906 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:36,909 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:37,289 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:37,291 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:37,654 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:37,656 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:38,016 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n",
"2018-04-16 18:41:38,018 - ALPHA_MIND - WARNING - winsorize_normal normally should not be done after neutralize\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 18.6 s\n"
]
}
],
"outputs": [],
"source": [
"%%time\n",
"\n",
......@@ -221,7 +105,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -260,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -344,30 +228,9 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x240ed0d7f60>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"ret_df = create_scenario(weight_gap)\n",
"ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
......@@ -375,13 +238,6 @@
" secondary_y='tc_cost')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -406,7 +262,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 5 - Style Factor Analysis.ipynb
View file @ 61bfd381
......@@ -4,7 +4,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"> The methodolegy is similar to The Barra China Equity Model (CNE5)'s documentation"
"* 方法参考自 The Barra China Equity Model (CNE5)'s 文档\n",
"\n",
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
......@@ -15,6 +17,7 @@
"source": [
"%matplotlib inline\n",
"\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
......@@ -54,7 +57,7 @@
"outputs": [],
"source": [
"def risk_factor_analysis(factor_name):\n",
" data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
" data_source = os.environ['DB_URI']\n",
" engine = SqlEngine(data_source)\n",
" risk_names = list(set(risk_styles).difference({factor_name}))\n",
" industry_names = list(set(industry_styles).difference({factor_name}))\n",
......@@ -115,13 +118,6 @@
"df[['factor', 'abs t.']].groupby('factor').mean().sort_values('abs t.', ascending=False).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -146,7 +142,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 6 - Target Volatility Builder.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -7,11 +14,13 @@
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"from PyFin.api import *\n",
"from alphamind.api import *\n",
"from alphamind.strategy.strategy import Strategy, RunningSetting\n",
"from alphamind.portfolio.meanvariancebuilder import target_vol_builder\n",
"\n",
"plt.style.use('ggplot')"
......@@ -32,7 +41,7 @@
"outputs": [],
"source": [
"ref_date = '2018-01-08'\n",
"engine = SqlEngine('postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha')\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['zz800'])"
]
},
......@@ -108,7 +117,7 @@
"# check the result\n",
"print(f\"total weight is {p_weight.sum(): .4f}\")\n",
"print(f\"portfolio activate weight forecasting vol is {np.sqrt((p_weight - bm) @ sec_cov @ (p_weight - bm)):.4f}\")\n",
"print(f\"portfolio expected return is {p_weight @ er:.4f} comparing with benchmark er {bm @ er:.4f}\")"
"print(f\"portfolio er: {p_weight @ er:.4f} comparing with benchmark er: {bm @ er:.4f}\")"
]
},
{
......@@ -140,13 +149,10 @@
"batch = 0\n",
"horizon = map_freq(freq)\n",
"universe = Universe(\"custom\", ['zz800'])\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
"data_source = os.environ['DB_URI']\n",
"benchmark_code = 906\n",
"target_vol = 0.05\n",
"\n",
"executor = NaiveExecutor()\n",
"ref_dates = makeSchedule(start_date, end_date, freq, 'china.sse')\n",
"engine = SqlEngine(data_source)"
"weights_bandwidth = 0.02"
]
},
{
......@@ -160,38 +166,18 @@
"\"\"\"\n",
"\n",
"alpha_factors = {'f01': CSRank(LAST('EPS'))}\n",
"\n",
"weights = dict(f01=1.)\n",
"\n",
"alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
"\n",
"def predict_worker(params):\n",
" data_meta = DataMeta(freq=freq,\n",
"data_meta = DataMeta(freq=freq,\n",
" universe=universe,\n",
" batch=batch,\n",
" neutralized_risk=neutralized_risk,\n",
" risk_model='short',\n",
" pre_process=[winsorize_normal, standardize],\n",
" post_process=[winsorize_normal, standardize],\n",
" post_process=[standardize],\n",
" warm_start=0,\n",
" data_source=data_source)\n",
" ref_date, model = params\n",
" er = predict_by_model(ref_date, model, data_meta)\n",
" return er"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"\"\"\"\n",
"Predicting Phase\n",
"\"\"\"\n",
"predicts = [predict_worker((d.strftime('%Y-%m-%d'), alpha_model)) for d in ref_dates]"
" data_source=data_source)"
]
},
{
......@@ -201,26 +187,32 @@
"outputs": [],
"source": [
"\"\"\"\n",
"Shared Data\n",
"Constraintes settings\n",
"\"\"\"\n",
"\n",
"constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
"total_risk_names = constraint_risk + ['total']\n",
"constraint_risk = ['SIZE', 'SIZENL', 'BETA'] + industry_names\n",
"total_risk_names = constraint_risk + ['benchmark', 'total']\n",
"\n",
"b_type = []\n",
"l_val = []\n",
"u_val = []\n",
"\n",
"previous_pos = pd.DataFrame()\n",
"rets = []\n",
"turn_overs = []\n",
"leverags = []\n",
"\n",
"for name in total_risk_names:\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.0)\n",
" u_val.append(0.0)\n",
" \n",
"bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)\n",
"industry_total = engine.fetch_industry_matrix_range(universe, dates=ref_dates, category=industry_name, level=industry_level)\n",
"benchmark_total = engine.fetch_benchmark_range(dates=ref_dates, benchmark=benchmark_code)\n",
"risk_cov_total, risk_exposure_total = engine.fetch_risk_model_range(universe, dates=ref_dates, risk_model=risk_model)\n",
"index_return = engine.fetch_dx_return_index_range(benchmark_code, start_date, end_date, horizon=horizon, offset=1).set_index('trade_date')"
" if name == 'benchmark':\n",
" b_type.append(BoundaryType.RELATIVE)\n",
" l_val.append(0.8)\n",
" u_val.append(1.0)\n",
" else:\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.0)\n",
" u_val.append(0.0)\n",
"\n",
"bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)"
]
},
{
......@@ -229,94 +221,18 @@
"metadata": {},
"outputs": [],
"source": [
"# rebalance\n",
"\n",
"def create_scenario(target_vol):\n",
" \n",
" all_styles = risk_styles + industry_styles + macro_styles\n",
" previous_pos = pd.DataFrame()\n",
" rets = []\n",
" turn_overs = []\n",
" leverags = []\n",
" ics = []\n",
"\n",
" for i, ref_date in enumerate(ref_dates):\n",
" ref_date = ref_date.strftime('%Y-%m-%d')\n",
" industry_matrix = industry_total[industry_total.trade_date == ref_date]\n",
" benchmark_w = benchmark_total[benchmark_total.trade_date == ref_date]\n",
" risk_exposure = risk_exposure_total[risk_exposure_total.trade_date == ref_date]\n",
" risk_cov = risk_cov_total[risk_cov_total.trade_date == ref_date]\n",
" \n",
" total_data = pd.merge(industry_matrix, benchmark_w, on=['code'], how='left').fillna(0.)\n",
" total_data = pd.merge(total_data, risk_exposure, on=['code'])\n",
" total_data = total_data.dropna()\n",
" codes = total_data.code.values.tolist()\n",
" \n",
" risk_exposure = total_data[all_styles].values\n",
" risk_cov = risk_cov[all_styles].values\n",
" special_risk = total_data.srisk.values\n",
" sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n",
"\n",
" benchmark_w = total_data.weight.values\n",
" \n",
" total_risk_exp = np.concatenate([total_data[constraint_risk].values.astype(float),\n",
" np.ones((len(benchmark_w),1))],\n",
" axis=1)\n",
" total_risk_exp = pd.DataFrame(total_risk_exp, columns=total_risk_names)\n",
" constraints = LinearConstraints(bounds, total_risk_exp, benchmark_w)\n",
" \n",
" lbound = np.zeros(len(total_data))\n",
" ubound = np.ones(len(total_data)) * 0.1\n",
"\n",
" er = predicts[i].loc[codes].values.flatten()\n",
" cons_mat = np.ones((len(er), 1))\n",
" risk_target = (benchmark_w.sum(), benchmark_w.sum())\n",
" \n",
" try:\n",
" target_pos, _ = er_portfolio_analysis(er,\n",
" total_data.industry_name.values,\n",
" None,\n",
" constraints,\n",
" False,\n",
" benchmark_w,\n",
" method='tv',\n",
" lbound=lbound,\n",
" ubound=ubound,\n",
" cov=sec_cov,\n",
" target_vol=target_vol)\n",
" except:\n",
" import pdb\n",
" pdb.set_trace()\n",
"\n",
" target_pos['code'] = codes\n",
" turn_over, executed_pos = executor.execute(target_pos=target_pos)\n",
"\n",
" executed_codes = executed_pos.code.tolist()\n",
" dx_returns = engine.fetch_dx_return(ref_date, executed_codes, horizon=horizon, offset=1)\n",
" result = pd.merge(executed_pos, total_data[['code', 'weight']], on=['code'], how='inner')\n",
" result = pd.merge(result, dx_returns, on=['code'])\n",
" \n",
" excess_return = np.exp(result.dx.values) - 1. - index_return.loc[ref_date, 'dx']\n",
" raw_weight = result.weight_x.values\n",
" activate_weight = raw_weight - result.weight_y.values\n",
" ret = raw_weight @ excess_return\n",
" risk_adjusted_ic = np.corrcoef(excess_return, activate_weight)[0, 1]\n",
" rets.append(np.log(1. + ret))\n",
" ics.append(risk_adjusted_ic)\n",
" executor.set_current(executed_pos)\n",
" turn_overs.append(turn_over)\n",
" \n",
" leverage = raw_weight.sum()\n",
" leverags.append(leverage)\n",
" alpha_logger.info(f\"{ref_date} is finished with expected vol {np.sqrt((target_pos.weight.values - benchmark_w) @ sec_cov @ (target_pos.weight.values - benchmark_w)):.2f}\")\n",
"\n",
" ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'IC': ics, 'leverage': leverags}, index=ref_dates)\n",
"\n",
" ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], freq)] = 0.\n",
" ret_df = ret_df.shift(1)\n",
" ret_df.iloc[0] = 0.\n",
" ret_df['tc_cost'] = ret_df.turn_over * 0.002\n",
" return ret_df"
"\"\"\"\n",
"Running Settings\n",
"\"\"\"\n",
"running_setting = RunningSetting(universe,\n",
" start_date,\n",
" end_date,\n",
" freq,\n",
" benchmark=benchmark_code,\n",
" weights_bandwidth=weights_bandwidth,\n",
" rebalance_method='tv',\n",
" bounds=bounds,\n",
" target_vol=target_vol)"
]
},
{
......@@ -325,7 +241,11 @@
"metadata": {},
"outputs": [],
"source": [
"ret_df = create_scenario(target_vol)"
"\"\"\"\n",
"Strategy run\n",
"\"\"\"\n",
"strategy = Strategy(alpha_model, data_meta, running_setting)\n",
"ret_df, positions = strategy.run()"
]
},
{
......@@ -334,9 +254,10 @@
"metadata": {},
"outputs": [],
"source": [
"ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),\n",
" title='Fixed freq rebalanced: {0} with benchmark {1}'.format(freq, 905),\n",
" secondary_y='tc_cost')"
"ret_df[['excess_return', 'turn_over']].cumsum().plot(figsize=(14, 7),\n",
" title='Fixed freq rebalanced with target vol \\\n",
" at {2}: {0} with benchmark {1}'.format(freq, benchmark_code, target_vol),\n",
" secondary_y='turn_over')"
]
},
{
......@@ -363,7 +284,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ 61bfd381
......@@ -12,15 +12,18 @@
"\n",
"* 由于``scipy``在``ashare_ex``上面性能太差，所以一般忽略``scipy``在这个股票池上的表现；\n",
"\n",
"* 时间单位都是毫秒。"
"* 时间单位都是毫秒。\n",
"\n",
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import timeit\n",
"import numpy as np\n",
"import pandas as pd\n",
......@@ -46,7 +49,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -57,7 +60,7 @@
"factor = 'EPS'\n",
"lb = 0.0\n",
"ub = 0.1\n",
"data_source = 'postgres+psycopg2://postgres:we083826@localhost/alpha'\n",
"data_source = os.environ['DB_URI']\n",
"engine = SqlEngine(data_source)\n",
"\n",
"universes = [Universe('custom', [u_name]) for u_name in u_names]\n",
......@@ -75,22 +78,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-30 16:52:12,784 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:12,796 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:12,813 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:12,842 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:12,879 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:52:13,184 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
"outputs": [],
"source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"\n",
......@@ -136,72 +126,9 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"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>sh50</th>\n",
" <th>hs300</th>\n",
" <th>zz500</th>\n",
" <th>zz800</th>\n",
" <th>zz1000</th>\n",
" <th>ashare_ex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>1.68</td>\n",
" <td>3.13</td>\n",
" <td>5.02</td>\n",
" <td>10.15</td>\n",
" <td>13.55</td>\n",
" <td>145.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>0.20</td>\n",
" <td>0.33</td>\n",
" <td>0.44</td>\n",
" <td>0.58</td>\n",
" <td>0.72</td>\n",
" <td>2.94</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 1.68 3.13 5.02 10.15 13.55 145.17\n",
"alphamind 0.20 0.33 0.44 0.58 0.72 2.94"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df"
]
......@@ -216,22 +143,9 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-30 16:52:13,374 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:13,509 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:13,732 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:14,220 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:14,956 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:52:21,246 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
"outputs": [],
"source": [
"from cvxpy import pnorm\n",
"\n",
......@@ -306,72 +220,9 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"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>sh50</th>\n",
" <th>hs300</th>\n",
" <th>zz500</th>\n",
" <th>zz800</th>\n",
" <th>zz1000</th>\n",
" <th>ashare_ex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>2.49</td>\n",
" <td>22.19</td>\n",
" <td>51.62</td>\n",
" <td>123.42</td>\n",
" <td>190.62</td>\n",
" <td>1,946.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>3.77</td>\n",
" <td>33.59</td>\n",
" <td>55.49</td>\n",
" <td>116.31</td>\n",
" <td>165.62</td>\n",
" <td>1,239.47</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 2.49 22.19 51.62 123.42 190.62 1,946.96\n",
"alphamind 3.77 33.59 55.49 116.31 165.62 1,239.47"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df"
]
......@@ -388,22 +239,9 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-30 16:52:21,418 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:21,888 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:23,360 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:29,066 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:37,334 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:56:15,174 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
"outputs": [],
"source": [
"from cvxpy import quad_form\n",
"\n",
......@@ -449,72 +287,9 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"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>sh50</th>\n",
" <th>hs300</th>\n",
" <th>zz500</th>\n",
" <th>zz800</th>\n",
" <th>zz1000</th>\n",
" <th>ashare_ex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>9.81</td>\n",
" <td>185.93</td>\n",
" <td>638.17</td>\n",
" <td>2,501.41</td>\n",
" <td>3,665.02</td>\n",
" <td>94,592.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>0.33</td>\n",
" <td>5.66</td>\n",
" <td>14.24</td>\n",
" <td>47.29</td>\n",
" <td>109.08</td>\n",
" <td>2,515.06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 9.81 185.93 638.17 2,501.41 3,665.02 94,592.70\n",
"alphamind 0.33 5.66 14.24 47.29 109.08 2,515.06"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df"
]
......@@ -529,22 +304,9 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-30 16:56:16,044 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:56:16,705 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:56:19,651 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:56:30,227 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:56:44,840 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 17:08:25,343 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
"outputs": [],
"source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n",
......@@ -590,72 +352,9 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th></th>\n",
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" <th>hs300</th>\n",
" <th>zz500</th>\n",
" <th>zz800</th>\n",
" <th>zz1000</th>\n",
" <th>ashare_ex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>10.78</td>\n",
" <td>300.36</td>\n",
" <td>1,377.87</td>\n",
" <td>5,244.64</td>\n",
" <td>6,758.95</td>\n",
" <td>334,990.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>0.32</td>\n",
" <td>4.78</td>\n",
" <td>16.02</td>\n",
" <td>41.63</td>\n",
" <td>64.54</td>\n",
" <td>1,134.95</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 10.78 300.36 1,377.87 5,244.64 6,758.95 334,990.46\n",
"alphamind 0.32 4.78 16.02 41.63 64.54 1,134.95"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df"
]
......@@ -672,19 +371,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-30 17:08:25,991 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 17:08:26,763 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 17:08:30,276 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 17:08:41,580 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 17:09:18,856 - ALPHA_MIND - INFO - zz1000 is finished\n"
]
}
],
"outputs": [],
"source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n",
......@@ -842,7 +529,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
notebooks/Example 8 - Neutralize Algorithm Comparison.ipynb
View file @ 61bfd381
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库\n",
"* 请在环境变量中设置`DATAYES_TOKEN`作为通联数据登陆凭证"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"from matplotlib import pyplot as plt\n",
"import uqer\n",
"import numpy as np\n",
......@@ -20,31 +29,23 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"16937@wmcloud.com 账号登录成功\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_ = uqer.Client(token='')"
"_ = uqer.Client(token=os.environ['DATAYES_TOKEN'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ref_date = '2017-06-23'\n",
"factor = 'EPS'\n",
"\n",
"engine = SqlEngine()\n",
"engine = SqlEngine(os.environ['DB_URI'])\n",
"universe = Universe('custom', ['zz800'])"
]
},
......@@ -96,7 +97,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -108,7 +109,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -118,20 +119,9 @@
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"800"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(total_data)"
]
......@@ -146,17 +136,9 @@
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"128 ms ± 4.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%timeit\n",
"neutralized_factor_uqer = uqer.neutralize(total_data[factor],\n",
......@@ -166,97 +148,9 @@
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>uqer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>000001</th>\n",
" <td>-0.076975</td>\n",
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" <tr>\n",
" <th>000009</th>\n",
" <td>0.029815</td>\n",
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" <tr>\n",
" <th>000012</th>\n",
" <td>0.089305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000021</th>\n",
" <td>0.034998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000025</th>\n",
" <td>-0.048613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000027</th>\n",
" <td>-0.003274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000028</th>\n",
" <td>0.347669</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uqer\n",
"000001 -0.076975\n",
"000002 -0.288382\n",
"000006 -0.054668\n",
"000008 -0.034123\n",
"000009 0.029815\n",
"000012 0.089305\n",
"000021 0.034998\n",
"000025 -0.048613\n",
"000027 -0.003274\n",
"000028 0.347669"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"neutralized_factor_uqer = uqer.neutralize(total_data[factor],\n",
" target_date=ref_date.replace('-', ''),\n",
......@@ -267,78 +161,18 @@
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"800"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(neutralized_factor_uqer)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"BETA 6.017409e-14\n",
"MOMENTUM 7.993606e-15\n",
"SIZE -6.938894e-14\n",
"EARNYILD -1.829648e-13\n",
"RESVOL 2.109424e-13\n",
"GROWTH -1.021405e-13\n",
"BTOP -1.425526e-13\n",
"LEVERAGE -1.541545e-13\n",
"LIQUIDTY 4.973799e-14\n",
"SIZENL 5.417888e-14\n",
"Bank -8.770762e-15\n",
"RealEstate -1.076916e-14\n",
"Health -6.661338e-16\n",
"Transportation 3.774758e-15\n",
"Mining -4.551914e-15\n",
"NonFerMetal 9.020562e-15\n",
"HouseApp -4.218847e-15\n",
"LeiService -4.163336e-16\n",
"MachiEquip 2.395306e-14\n",
"BuildDeco 9.547918e-15\n",
"CommeTrade 5.884182e-15\n",
"CONMAT 7.188694e-15\n",
"Auto 6.883383e-15\n",
"Textile 4.996004e-15\n",
"FoodBever -6.217249e-15\n",
"Electronics 1.132427e-14\n",
"Computer 8.604228e-15\n",
"LightIndus 1.346145e-15\n",
"Utilities -6.258882e-15\n",
"Telecom 3.608225e-16\n",
"AgriForest 8.160139e-15\n",
"CHEM -6.994405e-15\n",
"Media 1.088019e-14\n",
"IronSteel -1.915135e-15\n",
"NonBankFinan -9.880985e-15\n",
"ELECEQP -5.773160e-15\n",
"AERODEF 4.031497e-15\n",
"Conglomerates -5.023759e-15\n",
"dtype: float64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"risk_exposure_uqer = uqer.DataAPI.RMExposureDayGet(tradeDate=ref_date.replace('-', '')).set_index('ticker')\n",
"targeted_secs = risk_exposure_uqer.loc[neutralized_factor_uqer.index]\n",
......@@ -360,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -370,17 +204,9 @@
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"139 µs ± 8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
]
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%timeit\n",
"neutralized_factor_alphamind = neutralize(x, y, weights=np.ones(len(y)))"
......@@ -388,78 +214,9 @@
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
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" <td>-0.034123</td>\n",
" <td>-0.034123</td>\n",
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" <tr>\n",
" <th>000009</th>\n",
" <td>0.029815</td>\n",
" <td>0.029815</td>\n",
" </tr>\n",
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"text/plain": [
" uqer alpha-mind\n",
"000001 -0.076975 -0.076975\n",
"000002 -0.288382 -0.288382\n",
"000006 -0.054668 -0.054668\n",
"000008 -0.034123 -0.034123\n",
"000009 0.029815 0.029815"
]
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"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"neutralized_factor_alphamind = neutralize(x, y, weights=np.ones(len(y)))\n",
"alphamind_series = pd.Series(neutralized_factor_alphamind.flatten(), index=total_data.index)\n",
......@@ -469,20 +226,9 @@
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"800"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"len(alphamind_series)"
]
......@@ -497,7 +243,7 @@
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -506,96 +252,9 @@
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>code</th>\n",
" <th>isOpen</th>\n",
" <th>EPS</th>\n",
" <th>srisk</th>\n",
" <th>BETA</th>\n",
" <th>MOMENTUM</th>\n",
" <th>SIZE</th>\n",
" <th>EARNYILD</th>\n",
" <th>RESVOL</th>\n",
" <th>GROWTH</th>\n",
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" <th>Telecom</th>\n",
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" <th>NonBankFinan</th>\n",
" <th>ELECEQP</th>\n",
" <th>AERODEF</th>\n",
" <th>Conglomerates</th>\n",
" <th>COUNTRY</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ticker</th>\n",
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"<p>0 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [code, isOpen, EPS, srisk, BETA, MOMENTUM, SIZE, EARNYILD, RESVOL, GROWTH, BTOP, LEVERAGE, LIQUIDTY, SIZENL, Bank, RealEstate, Health, Transportation, Mining, NonFerMetal, HouseApp, LeiService, MachiEquip, BuildDeco, CommeTrade, CONMAT, Auto, Textile, FoodBever, Electronics, Computer, LightIndus, Utilities, Telecom, AgriForest, CHEM, Media, IronSteel, NonBankFinan, ELECEQP, AERODEF, Conglomerates, COUNTRY]\n",
"Index: []\n",
"\n",
"[0 rows x 43 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_data.loc[missed_codes]"
]
......@@ -610,7 +269,7 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -619,7 +278,7 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -629,7 +288,7 @@
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{
"cell_type": "code",
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -646,7 +305,7 @@
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{
"cell_type": "code",
"execution_count": 45,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
......@@ -656,126 +315,18 @@
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{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1fcc2856978>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fcc28465c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df[['uqer - ols', 'alphamind - ols']].plot(figsize=(14, 7), ylim=(-1e-4, 1e-4))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>uqer</th>\n",
" <th>alpha-mind</th>\n",
" <th>ols</th>\n",
" <th>uqer - ols</th>\n",
" <th>alphamind - ols</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>000001</th>\n",
" <td>-0.076975</td>\n",
" <td>-0.076975</td>\n",
" <td>-0.076975</td>\n",
" <td>-1.609823e-15</td>\n",
" <td>-1.165734e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000002</th>\n",
" <td>-0.288382</td>\n",
" <td>-0.288382</td>\n",
" <td>-0.288382</td>\n",
" <td>3.885781e-16</td>\n",
" <td>-2.220446e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000006</th>\n",
" <td>-0.054668</td>\n",
" <td>-0.054668</td>\n",
" <td>-0.054668</td>\n",
" <td>8.881784e-16</td>\n",
" <td>6.106227e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000008</th>\n",
" <td>-0.034123</td>\n",
" <td>-0.034123</td>\n",
" <td>-0.034123</td>\n",
" <td>-5.204170e-16</td>\n",
" <td>-7.910339e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>000009</th>\n",
" <td>0.029815</td>\n",
" <td>0.029815</td>\n",
" <td>0.029815</td>\n",
" <td>-2.567391e-16</td>\n",
" <td>2.775558e-16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" uqer alpha-mind ols uqer - ols alphamind - ols\n",
"000001 -0.076975 -0.076975 -0.076975 -1.609823e-15 -1.165734e-15\n",
"000002 -0.288382 -0.288382 -0.288382 3.885781e-16 -2.220446e-16\n",
"000006 -0.054668 -0.054668 -0.054668 8.881784e-16 6.106227e-16\n",
"000008 -0.034123 -0.034123 -0.034123 -5.204170e-16 -7.910339e-16\n",
"000009 0.029815 0.029815 0.029815 -2.567391e-16 2.775558e-16"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
......@@ -804,7 +355,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
},
"varInspector": {
"cols": {
......
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