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Commit e5dc9c53 authored by Dr.李's avatar Dr.李
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added strategy api

parent d18cf28c
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Showing with 265 additions and 35 deletions
alphamind/data/engines/sqlengine.py
View file @ e5dc9c53
......@@ -75,6 +75,17 @@ class SqlEngine(object):
sql = "select * from factor_master"
return pd.read_sql(sql, self.engine)
def fetch_strategy(self, ref_date: str, strategy: str) -> pd.DataFrame():
sql = "select strategyName, factor, weight from strategy " \
"where Date = '{ref_date}' and strategyName = '{strategy}'".format(ref_date=ref_date, strategy=strategy)
return pd.read_sql(sql, self.engine)
def fetch_strategy_names(self):
sql = "select distinct strategyName from strategy"
cursor = self.engine.execute(sql)
strategy_names = {c[0] for c in cursor.fetchall()}
return strategy_names
def fetch_codes(self, ref_date: str, univ: Universe) -> List[int]:
def get_universe(univ, ref_date):
......
notebooks/optimizer check.ipynb
View file @ e5dc9c53
......@@ -2,8 +2,10 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
......@@ -15,12 +17,13 @@
"from alphamind.data.winsorize import winsorize_normal\n",
"from alphamind.data.standardize import standardize\n",
"\n",
"engine = sa.create_engine('mysql+mysqldb://sa:We051253524522@rm-bp1psdz5615icqc0yo.mysql.rds.aliyuncs.com/uqer?charset=utf8')"
"#engine = sa.create_engine('mysql+mysqldb://sa:We051253524522@rm-bp1psdz5615icqc0yo.mysql.rds.aliyuncs.com/uqer?charset=utf8')\n",
"engine = sa.create_engine('mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 29,
"metadata": {
"collapsed": true
},
......@@ -43,7 +46,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 30,
"metadata": {
"collapsed": true
},
......@@ -55,28 +58,27 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 248 ms, sys: 40 ms, total: 288 ms\n",
"Wall time: 4.32 s\n"
"Wall time: 66.6 ms\n"
]
}
],
"source": [
"%%time\n",
"\n",
"risk_exposure_data = pd.read_sql(\"select * from risk_exposure where Date = '{0}'\".format(ref_date), engine).sort_values('Code')\n",
"risk_exposure_data.drop(['Date', 'exchangeCD', 'secShortName'], axis=1, inplace=True)\n",
"risk_exposure_data.dropna(inplace=True)\n",
"codes_list = risk_exposure_data.Code.astype(str).tolist()\n",
"codes = ','.join(codes_list)\n",
"# risk_exposure_data = pd.read_sql(\"select * from risk_exposure where Date = '{0}'\".format(ref_date), engine).sort_values('Code')\n",
"# risk_exposure_data.drop(['Date', 'exchangeCD', 'secShortName'], axis=1, inplace=True)\n",
"# risk_exposure_data.dropna(inplace=True)\n",
"# codes_list = risk_exposure_data.Code.astype(str).tolist()\n",
"# codes = ','.join(codes_list)\n",
"\n",
"factor_data = pd.read_sql(\"select Code, {1} from factors where Date = '{0}' and Code in ({2})\".format(ref_date, factors, codes), engine).sort_values('Code')\n",
"factor_data = pd.read_sql(\"select Code, {1} from uqer where Date = '{0}' and Code in ({2})\".format(ref_date, factors, codes), engine).sort_values('Code')\n",
"\n",
"risk_exposure_data = pd.read_sql(\"select * from risk_exposure where Date = '{0}' and Code in ({1})\".format(ref_date, codes), engine).sort_values('Code')\n",
"risk_exposure_data.drop(['Date', 'exchangeCD', 'secShortName'], axis=1, inplace=True)\n",
......@@ -88,7 +90,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 11,
"metadata": {
"collapsed": true
},
......@@ -99,15 +101,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
"Wall time: 3.08 ms\n"
"Wall time: 2 ms\n"
]
}
],
......@@ -120,15 +121,14 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.29 s, sys: 16 ms, total: 2.3 s\n",
"Wall time: 1.73 s\n"
"Wall time: 113 ms\n"
]
}
],
......@@ -140,16 +140,16 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3236, 38)"
"(581, 38)"
]
},
"execution_count": 9,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
......@@ -160,22 +160,21 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 192 ms, sys: 28 ms, total: 220 ms\n",
"Wall time: 56.2 ms\n"
"Wall time: 8 ms\n"
]
}
],
"source": [
"%%time\n",
"risk_cov_values = risk_cov_data.loc[risk_factor_columns, risk_factor_columns].astype(float) \n",
"sec_cov_values_full = (risk_exposure_values @ risk_cov_values @ risk_exposure_values.T + np.diag(specific_risk.SRISK.values ** 2)) / 100."
"sec_cov_values_full = (risk_exposure_values @ risk_cov_values @ risk_exposure_values.T + np.diag(specific_risk.SRISK.values ** 2)) / 10000."
]
},
{
......@@ -780,24 +779,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.savetxt(\"sec_cov_values.csv\", sec_cov_values_full, delimiter=\",\")"
"np.savetxt(\"sec_cov_values_sqlserver.csv\", sec_cov_values_full, delimiter=\",\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"signal.csv\", signal_full., delimiter=\",\")"
"np.savetxt(\"signal_sqlserver.csv\", signal_full, delimiter=\",\")"
]
},
{
......@@ -811,6 +808,228 @@
"sol['dual objective']"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"factor_data_sqlserver.csv\", factor_data.values, delimiter=\",\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" 1.79309338e-02, 1.04684474e-01, -4.10596016e-02,\n",
" 2.17147803e-02, -3.30579721e-02, 1.23981306e-01,\n",
" -4.72447376e-02, -2.73832625e-02, 7.93599676e-03,\n",
" -1.75565346e-01, -5.20879084e-03, 1.60830991e-02,\n",
" -5.85592245e-02, 2.65932684e-02, -1.87679629e-02,\n",
" -7.55003990e-02, 1.32831661e-02, 5.96110477e-02,\n",
" -5.93862848e-02, -2.80348415e-03, 8.68168816e-03,\n",
" 1.33779120e-01, 4.66782056e-02, 1.93152217e-02,\n",
" 3.44939435e-03, 3.46230307e-02, -2.57591807e-02,\n",
" 8.53029673e-02, -1.20312691e-01, -1.49691768e-02,\n",
" -4.73918118e-02, 8.20576187e-02, 1.82364019e-01,\n",
" 1.08211508e-01, 2.80542000e-02, 1.23508428e-01,\n",
" -5.59219426e-02, -5.06433770e-02, -5.12859160e-02,\n",
" -7.23309320e-02, 6.42195709e-02, -2.38411593e-03,\n",
" -6.14406959e-02, 2.26060862e-02, 3.14823611e-02,\n",
" 1.14303504e-02, -3.98017955e-01, 3.27112250e-02,\n",
" -1.91888119e-02, 4.48251075e-04, -3.58195830e-02,\n",
" 6.05167192e-02, 5.16925765e-02, -8.15079013e-02,\n",
" 5.99044732e-02, -3.80609425e-02, -1.50919054e-02,\n",
" 3.35160893e-02, -9.49373939e-02, 9.74936874e-03,\n",
" 9.28798242e-02, 2.27303122e-02, 1.79827433e-02,\n",
" 1.03069681e-02, 2.60655782e-03, 9.38127175e-02,\n",
" 4.36536284e-01, -1.77327346e-01, 1.21666176e-01,\n",
" -3.34637547e-02, -1.91385657e-02, -1.30247098e-02,\n",
" -4.22722793e-02, 2.55006640e-02, 5.09101967e-03,\n",
" -3.90486027e-02, -4.85443505e-02, -3.51819193e-02,\n",
" 1.06052324e-02, -9.01475647e-02, 4.74820575e-02,\n",
" -1.29657473e-02, 4.74335562e-02, 6.72075743e-04,\n",
" 9.30396533e-02, -9.80561940e-02, -5.34761284e-04,\n",
" 1.84546437e-02, -4.91005708e-02, -2.03496819e-01,\n",
" 3.09450920e-02, 3.99958848e-02, -1.51978938e-02,\n",
" -1.22142775e-02, -1.90168086e-02, -3.08715708e-02,\n",
" 8.83456421e-03, -5.80801511e-02, 4.44158930e-02,\n",
" 2.25741830e-02, 8.70872927e-03, -4.11430460e-02,\n",
" 4.95398509e-02, 1.04895895e-02, -5.65591215e-02,\n",
" 1.95155979e-02, 7.42283374e-02, 5.47088113e-02,\n",
" 8.94284264e-03, 1.42841454e-02])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"signal_full"
]
},
{
"cell_type": "code",
"execution_count": null,
......
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