FEATURE: added more engine stuff

alphamind/__init__.py

@@ -5,4 +5,4 @@ Created on 2017-4-25
 @author: cheng.li
 """
 
-__version__ = "0.2.1"
+__version__ = "0.2.3"

alphamind/api.py

@@ -5,14 +5,14 @@ Created on 2017-8-16
 @author: cheng.li
 """
 
+from alphamind.data.engines.sqlengine import SqlEngine
+from alphamind.data.engines.sqlengine import risk_styles
+from alphamind.data.engines.sqlengine import industry_styles
+from alphamind.data.engines.sqlengine import macro_styles
 from alphamind.analysis.factoranalysis import er_portfolio_analysis
 from alphamind.analysis.factoranalysis import factor_analysis
 from alphamind.analysis.quantileanalysis import er_quantile_analysis
 from alphamind.analysis.quantileanalysis import quantile_analysis
-from alphamind.data.engines.sqlengine import SqlEngine
-from alphamind.data.engines.sqlengine import industry_styles
-from alphamind.data.engines.sqlengine import macro_styles
-from alphamind.data.engines.sqlengine import risk_styles
 from alphamind.data.engines.universe import Universe
 from alphamind.data.engines.utilities import industry_list
 from alphamind.data.neutralize import neutralize

alphamind/data/dbmodel/models/__init__.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2020-11-14
+
+@author: cheng.li
+"""
+import os
+
+if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
+    from alphamind.data.dbmodel.models.models_rl import Market
+    from alphamind.data.dbmodel.models.models_rl import Universe
+    from alphamind.data.dbmodel.models.models_rl import Industry
+    from alphamind.data.dbmodel.models.models_rl import RiskExposure
+    from alphamind.data.dbmodel.models.models_rl import RiskCovDay
+    from alphamind.data.dbmodel.models.models_rl import RiskCovShort
+    from alphamind.data.dbmodel.models.models_rl import RiskCovLong
+    from alphamind.data.dbmodel.models.models_rl import SpecificRiskDay
+    from alphamind.data.dbmodel.models.models_rl import SpecificRiskShort
+    from alphamind.data.dbmodel.models.models_rl import SpecificRiskLong
+else:
+    from alphamind.data.dbmodel.models.models import Market
+    from alphamind.data.dbmodel.models.models import Universe
+    from alphamind.data.dbmodel.models.models import Industry
+    from alphamind.data.dbmodel.models.models import RiskExposure
+    from alphamind.data.dbmodel.models.models import RiskCovDay
+    from alphamind.data.dbmodel.models.models import RiskCovShort
+    from alphamind.data.dbmodel.models.models import RiskCovLong
+    from alphamind.data.dbmodel.models.models import SpecificRiskDay
+    from alphamind.data.dbmodel.models.models import SpecificRiskShort
+    from alphamind.data.dbmodel.models.models import SpecificRiskLong
+    from alphamind.data.dbmodel.models.models import FactorMaster
+    from alphamind.data.dbmodel.models.models import IndexComponent
+    from alphamind.data.dbmodel.models.models import IndexMarket
+    from alphamind.data.dbmodel.models.models import RiskMaster

alphamind/data/dbmodel/models_rl.py → alphamind/data/dbmodel/models/models_rl.py

@@ -29,6 +29,7 @@ class _StkDailyPricePro(Base):
     trade_date = Column(Date)
     code = Column("security_code", Text)
     chgPct = Column("change_pct", FLOAT)
+    secShortName = Column("name", Text)
     is_valid = Column(INT, nullable=False)
     flag = Column(INT, index=True, server_default=text("'1'"))
     is_verify = Column(INT, index=True, server_default=text("'0'"))

alphamind/data/engines/sqlengine/__init__.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2020-11-14
+
+@author: cheng.li
+"""
+
+import os
+
+if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
+    from alphamind.data.engines.sqlengine.sqlengine_rl import SqlEngine
+    from alphamind.data.engines.sqlengine.sqlengine_rl import total_risk_factors
+    from alphamind.data.engines.sqlengine.sqlengine_rl import industry_styles
+    from alphamind.data.engines.sqlengine.sqlengine_rl import risk_styles
+    from alphamind.data.engines.sqlengine.sqlengine_rl import macro_styles
+else:
+    from alphamind.data.engines.sqlengine import SqlEngine
+    from alphamind.data.engines.sqlengine.sqlengine import total_risk_factors
+    from alphamind.data.engines.sqlengine.sqlengine import industry_styles
+    from alphamind.data.engines.sqlengine.sqlengine import risk_styles
+    from alphamind.data.engines.sqlengine.sqlengine import macro_styles

alphamind/data/engines/sqlengine.py → alphamind/data/engines/sqlengine/sqlengine.py

@@ -20,14 +20,14 @@ from sqlalchemy.sql import func
 
 from PyFin.api import advanceDateByCalendar
 
-from alphamind.data.dbmodel.models import FactorMaster
-from alphamind.data.dbmodel.models import IndexComponent
-from alphamind.data.dbmodel.models import IndexMarket
-from alphamind.data.dbmodel.models import Industry
-from alphamind.data.dbmodel.models import Market
-from alphamind.data.dbmodel.models import RiskExposure
-from alphamind.data.dbmodel.models import RiskMaster
-from alphamind.data.dbmodel.models import Universe as UniverseTable
+from alphamind.data.dbmodel.models.models import FactorMaster
+from alphamind.data.dbmodel.models.models import IndexComponent
+from alphamind.data.dbmodel.models.models import IndexMarket
+from alphamind.data.dbmodel.models.models import Industry
+from alphamind.data.dbmodel.models.models import Market
+from alphamind.data.dbmodel.models.models import RiskExposure
+from alphamind.data.dbmodel.models.models import RiskMaster
+from alphamind.data.dbmodel.models.models import Universe as UniverseTable
 from alphamind.data.engines.universe import Universe
 from alphamind.data.engines.utilities import factor_tables
 from alphamind.data.engines.utilities import _map_factors
@@ -37,6 +37,7 @@ from alphamind.data.processing import factor_processing
 from alphamind.data.transformer import Transformer
 from alphamind.portfolio.riskmodel import FactorRiskModel
 
+
 risk_styles = ['BETA',
                'MOMENTUM',
                'SIZE',

alphamind/data/engines/sqlengine_rl.py → alphamind/data/engines/sqlengine/sqlengine_rl.py

@@ -5,14 +5,11 @@ Created on 2020-10-11
 @author: cheng.li
 """
 
-import os
-from typing import Dict
 from typing import Iterable
 from typing import List
 from typing import Tuple
 from typing import Union
 
-
 import numpy as np
 import pandas as pd
 import sqlalchemy as sa
@@ -21,26 +18,25 @@ from sqlalchemy import (
     and_,
     join,
     select,
+    outerjoin
 )
 
 from PyFin.api import advanceDateByCalendar
 
-from alphamind.data.dbmodel.models_rl import (
+from alphamind.data.dbmodel.models.models_rl import (
     Market,
     Industry,
-    RiskExposure
+    RiskExposure,
+    Universe as UniverseTable
 )
-
-if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
-    from alphamind.data.dbmodel.models_rl import Universe as UniverseTable
-else:
-    from alphamind.data.dbmodel.models import Universe as UniverseTable
-
+from alphamind.data.engines.utilities import factor_tables
+from alphamind.data.engines.utilities import _map_factors
 from alphamind.data.engines.universe import Universe
 from alphamind.data.processing import factor_processing
 
 from alphamind.data.engines.utilities import _map_risk_model_table
 from alphamind.portfolio.riskmodel import FactorRiskModel
+from alphamind.data.transformer import Transformer
 
 
 risk_styles = ['BETA',
@@ -214,6 +210,58 @@ class SqlEngine:
                           dates: Iterable[str] = None) -> pd.DataFrame:
         return universe.query(self, start_date, end_date, dates)
 
+    def fetch_factor(self,
+                     ref_date: str,
+                     factors: Iterable[object],
+                     codes: Iterable[int],
+                     warm_start: int = 0,
+                     used_factor_tables=None) -> pd.DataFrame:
+        if isinstance(factors, Transformer):
+            transformer = factors
+        else:
+            transformer = Transformer(factors)
+
+        dependency = transformer.dependency
+
+        if used_factor_tables:
+            factor_cols = _map_factors(dependency, used_factor_tables)
+        else:
+            factor_cols = _map_factors(dependency, factor_tables)
+
+        start_date = advanceDateByCalendar('china.sse', ref_date, str(-warm_start) + 'b').strftime(
+            '%Y-%m-%d')
+        end_date = ref_date
+
+        big_table = Market
+        joined_tables = set()
+        joined_tables.add(Market.__table__.name)
+
+        for t in set(factor_cols.values()):
+            if t.__table__.name not in joined_tables:
+                big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
+                                                         Market.code == t.code))
+
+                joined_tables.add(t.__table__.name)
+
+        query = select(
+            [Market.trade_date, Market.code.label("code"),
+             Market.chgPct.label("chgPct"),
+             Market.secShortName.label("secShortName")] + list(
+                factor_cols.keys())) \
+            .select_from(big_table).where(and_(Market.trade_date.between(start_date, end_date),
+                                               Market.code.in_(codes)))
+        df = pd.read_sql(query, self.engine) \
+            .replace([-np.inf, np.inf], np.nan) \
+            .sort_values(['trade_date', 'code']) \
+            .set_index('trade_date')
+        res = transformer.transform('code', df).replace([-np.inf, np.inf], np.nan)
+
+        res['chgPct'] = df.chgPct
+        res['secShortName'] = df['secShortName']
+        res = res.loc[ref_date:ref_date, :]
+        res.index = list(range(len(res)))
+        return res
+
     def fetch_industry(self,
                        ref_date: str,
                        codes: Iterable[int] = None,
@@ -405,6 +453,8 @@ if __name__ == "__main__":
     universe = Universe("hs300")
     start_date = '2020-09-29'
     end_date = '2020-10-10'
+    df = sql_engine.fetch_factor("2020-02-21", factors=["BETA"], codes=["2010031963"])
+    print(df)
     df = sql_engine.fetch_codes_range(start_date=start_date, end_date=end_date, universe=Universe("hs300"))
     print(df)
     df = sql_engine.fetch_dx_return("2020-10-09", codes=["2010031963"])

alphamind/data/engines/universe.py

@@ -7,7 +7,6 @@ Created on 2017-7-7
 
 import abc
 import sys
-import os
 
 import pandas as pd
 from sqlalchemy import and_
@@ -15,11 +14,7 @@ from sqlalchemy import not_
 from sqlalchemy import or_
 from sqlalchemy import select
 
-
-if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
-    from alphamind.data.dbmodel.models_rl import Universe as UniverseTable
-else:
-    from alphamind.data.dbmodel.models import Universe as UniverseTable
+from alphamind.data.dbmodel.models import Universe as UniverseTable
 
 
 class BaseUniverse(metaclass=abc.ABCMeta):

alphamind/data/engines/utilities.py

@@ -9,34 +9,16 @@ import os
 from typing import Dict
 from typing import Iterable
 
-if "DB_VENDOR" in os.environ and os.environ["DB_VENDOR"].lower() == "rl":
-    from alphamind.data.dbmodel.models import Categories
-    from alphamind.data.dbmodel.models import Market
-    from alphamind.data.dbmodel.models_rl import RiskCovDay
-    from alphamind.data.dbmodel.models_rl import RiskCovLong
-    from alphamind.data.dbmodel.models_rl import RiskCovShort
-    from alphamind.data.dbmodel.models_rl import RiskExposure
-    from alphamind.data.dbmodel.models_rl import SpecificRiskDay
-    from alphamind.data.dbmodel.models_rl import SpecificRiskLong
-    from alphamind.data.dbmodel.models_rl import SpecificRiskShort
-    from alphamind.data.dbmodel.models import Uqer
-    from alphamind.data.engines.industries import INDUSTRY_MAPPING
-
-    factor_tables = [Market, RiskExposure, Uqer, Categories]
-else:
-    from alphamind.data.dbmodel.models import Categories
-    from alphamind.data.dbmodel.models import Market
-    from alphamind.data.dbmodel.models import RiskCovDay
-    from alphamind.data.dbmodel.models import RiskCovLong
-    from alphamind.data.dbmodel.models import RiskCovShort
-    from alphamind.data.dbmodel.models import RiskExposure
-    from alphamind.data.dbmodel.models import SpecificRiskDay
-    from alphamind.data.dbmodel.models import SpecificRiskLong
-    from alphamind.data.dbmodel.models import SpecificRiskShort
-    from alphamind.data.dbmodel.models import Uqer
-    from alphamind.data.engines.industries import INDUSTRY_MAPPING
-
-    factor_tables = [Market, RiskExposure, Uqer, Categories]
+from alphamind.data.dbmodel.models import Market
+from alphamind.data.dbmodel.models import RiskCovDay
+from alphamind.data.dbmodel.models import RiskCovLong
+from alphamind.data.dbmodel.models import RiskCovShort
+from alphamind.data.dbmodel.models import RiskExposure
+from alphamind.data.dbmodel.models import SpecificRiskDay
+from alphamind.data.dbmodel.models import SpecificRiskLong
+from alphamind.data.dbmodel.models import SpecificRiskShort
+from alphamind.data.engines.industries import INDUSTRY_MAPPING
+factor_tables = [Market, RiskExposure]
 
 
 def _map_risk_model_table(risk_model: str) -> tuple:

notebooks/Example 10 - Quadratic Optimizer Comparison with CVXOPT.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,9 +32,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'SqlEngine' object has no attribute 'fetch_factor'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-f6a26cae2a7b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mrisk_cov\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrisk_exposure\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_risk_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mref_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcodes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mfactor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch_factor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mref_date\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'EPS'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcodes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0mtotal_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfactor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrisk_exposure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'code'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'SqlEngine' object has no attribute 'fetch_factor'"
+     ]
+    }
+   ],
    "source": [
     "risk_penlty = 0.5\n",
     "ref_date = '2018-02-08'\n",
@@ -59,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,17 +91,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wall time: 2.99 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
     "w = Variable(n)\n",
@@ -109,53 +113,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-----------------------------------------------------------------\n",
-      "           OSQP v0.4.1  -  Operator Splitting QP Solver\n",
-      "              (c) Bartolomeo Stellato,  Goran Banjac\n",
-      "        University of Oxford  -  Stanford University 2018\n",
-      "-----------------------------------------------------------------\n",
-      "problem:  variables n = 439, constraints m = 640\n",
-      "          nnz(P) + nnz(A) = 4419\n",
-      "settings: linear system solver = qdldl,\n",
-      "          eps_abs = 1.0e-03, eps_rel = 1.0e-03,\n",
-      "          eps_prim_inf = 1.0e-04, eps_dual_inf = 1.0e-04,\n",
-      "          rho = 1.00e-01 (adaptive),\n",
-      "          sigma = 1.00e-06, alpha = 1.60, max_iter = 4000\n",
-      "          check_termination: on (interval 25),\n",
-      "          scaling: on, scaled_termination: off\n",
-      "          warm start: on, polish: on\n",
-      "\n",
-      "iter  objective    pri res    dua res    rho        time\n",
-      "   1  -6.4413e+01   5.65e+00   6.37e+02   1.00e-01   1.36e-03s\n",
-      " 200  -2.4008e+00   4.49e-06   1.89e-03   1.55e+00   5.62e-03s\n",
-      "plsh  -2.4003e+00   5.55e-16   1.37e-14  ---------   6.38e-03s\n",
-      "\n",
-      "status:               solved\n",
-      "solution polish:      successful\n",
-      "number of iterations: 200\n",
-      "optimal objective:    -2.4003\n",
-      "run time:             6.38e-03s\n",
-      "optimal rho estimate: 2.29e-01\n",
-      "\n",
-      "Wall time: 29.9 ms\n"
+     "ename": "NameError",
+     "evalue": "name 'prob' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'prob' is not defined"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-2.4003282365506444"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -165,18 +135,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "('optimal', -2.4003282365506444)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'prob' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-10ee360ecec6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'prob' is not defined"
+     ]
     }
    ],
    "source": [
@@ -185,45 +156,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "ECOS 2.0.7 - (C) embotech GmbH, Zurich Switzerland, 2012-15. Web: www.embotech.com/ECOS\n",
-      "\n",
-      "It     pcost       dcost      gap   pres   dres    k/t    mu     step   sigma     IR    |   BT\n",
-      " 0  -3.332e-01  -1.048e+02  +2e+03  9e-01  1e-02  1e+00  5e+00    ---    ---    1  1  - |  -  - \n",
-      " 1  -3.461e-01  -1.811e+01  +1e+03  2e-01  2e-03  8e-01  3e+00  0.5430  3e-01   1  1  1 |  0  0\n",
-      " 2  -2.875e+00  -4.039e+00  +2e+02  1e-02  9e-05  9e-02  4e-01  0.9297  6e-02   1  1  1 |  0  0\n",
-      " 3  -2.573e+00  -2.939e+00  +5e+01  5e-03  3e-05  3e-02  1e-01  0.7733  9e-02   1  1  2 |  0  0\n",
-      " 4  -2.449e+00  -2.553e+00  +1e+01  1e-03  8e-06  6e-03  3e-02  0.7708  6e-02   1  1  1 |  0  0\n",
-      " 5  -2.407e+00  -2.418e+00  +2e+00  1e-04  9e-07  6e-04  4e-03  0.9066  2e-02   1  1  1 |  0  0\n",
-      " 6  -2.402e+00  -2.404e+00  +3e-01  3e-05  2e-07  1e-04  7e-04  0.8248  1e-02   1  1  2 |  0  0\n",
-      " 7  -2.401e+00  -2.401e+00  +6e-02  6e-06  4e-08  9e-06  2e-04  0.9466  2e-01   2  2  2 |  0  0\n",
-      " 8  -2.400e+00  -2.400e+00  +5e-03  4e-07  3e-09  7e-07  1e-05  0.9237  1e-03   1  1  1 |  0  0\n",
-      " 9  -2.400e+00  -2.400e+00  +2e-04  2e-08  1e-10  2e-08  5e-07  0.9890  3e-02   1  1  1 |  0  0\n",
-      "10  -2.400e+00  -2.400e+00  +2e-06  2e-10  1e-12  2e-10  5e-09  0.9890  1e-04   1  1  1 |  0  0\n",
-      "11  -2.400e+00  -2.400e+00  +2e-08  2e-12  1e-14  2e-12  6e-11  0.9890  1e-04   2  1  1 |  0  0\n",
-      "\n",
-      "OPTIMAL (within feastol=2.2e-12, reltol=9.8e-09, abstol=2.4e-08).\n",
-      "Runtime: 0.011237 seconds.\n",
-      "\n",
-      "Wall time: 32.9 ms\n"
+     "ename": "NameError",
+     "evalue": "name 'prob' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'prob' is not defined"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-2.400328236659518"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -531,9 +476,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:alpha-mind]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-alpha-mind-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -545,7 +490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.7.9"
   },
   "varInspector": {
    "cols": {
@@ -578,5 +523,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

requirements.txt

-arrow == 0.16.0
-cython == 0.29.21
-cvxpy == 1.1.5
-cvxopt == 1.2.5
-deprecated == 1.2.10
-ecos == 2.0.7.post1
-jupyter == 1.0.0
-jupyterlab == 2.2.6
-matplotlib == 3.3.1
-numba == 0.51.1
-numpy == 1.19.1
-pandas == 1.1.1
-psycopg2-binary == 2.8.5
-scikit-learn ==0.23.2
-scipy == 1.5.2
-simpleutils == 0.2.6
-sqlalchemy == 1.3.19
-xgboost == 1.2.0
\ No newline at end of file
+arrow
+cython
+cvxpy
+cvxopt
+deprecated
+ecos
+jupyter
+jupyterlab
+matplotlib
+numba
+numpy
+pandas
+psycopg2-binary
+scikit-learn
+scipy
+simpleutils
+sqlalchemy
+xgboost
\ No newline at end of file

setup.py

@@ -14,7 +14,7 @@ from Cython.Build import cythonize
 from distutils.extension import Extension
 import numpy as np
 
-VERSION = "0.2.1"
+VERSION = "0.2.3"
 
 if platform.system() != "Windows":
     import multiprocessing