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alpha-mind
Unverified Commit fbb26530 authored by iLampard's avatar iLampard Committed by GitHub
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Merge pull request #5 from alpha-miner/master 

merge update
parents 02e028fd a6f2d473
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README.md
View file @ fbb26530
...@@ -20,6 +20,16 @@ ...@@ -20,6 +20,16 @@
**Alpha - Mind** 是基于 **Python** 开发的股票多因子研究框架。 **Alpha - Mind** 是基于 **Python** 开发的股票多因子研究框架。
## 依赖
该项目主要有两个主要的github外部依赖：
* [portfolio - optimizer](https://github.com/alpha-miner/portfolio-optimizer)：该项目是相同作者编写的用于资产组合配置的优化器工具包；
* [xgboost](https://github.com/dmlc/xgboost)： 该项目是alpha - mind中一些机器模型的基础库。
这两个库都已经使用git子模块的方式打包到alpha-mind代码库中。
## 功能 ## 功能
alpha - mind 提供了多因子研究中常用的工具链，包括： alpha - mind 提供了多因子研究中常用的工具链，包括：
......
alphamind/analysis/crosssetctions.py
View file @ fbb26530
...@@ -33,7 +33,7 @@ def cs_impl(ref_date, ...@@ -33,7 +33,7 @@ def cs_impl(ref_date,
total_risk_exp = total_data[constraint_risk] total_risk_exp = total_data[constraint_risk]
er = total_data[[factor_name]].values.astype(float) er = total_data[[factor_name]].values.astype(float)
er = factor_processing(er, [winsorize_normal, standardize], total_risk_exp.values, [winsorize_normal, standardize]).flatten() er = factor_processing(er, [winsorize_normal, standardize], total_risk_exp.values, [standardize]).flatten()
industry = total_data.industry_name.values industry = total_data.industry_name.values
codes = total_data.code.tolist() codes = total_data.code.tolist()
...@@ -46,7 +46,10 @@ def cs_impl(ref_date, ...@@ -46,7 +46,10 @@ def cs_impl(ref_date,
total_risk_exp = target_pos[constraint_risk] total_risk_exp = target_pos[constraint_risk]
activate_weight = target_pos['weight'].values activate_weight = target_pos['weight'].values
excess_return = np.exp(target_pos[['dx']].values) - 1. excess_return = np.exp(target_pos[['dx']].values) - 1.
excess_return = factor_processing(excess_return, [winsorize_normal, standardize], total_risk_exp.values, [winsorize_normal, standardize]).flatten() excess_return = factor_processing(excess_return,
[winsorize_normal, standardize],
total_risk_exp.values,
[winsorize_normal, standardize]).flatten()
port_ret = np.log(activate_weight @ excess_return + 1.) port_ret = np.log(activate_weight @ excess_return + 1.)
ic = np.corrcoef(excess_return, activate_weight)[0, 1] ic = np.corrcoef(excess_return, activate_weight)[0, 1]
x = sm.add_constant(activate_weight) x = sm.add_constant(activate_weight)
......
alphamind/cython/optimizers.pyx
View file @ fbb26530
...@@ -7,13 +7,14 @@ Created on 2017-7-20 ...@@ -7,13 +7,14 @@ Created on 2017-7-20
""" """
cimport numpy as cnp cimport numpy as cnp
from libcpp.string cimport string
from libcpp.vector cimport vector from libcpp.vector cimport vector
import numpy as np import numpy as np
cdef extern from "lpoptimizer.hpp" namespace "pfopt": cdef extern from "lpoptimizer.hpp" namespace "pfopt":
cdef cppclass LpOptimizer: cdef cppclass LpOptimizer:
LpOptimizer(int, int, double*, double*, double*, double*) except + LpOptimizer(int, int, double*, double*, double*, double*, string) except +
vector[double] xValue() vector[double] xValue()
double feval() double feval()
int status() int status()
...@@ -26,12 +27,15 @@ cdef class LPOptimizer: ...@@ -26,12 +27,15 @@ cdef class LPOptimizer:
cdef int m cdef int m
def __cinit__(self, def __cinit__(self,
cnp.ndarray[double, ndim=2] cons_matrix, cnp.ndarray[double, ndim=2] cons_matrix,
double[:] lbound, double[:] lbound,
double[:] ubound, double[:] ubound,
double[:] objective): double[:] objective,
str method='simplex'):
self.n = lbound.shape[0] self.n = lbound.shape[0]
self.m = cons_matrix.shape[0] self.m = cons_matrix.shape[0]
py_bytes = method.encode('ascii')
cdef string c_str = py_bytes
cdef double[:] cons = cons_matrix.flatten(order='C'); cdef double[:] cons = cons_matrix.flatten(order='C');
self.cobj = new LpOptimizer(self.n, self.cobj = new LpOptimizer(self.n,
...@@ -39,7 +43,8 @@ cdef class LPOptimizer: ...@@ -39,7 +43,8 @@ cdef class LPOptimizer:
&cons[0], &cons[0],
&lbound[0], &lbound[0],
&ubound[0], &ubound[0],
&objective[0]) &objective[0],
c_str)
def __dealloc__(self): def __dealloc__(self):
del self.cobj del self.cobj
...@@ -151,9 +156,25 @@ cdef extern from "mvoptimizer.hpp" namespace "pfopt": ...@@ -151,9 +156,25 @@ cdef extern from "mvoptimizer.hpp" namespace "pfopt":
int status() int status()
cdef extern from "qpalglib.hpp" namespace "pfopt":
cdef cppclass QPAlglib:
QPAlglib(int,
double*,
double*,
double*,
double*,
double) except +
vector[double] xValue()
int status()
cdef class QPOptimizer: cdef class QPOptimizer:
cdef MVOptimizer* cobj cdef MVOptimizer* cobj
cdef QPAlglib* cobj2
cdef cnp.ndarray er
cdef cnp.ndarray cov
cdef double risk_aversion
cdef int n cdef int n
cdef int m cdef int m
...@@ -169,12 +190,15 @@ cdef class QPOptimizer: ...@@ -169,12 +190,15 @@ cdef class QPOptimizer:
self.n = lbound.shape[0] self.n = lbound.shape[0]
self.m = 0 self.m = 0
self.er = np.array(expected_return)
self.cov = np.array(cov_matrix)
self.risk_aversion = risk_aversion
cdef double[:] cov = cov_matrix.flatten(order='C') cdef double[:] cov = cov_matrix.flatten(order='C')
cdef double[:] cons cdef double[:] cons
if cons_matrix is not None: if cons_matrix is not None:
self.m = cons_matrix.shape[0] self.m = cons_matrix.shape[0]
cons = cons_matrix.flatten(order='C'); cons = cons_matrix.flatten(order='C')
self.cobj = new MVOptimizer(self.n, self.cobj = new MVOptimizer(self.n,
&expected_return[0], &expected_return[0],
...@@ -187,25 +211,39 @@ cdef class QPOptimizer: ...@@ -187,25 +211,39 @@ cdef class QPOptimizer:
&cubound[0], &cubound[0],
risk_aversion) risk_aversion)
else: else:
self.cobj = new MVOptimizer(self.n, self.cobj2 = new QPAlglib(self.n,
&expected_return[0], &expected_return[0],
&cov[0], &cov[0],
&lbound[0], &lbound[0],
&ubound[0], &ubound[0],
0, risk_aversion)
NULL,
NULL,
NULL,
risk_aversion)
def __dealloc__(self): def __dealloc__(self):
del self.cobj if self.cobj:
del self.cobj
else:
del self.cobj2
def feval(self): def feval(self):
return self.cobj.feval() if self.cobj:
return self.cobj.feval()
else:
x = np.array(self.cobj2.xValue())
return 0.5 * self.risk_aversion * x @ self.cov @ x - self.er @ x
def x_value(self): def x_value(self):
return np.array(self.cobj.xValue()) if self.cobj:
return np.array(self.cobj.xValue())
else:
return np.array(self.cobj2.xValue())
def status(self): def status(self):
return self.cobj.status() if self.cobj:
\ No newline at end of file return self.cobj.status()
else:
status = self.cobj2.status()
if 1 <= status <= 4:
return 0
else:
return status
alphamind/data/dbmodel/models.py
View file @ fbb26530
...@@ -12,614 +12,6 @@ Base = declarative_base() ...@@ -12,614 +12,6 @@ Base = declarative_base()
metadata = Base.metadata metadata = Base.metadata
class FullFactor(Base):
__tablename__ = 'full_factor'
__table_args__ = (
Index('full_factor_trade_date_code_uindex', 'trade_date', 'code', unique=True),
)
trade_date = Column(DateTime, primary_key=True, nullable=False)
code = Column(Integer, primary_key=True, nullable=False)
secShortName = Column(String(10))
exchangeCD = Column(String(4))
preClosePrice = Column(Float(53))
actPreClosePrice = Column(Float(53))
openPrice = Column(Float(53))
highestPrice = Column(Float(53))
lowestPrice = Column(Float(53))
closePrice = Column(Float(53))
turnoverVol = Column(BigInteger)
turnoverValue = Column(Float(53))
dealAmount = Column(BigInteger)
turnoverRate = Column(Float(53))
accumAdjFactor = Column(Float(53))
negMarketValue = Column(Float(53))
marketValue = Column(Float(53))
chgPct = Column(Float(53))
isOpen = Column(Integer)
vwap = Column(Float(53))
AccountsPayablesTDays = Column(Float(53))
AccountsPayablesTRate = Column(Float(53))
AdminiExpenseRate = Column(Float(53))
ARTDays = Column(Float(53))
ARTRate = Column(Float(53))
ASSI = Column(Float(53))
BLEV = Column(Float(53))
BondsPayableToAsset = Column(Float(53))
CashRateOfSales = Column(Float(53))
CashToCurrentLiability = Column(Float(53))
CMRA = Column(Float(53))
CTOP = Column(Float(53))
CTP5 = Column(Float(53))
CurrentAssetsRatio = Column(Float(53))
CurrentAssetsTRate = Column(Float(53))
CurrentRatio = Column(Float(53))
DAVOL10 = Column(Float(53))
DAVOL20 = Column(Float(53))
DAVOL5 = Column(Float(53))
DDNBT = Column(Float(53))
DDNCR = Column(Float(53))
DDNSR = Column(Float(53))
DebtEquityRatio = Column(Float(53))
DebtsAssetRatio = Column(Float(53))
DHILO = Column(Float(53))
DilutedEPS = Column(Float(53))
DVRAT = Column(Float(53))
EBITToTOR = Column(Float(53))
EGRO = Column(Float(53))
EMA10 = Column(Float(53))
EMA120 = Column(Float(53))
EMA20 = Column(Float(53))
EMA5 = Column(Float(53))
EMA60 = Column(Float(53))
EPS = Column(Float(53))
EquityFixedAssetRatio = Column(Float(53))
EquityToAsset = Column(Float(53))
EquityTRate = Column(Float(53))
ETOP = Column(Float(53))
ETP5 = Column(Float(53))
FinancialExpenseRate = Column(Float(53))
FinancingCashGrowRate = Column(Float(53))
FixAssetRatio = Column(Float(53))
FixedAssetsTRate = Column(Float(53))
GrossIncomeRatio = Column(Float(53))
HBETA = Column(Float(53))
HSIGMA = Column(Float(53))
IntangibleAssetRatio = Column(Float(53))
InventoryTDays = Column(Float(53))
InventoryTRate = Column(Float(53))
InvestCashGrowRate = Column(Float(53))
LCAP = Column(Float(53))
LFLO = Column(Float(53))
LongDebtToAsset = Column(Float(53))
LongDebtToWorkingCapital = Column(Float(53))
LongTermDebtToAsset = Column(Float(53))
MA10 = Column(Float(53))
MA120 = Column(Float(53))
MA20 = Column(Float(53))
MA5 = Column(Float(53))
MA60 = Column(Float(53))
MAWVAD = Column(Float(53))
MFI = Column(Float(53))
MLEV = Column(Float(53))
NetAssetGrowRate = Column(Float(53))
NetProfitGrowRate = Column(Float(53))
NetProfitRatio = Column(Float(53))
NOCFToOperatingNI = Column(Float(53))
NonCurrentAssetsRatio = Column(Float(53))
NPParentCompanyGrowRate = Column(Float(53))
NPToTOR = Column(Float(53))
OperatingExpenseRate = Column(Float(53))
OperatingProfitGrowRate = Column(Float(53))
OperatingProfitRatio = Column(Float(53))
OperatingProfitToTOR = Column(Float(53))
OperatingRevenueGrowRate = Column(Float(53))
OperCashGrowRate = Column(Float(53))
OperCashInToCurrentLiability = Column(Float(53))
PB = Column(Float(53))
PCF = Column(Float(53))
PE = Column(Float(53))
PS = Column(Float(53))
PSY = Column(Float(53))
QuickRatio = Column(Float(53))
REVS10 = Column(Float(53))
REVS20 = Column(Float(53))
REVS5 = Column(Float(53))
ROA = Column(Float(53))
ROA5 = Column(Float(53))
ROE = Column(Float(53))
ROE5 = Column(Float(53))
RSI = Column(Float(53))
RSTR12 = Column(Float(53))
RSTR24 = Column(Float(53))
SalesCostRatio = Column(Float(53))
SaleServiceCashToOR = Column(Float(53))
SUE = Column(Float(53))
TaxRatio = Column(Float(53))
TOBT = Column(Float(53))
TotalAssetGrowRate = Column(Float(53))
TotalAssetsTRate = Column(Float(53))
TotalProfitCostRatio = Column(Float(53))
TotalProfitGrowRate = Column(Float(53))
VOL10 = Column(Float(53))
VOL120 = Column(Float(53))
VOL20 = Column(Float(53))
VOL240 = Column(Float(53))
VOL5 = Column(Float(53))
VOL60 = Column(Float(53))
WVAD = Column(Float(53))
REC = Column(Float(53))
DAREC = Column(Float(53))
GREC = Column(Float(53))
FY12P = Column(Float(53))
DAREV = Column(Float(53))
GREV = Column(Float(53))
SFY12P = Column(Float(53))
DASREV = Column(Float(53))
GSREV = Column(Float(53))
FEARNG = Column(Float(53))
FSALESG = Column(Float(53))
TA2EV = Column(Float(53))
CFO2EV = Column(Float(53))
ACCA = Column(Float(53))
DEGM = Column(Float(53))
SUOI = Column(Float(53))
EARNMOM = Column(Float(53))
FiftyTwoWeekHigh = Column(Float(53))
Volatility = Column(Float(53))
Skewness = Column(Float(53))
ILLIQUIDITY = Column(Float(53))
BackwardADJ = Column(Float(53))
MACD = Column(Float(53))
ADTM = Column(Float(53))
ATR14 = Column(Float(53))
ATR6 = Column(Float(53))
BIAS10 = Column(Float(53))
BIAS20 = Column(Float(53))
BIAS5 = Column(Float(53))
BIAS60 = Column(Float(53))
BollDown = Column(Float(53))
BollUp = Column(Float(53))
CCI10 = Column(Float(53))
CCI20 = Column(Float(53))
CCI5 = Column(Float(53))
CCI88 = Column(Float(53))
KDJ_K = Column(Float(53))
KDJ_D = Column(Float(53))
KDJ_J = Column(Float(53))
ROC6 = Column(Float(53))
ROC20 = Column(Float(53))
SBM = Column(Float(53))
STM = Column(Float(53))
UpRVI = Column(Float(53))
DownRVI = Column(Float(53))
RVI = Column(Float(53))
SRMI = Column(Float(53))
ChandeSD = Column(Float(53))
ChandeSU = Column(Float(53))
CMO = Column(Float(53))
DBCD = Column(Float(53))
ARC = Column(Float(53))
OBV = Column(Float(53))
OBV6 = Column(Float(53))
OBV20 = Column(Float(53))
TVMA20 = Column(Float(53))
TVMA6 = Column(Float(53))
TVSTD20 = Column(Float(53))
TVSTD6 = Column(Float(53))
VDEA = Column(Float(53))
VDIFF = Column(Float(53))
VEMA10 = Column(Float(53))
VEMA12 = Column(Float(53))
VEMA26 = Column(Float(53))
VEMA5 = Column(Float(53))
VMACD = Column(Float(53))
VOSC = Column(Float(53))
VR = Column(Float(53))
VROC12 = Column(Float(53))
VROC6 = Column(Float(53))
VSTD10 = Column(Float(53))
VSTD20 = Column(Float(53))
KlingerOscillator = Column(Float(53))
MoneyFlow20 = Column(Float(53))
AD = Column(Float(53))
AD20 = Column(Float(53))
AD6 = Column(Float(53))
CoppockCurve = Column(Float(53))
ASI = Column(Float(53))
ChaikinOscillator = Column(Float(53))
ChaikinVolatility = Column(Float(53))
EMV14 = Column(Float(53))
EMV6 = Column(Float(53))
plusDI = Column(Float(53))
minusDI = Column(Float(53))
ADX = Column(Float(53))
ADXR = Column(Float(53))
Aroon = Column(Float(53))
AroonDown = Column(Float(53))
AroonUp = Column(Float(53))
DEA = Column(Float(53))
DIFF = Column(Float(53))
DDI = Column(Float(53))
DIZ = Column(Float(53))
DIF = Column(Float(53))
MTM = Column(Float(53))
MTMMA = Column(Float(53))
PVT = Column(Float(53))
PVT6 = Column(Float(53))
PVT12 = Column(Float(53))
TRIX5 = Column(Float(53))
TRIX10 = Column(Float(53))
UOS = Column(Float(53))
MA10RegressCoeff12 = Column(Float(53))
MA10RegressCoeff6 = Column(Float(53))
PLRC6 = Column(Float(53))
PLRC12 = Column(Float(53))
SwingIndex = Column(Float(53))
Ulcer10 = Column(Float(53))
Ulcer5 = Column(Float(53))
Hurst = Column(Float(53))
ACD6 = Column(Float(53))
ACD20 = Column(Float(53))
EMA12 = Column(Float(53))
EMA26 = Column(Float(53))
APBMA = Column(Float(53))
BBI = Column(Float(53))
BBIC = Column(Float(53))
TEMA10 = Column(Float(53))
TEMA5 = Column(Float(53))
MA10Close = Column(Float(53))
AR = Column(Float(53))
BR = Column(Float(53))
ARBR = Column(Float(53))
CR20 = Column(Float(53))
MassIndex = Column(Float(53))
BearPower = Column(Float(53))
BullPower = Column(Float(53))
Elder = Column(Float(53))
NVI = Column(Float(53))
PVI = Column(Float(53))
RC12 = Column(Float(53))
RC24 = Column(Float(53))
JDQS20 = Column(Float(53))
Variance20 = Column(Float(53))
Variance60 = Column(Float(53))
Variance120 = Column(Float(53))
Kurtosis20 = Column(Float(53))
Kurtosis60 = Column(Float(53))
Kurtosis120 = Column(Float(53))
Alpha20 = Column(Float(53))
Alpha60 = Column(Float(53))
Alpha120 = Column(Float(53))
Beta20 = Column(Float(53))
Beta60 = Column(Float(53))
Beta120 = Column(Float(53))
SharpeRatio20 = Column(Float(53))
SharpeRatio60 = Column(Float(53))
SharpeRatio120 = Column(Float(53))
TreynorRatio20 = Column(Float(53))
TreynorRatio60 = Column(Float(53))
TreynorRatio120 = Column(Float(53))
InformationRatio20 = Column(Float(53))
InformationRatio60 = Column(Float(53))
InformationRatio120 = Column(Float(53))
GainVariance20 = Column(Float(53))
GainVariance60 = Column(Float(53))
GainVariance120 = Column(Float(53))
LossVariance20 = Column(Float(53))
LossVariance60 = Column(Float(53))
LossVariance120 = Column(Float(53))
GainLossVarianceRatio20 = Column(Float(53))
GainLossVarianceRatio60 = Column(Float(53))
GainLossVarianceRatio120 = Column(Float(53))
RealizedVolatility = Column(Float(53))
REVS60 = Column(Float(53))
REVS120 = Column(Float(53))
REVS250 = Column(Float(53))
REVS750 = Column(Float(53))
REVS5m20 = Column(Float(53))
REVS5m60 = Column(Float(53))
REVS5Indu1 = Column(Float(53))
REVS20Indu1 = Column(Float(53))
Volumn1M = Column(Float(53))
Volumn3M = Column(Float(53))
Price1M = Column(Float(53))
Price3M = Column(Float(53))
Price1Y = Column(Float(53))
Rank1M = Column(Float(53))
CashDividendCover = Column(Float(53))
DividendCover = Column(Float(53))
DividendPaidRatio = Column(Float(53))
RetainedEarningRatio = Column(Float(53))
CashEquivalentPS = Column(Float(53))
DividendPS = Column(Float(53))
EPSTTM = Column(Float(53))
NetAssetPS = Column(Float(53))
TORPS = Column(Float(53))
TORPSLatest = Column(Float(53))
OperatingRevenuePS = Column(Float(53))
OperatingRevenuePSLatest = Column(Float(53))
OperatingProfitPS = Column(Float(53))
OperatingProfitPSLatest = Column(Float(53))
CapitalSurplusFundPS = Column(Float(53))
SurplusReserveFundPS = Column(Float(53))
UndividedProfitPS = Column(Float(53))
RetainedEarningsPS = Column(Float(53))
OperCashFlowPS = Column(Float(53))
CashFlowPS = Column(Float(53))
NetNonOIToTP = Column(Float(53))
NetNonOIToTPLatest = Column(Float(53))
PeriodCostsRate = Column(Float(53))
InterestCover = Column(Float(53))
NetProfitGrowRate3Y = Column(Float(53))
NetProfitGrowRate5Y = Column(Float(53))
OperatingRevenueGrowRate3Y = Column(Float(53))
OperatingRevenueGrowRate5Y = Column(Float(53))
NetCashFlowGrowRate = Column(Float(53))
NetProfitCashCover = Column(Float(53))
OperCashInToAsset = Column(Float(53))
CashConversionCycle = Column(Float(53))
OperatingCycle = Column(Float(53))
PEG3Y = Column(Float(53))
PEG5Y = Column(Float(53))
PEIndu = Column(Float(53))
PBIndu = Column(Float(53))
PSIndu = Column(Float(53))
PCFIndu = Column(Float(53))
PEHist20 = Column(Float(53))
PEHist60 = Column(Float(53))
PEHist120 = Column(Float(53))
PEHist250 = Column(Float(53))
StaticPE = Column(Float(53))
ForwardPE = Column(Float(53))
EnterpriseFCFPS = Column(Float(53))
ShareholderFCFPS = Column(Float(53))
ROEDiluted = Column(Float(53))
ROEAvg = Column(Float(53))
ROEWeighted = Column(Float(53))
ROECut = Column(Float(53))
ROECutWeighted = Column(Float(53))
ROIC = Column(Float(53))
ROAEBIT = Column(Float(53))
ROAEBITTTM = Column(Float(53))
OperatingNIToTP = Column(Float(53))
OperatingNIToTPLatest = Column(Float(53))
InvestRAssociatesToTP = Column(Float(53))
InvestRAssociatesToTPLatest = Column(Float(53))
NPCutToNP = Column(Float(53))
SuperQuickRatio = Column(Float(53))
TSEPToInterestBearDebt = Column(Float(53))
DebtTangibleEquityRatio = Column(Float(53))
TangibleAToInteBearDebt = Column(Float(53))
TangibleAToNetDebt = Column(Float(53))
NOCFToTLiability = Column(Float(53))
NOCFToInterestBearDebt = Column(Float(53))
NOCFToNetDebt = Column(Float(53))
TSEPToTotalCapital = Column(Float(53))
InteBearDebtToTotalCapital = Column(Float(53))
NPParentCompanyCutYOY = Column(Float(53))
SalesServiceCashToORLatest = Column(Float(53))
CashRateOfSalesLatest = Column(Float(53))
NOCFToOperatingNILatest = Column(Float(53))
TotalAssets = Column(Float(53))
MktValue = Column(Float(53))
NegMktValue = Column(Float(53))
TEAP = Column(Float(53))
NIAP = Column(Float(53))
TotalFixedAssets = Column(Float(53))
IntFreeCL = Column(Float(53))
IntFreeNCL = Column(Float(53))
IntCL = Column(Float(53))
IntDebt = Column(Float(53))
NetDebt = Column(Float(53))
NetTangibleAssets = Column(Float(53))
WorkingCapital = Column(Float(53))
NetWorkingCapital = Column(Float(53))
TotalPaidinCapital = Column(Float(53))
RetainedEarnings = Column(Float(53))
OperateNetIncome = Column(Float(53))
ValueChgProfit = Column(Float(53))
NetIntExpense = Column(Float(53))
EBIT = Column(Float(53))
EBITDA = Column(Float(53))
EBIAT = Column(Float(53))
NRProfitLoss = Column(Float(53))
NIAPCut = Column(Float(53))
FCFF = Column(Float(53))
FCFE = Column(Float(53))
DA = Column(Float(53))
TRevenueTTM = Column(Float(53))
TCostTTM = Column(Float(53))
RevenueTTM = Column(Float(53))
CostTTM = Column(Float(53))
GrossProfitTTM = Column(Float(53))
SalesExpenseTTM = Column(Float(53))
AdminExpenseTTM = Column(Float(53))
FinanExpenseTTM = Column(Float(53))
AssetImpairLossTTM = Column(Float(53))
NPFromOperatingTTM = Column(Float(53))
NPFromValueChgTTM = Column(Float(53))
OperateProfitTTM = Column(Float(53))
NonOperatingNPTTM = Column(Float(53))
TProfitTTM = Column(Float(53))
NetProfitTTM = Column(Float(53))
NetProfitAPTTM = Column(Float(53))
SaleServiceRenderCashTTM = Column(Float(53))
NetOperateCFTTM = Column(Float(53))
NetInvestCFTTM = Column(Float(53))
NetFinanceCFTTM = Column(Float(53))
GrossProfit = Column(Float(53))
Beta252 = Column(Float(53))
RSTR504 = Column(Float(53))
EPIBS = Column(Float(53))
CETOP = Column(Float(53))
DASTD = Column(Float(53))
CmraCNE5 = Column(Float(53))
HsigmaCNE5 = Column(Float(53))
SGRO = Column(Float(53))
EgibsLong = Column(Float(53))
STOM = Column(Float(53))
STOQ = Column(Float(53))
STOA = Column(Float(53))
NLSIZE = Column(Float(53))
ROEAfterNonRecurring = Column(Float(53))
EPSAfterNonRecurring = Column(Float(53))
EODPrice = Column(Float(53))
LogFloatCap = Column(Float(53))
BPS = Column(Float(53))
SPS = Column(Float(53))
DebtToAsset = Column(Float(53))
DROEAfterNonRecurring = Column(Float(53))
LogTotalCap = Column(Float(53))
BP = Column(Float(53))
SP = Column(Float(53))
EPAfterNonRecurring = Column(Float(53))
DivToB = Column(Float(53))
DivP = Column(Float(53))
EBITToSales = Column(Float(53))
EBITAToSales = Column(Float(53))
EVToSales = Column(Float(53))
EVToEBIT = Column(Float(53))
EVToEBITDA = Column(Float(53))
EVToNOPLAT = Column(Float(53))
EVToIC = Column(Float(53))
FCFFPS = Column(Float(53))
FCFFToEarningAfterNonRecurring = Column(Float(53))
FCFFP = Column(Float(53))
ProfitToAsset = Column(Float(53))
GrossProfitRatio = Column(Float(53))
LATO = Column(Float(53))
FATO = Column(Float(53))
TATO = Column(Float(53))
EquityTO = Column(Float(53))
PayableTO = Column(Float(53))
RecievableTO = Column(Float(53))
RevenueGrowth = Column(Float(53))
GrossProfitGrowth = Column(Float(53))
NetProfitGrowth = Column(Float(53))
GrossCFToRevenue = Column(Float(53))
CFToRevenue = Column(Float(53))
CFToProfit = Column(Float(53))
CFToAsset = Column(Float(53))
GrossCFGrowth = Column(Float(53))
CFGrowth = Column(Float(53))
ICFGrowth = Column(Float(53))
AveAmount60 = Column(Float(53))
PeriodReturn60 = Column(Float(53))
AmountRatio60to250 = Column(Float(53))
CFPS = Column(Float(53))
CFP = Column(Float(53))
NetCFGrowth = Column(Float(53))
NetCFGrowthP = Column(Float(53))
NetCash = Column(Float(53))
NetCashP = Column(Float(53))
BVPSGrowth = Column(Float(53))
EquityPSGrowth = Column(Float(53))
WholeSales = Column(Float(53))
WholeProfitAfterNonRecurring = Column(Float(53))
ExpenseRatio = Column(Float(53))
AcidTestRatio = Column(Float(53))
TimeInterestEarnedRatio = Column(Float(53))
DepositReceivedVsSale = Column(Float(53))
DebtRatioExcemptDepRec = Column(Float(53))
SNBARatio = Column(Float(53))
CFinc1 = Column(Float(53))
BDTO = Column(Float(53))
RVOL = Column(Float(53))
CHV = Column(Float(53))
VAL = Column(Float(53))
BETA = Column(Float(53))
MOMENTUM = Column(Float(53))
SIZE = Column(Float(53))
EARNYILD = Column(Float(53))
RESVOL = Column(Float(53))
GROWTH = Column(Float(53))
BTOP = Column(Float(53))
LEVERAGE = Column(Float(53))
LIQUIDTY = Column(Float(53))
SIZENL = Column(Float(53))
Bank = Column(BigInteger)
RealEstate = Column(BigInteger)
Health = Column(BigInteger)
Transportation = Column(BigInteger)
Mining = Column(BigInteger)
NonFerMetal = Column(BigInteger)
HouseApp = Column(BigInteger)
LeiService = Column(BigInteger)
MachiEquip = Column(BigInteger)
BuildDeco = Column(BigInteger)
CommeTrade = Column(BigInteger)
CONMAT = Column(BigInteger)
Auto = Column(BigInteger)
Textile = Column(BigInteger)
FoodBever = Column(BigInteger)
Electronics = Column(BigInteger)
Computer = Column(BigInteger)
LightIndus = Column(BigInteger)
Utilities = Column(BigInteger)
Telecom = Column(BigInteger)
AgriForest = Column(BigInteger)
CHEM = Column(BigInteger)
Media = Column(BigInteger)
IronSteel = Column(BigInteger)
NonBankFinan = Column(BigInteger)
ELECEQP = Column(BigInteger)
AERODEF = Column(BigInteger)
Conglomerates = Column(BigInteger)
COUNTRY = Column(BigInteger)
DROE = Column(Float(53))
IVR = Column(Float(53))
xueqiu_hotness = Column(Float(53))
con_eps = Column(Float(53))
con_eps_rolling = Column(Float(53))
con_na = Column(Float(53))
con_na_rolling = Column(Float(53))
con_np = Column(Float(53))
con_npcgrate_1w = Column(Float(53))
con_npcgrate_4w = Column(Float(53))
con_npcgrate_13w = Column(Float(53))
con_npcgrate_26w = Column(Float(53))
con_npcgrate_52w = Column(Float(53))
con_npcgrate_2y = Column(Float(53))
con_np_rolling = Column(Float(53))
con_np_yoy = Column(Float(53))
con_pb = Column(Float(53))
con_pb_order = Column(Float(53))
con_pb_rolling = Column(Float(53))
con_pb_rolling_order = Column(Float(53))
con_or = Column(Float(53))
con_pe = Column(Float(53))
con_pe_order = Column(Float(53))
con_pe_rolling = Column(Float(53))
con_pe_rolling_order = Column(Float(53))
con_peg = Column(Float(53))
con_peg_order = Column(Float(53))
con_peg_rolling = Column(Float(53))
con_peg_rolling_order = Column(Float(53))
con_roe = Column(Float(53))
con_target_price = Column(Float(53))
market_confidence_5d = Column(Float(53))
market_confidence_10d = Column(Float(53))
market_confidence_15d = Column(Float(53))
market_confidence_25d = Column(Float(53))
market_confidence_75d = Column(Float(53))
mcap = Column(Float(53))
optimism_confidence_5d = Column(Float(53))
optimism_confidence_10d = Column(Float(53))
optimism_confidence_15d = Column(Float(53))
optimism_confidence_25d = Column(Float(53))
optimism_confidence_75d = Column(Float(53))
pessimism_confidence_5d = Column(Float(53))
pessimism_confidence_10d = Column(Float(53))
pessimism_confidence_15d = Column(Float(53))
pessimism_confidence_25d = Column(Float(53))
pessimism_confidence_75d = Column(Float(53))
tcap = Column(Float(53))
d_srisk = Column(Float(53))
s_srisk = Column(Float(53))
l_srisk = Column(Float(53))
class DailyPortfolios(Base): class DailyPortfolios(Base):
__tablename__ = 'daily_portfolios' __tablename__ = 'daily_portfolios'
__table_args__ = ( __table_args__ = (
...@@ -924,29 +316,6 @@ class Positions(Base): ...@@ -924,29 +316,6 @@ class Positions(Base):
weight = Column(JSON) weight = Column(JSON)
class QuantileAnalysis(Base):
__tablename__ = 'quantile_analysis'
__table_args__ = (
Index('quantile_idx', 'trade_date', 'portfolio', 'universe', 'benchmark', unique=True),
)
trade_date = Column(DateTime, primary_key=True, nullable=False)
portfolio = Column(String(50), primary_key=True, nullable=False)
universe = Column(String(20), primary_key=True, nullable=False)
benchmark = Column(Integer, primary_key=True, nullable=False)
source = Column(String(20), primary_key=True, nullable=False)
q1 = Column(Float(53))
q2 = Column(Float(53))
q3 = Column(Float(53))
q4 = Column(Float(53))
q5 = Column(Float(53))
q6 = Column(Float(53))
q7 = Column(Float(53))
q8 = Column(Float(53))
q9 = Column(Float(53))
q10 = Column(Float(53))
class RebalanceLog(Base): class RebalanceLog(Base):
__tablename__ = 'rebalance_log' __tablename__ = 'rebalance_log'
__table_args__ = ( __table_args__ = (
...@@ -1124,8 +493,6 @@ class RiskExposure(Base): ...@@ -1124,8 +493,6 @@ class RiskExposure(Base):
trade_date = Column(DateTime, primary_key=True, nullable=False) trade_date = Column(DateTime, primary_key=True, nullable=False)
code = Column(Integer, primary_key=True, nullable=False) code = Column(Integer, primary_key=True, nullable=False)
exchangeCD = Column(String(4))
secShortName = Column(String(20))
BETA = Column(Float(53)) BETA = Column(Float(53))
MOMENTUM = Column(Float(53)) MOMENTUM = Column(Float(53))
SIZE = Column(Float(53)) SIZE = Column(Float(53))
...@@ -1165,7 +532,6 @@ class RiskExposure(Base): ...@@ -1165,7 +532,6 @@ class RiskExposure(Base):
AERODEF = Column(BigInteger) AERODEF = Column(BigInteger)
Conglomerates = Column(BigInteger) Conglomerates = Column(BigInteger)
COUNTRY = Column(BigInteger) COUNTRY = Column(BigInteger)
updateTime = Column(DateTime)
class RiskMaster(Base): class RiskMaster(Base):
......
alphamind/data/engines/universe.py
View file @ fbb26530
...@@ -14,8 +14,8 @@ from sqlalchemy import select ...@@ -14,8 +14,8 @@ from sqlalchemy import select
from sqlalchemy import join from sqlalchemy import join
from sqlalchemy import outerjoin from sqlalchemy import outerjoin
from alphamind.data.dbmodel.models import Universe as UniverseTable from alphamind.data.dbmodel.models import Universe as UniverseTable
from alphamind.data.dbmodel.models import FullFactor
from alphamind.data.engines.utilities import _map_factors from alphamind.data.engines.utilities import _map_factors
from alphamind.data.dbmodel.models import Market
from alphamind.data.engines.utilities import factor_tables from alphamind.data.engines.utilities import factor_tables
from alphamind.data.transformer import Transformer from alphamind.data.transformer import Transformer
from alphamind.utilities import encode from alphamind.utilities import encode
...@@ -86,22 +86,22 @@ class Universe(object): ...@@ -86,22 +86,22 @@ class Universe(object):
dependency = transformer.dependency dependency = transformer.dependency
factor_cols = _map_factors(dependency, factor_tables) factor_cols = _map_factors(dependency, factor_tables)
big_table = FullFactor big_table = Market
for t in set(factor_cols.values()): for t in set(factor_cols.values()):
if t.__table__.name != FullFactor.__table__.name: if t.__table__.name != Market.__table__.name:
big_table = outerjoin(big_table, t, and_(FullFactor.trade_date == t.trade_date, big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
FullFactor.code == t.code, Market.code == t.code,
FullFactor.trade_date.in_( Market.trade_date.in_(
dates) if dates else FullFactor.trade_date.between( dates) if dates else Market.trade_date.between(
start_date, end_date))) start_date, end_date)))
big_table = join(big_table, UniverseTable, big_table = join(big_table, UniverseTable,
and_(FullFactor.trade_date == UniverseTable.trade_date, and_(Market.trade_date == UniverseTable.trade_date,
FullFactor.code == UniverseTable.code, Market.code == UniverseTable.code,
universe_cond)) universe_cond))
query = select( query = select(
[FullFactor.trade_date, FullFactor.code] + list(factor_cols.keys())) \ [Market.trade_date, Market.code] + list(factor_cols.keys())) \
.select_from(big_table).distinct() .select_from(big_table).distinct()
df = pd.read_sql(query, engine.engine).sort_values(['trade_date', 'code']).dropna() df = pd.read_sql(query, engine.engine).sort_values(['trade_date', 'code']).dropna()
......
alphamind/data/processing.py
View file @ fbb26530
...@@ -9,6 +9,7 @@ from typing import Optional ...@@ -9,6 +9,7 @@ from typing import Optional
from typing import List from typing import List
import numpy as np import numpy as np
from alphamind.data.neutralize import neutralize from alphamind.data.neutralize import neutralize
from alphamind.utilities import alpha_logger
def factor_processing(raw_factors: np.ndarray, def factor_processing(raw_factors: np.ndarray,
...@@ -29,6 +30,8 @@ def factor_processing(raw_factors: np.ndarray, ...@@ -29,6 +30,8 @@ def factor_processing(raw_factors: np.ndarray,
if post_process: if post_process:
for p in post_process: for p in post_process:
if p.__name__ == 'winsorize_normal':
alpha_logger.warning("winsorize_normal normally should not be done after neutralize")
new_factors = p(new_factors, groups=groups) new_factors = p(new_factors, groups=groups)
return new_factors return new_factors
pfopt @ 1dcecd88
Subproject commit d04d8ac4ca33c4482ac2baae9b537a0df0d1b036 Subproject commit 1dcecd88728512ff50730f418d9d58195bf33851
alphamind/portfolio/linearbuilder.py
View file @ fbb26530
...@@ -17,7 +17,8 @@ def linear_builder(er: np.ndarray, ...@@ -17,7 +17,8 @@ def linear_builder(er: np.ndarray,
risk_constraints: np.ndarray, risk_constraints: np.ndarray,
risk_target: Tuple[np.ndarray, np.ndarray], risk_target: Tuple[np.ndarray, np.ndarray],
turn_over_target: float = None, turn_over_target: float = None,
current_position: np.ndarray = None) -> Tuple[str, np.ndarray, np.ndarray]: current_position: np.ndarray = None,
method: str='simplex') -> Tuple[str, np.ndarray, np.ndarray]:
er = er.flatten() er = er.flatten()
n, m = risk_constraints.shape n, m = risk_constraints.shape
...@@ -36,7 +37,7 @@ def linear_builder(er: np.ndarray, ...@@ -36,7 +37,7 @@ def linear_builder(er: np.ndarray,
if not turn_over_target: if not turn_over_target:
cons_matrix = np.concatenate((risk_constraints.T, risk_lbound, risk_ubound), axis=1) cons_matrix = np.concatenate((risk_constraints.T, risk_lbound, risk_ubound), axis=1)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er) opt = LPOptimizer(cons_matrix, lbound, ubound, -er, method)
status = opt.status() status = opt.status()
...@@ -77,7 +78,7 @@ def linear_builder(er: np.ndarray, ...@@ -77,7 +78,7 @@ def linear_builder(er: np.ndarray,
risk_ubound = np.concatenate((risk_ubound, np.inf * np.ones((n, 1))), axis=0) risk_ubound = np.concatenate((risk_ubound, np.inf * np.ones((n, 1))), axis=0)
cons_matrix = np.concatenate((risk_constraints, risk_lbound, risk_ubound), axis=1) cons_matrix = np.concatenate((risk_constraints, risk_lbound, risk_ubound), axis=1)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er) opt = LPOptimizer(cons_matrix, lbound, ubound, -er, method)
status = opt.status() status = opt.status()
......
alphamind/tests/portfolio/test_linearbuild.py
View file @ fbb26530
...@@ -37,6 +37,25 @@ class TestLinearBuild(unittest.TestCase): ...@@ -37,6 +37,25 @@ class TestLinearBuild(unittest.TestCase):
expected_risk = np.zeros(self.risk_exp.shape[1]) expected_risk = np.zeros(self.risk_exp.shape[1])
np.testing.assert_array_almost_equal(calc_risk, expected_risk) np.testing.assert_array_almost_equal(calc_risk, expected_risk)
def test_linear_build_with_interior(self):
bm = self.bm / self.bm.sum()
eplson = 1e-6
status, _, w = linear_builder(self.er,
0.,
0.01,
self.risk_exp,
(bm @ self.risk_exp, bm @ self.risk_exp),
method='interior')
self.assertEqual(status, 'optimal')
self.assertAlmostEqual(np.sum(w), 1.)
self.assertTrue(np.all(w <= 0.01 + eplson))
self.assertTrue(np.all(w >= -eplson))
calc_risk = (w - bm) @ self.risk_exp
expected_risk = np.zeros(self.risk_exp.shape[1])
np.testing.assert_array_almost_equal(calc_risk, expected_risk)
def test_linear_build_with_inequality_constraints(self): def test_linear_build_with_inequality_constraints(self):
bm = self.bm / self.bm.sum() bm = self.bm / self.bm.sum()
eplson = 1e-6 eplson = 1e-6
......
build_linux_dependencies.sh
View file @ fbb26530
...@@ -21,7 +21,10 @@ fi ...@@ -21,7 +21,10 @@ fi
cd ../.. cd ../..
cd alphamind/pfopt cd alphamind/pfopt
export BUILD_TEST=OFF
./build_linux.sh ./build_linux.sh
if [ $? -ne 0 ] ; then if [ $? -ne 0 ] ; then
cd ../.. cd ../..
exit 1 exit 1
......
build_windows_dependencies.bat
View file @ fbb26530
...@@ -19,6 +19,7 @@ cd ../.. ...@@ -19,6 +19,7 @@ cd ../..
cd alphamind\pfopt cd alphamind\pfopt
set BUILD_TEST=OFF
call build_windows.bat call build_windows.bat
if %errorlevel% neq 0 exit /b 1 if %errorlevel% neq 0 exit /b 1
......
notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ fbb26530
...@@ -44,7 +44,7 @@ ...@@ -44,7 +44,7 @@
"batch = 0\n", "batch = 0\n",
"horizon = map_freq(freq)\n", "horizon = map_freq(freq)\n",
"universe = Universe(\"custom\", ['zz800'])\n", "universe = Universe(\"custom\", ['zz800'])\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n", "data_source = 'postgres+psycopg2://postgres:we083826@localhost/alpha'\n",
"benchmark_code = 905\n", "benchmark_code = 905\n",
"method = 'tv'\n", "method = 'tv'\n",
"target_vol = 0.05\n", "target_vol = 0.05\n",
...@@ -269,9 +269,9 @@ ...@@ -269,9 +269,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"def create_report(ret_df, windows):\n", "def create_report(ret_df, windows):\n",
" sharp_calc = MovingSharp(windows)\n", " sharp_calc = MovingSharp(windows, x='ret', y='riskFree')\n",
" drawdown_calc = MovingMaxDrawdown(windows)\n", " drawdown_calc = MovingMaxDrawdown(windows, x='ret')\n",
" max_drawdown_calc = MovingMaxDrawdown(len(ret_df))\n", " max_drawdown_calc = MovingMaxDrawdown(len(ret_df), x='ret')\n",
"\n", "\n",
" res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n", " res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n",
" total_returns = 0.\n", " total_returns = 0.\n",
...@@ -285,8 +285,8 @@ ...@@ -285,8 +285,8 @@
"\n", "\n",
" res_df.loc[date, 'daily_return'] = ret\n", " res_df.loc[date, 'daily_return'] = ret\n",
" res_df.loc[date, 'cum_ret'] = total_returns\n", " res_df.loc[date, 'cum_ret'] = total_returns\n",
" res_df.loc[date, 'drawdown'] = drawdown_calc.result()[0]\n", " res_df.loc[date, 'drawdown'] = drawdown_calc.result()\n",
" res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()[0]\n", " res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()\n",
" res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n", " res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n",
"\n", "\n",
" if i < 5:\n", " if i < 5:\n",
...@@ -328,13 +328,6 @@ ...@@ -328,13 +328,6 @@
" alpha_logger.info(f\"target_vol: {target_vol:.4f} finished\")" " alpha_logger.info(f\"target_vol: {target_vol:.4f} finished\")"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
...@@ -349,18 +342,6 @@ ...@@ -349,18 +342,6 @@
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
},
"varInspector": { "varInspector": {
"cols": { "cols": {
"lenName": 16, "lenName": 16,
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ fbb26530
...@@ -57,7 +57,7 @@ ...@@ -57,7 +57,7 @@
"factor = 'EPS'\n", "factor = 'EPS'\n",
"lb = 0.0\n", "lb = 0.0\n",
"ub = 0.1\n", "ub = 0.1\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n", "data_source = 'postgres+psycopg2://postgres:we083826@localhost/alpha'\n",
"engine = SqlEngine(data_source)\n", "engine = SqlEngine(data_source)\n",
"\n", "\n",
"universes = [Universe('custom', [u_name]) for u_name in u_names]\n", "universes = [Universe('custom', [u_name]) for u_name in u_names]\n",
...@@ -82,12 +82,12 @@ ...@@ -82,12 +82,12 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2018-03-28 12:46:50,110 - ALPHA_MIND - INFO - sh50 is finished\n", "2018-03-30 16:52:12,784 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:46:50,131 - ALPHA_MIND - INFO - hs300 is finished\n", "2018-03-30 16:52:12,796 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:46:50,156 - ALPHA_MIND - INFO - zz500 is finished\n", "2018-03-30 16:52:12,813 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:46:50,193 - ALPHA_MIND - INFO - zz800 is finished\n", "2018-03-30 16:52:12,842 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:46:50,244 - ALPHA_MIND - INFO - zz1000 is finished\n", "2018-03-30 16:52:12,879 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-28 12:46:50,830 - ALPHA_MIND - INFO - ashare_ex is finished\n" "2018-03-30 16:52:13,184 - ALPHA_MIND - INFO - ashare_ex is finished\n"
] ]
} }
], ],
...@@ -171,21 +171,21 @@ ...@@ -171,21 +171,21 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>cvxpy</th>\n", " <th>cvxpy</th>\n",
" <td>2.69</td>\n", " <td>1.68</td>\n",
" <td>6.37</td>\n", " <td>3.13</td>\n",
" <td>7.28</td>\n", " <td>5.02</td>\n",
" <td>12.55</td>\n", " <td>10.15</td>\n",
" <td>20.09</td>\n", " <td>13.55</td>\n",
" <td>325.88</td>\n", " <td>145.17</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>alphamind</th>\n", " <th>alphamind</th>\n",
" <td>0.34</td>\n", " <td>0.20</td>\n",
" <td>0.33</td>\n",
" <td>0.44</td>\n",
" <td>0.58</td>\n", " <td>0.58</td>\n",
" <td>0.70</td>\n", " <td>0.72</td>\n",
" <td>0.92</td>\n", " <td>2.94</td>\n",
" <td>1.08</td>\n",
" <td>3.08</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
...@@ -193,8 +193,8 @@ ...@@ -193,8 +193,8 @@
], ],
"text/plain": [ "text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n", " sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 2.69 6.37 7.28 12.55 20.09 325.88\n", "cvxpy 1.68 3.13 5.02 10.15 13.55 145.17\n",
"alphamind 0.34 0.58 0.70 0.92 1.08 3.08" "alphamind 0.20 0.33 0.44 0.58 0.72 2.94"
] ]
}, },
"execution_count": 4, "execution_count": 4,
...@@ -223,19 +223,19 @@ ...@@ -223,19 +223,19 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2018-03-28 12:46:51,048 - ALPHA_MIND - INFO - sh50 is finished\n", "2018-03-30 16:52:13,374 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:46:51,157 - ALPHA_MIND - INFO - hs300 is finished\n", "2018-03-30 16:52:13,509 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:46:51,442 - ALPHA_MIND - INFO - zz500 is finished\n", "2018-03-30 16:52:13,732 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:46:52,102 - ALPHA_MIND - INFO - zz800 is finished\n", "2018-03-30 16:52:14,220 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:46:53,172 - ALPHA_MIND - INFO - zz1000 is finished\n", "2018-03-30 16:52:14,956 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-28 12:47:04,916 - ALPHA_MIND - INFO - ashare_ex is finished\n" "2018-03-30 16:52:21,246 - ALPHA_MIND - INFO - ashare_ex is finished\n"
] ]
} }
], ],
"source": [ "source": [
"from cvxpy import pnorm\n", "from cvxpy import pnorm\n",
"\n", "\n",
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n", "df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind (simplex)', 'alphamind (interior)'])\n",
"turn_over_target = 0.5\n", "turn_over_target = 0.5\n",
"number = 1\n", "number = 1\n",
"\n", "\n",
...@@ -254,8 +254,22 @@ ...@@ -254,8 +254,22 @@
" risk_constraints = np.ones((len(er), 1))\n", " risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\n", " risk_target = (np.array([1.]), np.array([1.]))\n",
"\n", "\n",
" status, y, x1 = linear_builder(er, lbound, ubound, risk_constraints, risk_target, turn_over_target=turn_over_target, current_position=current_position)\n", " status, y, x1 = linear_builder(er,\n",
" elasped_time1 = timeit.timeit(\"linear_builder(er, lbound, ubound, risk_constraints, risk_target, turn_over_target=turn_over_target, current_position=current_position)\", number=number, globals=globals()) / number * 1000\n", " lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='interior')\n",
" elasped_time1 = timeit.timeit(\"\"\"linear_builder(er,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='interior')\"\"\", number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = risk_constraints.T @ w\n",
...@@ -271,9 +285,21 @@ ...@@ -271,9 +285,21 @@
" prob.solve(solver='GLPK')\n", " prob.solve(solver='GLPK')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='GLPK')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='GLPK')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n",
" elasped_time3 = timeit.timeit(\"\"\"linear_builder(er,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" turn_over_target=turn_over_target,\n",
" current_position=current_position,\n",
" method='simplex')\"\"\", number=number, globals=globals()) / number * 1000\n",
" \n",
" \n",
" np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n", " np.testing.assert_almost_equal(x1 @ er, np.array(w.value).flatten() @ er, 4)\n",
"\n", "\n",
" df.loc['alphamind', u_name] = elasped_time1\n", " df.loc['alphamind (interior)', u_name] = elasped_time1\n",
" df.loc['alphamind (simplex)', u_name] = elasped_time3\n",
" df.loc['cvxpy', u_name] = elasped_time2\n", " df.loc['cvxpy', u_name] = elasped_time2\n",
" alpha_logger.info(f\"{u_name} is finished\")" " alpha_logger.info(f\"{u_name} is finished\")"
] ]
...@@ -315,21 +341,21 @@ ...@@ -315,21 +341,21 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>cvxpy</th>\n", " <th>cvxpy</th>\n",
" <td>4.26</td>\n", " <td>2.49</td>\n",
" <td>28.31</td>\n", " <td>22.19</td>\n",
" <td>67.48</td>\n", " <td>51.62</td>\n",
" <td>164.81</td>\n", " <td>123.42</td>\n",
" <td>256.14</td>\n", " <td>190.62</td>\n",
" <td>2,561.17</td>\n", " <td>1,946.96</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>alphamind</th>\n", " <th>alphamind</th>\n",
" <td>1.22</td>\n", " <td>3.77</td>\n",
" <td>20.28</td>\n", " <td>33.59</td>\n",
" <td>67.10</td>\n", " <td>55.49</td>\n",
" <td>154.95</td>\n", " <td>116.31</td>\n",
" <td>267.17</td>\n", " <td>165.62</td>\n",
" <td>3,314.10</td>\n", " <td>1,239.47</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
...@@ -337,8 +363,8 @@ ...@@ -337,8 +363,8 @@
], ],
"text/plain": [ "text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n", " sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 4.26 28.31 67.48 164.81 256.14 2,561.17\n", "cvxpy 2.49 22.19 51.62 123.42 190.62 1,946.96\n",
"alphamind 1.22 20.28 67.10 154.95 267.17 3,314.10" "alphamind 3.77 33.59 55.49 116.31 165.62 1,239.47"
] ]
}, },
"execution_count": 6, "execution_count": 6,
...@@ -362,19 +388,19 @@ ...@@ -362,19 +388,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2018-03-28 12:47:05,225 - ALPHA_MIND - INFO - sh50 is finished\n", "2018-03-30 16:52:21,418 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:47:06,047 - ALPHA_MIND - INFO - hs300 is finished\n", "2018-03-30 16:52:21,888 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:47:07,931 - ALPHA_MIND - INFO - zz500 is finished\n", "2018-03-30 16:52:23,360 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:47:13,124 - ALPHA_MIND - INFO - zz800 is finished\n", "2018-03-30 16:52:29,066 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:47:20,586 - ALPHA_MIND - INFO - zz1000 is finished\n", "2018-03-30 16:52:37,334 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-28 12:50:28,528 - ALPHA_MIND - INFO - ashare_ex is finished\n" "2018-03-30 16:56:15,174 - ALPHA_MIND - INFO - ashare_ex is finished\n"
] ]
} }
], ],
...@@ -423,7 +449,7 @@ ...@@ -423,7 +449,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -458,33 +484,33 @@ ...@@ -458,33 +484,33 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>cvxpy</th>\n", " <th>cvxpy</th>\n",
" <td>13.26</td>\n", " <td>9.81</td>\n",
" <td>166.37</td>\n", " <td>185.93</td>\n",
" <td>493.95</td>\n", " <td>638.17</td>\n",
" <td>1,635.71</td>\n", " <td>2,501.41</td>\n",
" <td>2,359.22</td>\n", " <td>3,665.02</td>\n",
" <td>51,708.15</td>\n", " <td>94,592.70</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>alphamind</th>\n", " <th>alphamind</th>\n",
" <td>68.57</td>\n", " <td>0.33</td>\n",
" <td>205.78</td>\n", " <td>5.66</td>\n",
" <td>317.68</td>\n", " <td>14.24</td>\n",
" <td>670.81</td>\n", " <td>47.29</td>\n",
" <td>915.77</td>\n", " <td>109.08</td>\n",
" <td>32,733.73</td>\n", " <td>2,515.06</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n", " sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 13.26 166.37 493.95 1,635.71 2,359.22 51,708.15\n", "cvxpy 9.81 185.93 638.17 2,501.41 3,665.02 94,592.70\n",
"alphamind 68.57 205.78 317.68 670.81 915.77 32,733.73" "alphamind 0.33 5.66 14.24 47.29 109.08 2,515.06"
] ]
}, },
"execution_count": 8, "execution_count": 9,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -497,24 +523,25 @@ ...@@ -497,24 +523,25 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 4. Mean - Variance 优化 （Box约束以及线性约束）" "## 4. Mean - Variance 优化 （Box约束）\n",
"---------------"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2018-03-28 12:50:29,489 - ALPHA_MIND - INFO - sh50 is finished\n", "2018-03-30 16:56:16,044 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:50:30,295 - ALPHA_MIND - INFO - hs300 is finished\n", "2018-03-30 16:56:16,705 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:50:33,286 - ALPHA_MIND - INFO - zz500 is finished\n", "2018-03-30 16:56:19,651 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:50:39,842 - ALPHA_MIND - INFO - zz800 is finished\n", "2018-03-30 16:56:30,227 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:50:55,691 - ALPHA_MIND - INFO - zz1000 is finished\n", "2018-03-30 16:56:44,840 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-28 12:57:46,604 - ALPHA_MIND - INFO - ashare_ex is finished\n" "2018-03-30 17:08:25,343 - ALPHA_MIND - INFO - ashare_ex is finished\n"
] ]
} }
], ],
...@@ -531,30 +558,25 @@ ...@@ -531,30 +558,25 @@
" sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n", " sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n",
" er = factor_data[factor].values\n", " er = factor_data[factor].values\n",
" n = len(er)\n", " n = len(er)\n",
" \n", "\n",
" bm = np.zeros(n)\n", " bm = np.zeros(n)\n",
" lbound = np.zeros(n)\n", " lbound = np.zeros(n)\n",
" ubound = np.ones(n) * 0.1\n", " ubound = np.ones(n) * 0.1\n",
" \n",
" risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, risk_constraints, risk_target, lam=1)\n", " status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n", " elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" risk = quad_form(w, sec_cov)\n", " risk = quad_form(w, sec_cov)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound,\n", " w <= ubound]\n",
" curr_risk_exposure == risk_target[0]]\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='CVXOPT')\n", " prob.solve(solver='CVXOPT')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
"\n", " \n",
" u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n", " u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n",
" x2 = np.array(w.value).flatten()\n", " x2 = np.array(w.value).flatten()\n",
" u2 = -x2 @ er + 0.5 * x2 @ sec_cov @ x2\n", " u2 = -x2 @ er + 0.5 * x2 @ sec_cov @ x2\n",
...@@ -568,7 +590,7 @@ ...@@ -568,7 +590,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -603,21 +625,21 @@ ...@@ -603,21 +625,21 @@
" <tbody>\n", " <tbody>\n",
" <tr>\n", " <tr>\n",
" <th>cvxpy</th>\n", " <th>cvxpy</th>\n",
" <td>20.25</td>\n", " <td>10.78</td>\n",
" <td>314.35</td>\n", " <td>300.36</td>\n",
" <td>1,306.26</td>\n", " <td>1,377.87</td>\n",
" <td>2,795.40</td>\n", " <td>5,244.64</td>\n",
" <td>7,386.27</td>\n", " <td>6,758.95</td>\n",
" <td>195,024.34</td>\n", " <td>334,990.46</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>alphamind</th>\n", " <th>alphamind</th>\n",
" <td>21.20</td>\n", " <td>0.32</td>\n",
" <td>41.34</td>\n", " <td>4.78</td>\n",
" <td>60.28</td>\n", " <td>16.02</td>\n",
" <td>91.30</td>\n", " <td>41.63</td>\n",
" <td>77.67</td>\n", " <td>64.54</td>\n",
" <td>868.86</td>\n", " <td>1,134.95</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
...@@ -625,11 +647,11 @@ ...@@ -625,11 +647,11 @@
], ],
"text/plain": [ "text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n", " sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 20.25 314.35 1,306.26 2,795.40 7,386.27 195,024.34\n", "cvxpy 10.78 300.36 1,377.87 5,244.64 6,758.95 334,990.46\n",
"alphamind 21.20 41.34 60.28 91.30 77.67 868.86" "alphamind 0.32 4.78 16.02 41.63 64.54 1,134.95"
] ]
}, },
"execution_count": 10, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -642,28 +664,97 @@ ...@@ -642,28 +664,97 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## 5. 线性优化（带二次限制条件）\n", "## 5. Mean - Variance 优化 （Box约束以及线性约束）\n",
"-------------------------" "----------------"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2018-03-28 12:57:47,308 - ALPHA_MIND - INFO - sh50 is finished\n", "2018-03-30 17:08:25,991 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:57:48,181 - ALPHA_MIND - INFO - hs300 is finished\n", "2018-03-30 17:08:26,763 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:57:51,075 - ALPHA_MIND - INFO - zz500 is finished\n", "2018-03-30 17:08:30,276 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:58:05,881 - ALPHA_MIND - INFO - zz800 is finished\n", "2018-03-30 17:08:41,580 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:58:19,775 - ALPHA_MIND - INFO - zz1000 is finished\n", "2018-03-30 17:09:18,856 - ALPHA_MIND - INFO - zz1000 is finished\n"
"2018-03-28 13:05:47,684 - ALPHA_MIND - INFO - ashare_ex is finished\n"
] ]
} }
], ],
"source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n",
"\n",
"for u_name, sample_data in zip(u_names, data_set):\n",
" all_styles = risk_styles + industry_styles + ['COUNTRY']\n",
" factor_data = sample_data['factor']\n",
" risk_cov = sample_data['risk_cov'][all_styles].values\n",
" risk_exposure = factor_data[all_styles].values\n",
" special_risk = factor_data.srisk.values\n",
" sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000\n",
" er = factor_data[factor].values\n",
" n = len(er)\n",
" \n",
" bm = np.zeros(n)\n",
" lbound = np.zeros(n)\n",
" ubound = np.ones(n) * 0.1\n",
" \n",
" risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\n",
"\n",
" status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, risk_constraints, risk_target, lam=1)\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n",
" number=number, globals=globals()) / number * 1000\n",
" \n",
" w = cvxpy.Variable(n)\n",
" risk = quad_form(w, sec_cov)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n",
" w <= ubound,\n",
" curr_risk_exposure == risk_target[0]]\n",
" prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='CVXOPT')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n",
" number=number, globals=globals()) / number * 1000\n",
"\n",
" u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n",
" x2 = np.array(w.value).flatten()\n",
" u2 = -x2 @ er + 0.5 * x2 @ sec_cov @ x2\n",
" \n",
" np.testing.assert_array_almost_equal(u1, u2, 4)\n",
"\n",
" df.loc['alphamind', u_name] = elasped_time1\n",
" df.loc['cvxpy', u_name] = elasped_time2\n",
" alpha_logger.info(f\"{u_name} is finished\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. 线性优化（带二次限制条件）\n",
"-------------------------"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [ "source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n", "df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n", "number = 1\n",
...@@ -720,72 +811,9 @@ ...@@ -720,72 +811,9 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
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" <th>hs300</th>\n",
" <th>zz500</th>\n",
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" <th>zz1000</th>\n",
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" <td>6,260.97</td>\n",
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" <tr>\n",
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" <td>22.55</td>\n",
" <td>32.70</td>\n",
" <td>60.94</td>\n",
" <td>59.65</td>\n",
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"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 23.83 360.10 1,261.50 9,555.63 6,260.97 212,776.88\n",
"alphamind 22.55 32.70 60.94 59.65 116.27 824.79"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
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"source": [ "source": [
"df" "df"
] ]
......
setup.py
View file @ fbb26530
...@@ -51,7 +51,16 @@ else: ...@@ -51,7 +51,16 @@ else:
"alphamind/pfopt/include/pfopt", "alphamind/pfopt/include/pfopt",
"alphamind/pfopt/include/eigen", "alphamind/pfopt/include/eigen",
"alphamind/pfopt/include/alglib"], "alphamind/pfopt/include/alglib"],
libraries=['pfopt', 'alglib', 'libClp', 'libCoinUtils', 'libipopt', 'libcoinhsl', 'libcoinblas', 'libcoinlapack', 'libcoinmetis'], libraries=['pfopt',
'alglib',
'libClp',
'libCoinUtils',
'libipopt',
'libcoinhsl',
'libcoinblas',
'libcoinlapack',
'libcoinmetis',
'libcoinmumps'],
library_dirs=['alphamind/pfopt/lib'], library_dirs=['alphamind/pfopt/lib'],
extra_compile_args=['/MD']), extra_compile_args=['/MD']),
] ]
......
xgboost @ a187ed6c
Subproject commit bf4367184164e593cd2856ef38f8dd4f8cc76999 Subproject commit a187ed6c8f3aa40b47d5be80667cbbe6a6fd563d
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