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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 @@
**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
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,
total_risk_exp = total_data[constraint_risk]
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
codes = total_data.code.tolist()
......@@ -46,7 +46,10 @@ def cs_impl(ref_date,
total_risk_exp = target_pos[constraint_risk]
activate_weight = target_pos['weight'].values
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.)
ic = np.corrcoef(excess_return, activate_weight)[0, 1]
x = sm.add_constant(activate_weight)
......
alphamind/cython/optimizers.pyx
View file @ fbb26530
......@@ -7,13 +7,14 @@ Created on 2017-7-20
"""
cimport numpy as cnp
from libcpp.string cimport string
from libcpp.vector cimport vector
import numpy as np
cdef extern from "lpoptimizer.hpp" namespace "pfopt":
cdef cppclass LpOptimizer:
LpOptimizer(int, int, double*, double*, double*, double*) except +
LpOptimizer(int, int, double*, double*, double*, double*, string) except +
vector[double] xValue()
double feval()
int status()
......@@ -29,9 +30,12 @@ cdef class LPOptimizer:
cnp.ndarray[double, ndim=2] cons_matrix,
double[:] lbound,
double[:] ubound,
double[:] objective):
double[:] objective,
str method='simplex'):
self.n = lbound.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');
self.cobj = new LpOptimizer(self.n,
......@@ -39,7 +43,8 @@ cdef class LPOptimizer:
&cons[0],
&lbound[0],
&ubound[0],
&objective[0])
&objective[0],
c_str)
def __dealloc__(self):
del self.cobj
......@@ -151,9 +156,25 @@ cdef extern from "mvoptimizer.hpp" namespace "pfopt":
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 MVOptimizer* cobj
cdef QPAlglib* cobj2
cdef cnp.ndarray er
cdef cnp.ndarray cov
cdef double risk_aversion
cdef int n
cdef int m
......@@ -169,12 +190,15 @@ cdef class QPOptimizer:
self.n = lbound.shape[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[:] cons
if cons_matrix is not None:
self.m = cons_matrix.shape[0]
cons = cons_matrix.flatten(order='C');
cons = cons_matrix.flatten(order='C')
self.cobj = new MVOptimizer(self.n,
&expected_return[0],
......@@ -187,25 +211,39 @@ cdef class QPOptimizer:
&cubound[0],
risk_aversion)
else:
self.cobj = new MVOptimizer(self.n,
self.cobj2 = new QPAlglib(self.n,
&expected_return[0],
&cov[0],
&lbound[0],
&ubound[0],
0,
NULL,
NULL,
NULL,
risk_aversion)
def __dealloc__(self):
if self.cobj:
del self.cobj
else:
del self.cobj2
def feval(self):
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):
if self.cobj:
return np.array(self.cobj.xValue())
else:
return np.array(self.cobj2.xValue())
def status(self):
if self.cobj:
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()
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):
__tablename__ = 'daily_portfolios'
__table_args__ = (
......@@ -924,29 +316,6 @@ class Positions(Base):
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):
__tablename__ = 'rebalance_log'
__table_args__ = (
......@@ -1124,8 +493,6 @@ class RiskExposure(Base):
trade_date = Column(DateTime, 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))
MOMENTUM = Column(Float(53))
SIZE = Column(Float(53))
......@@ -1165,7 +532,6 @@ class RiskExposure(Base):
AERODEF = Column(BigInteger)
Conglomerates = Column(BigInteger)
COUNTRY = Column(BigInteger)
updateTime = Column(DateTime)
class RiskMaster(Base):
......
alphamind/data/engines/universe.py
View file @ fbb26530
......@@ -14,8 +14,8 @@ from sqlalchemy import select
from sqlalchemy import join
from sqlalchemy import outerjoin
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.dbmodel.models import Market
from alphamind.data.engines.utilities import factor_tables
from alphamind.data.transformer import Transformer
from alphamind.utilities import encode
......@@ -86,22 +86,22 @@ class Universe(object):
dependency = transformer.dependency
factor_cols = _map_factors(dependency, factor_tables)
big_table = FullFactor
big_table = Market
for t in set(factor_cols.values()):
if t.__table__.name != FullFactor.__table__.name:
big_table = outerjoin(big_table, t, and_(FullFactor.trade_date == t.trade_date,
FullFactor.code == t.code,
FullFactor.trade_date.in_(
dates) if dates else FullFactor.trade_date.between(
if t.__table__.name != Market.__table__.name:
big_table = outerjoin(big_table, t, and_(Market.trade_date == t.trade_date,
Market.code == t.code,
Market.trade_date.in_(
dates) if dates else Market.trade_date.between(
start_date, end_date)))
big_table = join(big_table, UniverseTable,
and_(FullFactor.trade_date == UniverseTable.trade_date,
FullFactor.code == UniverseTable.code,
and_(Market.trade_date == UniverseTable.trade_date,
Market.code == UniverseTable.code,
universe_cond))
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()
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
from typing import List
import numpy as np
from alphamind.data.neutralize import neutralize
from alphamind.utilities import alpha_logger
def factor_processing(raw_factors: np.ndarray,
......@@ -29,6 +30,8 @@ def factor_processing(raw_factors: np.ndarray,
if 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)
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,
risk_constraints: np.ndarray,
risk_target: Tuple[np.ndarray, np.ndarray],
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()
n, m = risk_constraints.shape
......@@ -36,7 +37,7 @@ def linear_builder(er: np.ndarray,
if not turn_over_target:
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()
......@@ -77,7 +78,7 @@ def linear_builder(er: np.ndarray,
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)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er, method)
status = opt.status()
......
alphamind/tests/portfolio/test_linearbuild.py
View file @ fbb26530
......@@ -37,6 +37,25 @@ class TestLinearBuild(unittest.TestCase):
expected_risk = np.zeros(self.risk_exp.shape[1])
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):
bm = self.bm / self.bm.sum()
eplson = 1e-6
......
build_linux_dependencies.sh
View file @ fbb26530
......@@ -21,7 +21,10 @@ fi
cd ../..
cd alphamind/pfopt
export BUILD_TEST=OFF
./build_linux.sh
if [ $? -ne 0 ] ; then
cd ../..
exit 1
......
build_windows_dependencies.bat
View file @ fbb26530
......@@ -19,6 +19,7 @@ cd ../..
cd alphamind\pfopt
set BUILD_TEST=OFF
call build_windows.bat
if %errorlevel% neq 0 exit /b 1
......
notebooks/Example 3 - Multi Weight Gap Comparison.ipynb
View file @ fbb26530
......@@ -44,7 +44,7 @@
"batch = 0\n",
"horizon = map_freq(freq)\n",
"universe = Universe(\"custom\", ['zz800'])\n",
"data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'\n",
"data_source = 'postgres+psycopg2://postgres:we083826@localhost/alpha'\n",
"benchmark_code = 905\n",
"method = 'tv'\n",
"target_vol = 0.05\n",
......@@ -269,9 +269,9 @@
"outputs": [],
"source": [
"def create_report(ret_df, windows):\n",
" sharp_calc = MovingSharp(windows)\n",
" drawdown_calc = MovingMaxDrawdown(windows)\n",
" max_drawdown_calc = MovingMaxDrawdown(len(ret_df))\n",
" sharp_calc = MovingSharp(windows, x='ret', y='riskFree')\n",
" drawdown_calc = MovingMaxDrawdown(windows, x='ret')\n",
" max_drawdown_calc = MovingMaxDrawdown(len(ret_df), x='ret')\n",
"\n",
" res_df = pd.DataFrame(columns=['daily_return', 'cum_ret', 'sharp', 'drawdown', 'max_drawn', 'leverage'])\n",
" total_returns = 0.\n",
......@@ -285,8 +285,8 @@
"\n",
" res_df.loc[date, 'daily_return'] = ret\n",
" res_df.loc[date, 'cum_ret'] = total_returns\n",
" res_df.loc[date, 'drawdown'] = drawdown_calc.result()[0]\n",
" res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()[0]\n",
" res_df.loc[date, 'drawdown'] = drawdown_calc.result()\n",
" res_df.loc[date, 'max_drawn'] = max_drawdown_calc.result()\n",
" res_df.loc[date, 'leverage'] = ret_df.loc[date, 'leverage']\n",
"\n",
" if i < 5:\n",
......@@ -328,13 +328,6 @@
" alpha_logger.info(f\"target_vol: {target_vol:.4f} finished\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......@@ -349,18 +342,6 @@
"language": "python",
"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": {
"cols": {
"lenName": 16,
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ fbb26530
......@@ -57,7 +57,7 @@
"factor = 'EPS'\n",
"lb = 0.0\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",
"\n",
"universes = [Universe('custom', [u_name]) for u_name in u_names]\n",
......@@ -82,12 +82,12 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-28 12:46:50,110 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:46:50,131 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:46:50,156 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:46:50,193 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:46:50,244 - 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 16:52:12,784 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:12,796 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:12,813 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:12,842 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:12,879 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:52:13,184 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
......@@ -171,21 +171,21 @@
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>2.69</td>\n",
" <td>6.37</td>\n",
" <td>7.28</td>\n",
" <td>12.55</td>\n",
" <td>20.09</td>\n",
" <td>325.88</td>\n",
" <td>1.68</td>\n",
" <td>3.13</td>\n",
" <td>5.02</td>\n",
" <td>10.15</td>\n",
" <td>13.55</td>\n",
" <td>145.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>0.34</td>\n",
" <td>0.20</td>\n",
" <td>0.33</td>\n",
" <td>0.44</td>\n",
" <td>0.58</td>\n",
" <td>0.70</td>\n",
" <td>0.92</td>\n",
" <td>1.08</td>\n",
" <td>3.08</td>\n",
" <td>0.72</td>\n",
" <td>2.94</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -193,8 +193,8 @@
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 2.69 6.37 7.28 12.55 20.09 325.88\n",
"alphamind 0.34 0.58 0.70 0.92 1.08 3.08"
"cvxpy 1.68 3.13 5.02 10.15 13.55 145.17\n",
"alphamind 0.20 0.33 0.44 0.58 0.72 2.94"
]
},
"execution_count": 4,
......@@ -223,19 +223,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-28 12:46:51,048 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:46:51,157 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:46:51,442 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:46:52,102 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:46:53,172 - 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 16:52:13,374 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:13,509 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:13,732 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:14,220 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:14,956 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:52:21,246 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
"source": [
"from cvxpy import pnorm\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",
"number = 1\n",
"\n",
......@@ -254,8 +254,22 @@
" risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\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",
" 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",
" status, y, x1 = 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')\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",
" w = cvxpy.Variable(n)\n",
" curr_risk_exposure = risk_constraints.T @ w\n",
......@@ -271,9 +285,21 @@
" prob.solve(solver='GLPK')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='GLPK')\",\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",
"\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",
" alpha_logger.info(f\"{u_name} is finished\")"
]
......@@ -315,21 +341,21 @@
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>4.26</td>\n",
" <td>28.31</td>\n",
" <td>67.48</td>\n",
" <td>164.81</td>\n",
" <td>256.14</td>\n",
" <td>2,561.17</td>\n",
" <td>2.49</td>\n",
" <td>22.19</td>\n",
" <td>51.62</td>\n",
" <td>123.42</td>\n",
" <td>190.62</td>\n",
" <td>1,946.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>1.22</td>\n",
" <td>20.28</td>\n",
" <td>67.10</td>\n",
" <td>154.95</td>\n",
" <td>267.17</td>\n",
" <td>3,314.10</td>\n",
" <td>3.77</td>\n",
" <td>33.59</td>\n",
" <td>55.49</td>\n",
" <td>116.31</td>\n",
" <td>165.62</td>\n",
" <td>1,239.47</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -337,8 +363,8 @@
],
"text/plain": [
" sh50 hs300 zz500 zz800 zz1000 ashare_ex\n",
"cvxpy 4.26 28.31 67.48 164.81 256.14 2,561.17\n",
"alphamind 1.22 20.28 67.10 154.95 267.17 3,314.10"
"cvxpy 2.49 22.19 51.62 123.42 190.62 1,946.96\n",
"alphamind 3.77 33.59 55.49 116.31 165.62 1,239.47"
]
},
"execution_count": 6,
......@@ -362,19 +388,19 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-28 12:47:05,225 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:47:06,047 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:47:07,931 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:47:13,124 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:47:20,586 - 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 16:52:21,418 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:52:21,888 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:52:23,360 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:52:29,066 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:52:37,334 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 16:56:15,174 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
......@@ -423,7 +449,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -458,21 +484,21 @@
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>13.26</td>\n",
" <td>166.37</td>\n",
" <td>493.95</td>\n",
" <td>1,635.71</td>\n",
" <td>2,359.22</td>\n",
" <td>51,708.15</td>\n",
" <td>9.81</td>\n",
" <td>185.93</td>\n",
" <td>638.17</td>\n",
" <td>2,501.41</td>\n",
" <td>3,665.02</td>\n",
" <td>94,592.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>68.57</td>\n",
" <td>205.78</td>\n",
" <td>317.68</td>\n",
" <td>670.81</td>\n",
" <td>915.77</td>\n",
" <td>32,733.73</td>\n",
" <td>0.33</td>\n",
" <td>5.66</td>\n",
" <td>14.24</td>\n",
" <td>47.29</td>\n",
" <td>109.08</td>\n",
" <td>2,515.06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -480,11 +506,11 @@
],
"text/plain": [
" 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",
"alphamind 68.57 205.78 317.68 670.81 915.77 32,733.73"
"cvxpy 9.81 185.93 638.17 2,501.41 3,665.02 94,592.70\n",
"alphamind 0.33 5.66 14.24 47.29 109.08 2,515.06"
]
},
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
......@@ -497,24 +523,25 @@
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 9,
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-28 12:50:29,489 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:50:30,295 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:50:33,286 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:50:39,842 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:50:55,691 - 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 16:56:16,044 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 16:56:16,705 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 16:56:19,651 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 16:56:30,227 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 16:56:44,840 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-30 17:08:25,343 - ALPHA_MIND - INFO - ashare_ex is finished\n"
]
}
],
......@@ -531,30 +558,25 @@
" 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",
"\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",
" 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",
" 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",
" w <= ubound]\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",
" \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",
......@@ -568,7 +590,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -603,21 +625,21 @@
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>20.25</td>\n",
" <td>314.35</td>\n",
" <td>1,306.26</td>\n",
" <td>2,795.40</td>\n",
" <td>7,386.27</td>\n",
" <td>195,024.34</td>\n",
" <td>10.78</td>\n",
" <td>300.36</td>\n",
" <td>1,377.87</td>\n",
" <td>5,244.64</td>\n",
" <td>6,758.95</td>\n",
" <td>334,990.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>21.20</td>\n",
" <td>41.34</td>\n",
" <td>60.28</td>\n",
" <td>91.30</td>\n",
" <td>77.67</td>\n",
" <td>868.86</td>\n",
" <td>0.32</td>\n",
" <td>4.78</td>\n",
" <td>16.02</td>\n",
" <td>41.63</td>\n",
" <td>64.54</td>\n",
" <td>1,134.95</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
......@@ -625,11 +647,11 @@
],
"text/plain": [
" 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",
"alphamind 21.20 41.34 60.28 91.30 77.67 868.86"
"cvxpy 10.78 300.36 1,377.87 5,244.64 6,758.95 334,990.46\n",
"alphamind 0.32 4.78 16.02 41.63 64.54 1,134.95"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
......@@ -642,28 +664,97 @@
"cell_type": "markdown",
"metadata": {},
"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",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2018-03-28 12:57:47,308 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-28 12:57:48,181 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-28 12:57:51,075 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-28 12:58:05,881 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-28 12:58:19,775 - ALPHA_MIND - INFO - zz1000 is finished\n",
"2018-03-28 13:05:47,684 - ALPHA_MIND - INFO - ashare_ex is finished\n"
"2018-03-30 17:08:25,991 - ALPHA_MIND - INFO - sh50 is finished\n",
"2018-03-30 17:08:26,763 - ALPHA_MIND - INFO - hs300 is finished\n",
"2018-03-30 17:08:30,276 - ALPHA_MIND - INFO - zz500 is finished\n",
"2018-03-30 17:08:41,580 - ALPHA_MIND - INFO - zz800 is finished\n",
"2018-03-30 17:09:18,856 - ALPHA_MIND - INFO - zz1000 is finished\n"
]
}
],
"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": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n",
......@@ -720,72 +811,9 @@
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sh50</th>\n",
" <th>hs300</th>\n",
" <th>zz500</th>\n",
" <th>zz800</th>\n",
" <th>zz1000</th>\n",
" <th>ashare_ex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cvxpy</th>\n",
" <td>23.83</td>\n",
" <td>360.10</td>\n",
" <td>1,261.50</td>\n",
" <td>9,555.63</td>\n",
" <td>6,260.97</td>\n",
" <td>212,776.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>alphamind</th>\n",
" <td>22.55</td>\n",
" <td>32.70</td>\n",
" <td>60.94</td>\n",
" <td>59.65</td>\n",
" <td>116.27</td>\n",
" <td>824.79</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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,
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df"
]
......
setup.py
View file @ fbb26530
......@@ -51,7 +51,16 @@ else:
"alphamind/pfopt/include/pfopt",
"alphamind/pfopt/include/eigen",
"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'],
extra_compile_args=['/MD']),
]
......
xgboost @ a187ed6c
Subproject commit bf4367184164e593cd2856ef38f8dd4f8cc76999
Subproject commit a187ed6c8f3aa40b47d5be80667cbbe6a6fd563d
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