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Commit 1dcc5b0d authored by Dr.李's avatar Dr.李
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added more column to models

parent f5d14113
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Showing with 253 additions and 114 deletions
alphamind/data/dbmodel/models.py
View file @ 1dcc5b0d
......@@ -571,6 +571,51 @@ class FullFactor(Base):
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))
......
alphamind/examples/factor_analysis_example.py
View file @ 1dcc5b0d
# -*- coding: utf-8 -*-
"""
Created on 2017-8-16
Created on 2017-11-8
@author: cheng.li
"""
......@@ -10,120 +10,205 @@ import pandas as pd
from matplotlib import pyplot as plt
from alphamind.api import *
from PyFin.api import *
from PyFin.Math.Accumulators.StatefulAccumulators import MovingAverage
from PyFin.Math.Accumulators.StatefulAccumulators import MovingSharp
from PyFin.Math.Accumulators.StatefulAccumulators import MovingMaxDrawdown
strategies = {
'prod': {
# 'factors': ['RVOL', 'EPS', 'DROEAfterNonRecurring', 'DivP', 'CFinc1', 'BDTO'],
# 'weights': [0.05, 0.3, 0.35, 0.075, 0.15, 0.05]
# 'factors': ['RVOL', 'EPS', 'DROEAfterNonRecurring', 'DivP', 'CFinc1', 'BDTO'],
# 'weights': [0.05, 0.3, 0.35, 0.075, 0.15, 0.05]
'factors': ['VAL', 'RVOL', 'ROEDiluted', 'GREV', 'EPS', 'CHV', 'CFinc1', 'BDTO'],
'weights': [0.034129344,
0.015881607,
0.048765746,
0.042747382,
-0.015900173,
0.019044573,
-0.001792638,
0.014277867,
]
},
# 'candidate': {
# 'factors': ['RVOL', 'EPS', 'CFinc1', 'BDTO', 'VAL', 'GREV', 'ROEDiluted'],
# 'weights': [0.02, 0.2, 0.15, 0.05, 0.2, 0.2, 0.2]
# }
}
engine = SqlEngine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
universe = Universe('custom', ['zz500'])
benchmark_code = 905
neutralize_risk = industry_styles
constraint_risk = industry_styles
freq = '2w'
if freq == '1m':
horizon = 21
elif freq == '1w':
horizon = 4
elif freq == '2w':
horizon = 8
elif freq == '3w':
horizon = 12
elif freq == '1d':
horizon = 0
dates = makeSchedule('2012-01-01',
'2017-09-15',
tenor=freq,
calendar='china.sse',
dateGenerationRule=DateGeneration.Forward)
total_data_dict = {}
for strategy in strategies:
factors = strategies[strategy]['factors']
factor_weights = strategies[strategy]['weights']
all_data = engine.fetch_data_range(universe, factors, dates=dates, benchmark=905)
factor_all_data = all_data['factor']
factor_groups = factor_all_data.groupby('trade_date')
rets = []
for i, value in enumerate(factor_groups):
plt.style.use('ggplot')
"""
Back test parameter settings
"""
start_date = '2015-01-01'
end_date = '2017-11-28'
benchmark_code = 300
universe_name = ['hs300']
universe = Universe(universe_name, universe_name)
frequency = '5b'
method = 'risk_neutral'
use_rank = 100
industry_lower = 1.
industry_upper = 1.
neutralize_risk = ['SIZE'] + industry_styles
constraint_risk = ['SIZE'] + industry_styles
size_risk_lower = 0
size_risk_upper = 0
turn_over_target_base = 0.25
benchmark_total_lower = 1.
benchmark_total_upper = 1.
horizon = map_freq(frequency)
executor = NaiveExecutor()
engine = SqlEngine()
"""
Model phase: we need 1 constant linear model and one linear regression model
"""
alpha_name = ['alpha_factor']
#const_features = {alpha_name[0]: LAST('optimism_confidence_25d') + LAST('pessimism_confidence_25d')}
# const_features = {alpha_name[0]: CSRes(DIFF(1. / LAST('PE')), LAST('roe_q'))}
simple_expression = LAST('cfinc1_q') # CSRes(CSRes(LAST('DividendPS'), LAST('roe_q')), LAST('ep_q'))
const_features = {alpha_name[0]: simple_expression}
const_weights = np.array([1.])
const_model = ConstLinearModel(features=alpha_name,
weights=const_weights)
ref_dates = makeSchedule(start_date, end_date, frequency, 'china.sse')
const_model_factor_data = engine.fetch_data_range(universe,
const_features,
dates=ref_dates,
benchmark=benchmark_code)['factor'].dropna()
horizon = map_freq(frequency)
rets = []
turn_overs = []
leverags = []
previous_pos = pd.DataFrame()
index_dates = []
factor_groups = const_model_factor_data.groupby('trade_date')
for i, value in enumerate(factor_groups):
date = value[0]
data = value[1]
codes = data.code.tolist()
ref_date = date.strftime('%Y-%m-%d')
returns = engine.fetch_dx_return(ref_date, codes, horizon=horizon)
index_dates.append(date)
total_data = pd.merge(data, returns, on=['code']).dropna()
print(date, ': ', len(total_data))
total_data = data.fillna(data[alpha_name].median())
alpha_logger.info('{0}: {1}'.format(date, len(total_data)))
risk_exp = total_data[neutralize_risk].values.astype(float)
industry = total_data.industry_code.values
dx_return = total_data.dx.values
benchmark = total_data.weight.values
benchmark_w = total_data.weight.values
constraint_exp = total_data[constraint_risk].values
risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)
risk_names = constraint_risk + ['total']
risk_target = risk_exp_expand.T @ benchmark
risk_target = risk_exp_expand.T @ benchmark_w
lbound = np.maximum(0., benchmark_w - 0.02) # np.zeros(len(total_data))
ubound = 0.02 + benchmark_w
is_in_benchmark = (benchmark_w > 0.).astype(float)
lbound = np.zeros(len(total_data))
ubound = 0.01 + benchmark
risk_exp_expand = np.concatenate((risk_exp_expand, is_in_benchmark.reshape((-1, 1))), axis=1).astype(float)
risk_names.append('benchmark_total')
constraint = Constraints(risk_exp_expand, risk_names)
for i, name in enumerate(risk_names):
constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])
if name == 'total':
constraint.set_constraints(name,
lower_bound=risk_target[i],
upper_bound=risk_target[i])
elif name == 'SIZE':
base_target = abs(risk_target[i])
constraint.set_constraints(name,
lower_bound=risk_target[i] + base_target * size_risk_lower,
upper_bound=risk_target[i] + base_target * size_risk_upper)
elif name == 'benchmark_total':
base_target = benchmark_w.sum()
constraint.set_constraints(name,
lower_bound=benchmark_total_lower * base_target,
upper_bound=benchmark_total_upper * base_target)
else:
constraint.set_constraints(name,
lower_bound=risk_target[i] * industry_lower,
upper_bound=risk_target[i] * industry_upper)
factor_values = factor_processing(total_data[alpha_name].values,
pre_process=[winsorize_normal, standardize],
risk_factors=risk_exp,
post_process=[winsorize_normal, standardize])
# const linear model
er = const_model.predict(factor_values)
codes = total_data['code'].values
if previous_pos.empty:
current_position = None
turn_over_target = None
else:
previous_pos.set_index('code', inplace=True)
remained_pos = previous_pos.loc[codes]
remained_pos.fillna(0., inplace=True)
turn_over_target = turn_over_target_base
current_position = remained_pos.weight.values
f_data = total_data[factors]
try:
pos, analysis = factor_analysis(f_data,
factor_weights,
industry=industry,
d1returns=dx_return,
risk_exp=risk_exp,
benchmark=benchmark,
is_tradable=total_data.isOpen.values.astype(bool),
method='risk_neutral',
constraints=constraint,
use_rank=50,
target_pos, _ = er_portfolio_analysis(er,
industry,
None,
constraint,
False,
benchmark_w,
method=method,
use_rank=use_rank,
turn_over_target=turn_over_target,
current_position=current_position,
lbound=lbound,
ubound=ubound)
except ValueError:
alpha_logger.info('{0} full re-balance'.format(date))
target_pos, _ = er_portfolio_analysis(er,
industry,
None,
constraint,
False,
benchmark_w,
method=method,
use_rank=use_rank,
lbound=lbound,
ubound=ubound)
except Exception as e:
print(e)
rets.append(0.)
else:
rets.append(analysis.er[-1] / benchmark.sum())
total_data_dict[strategy] = rets
target_pos['code'] = total_data['code'].values
ret_df = pd.DataFrame(total_data_dict, index=dates)
ret_df.loc[advanceDateByCalendar('china.sse', dates[-1], freq)] = 0.
ret_df = ret_df.shift(1)
turn_over, executed_pos = executor.execute(target_pos=target_pos)
executed_codes = executed_pos.code.tolist()
dx_returns = engine.fetch_dx_return(date, executed_codes, horizon=horizon, offset=1)
result = pd.merge(executed_pos, total_data[['code', 'weight']], on=['code'], how='inner')
result = pd.merge(result, dx_returns, on=['code'])
leverage = result.weight_x.abs().sum()
ret = result.weight_x.values @ (np.exp(result.dx.values) - 1.)
rets.append(np.log(1. + ret))
executor.set_current(executed_pos)
turn_overs.append(turn_over)
leverags.append(leverage)
previous_pos = executed_pos
alpha_logger.info('{0} is finished'.format(date))
ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverage}, index=index_dates)
# index return
index_return = engine.fetch_dx_return_index_range(benchmark_code, start_date, end_date, horizon=horizon,
offset=1).set_index('trade_date')
ret_df['index'] = index_return['dx']
ret_df.loc[advanceDateByCalendar('china.sse', ref_dates[-1], frequency)] = 0.
ret_df = ret_df.shift(1)
ret_df.iloc[0] = 0.
ret_df['tc_cost'] = ret_df.turn_over * 0.002
ret_df['returns'] = ret_df['returns'] - ret_df['index'] * ret_df['leverage']
ret_df[['returns', 'tc_cost']].cumsum().plot(figsize=(12, 6),
title='Fixed frequency rebalanced: {0}'.format(frequency),
secondary_y='tc_cost')
ret_df.cumsum().plot(figsize=(12, 6))
plt.savefig("backtest_big_universe_20170814.png")
plt.show()
scripts/create_view.sql
View file @ 1dcc5b0d
......@@ -26,12 +26,20 @@ SELECT array_to_string(ARRAY(SELECT 'r' || '.' || '"' || c.column_name || '"'
), ',') || ' FROM risk_exposure AS r' As sqlstmt5;
create MATERIALIZED VIEW full_factor_view as SELECT m."trade_date",m."code",m."secShortName",m."exchangeCD",m."preClosePrice",m."actPreClosePrice",m."openPrice",m."highestPrice",m."lowestPrice",m."closePrice",m."turnoverVol",m."turnoverValue",m."dealAmount",m."turnoverRate",m."accumAdjFactor",m."negMarketValue",m."marketValue",m."chgPct",m."isOpen",m."vwap",
SELECT array_to_string(ARRAY(SELECT 'g' || '.' || '"' || c.column_name || '"'
FROM information_schema.columns As c
WHERE table_name = 'gogoal'
AND c.column_name NOT IN('trade_date', 'code')
), ',') || ' FROM gogoal AS g' As sqlstmt6;
create table full_factor as SELECT m."trade_date",m."code",m."secShortName",m."exchangeCD",m."preClosePrice",m."actPreClosePrice",m."openPrice",m."highestPrice",m."lowestPrice",m."closePrice",m."turnoverVol",m."turnoverValue",m."dealAmount",m."turnoverRate",m."accumAdjFactor",m."negMarketValue",m."marketValue",m."chgPct",m."isOpen",m."vwap",
u."AccountsPayablesTDays",u."AccountsPayablesTRate",u."AdminiExpenseRate",u."ARTDays",u."ARTRate",u."ASSI",u."BLEV",u."BondsPayableToAsset",u."CashRateOfSales",u."CashToCurrentLiability",u."CMRA",u."CTOP",u."CTP5",u."CurrentAssetsRatio",u."CurrentAssetsTRate",u."CurrentRatio",u."DAVOL10",u."DAVOL20",u."DAVOL5",u."DDNBT",u."DDNCR",u."DDNSR",u."DebtEquityRatio",u."DebtsAssetRatio",u."DHILO",u."DilutedEPS",u."DVRAT",u."EBITToTOR",u."EGRO",u."EMA10",u."EMA120",u."EMA20",u."EMA5",u."EMA60",u."EPS",u."EquityFixedAssetRatio",u."EquityToAsset",u."EquityTRate",u."ETOP",u."ETP5",u."FinancialExpenseRate",u."FinancingCashGrowRate",u."FixAssetRatio",u."FixedAssetsTRate",u."GrossIncomeRatio",u."HBETA",u."HSIGMA",u."IntangibleAssetRatio",u."InventoryTDays",u."InventoryTRate",u."InvestCashGrowRate",u."LCAP",u."LFLO",u."LongDebtToAsset",u."LongDebtToWorkingCapital",u."LongTermDebtToAsset",u."MA10",u."MA120",u."MA20",u."MA5",u."MA60",u."MAWVAD",u."MFI",u."MLEV",u."NetAssetGrowRate",u."NetProfitGrowRate",u."NetProfitRatio",u."NOCFToOperatingNI",u."NonCurrentAssetsRatio",u."NPParentCompanyGrowRate",u."NPToTOR",u."OperatingExpenseRate",u."OperatingProfitGrowRate",u."OperatingProfitRatio",u."OperatingProfitToTOR",u."OperatingRevenueGrowRate",u."OperCashGrowRate",u."OperCashInToCurrentLiability",u."PB",u."PCF",u."PE",u."PS",u."PSY",u."QuickRatio",u."REVS10",u."REVS20",u."REVS5",u."ROA",u."ROA5",u."ROE",u."ROE5",u."RSI",u."RSTR12",u."RSTR24",u."SalesCostRatio",u."SaleServiceCashToOR",u."SUE",u."TaxRatio",u."TOBT",u."TotalAssetGrowRate",u."TotalAssetsTRate",u."TotalProfitCostRatio",u."TotalProfitGrowRate",u."VOL10",u."VOL120",u."VOL20",u."VOL240",u."VOL5",u."VOL60",u."WVAD",u."REC",u."DAREC",u."GREC",u."FY12P",u."DAREV",u."GREV",u."SFY12P",u."DASREV",u."GSREV",u."FEARNG",u."FSALESG",u."TA2EV",u."CFO2EV",u."ACCA",u."DEGM",u."SUOI",u."EARNMOM",u."FiftyTwoWeekHigh",u."Volatility",u."Skewness",u."ILLIQUIDITY",u."BackwardADJ",u."MACD",u."ADTM",u."ATR14",u."ATR6",u."BIAS10",u."BIAS20",u."BIAS5",u."BIAS60",u."BollDown",u."BollUp",u."CCI10",u."CCI20",u."CCI5",u."CCI88",u."KDJ_K",u."KDJ_D",u."KDJ_J",u."ROC6",u."ROC20",u."SBM",u."STM",u."UpRVI",u."DownRVI",u."RVI",u."SRMI",u."ChandeSD",u."ChandeSU",u."CMO",u."DBCD",u."ARC",u."OBV",u."OBV6",u."OBV20",u."TVMA20",u."TVMA6",u."TVSTD20",u."TVSTD6",u."VDEA",u."VDIFF",u."VEMA10",u."VEMA12",u."VEMA26",u."VEMA5",u."VMACD",u."VOSC",u."VR",u."VROC12",u."VROC6",u."VSTD10",u."VSTD20",u."KlingerOscillator",u."MoneyFlow20",u."AD",u."AD20",u."AD6",u."CoppockCurve",u."ASI",u."ChaikinOscillator",u."ChaikinVolatility",u."EMV14",u."EMV6",u."plusDI",u."minusDI",u."ADX",u."ADXR",u."Aroon",u."AroonDown",u."AroonUp",u."DEA",u."DIFF",u."DDI",u."DIZ",u."DIF",u."MTM",u."MTMMA",u."PVT",u."PVT6",u."PVT12",u."TRIX5",u."TRIX10",u."UOS",u."MA10RegressCoeff12",u."MA10RegressCoeff6",u."PLRC6",u."PLRC12",u."SwingIndex",u."Ulcer10",u."Ulcer5",u."Hurst",u."ACD6",u."ACD20",u."EMA12",u."EMA26",u."APBMA",u."BBI",u."BBIC",u."TEMA10",u."TEMA5",u."MA10Close",u."AR",u."BR",u."ARBR",u."CR20",u."MassIndex",u."BearPower",u."BullPower",u."Elder",u."NVI",u."PVI",u."RC12",u."RC24",u."JDQS20",u."Variance20",u."Variance60",u."Variance120",u."Kurtosis20",u."Kurtosis60",u."Kurtosis120",u."Alpha20",u."Alpha60",u."Alpha120",u."Beta20",u."Beta60",u."Beta120",u."SharpeRatio20",u."SharpeRatio60",u."SharpeRatio120",u."TreynorRatio20",u."TreynorRatio60",u."TreynorRatio120",u."InformationRatio20",u."InformationRatio60",u."InformationRatio120",u."GainVariance20",u."GainVariance60",u."GainVariance120",u."LossVariance20",u."LossVariance60",u."LossVariance120",u."GainLossVarianceRatio20",u."GainLossVarianceRatio60",u."GainLossVarianceRatio120",u."RealizedVolatility",u."REVS60",u."REVS120",u."REVS250",u."REVS750",u."REVS5m20",u."REVS5m60",u."REVS5Indu1",u."REVS20Indu1",u."Volumn1M",u."Volumn3M",u."Price1M",u."Price3M",u."Price1Y",u."Rank1M",u."CashDividendCover",u."DividendCover",u."DividendPaidRatio",u."RetainedEarningRatio",u."CashEquivalentPS",u."DividendPS",u."EPSTTM",u."NetAssetPS",u."TORPS",u."TORPSLatest",u."OperatingRevenuePS",u."OperatingRevenuePSLatest",u."OperatingProfitPS",u."OperatingProfitPSLatest",u."CapitalSurplusFundPS",u."SurplusReserveFundPS",u."UndividedProfitPS",u."RetainedEarningsPS",u."OperCashFlowPS",u."CashFlowPS",u."NetNonOIToTP",u."NetNonOIToTPLatest",u."PeriodCostsRate",u."InterestCover",u."NetProfitGrowRate3Y",u."NetProfitGrowRate5Y",u."OperatingRevenueGrowRate3Y",u."OperatingRevenueGrowRate5Y",u."NetCashFlowGrowRate",u."NetProfitCashCover",u."OperCashInToAsset",u."CashConversionCycle",u."OperatingCycle",u."PEG3Y",u."PEG5Y",u."PEIndu",u."PBIndu",u."PSIndu",u."PCFIndu",u."PEHist20",u."PEHist60",u."PEHist120",u."PEHist250",u."StaticPE",u."ForwardPE",u."EnterpriseFCFPS",u."ShareholderFCFPS",u."ROEDiluted",u."ROEAvg",u."ROEWeighted",u."ROECut",u."ROECutWeighted",u."ROIC",u."ROAEBIT",u."ROAEBITTTM",u."OperatingNIToTP",u."OperatingNIToTPLatest",u."InvestRAssociatesToTP",u."InvestRAssociatesToTPLatest",u."NPCutToNP",u."SuperQuickRatio",u."TSEPToInterestBearDebt",u."DebtTangibleEquityRatio",u."TangibleAToInteBearDebt",u."TangibleAToNetDebt",u."NOCFToTLiability",u."NOCFToInterestBearDebt",u."NOCFToNetDebt",u."TSEPToTotalCapital",u."InteBearDebtToTotalCapital",u."NPParentCompanyCutYOY",u."SalesServiceCashToORLatest",u."CashRateOfSalesLatest",u."NOCFToOperatingNILatest",u."TotalAssets",u."MktValue",u."NegMktValue",u."TEAP",u."NIAP",u."TotalFixedAssets",u."IntFreeCL",u."IntFreeNCL",u."IntCL",u."IntDebt",u."NetDebt",u."NetTangibleAssets",u."WorkingCapital",u."NetWorkingCapital",u."TotalPaidinCapital",u."RetainedEarnings",u."OperateNetIncome",u."ValueChgProfit",u."NetIntExpense",u."EBIT",u."EBITDA",u."EBIAT",u."NRProfitLoss",u."NIAPCut",u."FCFF",u."FCFE",u."DA",u."TRevenueTTM",u."TCostTTM",u."RevenueTTM",u."CostTTM",u."GrossProfitTTM",u."SalesExpenseTTM",u."AdminExpenseTTM",u."FinanExpenseTTM",u."AssetImpairLossTTM",u."NPFromOperatingTTM",u."NPFromValueChgTTM",u."OperateProfitTTM",u."NonOperatingNPTTM",u."TProfitTTM",u."NetProfitTTM",u."NetProfitAPTTM",u."SaleServiceRenderCashTTM",u."NetOperateCFTTM",u."NetInvestCFTTM",u."NetFinanceCFTTM",u."GrossProfit",u."Beta252",u."RSTR504",u."EPIBS",u."CETOP",u."DASTD",u."CmraCNE5",u."HsigmaCNE5",u."SGRO",u."EgibsLong",u."STOM",u."STOQ",u."STOA",u."NLSIZE",
l."ROEAfterNonRecurring",l."EPSAfterNonRecurring",l."EODPrice",l."LogFloatCap",l."BPS",l."SPS",l."DebtToAsset",l."DROEAfterNonRecurring",l."LogTotalCap",l."BP",l."SP",l."EPAfterNonRecurring",l."DivToB",l."DivP",l."EBITToSales",l."EBITAToSales",l."EVToSales",l."EVToEBIT",l."EVToEBITDA",l."EVToNOPLAT",l."EVToIC",l."FCFFPS",l."FCFFToEarningAfterNonRecurring",l."FCFFP",l."ProfitToAsset",l."GrossProfitRatio",l."LATO",l."FATO",l."TATO",l."EquityTO",l."PayableTO",l."RecievableTO",l."RevenueGrowth",l."GrossProfitGrowth",l."NetProfitGrowth",l."GrossCFToRevenue",l."CFToRevenue",l."CFToProfit",l."CFToAsset",l."GrossCFGrowth",l."CFGrowth",l."ICFGrowth",l."AveAmount60",l."PeriodReturn60",l."AmountRatio60to250",l."CFPS",l."CFP",l."NetCFGrowth",l."NetCFGrowthP",l."NetCash",l."NetCashP",l."BVPSGrowth",l."EquityPSGrowth",l."WholeSales",l."WholeProfitAfterNonRecurring",l."ExpenseRatio",l."AcidTestRatio",l."TimeInterestEarnedRatio",l."DepositReceivedVsSale",l."DebtRatioExcemptDepRec",l."SNBARatio",
t."CFinc1",t."BDTO",t."RVOL",t."CHV",t."VAL",
r."BETA",r."MOMENTUM",r."SIZE",r."EARNYILD",r."RESVOL",r."GROWTH",r."BTOP",r."LEVERAGE",r."LIQUIDTY",r."SIZENL",r."Bank",r."RealEstate",r."Health",r."Transportation",r."Mining",r."NonFerMetal",r."HouseApp",r."LeiService",r."MachiEquip",r."BuildDeco",r."CommeTrade",r."CONMAT",r."Auto",r."Textile",r."FoodBever",r."Electronics",r."Computer",r."LightIndus",r."Utilities",r."Telecom",r."AgriForest",r."CHEM",r."Media",r."IronSteel",r."NonBankFinan",r."ELECEQP",r."AERODEF",r."Conglomerates",r."COUNTRY",
e."DROE",e."IVR",e."xueqiu_hotness",
g."con_eps",g."con_eps_rolling",g."con_na",g."con_na_rolling",g."con_np",g."con_npcgrate_1w",g."con_npcgrate_4w",g."con_npcgrate_13w",g."con_npcgrate_26w",g."con_npcgrate_52w",g."con_npcgrate_2y",g."con_np_rolling",g."con_np_yoy",g."con_pb",g."con_pb_order",g."con_pb_rolling",g."con_pb_rolling_order",g."con_or",g."con_pe",g."con_pe_order",g."con_pe_rolling",g."con_pe_rolling_order",g."con_peg",g."con_peg_order",g."con_peg_rolling",g."con_peg_rolling_order",g."con_roe",g."con_target_price",g."market_confidence_5d",g."market_confidence_10d",g."market_confidence_15d",g."market_confidence_25d",g."market_confidence_75d",g."mcap",g."optimism_confidence_5d",g."optimism_confidence_10d",g."optimism_confidence_15d",g."optimism_confidence_25d",g."optimism_confidence_75d",g."pessimism_confidence_5d",g."pessimism_confidence_10d",g."pessimism_confidence_15d",g."pessimism_confidence_25d",g."pessimism_confidence_75d",g."tcap",
s1."SRISK" as d_srisk, s2."SRISK" as s_srisk, s3."SRISK" as l_srisk
FROM market AS m left join uqer AS u on m.trade_date = u.trade_date and m.code = u.code
inner join risk_exposure AS r on m.trade_date = r.trade_date and m.code = r.code
......@@ -40,6 +48,7 @@ u."AccountsPayablesTDays",u."AccountsPayablesTRate",u."AdminiExpenseRate",u."ART
inner join specific_risk_long as s3 on m.trade_date = s3.trade_date and m.code = s3.code
left join legacy_factor as l on m.trade_date = l.trade_date and m.code = l.code
left join tiny as t on m.trade_date = t.trade_date and m.code = t.code
left join experimental as e on m.trade_date = e.trade_date and m.code = e.code;
left join experimental as e on m.trade_date = e.trade_date and m.code = e.code
left join gogoal as g on m.trade_date = g.trade_date and m.code = g.code;
create UNIQUE index on full_factor_view (trade_date, code);
\ No newline at end of file
create UNIQUE index on full_factor (trade_date, code);
\ No newline at end of file
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