# Porfolio（现金比率+负债现金+现金保障倍数）+市盈率 > 来源：https://uqer.io/community/share/566a896bf9f06c6c8a91cae7 ```py ?DataAPI.MktStockFactorsOneDayGet ``` ```py import numpy as np import pandas as pd start = '2015-01-01' # 回测起始时间 end = '2015-11-30' # 回测结束时间 benchmark = 'HS300' # 策略参考标准 universe = set_universe('HS300') # 证券池，支持股票和基金 capital_base = 100000 # 起始资金 freq = 'd' # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测 refresh_rate = 1 # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟 def initialize(account): # 初始化虚拟账户状态 pass def handle_data(account): # 每个交易日的买入卖出指令 market_val = DataAPI.MktEqudGet(tradeDate=account.current_date,field=u"secID,negMarketValue",pandas="1") #获取所有股票的市值 factor = DataAPI.MktStockFactorsOneDayGet(tradeDate=account.current_date,field='secID,ROE,ROA,CashRateOfSales,FinancialExpenseRate,CashToCurrentLiability,OperCashInToCurrentLiability,GrossIncomeRatio,NetProfitRatio,PE,PB',pandas="1") #获取所有股票的相关因子 # print factor factor.set_index('secID',inplace=True); sec_val_mkt = {'symbol':[], 'factor_value':[], 'market_value':[]} x='CashToCurrentLiability' y='OperCashInToCurrentLiability' z='PE' for stock in account.universe: sec_val_mkt['symbol'].append(stock) factor_va=float(1/3*factor.ix[stock][x]+1/3*factor.ix[stock][y]+1/3*factor.ix[stock][z]); sec_val_mkt['factor_value'].append(factor_va) sec_val_mkt['market_value'].append(float(market_val.negMarketValue[market_val.secID==stock])) sec_val_mkt = pd.DataFrame(sec_val_mkt).sort(columns='factor_value',ascending=True).reset_index() sec_val_mkt = sec_val_mkt[:int(len(sec_val_mkt)*0.1)] #排序并选择前10% buylist = list(sec_val_mkt.symbol) #买入股票列表 sum_market_val = sum(sec_val_mkt.market_value) position = np.array(sec_val_mkt.market_value)/sum_market_val*account.cash for stock in account.valid_secpos: if stock not in buylist: order_to(stock, 0) for stock in buylist: if stock not in account.valid_secpos: order(stock, position[buylist.index(stock)]) return ```  ```py bt.blotter None ```