积极投资法与史蒂夫•路佛价值选股法

def stock_sellection_Graham_positive_stock(context):
    '''股票的市盈率高于市场平均水平
    股票的市净率小于3
    企业的流动比率要大于1.1
    企业的长期负债 与营运资金（流动资产-流动负债）比率不超过5
    利润増长处于较高水平：大于80%的股票 '''
    previous_date = context.previous_date
    account =  context.get_account('fantasy_account')
    current_universe = context.get_universe( exclude_halt=True)
    
    #获取因子 5年收益增长率  收盘价  稀释每股收益
    data = DataAPI.MktStockFactorsOneDayGet(tradeDate=previous_date, secID=universe, 
                                                    field=['secID','tradeDate','PE','PB','CurrentRatio','LongDebtToWorkingCapital','NetProfitGrowRate'], pandas='1')
       
        
    data  = data.dropna()
                                                    
    #数据处理部分   #neg 流通市值
    data = data[(data.PB<3) & (data.PB)>0 &(data.PE > data.PE.quantile(0.9)) & (data.CurrentRatio>1.1) & (data.LongDebtToWorkingCapital < 5)
                &(data.NetProfitGrowRate >data.NetProfitGrowRate.quantile(0.9))]
    log.info(data)
    buy_list = data['secID'].tolist()
    
    print type(buy_list)
                                              
    return list(buy_list)

import numpy as np
import pandas as pd
import talib 
import datetime


start = '2015-01-01'                       # 回测起始时间
end = '2016-01-01'                         # 回测结束时间
universe = DynamicUniverse('A')        # 证券池，支持股票、基金、期货、指数四种资产
benchmark = 'HS300'                        # 策略参考标准
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 = 10                           # 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
  
# 配置账户信息，支持多资产多账户
accounts = {
    'fantasy_account': AccountConfig(account_type='security', capital_base=10000000)
}


##每只股票买入卖出阈值
Max_Position = 1000000           


def initialize(context):
    pass


def handle_data(context):  
    buy_list = stock_sellection_steve_stock(context)
    trading(buy_list,context)

def stock_sellection_steve_stock(context):
    previous_date = context.previous_date
    account =  context.get_account('fantasy_account')
    previous_date = previous_date.strftime('%Y%m%d')
    current_universe = context.get_universe( exclude_halt=True)
    security_position = account.get_positions()
    SV_factor = DataAPI.MktStockFactorsOneDayGet(tradeDate=previous_date, secID=current_universe, field=['secID','PB','ETP5','PCF','CurrentRatio','LongDebtToAsset'], pandas='1')
    SV_factor = SV_factor.dropna()
    SV_factor = SV_factor.set_index(['secID'])
    
    SV_factor = SV_factor[SV_factor['PCF']>0]       #去掉现金流负的企业
    
    # ETP5 5年平均收益市值比（Five-year average earnings to price）。计算方法：5年平均收益市值比=近五年净利润（TTM）/近五年总市值。属于价值类因子。数据更新时间：T日21点30分。
    # PCF 市现率（Price-to-cash-flow ratio）。计算方法：总市值/经营活动产生的现金流量净额（TTM） 
    #长期借款与资产总计之比（Long term loan to total assets）。计算方法：长期借款与资产总计之比=长期借款/总资产
    #流动比率（Current ratio），计算方法：流动比率=流动资产合计/流动负债合计。属于质量类因子
    SV_factor = SV_factor[(SV_factor['ETP5']>0.7*SV_factor['ETP5'].mean()) &(SV_factor['PCF']<0.75*SV_factor['PCF'].mean())& (SV_factor['LongDebtToAsset']<0.5) & 
                          (SV_factor['PB']< 0.8*SV_factor['PB'].mean())  & (SV_factor['CurrentRatio'] >SV_factor['CurrentRatio'].mean())]
    
    print(type(SV_factor))
    buy_list = SV_factor.index
    print(type(SV_factor))
    print(SV_factor)
    
    
    return list(buy_list)

d = min(len(buy_list),int(cash)//Max_Position)

def trading(buy_list,context):
    account =  context.get_account('fantasy_account')
    current_universe = context.get_universe( exclude_halt=True)
    security_position = account.get_positions()
    cash = account.cash
    
    for sec in current_universe:
        if sec not in buy_list and sec in security_position:
            account.order_to(sec,0)
            cash += security_position[sec].amount * context.current_price(sec)
            
    d = min(len(buy_list),int(cash)//Max_Position)
    for sec in  buy_list[:d]:
        if sec not in security_position:
            account.order(sec,Max_Position/ context.current_price(sec))