利用新闻情感数据炒股 python程序，新闻python,from CAL.PyC

利用新闻情感数据炒股 python程序，新闻python,from CAL.PyC

from CAL.PyCAL import Datestart = '2010-01-01'                       # 回测起始时间end = '2015-05-05'                         # 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 = 1000000                      # 起始资金longest_history = 0                        # handle_data 函数中可以使用的历史数据最长窗口长度refresh_rate = 1                          # 调仓频率，即每 refresh_rate 个交易日执行一次 handle_data() 函数longest_history = 1def initialize(account):                   # 初始化虚拟账户状态    account.isBuyPeriod = False    account.dayCount = 0def handle_data(account):                  # 每个交易日的买入卖出指令    account.dayCount += 1     if account.isBuyPeriod:                # 每60个工作日（3个月）调仓        hist = account.get_history(longest_history)        endDate = Date.fromDateTime(account.current_date)        startDate = endDate - 30        res =  DataAPI.NewsSentimentIndexGet(secID=account.universe, field=['secID', 'newsPublishDate', 'sentimentIndex'], beginDate=startDate.strftime('%Y%m%d'),endDate=endDate.strftime('%Y%m%d'))        res = res.groupby('secID')        # top 10%        top10 = res.mean().sort('sentimentIndex', ascending=False).head(int(0.1*len(res)))        buyList = list(top10.index)        print u"%s 买入 : %s" % (endDate, buyList)        # 等权重买入        if len(buyList) != 0:            singleCash = account.cash / len(buyList)            for stock in buyList:                approximationAmount = int(singleCash / hist[stock]['closePrice'][-1]/100.0) * 100                order(stock, approximationAmount)        account.isBuyPeriod = False        account.dayCount = 0    elif account.dayCount == 59:          # 调仓日前一日清空当前仓位        for stock in account.valid_secpos:            order_to(stock,0)        account.isBuyPeriod = True