3.2 分析师推荐 • 分析师的金手指?

在我们的观点中,分析师对股票的评级以及EPS的估计,更多的是对该之股票过去一段时间表现的总结,并没有明确的预测未来的能力。鉴于分析师估计的延迟特点,在我们的策略中我们将分析师估计作为反向指标使用。粗略的说,在固定的期限内,我们买入分析师调低预期的股票,卖出分析师调高预期的股票。

本策略的参数如下:

  • 起始日期: 2011年1月1日

  • 结束日期: 2015年3月19日

  • 股票池: 沪深300

  • 业绩基准: 沪深300

  • 起始资金: 100000元

  • 调仓周期: 3个月

本策略使用的主要数据API有:

这里我们使用了来自于第三方朝阳永续的数据API(需要在数据商城中购买)

  • CGRDReportGGGet 获取朝阳永续分析师一致评级

  • CESTReportGGGet 获取朝阳永续分析师一致预期

朝阳永续分析师分析数据相关链接

import pandas as pd

start = datetime(2011,1, 1)                # 回测起始时间
end   = datetime(2015, 3, 19)                # 回测结束时间
benchmark = 'HS300'                            # 策略参考标准
universe = set_universe('HS300')    # 股票池
#universe = ['600000.XSHG', '000001.XSHE']
capital_base = 100000                       # 起始资金
commission = Commission(0.0,0.0)
longest_history = 1

def CGRDwithBatch(universe, batch, startDate, endDate):
    res = pd.DataFrame()

    totalLength = len(universe)
    count = 0
    while totalLength > batch:
        tmp = DataAPI.GG.CGRDReportGGGet(secID = universe[count * batch : (count + 1) * batch], BeginPubDate = startDate, EndPubDate = endDate)
        count += 1
        totalLength -= batch
        res = res.append(tmp)

    tmp = DataAPI.GG.CGRDReportGGGet(secID = universe[(count * batch):], BeginPubDate = startDate, EndPubDate = endDate)
    res = res.append(tmp)

    return res

def CESTwithBatch(universe, batch, startDate, endDate):
    res = pd.DataFrame()

    totalLength = len(universe)
    count = 0
    while totalLength > batch:
        tmp = DataAPI.GG.CESTReportGGGet(secID = universe[count * batch : (count + 1) * batch], BeginPubDate = startDate, EndPubDate = endDate)
        count += 1
        totalLength -= batch
        res = res.append(tmp)

    tmp = DataAPI.GG.CGRDReportGGGet(secID = universe[(count * batch):], BeginPubDate = startDate, EndPubDate = endDate)
    res = res.append(tmp)

    return res

def MktEqudwithBatch(universe, batch, startDate, endDate):
    res = pd.DataFrame()

    totalLength = len(universe)
    count = 0
    while totalLength > batch:
        tmp = DataAPI.MktEqudGet(secID = universe[count * batch : (count + 1) * batch], beginDate = startDate, endDate = endDate)
        count += 1
        totalLength -= batch
        res = res.append(tmp)

    tmp = DataAPI.MktEqudGet(secID = universe[count * batch : (count + 1) * batch], beginDate = startDate, endDate = endDate)
    res = res.append(tmp)

    return res


def regressionTesting(universe, startDate, endDate):

    import statsmodels.api as sm

    res1 = CGRDwithBatch(universe, 50, startDate, endDate).sort('publishDate')
    res2 = CESTwithBatch(universe, 50, startDate, endDate).sort('publishDate')
    res1 = res1[res1.RatingType == 1]
    res2 = res2[res2.PnetprofitType == 1]

    # got expRating change
    lastRating = res1.groupby('secID').last()
    firstRating = res1.groupby('secID').first()
    lastRating['previousRating'] = firstRating.Rating
    lastRating['chg_exp'] = lastRating.Rating / firstRating.Rating - 1.0
    lowerP = lastRating['chg_exp'].quantile(0.05)
    highP = lastRating['chg_exp'].quantile(0.95)
    lastRating = lastRating[(lastRating['chg_exp']>lowerP) & (lastRating['chg_exp']<highP)]
    lastRating['chg_exp'] = (lastRating.chg_exp  - lastRating.chg_exp.mean())/lastRating.chg_exp.std()
    expRating = lastRating[['secShortName', 'publishDate', 'Rating', 'previousRating', 'chg_exp']]

    # got expEps change
    lastEps = res2.groupby('secID').last()
    firstEps = res2.groupby('secID').first()
    lastEps['previousEps'] = firstEps.EPS_con
    lastEps['chg_eps'] = lastEps.EPS_con / firstEps.EPS_con - 1.0
    lowerP = lastEps['chg_eps'].quantile(0.05)
    highP = lastEps['chg_eps'].quantile(0.95)
    lastEps = lastEps[(lastEps['chg_eps']>lowerP) & (lastEps['chg_eps']<highP)]
    lastEps['chg_eps'] = (lastEps.chg_eps  - lastEps.chg_eps.mean())/lastEps.chg_eps.std()
    expEps = lastEps[['secShortName', 'publishDate', 'EPS_con', 'previousEps', 'chg_eps']]

    # Weighted Average Ranking
    rankRes = expEps.copy()
    rankRes['chg_exp'] = expRating.chg_exp
    rankRes['ranking'] = expEps.chg_eps + expRating.chg_exp

    # Current period return
    mktDate = MktEqudwithBatch(universe, 50, startDate, endDate)
    group = mktDate.groupby('secID')
    returnRes = group.last().closePrice / group.first().closePrice - 1.0
    rankRes['currentReturn'] = (returnRes - returnRes.mean()) / returnRes.std()
    rankRes.dropna(inplace=True)

    # Do linear regression for current return
    x = rankRes[['chg_eps','chg_exp']].values
    y = rankRes.currentReturn.values
    x = sm.add_constant(x)
    model = sm.OLS(y, x)
    results = model.fit()


    rankRes['resid'] = results.resid

    return rankRes

def initialize(account):                    # 初始化虚拟账户状态
    account.traded = False
    account.universe = universe
    account.tradingMonth = set([1,4,7,10])
    account.currentTradedMonth = 0
    account.previousRatingExp = None
    account.previousEpsExp = None
    account.holdings = set()
    account.first = True
    account.chosen = 0.05

def handle_data(account):                   # 每个交易日的买入卖出指令
    today = Date(account.current_date.year, account.current_date.month, account.current_date.day)
    if today.month() in account.tradingMonth and not account.traded:
        hist = account.get_history(1)
        account.traded = True
        account.currentTradedMonth = today.month()
        endDate = today
        startDate = endDate - '3m'

        endStr = ''.join(endDate.toISO().split('-'))
        startStr = ''.join(startDate.toISO().split('-'))
        res = regressionTesting(account.universe, startStr, endStr)
        chosenNumber = int(account.chosen * len(res))

        secids = res.sort('resid')[:chosenNumber].index.values
        print today.toISO() + ' ' + str(chosenNumber) + u' 股票被选择:' + str(secids)

        # clean current position
        c = account.cash
        for s in account.holdings:
            c += hist[s]['closePrice'][-1] * account.secpos.get(s, 0)
            order_to(s, 0)

        equalAmount = c / chosenNumber
        # order equal amount
        for s in secids:
            approximationAmount = int(equalAmount / hist[s]['closePrice'][-1])
            order(s, approximationAmount)

        account.holdings = secids


    if today.month() != account.currentTradedMonth:

        account.traded = False

!{}(img/20160730104832.jpg)

2011-01-05 8 股票被选择:['002252.XSHE' '000338.XSHE' '600031.XSHG' '600741.XSHG' '002024.XSHE'
 '000869.XSHE' '600027.XSHG' '600588.XSHG']
2011-04-01 9 股票被选择:['600406.XSHG' '300024.XSHE' '002081.XSHE' '000776.XSHE' '002310.XSHE'
 '002375.XSHE' '601933.XSHG' '600570.XSHG' '002065.XSHE']
2011-07-01 9 股票被选择:['600873.XSHG' '600415.XSHG' '002344.XSHE' '002400.XSHE' '300133.XSHE'
 '002415.XSHE' '601166.XSHG' '002422.XSHE' '600887.XSHG']
2011-10-10 8 股票被选择:['600085.XSHG' '000598.XSHE' '002594.XSHE' '000157.XSHE' '600999.XSHG'
 '600208.XSHG' '600252.XSHG' '600585.XSHG']
2012-01-04 9 股票被选择:['600516.XSHG' '601901.XSHG' '600348.XSHG' '600395.XSHG' '601928.XSHG'
 '600352.XSHG' '600827.XSHG' '000629.XSHE' '600547.XSHG']
2012-04-05 9 股票被选择:['601929.XSHG' '300146.XSHE' '002450.XSHE' '300133.XSHE' '002603.XSHE'
 '600050.XSHG' '600252.XSHG' '601800.XSHG' '600267.XSHG']
2012-07-02 9 股票被选择:['002230.XSHE' '600143.XSHG' '002310.XSHE' '000729.XSHE' '600157.XSHG'
 '601258.XSHG' '600170.XSHG' '300133.XSHE' '002385.XSHE']
2012-10-08 9 股票被选择:['000869.XSHE' '002146.XSHE' '000338.XSHE' '601169.XSHG' '601336.XSHG'
 '000729.XSHE' '600031.XSHG' '002594.XSHE' '600115.XSHG']
2013-01-04 9 股票被选择:['002007.XSHE' '002065.XSHE' '601928.XSHG' '000858.XSHE' '600633.XSHG'
 '600519.XSHG' '600406.XSHG' '002603.XSHE' '603000.XSHG']
2013-04-01 9 股票被选择:['600809.XSHG' '000568.XSHE' '000060.XSHE' '000069.XSHE' '600549.XSHG'
 '000858.XSHE' '601377.XSHG' '002653.XSHE' '000338.XSHE']
2013-07-01 9 股票被选择:['600157.XSHG' '002475.XSHE' '000001.XSHE' '600886.XSHG' '002344.XSHE'
 '600028.XSHG' '600535.XSHG' '002429.XSHE' '600188.XSHG']
2013-10-08 9 股票被选择:['600372.XSHG' '600010.XSHG' '002146.XSHE' '002051.XSHE' '000999.XSHE'
 '600519.XSHG' '600518.XSHG' '000024.XSHE' '601117.XSHG']
2014-01-02 8 股票被选择:['300251.XSHE' '600880.XSHG' '600633.XSHG' '601928.XSHG' '002416.XSHE'
 '600637.XSHG' '600332.XSHG' '300058.XSHE']
2014-04-01 8 股票被选择:['002344.XSHE' '600880.XSHG' '002385.XSHE' '002310.XSHE' '600597.XSHG'
 '600315.XSHG' '600188.XSHG' '002415.XSHE']
2014-07-01 8 股票被选择:['300146.XSHE' '000413.XSHE' '002065.XSHE' '002456.XSHE' '300058.XSHE'
 '600633.XSHG' '000024.XSHE' '000400.XSHE']
2014-10-08 7 股票被选择:['600887.XSHG' '600863.XSHG' '300017.XSHE' '002292.XSHE' '002594.XSHE'
 '601169.XSHG' '000400.XSHE']
2015-01-05 8 股票被选择:['600880.XSHG' '002653.XSHE' '300017.XSHE' '603000.XSHG' '002456.XSHE'
 '002292.XSHE' '000963.XSHE' '300133.XSHE']

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