3.2 分析師推薦 ? 分析師的金手指？ · Python 量化交易教程

# 3.2 分析師推薦 ? 分析師的金手指？ > 在我們的觀點中，分析師對股票的評級以及EPS的估計，更多的是對該之股票過去一段時間表現的總結，并沒有明確的預測未來的能力。鑒于分析師估計的延遲特點，在我們的策略中我們將分析師估計作為反向指標使用。粗略的說，在固定的期限內，我們買入分析師調低預期的股票，賣出分析師調高預期的股票。本策略的參數如下： + 起始日期： 2011年1月1日 + 結束日期： 2015年3月19日 + 股票池：滬深300 + 業績基準：滬深300 + 起始資金： 100000元 + 調倉周期： 3個月本策略使用的主要數據API有：這里我們使用了來自于第三方朝陽永續的數據API（需要在數據商城中購買） + `CGRDReportGGGet` 獲取朝陽永續分析師一致評級 + `CESTReportGGGet` 獲取朝陽永續分析師一致預期 [朝陽永續分析師分析數據相關鏈接](https://api.wmcloud.com/docs/pages/viewpage.action?pageId=2392750) ```py import pandas as pd start = datetime(2011,1, 1) # 回測起始時間 end = datetime(2015, 3, 19) # 回測結束時間 benchmark = 'HS300' # 策略參考標準 universe = set_universe('HS300') # 股票池 #universe = ['600000.XSHG', '000001.XSHE'] capital_base = 100000 # 起始資金 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): # 初始化虛擬賬戶狀態 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): # 每個交易日的買入賣出指令 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' 股票被選擇：' + 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) ```py 2011-01-05 8 股票被選擇：['002252.XSHE' '000338.XSHE' '600031.XSHG' '600741.XSHG' '002024.XSHE' '000869.XSHE' '600027.XSHG' '600588.XSHG'] 2011-04-01 9 股票被選擇：['600406.XSHG' '300024.XSHE' '002081.XSHE' '000776.XSHE' '002310.XSHE' '002375.XSHE' '601933.XSHG' '600570.XSHG' '002065.XSHE'] 2011-07-01 9 股票被選擇：['600873.XSHG' '600415.XSHG' '002344.XSHE' '002400.XSHE' '300133.XSHE' '002415.XSHE' '601166.XSHG' '002422.XSHE' '600887.XSHG'] 2011-10-10 8 股票被選擇：['600085.XSHG' '000598.XSHE' '002594.XSHE' '000157.XSHE' '600999.XSHG' '600208.XSHG' '600252.XSHG' '600585.XSHG'] 2012-01-04 9 股票被選擇：['600516.XSHG' '601901.XSHG' '600348.XSHG' '600395.XSHG' '601928.XSHG' '600352.XSHG' '600827.XSHG' '000629.XSHE' '600547.XSHG'] 2012-04-05 9 股票被選擇：['601929.XSHG' '300146.XSHE' '002450.XSHE' '300133.XSHE' '002603.XSHE' '600050.XSHG' '600252.XSHG' '601800.XSHG' '600267.XSHG'] 2012-07-02 9 股票被選擇：['002230.XSHE' '600143.XSHG' '002310.XSHE' '000729.XSHE' '600157.XSHG' '601258.XSHG' '600170.XSHG' '300133.XSHE' '002385.XSHE'] 2012-10-08 9 股票被選擇：['000869.XSHE' '002146.XSHE' '000338.XSHE' '601169.XSHG' '601336.XSHG' '000729.XSHE' '600031.XSHG' '002594.XSHE' '600115.XSHG'] 2013-01-04 9 股票被選擇：['002007.XSHE' '002065.XSHE' '601928.XSHG' '000858.XSHE' '600633.XSHG' '600519.XSHG' '600406.XSHG' '002603.XSHE' '603000.XSHG'] 2013-04-01 9 股票被選擇：['600809.XSHG' '000568.XSHE' '000060.XSHE' '000069.XSHE' '600549.XSHG' '000858.XSHE' '601377.XSHG' '002653.XSHE' '000338.XSHE'] 2013-07-01 9 股票被選擇：['600157.XSHG' '002475.XSHE' '000001.XSHE' '600886.XSHG' '002344.XSHE' '600028.XSHG' '600535.XSHG' '002429.XSHE' '600188.XSHG'] 2013-10-08 9 股票被選擇：['600372.XSHG' '600010.XSHG' '002146.XSHE' '002051.XSHE' '000999.XSHE' '600519.XSHG' '600518.XSHG' '000024.XSHE' '601117.XSHG'] 2014-01-02 8 股票被選擇：['300251.XSHE' '600880.XSHG' '600633.XSHG' '601928.XSHG' '002416.XSHE' '600637.XSHG' '600332.XSHG' '300058.XSHE'] 2014-04-01 8 股票被選擇：['002344.XSHE' '600880.XSHG' '002385.XSHE' '002310.XSHE' '600597.XSHG' '600315.XSHG' '600188.XSHG' '002415.XSHE'] 2014-07-01 8 股票被選擇：['300146.XSHE' '000413.XSHE' '002065.XSHE' '002456.XSHE' '300058.XSHE' '600633.XSHG' '000024.XSHE' '000400.XSHE'] 2014-10-08 7 股票被選擇：['600887.XSHG' '600863.XSHG' '300017.XSHE' '002292.XSHE' '002594.XSHE' '601169.XSHG' '000400.XSHE'] 2015-01-05 8 股票被選擇：['600880.XSHG' '002653.XSHE' '300017.XSHE' '603000.XSHG' '002456.XSHE' '002292.XSHE' '000963.XSHE' '300133.XSHE'] ```