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61 lines
3.5 KiB
61 lines
3.5 KiB
## 股票池筛选，可交易，非新股，非PT，ST，涨跌停的股票
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def get_stocks(trDate):
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trDate = trDate.strftime('%Y-%m-%d')
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stocks_800 = w.wset("sectorconstituent", "date="+trDate+";windcode=000906.SH").Data[1]
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status = w.wss(stocks_800, "trade_status,maxupordown,riskwarning,ipo_date", tradeDate=trDate, usedf=True)[1]
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date_least=w.tdaysoffset(-6,trDate,'Period=M').Data[0][0]
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trade_codes=list(status[(status['TRADE_STATUS']=='交易')&(status['IPO_DATE']<=date_least)&(status['MAXUPORDOWN']==0)&(status['RISKWARNING']=='否')].index)
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return trade_codes
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trade_d = w.tdays("2014-01-02", "2019-05-30", Period='M',usedf=True).Data[0]
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stock_valid = []
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for i in range(len(trade_d)):
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stock_valid.append(get_stocks(trade_d[i]))
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stock_valid_df = pd.DataFrame(stock_valid,index = trade_d)
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## 获取下期收益数据
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Period = 'M'
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# 时间列表设置避免取到重复数据
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date_list = ['2014-01-02','2015-01-09','2016-01-08','2017-01-06','2018-01-05','2019-05-30']
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date_listd = ['2015-01-08','2016-01-07','2017-01-05','2018-01-04','2019-04-30']
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date_listw = ['2015-01-02','2016-01-01','2016-12-30','2017-12-29','2019-05-30']
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date_listm = ['2015-01-01','2016-01-01','2017-01-01','2018-01-01','2019-04-30']
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next_ret = []
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bench_date = [] # 之后分层测试可视化需要用到
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for i in range(5):
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date = w.wsd('000001.SZ','close',date_list[i],date_listm[i],period = Period,usedf = True)[1].index
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stocks = w.wset("sectorconstituent", "date="+date_listm[i]+";windcode=000906.SH").Data[1]
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d1 = w.tdaysoffset(1, date[0], Period = Period,usedf=True).Data[0][0].strftime('%Y-%m-%d') # 时间往后推一个周期，并且格式转化为字符串
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d2 = w.tdaysoffset(1, date[-1],Period = Period, usedf=True).Data[0][0].strftime('%Y-%m-%d')
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bench_date.append(d1)
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bench_date.append(d2)
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next_ret_ = w.wsd(stocks, "pct_chg", d1, d2, usedf=True, Period = Period)[1].fillna(value = 0)
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f = lambda x: x/100 # 万矿收益率数据单位为100%，这里换算成小数
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next_ret_ = next_ret_.applymap(f)
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next_ret.append(zhuanhuan(next_ret_,date,stocks,columns = ['NEXT_RET'])) # 转化下期收益数据为双重索引
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next_ret = pd.concat(next_ret) # 合并5年的下期收益数据
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## IC测试
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def ic_fenxi(df,next_ret,alpha = ['alpha']):
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d = list(next_ret.index.levels[0]) # 获取双索引中的日期索引
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ic_s = []
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df = df.fillna(value = 0) # 异常值填充为0
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for i in range(len(date_)):
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stock_v = stock_valid_df.loc[d[i]].dropna().values # 获取当期有效股票池
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stock_v = list(set(stock_v) & set(df.loc[d[i]].index))
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dff = df.loc[d[i]].loc[stock_v] # 提取当期可用因子值和下期收益数据
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# dff = dff.sort_values(alpha,ascending=False).iloc[:round(len(dff)/10)] # 提取top组的数据分析
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ic_s.append(dff['NEXT_RET'].corr(dff[alpha],method='spearman')) # 计算因子值与下期收益的秩相关系数，并存进ic值列表
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ic_s = pd.Series(ic_s) # 列表转化为Series，从而能够计算各统计量
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rate = len(ic_s[ic_s>=0])/len(ic_s) # ic值大于0的比例
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IC_mean = ic_s.mean() # ic均值
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IC_std = ic_s.std() # ic标准差
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IC_IR = ic_s.mean()/ic_s.std() # ic_IR用来衡量因子有效性
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stats = [IC_mean,IC_std,IC_IR,rate]
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return stats
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for i in range(len(data)):
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d = list(next_ret.index.levels[0])
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lc = data[i].loc[d] # 根据调仓频率筛选数据
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lc['NEXT_RET'] = next_ret.NEXT_RET
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list_ic = ic_fenxi(lc,next_ret,alpha = ['alpha'+str(i+1)]) # 计算IC统计量
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print(list_ic)
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