个股的beta系数的估算

代码

def test_gg_beta(symbol='000895.sz', start='2018-01-01',plot_price=True,align_to='gg',plot_k=True,):'''align_to: str, ['gg', 'dp'], 数据对齐的方式'gg': 表示对齐到个股, 改变大盘的数据, 以对齐到个股上'dp': 表示对齐到大盘, 改变个股的数据, 以对齐到大盘上>>> symbol='000895.sz'>>> start='2018-01-01'>>> beta, df = test_gg_beta(align_to='gg', plot_k=False)>>> beta2, df2 = test_gg_beta(align_to='dp', plot_k=False)>>> beta, df, plots = test_gg_beta('000933.sz')>>> beta, df = test_gg_beta('000933.sz', plot_price=False, plot_k=False)结果是: 0.83(双汇发展 @ 2018年)1.64(神火股份 @ 2018年)'''    _date = datetime.datetime.now().date().isoformat()title='QC图件: 用于计算个股的Beta系数(DP:399317)  \n   制作日期: {}'.format(_date)c=Context(symbol)stk = Stock(c,start)stk.grab_data_from_tdxhq()stk.qfq()stk.grab_index_from_tdxhq()stk.indicator()# 个股通常会有停牌的时候, 所以需要对齐# 比如以个股为准, 把大盘对齐到个股是时间戳里: # 将大盘的收盘线, 插入到个股的ohlc数据框里(用assign方法)if align_to == 'gg':f = stk.ohlc.close[0] / stk.aindex.close[0]df=stk.ohlc.assign(dp=stk.aindex.close * f)df=df.loc[:, ['close', 'dp']]beta = ttr.estimateBeta(df.close, df.dp)title += '\n{:s}({:s})的Beta系数: {:.4f}(数据对齐到个股)'.format(stk.context.name,stk.context.code, beta) if plot_price: plt.figure()df.plot(title=title)elif align_to == 'dp':f = stk.aindex.close[0] / stk.ohlc.close[0]df2 = stk.aindex.assign(gg=stk.ohlc.close * f)df2 = df2.loc[:, ['close', 'gg']]df2 = df2.fillna(method='ffill') # 向未来填充 (用老数据向下填充)beta2 = ttr.estimateBeta(df2.gg, df2.close)title += '\n{:s}({:s})的Beta系数: {:.4f}(数据对齐到大盘)'.format(stk.context.name,stk.context.code, beta2) if plot_price: plt.figure()df2.plot(title=title)if plot_k:
#        fig,ax = plt.subplots(1,1)#stk.mycandlestick_ohlc(ax, [20,60])#stk.mycandlestick_ohlc(ax, with_raw_quotes=True)
#        stk.mycandlestick_ohlc(ax, with_raw_quotes=False)subset = slice(-120*3,None) # '2017-07'  '2017'subset = Noneplots = pl.Plotter(stk.context, stk, subset) #plot stk data
#        plots.plot_candle_only( 'lday')plots.plot_candle_vol('lday')#plots.plot_candle_vol('lday', savefig=True)if align_to=='gg':return beta, dfelse:return beta2, df2

结果图

结论

用276天大盘交易日(同期个股是271个交易日)的数据, 采用两种方法, 得到的beta值为:
0.833 vs 0.830
非常接近.

小于1的beta, 揭示了该股在大盘下跌阶段的优异表现.

股票池的beta

代码

def study_block_beta(subset=(8,18,1),show=False):'''>>> beta, res = study_block_beta()>>> beta, res = study_block_beta(show=True)'''syms_, syms = read_zxg(subset)betas, names, codes=[],[],[]for enum, sym in enumerate(syms):stk=load_data(sym)sname = stk.context.namecode  = stk.context.code        names.append(sname)codes.append(code)pw=sname_print_width(sname)sdt = str(stk.sdt)[:10]edt = str(stk.edt)[:10]prompt = 'loaded data: {:<{pw}} {} {} {}'.format(sname, code, sdt, edt,pw=pw)print(prompt)beta=ttr.estimateBeta(stk.ohlc.close, stk.aindex.close)betas.append(beta)if show:#make plotclose = stk.ohlc.closeclose = close/close[0] * 10.0#if not isinstance(fig, plt.Figure):if enum==0:fig=plt.figure(); type(fig)aindex = stk.aindex.close/stk.aindex.close[0] * 10.ax = aindex.plot(label='国证A指', lw=4, logy=True, ) #use_index=)close.plot(axes=ax, label=sname, logy=True,)else:if enum< len(syms) - 1:close.plot(axes=ax, label=sname,logy=True,)else:close.plot(axes=ax, label=sname,logy=True, title='池子里的收盘线')ax.legend()resS = '{} {:8} {:6} {:6} \n'.format('Num','name', 'code', 'Beta')resS += '-' * 33 + '\n'for i in range(len(names)):pw=sname_print_width(names[i])resS += '{:3d} {:{pw}} {} {:.3f} \n'.format(i, names[i], codes[i], betas[i], pw=pw)print( resS)return betas, resS

结果:

Num name code Beta

0 深圳燃气 601139 0.732
1 分众传媒 002027 1.392
2 海康威视 002415 1.378
3 双汇发展 000895 0.839
4 柳 工 000528 1.336
5 上海银行 601229 0.561
6 华兰生物 002007 0.880
7 兖州煤业 600188 1.251
8 云铝股份 000807 1.586
9 神火股份 000933 1.627

300个交易日: 2017-11-28 -- 2019-02-22 时间段的数据:

520个交易日: 2017-01-03 -- 2019-02-22 时间段的数据: