布林通道
布林通道线是根据统计学的标准差来计算的，其具体可由上中下三条曲线展示。其中上下两线分别代表上升压力线和下降支撑线，故而可以根据K线图是否突破布林曲线来判断较好的买卖节点。三条曲线计算方法如下：
中轨线(MID)=收盘价的M日移动平均线;
上轨线(UPER)=中轨线+N倍的标准差;
下轨线(LOWER)=中轨线-N倍的标准差.、
实现
某些分析过程可以参考前面的博文，量化交易——传统技术分析相对强弱指数RSI的原理及实现，这里不细讲。
import numpy as npimport mathimport randomimport jsonimport matplotlib.pyplot as pltimport syssys.setrecursionlimit(10000)#date|open|high|low|close|volume|adjsuted def get_stock_hist(num):s_his=np.genfromtxt('C:/Users/Haipeng/Desktop/python/Korea/Korea_{:03d}.csv'.format(num), delimiter=',')s_hi=s_his[1:][:]days=s_hi.shape[0]this_stock = []for i in range(1,days,1):this_day = [i]for k in range(1,7):this_day.append(s_hi[i][k])this_stock.append(this_day)print 'Maximum date is ',len(this_stock)return this_stockdef get_price(D, p_tpe):if p_tpe=='close':pos=4;elif p_tpe=='open':pos=1;elif p_tpe=='high':pos=2;elif p_tpe=='low':pos=3;else:pos=5price=stock_hist[D-1][pos];return pricedef get_ma(D, N):p_used=np.zeros(N);for i in range(1,N+1,1):p_used[i-1]=stock_hist[(D-1)-(i-1)][4];ma=np.mean(p_used);return madef get_mar(fro,to,N):ma = []for i in range(fro,to+1):ma.append(get_ma(i,N))return ma#Date\Open\High\Low\Closedef get_tuples(fro,to):res =[]for d in range(fro,to+1):tmp = []tmp.append(d)tmp.append(get_price(d,'open'))tmp.append(get_price(d,'high'))tmp.append(get_price(d,'low'))tmp.append(get_price(d,'close')) res.append(tmp)return resdef get_volume(fro,to):res = []for d in range(fro,to+1):num = 1try:if get_price(d,'close')<get_price(d-1,'close'):num = -1except:passres.append(num*get_price(d,'volume'))return res # BB Band实现def get_BB(D,N):MD = 0.0UP = 0.0DN = 0.0SD = 0.0for i in range(N):MD += get_price(D-i,'close')MD = MD/Nfor i in range(N):SD += math.pow(MD-get_price(D-i,'close'),2)SD = math.sqrt(SD/N)UP = MD + 2*SDDN = MD - 2*SDreturn [UP,MD,DN]def get_bb(fro,to,N):res = [[],[],[]]for d in range(fro,to+1):if d<N:print 'Date number is not larger than N!'breaktmp = get_BB(d,N) res[0].append(tmp[0])res[1].append(tmp[1])res[2].append(tmp[2])return res
绘制k线图及BB指标
绘图代码：
def plot_BB(fro,to):volume = get_volume(fro,to)tmp = get_bb(fro,to,20)up = tmp[0]md = tmp[1]dn = tmp[2]tuples = get_tuples(fro,to)date = [d for d in range(fro,to+1)] fig = plt.figure(figsize=(18,10))p1 = plt.subplot2grid((4,4),(0,0),rowspan=3,colspan=4,axisbg='k') p1.set_title("Bollinger Bands:(20-Day Moving Average)")p1.set_ylabel("Price")p1.plot(date,up,'m')p1.plot(date,md,'b')p1.plot(date,dn,'y')p1.legend(('UP','MD','DN'))p1.grid(True,color='w')candlestick_ohlc(p1, tuples, width=0.7,colorup='r',colordown="g")p2 = plt.subplot2grid((4,4),(3,0),colspan=4,axisbg='c') p2.set_ylabel("Volume")colors = []for i in range(len(volume)):if volume[i]<0:colors.append('green')volume[i] = -volume[i]else:colors.append('red')p2.bar(date,volume,color=colors)p2.set_xlabel("Dates")plt.subplots_adjust(hspace=0)plt.show()# show the plot on the screenstock_hist = get_stock_hist(18)plot_BB(1250,1520)
图示结果1：
缩短时间范围：
#得图2plot_BB(220,320)
我们发现，上图有较大的不合理的跳跃。这是什么原因呢？仔细观察发现图像是在第278天发生了突变，应该就是数据出了问题。现在打开对应股票的CSV数据如下，果然：
可知，当天数据丢失了一位数，这才导致了以上的突变。所以在接下来深入挖掘数据时要考虑减少数据错误带来的影响。
