能量潮指标OBV
股市分析中有四个要素，分别是价、量、时、空。其中OBV便是从成交量作为分析的突破口。它反映的是在股市起伏波动时相关的市场人气变化，可以用来判断股市是否处于有较强的想上冲的牛市中还是即将要踏空。成交量越大，反映的是市场判断不一致程度越强，而这会如何影响股价走势则是研究的重点。
实现
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。
相关代码如下：
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 # OBV def get_OBV(D):if D == 1:return 0else:if get_price(D,'close')>get_price(D-1,'close'):return get_OBV(D-1)+get_price(D,'volume')elif get_price(D,'close')<get_price(D-1,'close'):return get_OBV(D-1)-get_price(D,'volume')else:return get_OBV(D-1)def get_obv(fro,to):res = []for d in range(fro,to+1):res.append(get_OBV(d))return res
绘制K线图及OBV曲线
代码：
def plot_OBV(fro,to):volume = get_volume(fro,to)obv =get_obv(fro,to)ma5 = get_mar(fro,to,5)ma10 = get_mar(fro,to,10)ma20 = get_mar(fro,to,20)tuples = get_tuples(fro,to)date = [d for d in range(fro,to+1)] fig = plt.figure(figsize=(8,5))p1 = plt.subplot2grid((5,4),(0,0),rowspan=3,colspan=4,axisbg='k') p1.set_title("On Balance Volume(OBV)")p1.set_ylabel("Price")p1.plot(date,ma5,'m')p1.plot(date,ma10,'b')p1.plot(date,ma20,'y')p1.legend(('MA5','MA10','MA20'))p1.grid(True,color='w')candlestick_ohlc(p1, tuples, width=0.7,colorup='r',colordown="g")p2 = plt.subplot2grid((5,4),(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)p3 = plt.subplot2grid((5,4),(4,0),colspan=4,axisbg='m') p3.plot(date,obv,'b')p3.set_ylabel("OBV")p3.set_xlabel("Dates")plt.subplots_adjust(hspace=0)plt.show()# show the plot on the screenstock_hist = get_stock_hist(17)plot_OBV(888,999)
图像1：
图像2：
stock_hist = get_stock_hist(18)plot_OBV(123,789)
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