量化交易——传统技术分析布林通道BollingerBands的原理及实现

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 np
import math
import random
import json
import matplotlib.pyplot as plt
import sys
sys.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_stock

def 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=5
    price=stock_hist[D-1][pos];
    return price

def 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 ma

def 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\Close
def 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 res

def get_volume(fro,to):
    res = []
    for d in range(fro,to+1):
        num = 1
        try:
            if get_price(d,'close')1,'close'):
                num = -1
        except:
            pass
        res.append(num*get_price(d,'volume'))
    return res  
# BB Band实现
def get_BB(D,N):
    MD = 0.0
    UP = 0.0
    DN = 0.0
    SD = 0.0
    for i in range(N):
        MD += get_price(D-i,'close')
    MD = MD/N
    for i in range(N):
        SD += math.pow(MD-get_price(D-i,'close'),2)
    SD = math.sqrt(SD/N)
    UP = MD + 2*SD
    DN = MD - 2*SD
    return [UP,MD,DN]
def get_bb(fro,to,N):
    res = [[],[],[]]
    for d in range(fro,to+1):
        if dprint 'Date number is not larger than N!'
            break
        tmp = 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 screen
stock_hist = get_stock_hist(18)
plot_BB(1250,1520)

图示结果1：

缩短时间范围：

#得图2
plot_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。