量化交易——传统技术分析顺势指标CCI的原理及实现

顺势指标CCI

唐纳德·蓝伯特于上世纪80年代提出比较新颖的顺势指标CCI，其引进了价格与固定期间的股价平均区间的偏离程度的概念，强调股价平均绝对偏差在股市技术分析中的重要性。CCI有两个与大多数常见的分析指标不一样的特点，第一个是其并非只利用股票的单一数据特征，第二个是指标波动于正无穷与负无穷之间而0并不代表它的中轴线。计算方法：
CCI（N日）=（TP－MA）÷MD÷0.015
其中，
TP=（最高价+最低价+收盘价）÷3
MA=近N日收盘价的累计之和÷N
MD=近N日（MA－收盘价）的累计之和÷N
0.015为计算系数，N为计算周期

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。
相关代码：

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  
#CCI实现
def get_CCI(D,N):
    TP = (get_price(D,'high')+get_price(D,'low')+get_price(D,'close'))/3.0
    MA = 0.0
    MD = 0.0
    for i in range(N):
        MA += get_price(D-i,'close')
    MA = MA/N
    for i in range(N):
        MD += abs(get_price(D-i,'close')-MA)
    MD = MD/N
    return (TP - MA)/MD/0.015
def get_cci(fro,to,N):
    res = []
    for d in range(fro,to+1):
        res.append(get_CCI(d,N))
    return res

绘制k线图及CCI指标

绘图代码：

def plot_CCI(fro,to):
    volume = get_volume(fro,to)
    cci7 = get_cci(fro,to,7)
    cci14 = get_cci(fro,to,14)
    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("Commodity Channel Index(CCI)")
    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='y') 
    p3.set_ylabel("CCI")
    p3.set_xlabel("Dates")
    p3.plot(date,cci7, 'm-')
    p3.plot(date,cci14, 'g-')
    p3.legend(('CCI7','CCI14'),loc='upper left')
    plt.subplots_adjust(hspace=0)
    plt.show()# show the plot on the screen
stock_hist = get_stock_hist(18)
plot_CCI(1000,1200)

图示结果1：

缩短时间范围：

#得图2
plot_CCI(1000,1080)