# 4.5 CCI • CCI 顺势指标探索

## 一、CCI指标简介与构造

``````def cci(stock,start_date,end_date,windows):   #设置股票，起始时间，以及CCI指标多少日
import pandas as pd
import numpy as np
from CAL.PyCAL import *
Alpha = 0.015
eq_TP = {}
eq_MATP = {}
eq_meanDev = {}
eq_CCI = {}
cal = Calendar('China.SSE')
windows = '-'+str(windows)+'B'
start_date = Date.strptime(start_date,"%Y%m%d")
end_date = Date.strptime(end_date,"%Y%m%d")
timeLength = cal.bizDatesList(start_date, end_date)

for i in xrange(len(timeLength)):

begin_date =begin_date.strftime("%Y%m%d")
timeLength[i] = timeLength[i].strftime("%Y%m%d")
for stk in stock:
try:
eq_TP[stk] = np.array(eq_static[eq_static['secID'] == stk].mean(axis=1))
eq_MATP[stk] = sum(eq_TP[stk])/len(eq_TP[stk])
eq_meanDev[stk] = sum(abs(eq_TP[stk] - eq_MATP[stk]))/len(eq_TP[stk])
eq_CCI[stk].append((eq_TP[stk][-1] - eq_MATP[stk])/(Alpha * eq_meanDev[stk]))
except:
eq_CCI[stk] = []

Date = pd.DataFrame(timeLength)
eq_CCI = pd.DataFrame(eq_CCI)
cciSeries = pd.concat([Date,eq_CCI],axis =1)
cciSeries.columns = ['Date','CCI']
return cciSeries

def cci_price_Plot(stock,start_date,end_date,windows):
cciSeries = cci(stock,start_date,end_date,windows)
table = pd.merge(cciSeries,closePrice, left_index=True, right_index=True, how = 'inner')
return table
``````
``````import pandas as pd
import numpy as np
from CAL.PyCAL import *
cal = Calendar('China.SSE')
table = cci_price_Plot(['600000.XSHG'],'20080531','20150901',30)  #绘制浦发银行的CCI与股价对比图
tableDate = table.set_index('Date')
tableDate.plot(figsize=(20,8),subplots = 1)

array([<matplotlib.axes.AxesSubplot object at 0x60037d0>,
<matplotlib.axes.AxesSubplot object at 0x602fa90>], dtype=object)
``````

## 二、CCI指标简单应用

``````def cci(account,N=20):
Alpha = 0.015
eq_TP = {}
eq_MATP = {}
eq_meanDev = {}
eq_CCI = {}
eq_highPrice = account.get_attribute_history('highPrice',N)
eq_closePrice = account.get_attribute_history('closePrice',N)
eq_lowPrice = account.get_attribute_history('lowPrice',N)
for stk in account.universe:
eq_TP[stk] = (eq_highPrice[stk] + eq_closePrice[stk] + eq_lowPrice[stk])/3
eq_MATP[stk] = sum(eq_TP[stk])/len(eq_TP[stk])
eq_meanDev[stk] = sum(abs(eq_TP[stk] - eq_MATP[stk]))/len(eq_TP[stk])
eq_CCI[stk] = (eq_TP[stk][-1] - eq_MATP[stk])/(Alpha * eq_meanDev[stk])
return eq_CCI
``````
``````start = '2010-08-01'                       # 回测起始时间
end = '2014-08-01'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')           # 证券池，支持股票和基金
capital_base = 100000                      # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 20                          # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

sim_params = quartz.sim_condition.env.SimulationParameters(start, end, benchmark, universe, capital_base)
idxmap_all, data_all = quartz.sim_condition.data_generator.get_daily_data(sim_params)
``````
``````from CAL.PyCAL import *
import pandas as pd
import numpy as np

def initialize(account):                   # 初始化虚拟账户状态
pass

def handle_data(account):                  # 每个交易日的买入卖出指令
eq_CCI = cci(account,window)
for stk in account.universe:
try:
if eq_CCI[stk] > 100 and eq_CCI[stk] < 150:
except:
pass

for stk in account.valid_secpos:
order_to(stk, 0)

if stk not in account.universe or account.referencePrice[stk] == 0 or np.isnan(account.referencePrice[stk]):
bulist.remove(stk)

print 'window   annualized_return   sharpe   max_drawdown'
for window in range(10, 100, 5):
bt_test, acct = quartz.quick_backtest(sim_params, strategy, idxmap_all, data_all,refresh_rate = refresh_rate)
perf = quartz.perf_parse(bt_test, acct)
print '  {0:2d}        {1:>7.4f}          {2:>7.4f}    {3:>7.4f}'.format(window, perf['annualized_return'], perf['sharpe'], perf['max_drawdown'])

window   annualized_return   sharpe   max_drawdown
10         0.0186          -0.0610     0.4161
15        -0.0367          -0.2818     0.5448
20         0.0753           0.1734     0.4531
25         0.0268          -0.0254     0.3098
30        -0.0440          -0.3198     0.5640
35         0.0481           0.0599     0.4794
40         0.1117           0.3270     0.4057
45         0.0619           0.1176     0.2353
50        -0.0425          -0.3442     0.4226
55         0.0227          -0.0577     0.3355
60         0.0513           0.0540     0.4461
65         0.0860           0.1969     0.2304
70         0.0434           0.0218     0.3005
75         0.0126          -0.1176     0.3672
80         0.0891           0.2084     0.3728
85         0.1002           0.2554     0.2971
90         0.0768           0.1687     0.2710
95         0.0243          -0.0588     0.3461
``````
``````from CAL.PyCAL import *
import pandas as pd
import numpy as np

start = '2010-08-01'                       # 回测起始时间
end = '2014-08-01'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')           # 证券池，支持股票和基金
capital_base = 100000                      # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 20                          # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

def initialize(account):                   # 初始化虚拟账户状态
pass

def handle_data(account):                  # 每个交易日的买入卖出指令
eq_CCI = cci(account,85)
for stk in account.universe:
try:
if eq_CCI[stk] > 100 and eq_CCI[stk] < 150:
except:
pass

for stk in account.valid_secpos:
order_to(stk, 0)

if stk not in account.universe or account.referencePrice[stk] == 0 or np.isnan(account.referencePrice[stk]):
bulist.remove(stk)

``````

``````from CAL.PyCAL import *
import pandas as pd
import numpy as np

start = '2014-08-01'                       # 回测起始时间
end = '2015-08-01'                         # 回测结束时间
benchmark = 'HS300'                        # 策略参考标准
universe = set_universe('HS300')           # 证券池，支持股票和基金
capital_base = 100000                      # 起始资金
freq = 'd'                                 # 策略类型，'d'表示日间策略使用日线回测，'m'表示日内策略使用分钟线回测
refresh_rate = 20                          # 调仓频率，表示执行handle_data的时间间隔，若freq = 'd'时间间隔的单位为交易日，若freq = 'm'时间间隔为分钟

def initialize(account):                   # 初始化虚拟账户状态
pass

def handle_data(account):                  # 每个交易日的买入卖出指令
eq_CCI = cci(account,85)
for stk in account.universe:
try:
if eq_CCI[stk] > 100 and eq_CCI[stk] < 150:
except:
pass

for stk in account.valid_secpos:
order_to(stk, 0)