# 4.5 CCI • CCI 顺势指标探索 > 来源：https://uqer.io/community/share/55f28179f9f06c1ea681fa40 ## 一、CCI指标简介与构造 顺势指标CCI由唐纳德拉姆伯特所创，是通过测量股价的波动是否已超出其正常范围，来预测股价变化趋势的技术分析指标。计算方法参考[《技术指标系列（五）——CCI的顺势而为 》](https://uqer.io/community/share/55f28179f9f06c1ea681fa40)。 下面描绘出CCI与股价时序图走势 ```py 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 = cal.advanceDate(timeLength[i],windows,BizDayConvention.Unadjusted) begin_date =begin_date.strftime("%Y%m%d") timeLength[i] = timeLength[i].strftime("%Y%m%d") eq_static = DataAPI.MktEqudAdjGet(secID=stock,beginDate=begin_date,endDate=timeLength[i],field=['secID','highestPrice','lowestPrice','closePrice'],pandas="1") 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) closePrice = DataAPI.MktEqudAdjGet(secID=stock,beginDate=start_date,endDate=end_date,field=['closePrice'],pandas="1") table = pd.merge(cciSeries,closePrice, left_index=True, right_index=True, how = 'inner') return table ``` ```py 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 选取CCI处于100和150之间，开始处于上涨趋势的股票。关于windows，我们用quick_backtest做一个简单的优化 ```py 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 ``` ```py 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) ``` ```py 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) buylist = [] for stk in account.universe: try: if eq_CCI[stk] > 100 and eq_CCI[stk] < 150: buylist.append(stk) except: pass for stk in account.valid_secpos: order_to(stk, 0) for stk in buylist[:]: if stk not in account.universe or account.referencePrice[stk] == 0 or np.isnan(account.referencePrice[stk]): bulist.remove(stk) for stk in buylist: order(stk, account.referencePortfolioValue/account.referencePrice[stk]/len(buylist)) print 'window annualized_return sharpe max_drawdown' for window in range(10, 100, 5): strategy = quartz.sim_condition.strategy.TradingStrategy(initialize, handle_data) 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 ``` ```py 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) buylist = [] for stk in account.universe: try: if eq_CCI[stk] > 100 and eq_CCI[stk] < 150: buylist.append(stk) except: pass for stk in account.valid_secpos: order_to(stk, 0) for stk in buylist[:]: if stk not in account.universe or account.referencePrice[stk] == 0 or np.isnan(account.referencePrice[stk]): bulist.remove(stk) for stk in buylist: order(stk, account.referencePortfolioValue/account.referencePrice[stk]/len(buylist)) ```  样本外测试 ```py 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) buylist = [] for stk in account.universe: try: if eq_CCI[stk] > 100 and eq_CCI[stk] < 150: buylist.append(stk) except: pass for stk in account.valid_secpos: order_to(stk, 0) for stk in buylist[:]: if stk not in account.universe or account.referencePrice[stk] == 0 or np.isnan(account.referencePrice[stk]): bulist.remove(stk) for stk in buylist: order(stk, account.referencePortfolioValue/account.referencePrice[stk]/len(buylist)) ```