金融量化-技术分析策略和交易系统_CCI指标的策略实现

技术分析策略和交易系统_CCI指标的策略实现

CCI指标详解及实战用法

import numpy as np
import pandas as pd
import talib as ta
import tushare as ts
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
# 确保可以显示‘-’号
mpl.rcParams['axes.unicode_minus']=False
# 确保中文显示正常
mpl.rcParams['font.sans-serif'] = ['SimHei']  

1. 数据获取和数据处理

# 获得历史数据并按时间升序排序
stock = ts.get_h_data('600030', '2016-06-01', '2017-06-30')
stock.sort_index(inplace=True)
stock.head()

	open	high	close	low	volume	amount
date
2016-06-01	16.00	16.14	15.79	15.78	116695922.0	1.949830e+09
2016-06-02	15.79	15.93	15.82	15.65	77142114.0	1.278391e+09
2016-06-03	15.86	16.09	15.84	15.71	115413775.0	1.920685e+09
2016-06-06	15.87	15.92	15.69	15.65	55176173.0	9.119408e+08
2016-06-07	15.74	15.86	15.72	15.66	59436762.0	9.817197e+08

# 计算CCI，取时间周期为20
stock['cci'] = ta.CCI(np.asarray(stock['high']), np.asarray(stock['low']), np.asarray(stock['close']), timeperiod=20)

stock.tail()

	open	high	close	low	volume	amount	cci
date
2017-06-26	16.62	17.08	16.94	16.58	211319532.0	3.640808e+09	229.588926
2017-06-27	16.93	16.96	16.79	16.74	92402416.0	1.585190e+09	172.362179
2017-06-28	16.76	16.95	16.77	16.69	87105025.0	1.494473e+09	135.465245
2017-06-29	16.76	16.87	16.77	16.66	66834132.0	1.140796e+09	107.891290
2017-06-30	16.70	16.79	16.70	16.50	75109183.0	1.274741e+09	73.369026

# 绘制cci指标图
plt.subplot(2, 1, 1)
plt.title('600030 CCI指标图')
plt.gca().axes.get_xaxis().set_visible(False)  # 只用一个坐标轴
stock['close'].plot(figsize = (10,8))
plt.legend()
plt.subplot(2, 1, 2)
stock['cci'].plot(figsize = (10,8))
plt.legend()
plt.show()

2. 交易信号和策略逻辑

2.1 产生交易信号；

# 产生开仓信号时应使用昨日及前日cci，以避免未来数据
stock['yes_cci'] = stock['cci'].shift(1)
stock['daybeforeyes_cci'] = stock['cci'].shift(2)

# 产生开平仓信号
# 开多信号：当前日cci小于-100，昨日cci大于-100则记为开多信号
stock['signal'] = np.where(np.logical_and(stock['daybeforeyes_cci']<-100, stock['yes_cci']>-100), 1, 0)

# stock['signal'] = np.where((stock['daybeforeyes_cci']<-100) & (stock['yes_cci']>-100), 1, 0)

# 开空信号：当前日cci大于100， 昨日cci小于100则记为开空信号
stock['signal'] = np.where(np.logical_and(stock['daybeforeyes_cci']>100, stock['yes_cci']<100),-1, stock['signal'])

plt.subplot(3, 1, 1)
plt.title('600030 CCI开仓图')
plt.gca().axes.get_xaxis().set_visible(False)
stock['close'].plot(figsize = (10,8))
plt.legend(loc='upper left')
plt.subplot(3, 1, 2)
stock['cci'].plot(figsize = (10,8))
plt.legend(loc='upper left')
plt.gca().axes.get_xaxis().set_visible(False)
plt.subplot(3, 1, 3)
stock['signal'].plot(figsize = (10,8),marker='o',linestyle='')

plt.legend(loc='upper left')
plt.show()

2.2 计算持仓

# 如果当天没有交易信号，设置为nan，如果有，取原来信号
stock['signal'] = np.where(stock['signal']==0, np.nan, stock['signal'])
# 通过前向填充生成持仓记录
stock['position'] = stock['signal'].fillna(method='ffill')

plt.subplot(3, 1, 1)
plt.title('600030 CCI持仓图')
plt.gca().axes.get_xaxis().set_visible(False)
stock['close'].plot(figsize = (12,12))
plt.legend(loc='upper left')
plt.subplot(3, 1, 2)
stock['cci'].plot(figsize = (12,12))
plt.legend(loc='upper left')
plt.gca().axes.get_xaxis().set_visible(False)
plt.subplot(3, 1, 3)
stock['position'].plot(marker='o', figsize=(10,8),linestyle='')
plt.legend(loc='upper left')
plt.show()

3. 收益计算和净值绘制

# 计算策略收益
# 计算股票每日收益率
stock['pct_change'] = stock['close'].pct_change()
# 计算策略每日收益率
stock['strategy_return'] = stock['pct_change'] * stock['position']
# 计算股票累积收益率
stock['return'] = (stock['pct_change']+1).cumprod()
# 计算策略累积收益率
stock['strategy_cum_return'] = (1 + stock['strategy_return']).cumprod()

stock.head()

# 将股票累积收益率和策略累积收益率绘图
stock[['return', 'strategy_cum_return']].plot(figsize = (12,6))

plt.title('600030 CCI收益图')
plt.legend(loc='upper left')
plt.show()