TA_Lib 趋势通道

均线百分比通道 布林带 平均波幅率通道 高低价通道

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通道

通道突破是技术分析中一个判断趋势的重要方法，其观念在于市场价格穿透了之前的价格压力或支撑，继而形成一股新的趋势，而交易策略的目标即是在突破发生时能够确认并建立仓位以获取趋势的利润。

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import 模块

import talib as ta
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt

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导入数据

data = ts.get_k_data('000001', start='2016-01-01', end='2016-12-31', ktype='D',autype='qfq')
data.index = pd.to_datetime(data['date'],format='%Y-%m-%d')

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均线百分比通道

middleband = ta.abstract.MA(data, timeperiod=20)
upperband = middleband*1.03
lowerband = middleband*0.97
data_B = pd.concat([middleband, upperband , lowerband], axis=1)
data_B.columns = ['middleband','upperband','lowerband']

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布林带通道线

data_B= ta.abstract.BBANDS(data, timeperiod=20)

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平均波幅率通道线

atr = ta.abstract.ATR(data, 20)
middleband = ta.abstract.MA(data,20)
upperband = middleband + atr
lowerband = middleband - atr
data_B = pd.concat([middleband, upperband, lowerband], axis=1)
data_B.columns = ['middleband', 'upperband', 'lowerband']

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高低价通道

upperband = ta.abstract.MAX(data, 20, price='high')
lowerband = ta.abstract.MIN(data, 20, price='low')
middleband = (upperband+lowerband)/2
data_B = pd.concat([middleband, upperband, lowerband], axis=1)
data_B.columns = ['middleband', 'upperband', 'lowerband']

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全部图的展示代码：

plt.plot(data['close'])
plt.plot(data_B['middleband'], 'r', alpha=0.3)
plt.plot(data_B['upperband'], 'g', alpha=0.3)
plt.plot(data_B['lowerband'], 'g', alpha=0.3)
plt.show()

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Donchian’s 5 and 20 day MA and Breakout system

• A 5-day moving average
• A 20-day moving average
• The average true range based on 20-day

Buy:

$$Close_t > MA5_{t−1} + ATR_{t−1} "and"Close_t > MA20_{t−1} + ATR_{t−1}$$

Exit:

$$Close_t < MA5_{t−1} - ATR_{t−1} "or" Close_t < MA20_{t−1} - ATR_{t−1}$$

import talib as ta
import tushare as ts
import pandas as pd
import matplotlib.pyplot as plt
data = ts.get_k_data('000001', start='2016-01-01', end='2016-12-31', ktype='D',autype='qfq')
data.index = pd.to_datetime(data['date'],format='%Y-%m-%d')
atr = ta.abstract.ATR(data, 20)
data['SMA20'] = ta.abstract.MA(data, 20)
data['SMA5'] = ta.abstract.MA(data, 5)
data['upperband20'] = data['SMA20'] + atr
data['lowerband20'] = data['SMA20'] - atr
data['upperband5'] = data['SMA5'] + atr
data['lowerband5'] = data['SMA5'] - atr
x_b=[]
y_b=[]
x_e=[]
y_e=[]
data = data.dropna()
it = data.iterrows()
t0, d0 = next(it)
pos = 0
for t1, d1 in it:
    if pos == 0 and d1.close > d0.upperband5 and d1.close > d0.upperband20:
        x_b.append(t1)
        y_b.append(d1.close)
        pos = 1
    elif pos == 1 and (d1.close < d0.lowerband5 or d1.close < d0.lowerband20):
        x_e.append(t1)
        y_e.append(d1.close)
        pos = 0
    t0, d0 = t1, d1
plt.plot(data['close'])
plt.plot(data['upperband20'], alpha=0.3)
plt.plot(data['lowerband20'], alpha=0.3)
plt.plot(data['upperband5'], alpha=0.3,c='r')
plt.plot(data['lowerband5'], alpha=0.3,c='r')
plt.scatter(x_b, y_b, c='r', marker='^', linewidths=3)
plt.scatter(x_e, y_e, c='g', marker='v', linewidths=3)
plt.show()