TA_Lib 趋势均线篇

均线类型 双均线策略

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均线与趋势是西蒙斯被TED采访时提到的关键词，做量化你必须懂，而且是深入地懂这两个词的真正意义。均线理论是当今应用最普遍的技术指标之一，它帮助交易者确认现有趋势、判断将出现的趋势、发现过度延生即将反转的趋势。接下来我们会从公式、代码、图表与策略来深入了解均线。

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$$\text{1. Simple Moving Average 平滑价格}$$
$$MA_n=\frac{X_1+X_2+...X_n}{n}$$

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$$\text{2. Weighted Moving Average 用权重方法突出前段}$$
$$W_t=\frac{w_1P_{t-n+1}+w_2P_{t-n+2}+...+w_{n-1}P_{t-1}+w_nP_t}{w_1+w_2+...+w_n}$$
$$w_1<w_2<...<w_n$$

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$$\text{3. Triangular Moving Average 突出中间,用于周期分析}$$
$$TMA_5=\frac{P_1+2P_2+3P_3+2P_4+P_5}{9}$$

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$$\text{4. Exponential Moving Average 用指数方法突出前段}$$
$$EMA_t=\frac{a^nP_{t-n+1}+a^{n-1}P_{t-n+2}+...+a^2P_{t-1}+aP_t}{a^n+a^{n-1}+...+a^2+a}$$
$$EMA_t=α*Price_t + ( 1 - α ) * EMA_{t-1}$$
$$α为平滑指数，一般取作2/(n+1)$$

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$$\text{5. Double Exponential Moving Average 突出EMA,减少滞后。}$$
$$DEMA = 2*EMA-EMA(EMA)$$

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$$\text{6. Kaufman Adaptive Moving Average 自适应自适应市场变化与波动}$$
$$ER = Change/Volatility$$

$$Change = ABS(Close - Close (10 periods ago))$$

$$Volatility = Sum10(ABS(Close_t - Close_{t-1}))$$
$$\text{Volatility is the sum of the absolute value of the last ten price changes (Close - Prior Close)}$$
$$SC = [ER * (fastest SC - slowest SC) + slowest SC]^2$$
$$SC = [ER * (2/(2+1) - 2/(30+1)) + 2/(30+1)]^2$$
$$Current KAMA = Prior KAMA + SC * (Price - KAMA_{t-1})$$

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import talib as ta
import tushare as ts
import pandas as pd
import numpy as np
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')
dc = np.array(data['close'])

data['SMA'] = ta.MA(dc, 20)
data['WMA'] = ta.WMA(dc, 20)
data['TRIMA'] = ta.TRIMA(dc, 20)
data['EMA']  = ta.EMA(dc, 20)
data['DEMA'] = ta.DEMA(dc, 20)
data['KAMA'] = ta.KAMA(dc, 20)

plt.plot(data['close'])
plt.plot(data['SMA'], alpha=0.3)
plt.plot(data['WMA'], alpha=0.3)
plt.plot(data['TRIMA'], alpha=0.3)
plt.plot(data['EMA'], alpha=0.3)
plt.plot(data['DEMA'], alpha=0.3)
plt.plot(data['KAMA'], alpha=0.3)
plt.legend(loc='lower right')
plt.show()

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两条均线的三种交易方法
1. 当均线金叉（短期大于长期均线）时候买进，死叉（短期小于长期）时卖出。
2. 当价格上穿两条均线时买入，但价格下穿其中一条均线时卖出。
3. 当两条均线都处于向上方向时买入，当两条均线都处于下跌方向时卖出。

data['EMA']  = ta.EMA(dc, 20)
data['EMA60'] = ta.EMA(dc, 60)
x1=[]
y1=[]
x0=[]
y0=[]
data = data.dropna()
it = data.iterrows()
t0, d0 = next(it)
pos = 0

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第一种进出场策略:
对数据进行遍历，获取所需要的值存入x,y列表。

for t1, d1 in it:
    if d1.EMA > d1.EMA60 and d0.EMA < d0.EMA60:
        x1.append(t1)
        y1.append(d1.EMA)
    elif d1.EMA < d1.EMA60 and d0.EMA > d0.EMA60:
        x0.append(t1)
        y0.append(d1.EMA)
    t0, d0 = t1, d1

策略一的图表：

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第二种进出场策略:
需要添加目前是否有持仓的判断。

for t1, d1 in it:
    if  pos == 0 and d1.close > d1.EMA and d1.close > d1.EMA60 and (d0.close < d0.EMA60 or d0.close < d0.EMA):
        x1.append(t1)
        y1.append(d1.close)
        pos = 1
    elif pos == 1 and (d1.close < d1.EMA or d1.close < d1.EMA60) :
        x0.append(t1)
        y0.append(d1.close)
        pos = 0
    t0, d0 = t1, d1

策略二的图片：

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第三种进出场策略

for t1, d1 in it:
    if pos == 0 and d1.EMA > d0.EMA and d1.EMA60 > d0.EMA60:
        x1.append(t1)
        y1.append(d1.close)
        pos = 1
    elif pos == 1 and d1.EMA < d0.EMA and d1.EMA60 < d0.EMA60 :
        x0.append(t1)
        y0.append(d1.close)
        pos = 0
    t0, d0 = t1, d1

策略三的图片：

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显示图片需要添加的可视化代码：

import matplotlib as mpl
mpl.rc('xtick', labelsize=8)
plt.plot(data['close'])
plt.plot(data['EMA'], alpha=0.3)
plt.plot(data['EMA60'], alpha=0.3)
plt.scatter(x1, y1, c='r', marker='^', linewidths=3)
plt.scatter(x0, y0, c='g', marker='v', linewidths=3)
plt.show()