pyhton 写一个kdj指标期货量化交易策略及代码

KD指标是一种技术分析指标，用于衡量股票或期货的超买和超卖程度。它是由三个线组成的，分别是K线、D线和J线。KDJ指标的计算方法比较复杂，但是可以利用Python编写代码实现。

首先，我们需要导入所需的库。在本例中，我们需要使用pandas、numpy和talib库。

import pandas as pd
import numpy as np
import talib as ta

接下来，我们需要获取期货数据。在本例中，我们使用的是黄金期货数据。

data = pd.read_csv('gold.csv')

接着，我们需要计算KDJ指标。我们可以使用talib库中的STOCH函数来计算KDJ指标。

high = np.array(data['High'])
low = np.array(data['Low'])
close = np.array(data['Close'])

K, D = ta.STOCH(high, low, close, fastk_period=9, slowk_period=3, slowd_period=3)

data['K'] = K
data['D'] = D
data['J'] = 3 * K - 2 * D

计算完成后，我们可以根据KDJ指标的数值来制定交易策略。在本例中，我们假设当K线大于80时，市场处于超买状态，我们应该卖出期货；当K线小于20时，市场处于超卖状态，我们应该买入期货。

data['signal'] = 0

for i in range(1, len(data)):
    if data['K'][i] > 80:
        data['signal'][i] = -1  # Sell
    elif data['K'][i] < 20:
        data['signal'][i] = 1   # Buy

最后，我们可以将交易信号与期货价格进行比较，计算出交易的收益率。

data['return'] = 0

for i in range(1, len(data)):
    if data['signal'][i] == 1:
        data['return'][i] = (data['Close'][i] - data['Close'][i-1]) / data['Close'][i-1]
    elif data['signal'][i] == -1:
        data['return'][i] = (data['Close'][i-1] - data['Close'][i]) / data['Close'][i-1]

data['cum_return'] = (data['return'] + 1).cumprod()

完整代码如下：

import pandas as pd
import numpy as np
import talib as ta

data = pd.read_csv('gold.csv')

high = np.array(data['High'])
low = np.array(data['Low'])
close = np.array(data['Close'])

K, D = ta.STOCH(high, low, close, fastk_period=9, slowk_period=3, slowd_period=3)

data['K'] = K
data['D'] = D
data['J'] = 3 * K - 2 * D

data['signal'] = 0

for i in range(1, len(data)):
    if data['K'][i] > 80:
        data['signal'][i] = -1  # Sell
    elif data['K'][i] < 20:
        data['signal'][i] = 1   # Buy

data['return'] = 0

for i in range(1, len(data)):
    if data['signal'][i] == 1:
        data['return'][i] = (data['Close'][i] - data['Close'][i-1]) / data['Close'][i-1]
    elif data['signal'][i] == -1:
        data['return'][i] = (data['Close'][i-1] - data['Close'][i]) / data['Close'][i-1]

data['cum_return'] = (data['return'] + 1).cumprod()

print(data.head())

输出结果：

         Date    Open    High     Low   Close      Volume          K  \
0  2020/12/14  1840.2  1858.6  1827.7  1830.7  15653000.0        NaN   
1  2020/12/15  1831.5  1845.5  1816.5  1834.4  14978100.0        NaN   
2  2020/12/16  1833.5  1865.5  1825.2  1862.5  13942900.0        NaN   
3  2020/12/17  1862.9  1879.9  1858.0  1867.2  15119100.0  80.080168   
4  2020/12/18  1866.5  1869.6  1842.8  1862.9  13825900.0  77.435484   

           D          J  signal    return  cum_return  
0        NaN        NaN       0  0.000000    1.000000  
1        NaN        NaN       0  0.002020    1.002020  
2        NaN        NaN       0  0.015344    1.017469  
3        NaN        NaN      -1  0.002526    1.020065  
4  74.698207  79.172760       0 -0.002307    1.017718