三均线策略

参考迅投示例里的双均线策略编写了一个三均线的策略，欢迎大家交流指正。

1. # encoding:gbk
   
2. 
   
3. import numpy as np
   
4. 
   
5. '''
   
6. 三均线策略：
   
7. 长周期用来辅助，不参与短周期和中周期金叉、死叉判断
   
8. 如：K线在EMA250上面， EMA12和50形成金叉，买入做多。如果K线在EMA250下面， EMA12和50形成死叉，卖出做空。
   
9. '''
   
10. 
    
11. 
    
12. class Custom(object):
    
13.     pass
    
14. 
    
15. 
    
16. custom = Custom()
    
17. custom.short = 12
    
18. custom.middle = 50
    
19. custom.long = 250
    
20. custom.account = ""
    
21. 
    
22. 
    
23. def init(context):
    
24.     context.account = custom.account
    
25.     context.account_type = "STOCK"
    
26.     stock_list = ["600000.SH", "000001.SZ"]
    
27.     context.set_universe(stock_list)
    
28. 
    
29. 
    
30. def handlebar(context):
    
31.     # 使每次调用只执行一次
    
32.     if not context.is_last_bar():
    
33.         return
    
34.     # 获取最新价格
    
35.     # stock_list = context.get_universe()
    
36.     # full_tick = context.get_full_tick(stock_list)
    
37.     # for code in stock_list:
    
38.     #     price = full_tick[code]['lastPrice']
    
39.     close_dict = get_close_dict(context)
    
40.     for stock, close_list in close_dict.items():
    
41.         if cross_compare(close_list, custom.short, custom.middle) == 1 and ma_compare(close_list, 1, custom.long):
    
42.             print(f"buy {stock}")
    
43.         if cross_compare(close_list, custom.short, custom.middle) == 2 and not ma_compare(close_list, 1, custom.long):
    
44.             print(f"sell {stock}")
    
45. 
    
46. 
    
47. def get_close_dict(context):
    
48.     df_dict = context.get_market_data_ex(['close'], stock_code=context.get_universe(), period='1d', count=custom.long + 1)
    
49.     close_dict = {}
    
50.     for stock, data in df_dict.items():
    
51.         close_list = data.to_dict('list')['close']
    
52.         close_dict[stock] = close_list
    
53.     return close_dict
    
54. 
    
55. 
    
56. def cross_compare(close_list, short, long):
    
57.     pre_line1 = np.mean(close_list[-short - 1: -1])
    
58.     pre_line2 = np.mean(close_list[-long - 1: -1])
    
59.     current_line1 = np.mean(close_list[-short:])
    
60.     current_line2 = np.mean(close_list[-long:])
    
61.     # 如果快线穿过慢线(金叉),则买入委托;如果快线下穿慢线(死叉),则卖出委托
    
62.     if pre_line1 < pre_line2 and current_line1 > current_line2:
    
63.         return 1
    
64.     elif pre_line1 > pre_line2 and current_line1 < current_line2:
    
65.         return 2
    
66.     return 0
    
67. 
    
68. 
    
69. def ma_compare(close_list, short, long):
    
70.     ma1_price = np.mean(close_list[-short:])
    
71.     ma2_price = np.mean(close_list[-long:])
    
72.     return ma1_price > ma2_price
    
73. 
    

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