Coverage for source/indicators/donchain_channels_indicator_handler.py: 92%

1# indicators/donchain_channels_indicator_handler.py 

2 

3# global imports 

4import pandas as pd 

5 

6# local imports 

7from source.indicators import IndicatorHandlerBase 

8 

9class DonchainChannelsIndicatorHandler(IndicatorHandlerBase): 

10 """ 

11 Implements donchain channels indicator. It indicates the highest and the 

12 lowest price for the certain period, creating so called channel. It also 

13 calculates average price as the average of the lowest and the highest price. 

14 """ 

15 

16 def __init__(self, window_size: int = 20) -> None: 

17 """ 

18 Class constructor. 

19 

20 Parameters: 

21 window_size (int): Length of window that indicator should be applied over. 

22 """ 

23 

24 self.__window_size = window_size 

25 

26 def calculate(self, data: pd.DataFrame) -> pd.DataFrame: 

27 """ 

28 Calculates donchain channels indicator values for given data. 

29 

30 Parameters: 

31 data (pd.DataFrame): Data frame with input data. 

32 

33 Returns: 

34 (pd.DataFrame): Output data with calculated donchain channels values. 

35 """ 

36 

37 donchian_df = pd.DataFrame(index = data.index) 

38 donchian_df['dc_up'] = data['high'].rolling(window = self.__window_size, min_periods = 1).max() 

39 donchian_df['dc_low'] = data['low'].rolling(window = self.__window_size, min_periods = 1).min() 

40 donchian_df['dc_mid'] = (donchian_df['dc_up'] + donchian_df['dc_low']) / 2 

41 

42 return donchian_df 

43 

44 def can_be_normalized(self) -> bool: 

45 """ 

46 Checks if the indicator can be normalized. 

47 

48 Returns: 

49 (bool): True if the indicator can be normalized, False otherwise. 

50 """ 

51 

52 return True