https://github.com/keithorange/PatternPy/blob/2b1f29b17986acafffeb32c83e772417d540c100/tradingpatterns/tradingpatterns.py#L31C5-L34C5
The following logic is problematic because close_roll_max cannot/will not ever be less than the prior close (which is retrieved with df['Close'].shift(1)). I am operating under the assumption though that the underlying df is in chronological order (which is not always the case depending on the API you use for historical data).
# Create a boolean mask for multiple top pattern mask_top = (df['high_roll_max'] >= df['High'].shift(1)) & (**df['close_roll_max'] < df['Close'].shift(1)**) # Create a boolean mask for multiple bottom pattern mask_bottom = (df['low_roll_min'] <= df['Low'].shift(1)) & (**df['close_roll_min'] > df['Close'].shift(1)**)
https://github.com/keithorange/PatternPy/blob/2b1f29b17986acafffeb32c83e772417d540c100/tradingpatterns/tradingpatterns.py#L31C5-L34C5
The following logic is problematic because close_roll_max cannot/will not ever be less than the prior close (which is retrieved with df['Close'].shift(1)). I am operating under the assumption though that the underlying df is in chronological order (which is not always the case depending on the API you use for historical data).
# Create a boolean mask for multiple top pattern mask_top = (df['high_roll_max'] >= df['High'].shift(1)) & (**df['close_roll_max'] < df['Close'].shift(1)**) # Create a boolean mask for multiple bottom pattern mask_bottom = (df['low_roll_min'] <= df['Low'].shift(1)) & (**df['close_roll_min'] > df['Close'].shift(1)**)