I got a "Building a comprehensive set of Technical Indicators in Python for quantitative trading" code sample from:

https://towardsdatascience.com/building-a-comprehensive-set-of-technical-indicators-in-python-for-quantitative-trading-8d98751b5fb

However, groupby method (for Simple Moving Average-SMA) is resolved based on it's 'symbol' (AAPL or NFLX):

all_data['SMA_5'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window = 5).mean())

all_data['SMA_15'] = all_data.groupby('symbol')['Close'].transform(lambda x: x.rolling(window = 15).mean())

If I have my own DataFrame without the symbol column (only open, close, high, low and volume), is there a way to skip it and perform this method (together with mentioned transform method call)? Calling the method this way seem to require it's symbol column.

Thank you!

I have tried erasing the ('symbol') requirement, however the method does not seem to respond to this type of call. I would like to receive an SMA for a DataFrame that only have open, close, high, low and volume columns using groupby.

CodePudding user response：

Try this for 5 day moving average

all_data['SMA_5'] = all_data['column_name'].ewm(span=5).mean()

CodePudding user response：

If you don't need to group by "symbol" and just want to apply the transformation on your whole data, just remove the whole groupby call:

all_data['SMA_5'] = all_data['Close'].transform(lambda x: x.rolling(window = 5).mean())