Signal conditional for column [duplicate]

Question

Reading from yahoo finance download ohlcv for nvidia, I am creating a column for signal buy/dontbuy, when I try to define which passes the avg>volume test everything either comes out all 'buy' or don't buy.

df=pd.read_csv('NVDA.csv',dtype={'label':str})
df['Price%delta']=((df['Close']/df['Open'])*100)                       

df['Avg_volume']=df['Volume'].rolling(7).mean()

df['Signal']=0

for index, row in df.iterrows():
    if row['Volume'] > row['Avg_volume']:
    df['Signal']='Buy'
    else:
        df['Signal']='Dont Buy'

Use something like df['Signal']=np.where(row['Volume'] > row['Avg_volume'],'Buy','Dont Buy'). Avoid using for loops for this when it can be done in a vectoized way — Vivek Kalyanarangan
– Vivek Kalyanarangan, Commented Nov 30, 2018 at 20:23
@IanQuah Notice how in each iteration OP is just setting the whole series and not every row. Hence he is getting all duplicate values which is either all Buy or Dont Buy based on his last iteration — Vivek Kalyanarangan
– Vivek Kalyanarangan, Commented Nov 30, 2018 at 20:24

Christian Sloper · Accepted Answer · 2018-11-30 20:33:10Z

1

You don't really need the for loop at all:

mask = df["Volume"] > df["Avg_volume"] 

df.loc[mask, "Signal"] = "Buy"
df.loc[~mask, "Signal"] = 'Don't buy'

answered Nov 30, 2018 at 20:33

Christian Sloper

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yatu · Accepted Answer · 2018-11-30 20:29:07Z

1

You are not specifying any index where to assign 'Buy' or 'Don't buy'. Use loc instead:

for index, row in df.iterrows(): 
    if row['Volume'] > row['Avg_volume']:
        df.loc[index, 'Signal']='Buy'
    else:
        df.loc[index, 'Signal']='Dont Buy'

answered Nov 30, 2018 at 20:29

yatu

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6 Comments

J.J. Over a year ago

it finally worked thank you nixon

yatu Over a year ago

Good to know. Please let me know iI solved it for you by setting the answer as correct.

rahlf23 Over a year ago

Just an FYI - this solution will be considerably slower than the vectorized solution I have posted for larger dataframes.

yatu Over a year ago

I know I'm aware @rahlf23 it can be vectorized, however, given that the user is unaware of how to properly index data, i found more appropriate to give some help on correcting the actual code

rahlf23 Over a year ago

Not saying your answer is incorrect @nixon, it definitely produces the desired output for the OP. Just pointing something out :)

|

rahlf23 · Accepted Answer · 2018-11-30 20:33:24Z

1

A vectorized solution using np.where():

df['Signal'] = np.where(df['Volume'] > df['Avg_volume'], 'Buy', 'Dont Buy')

answered Nov 30, 2018 at 20:33

rahlf23

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