Cumulative sum and calculation on a condition

Question

So I have a dataframe with closing and opening prices, total length of 1066 rows. I want to iterate over the column 'signal' (values are available from 700th row onward, which I want to skip all the way), check if 1, then perform a calculation using the values from the corresponding columns, then compute cumulative sum in a new column. I want the code to perform the calculation only when 1 is checked.

let's say I have this dataframe:

(I don't know how to post a dataframe)

df = pd.DataFrame({'open':[2,4,5,7,5,11], 
                   'close':[0,2,4,3,10,5], 
                   'signal':[np.nan, 0, 1, -1, 1, -1]})

the desired results are as if the dataframe is constructed as follows:

df = pd.DataFrame({'open':[2,4,5,7,5,11], 
                   'close':[0,2,4,3,10,5], 
                   'signal':[np.nan, 0, 1, -1, 1, -1], 
                   'profits':['','',-1,-1,4, 4]})

I have edited the code per the answer.

My code still ignore 1 in 'signal' and it just calculates the cumulative sum for the whole column, although transaction_count passes.

here is my code:

transaction_count = 0
for i in df['signal']:
    if i == 1:
        df['profits'] = (df['close'] - df['open']).cumsum()
        transaction_count +=1
print('transaction count:', transaction_count)
print(df['profits_rf'])

Answer 1

[EDIT]

In this updated case you are mixing two answers. As @quest said, you dont need to loop. But if you want to do so anyways, you could create an array and append this array to your dataframe:

transaction_count = 0
profits = []
cum_sum = 0

for i in range(len(df)):
    if df['signal'][i] == 1:
        cum_sum += df['close'][i] - df['open'][i]
        profits.append(cum_sum)
    else:
        profits.append(cum_sum)

df['profits'] = profits

[OLD]

This part seems to be the problem:

if i in df['signal'] == 1[i]:

You are already iterating over df['signal'] so you just could check if i == 1:

if i == 1: