How to remove duplicated buy signal-CodePudding

I'm testing my stock trading logic and I made a position column to check the buying / selling signal

df = pd.DataFrame({'position': [1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 1.0, 0.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]})

I want to replace 1.0 value occurs between 1.0 and -1.0 with 0.0, and replace -1.0 value occurs between -1.0 and 1.0 with 0.0

here is the desired output:

df = pd.DataFrame({'position': [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]})

NOTE: the output only keeps the initial signal of 1.0 or -1.0

CodePudding user response：

Here is a basic implementation based on the approach described by the previous answer:

lastseen = 0

for n,el in enumerate(df["position"]):
    if lastseen == 0 and el == -1:
        raise Exception("Inconsistent data")
    
    if (el in [1, -1] and el != lastseen) or lastseen == 0:
        lastseen = el
    else:
        df["position"][n] = 0

I added the first check by considering the domain you described. If it's not correct for your problem feel free to remove it

CodePudding user response：

can you show us what you tried to do and didn't work, so we can help? anyway, maybe start with a simple solution:

loop over the array
keep track of what you saw most recently: -1 or 1
change every entry that matches the most recent
deal with edge cases (eg. loop only from first non 0 to last non 0)

CodePudding user response：

Vectorized solution that uses the capabilities of Pandas in full:

s = pd.Series([1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 1.0, 0.0, -1.0, -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0])

s_ = s.mask(s == 0).ffill()
result = s.where(s_ != s_.shift(), 0)

print(pd.DataFrame({'input': s, 'result': result}))

Output:

    input  result
0     1.0     1.0
1     0.0     0.0
2     0.0     0.0
3     1.0     0.0
4     0.0     0.0
5     0.0     0.0
6     0.0     0.0
7     0.0     0.0
8     0.0     0.0
9     0.0     0.0
10   -1.0    -1.0
11    1.0     1.0
12    0.0     0.0
13   -1.0    -1.0
14   -1.0     0.0
15    0.0     0.0
16    0.0     0.0
17    0.0     0.0
18    0.0     0.0
19    0.0     0.0
20    1.0     1.0