I'm trying to flag some price data as "stale" if the quoted price of the security hasn't changed over lets say 3 trading days. I'm currently trying it with:
firm["dev"] = np.std(firm["Price"],firm["Price"].shift(1),firm["Price"].shift(2))
firm["flag"] == np.where(firm["dev"] = 0, 1, 0)
But I'm getting nowhere with it. This is what my dataframe would look like.
| Index | Price | Flag |
|---|---|---|
| 1 | 10 | 0 |
| 2 | 11 | 0 |
| 3 | 12 | 0 |
| 4 | 12 | 0 |
| 5 | 12 | 1 |
| 6 | 11 | 0 |
| 7 | 13 | 0 |
Any help is appreciated!
If you are okay with other conditions, you can first check if series.diff equals 0 and take cumsum to check if you have a cumsum of 2 (n-1). Also check if the next row is equal to current, when both these conditions suffice, assign a flag of 1 else 0.
n=3
firm['Flag'] = (firm['Price'].diff().eq(0).cumsum().eq(n-1) &
firm['Price'].eq(firm['Price'].shift())).astype(int)
EDIT, to make it a generalized function with consecutive n, use this:
def fun(df,col,n):
c = df[col].diff().eq(0)
return (c|c.shift(-1)).cumsum().ge(n) & df[col].eq(df[col].shift())
firm['flag_2'] = fun(firm,'Price',2).astype(int)
firm['flag_3'] = fun(firm,'Price',3).astype(int)
print(firm)
Price Flag flag_2 flag_3
Index
1 10 0 0 0
2 11 0 0 0
3 12 0 0 0
4 12 0 1 0
5 12 1 1 1
6 11 0 0 0
7 13 0 0 0