numbering elements in a pandas dataframe
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
add a comment |
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
add a comment |
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
I am trying to build a small stock trading reporting in pandas. It's getting a little complicated because of subsequent buys and sells.
Assuming I have my buys and sells in a dataframe:
import pandas as pd
data = pd.read_csv("ticker1.csv", delimiter=";")
data['cumsum']=data['quantity'].cumsum(axis=0)
data
Date qty price cumsum
0 2018-01-20 80 20.70 80
1 2018-02-14 90 20.82 170
2 2018-02-19 -100 20.62 70
3 2018-02-27 -70 20.55 0
4 2018-03-13 30 19.80 30
5 2018-03-14 10 19.55 40
6 2018-03-30 -20 20.92 20
7 2018-04-01 -10 20.95 10
8 2018-04-10 -10 21.03 0
9 2018-05-04 25 19.77 25
10 2018-05-31 -10 20.22 15
So there can be "completed" cycles of buying and selling whenever cumsum =0 (no short-selling). In this example, there would be an open position of 15 at the end.
In order to analyze the trades, I'd like to group them like this:
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
I am trying to group the transactions until the next time cumsum =0.
Then I could loop over the groupings for further analysis (e.g. see if it was a winning or losing trade, # days between first buy and last sale etc.) and I would be able to see that in this case there is an open position at the moment (if last value for cumsum != 0).
Could someone please give me a hint how I could realize the grouping?
Thanks
python pandas dataframe
python pandas dataframe
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
asked Nov 24 '18 at 17:13
RazzleDazzleRazzleDazzle
237
237
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
Coincidentally, one solution is to apply Series.cumsum() on the column named cumsum:
df['group'] = (df['cumsum'].shift() == 0).astype(int).cumsum() + 1
df
Date qty price cumsum group
0 2018-01-20 80 20.70 80 1
1 2018-02-14 90 20.82 170 1
2 2018-02-19 -100 20.62 70 1
3 2018-02-27 -70 20.55 0 1
4 2018-03-13 30 19.80 30 2
5 2018-03-14 10 19.55 40 2
6 2018-03-30 -20 20.92 20 2
7 2018-04-01 -10 20.95 10 2
8 2018-04-10 -10 21.03 0 2
9 2018-05-04 25 19.77 25 3
10 2018-05-31 -10 20.22 15 3
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
answered Nov 24 '18 at 17:17
Peter LeimbiglerPeter Leimbigler
4,3731415
4,3731415
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
Thanks - that’s really an elegant way to this! I would not have thought of that myself!
– RazzleDazzle
Nov 24 '18 at 18:05
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
@RazzleDazzle, you're welcome! You'll learn fast as you continue writing and reading code, and this will become second nature to you :)
– Peter Leimbigler
Nov 24 '18 at 18:31
add a comment |
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