There is a list of lists of stock data. Every list consists data for a particular stock ticker. I need to find out
dfList
For each event, I will buy with the open price on Day 1 after the crash event, then we sell it when the price increases by x=20% or decreases by y=10%. If no selling happens, then we sell it on the last day (Day T+30) with the closing price.
Finally create a dataframe with
ticker, buying date, buying price, selling date, selling price, %price-change.
Hello there,
Do you have a question? Is this related to your first post?: Urgent need with the python code in time series stocks data - Python - The freeCodeCamp Forum
We do not know what you need help with, unless you ask a question.
That is a different question and this is a different question. Here I need to iterate through list of dataframes. I think i can remove last post becasue I got a sort of solution.
Right, thank you, for telling.
But, what are you struggling with? Do you need help with iterating in Python? Or, do you need help with working with Dataframes?
So if you see my question,
There is a list “dfList” which contains multiple dataframes. Each dataframe has 10 columns which stores data for a particular stock. I need to iterate through the dfList and for 3rd row of every dataframe , the Open price will be my buying price. Now will check in the rest of data for the particular data if there is decrease of 10% in the buying price or increase of 20% in the buying price , which willbe our selling price. I need to do this for all the stocks.
I tried this code.
for i in range(0,len(dfList)):
tempval = dfList[i].loc[2]['Open']
for j in range(0,len(dfList[i])):
if ((dfList[i].loc[j]['Open'] > (1.2 * tempval)) | (dfList[i].loc[j]['Open'] < (0.9 * tempval))):
print(dfList[i].loc[j])
break
but it is not working
This is how my dfList is
dfList[:2]
[ Event_index Date Open ... Volume Name Loss percent
0 1 2018-02-05 830.0 ... 1324579.0 TVIX 68.000000
1 1 2018-02-06 1489.0 ... 987706.0 TVIX -40.793651
2 1 2018-02-07 914.0 ... 683893.0 TVIX 21.849866
3 1 2018-02-08 924.0 ... 1122493.0 TVIX 48.514851
4 1 2018-02-09 1160.0 ... 949449.0 TVIX -16.296296
5 1 2018-02-12 1058.5 ... 545203.0 TVIX -7.876106
6 1 2018-02-13 1096.0 ... 325524.0 TVIX -0.288184
7 1 2018-02-14 1030.0 ... 432958.0 TVIX -21.772640
8 1 2018-02-15 756.0 ... 257326.0 TVIX -4.187192
9 1 2018-02-16 820.0 ... 398125.0 TVIX 2.185090
10 1 2018-02-20 847.0 ... 282795.0 TVIX 8.553459
11 1 2018-02-21 840.0 ... 312487.0 TVIX 2.201622
12 1 2018-02-22 834.0 ... 280997.0 TVIX -2.267574
13 1 2018-02-23 819.0 ... 295986.0 TVIX -14.617169
14 1 2018-02-26 698.0 ... 139378.0 TVIX -7.336957
15 1 2018-02-27 699.0 ... 390828.0 TVIX 16.568915
16 1 2018-02-28 750.0 ... 413212.0 TVIX 8.930818
17 1 2018-03-01 865.0 ... 687815.0 TVIX 14.434180
18 1 2018-03-02 1076.0 ... 672461.0 TVIX -8.678103
19 1 2018-03-05 937.0 ... 322371.0 TVIX -9.060773
20 1 2018-03-06 810.0 ... 305395.0 TVIX 1.579587
21 1 2018-03-07 900.0 ... 418296.0 TVIX -2.033493
22 1 2018-03-08 797.5 ... 340665.0 TVIX -6.471306
23 1 2018-03-09 741.0 ... 323610.0 TVIX -13.838120
24 1 2018-03-12 681.0 ... 178714.0 TVIX 6.060606
25 1 2018-03-13 681.0 ... 378357.0 TVIX 3.571429
26 1 2018-03-14 704.0 ... 387484.0 TVIX 3.862069
27 1 2018-03-15 733.0 ... 311277.0 TVIX -5.710491
28 1 2018-03-16 702.0 ... 255317.0 TVIX -3.098592
29 1 2018-03-19 716.0 ... 708294.0 TVIX 17.587209
30 1 2018-03-20 800.0 ... 321277.0 TVIX -4.079110
[31 rows x 10 columns],
Event_index Date Open ... Volume Name Loss percent
0 2 2018-02-23 28.799999 ... 3656.0 ABIO 3.225805
1 2 2018-02-26 13.500000 ... 580117.0 ABIO -71.874998
2 2 2018-02-27 9.540000 ... 459467.0 ABIO 42.222221
3 2 2018-02-28 11.700000 ... 776422.0 ABIO 26.562494
4 2 2018-03-01 16.200001 ... 255061.0 ABIO -11.111110
5 2 2018-03-02 12.960000 ... 95650.0 ABIO -4.166666
6 2 2018-03-05 12.600000 ... 86344.0 ABIO -1.449278
7 2 2018-03-06 12.420000 ... 64228.0 ABIO 0.000000
8 2 2018-03-07 12.420000 ... 49217.0 ABIO 0.000000
9 2 2018-03-08 12.060000 ... 59811.0 ABIO -8.823529
10 2 2018-03-09 11.520000 ... 54211.0 ABIO 4.838709
11 2 2018-03-12 11.700000 ... 32844.0 ABIO -3.076920
12 2 2018-03-13 11.340000 ... 22644.0 ABIO -3.174609
13 2 2018-03-14 10.980000 ... 102511.0 ABIO 16.393445
14 2 2018-03-15 12.960000 ... 44994.0 ABIO -7.042251
15 2 2018-03-16 11.700000 ... 18222.0 ABIO -1.515154
16 2 2018-03-19 11.880000 ... 62517.0 ABIO 0.000000
17 2 2018-03-20 11.880000 ... 63794.0 ABIO -4.615384
18 2 2018-03-21 11.160000 ... 59850.0 ABIO 0.000000
19 2 2018-03-22 11.340000 ... 20611.0 ABIO -1.612906
20 2 2018-03-23 10.980000 ... 62706.0 ABIO -9.836065
21 2 2018-03-26 9.900000 ... 56239.0 ABIO -7.272721
22 2 2018-03-27 9.540000 ... 58111.0 ABIO 1.960777
23 2 2018-03-28 9.180000 ... 29439.0 ABIO -5.769231
24 2 2018-03-29 9.000000 ... 14767.0 ABIO 2.040820
25 2 2018-04-02 9.000000 ... 39111.0 ABIO 3.999996
26 2 2018-04-03 9.540000 ... 15883.0 ABIO -5.769231
27 2 2018-04-04 9.000000 ... 15444.0 ABIO 2.040820
28 2 2018-04-05 9.000000 ... 13350.0 ABIO 2.000003
29 2 2018-04-06 9.360000 ... 13128.0 ABIO 0.000000
30 2 2018-04-09 9.360000 ... 7161.0 ABIO 0.000000
[31 rows x 10 columns]]
Are you aware you are doing bitwise comparison here? The or operator in Python is the word or (in JavaScript ||)
I also tried this using “or” but when we print it it only printing the tempval values.
Can you help me with how to take only the first occurance which matches the condition in this situation ?