test files

2 years ago · f48d79afb9
9 changed files with 4910 additions and 5 deletions
--- a/dict_iter.py
+++ b/dict_iter.py
@ -0,0 +1,29 @@
+import pandas
+
+from fincal.fincal import TimeSeries
+
+dfd = pandas.read_csv('test_files/nav_history_daily - Copy.csv')
+dfm = pandas.read_csv('test_files/nav_history_monthly.csv')
+
+data_d = [(i.date, i.nav) for i in dfd.itertuples() if i.amfi_code == 118825]
+data_d.sort()
+data_m = [{'date': i.date, 'value': i.nav} for i in dfm.itertuples()]
+
+tsd = TimeSeries(data_d, frequency='D')
+
+md = dict(data_d)
+counter = 1
+for i in iter(md):
+    print(i)
+    counter += 1
+    if counter >= 5: break
+
+print('\n')
+counter = 1
+for i in reversed(md):
+    print('rev', i)
+    counter += 1
+    if counter >= 5: break
+
+x = [next(i) for i in iter(md)]
+print(x)
--- a/fincal/core.py
+++ b/fincal/core.py
@ -1,7 +1,6 @@
 import datetime
 from dataclasses import dataclass
-from msilib import sequence
-from typing import Dict, Iterable, List, Literal, Mapping, Sequence, Tuple, Union
+from typing import Iterable, List, Literal, Mapping, Sequence, Tuple, Union


@dataclass
@ -134,7 +133,9 @@ class TimeSeriesCore:

    def __repr__(self):
        if len(self.time_series) > 6:
-            printable_data_1 = list(self.time_series)[:3]
+            iter_f = iter(self.time_series)
+            iter_b = reversed(self.time_series)
+            printable_data_1 = [next(i) for i in iter(dict)]
            printable_data_2 = list(self.time_series)[-3:]
            printable_str = "TimeSeries([{}\n\t...\n\t{}])".format(
                                ',\n\t'.join([str((i, self.time_series[i])) for i in printable_data_1]),
@ -160,7 +161,7 @@ class TimeSeriesCore:
        return printable_str

    def __getitem__(self, n):
-        all_keys = list(self.time_series.keys())
+        all_keys = list(self.time_series)
        if isinstance(n, int):
            keys = [all_keys[n]]
        else:
@ -172,7 +173,7 @@ class TimeSeriesCore:
        return item

    def __len__(self):
-        return len(self.time_series.keys())
+        return len(self.time_series)

    def head(self, n: int = 6):
        keys = list(self.time_series.keys())
--- a/my_test.py
+++ b/my_test.py
@ -0,0 +1,23 @@
+import time
+
+import pandas
+
+from fincal.fincal import AllFrequencies, Frequency, TimeSeries, create_date_series
+
+dfd = pandas.read_csv('test_files/nav_history_daily - Copy.csv')
+dfm = pandas.read_csv('test_files/nav_history_monthly.csv')
+dfq = pandas.read_csv('test_files/nav_history_quarterly.csv')
+
+data_d = [(i.date, i.nav) for i in dfd.itertuples()]
+data_m = [{'date': i.date, 'value': i.nav} for i in dfm.itertuples()]
+data_q = {i.date: i.nav for i in dfq.itertuples()}
+
+tsd = TimeSeries(data_d, frequency='D')
+tsm = TimeSeries(data_m, frequency='M', date_format='%d-%m-%Y')
+tsq = TimeSeries(data_q, frequency='Q', date_format='%d-%m-%Y')
+
+start = time.time()
+# ts.calculate_rolling_returns(datetime.datetime(2015, 1, 1), datetime.datetime(2022, 2, 1), years=1)
+# fdata = tsd.ffill()
+# rr = tsd.calculate_rolling_returns(datetime.datetime(2022, 1, 1), datetime.datetime(2022, 2, 1), years=1)
+print(time.time() - start)
--- a/test_files/nav_history_daily
+++ b/test_files/nav_history_daily
--- a/test_files/nav_history_daily.csv
+++ b/test_files/nav_history_daily.csv
@ -0,0 +1,139 @@
+amfi_code,date,nav
+118825,01-11-2021,87.925
+119528,02-11-2021,378.51
+118825,02-11-2021,87.885
+119528,03-11-2021,377.79
+118825,03-11-2021,87.553
+119528,08-11-2021,383.13
+118825,08-11-2021,88.743
+119528,09-11-2021,383.06
+118825,09-11-2021,88.793
+119528,10-11-2021,382.71
+118825,10-11-2021,88.723
+118825,10-11-2021,88.78
+119528,11-11-2021,379.28
+118825,11-11-2021,88.205
+119528,12-11-2021,383.94
+118825,12-11-2021,89.025
+119528,15-11-2021,383.31
+118825,15-11-2021,89.182
+119528,16-11-2021,381.08
+118825,16-11-2021,88.569
+119528,17-11-2021,379.17
+118825,17-11-2021,88.09
+119528,18-11-2021,375.09
+118825,18-11-2021,87.202
+119528,22-11-2021,368.16
+118825,22-11-2021,85.382
+119528,23-11-2021,370.64
+118825,23-11-2021,85.978
+119528,24-11-2021,369.91
+118825,24-11-2021,85.635
+119528,25-11-2021,371.33
+118825,25-11-2021,86.212
+119528,26-11-2021,360.66
+118825,26-11-2021,83.748
+119528,29-11-2021,360.05
+118825,29-11-2021,83.523
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+118825,30-11-2021,83.475
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+119528,24-12-2021,359.11
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+119528,27-12-2021,360.71
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+119528,31-12-2021,366.98
+118825,31-12-2021,85.759
+119528,03-01-2022,371.76
+118825,03-01-2022,87.111
+119528,04-01-2022,374.22
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+119528,11-01-2022,379.34
+118825,11-01-2022,88.678
+119528,12-01-2022,382.86
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+119528,14-01-2022,384.02
+118825,14-01-2022,89.729
+119528,17-01-2022,384.36
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+119528,18-01-2022,380
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+119528,24-01-2022,361.01
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+118825,25-01-2022,84.763
+119528,27-01-2022,361.95
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+119528,28-01-2022,361.91
+118825,28-01-2022,83.829
+119528,31-01-2022,367.31
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+119528,04-02-2022,371.01
+118825,04-02-2022,86.079
+119528,07-02-2022,365.04
+118825,07-02-2022,84.867
+119528,08-02-2022,365.74
+118825,08-02-2022,84.945
+119528,09-02-2022,369.85
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+119528,10-02-2022,372.29
+118825,10-02-2022,86.5
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+118825,11-02-2022,85.226
+119528,14-02-2022,355.47
+118825,14-02-2022,82.533
--- a/test_files/nav_history_monthly
+++ b/test_files/nav_history_monthly
@ -0,0 +1,219 @@
+"amfi_code","date","nav"
+118825,2013-01-31,18.913
+118825,2013-02-28,17.723
+118825,2013-03-28,17.563
+118825,2013-04-30,18.272
+118825,2013-05-31,18.383
+118825,2013-06-28,17.802
+118825,2013-07-31,17.588
+118825,2013-08-30,16.993
+118825,2013-09-30,17.732
+118825,2013-10-31,19.665
+118825,2013-11-29,19.787
+118825,2013-12-31,20.499
+118825,2014-01-31,19.994
+118825,2014-02-28,20.942
+118825,2014-03-31,22.339
+118825,2014-04-30,22.599
+118825,2014-05-30,24.937
+118825,2014-06-30,27.011
+118825,2014-07-31,27.219
+118825,2014-08-28,28.625
+118825,2014-09-30,29.493
+118825,2014-10-31,30.685
+118825,2014-11-28,31.956
+118825,2014-12-31,31.646
+118825,2015-01-30,33.653
+118825,2015-02-27,33.581
+118825,2015-03-31,33.14
+118825,2015-04-30,32.181
+118825,2015-05-29,33.256
+118825,2015-06-30,33.227
+118825,2015-07-31,34.697
+118825,2015-08-31,32.833
+118825,2015-09-30,32.94
+118825,2015-10-30,33.071
+118825,2015-11-30,33.024
+118825,2015-12-31,33.267
+118825,2016-01-29,31.389
+118825,2016-02-29,28.751
+118825,2016-03-31,32.034
+118825,2016-04-29,32.848
+118825,2016-05-31,34.135
+118825,2016-06-30,35.006
+118825,2016-07-29,37.148
+118825,2016-08-31,38.005
+118825,2016-09-30,37.724
+118825,2016-10-28,38.722
+118825,2016-11-30,36.689
+118825,2016-12-30,36.239
+118825,2017-01-31,38.195
+118825,2017-02-28,39.873
+118825,2017-03-31,41.421
+118825,2017-04-28,42.525
+118825,2017-05-31,43.977
+118825,2017-06-30,43.979
+118825,2017-07-31,46.554
+118825,2017-08-31,46.383
+118825,2017-09-29,46.085
+118825,2017-10-31,48.668
+118825,2017-11-30,48.824
+118825,2017-12-29,50.579
+118825,2018-01-31,51.799
+118825,2018-02-28,49.041
+118825,2018-03-28,46.858
+118825,2018-04-30,49.636
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+118825,2018-08-31,53.494
+118825,2018-09-28,49.863
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+118825,2018-12-31,50.691
+118825,2019-01-31,50.517
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+118825,2019-04-30,54.402
+118825,2019-05-31,55.334
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+118825,2019-12-31,57.771
+118825,2020-01-31,57.135
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+118825,2020-03-31,41.452
+118825,2020-04-30,47.326
+118825,2020-05-29,45.845
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+118825,2020-07-31,53.306000000000004
+118825,2020-08-19,55.747
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+119528,2017-02-28,193.2
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+119528,2018-02-28,226.55
+119528,2018-03-28,219.73
+119528,2018-04-30,232.04
+119528,2018-05-31,228.49
+119528,2018-06-29,225.27
+119528,2018-07-31,237.11
+119528,2018-08-31,243.79
+119528,2018-09-28,223.83
+119528,2018-10-31,218.61
+119528,2018-11-30,226.99
+119528,2018-12-31,228.61
+119528,2019-01-31,224.26
+119528,2019-02-28,222.71
+119528,2019-03-29,240.21
+119528,2019-04-30,240.01
+119528,2019-05-31,243.72
+119528,2019-06-28,241.28
+119528,2019-07-31,229.54
+119528,2019-08-30,226.0
+119528,2019-09-30,234.75
+119528,2019-10-31,242.11
+119528,2019-11-29,246.75
+119528,2019-12-31,247.81
+119528,2020-01-31,246.14
+119528,2020-02-28,231.91
+119528,2020-03-31,175.98
+119528,2020-04-30,200.77
+119528,2020-05-29,196.75
+119528,2020-06-30,210.55
+119528,2020-07-31,224.93
+119528,2020-08-19,233.78
+119528,2020-10-30,235.83
+119528,2020-11-27,264.04
+119528,2020-12-31,285.02
+119528,2021-01-29,280.52
+119528,2021-02-26,300.56
+119528,2021-03-31,301.57
+119528,2021-04-30,301.1
+119528,2021-05-31,320.98
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+119528,2021-07-30,336.6
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+119528,2021-12-31,366.98
+119528,2022-01-31,367.31
+119528,2022-02-17,363.56
--- a/test_files/nav_history_monthly.csv
+++ b/test_files/nav_history_monthly.csv
@ -0,0 +1,11 @@
+amfi_code,date,nav
+118825,31-03-2021,70.69
+118825,30-04-2021,70.39
+118825,31-05-2021,74.85
+118825,30-07-2021,78.335
+118825,31-08-2021,83.691
+118825,30-09-2021,86.128
+118825,29-10-2021,86.612
+118825,30-11-2021,83.475
+118825,31-01-2022,85.18
+118825,17-02-2022,84.33
--- a/test_files/nav_history_quarterly
+++ b/test_files/nav_history_quarterly
@ -0,0 +1,71 @@
+"amfi_code","date","nav"
+118825,2013-03-28,17.563
+118825,2013-06-28,17.802
+118825,2013-09-30,17.732
+118825,2013-12-31,20.499
+118825,2014-03-31,22.339
+118825,2014-06-30,27.011
+118825,2014-09-30,29.493
+118825,2014-12-31,31.646
+118825,2015-03-31,33.14
+118825,2015-06-30,33.227
+118825,2015-09-30,32.94
+118825,2015-12-31,33.267
+118825,2016-03-31,32.034
+118825,2016-06-30,35.006
+118825,2016-09-30,37.724
+118825,2016-12-30,36.239
+118825,2017-03-31,41.421
+118825,2017-06-30,43.979
+118825,2017-09-29,46.085
+118825,2017-12-29,50.579
+118825,2018-03-28,46.858
+118825,2018-06-29,48.716
+118825,2018-09-28,49.863
+118825,2018-12-31,50.691
+118825,2019-03-31,54.017
+118825,2019-06-28,55.181
+118825,2019-09-30,54.058
+118825,2019-12-31,57.771
+118825,2020-03-31,41.452
+118825,2020-06-30,49.526
+118825,2020-12-31,66.415
+118825,2021-03-31,70.69
+118825,2021-06-30,77.109
+118825,2021-09-30,86.128
+118825,2021-12-31,85.759
+119528,2013-03-28,94.81
+119528,2013-06-28,97.52
+119528,2013-09-30,97.45
+119528,2013-12-31,109.3
+119528,2014-03-31,117.28
+119528,2014-06-30,139.48
+119528,2014-09-30,147.4
+119528,2014-12-31,159.62
+119528,2015-03-31,166.8
+119528,2015-06-30,166.67
+119528,2015-09-30,162.25
+119528,2015-12-31,162.83
+119528,2016-03-31,159.88
+119528,2016-06-30,174.61
+119528,2016-09-30,187.16
+119528,2016-12-30,176.66
+119528,2017-03-31,200.54
+119528,2017-06-30,209.83
+119528,2017-09-29,217.7
+119528,2017-12-29,233.26
+119528,2018-03-28,219.73
+119528,2018-06-29,225.27
+119528,2018-09-28,223.83
+119528,2018-12-31,228.61
+119528,2019-03-29,240.21
+119528,2019-06-28,241.28
+119528,2019-09-30,234.75
+119528,2019-12-31,247.81
+119528,2020-03-31,175.98
+119528,2020-06-30,210.55
+119528,2020-12-31,285.02
+119528,2021-03-31,301.57
+119528,2021-06-30,327.64
+119528,2021-09-30,369.42
+119528,2021-12-31,366.98
--- a/test_files/nav_history_quarterly.csv
+++ b/test_files/nav_history_quarterly.csv
@ -0,0 +1,9 @@
+amfi_code,date,nav
+118825,31-03-2019,54.017
+118825,28-06-2019,55.181
+118825,31-12-2019,57.771
+118825,31-03-2020,41.452
+118825,30-06-2020,49.526
+118825,30-06-2021,77.109
+118825,30-09-2021,86.128
+118825,31-12-2021,85.759