import datetime
import time
import timeit

import pandas

from fincal.fincal import AllFrequencies, TimeSeries, create_date_series

dfd = pandas.read_csv('test_files/msft.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()}
data_q.update({'14-02-2022': 93.7})

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)
bdata = tsq.bfill()
# rr = tsd.calculate_rolling_returns(datetime.datetime(2022, 1, 1), datetime.datetime(2022, 2, 1), years=1)
print(time.time() - start)