def test_cumsum():
dt = mn.sum_money(rt.read_file('money'))
# Assert at datapoint
assert float(prg.transaction_at("2014-05-08", dt).total_value) == 4000.0
assert float(prg.transaction_at("2014-05-10", dt).total_value) == 5000.0
# Assert between datapoints (should get nearest smaller index)
assert float(prg.transaction_at("2014-05-09", dt).total_value) == 4000.0
def plot_cumulative_state(transactions_file):
cs = cumulative_state(rt.read_file(transactions_file))
cs = cs.drop('tsla_q', 1)
cs = cs.drop('tsla_p', 1)
cs = cs.drop('scty_q', 1)
cs = cs.drop('scty_p', 1)
cs = cs.drop('vgk_q', 1)
cs = cs.drop('vgk_p', 1)
cs = cs.drop('vig_q', 1)
cs = cs.drop('vig_p', 1)
cs = cs.drop('csco_q', 1)
cs = cs.drop('csco_p', 1)
cs = cs.drop('kndi_q', 1)
cs = cs.drop('kndi_p', 1)
cs = cs.drop('total_value', 1)
print cs
cs.plot(kind='area', x='pd_timestamp')
plt.show()
def test_cumulative_state():
df = prg.cumulative_state(rt.read_file('transactions'))
assert int(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].tsla_q) == 8
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].tsla_p) == 204.190002
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].tsla_v) == 1633.520016
assert int(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].scty_q) == 4
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].scty_p) == 55.130001
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].scty_v) == 220.520004
assert int(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].ibm_q) == 4
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].ibm_p) == 195.679993
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].ibm_v) == 782.719972
assert int(df[df.pd_timestamp == pd.Timestamp("2014-04-10")]['sie.de_q']) == 0
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")]['sie.de_p']) == 97.26
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")]['sie.de_v']) == 0
assert float(df[df.pd_timestamp == pd.Timestamp("2014-04-10")].total_value) == 2636.759992
def plot_total_value_progress(transactions_file):
cs = cumulative_state(rt.read_file(transactions_file))
cs.plot(x='pd_timestamp', y='total_value')
plt.show()
def test_yield():
assert mn.count_yield(rt.read_file('money')) == 0.15
def test_worth_at_date():
moneystr = mn.worth_at_date(rt.read_file('money'), date(2014, 5, 9))
assert "bruce 59331.9" in moneystr
assert "alice 19777.3" in moneystr
def test_invested():
moneystr = mn.invested(rt.read_file('money'))
assert "alice 2000" in moneystr
assert "bruce 4000" in moneystr
def test_read_file():
lines = rt.read_file('transactions')
assert lines[0]['ticker'] == 'tsla'
assert lines[1]['ticker'] == 'ibm'
assert lines[2]['ticker'] == 'scty'
def test_transaction_at_missing_inter():
df = prg.to_dataframe(rt.read_file('transactions'))
tr = prg.transaction_at("2014-04-09", df)
field_by_field_equal(tr, example_dict_inter)
def test_transaction_at_direct():
df = prg.to_dataframe(rt.read_file('transactions'))
tr = prg.transaction_at("2014-04-10", df)
field_by_field_equal(tr, example_dict)
def test_money_cumulative_state():
df = prg.money_cumulative_state(rt.read_file('money'))
assert int(df[df.pd_timestamp == pd.Timestamp("2014-05-10")].bruce_q) == 4000
assert int(df[df.pd_timestamp == pd.Timestamp("2014-05-11")].alice_q) == 2000
def test_overview():
rt.read_file('transactions')
def test_all_timestamps():
t = rt.read_file('transactions')
l = rt.all_timestamps(t)
assert l.count(1389312000000000000) == 1
assert l.count(1391990400000000000) == 1
assert l.count(1397088000000000000) == 1
def test_all_dates():
t = rt.read_file('transactions')
l = rt.all_dates(t)
assert l.count(date(2014, 1, 10)) == 1
assert l.count(date(2014, 2, 10)) == 1
assert l.count(date(2014, 4, 10)) == 1
def test_all_tickers():
t = rt.read_file('transactions')
l = rt.all_tickers(t)
assert l.count('tsla') == 1
assert l.count('ibm') == 1
assert l.count('scty') == 1
def read_file(transaction_file):
return prg.to_dataframe(rt.read_file(transaction_file))
import stocks as st
import report as rp
## This is how the lambda is done
# ds['total_czk'] = ds.total_value.apply(lambda x: x * cv.getUSDCZK())
## Bezny check
print "Akcie v USD"
print "Dne: " + str(date.today())
print str(st.worth_at_date('../transactions/us_stocks', date.today()))
print "V czk:"
print str(st.worth_at_date_czk('../transactions/us_stocks', date.today()))
print "Investovane penize"
print mn.invested(rt.read_file('../transactions/czk'))
print "Prodat lze"
print str(st.three_years_test('../transactions/us_stocks'))
print "Zhodnoceni"
print "Honza"
print str(rp.appreciation(rt.read_file('../transactions/czk'), '../transactions/us_stocks', 'jd'))
print "Deni"
print str(rp.appreciation(rt.read_file('../transactions/czk'), '../transactions/us_stocks', 'deni'))
print "Mama"
print str(rp.appreciation(rt.read_file('../transactions/czk'), '../transactions/us_stocks', 'mama'))
print "Terka"
print str(rp.appreciation(rt.read_file('../transactions/czk'), '../transactions/us_stocks', 'terka'))
print "Laura"
print str(rp.appreciation(rt.read_file('../transactions/czk'), '../transactions/us_stocks', 'laura'))
def test_apprec():
assert rep.appreciation(rt.read_file('money'), 'transactions', 'alice') > 0
