def test_calc_stats():
# test twelve_month_win_perc divide by zero
prices = df.C['2010-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert 'twelve_month_win_perc' not in stats.index
prices = df.C['2009-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert 'twelve_month_win_perc' in stats.index
# test yearly_sharpe divide by zero
prices = df.C['2009-01-01':'2012-01-01']
stats = ffn.calc_stats(prices).stats
assert 'yearly_sharpe' in stats.index
prices[prices > 0.0] = 1.0
stats = ffn.calc_stats(prices).stats
assert 'yearly_sharpe' not in stats.index
def test_calc_stats():
# test twelve_month_win_perc divide by zero
prices = df.C['2010-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert pd.isnull(stats['twelve_month_win_perc'])
prices = df.C['2009-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert not pd.isnull(stats['twelve_month_win_perc'])
# test yearly_sharpe divide by zero
prices = df.C['2009-01-01':'2012-01-01']
stats = ffn.calc_stats(prices).stats
assert 'yearly_sharpe' in stats.index
prices[prices > 0.0] = 1.0
# throws warnings
stats = ffn.calc_stats(prices).stats
assert pd.isnull(stats['yearly_sharpe'])
def test_calc_stats():
# test twelve_month_win_perc divide by zero
prices = df.C['2010-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert pd.isnull(stats['twelve_month_win_perc'])
prices = df.C['2009-10-01':'2011-08-01']
stats = ffn.calc_stats(prices).stats
assert not pd.isnull(stats['twelve_month_win_perc'])
# test yearly_sharpe divide by zero
prices = df.C['2009-01-01':'2012-01-01']
stats = ffn.calc_stats(prices).stats
assert 'yearly_sharpe' in stats.index
prices[prices > 0.0] = 1.0
# throws warnings
stats = ffn.calc_stats(prices).stats
assert pd.isnull(stats['yearly_sharpe'])
def getWatchlist(source='qa'):
'''
get stats from all the symbols
:param source:
:return: stats
'''
startdate, enddate = getDateFromSetting()
if source == 'ffn':
data = getDataFromFFN(','.join(getSymbolListFromSetting()), startdate,
enddate)
perf = data.calc_stats()
return perf.stats
else:
data = getDataFromQA([s.upper() for s in getSymbolListFromSetting()],
startdate=startdate,
enddate=enddate)
tdata = transQAToFFN(data)
tdata.columns = map(str.lower, tdata.columns)
perf = ffn.calc_stats(tdata)
return perf.stats
import ffn as fn
import QUANTAXIS as QA
import pandas as pd
symbols = ['WBAI', 'TSLA']
startdate = "2019-05-01"
enddate = "2019-05-30"
data = QA.QA_fetch_stock_day_adv(symbols, startdate, enddate)
dn = data.to_pd().close.reset_index().set_index("date")
frame = {}
for s in symbols:
frame.update({s: dn[dn.code == s].close})
df = pd.DataFrame(frame)
perf = fn.calc_stats(df)
print(perf.stats)