def _calc_swarm_member(self, opts):
# In bi-directional mode 1-st opt argument must be *direction* of the swarm
direction = opts[0]
if direction != 1 and direction != -1:
raise Exception(
'In bi-direction mode fist opt argument must be Direction: 1 or -1'
)
swarm_name, entry_rule, exit_rule, calc_info = self.calculate(opts)
# Backtesting routine
pl, inposition = backtester.backtest(self.data, entry_rule, exit_rule,
direction)
# Apply global filter to trading system entries
if self._filtered_swarm is not None:
filtered_inpos = self._filtered_swarm[self.get_member_name(opts)]
# Binary AND to arrays
if self.global_filter is not None:
inposition = inposition & filtered_inpos & self.global_filter
else:
inposition = inposition & filtered_inpos
# Do backtest
equity, stats = backtester.stats(pl, inposition, self.positionsize,
self.costs)
return (swarm_name, equity, stats, inposition)
def test_stats_1bar_inposition(self):
pl = pd.Series([0, 1, 1, 1, 1, 1, 1, 1, 1, 1])
inpos = pd.Series([0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
equity, statsistics = stats(pl,
inpos,
positionsize=pd.Series(1,
index=inpos.index),
costs=pd.Series(1, index=inpos.index))
for i in range(len(equity)):
exp = pd.Series([0, -2, -1, -1, -1, -1, -1, -1, -1, -1])
self.assertEqual(exp[i], equity.values[i])
self.assertEqual(1, statsistics['count'])
self.assertEqual(-1, statsistics['netprofit'])
self.assertEqual(-1, statsistics['avg'])
def test_stats_no_costs_position_size(self):
data = {'exo': pd.Series(range(10))}
entry_rule = pd.Series([0, 1, 0, 0, 0, 1, 0, 0, 0, 0])
exit_rule = pd.Series([0, 0, 0, 1, 0, 0, 0, 0, 1, 0])
pl, inpos = backtest(data, entry_rule, exit_rule, direction=1)
equity, statsistics = stats(pl,
inpos,
positionsize=pd.Series(2,
index=inpos.index),
costs=None)
for i in range(len(equity)):
exp = pd.Series([0, 0, 2, 4, 4, 4, 6, 8, 10, 10])
self.assertEqual(exp[i], equity.values[i])
self.assertEqual(2, statsistics['count'])
self.assertEqual(10, statsistics['netprofit'])
self.assertEqual(5, statsistics['avg'])