def test_laststate_update(self):
def reblance_every_5th(swarm):
return pd.Series(swarm.index % 5 == 0, index=swarm.index)
STRATEGY_CONTEXT = {
'strategy': {
'class': StrategyMACrossTrail,
'exo_name': './mat/strategy_270225.mat',
'direction': -1,
'opt_params': [
# OptParam(name, default_value, min_value, max_value, step)
OptParamArray('Direction', [-1]),
OptParam('SlowMAPeriod', 20, 10, 40, 5),
OptParam('FastMAPeriod', 2, 5, 20, 5),
OptParam('MedianPeriod', 5, 2, 10, 1)
],
},
'swarm': {
'members_count': 1,
'ranking_class': RankerHighestReturns(return_period=1),
'rebalance_time_function': reblance_every_5th
}
}
swm_index = np.array(range(11))
swm_values = np.array([
[ 0., 0.], # 0
[ 1., -1.],
[ 2., -2.],
[ 3., -3.],
[ 4., -4.],
[ 5., -5.], # 5
[ 6., -6.],
[ 7., -7.],
[ 8., -8.],
[ 9., -9.],
[ 6., -6.], # 10
])
exo_price = np.array([
0,
1,
2,
3,
4,
5, # 5
6,
7,
8,
9,
6 # 10
])
exposure_values = np.array([
[1., -1.], # 0
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 5
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 10
])
swm = Swarm(STRATEGY_CONTEXT)
swm._swarm = pd.DataFrame(swm_values, swm_index)
swm._swarm_inposition = pd.DataFrame(np.ones((11, 2)), swm_index)
swm._swarm_exposure = pd.DataFrame(exposure_values, swm_index, dtype=np.float)
swm.strategy.data = pd.DataFrame({'exo': pd.Series(exo_price, index=swm_index, dtype=np.float)})
swm.strategy.costs = None
swm_res = np.array([
0., #0 - ignored by default
0.,
0.,
0.,
0.,
0., #5 - first rebalance (pick system #0)
0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6
1.,
2.,
3.,
0., #10 - pick another systems (but keep prev system change to next day)
])
expected = pd.DataFrame(swm_res, index=swm_index)
swm.pick()
self.assertEqual(2, len(swm.rebalance_info))
self.assertEqual(5, swm.rebalance_info[0]['rebalance_date'])
self.assertEqual(10, swm.rebalance_info[1]['rebalance_date'])
for k, v in expected[0].items():
#print(k)
self.assertEqual(k, swm.picked_swarm.index[k])
self.assertEqual(v, swm.picked_swarm[0][k])
self.assertEqual(swm.last_date, 10)
self.assertEqual(swm.last_rebalance_date, 10)
self.assertEqual(swm.last_exposure, 1)
self.assertEqual(swm.last_members_list, [1])
self.assertEqual(True, np.all(swm.picked_equity.values == expected[0].values))
#
# DO swarm update with new quotes
#
swm_index = np.array(range(14))
exo_price = np.array([
0., # 0 - ignored by default
0.,
0.,
0.,
0.,
0., # 5 - first rebalance (pick system #0)
0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6
1.,
2.,
3.,
0., # 10 - pick another systems (but keep prev system change to next day)
10., # Should be added with last exposure
11.,
13.
])
swarm_exposure = np.array([
0., # 0 - ignored by default
0.,
0.,
0.,
0.,
0., # 5 - first rebalance (pick system #0)
0.,
0.,
0.,
0.,
0., # 10
2., # Should be added with last exposure
2.,
2.,
])
#swm = Swarm(STRATEGY_CONTEXT)
# Little hack
swm._last_exoquote = 0.0 # exo_price on #10
# Little hack
swm._last_exposure = -1
self.assertEqual(swm.last_date, 10)
self.assertEqual(swm.last_rebalance_date, 10)
self.assertEqual(swm.last_exposure, -1)
self.assertEqual(swm.last_exoquote, 0)
self.assertEqual(swm.last_members_list, [1])
exo_df = pd.DataFrame({'exo': exo_price}, index=swm_index)
swm._laststate_update(exo_df, pd.Series(swarm_exposure, index=swm_index))
self.assertEqual(swm.last_date, 13)
self.assertEqual(swm.last_rebalance_date, 10)
self.assertEqual(swm.last_exposure, 2)
self.assertEqual(swm.last_exoquote, 13)
self.assertEqual(swm.last_members_list, [1])
swm_res = np.array([
0., # 0 - ignored by default
0.,
0.,
0.,
0.,
0., # 5 - first rebalance (pick system #0)
0., # Apply delayed rebalance we checked rebalance on #5 but change the position at #6
1.,
2.,
3.,
0., # 10 - pick another systems (but keep prev system change to next day)
-10., # Apply delayed rebalance we checked rebalance on #10 but change the position at #11
-8.,
-4.,
])
expected = pd.Series(swm_res, index=swm_index)
self.assertEqual(len(swm.picked_equity), len(expected))
for k, v in expected.items():
print(k)
self.assertEqual(k, swm.picked_equity.index[k])
self.assertEqual(v, swm.picked_equity.values[k])
def test_pick_equity_calculation(self):
def reblance_every_5th(swarm):
return pd.Series(swarm.index % 5 == 0, index=swarm.index)
STRATEGY_CONTEXT = {
'strategy': {
'class': StrategyMACrossTrail,
'exo_name': './strategy_270225.mat',
'direction': -1,
'opt_params': [
# OptParam(name, default_value, min_value, max_value, step)
OptParamArray('Direction', [-1]),
OptParam('SlowMAPeriod', 20, 10, 40, 5),
OptParam('FastMAPeriod', 2, 5, 20, 5),
OptParam('MedianPeriod', 5, 2, 10, 1)
],
},
'swarm': {
'members_count': 1,
'ranking_class': RankerHighestReturns(return_period=1),
'rebalance_time_function': reblance_every_5th
}
}
swm_index = np.array(range(20))
swm_values = np.array([
[ 0., 0.], # 0
[ 1., -1.],
[ 2., -2.],
[ 3., -3.],
[ 4., -4.],
[ 5., -5.], # 5
[ 6., -6.],
[ 7., -7.],
[ 8., -8.],
[ 9., -9.],
[ 6., -6.], # 10
[ 5., -5.],
[ 4., -4.],
[ 3., -3.],
[ 2., -2.],
[ 1., -1.], #15
[ 0., 0.],
[ -1., 1.],
[ -2., 2.],
[ -3., 3.]])
exo_price = np.array([
0,
1,
2,
3,
4,
5, # 5
6,
7,
8,
9,
6, # 10
5,
4,
3,
2,
1,
0,
-1,
-2,
-3
])
exposure_values = np.array([
[1., -1.], # 0
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 5
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 10
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
])
swm = Swarm(STRATEGY_CONTEXT)
swm._swarm = pd.DataFrame(swm_values, swm_index)
swm._swarm_inposition = pd.DataFrame(np.ones((20, 2)), swm_index)
swm._swarm_exposure = pd.DataFrame(exposure_values, swm_index, dtype=np.float)
swm.strategy.data = pd.DataFrame({'exo': pd.Series(exo_price, index=swm_index, dtype=np.float)})
swm.strategy.costs = None
swm_res = np.array([
[ 0.], #0 - ignored by default
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.], #5 - first rebalance (pick system #0)
[ 0.], # Apply delayed rebalance we checked rebalance on #5 but change the position at #6
[ 1.],
[ 2.],
[ 3.],
[ 0.], #10 - pick another systems (but keep prev system change to next day)
[ -1.], # Apply delayed rebalance we checked rebalance on #10 but change the position at #11
[ 0.],
[ 1.],
[ 2.],
[ 3.], #15 - keep system #1
[ 4.],
[ 5.],
[ 6.],
[ 7.]] )
expected = pd.DataFrame(swm_res, index=swm_index)
swm.pick()
for k, v in expected[0].items():
print(k)
self.assertEqual(k, swm.picked_swarm.index[k])
self.assertEqual(v, swm.picked_swarm[0][k])
def test_rebalanceinformation_before_next_day(self):
def riseup(swarm_slice, nsystems):
result = []
rank_info = []
# Calculate 14-period equity returns and sort values
last_diff = swarm_slice.diff(periods=1).iloc[-1, :].sort_values(ascending=False)
# Pick best nsystems
best = last_diff[:nsystems]
for k, v in best.items():
if not np.isnan(v) and v > 0:
result.append(k)
rank_info.append({'rank_value': v})
return result, rank_info
def reblance_every_5th(swarm):
return pd.Series(swarm.index % 5 == 0, index=swarm.index)
STRATEGY_CONTEXT = {
'strategy': {
'class': StrategyMACrossTrail,
'exo_name': 'strategy_270225',
'direction': -1,
'opt_params': [
# OptParam(name, default_value, min_value, max_value, step)
OptParamArray('Direction', [-1]),
OptParam('SlowMAPeriod', 20, 10, 40, 5),
OptParam('FastMAPeriod', 2, 5, 20, 5),
OptParam('MedianPeriod', 5, 2, 10, 1)
],
},
'swarm': {
'members_count': 1,
'ranking_class': RankerHighestReturns(return_period=1),
'rebalance_time_function': reblance_every_5th
}
}
swm_index = np.array(range(11))
swm_values = np.array([
[ 0., 0.], # 0
[ 1., -1.],
[ 2., -2.],
[ 3., -3.],
[ 4., -4.],
[ 5., -5.], # 5
[ 6., -6.],
[ 7., -7.],
[ 8., -8.],
[ 9., -9.],
[ 6., -6.], # 10
])
exposure_values = np.array([
[1., -1.], # 0
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 5
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.],
[1., -1.], # 10
])
exo_price = np.array([
0,
1,
2,
3,
4,
5, # 5
6,
7,
8,
9,
6 #10
])
swm = Swarm(STRATEGY_CONTEXT)
swm._swarm = pd.DataFrame(swm_values, swm_index)
swm._swarm_inposition = pd.DataFrame(np.ones((11, 2)), swm_index)
swm._swarm_exposure = pd.DataFrame(exposure_values, swm_index, dtype=np.float)
swm.strategy.data = pd.DataFrame({'exo': pd.Series(exo_price, index=swm_index, dtype=np.float)})
swm.strategy.costs = None
#equity1, _st_pass = stats_exposure(swm.strategy.data['exo'], swm._swarm_exposure[1], costs=None)
swm_res = np.array([
[ 0.], #0 - ignored by default
[ 0.],
[ 0.],
[ 0.],
[ 0.],
[ 0.], #5 - first rebalance (pick system #0)
[ 0.], # Apply delayed rebalance we checked rebalance on #5 but change the position at #6
[ 1.],
[ 2.],
[ 3.],
[ 0.], #10 - pick another systems (but keep prev system change to next day)
])
expected = pd.DataFrame(swm_res, index=swm_index)
swm.pick()
self.assertEqual(2, len(swm.rebalance_info))
self.assertEqual(5, swm.rebalance_info[0]['rebalance_date'])
self.assertEqual(10, swm.rebalance_info[1]['rebalance_date'])
for k, v in expected[0].items():
print(k)
self.assertEqual(k, swm.picked_swarm.index[k])
self.assertEqual(v, swm.picked_swarm[0][k])