def test_strategybase_tree_allocate():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
# since children have w == 0 this should stay in s
s.allocate(1000)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# now allocate directly to child
c1.allocate(500)
assert c1.position == 5
assert c1.value == 500
assert s.capital == 1000 - 501
assert s.value == 999
assert c1.weight == 500.0 / 999
assert c2.weight == 0
def test_strategybase_tree_setup():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
assert len(s.data) == 3
assert len(c1.data) == 3
assert len(c2.data) == 3
assert len(s.prices) == 0
assert len(c1.prices) == 0
assert len(c2.prices) == 0
assert len(s.values) == 0
assert len(c1.values) == 0
assert len(c2.values) == 0
def test_degenerate_shorting():
# can have situation where you short infinitely if commission/share > share
# price
c1 = SecurityBase('c1')
s = StrategyBase('p', [c1])
# $1/share commission
s.set_commissions(lambda q, p: abs(q) * 1)
c1 = s['c1']
dts = pd.date_range('2010-01-01', periods=3)
# c1 trades at 0.01
data = pd.DataFrame(index=dts, columns=['c1'], data=0.01)
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
try:
c1.allocate(-10)
assert False
except Exception as e:
assert 'full_outlay should always be approaching amount' in str(e)
def test_strategybase_tree_rebalance():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# now rebalance c1
s.rebalance(0.5, 'c1')
assert c1.position == 5
assert c1.value == 500
assert s.capital == 1000 - 501
assert s.value == 999
assert c1.weight == 500.0 / 999
assert c2.weight == 0
def test_strategybase_tree_setup():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
assert len(s.data) == 3
assert len(c1.data) == 3
assert len(c2.data) == 3
assert len(s._prices) == 3
assert len(c1._prices) == 3
assert len(c2._prices) == 3
assert len(s._values) == 3
assert len(c1._values) == 3
assert len(c2._values) == 3
def test_strategybase_tree_update():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
c1.price == 100
c2.price == 100
i = 1
s.update(dts[i], data.ix[dts[i]])
c1.price == 105
c2.price == 95
i = 2
s.update(dts[i], data.ix[dts[i]])
c1.price == 100
c2.price == 100
def test_strategybase_tree_rebalance():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# now rebalance c1
s.rebalance(0.5, 'c1')
assert c1.position == 5
assert c1.value == 500
assert s.capital == 1000 - 501
assert s.value == 999
assert c1.weight == 500.0 / 999
assert c2.weight == 0
def test_strategybase_tree_update():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
c1.price == 100
c2.price == 100
i = 1
s.update(dts[i], data.ix[dts[i]])
c1.price == 105
c2.price == 95
i = 2
s.update(dts[i], data.ix[dts[i]])
c1.price == 100
c2.price == 100
def test_security_setup_prices():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert c1.price == 105
assert len(c1.prices) == 1
assert c1.prices[0] == 105
assert c2.price == 95
assert len(c2.prices) == 1
assert c2.prices[0] == 95
# now with setup
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert c1.price == 105
assert len(c1.prices) == 1
assert c1.prices[0] == 105
assert c2.price == 95
assert len(c2.prices) == 1
assert c2.prices[0] == 95
def test_securitybase_allocate():
c1 = SecurityBase('c1')
s = StrategyBase('p', [c1])
c1 = s['c1']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1'], data=100.)
# set the price
data['c1'][dts[0]] = 91.40246706608193
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
# allocate 100000 to strategy
original_capital = 100000.
s.adjust(original_capital)
# not integer positions
c1.integer_positions = False
# set the full_outlay and amount
full_outlay = 1999.693706988672
amount = 1999.6937069886717
c1.allocate(amount)
# the results that we want to be true
assert np.isclose(full_outlay, amount, rtol=0.)
# check that the quantity wasn't decreased and the full_outlay == amount
# we can get the full_outlay that was calculated by
# original capital - current capital
assert np.isclose(full_outlay, original_capital - s._capital, rtol=0.)
def test_child_weight_above_1():
# check for child weights not exceeding 1
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(np.random.randn(3, 2) + 100,
index=dts, columns=['c1', 'c2'])
s.setup(data)
i = 0
s.update(dts[i])
s.adjust(1e6)
s.allocate(1e6, 'c1')
c1 = s['c1']
assert c1.weight <= 1
def test_node_full_name():
s1 = SecurityBase('s1')
s2 = SecurityBase('s2')
s = StrategyBase('p', [s1, s2])
assert s.full_name == 'p'
assert s1.full_name == 'p>s1'
assert s2.full_name == 'p>s2'
def test_strategybase_tree_allocate_update():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert s.price == 100
s.adjust(1000)
assert s.price == 100
assert s.value == 1000
assert s._value == 1000
c1.allocate(500)
assert c1.position == 5
assert c1.value == 500
assert c1.weight == 500.0 / 1000
assert s.capital == 1000 - 500
assert s.value == 1000
assert s.price == 100
i = 1
s.update(dts[i], data.ix[dts[i]])
assert c1.position == 5
assert c1.value == 525
assert c1.weight == 525.0 / 1025
assert s.capital == 1000 - 500
assert s.value == 1025
assert np.allclose(s.price, 102.5)
def test_node_full_name():
s1 = SecurityBase('s1')
s2 = SecurityBase('s2')
s = StrategyBase('p', [s1, s2])
# we cannot access s1 and s2 directly since they are copied
# we must therefore access through s
assert s.full_name == 'p'
assert s['s1'].full_name == 'p>s1'
assert s['s2'].full_name == 'p>s2'
def test_strategybase_tree():
s1 = SecurityBase('s1')
s2 = SecurityBase('s2')
s = StrategyBase('p', [s1, s2])
assert len(s.children) == 2
assert 's1' in s.children
assert 's2' in s.children
assert s == s1.parent
assert s == s2.parent
def test_strategybase_allocate():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i])
s.adjust(1000)
s.allocate(100, 'c1')
c1 = s['c1']
assert c1.position == 1
assert c1.value == 100
assert s.value == 1000
def test_rebalance_child_not_in_tree():
s = StrategyBase('p')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i])
s.adjust(1000)
# rebalance to 0 w/ child that is not present - should ignore
s.rebalance(0, 'c2')
assert s.value == 1000
assert s.capital == 1000
assert len(s.children) == 0
def test_strategybase_tree_adjust():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
s.adjust(1000)
assert s.capital == 1000
assert s.value == 1000
assert c1.value == 0
assert c2.value == 0
assert c1.weight == 0
assert c2.weight == 0
def test_strategybase_tree_allocate_long_short():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
c1.allocate(500)
assert c1.position == 5
assert c1.value == 500
assert c1.weight == 500.0 / 1000
assert s.capital == 1000 - 500
assert s.value == 1000
c1.allocate(-200)
assert c1.position == 3
assert c1.value == 300
assert c1.weight == 300.0 / 1000
assert s.capital == 1000 - 500 + 200
assert s.value == 1000
c1.allocate(-400)
assert c1.position == -1
assert c1.value == -100
assert c1.weight == -100.0 / 1000
assert s.capital == 1000 - 500 + 200 + 400
assert s.value == 1000
# close up
c1.allocate(-c1.value)
assert c1.position == 0
assert c1.value == 0
assert c1.weight == 0
assert s.capital == 1000 - 500 + 200 + 400 - 100
assert s.value == 1000
def test_strategybase_universe():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i])
assert len(s.universe) == 1
assert 'c1' in s.universe
assert 'c2' in s.universe
assert s.universe['c1'][dts[i]] == 105
assert s.universe['c2'][dts[i]] == 95
# should not have children unless allocated
assert len(s.children) == 0
def test_strategybase_tree_allocate_update():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert s.price == 100
s.adjust(1000)
assert s.price == 100
assert s.value == 1000
assert s._value == 1000
c1.allocate(500)
assert c1.position == 5
assert c1.value == 500
assert c1.weight == 500.0 / 999
assert s.capital == 1000 - 501
assert s.value == 999
assert s.price == 99.9
i = 1
s.update(dts[i], data.ix[dts[i]])
assert c1.position == 5
assert c1.value == 525
assert c1.weight == 525.0 / 1024
assert s.capital == 1000 - 501
assert s.value == 1024
assert s.price == 102.4
def test_fail_if_root_value_negative():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
try:
s.adjust(-100)
# trigger update
s.update(dts[0])
assert False
except ValueError, e:
if not 'negative root node value' in str(e):
assert False
def test_strategybase_allocate():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i])
s.adjust(1000)
s.allocate(100, 'c1')
c1 = s['c1']
assert c1.position == 1
assert c1.value == 100
assert s.value == 999
def test_rebalance_child_not_in_tree():
s = StrategyBase('p')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i])
s.adjust(1000)
# rebalance to 0 w/ child that is not present - should ignore
s.rebalance(0, 'c2')
assert s.value == 1000
assert s.capital == 1000
assert len(s.children) == 0
def test_strategybase_tree_allocate_long_short():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
c1.allocate(500)
assert c1.position == 5
assert c1.value == 500
assert c1.weight == 500.0 / 999
assert s.capital == 1000 - 501
assert s.value == 999
c1.allocate(-200)
assert c1.position == 3
assert c1.value == 300
assert c1.weight == 300.0 / 998
assert s.capital == 1000 - 501 + 199
assert s.value == 998
c1.allocate(-400)
assert c1.position == -1
assert c1.value == -100
assert c1.weight == -100.0 / 997
assert s.capital == 1000 - 501 + 199 + 399
assert s.value == 997
# close up
c1.allocate(-c1.value)
assert c1.position == 0
assert c1.value == 0
assert c1.weight == 0
assert s.capital == 1000 - 501 + 199 + 399 - 101
assert s.value == 996
def test_node_members():
s1 = SecurityBase('s1')
s2 = SecurityBase('s2')
s = StrategyBase('p', [s1, s2])
actual = s.members
assert len(actual) == 3
assert s1 in actual
assert s2 in actual
assert s in actual
actual = s1.members
assert len(actual) == 1
assert s1 in actual
actual = s2.members
assert len(actual) == 1
assert s2 in actual
def test_strategybase_tree_adjust():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
s.adjust(1000)
assert s.capital == 1000
assert s.value == 1000
assert c1.value == 0
assert c2.value == 0
assert c1.weight == 0
assert c2.weight == 0
def test_strategybase_universe():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i])
assert len(s.universe) == 1
assert 'c1' in s.universe
assert 'c2' in s.universe
assert s.universe['c1'][dts[i]] == 105
assert s.universe['c2'][dts[i]] == 95
# should not have children unless allocated
assert len(s.children) == 0
def test_strategybase_tree_rebalance_level2():
c1 = SecurityBase('c1')
c12 = copy.deepcopy(c1)
c2 = SecurityBase('c2')
c22 = copy.deepcopy(c2)
s1 = StrategyBase('s1', [c1, c2])
s2 = StrategyBase('s2', [c12, c22])
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
c1 = s1['c1']
c2 = s1['c2']
c12 = s2['c1']
c22 = s2['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
assert m.value == 1000
assert m.capital == 1000
assert s1.value == 0
assert s2.value == 0
assert c1.value == 0
assert c2.value == 0
# now rebalance child s1 - since its children are 0, no waterfall alloc
m.rebalance(0.5, 's1')
assert s1.value == 500
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
# now allocate directly to child of child
s1.rebalance(0.4, 'c1')
assert s1.value == 499
assert s1.capital == 500 - 201
assert c1.value == 200
assert c1.weight == 200.0 / 499
assert c1.position == 2
assert m.capital == 1000 - 500
assert m.value == 999
assert s1.weight == 499.0 / 999
assert s2.weight == 0
assert c12.value == 0
# now rebalance child s1 again and make sure c1 also gets proportional
# increase
m.rebalance(0.8, 's1')
assert s1.value == 798.2
aae(m.capital, 199.8, 1)
assert m.value == 998
assert s1.weight == 798.2 / 998
assert s2.weight == 0
assert c1.value == 300.0
assert c1.weight == 300.0 / 798.2
assert c1.position == 3
# now rebalance child s1 to 0 - should close out s1 and c1 as well
m.rebalance(0, 's1')
assert s1.value == 0
assert m.capital == 997
assert m.value == 997
assert s1.weight == 0
assert s2.weight == 0
assert c1.weight == 0
def test_fail_if_root_value_negative():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
s.adjust(-100)
# trigger update
s.update(dts[0])
assert s.bankrupt
# make sure only triggered if root negative
c1 = StrategyBase('c1')
s = StrategyBase('s', children=[c1])
c1 = s['c1']
s.setup(data)
s.adjust(1000)
c1.adjust(-100)
s.update(dts[0])
# now make it trigger
c1.adjust(-1000)
# trigger update
s.update(dts[0])
assert s.bankrupt
def test_strategybase_tree_allocate_level2():
c1 = SecurityBase('c1')
c12 = copy.deepcopy(c1)
c2 = SecurityBase('c2')
c22 = copy.deepcopy(c2)
s1 = StrategyBase('s1', [c1, c2])
s2 = StrategyBase('s2', [c12, c22])
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
c1 = s1['c1']
c2 = s1['c2']
c12 = s2['c1']
c22 = s2['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
# since children have w == 0 this should stay in s
m.allocate(1000)
assert m.value == 1000
assert m.capital == 1000
assert s1.value == 0
assert s2.value == 0
assert c1.value == 0
assert c2.value == 0
# now allocate directly to child
s1.allocate(500)
assert s1.value == 500
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
# now allocate directly to child of child
c1.allocate(200)
assert s1.value == 500
assert s1.capital == 500 - 200
assert c1.value == 200
assert c1.weight == 200.0 / 500
assert c1.position == 2
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
assert c12.value == 0
def test_strategy_tree_proper_return_calcs():
s1 = StrategyBase('s1')
s2 = StrategyBase('s2')
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data.loc['c1', dts[1]] = 105
data.loc['c2', dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
# since children have w == 0 this should stay in s
m.allocate(1000)
assert m.value == 1000
assert m.capital == 1000
assert m.price == 100
assert s1.value == 0
assert s2.value == 0
# now allocate directly to child
s1.allocate(500)
assert m.capital == 500
assert m.value == 1000
assert m.price == 100
assert s1.value == 500
assert s1.weight == 500.0 / 1000
assert s1.price == 100
assert s2.weight == 0
# allocate to child2 via master method
m.allocate(500, 's2')
assert m.capital == 0
assert m.value == 1000
assert m.price == 100
assert s1.value == 500
assert s1.weight == 500.0 / 1000
assert s1.price == 100
assert s2.value == 500
assert s2.weight == 500.0 / 1000
assert s2.price == 100
# now allocate and incur commission fee
s1.allocate(500, 'c1')
assert m.capital == 0
assert m.value == 999
assert m.price == 99.9
assert s1.value == 499
assert s1.weight == 499.0 / 999.0
assert s1.price == 99.8
assert s2.value == 500
assert s2.weight == 500.0 / 999.0
assert s2.price == 100
def test_security_setup_prices():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert c1.price == 105
assert len(c1.prices) == 1
assert c1.prices[0] == 105
assert c2.price == 95
assert len(c2.prices) == 1
assert c2.prices[0] == 95
# now with setup
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
assert c1.price == 105
assert len(c1.prices) == 1
assert c1.prices[0] == 105
assert c2.price == 95
assert len(c2.prices) == 1
assert c2.prices[0] == 95
def test_strategybase_multiple_calls_preset_secs():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('s', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=5)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data.c2[dts[0]] = 95
data.c1[dts[1]] = 95
data.c2[dts[2]] = 95
data.c2[dts[3]] = 95
data.c2[dts[4]] = 95
data.c1[dts[4]] = 105
s.setup(data)
# define strategy logic
def algo(target):
# close out any open positions
target.flatten()
# get stock w/ lowest price
c = target.universe.ix[target.now].idxmin()
# allocate all capital to that stock
target.allocate(target.value, c)
# replace run logic
s.run = algo
# start w/ 1000
s.adjust(1000)
# loop through dates manually
i = 0
# update t0
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1000
# run t0
s.run(s)
assert len(s.children) == 2
assert s.value == 1000
assert s.capital == 50
assert c2.value == 950
assert c2.weight == 950.0 / 1000
assert c2.price == 95
# update out t0
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1000
assert s.capital == 50
assert c2.value == 950
assert c2.weight == 950.0 / 1000
assert c2.price == 95
# update t1
i = 1
s.update(dts[i])
assert s.value == 1050
assert s.capital == 50
assert len(s.children) == 2
assert c2.value == 1000
assert c2.weight == 1000.0 / 1050.
assert c2.price == 100
# run t1 - close out c2, open c1
s.run(s)
assert c1.value == 1045
assert c1.weight == 1045.0 / 1050
assert c1.price == 95
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 100
assert len(s.children) == 2
assert s.value == 1050
assert s.capital == 5
# update out t1
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1050
assert s.capital == 5
assert c1.value == 1045
assert c1.weight == 1045.0 / 1050
assert c1.price == 95
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 100
# update t2
i = 2
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 5
assert c1.value == 1100
assert c1.weight == 1100.0 / 1105
assert c1.price == 100
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 95
# run t2
s.run(s)
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# update out t2
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# update t3
i = 3
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# run t3
s.run(s)
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# update out t3
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# update t4
i = 4
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
# accessing price should refresh - this child has been idle for a while -
# must make sure we can still have a fresh prices
assert c1.price == 105
assert len(c1.prices) == 5
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# run t4
s.run(s)
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 105
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
# update out t4
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1105
assert s.capital == 60
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 105
assert c2.value == 1045
assert c2.weight == 1045.0 / 1105
assert c2.price == 95
def test_strategybase_tree_decimal_position_rebalance():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
s.use_integer_positions(False)
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000.2)
s.rebalance(0.42, 'c1')
s.rebalance(0.58, 'c2')
aae(c1.value, 420.084)
aae(c2.value, 580.116)
aae(c1.value + c2.value, 1000.2)
def test_strategybase_tree_rebalance_level2():
c1 = SecurityBase('c1')
c12 = copy.deepcopy(c1)
c2 = SecurityBase('c2')
c22 = copy.deepcopy(c2)
s1 = StrategyBase('s1', [c1, c2])
s2 = StrategyBase('s2', [c12, c22])
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
c1 = s1['c1']
c2 = s1['c2']
c12 = s2['c1']
c22 = s2['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
assert m.value == 1000
assert m.capital == 1000
assert s1.value == 0
assert s2.value == 0
assert c1.value == 0
assert c2.value == 0
# now rebalance child s1 - since its children are 0, no waterfall alloc
m.rebalance(0.5, 's1')
assert s1.value == 500
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
# now allocate directly to child of child
s1.rebalance(0.4, 'c1')
assert s1.value == 500
assert s1.capital == 500 - 200
assert c1.value == 200
assert c1.weight == 200.0 / 500
assert c1.position == 2
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
assert c12.value == 0
# now rebalance child s1 again and make sure c1 also gets proportional
# increase
m.rebalance(0.8, 's1')
assert s1.value == 800
aae(m.capital, 200, 1)
assert m.value == 1000
assert s1.weight == 800 / 1000
assert s2.weight == 0
assert c1.value == 300.0
assert c1.weight == 300.0 / 800
assert c1.position == 3
# now rebalance child s1 to 0 - should close out s1 and c1 as well
m.rebalance(0, 's1')
assert s1.value == 0
assert m.capital == 1000
assert m.value == 1000
assert s1.weight == 0
assert s2.weight == 0
assert c1.weight == 0
def test_strategybase_tree_rebalance_base():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
s.set_commissions(lambda q, p: 1)
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# check that 2 rebalances of equal weight lead to two different allocs
# since value changes after first call
s.rebalance(0.5, 'c1')
assert c1.position == 4
assert c1.value == 400
assert s.capital == 1000 - 401
assert s.value == 999
assert c1.weight == 400.0 / 999
assert c2.weight == 0
s.rebalance(0.5, 'c2')
assert c2.position == 4
assert c2.value == 400
assert s.capital == 1000 - 401 - 401
assert s.value == 998
assert c2.weight == 400.0 / 998
assert c1.weight == 400.0 / 998
# close out everything
s.flatten()
# adjust to get back to 1000
s.adjust(4)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# now rebalance but set fixed base
base = s.value
s.rebalance(0.5, 'c1', base=base)
assert c1.position == 4
assert c1.value == 400
assert s.capital == 1000 - 401
assert s.value == 999
assert c1.weight == 400.0 / 999
assert c2.weight == 0
s.rebalance(0.5, 'c2', base=base)
assert c2.position == 4
assert c2.value == 400
assert s.capital == 1000 - 401 - 401
assert s.value == 998
assert c2.weight == 400.0 / 998
assert c1.weight == 400.0 / 998
def test_strategy_tree_proper_return_calcs():
s1 = StrategyBase('s1')
s2 = StrategyBase('s2')
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data.loc['c1', dts[1]] = 105
data.loc['c2', dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
# since children have w == 0 this should stay in s
m.allocate(1000)
assert m.value == 1000
assert m.capital == 1000
assert m.price == 100
assert s1.value == 0
assert s2.value == 0
# now allocate directly to child
s1.allocate(500)
assert m.capital == 500
assert m.value == 1000
assert m.price == 100
assert s1.value == 500
assert s1.weight == 500.0 / 1000
assert s1.price == 100
assert s2.weight == 0
# allocate to child2 via master method
m.allocate(500, 's2')
assert m.capital == 0
assert m.value == 1000
assert m.price == 100
assert s1.value == 500
assert s1.weight == 500.0 / 1000
assert s1.price == 100
assert s2.value == 500
assert s2.weight == 500.0 / 1000
assert s2.price == 100
# now allocate and incur commission fee
s1.allocate(500, 'c1')
assert m.capital == 0
assert m.value == 1000
assert m.price == 100
assert s1.value == 500
assert s1.weight == 500.0 / 1000
assert s1.price == 100
assert s2.value == 500
assert s2.weight == 500.0 / 1000.0
assert s2.price == 100
def test_strategybase_tree_rebalance_base():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
s.adjust(1000)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# check that 2 rebalances of equal weight lead to two different allocs
# since value changes after first call
s.rebalance(0.5, 'c1')
assert c1.position == 5
assert c1.value == 500
assert s.capital == 1000 - 501
assert s.value == 999
assert c1.weight == 500.0 / 999
assert c2.weight == 0
s.rebalance(0.5, 'c2')
assert c2.position == 4
assert c2.value == 400
assert s.capital == 1000 - 501 - 401
assert s.value == 998
assert c2.weight == 400.0 / 998
assert c1.weight == 500.0 / 998
# close out everything
s.flatten()
# adjust to get back to 1000
s.adjust(4)
assert s.value == 1000
assert s.capital == 1000
assert c1.value == 0
assert c2.value == 0
# now rebalance but set fixed base
base = s.value
s.rebalance(0.5, 'c1', base=base)
assert c1.position == 5
assert c1.value == 500
assert s.capital == 1000 - 501
assert s.value == 999
assert c1.weight == 500.0 / 999
assert c2.weight == 0
s.rebalance(0.5, 'c2', base=base)
assert c2.position == 5
assert c2.value == 500
assert s.capital == 1000 - 501 - 501
assert s.value == 998
assert c2.weight == 500.0 / 998
assert c1.weight == 500.0 / 998
def test_set_commissions():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
s.setup(data)
s.update(dts[0])
s.adjust(1000)
s.allocate(500, 'c1')
assert s.capital == 499
s.set_commissions(lambda x, y: 0.0)
s.allocate(-500, 'c1')
assert s.capital == 999
def test_strategybase_prices():
dts = pd.date_range('2010-01-01', periods=21)
rawd = [13.555, 13.75, 14.16, 13.915, 13.655,
13.765, 14.02, 13.465, 13.32, 14.65,
14.59, 14.175, 13.865, 13.865, 13.89,
13.85, 13.565, 13.47, 13.225, 13.385,
12.89]
data = pd.DataFrame(index=dts, data=rawd, columns=['a'])
s = StrategyBase('s')
s.setup(data)
# buy 100 shares on day 1 - hold until end
# just enough to buy 100 shares + 1$ commission
s.adjust(1356.50)
s.update(dts[0])
# allocate all capital to child a
# a should be dynamically created and should have
# 100 shares allocated. s.capital should be 0
s.allocate(s.value, 'a')
assert s.capital == 0
assert s.value == 1355.50
assert len(s.children) == 1
aae(s.price, 99.92628, 5)
a = s['a']
assert a.position == 100
assert a.value == 1355.50
assert a.weight == 1
assert a.price == 13.555
assert len(a.prices) == 1
# update through all dates and make sure price is ok
s.update(dts[1])
aae(s.price, 101.3638, 4)
s.update(dts[2])
aae(s.price, 104.3863, 4)
s.update(dts[3])
aae(s.price, 102.5802, 4)
# finish updates and make sure ok at end
for i in range(4, 21):
s.update(dts[i])
assert len(s.prices) == 21
aae(s.prices[-1], 95.02396, 5)
aae(s.prices[-2], 98.67306, 5)
def test_outlays():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
# allocate 1000 to strategy
s.adjust(1000)
# now let's see what happens when we allocate 500 to each child
c1.allocate(500)
c2.allocate(500)
# out update
s.update(dts[i])
assert c1.data['outlay'][dts[0]] == (4 * 105)
assert c2.data['outlay'][dts[0]] == (5 * 95)
i = 1
s.update(dts[i], data.ix[dts[i]])
c1.allocate(-400)
c2.allocate(100)
# out update
s.update(dts[i])
print c1.data['outlay']
assert c1.data['outlay'][dts[1]] == (-4 * 100)
assert c2.data['outlay'][dts[1]] == 100
def test_strategybase_multiple_calls_preset_secs():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('s', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=5)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data.c2[dts[0]] = 95
data.c1[dts[1]] = 95
data.c2[dts[2]] = 95
data.c2[dts[3]] = 95
data.c2[dts[4]] = 95
data.c1[dts[4]] = 105
s.setup(data)
# define strategy logic
def algo(target):
# close out any open positions
target.flatten()
# get stock w/ lowest price
c = target.universe.ix[target.now].idxmin()
# allocate all capital to that stock
target.allocate(target.value, c)
# replace run logic
s.run = algo
# start w/ 1000
s.adjust(1000)
# loop through dates manually
i = 0
# update t0
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1000
# run t0
s.run(s)
assert len(s.children) == 2
assert s.value == 999
assert s.capital == 49
assert c2.value == 950
assert c2.weight == 950.0 / 999
assert c2.price == 95
# update out t0
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 999
assert s.capital == 49
assert c2.value == 950
assert c2.weight == 950.0 / 999
assert c2.price == 95
# update t1
i = 1
s.update(dts[i])
assert s.value == 1049
assert s.capital == 49
assert len(s.children) == 2
assert c2.value == 1000
assert c2.weight == 1000.0 / 1049.0
assert c2.price == 100
# run t1 - close out c2, open c1
s.run(s)
assert c1.value == 1045
assert c1.weight == 1045.0 / 1047
assert c1.price == 95
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 100
assert len(s.children) == 2
assert s.value == 1047
assert s.capital == 2
# update out t1
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1047
assert s.capital == 2
assert c1.value == 1045
assert c1.weight == 1045.0 / 1047
assert c1.price == 95
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 100
# update t2
i = 2
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1102
assert s.capital == 2
assert c1.value == 1100
assert c1.weight == 1100.0 / 1102
assert c1.price == 100
assert c2.value == 0
assert c2.weight == 0
assert c2.price == 95
# run t2
s.run(s)
assert len(s.children) == 2
assert s.value == 1100
assert s.capital == 55
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1100
assert c2.price == 95
# update out t2
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1100
assert s.capital == 55
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1100
assert c2.price == 95
# update t3
i = 3
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1100
assert s.capital == 55
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1100
assert c2.price == 95
# run t3
s.run(s)
assert len(s.children) == 2
assert s.value == 1098
assert s.capital == 53
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1098
assert c2.price == 95
# update out t3
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1098
assert s.capital == 53
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 100
assert c2.value == 1045
assert c2.weight == 1045.0 / 1098
assert c2.price == 95
# update t4
i = 4
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1098
assert s.capital == 53
assert c1.value == 0
assert c1.weight == 0
# accessing price should refresh - this child has been idle for a while -
# must make sure we can still have a fresh prices
assert c1.price == 105
assert len(c1.prices) == 5
assert c2.value == 1045
assert c2.weight == 1045.0 / 1098
assert c2.price == 95
# run t4
s.run(s)
assert len(s.children) == 2
assert s.value == 1096
assert s.capital == 51
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 105
assert c2.value == 1045
assert c2.weight == 1045.0 / 1096
assert c2.price == 95
# update out t4
s.update(dts[i])
assert len(s.children) == 2
assert s.value == 1096
assert s.capital == 51
assert c1.value == 0
assert c1.weight == 0
assert c1.price == 105
assert c2.value == 1045
assert c2.weight == 1045.0 / 1096
assert c2.price == 95
def test_strategybase_tree_allocate_level2():
c1 = SecurityBase('c1')
c12 = copy.deepcopy(c1)
c2 = SecurityBase('c2')
c22 = copy.deepcopy(c2)
s1 = StrategyBase('s1', [c1, c2])
s2 = StrategyBase('s2', [c12, c22])
m = StrategyBase('m', [s1, s2])
s1 = m['s1']
s2 = m['s2']
c1 = s1['c1']
c2 = s1['c2']
c12 = s2['c1']
c22 = s2['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[1]] = 105
data['c2'][dts[1]] = 95
m.setup(data)
i = 0
m.update(dts[i], data.ix[dts[i]])
m.adjust(1000)
# since children have w == 0 this should stay in s
m.allocate(1000)
assert m.value == 1000
assert m.capital == 1000
assert s1.value == 0
assert s2.value == 0
assert c1.value == 0
assert c2.value == 0
# now allocate directly to child
s1.allocate(500)
assert s1.value == 500
assert m.capital == 1000 - 500
assert m.value == 1000
assert s1.weight == 500.0 / 1000
assert s2.weight == 0
# now allocate directly to child of child
c1.allocate(200)
assert s1.value == 499
assert s1.capital == 500 - 201
assert c1.value == 200
assert c1.weight == 200.0 / 499
assert c1.position == 2
assert m.capital == 1000 - 500
assert m.value == 999
assert s1.weight == 499.0 / 999
assert s2.weight == 0
assert c12.value == 0
def test_fixed_commissions():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
# fixed $1 commission per transaction
s.set_commissions(lambda q, p: 1)
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
# allocate 1000 to strategy
s.adjust(1000)
# now let's see what happens when we allocate 500 to each child
c1.allocate(500)
c2.allocate(500)
# out update
s.update(dts[i])
assert c1.value == 400
assert c2.value == 400
assert s.capital == 198
# de-alloc 100 from c1. This should force c1 to sell 2 units to raise at
# least 100 (because of commissions)
c1.allocate(-100)
s.update(dts[i])
assert c1.value == 200
assert s.capital == 198 + 199
# allocate 100 to c2. This should leave things unchaged, since c2 cannot
# buy one unit since the commission will cause total outlay to exceed
# allocation
c2.allocate(100)
s.update(dts[i])
assert c2.value == 400
assert s.capital == 198 + 199
# ok try again w/ 101 allocation. This time, it should work
c2.allocate(101)
s.update(dts[i])
assert c2.value == 500
assert s.capital == 198 + 199 - 101
# ok now let's close the whole position. Since we are closing, we expect
# the allocation to go through, even though the outlay > amount
c2.allocate(-500)
s.update(dts[i])
assert c2.value == 0
assert s.capital == 198 + 199 - 101 + 499
# now we are going to go short c2
# we want to 'raise' 100 dollars. Since we need at a minimum 100, but we
# also have commissions, we will actually short 2 units in order to raise
# at least 100
c2.allocate(-100)
s.update(dts[i])
assert c2.value == -200
assert s.capital == 198 + 199 - 101 + 499 + 199
def test_fail_if_root_value_negative():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
s.adjust(-100)
# trigger update
s.update(dts[0])
assert s.bankrupt
# make sure only triggered if root negative
c1 = StrategyBase('c1')
s = StrategyBase('s', children=[c1])
c1 = s['c1']
s.setup(data)
s.adjust(1000)
c1.adjust(-100)
s.update(dts[0])
# now make it trigger
c1.adjust(-1000)
# trigger update
s.update(dts[0])
assert s.bankrupt
def test_set_commissions():
s = StrategyBase('s')
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
s.set_commissions(lambda x, y: 1.0)
s.setup(data)
s.update(dts[0])
s.adjust(1000)
s.allocate(500, 'c1')
assert s.capital == 599
s.set_commissions(lambda x, y: 0.0)
s.allocate(-400, 'c1')
assert s.capital == 999
def test_fail_if_0_base_in_return_calc():
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
# must setup tree because if not negative root error pops up first
c1 = StrategyBase('c1')
s = StrategyBase('s', children=[c1])
c1 = s['c1']
s.setup(data)
s.adjust(1000)
c1.adjust(100)
s.update(dts[0])
c1.adjust(-100)
s.update(dts[1])
try:
c1.adjust(-100)
s.update(dts[1])
assert False
except ZeroDivisionError, e:
if 'Could not update' not in str(e):
assert False
def test_fail_if_0_base_in_return_calc():
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
# must setup tree because if not negative root error pops up first
c1 = StrategyBase('c1')
s = StrategyBase('s', children=[c1])
c1 = s['c1']
s.setup(data)
s.adjust(1000)
c1.adjust(100)
s.update(dts[0])
c1.adjust(-100)
s.update(dts[1])
try:
c1.adjust(-100)
s.update(dts[1])
assert False
except ZeroDivisionError as e:
if 'Could not update' not in str(e):
assert False
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 100
data['c2'][dts[0]] = 95
s.setup(data)
try:
s.adjust(-100)
# trigger update
s.update(dts[0])
assert False
except ValueError, e:
if not 'negative root node value' in str(e):
assert False
# make sure only triggered if root negative
c1 = StrategyBase('c1')
s = StrategyBase('s', children=[c1])
s.setup(data)
s.adjust(1000)
c1.adjust(-100)
s.update(dts[0])
# now make it trigger
try:
c1.adjust(-1000)
# trigger update
s.update(dts[0])
assert False
except ValueError, e:
if not 'negative root node value' in str(e):
def test_outlays():
c1 = SecurityBase('c1')
c2 = SecurityBase('c2')
s = StrategyBase('p', [c1, c2])
c1 = s['c1']
c2 = s['c2']
dts = pd.date_range('2010-01-01', periods=3)
data = pd.DataFrame(index=dts, columns=['c1', 'c2'], data=100)
data['c1'][dts[0]] = 105
data['c2'][dts[0]] = 95
s.setup(data)
i = 0
s.update(dts[i], data.ix[dts[i]])
# allocate 1000 to strategy
s.adjust(1000)
# now let's see what happens when we allocate 500 to each child
c1.allocate(500)
c2.allocate(500)
# out update
s.update(dts[i])
assert c1.data['outlay'][dts[0]] == (4 * 105)
assert c2.data['outlay'][dts[0]] == (5 * 95)
i = 1
s.update(dts[i], data.ix[dts[i]])
c1.allocate(-400)
c2.allocate(100)
# out update
s.update(dts[i])
#print(c1.data['outlay'])
assert c1.data['outlay'][dts[1]] == (-4 * 100)
assert c2.data['outlay'][dts[1]] == 100
