wassname/catalyst
1
0
Fork 0
mirror of https://github.com/wassname/catalyst.git synced 2026-07-01 21:12:54 +08:00
Code Issues Packages Projects Releases Wiki Activity

Fix weights calculation to use portfolio value as denominator

Browse Source
This commit is contained in:
David Michalowicz
2017-06-07 15:46:45 -04:00
parent 94b1d5a40e
commit 49a7b78b2e
3 changed files with 26 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_algorithm.py
+20 -15
View File
@@ -1095,6 +1095,7 @@ class TestPositions(WithLogger,
ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-01-06', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 1000
ASSET_FINDER_EQUITY_SIDS = (1, 133)
@@ -1183,7 +1184,7 @@ class TestPositions(WithLogger,
self.asset_finder.retrieve_all(sids)
algo = TestPositionWeightsAlgorithm(
sids_and_amounts=zip(sids, [1, -1, 1]),
sids_and_amounts=zip(sids, [2, -1, 1]),
sim_params=self.sim_params,
env=self.env,
)
@@ -1192,25 +1193,29 @@ class TestPositions(WithLogger,
expected_position_weights = [
# No positions held on the first day.
pd.Series({}),
# Each equity's weight is its price times the number of shares
# held. For example, we hold a long position in equity_1 so its
# weight is (+95.0 * 1) = 95. For a futures contract, its weight is
# the unit price times number of shares held times the multiplier.
# For future_1000, this is (2.0 * 1 * 100) = 200.
# Each equity's position value is its price times the number of
# shares held. In this example, we hold a long position in 2 shares
# of equity_1 so its weight is (95.0 * 2) = 190.0 divided by the
# total portfolio value. The total portfolio value is the sum of
# cash ($905.00) plus the value of all equity positions.
#
# For a futures contract, its weight is the unit price times number
# of shares held times the multiplier. For future_1000, this is
# (2.0 * 1 * 100) = 200.0 divided by total portfolio value.
pd.Series({
equity_1: 95.0 / (95 + 95 + 200),
equity_133: -95.0 / (95 + 95 + 200),
future_1000: 200.0 / (95 + 95 + 200),
equity_1: 190.0 / (190.0 - 95.0 + 905.0),
equity_133: -95.0 / (190.0 - 95.0 + 905.0),
future_1000: 200.0 / (190.0 - 95.0 + 905.0),
}),
pd.Series({
equity_1: 100.0 / (100 + 100 + 200),
equity_133: -100.0 / (100 + 100 + 200),
future_1000: 200.0 / (100 + 100 + 200),
equity_1: 200.0 / (200.0 - 100.0 + 905.0),
equity_133: -100.0 / (200.0 - 100.0 + 905.0),
future_1000: 200.0 / (200.0 - 100.0 + 905.0),
}),
pd.Series({
equity_1: 105.0 / (105 + 105 + 200),
equity_133: -105.0 / (105 + 105 + 200),
future_1000: 200.0 / (105 + 105 + 200),
equity_1: 210.0 / (210.0 - 105.0 + 905.0),
equity_133: -105.0 / (210.0 - 105.0 + 905.0),
future_1000: 200.0 / (210.0 - 105.0 + 905.0),
}),
]