| Overall Statistics |
|
Total Orders 120 Average Win 5.08% Average Loss -5.90% Compounding Annual Return -34.380% Drawdown 77.700% Expectancy -0.393 Start Equity 100000 End Equity 28221.44 Net Profit -71.779% Sharpe Ratio -0.167 Sortino Ratio -0.174 Probabilistic Sharpe Ratio 0.805% Loss Rate 67% Win Rate 33% Profit-Loss Ratio 0.86 Alpha -0.117 Beta 0.191 Annual Standard Deviation 0.621 Annual Variance 0.386 Information Ratio -0.279 Tracking Error 0.627 Treynor Ratio -0.543 Total Fees $360.62 Estimated Strategy Capacity $0 Lowest Capacity Asset GNF WTDK2AZMTCZL Portfolio Turnover 9.58% |
#region imports
from AlgorithmImports import *
#endregion
class CommidityMomentumEffect(QCAlgorithm):
def initialize(self):
self.set_start_date(2015,1, 1)
self.set_end_date(2018, 1, 1)
self.set_cash(100000)
self.set_security_initializer(
BrokerageModelSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices))
)
symbols = [
Futures.Dairy.CASH_SETTLED_BUTTER,
Futures.Dairy.CASH_SETTLED_CHEESE,
Futures.Dairy.CLASS_III_MILK,
Futures.Dairy.DRY_WHEY,
Futures.Dairy.CLASS_IV_MILK,
Futures.Dairy.NONFAT_DRY_MILK,
Futures.Meats.LIVE_CATTLE,
Futures.Meats.FEEDER_CATTLE,
Futures.Meats.LEAN_HOGS,
Futures.Forestry.RANDOM_LENGTH_LUMBER
]
period = 252
self._data = {}
for symbol in symbols:
future = self.add_future(symbol,
resolution=Resolution.MINUTE,
extended_market_hours=True,
data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
data_mapping_mode=DataMappingMode.OPEN_INTEREST,
contract_depth_offset=0
)
future.set_leverage(1)
self._data[future.symbol] = SymbolData(self, future, period)
# Rebalance the portfolio every month
self._rebalance = self.time
def on_data(self, slice):
if self._rebalance <= self.time:
# sorted futures by 12-month return reversely
sorted_roc = sorted([x for x in self._data.values() if x.is_ready], key = lambda x: x.value, reverse=True)
number_futures = int(0.25*len(sorted_roc))
if number_futures == 0: return
longs = [x for x in sorted_roc[:number_futures]]
shorts = [x for x in sorted_roc[-number_futures:]]
for symbol, security_holding in self.portfolio.items():
# liquidate the futures which is no longer in the trading list
if security_holding.invested and symbol not in [x.mapped for x in longs + shorts]:
self.liquidate(symbol)
for long_ in longs:
qty = self.calculate_order_quantity(long_.mapped, 0.5/number_futures) // long_.multiplier
if qty:
self.market_order(long_.mapped, qty)
for short in shorts:
qty = self.calculate_order_quantity(short.mapped, -0.5/number_futures) // short.multiplier
if qty:
self.market_order(short.mapped, qty)
self._rebalance = Expiry.end_of_month(self.time)
class SymbolData:
def __init__(self, algorithm, future, period):
self._future = future
symbol = future.symbol
self._roc = algorithm.roc(symbol, period, Resolution.DAILY)
# warm up indicator
algorithm.warm_up_indicator(symbol, self._roc, Resolution.DAILY)
@property
def is_ready(self):
return self._roc.is_ready and self._future.mapped is not None
@property
def value(self):
return self._roc.current.value
@property
def mapped(self):
return self._future.mapped
@property
def multiplier(self):
return self._future.symbol_properties.contract_multiplier