| Overall Statistics |
|
Total Orders 2 Average Win 0% Average Loss 0% Compounding Annual Return 13.298% Drawdown 7.000% Expectancy 0 Start Equity 10000000 End Equity 10061759.4 Net Profit 0.618% Sharpe Ratio 0.326 Sortino Ratio 0.572 Probabilistic Sharpe Ratio 44.235% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 1.445 Beta -2.461 Annual Standard Deviation 0.327 Annual Variance 0.107 Information Ratio -0.983 Tracking Error 0.445 Treynor Ratio -0.043 Total Fees $735.30 Estimated Strategy Capacity $2900000000.00 Lowest Capacity Asset NQ Y6URRFPZ86BL Portfolio Turnover 37.84% |
# region imports
from AlgorithmImports import *
# endregion
class FocusedFluorescentYellowGoshawk(QCAlgorithm):
def initialize(self):
self.set_start_date(2023, 1, 7)
self.set_end_date(2023, 1, 24)
self.set_cash(10000000)
tickers = [
Futures.Indices.NASDAQ_100_E_MINI,
Futures.Indices.SP_500_E_MINI
]
self._futures = []
for ticker in tickers:
future = self.add_future(ticker)
self.log(f"{future.symbol}: {future.symbol_properties.contract_multiplier}")
self._futures.append(future)
def on_data(self, data):
if self.portfolio.invested:
return
usd_per_position = 1_000_000
self.market_order(self._futures[0].mapped, int(usd_per_position / self.securities[self._futures[0].mapped].price))
self.market_order(self._futures[1].mapped, -int(usd_per_position / self.securities[self._futures[1].mapped].price))