| Overall Statistics |
|
Total Orders 69 Average Win 14.22% Average Loss -10.58% Compounding Annual Return 45.182% Drawdown 66.300% Expectancy 0.233 Start Equity 100000 End Equity 169732.29 Net Profit 69.732% Sharpe Ratio 1.297 Sortino Ratio 2.237 Probabilistic Sharpe Ratio 33.905% Loss Rate 47% Win Rate 53% Profit-Loss Ratio 1.34 Alpha 1.572 Beta 1.2 Annual Standard Deviation 1.378 Annual Variance 1.898 Information Ratio 1.193 Tracking Error 1.348 Treynor Ratio 1.489 Total Fees $20732.10 Estimated Strategy Capacity $23000.00 Lowest Capacity Asset ABP RXBFGHC4AV6T Portfolio Turnover 19.40% |
from AlgorithmImports import *
from QuantConnect.DataSource import *
class USEnergyDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2020, 1, 1)
self.set_end_date(2021, 6, 1)
self.set_cash(100000)
# Requesting data
self.axas = self.add_equity("AXAS", Resolution.DAILY).symbol
us_energy_symbol = self.add_data(USEnergy, USEnergy.Petroleum.UnitedStates.WeeklyNetImportsOfTotalPetroleumProducts).symbol
# Historical data
history = self.history(USEnergy, us_energy_symbol, 60, Resolution.DAILY)
self.log(f"We got {len(history)} items from our history request")
# Get latest value for net imports of petroleum products
self.previous_value = history.loc[us_energy_symbol].values[-1, -1]
def on_data(self, slice: Slice) -> None:
# Gather the current net imports of petroleum products
points = slice.Get(USEnergy)
current_value = None
for point in points.Values:
current_value = point.Value
if current_value is None:
return
# Buy when net imports of petroleum products are increasing
if current_value > self.previous_value:
self.set_holdings(self.axas, 1)
# Short sell when net imports of petroleum products are decreasing
elif current_value < self.previous_value:
self.set_holdings(self.axas, -1)
self.previous_value = current_value