| Overall Statistics |
|
Total Trades 6 Average Win 0.63% Average Loss -0.07% Compounding Annual Return 6.498% Drawdown 0.300% Expectancy 4.100 Net Profit 1.093% Sharpe Ratio 3.811 Probabilistic Sharpe Ratio 91.319% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 9.20 Alpha 0.045 Beta -0.032 Annual Standard Deviation 0.011 Annual Variance 0 Information Ratio -0.19 Tracking Error 0.107 Treynor Ratio -1.359 Total Fees $11.10 Estimated Strategy Capacity $62000000.00 Lowest Capacity Asset MGC VOFJUCDY9XNH |
from AlgorithmImports import *
class USFuturesDataAlgorithm(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2013, 12, 20)
self.SetEndDate(2014, 2, 20)
self.SetCash(1000000)
# Requesting data
self.mini_gold = self.AddFuture(Futures.Metals.Gold)
self.mini_gold.SetFilter(0, 90)
self.micro_gold = self.AddFuture(Futures.Metals.MicroGold)
self.micro_gold.SetFilter(0, 90)
self.contract = {self.mini_gold.Symbol: None, self.micro_gold.Symbol: None}
def OnData(self, data):
for kvp in data.FutureChains:
symbol = kvp.Key
if symbol in self.contract:
chain = kvp.Value
# Select the contract with the greatest open interest
most_liquid_contract = sorted(chain, key=lambda contract: contract.OpenInterest, reverse=True)[0]
if self.contract[symbol] is None or most_liquid_contract.Symbol != self.contract[symbol].Symbol:
if self.contract[symbol] is not None:
self.Liquidate(self.contract[symbol].Symbol)
self.contract[symbol] = most_liquid_contract
if symbol == self.mini_gold.Symbol:
self.MarketOrder(self.contract[symbol].Symbol, 1)
elif symbol == self.micro_gold.Symbol:
self.MarketOrder(self.contract[symbol].Symbol, -1)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
# Historical data
history = self.History(security.Symbol, 10, Resolution.Minute)
self.Debug(f"We got {len(history)} from our history request for {security.Symbol}")