| Overall Statistics |
|
Total Orders 72 Average Win 0.59% Average Loss -0.12% Compounding Annual Return -0.885% Drawdown 11.200% Expectancy 1.649 Start Equity 2000000 End Equity 1955116 Net Profit -2.244% Sharpe Ratio -1.358 Sortino Ratio -0.212 Probabilistic Sharpe Ratio 1.009% Loss Rate 56% Win Rate 44% Profit-Loss Ratio 4.96 Alpha -0.052 Beta -0.003 Annual Standard Deviation 0.038 Annual Variance 0.001 Information Ratio -1.004 Tracking Error 0.143 Treynor Ratio 16.104 Total Fees $0.00 Estimated Strategy Capacity $3000.00 Lowest Capacity Asset SPXW YA41I8A1DZPQ|SPX 31 Portfolio Turnover 0.01% |
from AlgorithmImports import *
from datetime import datetime, timedelta
class FedAnnouncementIronCondorStrategy(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 5, 13)
self.SetEndDate(2024, 12, 1)
self.SetCash(2000000)
self.SetTimeZone(TimeZones.NewYork)
# Add SPX index and options
self.index = self.AddIndex("SPX")
# Add SPXW options with iron condor filter
option = self.AddIndexOption(self.index.Symbol, "SPXW")
option.SetFilter(lambda x: x.IncludeWeeklys().IronCondor(0, 20, 40))
self._symbol = option.Symbol
# Risk management parameters
self.max_portfolio_risk = 0.20 # 20%! max risk per trade
self.profit_target = 1.5 # 150% of initial credit
self.stop_loss = 1 # 150% of max potential loss
self.trade_open = False
self.initial_credit = 0
self.max_potential_loss = 0
self.target_delta = 0.15 # Target delta for option selection
# Kurtosis Open IV distribution HIGH dates
# self.announcement_dates = [
# datetime(2022, 5, 16), datetime(2022, 5, 18), datetime(2022, 5, 19), datetime(2022, 5, 20),
# datetime(2022, 5, 23), datetime(2022, 5, 25), datetime(2022, 5, 26), datetime(2022, 5, 31),
# datetime(2022, 6, 3), datetime(2022, 6, 7), datetime(2022, 6, 8), datetime(2022, 6, 9),
# datetime(2022, 6, 14), datetime(2022, 6, 15), datetime(2022, 6, 16), datetime(2022, 6, 17),
# datetime(2022, 6, 27), datetime(2022, 6, 28), datetime(2022, 6, 29), datetime(2022, 6, 30),
# datetime(2022, 7, 6), datetime(2022, 7, 20), datetime(2022, 7, 21), datetime(2022, 7, 28),
# datetime(2022, 8, 1), datetime(2022, 8, 11), datetime(2022, 8, 15), datetime(2022, 8, 16),
# datetime(2022, 9, 13), datetime(2022, 9, 14), datetime(2022, 9, 16), datetime(2022, 9, 21),
# datetime(2022, 9, 28), datetime(2022, 9, 29), datetime(2022, 10, 5), datetime(2022, 10, 7),
# datetime(2022, 10, 12), datetime(2022, 10, 14), datetime(2022, 10, 19), datetime(2022, 10, 24),
# datetime(2022, 10, 25), datetime(2022, 11, 2), datetime(2022, 11, 9), datetime(2022, 11, 23),
# datetime(2022, 11, 28), datetime(2022, 12, 1), datetime(2022, 12, 5), datetime(2022, 12, 7),
# datetime(2022, 12, 14), datetime(2023, 1, 12), datetime(2023, 1, 25), datetime(2023, 1, 30),
# datetime(2023, 2, 1), datetime(2023, 2, 14), datetime(2023, 3, 13), datetime(2023, 3, 15),
# datetime(2023, 3, 16), datetime(2023, 3, 22), datetime(2023, 3, 30), datetime(2023, 4, 3),
# datetime(2023, 4, 4), datetime(2023, 4, 19), datetime(2023, 5, 1), datetime(2023, 6, 5),
# datetime(2023, 6, 12), datetime(2023, 6, 14), datetime(2023, 7, 19), datetime(2023, 10, 23),
# ]
self.announcement_dates = [
datetime(2022, 5, 17), datetime(2022, 5, 19), datetime(2022, 5, 22), datetime(2022, 5, 24), datetime(2022, 5, 27), datetime(2022, 6, 2), datetime(2022, 6, 5), datetime(2022, 6, 10), datetime(2022, 6, 13), datetime(2022, 6, 18), datetime(2022, 6, 22), datetime(2022, 6, 26), datetime(2022, 7, 3), datetime(2022, 7, 15), datetime(2022, 7, 22), datetime(2022, 7, 25), datetime(2022, 8, 4), datetime(2022, 8, 9), datetime(2022, 8, 12), datetime(2022, 8, 17), datetime(2022, 9, 8), datetime(2022, 9, 12), datetime(2022, 9, 17), datetime(2022, 9, 23), datetime(2022, 9, 27), datetime(2022, 10, 2), datetime(2022, 10, 9), datetime(2022, 10, 13), datetime(2022, 10, 16), datetime(2022, 10, 20), datetime(2022, 10, 26), datetime(2022, 11, 5), datetime(2022, 11, 12), datetime(2022, 11, 19), datetime(2022, 11, 25), datetime(2022, 12, 3), datetime(2022, 12, 6), datetime(2022, 12, 10), datetime(2022, 12, 15), datetime(2023, 1, 14), datetime(2023, 1, 22), datetime(2023, 1, 27), datetime(2023, 2, 5), datetime(2023, 2, 11), datetime(2023, 3, 8), datetime(2023, 3, 14), datetime(2023, 3, 18), datetime(2023, 3, 25), datetime(2023, 3, 29), datetime(2023, 4, 2), datetime(2023, 4, 7), datetime(2023, 4, 16), datetime(2023, 4, 20), datetime(2023, 5, 3), datetime(2023, 6, 8), datetime(2023, 6, 11), datetime(2023, 6, 15), datetime(2023, 7, 14), datetime(2023, 10, 20)]
# Schedule to monitor and potentially close positions
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(15, 50), self.CheckPositionManagement)
def OnData(self, slice):
if self.portfolio.invested or not self.Time.date() in [date.date() for date in self.announcement_dates]:
return
chain = slice.OptionChains.get(self._symbol)
if not chain:
return
# Find contracts with the farthest expiry
expiry = max([x.Expiry for x in chain])
chain = sorted([x for x in chain if x.Expiry == expiry], key=lambda x: x.Strike)
# Delta-filtered option selection
put_contracts = [x for x in chain if x.Right == OptionRight.PUT and abs(x.Greeks.Delta) <= self.target_delta]
call_contracts = [x for x in chain if x.Right == OptionRight.CALL and abs(x.Greeks.Delta) <= self.target_delta]
if len(call_contracts) < 2 or len(put_contracts) < 2:
return
# Select strategy legs with delta consideration
near_call = min(call_contracts, key=lambda x: abs(x.Greeks.Delta - self.target_delta))
far_call = min([x for x in call_contracts if x.Strike > near_call.Strike],
key=lambda x: abs(x.Greeks.Delta - self.target_delta))
near_put = min(put_contracts, key=lambda x: abs(x.Greeks.Delta + self.target_delta))
far_put = min([x for x in put_contracts if x.Strike < near_put.Strike],
key=lambda x: abs(x.Greeks.Delta + self.target_delta))
# Calculate credit and potential loss
credit = (near_call.BidPrice - far_call.AskPrice) + (near_put.BidPrice - far_put.AskPrice)
spread_width = max(far_call.Strike - near_call.Strike, near_put.Strike - far_put.Strike)
max_potential_loss = spread_width * 100 - credit * 100
# Position sizing
total_portfolio_value = self.Portfolio.TotalPortfolioValue
max_trade_risk = total_portfolio_value * self.max_portfolio_risk
contracts = int(max_trade_risk / max_potential_loss)
if contracts == 0:
return
# Create iron condor strategy
iron_condor = OptionStrategies.IronCondor(
self._symbol,
far_put.Strike,
near_put.Strike,
near_call.Strike,
far_call.Strike,
expiry)
# Buy the iron condor
self.Buy(iron_condor, contracts)
# Store trade details
self.initial_credit = credit * 100 * contracts
self.max_potential_loss = max_potential_loss * contracts
self.trade_open = True
self.Debug(f"Opened iron condor at {self.Time}, Contracts: {contracts}, Credit: ${self.initial_credit:.2f}")
def CheckPositionManagement(self):
if not self.Portfolio.Invested:
return
# Calculate total unrealized PnL
total_pnl = sum([holding.UnrealizedProfit for holding in self.Portfolio.Values if holding.Invested])
# Profit target exit
if total_pnl >= self.initial_credit * self.profit_target:
self.Liquidate()
self.Debug(f"Closed position at profit target on {self.Time}")
self.trade_open = False
# Stop loss exit
elif total_pnl <= -self.max_potential_loss * self.stop_loss:
self.Liquidate()
self.Debug(f"Closed position at stop loss on {self.Time}")
self.trade_open = False