| Overall Statistics |
|
Total Orders 2208 Average Win 0.53% Average Loss -0.30% Compounding Annual Return -2.412% Drawdown 20.200% Expectancy 0.200 Start Equity 100000 End Equity 94462 Net Profit -5.538% Sharpe Ratio -0.437 Sortino Ratio -0.424 Probabilistic Sharpe Ratio 2.163% Loss Rate 56% Win Rate 44% Profit-Loss Ratio 1.75 Alpha -0.049 Beta 0.369 Annual Standard Deviation 0.111 Annual Variance 0.012 Information Ratio -0.357 Tracking Error 0.136 Treynor Ratio -0.132 Total Fees $1164.00 Estimated Strategy Capacity $1400000.00 Lowest Capacity Asset SPXW 32H6KRH2DRL9Q|SPX 31 Portfolio Turnover 1.13% |
#region imports
from AlgorithmImports import *
#endregion
"""
To support MANY symbols -- we would replace much of main with this.
we would create these symbols on equity/index and option add.
the UNIVERSE (set filter) stays in main.
self.strategies[underlying] = Consolidator( ... )
in on_data -- we loop through the self.strategies ...
call on_data, and pass it the slice.
We REMOVE all regions, except order_handling, from main.
EXAMPLE:
# <Class variables>
underlying_universe = ['SPY','QQQ','IWM']
# <Initialize>
self.strategies = {}
for underlying in underlying_universe:
# (special case for SPX -- or indexes probably -- diff list?)
if underlying in ['SPX','VIX']:
underlying_symbol = self.add_index(underlying).symbol
option = self.add_index_option(underlying_symbol, f'{underlying}W')
else:
underlying_symbol = self.add_equity(underlying).symbol
option = self.add_option(underlying)
option.SetFilter(lambda x: x.IncludeWeeklys().Strikes(0, 100).Expiration(timedelta(days=0), timedelta(days=1)))
self.strategies[underlying_symbol] = Consolidator(underlying_symbol, option.symbol, self)
# <OnData>
for underlying_symbol, strat in self.strategies.items():
strat.on_data(slice)
"""
class Consolidator:
def __init__(self, underlying_symbol, option_symbol, algo):
# universe created in init
self.a = algo
idx = underlying_symbol
self.underlying_symbol = underlying_symbol
self._symbol = option_symbol
self._contracts = 1
# Note -- we could simply pull this down via a history call (each day) -- but this is more efficient.
self.cons = self.a.consolidate(idx, timedelta(minutes=15), self.on_15m)
self.atr = AverageTrueRange(self.a.length, MovingAverageType.Simple)
self.register_indicator(idx, self.atr, self.cons)
self.warmup_data(idx)
self.open_prices = {}
self.long_ct = None
self.short_ct = None
# region Data Handling
def warmup_data(self, idx):
"""
auto-warmup, with history -- need to warmup with sufficient bars, to cover n bars at x interval.
(data formats changed -- be careful, volume NOT in the default history object)
# https://www.quantconnect.com/docs/v2/research-environment/datasets/indices
"""
hist = self.a.history([idx], self.a.length * 15 + 1, Resolution.Minute).loc[idx]
for row in hist.itertuples():
bar = TradeBar(row.Index, idx, row.open, row.high, row.low, row.close, 0)
self.cons.Update(bar)
self.a.log(f'successfully warmed up.')
def on_15m(self, bar: TradeBar) -> None:
self.atr.update(bar)
def on_data(self, data: Slice):
bars = data.bars
if not data.contains_key(self.underlying_symbol): return
ul = bars[self.underlying_symbol]
# first bar of day?
if (ul.end_time).hour == 9 and (ul.end_time).minute == 31:
open_price = ul.open
self.open_prices[ul.end_time.date()] = open_price
if self.IsReady:
self.sell_price = open_price + self.atr.current.value * self.a.entry_strike_atrs
self.buy_price = self.sell_price + self.atr.current.value * self.a.exit_strike_atrs
self.a.log(f'open: {open_price}, atr: {self.atr.current.value}, sell: {self.sell_price}, buy: {self.buy_price}')
# self.max_risk_pts = abs(self.sell_price - self.buy_price)
self.ul_stop_lvl = self.buy_price
if not self.a.portfolio[self.underlying_symbol].invested:
self.entry_logic(data)
else:
self.exit_logic()
# endregion
# region Strategy Logic
def entry_logic(self, slice):
"""
After recording the opening figures, the algorithm calculates the two components of the
credit spread. Both legs of the spread share the same day of expiration and are of equal size.
(Note: For testing purposes, both legs contain 1 contract each.) The strikes for each leg are
determined as follows:
- The Write Strike equals the Open Price of $XSP (9:30 AM EST) plus one ATR,
rounded to the nearest whole dollar.
- The Buy Strike equals the Write Strike plus 2 ATRs, rounded to the nearest whole dollar.
"""
if self.a.Portfolio[self.underlying_symbol].Invested:
return
chain = slice.OptionChains.get(self._symbol)
if not chain:
return
# nearest expiry
expiry = sorted(chain, key=lambda x: x.Expiry)[0].Expiry
self.a.log(f'date: {self.a.time.date()} -- expiry: {expiry}')
calls = [i for i in chain if i.Expiry == expiry and i.Right == OptionRight.Call]
if len(calls) < 1: return
# sorted the contracts according to their strike prices
calls = sorted(calls, key=lambda x: x.Strike)
sell_contract = sorted(calls, key=lambda x: abs(self.sell_price - x.strike))[0]
buy_contract = sorted(calls, key=lambda x: abs(self.buy_price - x.strike))[0]
self.short_ct = sell_contract
self.long_ct = buy_contract
self.a.log(f'symbol: {sell_contract.Symbol} -- {sell_contract.BidPrice} / {sell_contract.AskPrice}')
self.a.log(f'symbol: {buy_contract.Symbol} -- {buy_contract.BidPrice} / {buy_contract.AskPrice}')
self.max_gain, self.max_loss = self.credit_spread_max_gain_loss(buy_contract, sell_contract)
self.initialCredit = self.max_gain
self.a.Buy(buy_contract.Symbol, self._contracts)
self.a.Sell(sell_contract.Symbol, self._contracts)
def exit_logic(self):
"""
An unrealized loss of 50% or more of the spread's maximum loss.
● An unrealized gain exceeding 90% of the spreads maximum gain.
● The underlying price of $XSP surpasses our stop loss level.
Stop Loss Level = the Open Price of $XSP plus 2 ATRs.
"""
# so -- we sold it for 100, can buy it back for 50. pnl is 50.
# or -- we sold it for 100, can buy it back for 150, pnl is -50
current_pnl = self.initialCredit - self.spread_price()
# If our pnl if <= 50% of max loss -- stop out.
stop_condition = current_pnl <= self.a.pct_of_max_loss_stop * self.max_loss
if stop_condition:
self.a.log(f'stop condition: {current_pnl} < {self.a.pct_of_max_loss_stop * self.max_loss} -- ({self.a.pct_of_max_loss_stop} * {self.max_loss})')
self.a.liquidate(self.underlying_symbol, tag="SL")
# if our pnl is >= 90% of max gain -- take profit.
tgt_condition = current_pnl >= self.a.pct_of_max_gain_tgt * self.max_gain
if tgt_condition:
self.a.liquidate(self.underlying_symbol, tag="TP")
self.a.log(f'tp condition: {current_pnl} >= {self.pct_of_max_gain_tgt * self.max_gain} -- ({self.a.pct_of_max_gain_tgt} * {self.max_gain})')
ul_stop_condition = self.a.portfolio[self.underlying_symbol].price >= self.buy_price
if ul_stop_condition:
self.a.liquidate(self.underlying_symbol, tag="Underlying SL")
self.a.log(f"tp condition: {ul_stop_condition} = {self.a.portfolio[self.underlying_symbol].price} >= {self.buy_price}")
#endregion
# region Utilities
def spread_price(self):
""" Here we are really pricing the cost to exit """
if self.long_ct and self.short_ct:
# credit - debit
return self.short_ct.AskPrice - self.long_ct.BidPrice
def credit_spread_max_gain_loss(self, buy, sell):
"""This returns in points -- share points -- not $$"""
buyprice = buy.AskPrice
sellprice = sell.BidPrice
buystk = buy.Strike
sellstk = sell.Strike
stk_distance = abs(buystk - sellstk)
credit = sellprice - buyprice
max_gain = credit
max_loss = (stk_distance) - credit
self.a.log(f'Strike distance: {stk_distance}')
self.a.log(f'max gain: {credit}')
self.a.log(f'max loss: {max_loss}')
return max_gain, max_loss
@property
def IsReady(self):
return self.atr.is_ready
#endregion # region imports
from AlgorithmImports import *
from consolidator import Consolidator
# endregion
"""
0DTE Call Write
dev: @zoakes
Commit Equivalent History
- initial commits, data handling, aggregation, consolidator + time bars.
- handling of pricing, risk, reward, and basics of entry logic.
- handling of orders, entries exits, and pricing wrt risk.
Version History:
0.1 -- Initial Build
0.2 -- Index support performs better -- use that.
0.3 -- Consolidator (supported, not needed) -- for multi-product design.
NOTES:
XSP is not supported, SPX is
https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/algoseek/us-index-options#07-Supported-Assets
"""
class DeterminedSkyBlueFly(QCAlgorithm):
# # can create custom alerts, and a mailing list here
# # may start there -- we can extend as desired.
to_notify = [
"test@test.com"
]
length = 130
entry_strike_atrs = 1
exit_strike_atrs = 2
# exit strike atrs are counted from the entry strike
# -- i.e. it would be entry_strike_atrs + exit_strike_atrs from open
pct_of_max_loss_stop = .5
# .5 == 50%
pct_of_max_gain_tgt = .9
# .9 == 90%
target_dte = 0
_contracts = 1
_debug_lvl = 3
# higher debug_lvl is more details logged. (0 - 3)
def Initialize(self):
self.SetStartDate(2022,1,1)
# self.SetStartDate(2022, 10, 30)
# self.SetEndDate(2022,11,15)
self.SetCash(100000)
# Benchmark
self.spy = self.AddEquity("SPY", Resolution.Minute).symbol
self.set_benchmark(self.spy)
self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
# Index Options (SPX)
# https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options
self.idx = self.add_index("SPX").symbol
option = self.add_index_option(self.idx, "SPXW")
self.underlyingsymbol = self.idx
self._symbol = option.symbol
# Equity Options (SPY)
## self.idx = self.add_equity("SPY")
# self.idx = self.spy
# option = self.add_option("SPY")
# self.underlyingsymbol = self.idx
# self._symbol = option.symbol
# https://www.quantconnect.com/docs/v2/writing-algorithms/universes/equity-options
option.SetFilter(
lambda x: x.IncludeWeeklys()
.Strikes(-100, 100)
.Expiration(
timedelta(days=self.target_dte),
timedelta(days=self.target_dte + 1)
)
)
self.open_prices = {}
self.long_ct = None
self.short_ct = None
self.SetSecurityInitializer(self.reality_model)
# Note -- we could simply pull this down via a history call (each day) -- but this is more efficient.
self.cons = self.consolidate(self.idx, timedelta(minutes=15), self.on_15m)
self.atr = AverageTrueRange(self.length, MovingAverageType.Simple)
self.register_indicator(self.idx, self.atr, self.cons)
self.warmup_data(self.idx)
def reality_model(self, security):
security.SetFeeModel(InteractiveBrokersFeeModel())
# region Data Handling
def warmup_data(self, idx):
"""
auto-warmup, with history -- need to warmup with sufficient bars, to cover n bars at x interval.
(data formats changed -- be careful, volume NOT in the default history object)
# https://www.quantconnect.com/docs/v2/research-environment/datasets/indices
"""
hist = self.history([idx], self.length * 15 + 1, Resolution.Minute).loc[idx]
for row in hist.itertuples():
bar = TradeBar(row.Index, idx, row.open, row.high, row.low, row.close, 0)
self.cons.Update(bar)
self.log(f'successfully warmed up.')
def on_15m(self, bar: TradeBar) -> None:
self.atr.update(bar)
def OnData(self, data: Slice):
bars = data.bars
if not data.contains_key(self.underlyingsymbol): return
ul = bars[self.underlyingsymbol]
# first bar of day?
if (ul.end_time).hour == 9 and (ul.end_time).minute == 31:
open_price = ul.open
self.open_prices[ul.end_time.date()] = open_price
if self.IsReady:
self.sell_price = open_price - self.atr.current.value * self.entry_strike_atrs
self.buy_price = self.sell_price - self.atr.current.value * self.exit_strike_atrs
if self._debug_lvl >= 2:
self.log(f'open: {open_price}, atr: {self.atr.current.value}, \
sell: {self.sell_price}, buy: {self.buy_price}')
# self.max_risk_pts = abs(self.sell_price - self.buy_price)
self.ul_stop_lvl = self.buy_price
if not self.portfolio.invested:
self.entry_logic(data)
else:
self.exit_logic()
#endregion
# region Strategy Logic
def entry_logic(self, slice):
"""
After recording the opening figures, the algorithm calculates the two components of the
credit spread. Both legs of the spread share the same day of expiration and are of equal size.
(Note: For testing purposes, both legs contain 1 contract each.) The strikes for each leg are
determined as follows:
- The Write Strike equals the Open Price of $XSP (9:30 AM EST) plus one ATR,
rounded to the nearest whole dollar.
- The Buy Strike equals the Write Strike plus 2 ATRs, rounded to the nearest whole dollar.
"""
if self.Portfolio.Invested:
return
chain = slice.OptionChains.get(self._symbol)
if not chain:
return
# nearest expiry
expiry = sorted(chain, key=lambda x: x.Expiry)[0].Expiry
if self._debug_lvl >= 2: self.log(f'date: {self.time.date()} -- expiry: {expiry}')
calls = [i for i in chain if i.Expiry == expiry and i.Right == OptionRight.PUT]
if len(calls) < 1: return
# sorted the contracts according to their strike prices
calls = sorted(calls, key=lambda x: x.Strike)
sell_contract = sorted(calls, key=lambda x: abs(self.sell_price - x.strike))[0]
buy_contract = sorted(calls, key=lambda x: abs(self.buy_price - x.strike))[0]
self.short_ct = sell_contract
self.long_ct = buy_contract
if self._debug_lvl >= 2:
self.log(f'symbol: {sell_contract.Symbol} -- {sell_contract.BidPrice} / {sell_contract.AskPrice}')
self.log(f'symbol: {buy_contract.Symbol} -- {buy_contract.BidPrice} / {buy_contract.AskPrice}')
self.max_gain, self.max_loss = self.credit_spread_max_gain_loss(buy_contract, sell_contract)
self.initialCredit = self.max_gain
self.Buy(buy_contract.Symbol, self._contracts)
self.Sell(sell_contract.Symbol, self._contracts)
if self._debug_lvl >= 1:
self.log(f'selling {sell_contract.symbol} at {sell_contract.BidPrice}')
self.log(f'buying {buy_contract.symbol} at {buy_contract.AskPrice}')
def exit_logic(self):
"""
An unrealized loss of 50% or more of the spread's maximum loss.
● An unrealized gain exceeding 90% of the spreads maximum gain.
● The underlying price of $XSP surpasses our stop loss level.
Stop Loss Level = the Open Price of $XSP plus 2 ATRs.
"""
# so -- we sold it for 100, can buy it back for 50. pnl is 50.
# or -- we sold it for 100, can buy it back for 150, pnl is -50
current_pnl = self.initialCredit - self.spread_price()
# If our pnl if <= 50% of max loss -- stop out.
stop_condition = current_pnl <= self.pct_of_max_loss_stop * self.max_loss
if stop_condition:
log_message = (
f'stop condition: {current_pnl} < {self.pct_of_max_loss_stop * self.max_loss}'
f'-- ({self.pct_of_max_loss_stop} * {self.max_loss})'
)
self.log(log_message)
self.liquidate(tag="SL")
# if our pnl is >= 90% of max gain -- take profit.
tgt_condition = current_pnl >= self.pct_of_max_gain_tgt * self.max_gain
if tgt_condition:
self.liquidate(tag="TP")
log_message = (
f"tp condition: {current_pnl} >= {target_pnl} "
f"-- ({self.pct_of_max_gain_tgt} * {self.max_gain})"
)
self.log(log_message)
ul_stop_condition = self.portfolio[self.underlyingsymbol].price >= self.buy_price
if ul_stop_condition:
self.liquidate(tag="Underlying SL")
self.log(f"tp condition: {self.portfolio[self.underlyingsymbol].price} >= {self.buy_price}")
# endregion
# region Utilities
# Not in use, currently -- QC has alerting natively, dont think we need this.
def notify_mailing_list(self, msg: str, subject: Optional[str] = None):
"""
https://www.quantconnect.com/docs/v2/cloud-platform/live-trading/notifications
# https://www.quantconnect.com/forum/discussion/11677/formatting-problems-with-self-notify-email/
self.Notify.Email('email-id', 'Subject Text', 'Message Text', 'Attachment Text')
"""
if not subject:
subject = "QuantConnect Notification"
for addr in self.to_notify:
self.notify.email(addr, subject, msg, None, None)
@property
def IsReady(self):
return self.atr.is_ready
def spread_price(self):
""" Here we are really pricing the cost to exit """
if self.long_ct and self.short_ct:
# credit - debit
return self.short_ct.AskPrice - self.long_ct.BidPrice
def credit_spread_max_gain_loss(self, buy, sell):
"""This returns in points -- share points -- not $$"""
buyprice = buy.AskPrice
sellprice = sell.BidPrice
buystk = buy.Strike
sellstk = sell.Strike
stk_distance = abs(buystk - sellstk)
credit = sellprice - buyprice
max_gain = credit
max_loss = (stk_distance) - credit
if self._debug_lvl >= 2:
self.log(f'Strike distance: {stk_distance}')
self.log(f'max gain: {credit}')
self.log(f'max loss: {max_loss}')
return max_gain, max_loss
#endregion
# region Order Events
def OnOrderEvent(self, order_event: OrderEvent):
"""
https://www.quantconnect.com/docs/v2/writing-algorithms/trading-and-orders/order-events
"""
order = self.transactions.get_order_by_id(order_event.order_id)
if order_event.status == OrderStatus.FILLED:
# Grab the fill price, etc.
fill_price = order_event.fill_price
fill_qty = order_event.fill_quantity
dir = order_event.direction
self.Log(str(order_event))
def on_assignment_order_event(self, assignment_event):
"""
https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/options-models/assignment
"""
# Liquidate all positions related to the underlying symbol
underlying_symbol = assignment_event.Symbol.Underlying
self.Liquidate(underlying_symbol)
self.log(f"Option assigned: {assignment_event.Symbol} -- flattening all {underlying_symbol}")
#endregion
#region imports
from AlgorithmImports import *
#endregion
"""
class Garbage:
def TradeOptions(self):
# https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options
# seems to do it differently with set_filter -- this is slower, but maybe betteR?
contracts = self.OptionChainProvider.GetOptionContractList(self.underlyingsymbol, self.Time.date())
if len(contracts) > 1:
# get the NEAREST expiry available -- great. (I think this is right -- TEST)
# TODO: why no 0 dte options?
expiry = sorted(contracts,key = lambda x: x.ID.Date, reverse=False)[0].ID.Date
self.log(f'now: {self.time.date()} exp: {expiry}')
# filter the call options from the contracts expires on that date
call = [i for i in contracts if i.ID.Date == expiry and i.ID.OptionRight == 0]
self.log(f'calls: {[i for i in call]}')
# # get options that are nearest our target levels.
self.call_to_sell = sorted(call, key=lambda x: abs(x.ID.StrikePrice - self.sell_price))[0]
self.call_to_buy = sorted(call, key=lambda x: abs(x.ID.StrikePrice - self.buy_price))[0]
sell = self.AddOptionContract(self.call_to_sell, Resolution.Minute)
buy = self.AddOptionContract(self.call_to_buy, Resolution.Minute)
# TODO: how do we get the prices?
# https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/equity-options/requesting-data
# # Works -- but it returns 0.0?
# try:
# self.log(f'sell price? : {sell.ask_price}')
# except:
# self.log(f'nope2 ')
# # works -- but it returns 0.0?
# try:
# self.log(f'sell price? : {sell.AskPrice}')
# except:
# self.log(f'nope4 ')
# try:
# self.log(f'price? : {sell.price}')
# except:
# self.log(f'nope 5')
# # TODO: deal with position sizes... hmm.
self.Buy(self.call_to_buy, 1)
self.Sell(self.call_to_sell, 1)
# Why arent these firing?
# QC seems to have made alot of changes, broken alot of things,
# and make alot of documentation partially accurate.
# self.market_order(sell, -1)
# self.market_order(buy, 1)
"""