The strategy trades US stocks with liquid options, shorting high O/S ratio stocks, going long on low O/S ratio stocks, equally weighted, and rebalanced monthly to exploit trading disparities.
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Option/Stock, Volume, Ratio, Stock, Returns
I. STRATEGY IN A NUTSHELL
This strategy targets U.S. stocks with liquid options (excluding CEFs, REITs, ADRs, and stocks under $1). Each month, it calculates the option-to-stock (O/S) volume ratio for near-term options. Stocks with high O/S ratios are shorted, and stocks with low O/S ratios are bought. The portfolio is equally weighted, rebalanced monthly, aiming to exploit differences between option and stock market activity.
II. ECONOMIC RATIONALE
The strategy exploits the predictive power of the option-to-stock volume ratio (O/S). High O/S signals indicate bearish sentiment as informed traders use options to express negative views due to short-sale constraints in equities. Low O/S stocks tend to outperform, allowing the strategy to capture future return differences driven by asymmetric information and market frictions.
III. SOURCE PAPER
The Option to Stock Volume Ratio and Future Returns [Click to Open PDF]
Johnson, So, Stanford University Graduate School of Business, Stanford University Graduate School of Business
<Abstract>
We examine the information content of option and equity volumes when trade direction is unobserved. In a multimarket symmetric information model, we show that equity short-sale costs result in a negative relation between relative option volume and future firm value. In our empirical tests, firms in the lowest decile of the option to stock volume ratio (O/S) outperform the highest decile by 1.47% per month on a risk-adjusted basis. Our model and empirics both indicate that O/S is a stronger signal when short-sale costs are high or option leverage is low. O/S also predicts future firm-specific earnings news, consistent with O/S reflecting private information.
IV. BACKTEST PERFORMANCE
| Annualised Return | 14.54% |
| Volatility | 19.2% |
| Beta | 0.06 |
| Sharpe Ratio | 0.55 |
| Sortino Ratio | -0.049 |
| Maximum Drawdown | N/A |
| Win Rate | 49% |
V. FULL PYTHON CODE
from AlgorithmImports import *
from typing import List, Dict
#endregion
class OptionStockVolumeRatioPredictsStockReturns(QCAlgorithm):
def Initialize(self) -> None:
self.SetStartDate(2020, 1, 1)
self.SetCash(100_000)
self.min_expiry: int = 20
self.max_expiry: int = 30
self.min_period_len: int = 14 # need at least n daily volumes
self.quantile: int = 5
self.leverage: int = 5
self.min_share_price: int = 5
self.last_fundamental: List[Symbol] = []
self.data: Dict[Symbol, SymbolData] = {} # list of stocks volumes and list of total option volumes in selection
self.subscribed_contracts: Dict[Symbol, Contracts] = {} # subscribed option universe
# initial data feed
self.AddEquity('SPY', Resolution.Minute)
self.fundamental_count: int = 50
self.fundamental_sorting_key = lambda x: x.DollarVolume
self.selection_flag: bool = True
self.subscribing_flag: bool = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.settings.daily_precise_end_time = False
self.UniverseSettings.Resolution = Resolution.Minute
self.AddUniverse(self.FundamentalSelectionFunction)
self.SetSecurityInitializer(lambda x: x.SetDataNormalizationMode(DataNormalizationMode.Raw))
self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# rebalance monthly
if not self.selection_flag:
return Universe.Unchanged
# change flags values
self.selection_flag = False
self.subscribing_flag = True
# filter top n U.S. stocks by dollar volume
selected: List[Fundamental] = [
x for x in fundamental if x.HasFundamentalData
and x.Market == 'usa'
and x.Price > self.min_share_price
and x.Symbol.Value != 'GOOG']
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
# filter top n U.S. stocks by dollar volume
# initialize new fundamental
self.last_fundamental = [x.Symbol for x in selected]
# return newly selected symbols
return self.last_fundamental
def OnData(self, data: Slice) -> None:
for stock_symbol in self.last_fundamental:
# stock has to have subscribed option contracts
if stock_symbol not in self.subscribed_contracts:
continue
# check if any of the subscribed contracts expired
if self.subscribed_contracts[stock_symbol].expiry_date - timedelta(days=1) <= self.Time.date():
for c in self.subscribed_contracts[stock_symbol].contracts:
self.RemoveOptionContract(c)
self.subscribed_contracts[stock_symbol].contracts.clear()
# remove Contracts object for current symbol
del self.subscribed_contracts[stock_symbol]
else:
# collect volumes
stock_volume: Union[None, float] = data[stock_symbol].Value if stock_symbol in data and data[stock_symbol] else None
option_volumes: List[float] = []
option_contracts: List[Symbol] = self.subscribed_contracts[stock_symbol].contracts
for option_contract in option_contracts:
if option_contract in data and data[option_contract]:
# option volume isn't in data object
option_volumes.append(self.Securities[option_contract].Volume)
# make sure all volumes were collected
if stock_volume is not None and len(option_volumes) == len(option_contracts):
# store volumes
if stock_symbol not in self.data:
self.data[stock_symbol] = SymbolData()
# store stock volume in all stocks volumes in this day
self.data[stock_symbol].stock_minute_volumes.append(stock_volume)
# store total option volume in all total option volumes in this day
self.data[stock_symbol].options_minute_volumes.append(sum(option_volumes))
# execute once a day for storing stock and options volumes
if self.Time.hour == 16 and self.Time.minute == 00 and stock_symbol in self.data:
self.data[stock_symbol].update_daily_volumes()
# perform trade, then perform next selection, when there are no active contracts for current selection
if len(self.subscribed_contracts) == 0 and not self.subscribing_flag and self.Time.hour != 0:
# calculate OS ratio
OS_ratio: Dict[Symbol, float] = {}
for stock_symbol, symbol_obj in self.data.items():
# make sure volumes data are ready
if symbol_obj.is_ready(self.min_period_len):
month_stock_volume: float = sum(symbol_obj.stock_daily_volumes)
if month_stock_volume != 0:
month_total_option_volume: float = sum(symbol_obj.total_option_daily_volumes)
OS_ratio_value: float = month_total_option_volume / month_stock_volume
# store OS ratio keyed by stock symbol
OS_ratio[stock_symbol] = OS_ratio_value
# clear last selection data
symbol_obj.clear_data()
if len(OS_ratio) >= self.quantile:
# perform selection
quantile: int = int(len(OS_ratio) / self.quantile)
sorted_by_ratio: List[Symbol] = [x[0] for x in sorted(OS_ratio.items(), key=lambda item: item[1])]
# long low and short high
long: List[Symbol] = sorted_by_ratio[:quantile]
short: List[Symbol] = sorted_by_ratio[-quantile:]
targets: List[PortfolioTarget] = []
for i, portfolio in enumerate([long, short]):
for symbol in portfolio:
if symbol in data and data[symbol]:
targets.append(PortfolioTarget(symbol, ((-1) ** i) / len(portfolio)))
self.SetHoldings(targets, True)
elif not self.selection_flag:
# liquidate all positions from previous selection
self.Liquidate()
# clear for next selection
self.last_fundamental.clear()
self.selection_flag = True
return # skip to firstly perform fundamental selection and then contracts subscribing
# subscribe to new contracts after selection
if len(self.subscribed_contracts) == 0 and self.subscribing_flag:
for symbol in self.last_fundamental:
# get all contracts for current stock symbol
contracts: List[Symbol] = self.OptionChainProvider.GetOptionContractList(symbol, self.Time)
# get current price for etf
underlying_price: float = self.Securities[symbol].Price
# get strikes from commodity future contracts
strikes: List[float] = [i.ID.StrikePrice for i in contracts]
# can't filter contracts, if there isn't any strike price
if len(strikes) <= 0 or underlying_price == 0:
continue
atm_strike: float = min(strikes, key=lambda x: abs(x-underlying_price))
itm_strike: float = min(strikes, key=lambda x: abs(x-(underlying_price*0.95)))
otm_strike: float = min(strikes, key=lambda x: abs(x-(underlying_price*1.05)))
# filter calls and puts contracts with one month expiry
atm_calls, atm_puts = self.FilterContracts(atm_strike, contracts, underlying_price)
itm_calls, itm_puts = self.FilterContracts(itm_strike, contracts, underlying_price)
otm_calls, otm_puts = self.FilterContracts(otm_strike, contracts, underlying_price)
# make sure, there is at least one call and put contract
if len(atm_calls) > 0 and len(atm_puts) > 0 and len(itm_calls) > 0 and len(itm_puts) > 0 and len(otm_calls) > 0 and len(otm_puts) > 0:
# sort by expiry
atm_call, atm_put = self.SortByExpiry(atm_calls, atm_puts)
itm_call, itm_put = self.SortByExpiry(itm_calls, itm_puts)
otm_call, otm_put = self.SortByExpiry(otm_calls, otm_puts)
atm_call_subscriptions: List[SubscriptionDataConfig] = self.SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(atm_call.Underlying)
# check if stock's call and put contract was successfully subscribed
if atm_call_subscriptions:
selected_contracts: List[Symbol] = [atm_call, atm_put, itm_call, itm_put, otm_call, otm_put]
for contract in selected_contracts:
# add contract
self.AddOptionContract(contract, Resolution.Minute)
# retrieve expiry date for contracts
expiry_date: datetime.date = min([c.ID.Date.date() for c in selected_contracts])
# store contracts with expiry date under stock's symbol
self.subscribed_contracts[symbol] = Contracts(expiry_date, underlying_price, selected_contracts)
# at least one stock has to have successfully subscribed all option contracts, to stop subscribing
if len(self.subscribed_contracts) > 0:
self.subscribing_flag = False
def FilterContracts(self, strike: float, contracts: List[Symbol], underlying_price: float) -> List[Symbol]:
''' filter call and put contracts from contracts parameter '''
''' return call and put contracts '''
calls: List[Symbol] = [] # storing call contracts
puts: List[Symbol] = [] # storing put contracts
for contract in contracts:
# check if contract has one month expiry
if self.min_expiry < (contract.ID.Date - self.Time).days < self.max_expiry:
# check if contract is call
if contract.ID.OptionRight == OptionRight.Call and contract.ID.StrikePrice == strike:
calls.append(contract)
# check if contract is put
elif contract.ID.OptionRight == OptionRight.Put and contract.ID.StrikePrice == strike:
puts.append(contract)
# return filtered calls and puts with one month expiry
return calls, puts
def SortByExpiry(self, calls: List[Symbol], puts: List[Symbol]) -> List[Symbol]:
''' return option call and option put with farest expiry '''
call: List[Symbol] = sorted(calls, key = lambda x: x.ID.Date, reverse=True)[0]
put: List[Symbol] = sorted(puts, key = lambda x: x.ID.Date, reverse=True)[0]
return call, put
class SymbolData:
def __init__(self) -> None:
self.stock_minute_volumes: List[float] = []
self.options_minute_volumes: List[float] = []
self.stock_daily_volumes: List[float] = []
self.total_option_daily_volumes: List[float] = []
def update_daily_volumes(self) -> None:
self.stock_daily_volumes.append(sum(self.stock_minute_volumes))
self.total_option_daily_volumes.append(sum(self.options_minute_volumes))
self.stock_minute_volumes.clear()
self.options_minute_volumes.clear()
def clear_data(self) -> None:
self.stock_minute_volumes.clear()
self.options_minute_volumes.clear()
self.stock_daily_volumes.clear()
self.total_option_daily_volumes.clear()
def is_ready(self, period: int) -> bool:
return len(self.stock_daily_volumes) >= period and len(self.total_option_daily_volumes) >= period
class Contracts():
def __init__(self, expiry_date: datetime.date, underlying_price: float, contracts: List[Symbol]) -> None:
self.expiry_date: datetime.date = expiry_date
self.underlying_price: float = underlying_price
self.contracts: List[Symbol] = contracts
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee:
fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))