In This Page

Introduction

Trades

Quotes

Slices

Open Interest

Daily Option Chains

Indicators

Examples

Asset Classes

Equity Options

Introduction

This page explains how to get historical data for Equity Options. Some of the data you can get include prices, open interest, Greeks, and universe selection data.

Trades

To get historical trade data, call the History<TradeBar> method with a security's Symbol.

To get historical trade data, call the history method with the TradeBar type and a security's Symbol. This method returns a DataFrame with columns for the open, high, low, close, and volume.

Select Language:

public class EquityOptionTradeBarHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var equity = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw);
        var symbol = OptionChain(equity.Symbol).OrderBy(c => c.OpenInterest).Last().Symbol;
        // Get the 5 trailing daily TradeBar objects of the security. 
        var history = History<TradeBar>(symbol, 5, Resolution.Daily);
        // Iterate through each TradeBar and calculate its dollar volume.
        foreach (var bar in history)
        {
            var t = bar.EndTime;
            var dollarVolume = bar.Close * bar.Volume;
        }
    }
}

class EquityOptionTradeBarHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        equity = self.add_equity('SPY', data_normalization_mode=DataNormalizationMode.RAW)
        symbol = sorted(self.option_chain(equity.symbol), key=lambda c: c.open_interest)[-1].symbol
        # Get the 5 trailing daily TradeBar objects of the security in DataFrame format. 
        history = self.history(TradeBar, symbol, 5, Resolution.DAILY)

					close	high	low	open	volume
expiry	strike	type	symbol	time
2025-01-17	440.0	1	SPY 32OCGBPPW6DTY\|SPY R735QTJ8XC9X	2024-12-12 16:00:00	0.20	0.23	0.20	0.21	99.0
				2024-12-13 16:00:00	0.20	0.21	0.17	0.18	543.0
				2024-12-16 16:00:00	0.18	0.19	0.17	0.17	58.0
				2024-12-17 16:00:00	0.24	0.24	0.19	0.19	101.0
				2024-12-18 16:00:00	0.85	0.85	0.21	0.21	160.0

# Calculate the daily returns.
daily_returns = history.close.pct_change().iloc[1:]

expiry      strike  type  symbol                              time               
2025-01-17  440.0   1     SPY 32OCGBPPW6DTY|SPY R735QTJ8XC9X  2024-12-13 16:00:00    0.000000
                                                              2024-12-16 16:00:00   -0.100000
                                                              2024-12-17 16:00:00    0.333333
                                                              2024-12-18 16:00:00    2.541667
Name: close, dtype: float64

If you intend to use the data in the DataFrame to create TradeBar objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of TradeBar objects instead of a DataFrame, call the history[TradeBar] method.

# Get the 5 trailing daily TradeBar objects of the security in TradeBar format. 
history = self.history[TradeBar](symbol, 5, Resolution.DAILY)
# Iterate through the TradeBar objects and access their volumes.
for trade_bar in history:
    t = trade_bar.end_time
    volume = trade_bar.volume

Request minute, hour, or daily resolution data. Otherwise, the history request won't return any data.

Quotes

To get historical quote data, call the History<QuoteBar> method with a security's Symbol.

To get historical quote data, call the history method with the QuoteBar type and a security's Symbol. This method returns a DataFrame with columns for the open, high, low, close, and size of the bid and ask quotes. The columns that don't start with "bid" or "ask" are the mean of the quote prices on both sides of the market.

Select Language:

public class EquityOptionsQuoteBarHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var equity = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw);
        var symbol = OptionChain(equity.Symbol).OrderBy(c => c.OpenInterest).Last().Symbol;
        // Get the 5 trailing minute QuoteBar objects of the security. 
        var history = History<QuoteBar>(symbol, 5, Resolution.Minute);
        // Iterate through the QuoteBar objects and calculate the spread.
        foreach (var bar in history)
        {
            var t = bar.EndTime;
            var spread = bar.Ask.Close - bar.Bid.Close;
        }
    }
}

class EquityOptionsQuoteBarHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        equity = self.add_equity('SPY', data_normalization_mode=DataNormalizationMode.RAW)
        symbol = sorted(self.option_chain(equity.symbol), key=lambda c: c.open_interest)[-1].symbol
        # Get the 5 trailing minute QuoteBar objects of the security in DataFrame format. 
        history = self.history(QuoteBar, symbol, 5, Resolution.MINUTE)

					askclose	askhigh	asklow	askopen	asksize	bidclose	bidhigh	bidlow	bidopen	bidsize	close	high	low	open
expiry	strike	type	symbol	time
2025-01-17	440.0	1	SPY 32OCGBPPW6DTY\|SPY R735QTJ8XC9X	2024-12-18 15:56:00	0.76	0.85	0.71	0.75	72.0	0.74	0.74	0.17	0.60	72.0	0.75	0.795	0.440	0.675
				2024-12-18 15:57:00	0.79	0.80	0.76	0.76	143.0	0.77	0.78	0.73	0.74	60.0	0.78	0.790	0.745	0.750
				2024-12-18 15:58:00	0.80	0.80	0.78	0.79	169.0	0.78	0.78	0.75	0.77	60.0	0.79	0.790	0.765	0.780
				2024-12-18 15:59:00	0.82	0.83	0.79	0.80	72.0	0.80	0.80	0.76	0.78	72.0	0.81	0.815	0.775	0.790
				2024-12-18 16:00:00	0.91	0.91	0.82	0.82	346.0	0.87	0.89	0.80	0.80	72.0	0.89	0.900	0.810	0.810

# Calculate the spread at each minute.
spread = history.askclose - history.bidclose

expiry      strike  type  symbol                              time               
2025-01-17  440.0   1     SPY 32OCGBPPW6DTY|SPY R735QTJ8XC9X  2024-12-18 15:56:00    0.02
                                                              2024-12-18 15:57:00    0.02
                                                              2024-12-18 15:58:00    0.02
                                                              2024-12-18 15:59:00    0.02
                                                              2024-12-18 16:00:00    0.04
dtype: float64

If you intend to use the data in the DataFrame to create QuoteBar objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of QuoteBar objects instead of a DataFrame, call the history[QuoteBar] method.

# Get the 5 trailing minute QuoteBar objects of the security in QuoteBar format. 
history = self.history[QuoteBar](symbol, 5, Resolution.MINUTE)
# Iterate through each QuoteBar and calculate the dollar volume on the bid.
for quote_bar in history:
    t = quote_bar.end_time
    bid_dollar_volume = quote_bar.last_bid_size * quote_bar.bid.close

Request minute, hour, or daily resolution data. Otherwise, the history request won't return any data.

Slices

To get historical Slice data, call the Historyhistory method without passing any Symbol objects. This method returns Slice objects, which contain data points from all the datasets in your algorithm. If you omit the resolution argument, it uses the resolution that you set for each security and dataset when you created the subscriptions.

Select Language:

public class SliceHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 1);
        // Add some securities and datasets.
        var equity = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw);
        var contract = OptionChain(equity.Symbol).OrderBy(c => c.OpenInterest).Last();
        AddOptionContract(contract.Symbol);
        // Get the historical Slice objects over the last 5 days for all the subcriptions in your algorithm.
        var history = History(5, Resolution.Daily);
        // Iterate through each historical Slice.
        foreach (var slice in history)
        {
            // Iterate through each TradeBar in this Slice.
            foreach (var kvp in slice.Bars)
            {
                var symbol = kvp.Key;
                var bar = kvp.Value;
            }
        }
    }
}

class SliceHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 1)
        # Add some securities and datasets.
        equity = self.add_equity('SPY', data_normalization_mode=DataNormalizationMode.RAW)
        contract = sorted(self.option_chain(equity.symbol), key=lambda c: c.open_interest)[-1]
        self.add_option_contract(contract.symbol)
        # Get the historical Slice objects over the last 5 days for all the subcriptions in your algorithm.
        history = self.history(5, Resolution.DAILY)
        # Iterate through each historical Slice.
        for slice_ in history:
            # Iterate through each TradeBar in this Slice.
            for symbol, trade_bar in slice_.bars.items():
                close = trade_bar.close

Open Interest

To get historical open interest data, call the History<OpenInterest> method with a security's Symbol.

To get historical open interest data, call the history method with the OpenInterest type and a security's Symbol. This method returns a DataFrame with a single column.

Select Language:

public class EquityOptionOpenInterestHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of a security.
        var equity = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw);
        var symbol = OptionChain(equity.Symbol).OrderBy(c => c.OpenInterest).Last().Symbol;
        // Get the 5 trailing daily OpenInterest objects of the security. 
        var history = History<OpenInterest>(symbol, 5, Resolution.Daily);
        // Iterate through each OpenInterest and get it's value.
        foreach (var oi in history)
        {
            var t = oi.EndTime;
            var openInterest = oi.Value;
        }
    }
}

class EquityOptionOpenInterestHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of a security.
        equity = self.add_equity('SPY', data_normalization_mode=DataNormalizationMode.RAW)
        symbol = sorted(self.option_chain(equity.symbol), key=lambda c: c.open_interest)[-1].symbol
        # Get the 5 trailing daily OpenInterest objects of the security in DataFrame format. 
        history = self.history(OpenInterest, symbol, 5, Resolution.DAILY)

					openinterest
expiry	strike	type	symbol	time
2025-01-17	440.0	1	SPY 250117P00440000	2024-12-13	171751.0
				2024-12-16	172190.0
				2024-12-17	172157.0
				2024-12-18	172147.0
				2024-12-19	172099.0

# Calculate the daily change in open interest.
oi_delta = history.openinterest.diff().iloc[1:]

expiry      strike  type  symbol                 time      
2025-01-17  440.0   1     SPY   250117P00440000  2024-12-16    439.0
                                                 2024-12-17    -33.0
                                                 2024-12-18    -10.0
                                                 2024-12-19    -48.0
Name: openinterest, dtype: float64

If you intend to use the data in the DataFrame to create OpenInterest objects, request that the history request returns the data type you need. Otherwise, LEAN consumes unnecessary computational resources populating the DataFrame. To get a list of OpenInterest objects instead of a DataFrame, call the history[OpenInterest] method.

# Get the 5 trailing daily OpenInterest objects of the security in OpenInterest format. 
history = self.history[OpenInterest](symbol, 5, Resolution.DAILY)
# Iterate through the TradeBar objects and access their volumes.
for oi in history:
    t = oi.end_time
    open_interest = oi.value

Daily Option Chains

To get historical daily Option chain data, call the History method with the Option Symbol object. The data this method returns contains information on all the currently tradable contracts, not just the contracts that pass your filter.

To get historical daily Option chain data, call the history method with the Option Symbol object. The data this method returns contains information on all the currently tradable contracts, not just the contracts that pass your filter. If you pass flatten=True, this method returns a DataFrame with columns for the data point attributes.

Select Language:

public class EquityOptionDailyOptionChainHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 23);
        // Add an Equity Option universe.
        var option = AddOption("SPY");
        // Get the trailing 5 daily Option chains.
        var history = History<OptionUniverse>(option.Symbol, 5);
        // Iterate through each day of the history.
        foreach (var optionUniverse in history)
        {
            var t = optionUniverse.EndTime;
            // Select the 2 contracts with the most volume.
            var mostTraded = optionUniverse.Select(c => c as OptionUniverse).OrderByDescending(c => c.Volume).Take(2);
        }
    }
}

class EquityOptionDailyOptionChainHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 23)
        # Add an Equity Option universe.
        option = self.add_option('SPY')
        # Get the trailing 5 daily Option chains in DataFrame format.
        history = self.history(option.symbol, 5, flatten=True)

		close	delta	gamma	high	impliedvolatility	low	open	openinterest	rho	theta	underlying	value	vega	volume
time	symbol
2024-12-13	SPY YOGVNNCO8QDI\|SPY R735QTJ8XC9X	484.815	1.000000	0.000000	487.945	3.212331	484.225	486.74	1539.0	0.000000	0.000000	SPY: ¤604.33	484.815	0.000000	0.0
	SPY YOGVNNCU72FA\|SPY R735QTJ8XC9X	474.210	1.000000	0.000000	477.970	3.055466	474.210	476.91	26.0	0.000000	0.000000	SPY: ¤604.33	474.210	0.000000	0.0
	SPY YOGVNND05EH2\|SPY R735QTJ8XC9X	464.835	1.000000	0.000000	467.965	2.910873	464.230	466.91	4.0	0.000000	0.000000	SPY: ¤604.33	464.835	0.000000	0.0
	SPY YOGVNND63QIU\|SPY R735QTJ8XC9X	454.845	1.000000	0.000000	458.000	2.775398	454.245	456.92	18.0	0.000000	0.000000	SPY: ¤604.33	454.845	0.000000	0.0
	SPY YTG30NXW11QE\|SPY R735QTJ8XC9X	456.345	1.000000	0.000000	459.235	0.687831	456.270	458.77	33.0	0.000000	0.000000	SPY: ¤604.33	456.345	0.000000	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2024-12-19	SPY 33899RRUZK23Q\|SPY R735QTJ8XC9X	314.995	-0.865013	0.000496	317.045	0.145628	293.900	297.42	1.0	-5.866155	0.038392	SPY: ¤586.28	314.995	0.573632	0.0
	SPY 337HP99QFUJJA\|SPY R735QTJ8XC9X	319.995	-0.874953	0.000412	321.765	0.142599	299.020	302.42	0.0	-5.754009	0.039155	SPY: ¤586.28	319.995	0.452253	0.0
	SPY 33899RVZ5ZO12\|SPY R735QTJ8XC9X	319.990	-0.868866	0.000447	321.805	0.143224	298.995	302.47	0.0	-5.954058	0.039088	SPY: ¤586.28	319.990	0.511173	0.0
	SPY 337HP95ZTK8HY\|SPY R735QTJ8XC9X	324.995	-0.876073	0.000389	326.715	0.142820	304.030	307.43	0.0	-5.842472	0.040096	SPY: ¤586.28	324.995	0.428058	0.0
	SPY 33899RS8JPCZQ\|SPY R735QTJ8XC9X	324.995	-0.868426	0.000446	326.745	0.145502	303.975	307.46	1.0	-6.048355	0.039695	SPY: ¤586.28	324.995	0.517696	0.0

# Select the 2 contracts with the greatest volume each day.
most_traded = history.groupby('time').apply(lambda x: x.nlargest(2, 'volume')).reset_index(level=1, drop=True).volume

time        symbol                            
2024-12-13  SPY 32NDZOIHRHLRA|SPY R735QTJ8XC9X    166689.0
            SPY 32NDZOIHXFXT2|SPY R735QTJ8XC9X    160265.0
2024-12-14  SPY YOCXVCLWHPT2|SPY R735QTJ8XC9X     137597.0
            SPY YOCXVCM2G1UU|SPY R735QTJ8XC9X     100401.0
2024-12-17  SPY 32NHXGVYLQYG6|SPY R735QTJ8XC9X    104700.0
            SPY YODXBFZKFH46|SPY R735QTJ8XC9X     103635.0
2024-12-18  SPY 32NIWWZBFX1IE|SPY R735QTJ8XC9X    106804.0
            SPY YOEWRJCELK6E|SPY R735QTJ8XC9X      75769.0
2024-12-19  SPY 32NKVT60AHJDY|SPY R735QTJ8XC9X    109298.0
            SPY YOFW7MPKOBC6|SPY R735QTJ8XC9X      94823.0
Name: volume, dtype: float64

To get the data in the format of OptionUniverse objects instead of a DataFrame, call the history[OptionUniverse] method.

# Get the historical OptionUniverse data over the last 30 days.
history = self.history[OptionUniverse](option.symbol, timedelta(30))
# Iterate through each daily Option chain.
for option_universe in history:
    t = option_universe.end_time
    # Select the contract with the most volume.
    most_traded = sorted(option_universe, key=lambda contract: contract.volume)[-1]

Indicators

To get historical indicator values, call the IndicatorHistoryindicator_history method with an indicator and the security's Symbol.

Select Language:

public class EquityOptionIndicatorHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of the Option contract.
        var underlying = AddEquity("SPY").Symbol;
        var symbol = OptionChain(underlying).OrderBy(c => c.OpenInterest).Last().Symbol;
        // Get the 21-day SMA values of the contract for the last 5 trading days. 
        var history = IndicatorHistory(new SimpleMovingAverage(21), symbol, 5, Resolution.Daily);
        // Get the maximum of the SMA values.
        var maxSMA = history.Max(indicatorDataPoint => indicatorDataPoint.Current.Value);
    }
}

class EquityOptionIndicatorHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of the Option contract.
        underlying = self.add_equity('SPY').symbol
        symbol = sorted(self.option_chain(underlying), key=lambda c: c.open_interest)[-1].symbol
        # Get the 21-day SMA values of the contract for the last 5 trading days. 
        history = self.indicator_history(SimpleMovingAverage(21), symbol, 5, Resolution.DAILY)

To organize the data into a DataFrame, use the data_frame property of the result.

# Organize the historical indicator data into a DataFrame to enable pandas wrangling.
history_df = history.data_frame

	current	rollingsum
2024-12-12 16:00:00	0.377619	7.930
2024-12-13 16:00:00	0.366190	7.690
2024-12-16 16:00:00	0.353810	7.430
2024-12-17 16:00:00	0.336667	7.070
2024-12-18 16:00:00	0.356429	7.485

# Get the maximum of the SMA values.
sma_max = history_df.current.max()

The IndicatorHistoryindicator_history method resets your indicator, makes a history request, and updates the indicator with the historical data. Just like with regular history requests, the IndicatorHistoryindicator_history method supports time periods based on a trailing number of bars, a trailing period of time, or a defined period of time. If you don't provide a resolution argument, it defaults to match the resolution of the security subscription.

To make the IndicatorHistoryindicator_history method update the indicator with an alternative price field instead of the close (or mid-price) of each bar, pass a selector argument.

Select Language:

// Get the historical values of an indicator over the last 30 days, applying the indicator to the security's volume.
var history = IndicatorHistory(indicator, symbol, TimeSpan.FromDays(30), selector: Field.Volume);

# Get the historical values of an indicator over the last 30 days, applying the indicator to the security's volume.
history = self.indicator_history(indicator, symbol, timedelta(30), selector=Field.VOLUME)

Some indicators require the prices of multiple securities to compute their value (for example, the indicators for the Greeks and implied volatility). In this case, pass a list of the Symbol objects to the method.

Select Language:

public class EquityOptionMultiAssetIndicatorHistoryAlgorithm : QCAlgorithm
{
    public override void Initialize()
    {
        SetStartDate(2024, 12, 19);
        // Get the Symbol of the underlying asset.
        var underlying = AddEquity("SPY").Symbol;
        // Get the Option contract Symbol.
        var option = OptionChain(underlying).OrderBy(c => c.OpenInterest).Last().Symbol;
        // Get the Symbol of the mirror contract.
        var mirror = QuantConnect.Symbol.CreateOption(
            underlying, option.ID.Market, option.ID.OptionStyle,
            option.ID.OptionRight == OptionRight.Put ? OptionRight.Call : OptionRight.Put,
            option.ID.StrikePrice, option.ID.Date
        );
        // Create the indicator.
        var indicator = new ImpliedVolatility(option, RiskFreeInterestRateModel, new DividendYieldProvider(underlying), mirror, OptionPricingModelType.ForwardTree);
        // Get the historical values of the indicator over the last 60 trading minutes.
        var history = IndicatorHistory(indicator, new[] {underlying, option, mirror}, 60, Resolution.Minute);
        // Get the average IV value.
        var avgIV = history.Average(indicatorDataPoint => indicatorDataPoint.Current.Value);
    }
}

class EquityOptionMultiAssetIndicatorHistoryAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 12, 19)
        # Get the Symbol of the underlying asset.
        underlying = self.add_equity('SPY').symbol
        # Get the Option contract Symbol.
        option = sorted(self.option_chain(underlying), key=lambda c: c.open_interest)[-1].symbol
        # Get the Symbol of the mirror contract.
        mirror = Symbol.create_option(
            underlying, option.id.market, option.id.option_style, 
            OptionRight.Call if option.id.option_right == OptionRight.PUT else OptionRight.PUT,
            option.id.strike_price, option.id.date
        )
        # Create the indicator.
        indicator = ImpliedVolatility(
            option, self.risk_free_interest_rate_model, DividendYieldProvider(underlying),
            mirror, OptionPricingModelType.FORWARD_TREE
        )
        # Get the historical values of the indicator over the last 60 trading minutes.
        history = self.indicator_history(indicator, [underlying, option, mirror], 60, Resolution.MINUTE)
        # Get the average IV value.
        iv_avg = history.data_frame.current.mean()

If you already have a list of Slice objects, you can pass them to the IndicatorHistoryindicator_history method to avoid the internal history request.

var slices = History(new[] {symbol}, 30, Resolution.Daily);
var history = IndicatorHistory(indicator, slices);

Examples

The following examples demonstrate some common practices for trading using historical requests.

Example 1: Trend Following on 0DTE Option Contract

This algorithm strategically trades 0DTE SPY options by analyzing bid and ask volumes shortly after the market opens. Using scheduled events, it effectively executes trades based on historical quote data, optimizing decision-making. The algorithm aims for timely entries and exits, ensuring efficient capital management and quick adaptability in volatile markets.

Select Language:

public class ZeroDTEOptionsTradingAlgorithm : QCAlgorithm
{
    private Symbol _option;

    public override void Initialize()
    {
        SetStartDate(2024, 12, 1);
        SetEndDate(2024, 12, 10); 
        SetCash(100000);

        // Request SPY option data for trading and signal generation.
        var option = AddOption("SPY");
        // We are interested in ATM 0DTE options since they are the most popular.
        option.SetFilter(u => u.IncludeWeeklys().Expiration(0, 0).Strikes(-3, 3));
        _option = option.Symbol;

        // Schedule event to enter and exit option contract position.
        Schedule.On(DateRules.EveryDay(_option), TimeRules.AfterMarketOpen(_option, 16), TradeOption);
        Schedule.On(DateRules.EveryDay(_option), TimeRules.BeforeMarketClose(_option, 15), () => Liquidate());
    }

    public void TradeOption()
    {
        // Get the option chain for SPY to trade.
        if (CurrentSlice.OptionChains.TryGetValue(_option, out var optionChain))
        {
            foreach (var option in optionChain)
            {
                // Request historical quote data for signal generation.
                var history = History<QuoteBar>(_option, 15, Resolution.Minute);
                // Calculate the total bid and ask for dollar volume to determine the capital directional force.
                var totalBidVolume = 0m;
                var totalAskVolume = 0m;
                foreach (var bar in history)
                {
                    totalBidVolume += bar.Bid.Close * bar.LastBidSize;
                    totalAskVolume += bar.Ask.Close * bar.LastAskSize;
                }

                // Follow the capital flow to trade.
                if (totalBidVolume > totalAskVolume)
                {
                    MarketOrder(option.Symbol, 1);
                }
                else
                {
                    MarketOrder(option.Symbol, -1);
                }
            }
        }
    }
}

class ZeroDTEOptionsTradingAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2024, 12, 1)
        self.set_end_date(2024, 12, 10)  # Limit to a single day for 0DTE
        self.set_cash(100000)  # Starting cash

        # Request SPY option data for trading and signal generation.
        option = self.add_option("SPY")
        # We are interested in ATM 0DTE options since they are the most popular.
        option.set_filter(lambda u: u.include_weeklys().expiration(0, 0).strikes(-3, 3))
        self._option = option.symbol
        
        # Schedule event to enter and exit option contract position.
        self.schedule.on(self.date_rules.every_day(self._option), self.time_rules.after_market_open(self._option, 16), self.trade_option)
        self.schedule.on(self.date_rules.every_day(self._option), self.time_rules.before_market_close(self._option, 15), self.liquidate)

    def trade_option(self) -> None:
        # Get the option chain for SPY to trade.
        option_chain = self.current_slice.option_chains.get(self._option, None)
        if option_chain:
            for option in option_chain:
                # Request historical quote data for signal generation.
                history = self.history(QuoteBar, option.symbol, 15, Resolution.Minute)
                if not history.empty:
                    # Calculate total bid and ask dollar volume to determine the capital directional force.
                    total_bid_volume = (history['bidclose'] * history['bidsize']).sum()
                    total_ask_volume = (history['askclose'] * history['asksize']).sum()

                    # Follow the capital flow to trade.
                    if total_bid_volume > total_ask_volume:
                        self.market_order(option.symbol, 1)
                    else:
                        self.market_order(option.symbol, -1)

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Example 1: Trend Following on 0DTE Option Contract

▶
Key Concepts

▶
Securities

▶
Portfolio

▶
Universes

▶
Datasets

▶
Importing Data

▶
Consolidating Data

▶
Historical Data

▶
Trading and Orders

▶
Reality Modeling

▶
Indicators

▶
Optimization

▶
Machine Learning

▶
Algorithm Framework

▶
Statistics

▶
Live Trading

▶
Migrations