---
title: "Individual Contracts"
description: "How to get data for individual Equity Options in the research environment in QuantConnect."
canonical: https://www.quantconnect.com/docs/v2/research-environment/datasets/equity-options/individual-contracts
markdown: https://www.quantconnect.com/docs/v2/research-environment/datasets/equity-options/individual-contracts.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/04%20Research%20Environment/03%20Datasets/08%20Equity%20Options/03%20Individual%20Contracts
modified: 2026-11-02
index: https://www.quantconnect.com/llms.txt
---

# Individual Contracts

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Research Environment](https://www.quantconnect.com/docs/v2/research-environment.md) > Datasets > Equity Options > Individual Contracts

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

This page explains how to request historical data for individual Equity Option contracts. The [history requests](https://www.quantconnect.com/docs/v2/research-environment/datasets/key-concepts.md) on this page only return the prices and open interest of the Option contracts, not their implied volatility or Greeks. For information about history requests that return the daily implied volatility and Greeks, see [Universes](https://www.quantconnect.com/docs/v2/research-environment/datasets/equity-options/universes.md).

## Create Subscriptions

Follow these steps to subscribe to individual Equity Option contracts:

1. Create a `QuantBook`.
   ```python
   qb = QuantBook()
   ```
2. [Add the underlying Equity](https://www.quantconnect.com/docs/v2/research-environment/datasets/us-equity.md#03-Create-Subscriptions) with raw [data normalization](https://www.quantconnect.com/docs/v2/research-environment/datasets/us-equity.md#07-Data-Normalization).
   ```python
   underlying_symbol = qb.add_equity("SPY", data_normalization_mode=DataNormalizationMode.RAW).symbol
   ```
   To view the supported underlying assets in the US Equity Options dataset, see the [Data Explorer](https://www.quantconnect.com/datasets/algoseek-us-equity-options/explorer).
3. [Set the start date](https://www.quantconnect.com/docs/v2/research-environment/initialization.md#02-Set-Dates) to a date in the past that you want to use as the analysis date.
   ```python
   qb.set_start_date(2024, 1, 1)
   ```
   The method that you call in the next step returns data on all the contracts that were tradable on this date.
4. Call the `option_chain` method with the underlying `Equity` `Symbol`.
   ```python
   # Get the Option contracts that were tradable on January 1st, 2024.
   chain = qb.option_chain(underlying_symbol, flatten=True)
   ```
   This method returns an `OptionChain` object, which represent an entire chain of Option contracts for a single underlying security. You can even format the chain data into a DataFrame where each row in the DataFrame represents a single contract.
5. Sort and filter the data to select the specific contract(s) you want to analyze.
   ```python
   # Get the contracts available to trade (in DataFrame format).
   chain = chain.data_frame

   # Select a contract.
   expiry = chain.expiry.min()
   contract_symbol = chain[
       # Select call contracts with the closest expiry.
       (chain.expiry == expiry) &
       (chain.right == OptionRight.CALL) &
       # Select contracts with a 0.3-0.7 delta.
       (chain.delta > 0.3) &
       (chain.delta < 0.7)
       # Select the contract with the largest open interest.
   ].sort_values('openinterest').index[-1]
   ```
6. Call the `add_option_contract` method with an `OptionContract` `Symbol` and disable fill-forward.
   ```python
   # Subscribe to the target contract.
   option_contract = qb.add_option_contract(contract_symbol, fill_forward=False)
   ```
   Disable fill-forward because there are only a few `OpenInterest` data points per day.

## Trade History

`TradeBar` objects are price bars that consolidate individual trades from the exchanges. They contain the open, high, low, close, and volume of trading activity over a period of time.

![Tradebar decomposition](https://cdn.quantconnect.com/docs/i/dataformat-tradebar.png)

To get trade data, call the `history` or `history[TradeBar]` method with the contract `Symbol` object(s).

```python
# DataFrame format
history_df = qb.history(TradeBar, contract_symbol, timedelta(3))
display(history_df)

# TradeBar objects
history = qb.history[TradeBar](contract_symbol, timedelta(3))
for trade_bar in history:
    print(trade_bar)
```

`TradeBar` objects have the following properties:

*Members of `QuantConnect.Data.Market.TradeBar` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.TradeBar&language=python*

## Quote History

`QuoteBar` objects are bars that consolidate NBBO quotes from the exchanges. They contain the open, high, low, and close prices of the bid and ask. The `open`, `high`, `low`, and `close` properties of the `QuoteBar` object are the mean of the respective bid and ask prices. If the bid or ask portion of the `QuoteBar` has no data, the `open`, `high`, `low`, and `close` properties of the `QuoteBar` copy the values of either the `bid` or `ask` instead of taking their mean.

![Quotebar decomposition](https://cdn.quantconnect.com/docs/i/dataformat-quotebar.png)

To get quote data, call the `history` or `history[QuoteBar]` method with the contract `Symbol` object(s).

```python
# DataFrame format
history_df = qb.history(QuoteBar, contract_symbol, timedelta(3))
display(history_df)

# QuoteBar objects
history = qb.history[QuoteBar](contract_symbol, timedelta(3))
for quote_bar in history:
    print(quote_bar)
```

`QuoteBar` objects have the following properties:

*Members of `QuantConnect.Data.Market.QuoteBar` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.QuoteBar&language=python*

## Open Interest History

Open interest is the number of outstanding contracts that haven't been settled. It provides a measure of investor interest and the market liquidity, so it's a popular metric to use for contract selection. Open interest is calculated once per day.

To get open interest data, call the `history` or `history[OpenInterest]` method with the contract `Symbol` object(s).

```python
# DataFrame format
history_df = qb.history(OpenInterest, contract_symbol, timedelta(3))
display(history_df)

# OpenInterest objects
history = qb.history[OpenInterest](contract_symbol, timedelta(3))
for open_interest in history:
    print(open_interest)
```

`OpenInterest` objects have the following properties:

*Members of `QuantConnect.Data.Market.OpenInterest` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Data.Market.OpenInterest&language=python*

## Greeks and IV History

The Greeks are measures that describe the sensitivity of an Option's price to various factors like underlying price changes (Delta), time decay (Theta), volatility (Vega), and interest rates (Rho), while Implied Volatility (IV) represents the market's expectation of the underlying asset's volatility over the life of the Option.

Follow these steps to get the Greeks and IV data:

1. Create the mirror contract `Symbol`.
   ```python
   mirror_contract_symbol = Symbol.create_option(
       option_contract.underlying.symbol,     contract_symbol.id.market,
       option_contract.style,
       OptionRight.CALL if option_contract.right == OptionRight.PUT else OptionRight.PUT,
       option_contract.strike_price,
       option_contract.expiry
   )
   ```
2. Set up the [risk free interest rate](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/risk-free-interest-rate/key-concepts.md), [dividend yield](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/dividend-yield/key-concepts.md), and [Option pricing](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/options-models/pricing.md) models.
   In [our research](https://www.quantconnect.com/research/16977/greeks-and-iv-implementation/p1), we found the Forward Tree model to be the best pricing model for indicators.
   ```python
   risk_free_rate_model = qb.risk_free_interest_rate_model
   dividend_yield_model = DividendYieldProvider(underlying_symbol)
   option_model = OptionPricingModelType.FORWARD_TREE
   ```
3. Define a method to return the [IV & Greeks indicator](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/equity-options/greeks-and-implied-volatility/indicators.md) values for each contract.
   ```python
   def greeks_and_iv(contracts, period, risk_free_rate_model, dividend_yield_model, option_model):
       # Get the call and put contract.
       call, put = sorted(contracts, key=lambda s: s.id.option_right)

       def get_values(indicator_class, contract, mirror_contract):
           return qb.indicator_history(
               indicator_class(contract, risk_free_rate_model, dividend_yield_model, mirror_contract, option_model),
               [contract, mirror_contract, contract.underlying],
               period
           ).data_frame.current

       return pd.DataFrame({
           'iv_call': get_values(ImpliedVolatility, call, put),
           'iv_put': get_values(ImpliedVolatility, put, call),
           'delta_call': get_values(Delta, call, put),
           'delta_put': get_values(Delta, put, call),
           'gamma_call': get_values(Gamma, call, put),
           'gamma_put': get_values(Gamma, put, call),
           'rho_call': get_values(Rho, call, put),
           'rho_put': get_values(Rho, put, call),
           'vega_call': get_values(Vega, call, put),
           'vega_put': get_values(Vega, put, call),
           'theta_call': get_values(Theta, call, put),
           'theta_put': get_values(Theta, put, call),
       })
   ```
4. Call the preceding method and display the results.
   ```python
   greeks_and_iv([contract_symbol, mirror_contract_symbol], 15, risk_free_rate_model, dividend_yield_model, option_model)
   ```

![DataFrame result of the preceding code snippets, containing the greeks and IV history.](https://cdn.quantconnect.com/i/tu/equity-options-greeks-and-iv-history.png)

The DataFrame can have NaN entries if there is no data for the contracts or the underlying asset at a moment in time.

## Examples

The following examples demonstrate some common practices for analyzing individual Equity Option contracts in the Research Environment.

### Example 1: Contract Trade History

The following notebook plots the historical prices of an SPY Equity Option contract using [Plotly](https://www.quantconnect.com/docs/v2/research-environment/charting/plotly.md):

```python
import plotly.graph_objects as go

# Get the SPY Option chain for January 1, 2024.
qb = QuantBook()
underlying_symbol = qb.add_equity("SPY", data_normalization_mode=DataNormalizationMode.RAW).symbol
qb.set_start_date(2024, 1, 1)
chain = qb.option_chain(underlying_symbol, flatten=True).data_frame

# Select a contract from the chain.
expiry = chain.expiry.min()
contract_symbol = chain[
    (chain.expiry == expiry) &
    (chain.right == OptionRight.CALL) &
    (chain.delta > 0.3) &
    (chain.delta < 0.7)
].sort_values('openinterest').index[-1]

# Add the target contract.
qb.add_option_contract(contract_symbol)

# Get the contract history.
history = qb.history(contract_symbol, timedelta(3))

# Plot the price history.
go.Figure(
    data=go.Candlestick(
        x=history.index.levels[4],
        open=history['open'],
        high=history['high'],
        low=history['low'],
        close=history['close']
    ),
    layout=go.Layout(
        title=go.layout.Title(text=f'{contract_symbol.value} OHLC'),
        xaxis_title='Date',
        yaxis_title='Price',
        xaxis_rangeslider_visible=False
    )
).show()
```

![Candlestick plot of the prices for a SPY Option contract](https://cdn.quantconnect.com/i/tu/equity-options-price-example-python.png)

### Example 2: Contract Open Interest History

The following notebook plots the historical open interest of a TSLA Equity Option contract using [Matplotlib](https://www.quantconnect.com/docs/v2/research-environment/charting/matplotlib.md):

```python
# Get the TSLA Option chain for January 1, 2024.
qb = QuantBook()
underlying_symbol = qb.add_equity("TSLA", data_normalization_mode=DataNormalizationMode.RAW).symbol
qb.set_start_date(2024, 1, 1)
chain = qb.option_chain(underlying_symbol, flatten=True).data_frame

# Select a contract from the chain.
strike_distance = (chain.strike - chain.underlyinglastprice).abs()
target_strike_distance = strike_distance.min()
chain = chain.loc[strike_distance[strike_distance == target_strike_distance].index]
contract_symbol = chain.sort_values('impliedvolatility').index[-1]

# Add the target contract.
qb.add_option_contract(contract_symbol, fill_forward=False)

# Get the contract's open interest history.
history = qb.history(OpenInterest, contract_symbol, timedelta(90))
history.index = history.index.droplevel([0, 1, 2])
history = history['openinterest'].unstack(0)[contract_symbol]

# Plot the open interest history.
history.plot(title=f'{contract_symbol.value} Open Interest')
plt.show()
```

![Line plot of the open interest for a TSLA Option contract](https://cdn.quantconnect.com/i/tu/equity-options-open-interest-example-python.png)