---
title: "Universes"
description: "How to get data for an Equity Options universe in the research environment in QuantConnect."
canonical: https://www.quantconnect.com/docs/v2/research-environment/datasets/futures-options/universes
markdown: https://www.quantconnect.com/docs/v2/research-environment/datasets/futures-options/universes.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/04%20Research%20Environment/03%20Datasets/14%20Futures%20Options/02%20Universes
modified: 2026-11-02
index: https://www.quantconnect.com/llms.txt
---

# Universes

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

> 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 a universe of Future Option contracts.

## Create Subscriptions

Follow these steps to subscribe to a Futures Options universe:

1. Create a `QuantBook`.
   ```python
   qb = QuantBook()
   ```
2. [Add the underlying Future](https://www.quantconnect.com/docs/v2/research-environment/datasets/futures/universes.md#02-Create-Subscriptions).
   ```python
   future = qb.add_future(Futures.Indices.SP_500_E_MINI)
   ```
   To view the available underlying Futures in the US Future Options dataset, see [Supported Assets](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/algoseek/us-future-options.md#08-Supported-Assets).

## Price History

The [contract filter](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options.md#05-Filter-by-Other-Contract-Properties) determines which Future Option contracts are in your universe each trading day. The default filter selects the contracts with the following characteristics:

- Standard type (weeklies and non-standard contracts are not available)
- Within 1 strike price of the underlying asset price
- Expire within 35 days

To get the prices and volumes for all of the Future Option contracts that pass your filter during a specific period of time, get the underlying Future contract and then call the `option_history` method with the Future contract's `Symbol` object, a start `datetime`, and an end `datetime`.

```python
start_date = datetime(2024, 1, 1)

# Select an underlying Futures contract. For example, get the front-month contract.
chain = list(qb.history[FutureUniverse](future.symbol, start_date, start_date+timedelta(2)))[0]
futures_contract = list(chain)[0].symbol

# Get the Options data for the selected Futures contract.
option_history = qb.option_history(
    futures_contract, start_date, futures_contract.id.date, Resolution.HOUR,
    fill_forward=False, extended_market_hours=False
)
```

To convert the `OptionHistory` object to a `DataFrame` that contains the trade and quote information of each contract and the underlying, use the `data_frame` property.

```python
option_history.data_frame
```

| expiry | strike | type | symbol | time | askclose | askhigh | asklow | askopen | asksize | bidclose | bidhigh | bidlow | bidopen | bidsize | close | high | low | open | volume |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2024-03-15 | 4575.0 | 0 | ES YGT6I1W1ONB8\|ES YGT6HGVF2SQP | 2024-01-02 10:00:00 | 262.0 | 316.50 | 248.75 | 256.0 | 1.0 | 247.25 | 257.25 | 187.25 | 253.50 | 1.0 | 254.625 | 286.875 | 218.000 | 254.750 | NaN |
| 2024-03-15 | 4575.0 | 0 | ES YGT6I1W1ONB8\|ES YGT6HGVF2SQP | 2024-01-02 11:00:00 | 250.5 | 300.00 | 248.25 | 262.0 | 2.0 | 248.50 | 256.50 | 215.00 | 247.25 | 2.0 | 249.500 | 278.250 | 231.625 | 254.625 | NaN |
| 2024-03-15 | 4575.0 | 0 | ES YGT6I1W1ONB8\|ES YGT6HGVF2SQP | 2024-01-02 12:00:00 | 261.0 | 300.00 | 250.00 | 250.5 | 1.0 | 259.25 | 259.75 | 216.25 | 248.50 | 1.0 | 260.125 | 279.875 | 233.125 | 249.500 | NaN |
| 2024-03-15 | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2024-03-15 | 5215.0 | 1 | ES 32FX6NKT3COAS\|ES YGT6HGVF2SQP | 2024-03-14 15:00:00 | 70.5 | 92.25 | 58.00 | 64.5 | 2.0 | 69.75 | 70.25 | 36.75 | 52.00 | 1.0 | 70.125 | 81.250 | 47.375 | 58.250 | NaN |
| 2024-03-15 | 5215.0 | 1 | ES 32FX6NKT3COAS\|ES YGT6HGVF2SQP | 2024-03-14 16:00:00 | 75.0 | 183.50 | 63.25 | 70.5 | 10.0 | 44.75 | 89.75 | 2.10 | 69.75 | 10.0 | 67.750 | 67.750 | 67.750 | 67.750 | 1.0 |
| 2024-03-15 | 5215.0 | 1 | ES 32FX6NKT3COAS\|ES YGT6HGVF2SQP | 2024-03-14 17:00:00 | 63.5 | 84.00 | 58.00 | 75.0 | 1.0 | 58.50 | 61.50 | 35.50 | 44.75 | 1.0 | 61.000 | 72.750 | 46.750 | 59.875 | NaN |

To get the expiration dates of all the contracts in an `OptionHistory` object, call the method.

```python
option_history.get_expiry_dates()
```

![list of expiry dates](https://cdn.quantconnect.com/i/tu/future-options-uiverse-history-get-expiry-dates-python.png)

To get the strike prices of all the contracts in an `OptionHistory` object, call the method.

```python
option_history.get_strikes()
```

![List of strike prices](https://cdn.quantconnect.com/i/tu/future-options-uiverse-history-get-strikes-python.png)

## Examples

The following examples demonstrate some common practices for applying the Future Options dataset.

### Example 1: Implied Volatility Line Chart

The following example plots a line chart on the implied volatility curve of the closest expiring calls.

```python
# Instantiate a QuantBook instance.
qb = QuantBook()
# Set the date being studied.
date = datetime(2024, 1, 4)

# Subscribe to the underlying Future.
future = qb.add_future(Futures.Indices.SP_500_E_MINI)
# Select an underlying Futures contract. For example, get the front-month contract.
chain = list(qb.history[FutureUniverse](future.symbol, date, date+timedelta(2)))[0]
futures_contract = list(chain)[0].symbol
# Get the Options data for the selected Futures contract.
option_history = qb.option_history(
    futures_contract, date - timedelta(1), date, Resolution.DAILY,
    fill_forward=False, extended_market_hours=False
)

chain = list(option_history)[-1].option_chains.values()[0]
# Study the closest expiring contracts.
expiry = min(x.expiry for x in chain)
# Filter for the closest expiring calls to study only.
filter_contracts = [x for x in chain if x.expiry == expiry and x.right == OptionRight.CALL]

# Obtain the strike and IV for plotting the IV curve.
iv_by_strike = pd.Series({x.strike: x.implied_volatility for x in filter_contracts})
iv_by_strike.plot(title=f"IV Curve of {futures_contract}", ylabel="Implied Volatility", xlabel="Strike")
```