---
title: "Cross Asset Model"
description: "Cross Asset Model dataset from ExtractAlpha."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/extractalpha/cross-asset-model
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/extractalpha/cross-asset-model.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/14%20Datasets/16%20ExtractAlpha/01%20Cross%20Asset%20Model
modified: 2026-10-28
index: https://www.quantconnect.com/llms.txt
---

# Cross Asset Model

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Datasets > [ExtractAlpha](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/extractalpha.md) > Cross Asset Model

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

## Introduction

The Cross Asset Model by ExtractAlpha provides stock scoring values based on the trading activity in the Options market. Since the Options market has a higher proportion of institutional traders than the Equities market, the Options market is composed of investors who are more informed and information-driven on average. The data covers a dynamic universe of over 3,000 US Equities, starts in July 2005, and is delivered on a daily frequency. This dataset is created by feature engineering on the Options market put-call spread, volatility skewness, and volume.

This dataset depends on the [US Equity Security Master](https://www.quantconnect.com/datasets/quantconnect-us-equity-security-master) dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.

For more information about the Cross Asset Model dataset, including CLI commands and pricing, see the [dataset listing](https://www.quantconnect.com/datasets/extractalpha-cross-asset-model).

## About the Provider

[ExtractAlpha](https://extractalpha.com/) was founded by Vinesh Jha in 2013 with the goal of providing alternative data for investors. ExtractAlpha's rigorously researched data sets and quantitative stock selection models leverage unique sources and analytical techniques, allowing users to gain an investment edge.

## Getting Started

The following snippet demonstrates how to request data from the Cross Asset Model dataset:

```python
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(ExtractAlphaCrossAssetModel, self.aapl).symbol
```

## Data Summary

The following table describes the dataset properties:

| Property | Value |
| --- | --- |
| Start Date | July 2005 |
| Asset Coverage | Over 3,000 US Equities |
| Data Density | Sparse |
| Resolution | Daily |
| Timezone | New York |
| Timestamp | 9 AM |

## Requesting Data

To add Cross Asset Model data to your algorithm, call the `add_data` method. Save a reference to the dataset `Symbol` so you can access the data later in your algorithm.

```python
class ExtractAlphaCrossAssetModelDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(ExtractAlphaCrossAssetModel, self.aapl).symbol
```

## Accessing Data

To get the current Cross Asset Model data, index the current [`Slice`](https://www.quantconnect.com/docs/v2/writing-algorithms/key-concepts/time-modeling/timeslices.md) with the dataset `Symbol`. Slice objects deliver unique events to your algorithm as they happen, but the `Slice` may not contain data for your dataset at every time step. To avoid issues, check if the `Slice` contains the data you want before you index it.

```python
def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.dataset_symbol):
        data_point = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} score at {slice.time}: {data_point.score}")
```

To iterate through all of the dataset objects in the current `Slice`, call the `get` method.

```python
def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(ExtractAlphaCrossAssetModel).items():
        self.log(f"{dataset_symbol} score at {slice.time}: {data_point.score}")
```

## Historical Data

To get historical Cross Asset Model data, call the `history` method with the dataset `Symbol`. If there is no data in the period you request, the history result is empty.

```python
# DataFrame
history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)

# Dataset objects
history_bars = self.history[ExtractAlphaCrossAssetModel](self.dataset_symbol, 100, Resolution.DAILY)
```

For more information about historical data, see [History Requests](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md).

## Remove Subscriptions

To remove a subscription, call the `remove_security` method.

```python
self.remove_security(self.dataset_symbol)
```

If you subscribe to Cross Asset Model data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see [Track Security Changes](https://www.quantconnect.com/docs/v2/writing-algorithms/algorithm-framework/alpha/key-concepts.md#05-Track-Security-Changes).

## Example Applications

The Cross Asset Model dataset by ExtractAlpha enables you to utilize Options market information to extract alpha. Examples include the following strategies:

- Predicting price and volatility changes in Equities.
- Signaling arbitrage opportunities between Options and underlying assets.
- Using it as a stock selection indicator.

### Classic Algorithm Example

The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, the algorithm forms an equal-weighted dollar-neutral portfolio of the 10 companies most likely to outperform and the 10 companies most likely to underperform.

```python
from AlgorithmImports import *

class ExtractAlphaCrossAssetModelAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(100_000)
        self._points = {}
        self.add_universe(self._select_assets)
        # Add a Scheduled Event to rebalance the portfolio each day.
        spy = Symbol.create('SPY', SecurityType.EQUITY, Market.USA)
        self.schedule.on(
            self.date_rules.every_day(spy),
            self.time_rules.after_market_open(spy, 30),
            self._rebalance
        )

    def _select_assets(self, coarse: List[Fundamental]) -> List[Symbol]:
        # Select non-penny stocks with highest dollar volume due to better informed information from more market activities
        # Only the ones with fundamental data are supported by cross asset model data
        sorted_by_dollar_volume = sorted(
            [x for x in coarse if x.has_fundamental_data and x.price > 4],
            key=lambda x: x.dollar_volume
        )
        return [x.symbol for x in sorted_by_dollar_volume[-100:]]

    def on_data(self, slice: Slice) -> None:
        # Get the current data from the cross asset model.
        points = slice.get(ExtractAlphaCrossAssetModel)
        if points:
            self._points = points
            ## Demonstrate how to iterate through the data and access its members:
            #for dataset_symbol, model in points.items():
            #    self.quit(
            #        f"{self.time} -- "
            #        f"Asset Symbol: {dataset_symbol.underlying}; "
            #        f"Score: {model.score} "
            #    )

    def _rebalance(self):
        # Long the ones with the highest return estimates based on option trade data
        # Short the lowest return ones
        sorted_by_score = sorted(
            [
                x for x in self._points.items()
                # Remove assets that have no price or score.
                if self.securities[x[0].underlying].price and x[1].score
            ],
            key=lambda x: x[1].score
        )
        long_symbols = [x[0].underlying for x in sorted_by_score[-10:]]
        short_symbols = [x[0].underlying for x in sorted_by_score[:10]]

        # Liquidate the ones without a strong trading signal
        # Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.set_holdings(long_targets + short_targets, True)

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting cross asset model data for trading signal generation
            security.cross_asset_model = self.add_data(
                ExtractAlphaCrossAssetModel, security.symbol
            ).symbol
            # Historical Data
            history = self.history(security.cross_asset_model, 2, Resolution.DAILY)
        for security in changes.removed_securities:
            # Remove the cross asset model data for this asset when it leaves the universe.
            self.remove_security(security.cross_asset_model)
```

### Framework Algorithm Example

The following example algorithm creates a dynamic universe of the 100 most liquid US Equities. Each day, the algorithm forms an equal-weighted dollar-neutral portfolio of the 10 companies most likely to outperform and the 10 companies most likely to underperform.

```python
from AlgorithmImports import *

class ExtractAlphaCrossAssetModelFrameworkAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(100000)
        self.add_universe_selection(LiquidEquitiesUniverseSelectionModel())
        # Custom alpha model emits insights based on cross asset model data
        self.add_alpha(ExtractAlphaCrossAssetModelAlphaModel())
        # Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
        self.set_execution(ImmediateExecutionModel())

class LiquidEquitiesUniverseSelectionModel(FundamentalUniverseSelectionModel):

    def select(self, algorithm: QCAlgorithm, fundamentals: List[Fundamental]) -> List[Symbol]:
        # Select non-penny stocks with highest dollar volume due to better informed information from more market activities
        # Only the ones with fundamental data are supported by cross asset model data
        sorted_by_dollar_volume = sorted(
            [x for x in fundamentals if x.has_fundamental_data and x.price > 4],
            key=lambda x: x.dollar_volume
        )
        return [x.symbol for x in sorted_by_dollar_volume[-100:]]

class ExtractAlphaCrossAssetModelAlphaModel(AlphaModel):

    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
        # Get the current data from the cross asset model.
        points = slice.get(ExtractAlphaCrossAssetModel)
        ## Demonstrate how to iterate through the data and access its members:
        #for dataset_symbol, model in points.items():
        #    algorithm.quit(
        #        f"{algorithm.time} -- "
        #        f"Asset Symbol: {dataset_symbol.underlying}; "
        #        f"Score: {model.score} "
        #    )

        # Drop factors for assets that have no price or score.
        points = [
            x for x in points.items()
            if algorithm.securities[x[0].underlying].price and x[1].score
        ]
        # Only rebalance when there are new data points from the cross asset models.
        if not points:
            return []

        # Long the ones with the highest return estimates based on option trade data
        # Short the lowest return ones
        sorted_by_score = sorted(points, key=lambda x: x[1].score)
        insight_directions = (
            [(x, InsightDirection.DOWN) for x in sorted_by_score[:10]] +
            [(x, InsightDirection.UP) for x in sorted_by_score[-10:]]
        )
        return [
            Insight.price(x[0].underlying, Expiry.END_OF_DAY, direction)
            for x, direction in insight_directions
        ]

    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting cross asset model data for trading signal generation
            security.cross_asset_model = algorithm.add_data(
                ExtractAlphaCrossAssetModel, security.symbol
            ).symbol
            # Historical Data
            history = algorithm.history(security.cross_asset_model, 2, Resolution.DAILY)
        for security in changes.removed_securities:
            # Remove the cross asset model data for this asset when it leaves the universe.
            algorithm.remove_security(security.cross_asset_model)
```

## Data Point Attributes

The Cross Asset Model dataset provides `ExtractAlphaCrossAssetModel` objects, which have the following attributes:

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