---
title: "Corporate Lobbying"
description: "Corporate Lobbying dataset from Quiver Quantitative."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quiver-quantitative/corporate-lobbying
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quiver-quantitative/corporate-lobbying.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/14%20Datasets/23%20Quiver%20Quantitative/02%20Corporate%20Lobbying
modified: 2026-11-01
index: https://www.quantconnect.com/llms.txt
---

# Corporate Lobbying

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Datasets > [Quiver Quantitative](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quiver-quantitative.md) > Corporate Lobbying

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

## Introduction

The Corporate Lobbying dataset by Quiver Quantitative tracks the lobbying activity of US Equities. The Lobbying Disclosure Act of 1995 requires lobbyists in the United States to disclose information about their activities, such as their clients, which issues they are lobbying on, and how much they are being paid. Quiver Quantiative scrapes this data and maps it to stock tickers to track which companies are spending money for legislative influence.

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 Corporate Lobbying dataset, including CLI commands and pricing, see the [dataset listing](https://www.quantconnect.com/datasets/quiver-quantitative-corporate-lobbying).

## About the Provider

[Quiver Quantitative](https://www.quiverquant.com/) was founded by two college students in February 2020 with the goal of bridging the information gap between Wall Street and non-professional investors. Quiver allows retail investors to tap into the power of big data and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street.

## Getting Started

The following snippet demonstrates how to request data from the Corporate Lobbying dataset:

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

self._universe = self.add_universe(QuiverLobbyingUniverse, self.universe_selection_filter)
```

## Data Summary

The following table describes the dataset properties:

| Property | Value |
| --- | --- |
| Start Date | January 4, 1999 |
| Asset Coverage | 1,418 US Equities |
| Data Density | Sparse |
| Resolution | Daily |
| Timezone | UTC |

## Requesting Data

To add Corporate Lobbying 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 QuiverLobbyingDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

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

## Accessing Data

To get the current Corporate Lobbying 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_points = slice[self.dataset_symbol]
        for data_point in data_points:
            self.log(f"{self.dataset_symbol} amount at {slice.time}: {data_point.amount}")
```

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_points in slice.get(QuiverLobbyings).items():
        for data_point in data_points:
            self.log(f"{dataset_symbol} amount at {slice.time}: {data_point.amount}")
```

## Historical Data

To get historical Corporate Lobbying 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[QuiverLobbyings](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).

## Universe Selection

To select a dynamic universe of US Equities based on Corporate Lobbying data, call the `add_universe` method with the `QuiverLobbyingUniverse` class and a selection function.

```python
def initialize(self):
    self._universe = self.add_universe(QuiverLobbyingUniverse, "QuiverLobbyingUniverse", Resolution.DAILY, self.universe_selection)

def universe_selection(self, alt_coarse: List[QuiverLobbyingUniverse]) -> List[Symbol]:
    lobby_data_by_symbol = {}

    for datum in alt_coarse:
        symbol = datum.symbol

        if symbol not in lobby_data_by_symbol:
            lobby_data_by_symbol[symbol] = []
        lobby_data_by_symbol[symbol].append(datum)

    return [symbol for symbol, d in lobby_data_by_symbol.items()
            if sum([x.amount for x in d]) >= 100000]
```

## Universe History

You can get historical universe data in an algorithm and in the Research Environment.

### Historical Universe Data in Algorithms

To get historical universe data in an algorithm, call the `history` method with the `Universe` object and the lookback period. If there is no data in the period you request, the history result is empty.

```python
# DataFrame example where the columns are the QuiverLobbyingUniverse attributes:
history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)

# Series example where the values are lists of QuiverLobbyingUniverse objects:
universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (symbol, time), lobbyings in universe_history.items():
    for lobbying in lobbyings:
        print(f"{lobbying.symbol} issue at {lobbying.end_time}: {lobbying.issue}")
```

### Historical Universe Data in Research

To get historical universe data in research, call the `universe_history` method with the `Universe` object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.

```python
# DataFrame example where the columns are the QuiverLobbyingUniverse attributes:
history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)

# Series example where the values are lists of QuiverLobbyingUniverse objects:
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (symbol, time), lobbyings in universe_history.items():
    for lobbying in lobbyings:
        print(f"{lobbying.symbol} issue at {lobbying.end_time}: {lobbying.issue}")
```

You can call the `history` method in Research.

## Remove Subscriptions

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

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

If you subscribe to Corporate Lobbying 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 Corporate Lobbying dataset enables you to create strategies using the latest information on lobbying activity. Examples include the following strategies:

- Trading securities that have spent the most on lobbying over the last quarter
- Trading securities that have had the biggest change in lobbying spend for privacy legislation over the last year

### Classic Algorithm Example

The following example algorithm tracks the lobbying activity of Apple. When the company initiates a lobbying activity worth more than $50K, the algorithm buys Apple stock. When the company initiates a lobbying activity worth less than $10K, the algorithm short sells Apple stock.

```python
from AlgorithmImports import *

class QuiverLobbyingDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self._equity = self.add_equity("AAPL", Resolution.DAILY)
        self.settings.seed_initial_prices = True
        # Subscribe to lobbying data for AAPL to generate the trade signal.
        self._dataset_symbol = self.add_data(QuiverLobbyings, self._equity).symbol
        # Request recent history to confirm the custom data subscription.
        history = self.history(self._dataset_symbol, 10, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from historical data request of {self._dataset_symbol}.")

    def on_data(self, data: Slice) -> None:
        # Trade only based on lobbying data.
        for lobbyings in data.get(QuiverLobbyings).values():
            # Buy when lobbying amount is over 50000, suggesting favorable political sentiment.
            if any([lobbying.amount > 50000 for lobbying in lobbyings]):
                self.set_holdings(self._equity, 1)
            # Sell when lobbying amount is below 10000, suggesting less favorable political sentiment.
            elif any([lobbying.amount < 10000 for lobbying in lobbyings]):
                self.set_holdings(self._equity, -1)
```

### Framework Algorithm Example

The following example algorithm creates a dynamic universe of US Equities that have at least $100K worth of new lobbying activity. Each day, it then forms an equal-weighted portfolio with all of the securities in the universe.

```python
from AlgorithmImports import *

class QuiverLobbyingDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2024, 9, 1)
        self.set_end_date(2024, 12, 31)
        self.set_cash(100000)
        # To hold the lobbying dataset symbol for managing subscription
        self.dataset_symbol_by_symbol = {}

        # Filter universe based on the lobbying data
        self.add_universe(QuiverLobbyingUniverse, self.universe_selection)

        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))

        # Invest equally to evenly dissipate the capital concentration risk
        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())

    def universe_selection(self, data: List[QuiverLobbyingUniverse]) -> List[Symbol]:
        lobby_data_by_symbol = {}

        for datum in data:
            symbol = datum.symbol

            if symbol not in lobby_data_by_symbol:
                lobby_data_by_symbol[symbol] = []
            lobby_data_by_symbol[symbol].append(datum)

        # Select and invest all stocks with lobbying amount above 100000, suggesting a more favorable political prospect and sentiment
        return [symbol for symbol, d in lobby_data_by_symbol.items()
                if sum([x.amount for x in d if x.amount]) >= 100000]

    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting lobbying data for trading
            symbol = security.symbol
            dataset_symbol = self.add_data(QuiverLobbyings, symbol).symbol
            self.dataset_symbol_by_symbol[symbol] = dataset_symbol
            # Historical Data
            history = self.history(dataset_symbol, 10, Resolution.DAILY)

        for security in changes.removed_securities:
            dataset_symbol = self.dataset_symbol_by_symbol.pop(security.symbol, None)
            if dataset_symbol:
                # Remove lobbying data subscription to release computation resources
                self.remove_security(dataset_symbol)
```

### Research Example

The following example lists US Equities that have brought health related issues.

```python
qb = QuantBook()

# Requesting Data
aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol
symbol = qb.add_data(QuiverLobbyings, aapl).symbol

# Historical data
history = qb.history(QuiverLobbyings, symbol, 60, Resolution.DAILY)
for (symbol, time), lobbyings in history.items():
    for lobbying in lobbyings:
        print(f"{lobbying.symbol} issue at {lobbying.end_time}: {lobbying.issue}")

# Add Universe Selection
def universe_selection(alt_coarse: List[QuiverLobbyingUniverse]) -> List[Symbol]:
    return [d.symbol for d in alt_coarse if 'health' in d.issue.lower()]

universe = qb.add_universe(QuiverLobbyingUniverse, universe_selection)

# Historical Universe data
history = qb.universe_history(universe, qb.time-timedelta(60), qb.time)
for (symbol, time), lobbyings in history.items():
    for lobbying in lobbyings:
        print(f"{lobbying.symbol} issue at {lobbying.end_time}: {lobbying.issue}")
```

## Data Point Attributes

The Quiver Quantitative Corporate Lobbying dataset provides `QuiverLobbyings`, `QuiverLobbying`, and `QuiverLobbyingUniverse` objects.

### QuiverLobbyings

`QuiverLobbyings` objects have the following attributes:

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

### QuiverLobbying

`QuiverLobbying` objects have the following attributes:

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

### QuiverLobbyingUniverse

`QuiverLobbyingUniverse` objects have the following attributes:

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