In This Page

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

Requesting Data

Accessing Data

Historical Data

Universe Selection

Universe History

Remove Subscriptions

Example Applications

Quiver Quantitative

Corporate Lobbying

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 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.

About the Provider

Quiver Quantitative 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:

Select Language:

self.aapl = 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)

_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<QuiverLobbyings>(_symbol).Symbol;

_universe = AddUniverse<QuiverLobbyingUniverse>(UniverseSelectionFilter);

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

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

For more example algorithms, see Examples.

Data Point Attributes

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

QuiverLobbyings

QuiverLobbyings objects have the following attributes:

QuiverLobbyings Select Language:

The associated underlying price data if any

underlying: BaseData

Gets or sets the contracts selected by the universe

filtered_contracts: HashSet<Symbol>

Gets the data list

data: List<BaseData>

Gets or sets the end time of this data

end_time: DateTime

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.

value: decimal

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

QuiverLobbying

QuiverLobbying objects have the following attributes:

QuiverLobbying Select Language:

Full name of the lobbying client

client: string

Category of legislation that is being lobbied for

issue: string

Specific piece of legislation being lobbied for

specific_issue: string

The Size of spending instance (USD)

amount: decimal

Time the data became available

end_time: DateTime

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.

value: decimal

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

QuiverLobbyingUniverse

QuiverLobbyingUniverse objects have the following attributes:

QuiverLobbyingUniverse Select Language:

Full name of the lobbying client

client: string

Category of legislation that is being lobbied for

issue: string

Specific piece of legislation being lobbied for

specific_issue: string

The Size of spending instance (USD)

amount: decimal

Time the data became available

end_time: DateTime

The associated underlying price data if any

underlying: BaseData

Gets or sets the contracts selected by the universe

filtered_contracts: HashSet<Symbol>

Gets the data list

data: List<BaseData>

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.

value: decimal

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

Requesting Data

To add Corporate Lobbying data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.

Select Language:

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

namespace QuantConnect.Algorithm.CSharp.AltData
{
    public class QuiverLobbyingDataAlgorithm: QCAlgorithm
    {
        private Symbol _datasetSymbol;

        public override void Initialize()
        {
            SetStartDate(2019, 1, 1);
            SetEndDate(2020, 6, 1);
            SetCash(100000);
            var symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
            _datasetSymbol= AddData<QuiverLobbyings>(symbol).Symbol;
        }
    }
}

Accessing Data

To get the current Corporate Lobbying data, index the current Slice 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.

Select Language:

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}")

public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var dataPoints = slice[_datasetSymbol];
        foreach (var dataPoint in dataPoints)
        {
            Log($"{_datasetSymbol} amount at {slice.Time}: {dataPoint.Amount}");
        }
    }
}

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

Select Language:

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}")

public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<QuiverLobbyings>())
    {
        var datasetSymbol = kvp.Key;
        var dataPoints = kvp.Value;
        foreach(var dataPoint in dataPoints)
        {
            Log($"{datasetSymbol} amount at {slice.Time}: {dataPoint.Amount}");
        }
    }
}

Historical Data

To get historical Corporate Lobbying data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.

Select Language:

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

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

var history = History<QuiverLobbyings>(_datasetSymbol, 100, Resolution.Daily);

For more information about historical data, see History Requests.

Universe Selection

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

Select Language:

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]

private Universe _universe;
public override void Initialize()
{
    _universe = AddUniverse<QuiverLobbyingUniverse>("QuiverLobbyingUniverse", Resolution.Daily, altCoarse =>
    {
        var lobbyDataBySymbol = new Dictionary<Symbol, List<QuiverLobbyingUniverse>>();

        foreach (var datum in altCoarse.OfType<QuiverLobbyingUniverse>())
        {
            var symbol = datum.Symbol;

            if (!lobbyDataBySymbol.ContainsKey(symbol))
            {
                lobbyDataBySymbol.Add(symbol, new List<QuiverLobbyingUniverse>());
            }
            lobbyDataBySymbol[symbol].Add(datum);
        }

        return from kvp in lobbyDataBySymbol
              where kvp.Value.Sum(x => x.Amount) >= 100000
              select kvp.Key;
    })
};

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 Historyhistory method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.

Select Language:

var universeHistory = History(universe, 30, Resolution.Daily);
foreach (var lobbyings in universeHistory)
{
    foreach (QuiverLobbyingUniverse lobbying in lobbyings)
    {
        Log($"{lobbying.Symbol} issue at {lobbying.EndTime}: {lobbying.Issue}");
    }
}

# 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 UniverseHistoryuniverse_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.

Select Language:

var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
foreach (var lobbyings in universeHistory)
{
    foreach (QuiverLobbyingUniverse lobbying in lobbyings)
    {
        Consolte.WriteLine($"{lobbying.Symbol} issue at {lobbying.EndTime}: {lobbying.Issue}");
    }
}

# 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 Historyhistory method in Research.

Remove Subscriptions

To remove a subscription, call the RemoveSecurityremove_security method.

Select Language:

self.remove_security(self.dataset_symbol)

RemoveSecurity(_datasetSymbol);

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.

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

For more example algorithms, see Examples.

You can also see our Videos. You can also get in touch with us via Discord.

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Hello

Quiver Quantitative

Corporate Lobbying

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

QuiverLobbyings

QuiverLobbying

QuiverLobbyingUniverse

Requesting Data

Accessing Data

Historical Data

Universe Selection

Universe History

Historical Universe Data in Algorithms

Historical Universe Data in Research

Remove Subscriptions

Example Applications

▶
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