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:
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);
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:
QuiverLobbying
QuiverLobbying objects have the following attributes:
QuiverLobbyingUniverse
QuiverLobbyingUniverse objects have the following attributes:
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.
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.
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($"{_datasetSymbol} amount at {slice.Time}: {dataPoint.Amount}");
}
}
}
To iterate through all of the dataset objects in the current Slice, call the Getget method.
def on_data(self, slice: Slice) -> None:
for dataset_symbol, data_points in slice.get(QuiverLobbyings).items():
for data_point in data_points:
self.log(f"{dataset_symbol} amount at {slice.time}: {data_point.amount}")
public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<QuiverLobbyings>())
{
var datasetSymbol = kvp.Key;
var dataPoints = kvp.Value;
foreach(var dataPoint in dataPoints)
{
Log($"{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.
# 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.
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.
var universeHistory = History(universe, 30, Resolution.Daily);
foreach (var lobbyings in universeHistory)
{
foreach (QuiverLobbyingUniverse lobbying in lobbyings)
{
Log($"{lobbying.Symbol} issue at {lobbying.EndTime}: {lobbying.Issue}");
}
} universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (symbol, time), lobbyings in universe_history.items():
for lobbying in lobbyings:
print(f"{lobbying.symbol} issue at {lobbying.end_time}: {lobbying.issue}")
Historical Universe Data in Research
To get historical universe data in research, call the UniverseHistoryuniverse_history method with the Universe object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
foreach (var lobbyings in universeHistory)
{
foreach (QuiverLobbyingUniverse lobbying in lobbyings)
{
Consolte.WriteLine($"{lobbying.Symbol} issue at {lobbying.EndTime}: {lobbying.Issue}");
}
} 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.
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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