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Quiver Quantitative

US Congress Trading

Introduction

The US Congress Trading dataset by Quiver Quantitative tracks US Equity trades made by members of Congress in the Senate and the House of Representatives. The data covers 1,800 US Equities, starts in January 2016, and is delivered on a daily frequency. This dataset is created by scraping SEC reports.

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 US Congress Trading 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 US Congress Trading dataset:

Select Language: 
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverCongress, self.aapl).symbol

self._universe = self.add_universe(QuiverQuantCongressUniverse, self.universe_selection)

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2016
Asset Coverage1,800 US Equities
Data DensitySparse
ResolutionDaily
TimezoneUTC

Example Applications

The US Congress Trading dataset enables you to take immediate action on trades made by informed Members of Congress. Examples include the following strategies:

  • Following the trades of specific representatives on the premise that the representatives are more informed
  • Assigning a long/short-bias to securities on a daily frequency based on how Members of Congress are trading them

For more example algorithms, see Examples.

Data Point Attributes

The US Congress Trading dataset provides QuiverCongressDataPoint, QuiverCongress, and QuiverQuantCongressUniverseobjects.

QuiverCongressDataPoint Attributes

QuiverCongressDataPoint object has the following attributes:

QuiverCongressDataPoint Select Language:
    The date the transaction was recorded by QuiverQuant. Value will always exist.
  • record_date: DateTime
    • The date the recorded transaction was updated by QuiverQuant. Alias for EndTime.
  • updated_at: DateTime
    • The date the transaction was reported. Value will always exist.
  • report_date: DateTime
    • The date the transaction took place
  • transaction_date: DateTime
    • The Representative making the transaction
  • representative: string
    • The type of transaction
  • transaction: OrderDirection
    • The amount of the transaction (in USD). The Representative can report a range (see QuantConnect.DataSource.QuiverCongressDataPoint.MaximumAmount).
  • amount: decimal
    • The maximum amount of the transaction (in USD). The Representative can report a range (see QuantConnect.DataSource.QuiverCongressDataPoint.Amount).
  • maximum_amount: decimal
    • The Chamber of Congress that the trader belongs to
  • house: Congress
    • The political party that the trader belongs to
  • party: Party
    • The district that the trader belongs to (null or empty for Senators)
  • district: string
    • The state that the trader belongs to
  • state: string
    • The time the data point ends at and becomes available to the algorithm
  • 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

QuiverCongress Attributes

QuiverCongress object has the following attributes:

QuiverCongress 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

QuiverQuantCongressUniverse Attributes

QuiverQuantCongressUniverse object has the following attributes:

QuiverQuantCongressUniverse 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

Requesting Data

To add US Congress Trading 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.

Select Language: 
class QuiverCongressDataAlgorithm(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(QuiverCongress, symbol).symbol

Accessing Data

To get the current US Congress Trading 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} transaction amount at {slice.time}: {data_point.amount}")

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

Select Language: 
def on_data(self, slice: Slice) -> None:
    for dataset_symbol, data_point in slice.get(QuiverCongress).items():
        self.log(f"{dataset_symbol} transaction amount at {slice.time}: {data_point.amount}")

Historical Data

To get historical US Congress Trading data, call the history 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[QuiverCongress](self.dataset_symbol, 100, Resolution.DAILY)

For more information about historical data, see History Requests.

Universe Selection

To select a dynamic universe of US Equities based on US Congress Trading data, call the add_universe method with the QuiverQuantCongressUniverse class and a selection function.

Select Language: 
def initialize(self) -> None:
    self._universe = self.add_universe(QuiverQuantCongressUniverse, self.universe_selection)

def universe_selection(self, alt_coarse: List[QuiverQuantCongresssUniverse]) -> List[Symbol]:
    return [d.symbol for d in alt_coarse \
        if d.amount > 200000 and d.transaction == OrderDirection.BUY]

For more information about dynamic universes, see Universes.

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.

Select Language: 
# DataFrame example where the columns are the QuiverQuantCongressUniverse attributes: 
history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)

# Series example where the values are lists of QuiverQuantCongressUniverse objects: 
history = self.history(self._universe, 30, Resolution.DAILY)
for (univere_symbol, time), trades in universe_history.items():
    for trade in trades:
        self.log(f"{trade.symbol} amount at {trade.end_time}: {trade.amount} {trade.representative}") {trade.Representative}")

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.

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

# Series example where the values are lists of QuiverQuantCongressUniverse objects: 
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (univere_symbol, time), trades in universe_history.items():
    for trade in trades:
        print(f"{trade.symbol} amount at {trade.end_time}: {trade.amount} {trade.representative}")

You can call the history method in Research.

Remove Subscriptions

To remove a subscription, call the remove_security method.

Select Language: 
self.remove_security(self.dataset_symbol)

If you subscribe to US Congress Trading 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 US Congress Trading dataset enables you to take immediate action on trades made by informed Members of Congress. Examples include the following strategies:

  • Following the trades of specific representatives on the premise that the representatives are more informed
  • Assigning a long/short-bias to securities on a daily frequency based on how Members of Congress are trading them

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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