About US Congress Trading
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.
About Quiver Quantitative
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.
About QuantConnect
QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
Algorithm Example
from AlgorithmImports import *
from QuantConnect.DataSource import *
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)
# Requesting data per underlying data to subscribe to updated congress members' trade information
aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
quiver_congress_symbol = self.add_data(QuiverCongress, aapl).symbol
# Historical data
history = self.history(QuiverCongress, quiver_congress_symbol, 60, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request");
def on_data(self, slice: Slice) -> None:
congress_by_symbol = slice.Get(QuiverCongress)
# Determine net direction of Congress trades for each security to estimate the sentiment direction due to political factor
net_quantity_by_symbol = {}
for symbol, points in congress_by_symbol.items():
symbol = symbol.underlying
if symbol not in net_quantity_by_symbol:
net_quantity_by_symbol[symbol] = 0
# Voting weight is by order size
for point in points:
net_quantity_by_symbol[symbol] += (1 if point.transaction == OrderDirection.BUY else -1) * point.amount
for symbol, net_quantity in net_quantity_by_symbol.items():
if net_quantity == 0:
self.liquidate(symbol)
continue
# Buy when Congress members have bought, short otherwise
self.set_holdings(symbol, 1 if net_quantity > 0 else -1)
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
Pricing
Cloud Access
Cloud access to the US Equity trades made by Members of Congress cached and updated daily for use in your research and live trading strategies.
Download On Premise
Data of US Equity trades made by Members of Congress archived in LEAN format for on premise backtesting and research. One file per ticker.
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