About US Regulatory Alerts - Financial Sector
The US Regulatory Alerts dataset by RegAlytics tracks changes from over 8,000 globally governing bodies. The data covers over 2.5 million alerts, starts from January 2020, and is delivered on a daily basis. This dataset is created by sourcing information from over 8,000 regulators and using proprietary technology to gather and structure the regulatory data. Once prepared, the data is thoroughly reviewed by RegAlytics' team of regulatory experts and delivered each morning by 8AM for industry use.
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 RegAlytics
RegAlytics was founded by Mary Kopczynski, Aaron Heisler, Alexander Appugliese, and Werner Pauliks in 2019 with the goal of significantly reducing the time and cost required to mitigate regulatory risk. RegAlytics provides access to accurate and clean regulatory data from all global regulators in all sectors that is enriched by regulatory experts for risk and compliance teams everywhere. Please come to RegAlytics directly if you would like data on other sectors or countries!
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 RegalyticsDataAlgorithm(QCAlgorithm):
# Pre-defined list of negative sentiment phrases as indicator for short selling, since these words will decrease confident in equity performance
negative_sentiment_phrases = ["emergency rule", "proposed rule change", "development of rulemaking"]
def initialize(self) -> None:
self.set_start_date(2022, 7, 10)
self.set_end_date(2022, 7, 15)
self.set_cash(100000)
self.spy = self.add_equity("SPY", Resolution.DAILY).symbol
# Requesting data to receive updated regulatory news for timely short selling
self.regalytics_symbol = self.add_data(RegalyticsRegulatoryArticles, "REG").symbol
# Historical data for past articles
history = self.history(self.regalytics_symbol, 7, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request")
def on_data(self, slice: Slice) -> None:
# Only trade on regulatory news data
data = slice.Get(RegalyticsRegulatoryArticles)
if data:
for articles in data.values():
self.log(articles.to_string())
# If any of the negative phrases appeared in regulatory news, we expect a market drop for the day
if any([p in article.title.lower() for p in self.negative_sentiment_phrases for article in articles]):
self.set_holdings(self.spy, -1)
else:
self.set_holdings(self.spy, 1)
Example Applications
This regulatory dataset enables you to accurately design strategies while mitigating regulatory risk. Examples include the following strategies:
- Temporarily increasing/decreasing exposure to securities when new regulations are announced
- Parsing the content of regulatory announcements to determine market or sector impact
- Country rotation based on regulatory alert sentiment
Pricing
Cloud Access US Regulatory Alerts
Cloud access to RegAlytics Regulatory Articles delivered nightly for use in backtesting or live-trading.
On Premise Download
On premise download of RegAlytics daily alert data for use in LEAN backtesting.
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