About Corporate Buybacks
The Corporate Buybacks dataset by Smart Insider tracks US Equities share buyback programs. The data covers 3,000 US Equities, starts in May 2015, and is delivered on a second frequency. This dataset is created by analyzing daily buyback announcements and by using secondary data sources to ensure records are accurate and complete.
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 Smart Insider
Smart Insider was founded by Michael Tindale in 2016 with the goal of forming the most progressive insider data vendor in the field. Smart Insider provides access to buyback intention and transactions for quantitative researchers. In addition to their Corporate Buybacks dataset, Smart Insider provides data on stock trades made by US politicians and thousands of high net worth individuals around the globe.
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 CorporateBuybacksDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2016, 1, 1)
self.set_end_date(2021, 1, 1)
self.set_cash(100000)
self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
# Requesting insider trade intention news and actual trades to estimate the return, since insiders may have better information of the future confidence
self.smart_insider_intention = self.add_data(SmartInsiderIntention, self.aapl).symbol
self.smart_insider_transaction = self.add_data(SmartInsiderTransaction, self.aapl).symbol
# Historical data
history = self.history(self.smart_insider_intention, 365, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request for intentions")
history = self.history(self.smart_insider_transaction, 365, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request for transactions")
def on_data(self, slice: Slice) -> None:
# Buy Apple whenever we receive a buyback intention or transaction notification, given the insiders may have confidence in the future to buy more
# This news may stimulate market popularity
if slice.contains_key(self.smart_insider_intention) or slice.contains_key(self.smart_insider_transaction):
self.set_holdings(self.aapl, 1)
self.entry_time = self.time
# Liquidate holdings 3 days after the latest entry
# The market popularity and possible overbrought is cooled
if self.portfolio.invested and self.time >= self.entry_time + timedelta(days=3):
self.liquidate()
Example Applications
The Corporate Buybacks dataset enables you to design strategies using information on company buyback programs. Examples include the following strategies:
- Buying securities when the company announces an upcoming share buyback on the premise that the reduction in supply (shares outstanding) will drive up the remaining shares price
- Buying securities when the company executes an upcoming share buyback on the premise that the reduction in supply (shares outstanding) will drive up the remaining shares price
Pricing
Intentions Cloud Access
Harness Corporate Buybacks Intentions data in the QuantConnect Cloud for your backtesting and live trading purposes.
Transactions Cloud Access
Harness Corporate Buybacks Transactions data in the QuantConnect Cloud for your backtesting and live trading purposes.
Explore Other Datasets
FOREX Data
Dataset by QuantConnect
US Government Contracts
Dataset by Quiver Quantitative
US Equity Options
Dataset by AlgoSeek
