About Upcoming Splits
The Upcoming Splits dataset, provided by EODHD, offers daily alerts for US Equities that will have a split event within the upcoming 7 days. The data starts in January 2010, and is delivered on a daily frequency.
About EOD Historical Data
EOD Historical Data (EODHD) is a financial data provider based in France, and founded in April 2015. They focus on providing clean financial data, including stock prices, splits, dividends, fundamentals, macroeconomic indicators, technical indicators, and alternative data sources, through 24/7 API seamlessly. For more information about EODHD, visit https://eodhd.com/.
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
class UpcomingSplitsExampleAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2020, 1, 1)
self.set_end_date(2024, 10, 1)
self.set_cash(100000)
# Trade on daily basis based on daily upcoming splits signals.
self.universe_settings.resolution = Resolution.DAILY
# Universe consists of equities with upcoming splits events.
self._universe = self.add_universe(EODHDUpcomingSplits, self.selection)
def selection(self, splits: List[EODHDUpcomingSplits]) -> List[Symbol]:
# Split (more shares with lower price) will make the stock more affordable and drive up the demand.
# Hence, include all stocks that will have a split within the next 7 days.
# Note that spliting up the stock means the split factor > 1.
return [x.symbol for x in splits if x.split_factor > 1]
def on_data(self, slice: Slice) -> None:
# Equally invest in each member of the universe to evenly dissipate the capital risk.
total_count = len(self._universe.members)
targets = [PortfolioTarget.percent(self, symbol, 1. / total_count) for symbol in self._universe.members.keys()]
self.set_holdings(targets)
def on_securities_changed(self, changes: SecurityChanges) -> None:
for removed in changes.removed_securities:
# Liquidate the ones exit the universe (already split) to capitalize the microeconomic demand.
self.liquidate(removed.symbol)
Example Applications
The Upcoming Splits dataset provides timely notifications about upcoming share split or reverse split events, allowing traders to capitalize on potential price movements and manage risks effectively. Examples include the following strategies:
- Splits into shares with lower price will provide higher liquidity for market making algorithms.
- Select a universe of stocks with upcoming splits event and trade their volatility using options.
- Buy stocks with split factor higher than 1, sell vice versa for self-defeating ones.
Pricing
Cloud Access
Using Upcoming Splits data in the QuantConnect Cloud for your backtesting and live trading purposes.
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