About Upcoming Dividends
The Upcoming Dividends dataset, provided by EODHD, offers daily alerts for US Equities that will have a dividend event within the upcoming 7 days. The data starts in January 2015 and is delivered on a daily frequency.
Compared to US Equity Security Master as a benchmark, the Upcoming Dividends dataset has a 98.56% coverage of all dividend events, while having a 99.71% precision on the exact dividend dates of the covered ones and a 99.90% precision within +/- 3 days.
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 UpcomingDividendsExampleAlgorithm(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 a daily basis based on daily upcoming dividend signals.
self.universe_settings.resolution = Resolution.DAILY
# Universe consists of equities with upcoming dividend events.
self._universe = self.add_universe(EODHDUpcomingDividends, self.selection)
def selection(self, dividends: List[EODHDUpcomingDividends]) -> List[Symbol]:
# Select the stocks with upcoming dividend record date, with a sufficient dividend size.
return [x.symbol for x in dividends if x.dividend_date < self.time + timedelta(1) and x.dividend > 0.5]
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.selected)
targets = [PortfolioTarget.percent(self, symbol, -1. / total_count) for symbol in self._universe.selected]
self.set_holdings(targets, liquidate_existing_holdings=True)
Example Applications
The Upcoming Dividends dataset allows traders to trade the price change due to dividends. Examples include the following strategies:
- Short the stocks on dividend report day to earn the dividend discount on pricing.
- Avoid volatility on securities with upcoming dividends
Pricing
Cloud Access
Using Upcoming Dividends data in the QuantConnect Cloud for your backtesting and live trading purposes.
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