About Upcoming IPOs
The Upcoming IPOs dataset, provided by EODHD, offers daily alerts for US Equities that will start their IPOs or have any updates on their IPO registrations within the upcoming 7 days. The data started in February 2013 and is delivered on a daily basis.
Notice that this dataset might have a +/-2 days accuracy due to the data provider.
About EOD Historical Data
EODHD was a France financial data provider founded in April 2015. They focus on providing clean financial data, including stock prices, splits, dividends, fundamentals, macroeconomy indicators, technical indicators, and alternative data sources, through 24/7 API seamlessly.
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 UpcomingIPOsExampleAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 7, 1)
self.set_end_date(2022, 1, 1)
self.set_cash(100000)
# Create a EMA indicator to estimate the trend of IPO number to reflect the market popularity.
self._ema = ExponentialMovingAverage(50)
self.universe_settings.resolution = Resolution.DAILY
# Filter for new stocks to trade their hype using EODHDUpcomingIPOs.
self.add_universe(EODHDUpcomingIPOs, self.selection)
# Request QQQ data to trade.
self.qqq = self.add_equity("QQQ", Resolution.DAILY).symbol
self.set_warm_up(50, Resolution.DAILY)
def selection(self, ipos: List[EODHDUpcomingIPOs]) -> List[Symbol]:
# Filter for the stocks that IPO starts today and traded in Nasdaq.
universe = [x.symbol for x in ipos if x.ipo_date and x.ipo_date <= self.time + timedelta(1) and x.exchange == Exchange.NASDAQ]
# Feed to the EMA indicator.
self._ema.update(self.time, len(universe))
return Universe.UNCHANGED
def on_data(self, slice: Slice) -> None:
if self._ema.is_ready:
# If the EMA is decreasing, we estimate the market popularity is decreasing, so we sell QQQ.
if self._ema.previous.value > self._ema.current.value:
self.set_holdings(self.qqq, -1)
# Otherwise, we estimate the market popularity is increasing, so we buy QQQ.
else:
self.set_holdings(self.qqq, 1)
Example Applications
The Upcoming IPOs dataset provides timely notifications about upcoming IPOs start, allowing traders to capitalize on the high volatility of new stocks. Examples include the following strategies:
- Long straddle to trade the volatility of the new IPO stock.
- Arbitration on fair price versus IPO price.
- Use SMA of IPO number to estimate the IPO trend and market popularity.
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