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EOD Historical Data

Upcoming Splits

Introduction

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.

Compared to US Equity Security Master as a benchmark, the Upcoming Splits dataset has an 85.18% coverage of all splits since 2015, while having a 92.74% precision on the exact split dates of the covered ones and a 97.18% precision within +/- 3 days.

For more information about the Upcoming Splits dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

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/.

Getting Started

The following snippet demonstrates how to request data from the Upcoming Splits dataset:

Select Language: 
self.add_universe(EODHDUpcomingSplits, self.selection_function)

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateJanuary 2010
Data DensitySparse
ResolutionDaily
TimezoneNew York

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.

Data Point Attributes

The EODHD Upcoming Splits dataset provides EODHDUpcomingSplits objects, which have the following attributes:

EODHDUpcomingSplits Select Language:
    Date of the split will happen
  • split_date: DateTime
    • If this is split optionable for shareholders.
  • optionable: bool
    • Ratio of old shares / new shares.
  • split_factor: decimal
    • Time the data became available
  • end_time: DateTime
    • Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.
  • data_type: MarketDataType
    • True if this is a fill forward piece of data
  • is_fill_forward: bool
    • Current time marker of this data packet.
  • time: DateTime
    • Symbol representation for underlying Security
  • symbol: Symbol
    • Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.
  • value: decimal
    • As this is a backtesting platform we'll provide an alias of value as price.
  • price: decimal

Universe Selection

To select a dynamic universe of US Equities based on the Upcoming Splits dataset, call the add_universe method with a EODHDUpcomingSplits cast.

Select Language: 
def initialize(self) -> None:
    self._universe = self.add_universe(EODHDUpcomingSplits, self.universe_selection_filter)

def universe_selection_filter(self, splits: list[EODHDUpcomingSplits]) -> list[Symbol]:
    return [d.symbol for d in splits if d.split_date <= self.time + timedelta(3) and d.split_factor > 1]

For more information about universe settings, see Settings.

Requesting Data

To add Upcoming Splits data to your algorithm, call the add_data method.

Select Language: 
class UpcomingSplitsDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)

        self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.dataset_symbol = self.add_data(EODHDUpcomingSplits, "splits").symbol

Accessing Data

To get the current Upcoming Splits data, call the get(EODHDUpcomingSplits) method from the current Slice and index the result with the security Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your security at every time step. To avoid issues, check if the Slice contains the data you want before you index it.

Select Language: 
def on_data(self, slice: Slice) -> None:
    upcomings_splits = slice.get(EODHDUpcomingSplits)
    if upcomings_splits and self._symbol in upcomings_splits:
        upcomings_splits_data_point = upcomings_splits[self._symbol]
        self.log(f"{self._symbol} will split at {upcomings_splits_data_point.split_date} with split factor {upcomings_splits_data_point.split_factor}")

You can also iterate through all of the dataset objects in the current Slice

Select Language: 
def on_data(self, slice: Slice) -> None:
    for equity_symbol, upcomings_splits_data_point in slice.get(EODHDUpcomingSplits).items():
        self.log(f"{equity_symbol} will split at {upcomings_splits_data_point.split_date} with split factor {upcomings_splits_data_point.split_factor}")

Historical Data

To get historical Upcoming Splits data, call the history method with the type EODHDUpcomingSplits cast and the period of request. If there is no data in the period you request, the history result is empty.

Select Language: 
history = self.history[EODHDUpcomingSplits](timedelta(100), Resolution.DAILY)

For more information about historical data, see History Requests.

Remove Subscriptions

To remove a subscription, call the remove_security method.

Select Language: 
self.remove_security(self.dataset_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.

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