About US Equities Short Availability
The US Equity Short Availability dataset provides the available shares for open short positions and their borrowing cost in the US Equity market. The data covers 10,500 US Equities, starts in January 2018, and is delivered on a daily frequency. This dataset is created using information from the exchanges.
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 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.
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 *
class ShortAvailabilityDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 1, 1)
self.set_end_date(2021, 7, 1)
self.set_cash(1000)
# Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
self.set_security_initializer(MySecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices)))
self.equity = self.add_equity("GME")
# Set up daily rebalance scheduled event, since shortable quantity is updated daily
self.schedule.on(
self.date_rules.every_day(self.equity.symbol),
self.time_rules.after_market_open(self.equity.symbol, 10),
self.rebalance)
def rebalance(self) -> None:
symbol = self.equity.symbol
# You can obtain the shortable quantity to decide the submission of a short order
shortable_quantity = self.equity.shortable_provider.shortable_quantity(symbol, self.time)
if not shortable_quantity:
shortable_quantity = 0
# Fee and rebate rate is also available, such that you can calculate the expected return and decide if the margin is worthwhile
self.plot('Total Shortable Quantity', symbol, shortable_quantity)
self.plot('Borrowing Cost', "Fee Rate", self.equity.shortable_provider.fee_rate(symbol, self.time))
self.plot('Borrowing Cost', "Rebate Rate", self.equity.shortable_provider.rebate_rate(symbol, self.time))
# Test whether we can short the desired quantity
quantity = self.calculate_order_quantity(symbol, -1)
if self.shortable(symbol, quantity):
self.market_order(symbol, quantity)
def on_margin_call_warning(self) -> None:
self.liquidate()
class MySecurityInitializer(BrokerageModelSecurityInitializer):
def __init__(self, brokerage_model: IBrokerageModel, security_seeder: ISecuritySeeder) -> None:
super().__init__(brokerage_model, security_seeder)
def initialize(self, security: Security) -> None:
super().initialize(security)
# Set the shortable provider as your broker for accurate short reality modeling
security.set_shortable_provider(InteractiveBrokersShortableProvider())
Example Applications
The US Equities Short Availability dataset enables you to accurately design strategies harnessing information about short availability. Examples include the following use cases:
- Avoiding short orders that the brokerage will reject
- Selecting securities based on how many shares are available to short
Pricing
Cloud Access
Enable realistic modeling of shortable stock limits for your QuantConnect Cloud backtesting. Plot and explore the data in backtesting and research.
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