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QuantConnect

Binance Crypto Future Margin Rate Data

Introduction

The Binance Crypto Future Margin Rate Data by QuantConnect is for crypto-currency futures margin interest data points. The data covers 421 Cryptocurrency pairs, starts in August 2020, and is delivered on a daily update frequency. This dataset is created by downloading data using Binance API.

This dataset is an important companion to the Binance Crypto Future Price Data dataset because it contains information on margin interest data to model margin costs.

For more information about the Binance Crypto Future Margin Rate Data dataset, including CLI commands and pricing, see the dataset listing.

About the Provider

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.

Getting Started

The following snippet demonstrates how to request data from the Binance Crypto Future Margin Rate dataset:

Select Language: 
def initialize(self) -> None:
    self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
    self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)

    self.crypto_future_symbol = self.add_crypto_future("BTCBUSD", Resolution.MINUTE).symbol

The Binance Crypto Future Margin Rate data is added to your algorithm along with the market data when you add a crypto future subscription.

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateAugust 2020
Asset Coverage421 Crypto Futures Pairs
Data DensityRegular
ResolutionDaily
TimezoneUTC
Market HoursAlways Open

Example Applications

The Binance Crypto Future Margin Rate dataset enables correct margin cost so you can accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies:

  • Horizontal/Diagonal arbitrage with the underlying cryptocurrencies
  • Trade Contango/Backwardation predictions
  • Hedge for illiquid cryptocurrencies

For more example algorithms, see Examples.

Data Point Attributes

The Binance Crypto Future Margin Rate dataset provides MarginInterestRates objects with the following attributes:

MarginInterestRate Select Language:
    The interest rate value
  • interest_rate: decimal
    • 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
    • The end time of this data. Some data covers spans (trade bars) and as such we want to know the entire time span covered
  • end_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

Supported Assets

The following table shows the available Crypto Future pairs:

Requesting Data

To add Binance Crypto Future Margin Rate data to your algorithm, call the add_crypto_future method. Save a reference to the Crypto Future Symbol so you can access the data later in your algorithm.

Select Language: 
class CoinAPIDataAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2020, 6, 1)
        self.set_end_date(2021, 6, 1)

        # Set Account Currency to Binance Stable Coin for USD
        self.set_account_currency("BUSD")
        self.set_cash(100000)

        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
        self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)
        
        crypto_future = self.add_crypto_future("BTCBUSD", Resolution.MINUTE)
        # perpetual futures does not have a filter function
        self.btcbusd = crypto_future.symbol

For more information about creating Crypto Future subscriptions, see Requesting Data.

Accessing Data

To get the current Binance Crypto Margin Rate data, index the margin_interest_rates property of the current Slice with the Crypto Future 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:
    if self.btcbusd in slice.margin_interest_rates:
        interest_rate = slice.margin_interest_rates[self.btcbusd].interest_rate
        self.log(f"{self.btcbusd} close at {slice.time}: {interest_rate}")

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

Select Language: 
def on_data(self, slice: Slice) -> None:
    for symbol, margin_interest_rate in slice.margin_interest_rates.items():
        interest_rate = margin_interest_rate.interest_rate
        self.log(f"{symbol} close at {slice.time}: {interest_rate}")

For more information about accessing Crypto Future data, see Handling Data.

Historical Data

To get historical Binance Crypto Future Margin Rate data, call the history method with MarginInterestRate data type and the Crypto Future Symbol. If there is no data in the period you request, the history result is empty.

Select Language: 
# DataFrame
history_df = self.history(MarginInterestRate, self.btcbusd, 100, Resolution.DAILY)

# MarginInterestRate objects
history = self.history[MarginInterestRate](self.btcbusd, 100, Resolution.DAILY)

For more information about historical data, see History Requests.

Remove Subscriptions

To unsubscribe from a Crypto pair that you added with the add_crypto method, call the remove_security method.

Select Language: 
self.remove_security(self.btcbusd)

The remove_security method cancels your open orders for the security and liquidates your holdings in the virtual pair.

Example Applications

The Binance Crypto Future Margin Rate dataset enables correct margin cost so you can accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies:

  • Horizontal/Diagonal arbitrage with the underlying cryptocurrencies
  • Trade Contango/Backwardation predictions
  • Hedge for illiquid cryptocurrencies

For more example algorithms, see Examples.

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