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CoinAPI

Binance Crypto Future Price Data

Introduction

The Binance Crypto Future Price Data by CoinAPI is for Crypto-currency futures price and volume data points. The data covers 421 Cryptocurrency pairs, starts in August 2020, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance.

The Binance Crypto Future Margin Rate Data dataset provides margin interest data to model margin costs.

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

About the Provider

CoinAPI was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.

Getting Started

The following snippet demonstrates how to request data from the Binance Crypto Future Price 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

Data Summary

The following table describes the dataset properties:

PropertyValue
Start DateAugust 2020
Asset Coverage421 Crypto Futures Pairs
Data DensityDense
ResolutionTick, Second, Minute, Hourly, & Daily
TimezoneUTC
Market HoursAlways Open

Example Applications

The Binance Crypto Future Price dataset enables you to 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 Price dataset provides TradeBar, QuoteBar, and Tick objects.

TradeBar Attributes

TradeBar objects have the following attributes:

TradeBar Select Language:
    Volume:
  • volume: decimal
    • Opening price of the bar: Defined as the price at the start of the time period.
  • open: decimal
    • High price of the TradeBar during the time period.
  • high: decimal
    • Low price of the TradeBar during the time period.
  • low: decimal
    • Closing price of the TradeBar. Defined as the price at Start Time + TimeSpan.
  • close: decimal
    • The closing time of this bar, computed via the Time and Period
  • end_time: DateTime
    • The period of this trade bar, (second, minute, daily, ect...)
  • period: TimeSpan
    • 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

QuoteBar Attributes

QuoteBar objects have the following attributes:

QuoteBar Select Language:
    Average bid size
  • last_bid_size: decimal
    • Average ask size
  • last_ask_size: decimal
    • Bid OHLC
  • bid: Bar
    • Ask OHLC
  • ask: Bar
    • Opening price of the bar: Defined as the price at the start of the time period.
  • open: decimal
    • High price of the QuoteBar during the time period.
  • high: decimal
    • Low price of the QuoteBar during the time period.
  • low: decimal
    • Closing price of the QuoteBar. Defined as the price at Start Time + TimeSpan.
  • close: decimal
    • The closing time of this bar, computed via the Time and Period
  • end_time: DateTime
    • The period of this quote bar, (second, minute, daily, ect...)
  • period: TimeSpan
    • 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

Tick Attributes

Tick objects have the following attributes:

Tick Select Language:
    Type of the Tick: Trade or Quote.
  • tick_type: TickType
    • Quantity exchanged in a trade.
  • quantity: decimal
    • Exchange code this tick came from QuantConnect.Exchanges
  • exchange_code: string
    • Exchange name this tick came from QuantConnect.Exchanges
  • exchange: string
    • Sale condition for the tick.
  • sale_condition: string
    • For performance parsed sale condition for the tick.
  • parsed_sale_condition: UInt32
    • Bool whether this is a suspicious tick
  • suspicious: bool
    • Bid Price for Tick
  • bid_price: decimal
    • Asking price for the Tick quote.
  • ask_price: decimal
    • Alias for "Value" - the last sale for this asset.
  • last_price: decimal
    • Size of bid quote.
  • bid_size: decimal
    • Size of ask quote.
  • ask_size: 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 Price 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 Future Price data, index the bars, quote_bars, or ticks properties 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.bars:
        trade_bar = slice.bars[self.btcbusd]
        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")

    if self.btcbusd in slice.quote_bars:
        quote_bar = slice.quote_bars[self.btcbusd]
        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")

    if self.btcbusd in slice.ticks:
        ticks = slice.ticks[self.btcbusd]
        for tick in ticks:
            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")

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, trade_bar in slice.bars.items():
        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")

    for symbol, quote_bar in slice.quote_bars.items():
        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")

    for symbol, ticks in slice.ticks.items():
        for tick in ticks:
            self.log(f"{symbol} price at {slice.time}: {tick.price}")

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

Historical Data

To get historical Binance Crypto Future Price data, call the history method with 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(self.btcbusd, 100, Resolution.DAILY)

# TradeBar objects
history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)

# QuoteBar objects
history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)

# Tick objects
history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)

For more information about historical data, see History Requests.

Remove Subscriptions

To unsubscribe from a Crypto Future contract that you added with the add_crypto_future 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 Crypto Future holdings.

Example Applications

The Binance Crypto Future Price dataset enables you to 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.

You can also see our Videos. You can also get in touch with us via Discord.

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