Binance crypto data and other data

QuantConnect provides data for backtesting and live trading. For backtesting, you can use the data provided by QuantConnect, which includes Binance data. However, the availability of historical data depends on the specific asset and exchange. For Bitcoin on Binance, the data might only go back to May 2018 due to the limitations of the data source.

For live trading, you can use the data provided by the brokerage. In your case, you can use Binance data for live trading. You can set the brokerage model to Binance using the SetBrokerageModel method and add the specific crypto asset using the AddCrypto method. Here is an example:

self.SetBrokerageModel(BrokerageName.Binance, AccountType.Cash)
self.AddCrypto('BTCUSDT', Resolution.Minute, Market.Binance)

If you want to use a different Binance branch, you can specify it in the Market argument of the AddCrypto method. For example, to use Binance US, you can do:

self.AddCrypto('BTCUSDT', Resolution.Minute, Market.BinanceUS)

However, please note that QuantConnect's Binance and Binance US integrations don't support trading with sub-accounts. You must use your main account (source).

If you need to use specific exchanges or data that are not provided by QuantConnect, you might need to use custom data. However, this might require more advanced programming skills and understanding of the QuantConnect API.

For more information, you can refer to the QuantConnect documentation.

So only BinanceUS is currently available, and I would need to add any other of Binance's regional exchanges by implementing them. How can I do this, and implement the Binance market for an account in the the EU or Asia or the UK?

QuantConnect currently supports Binance and Binance US for live trading. If you want to use a different regional exchange from Binance, you would need to add a new brokerage to the platform. However, this requires the brokerage to have an API that is popular, stable, and officially supported by the brokerage (source).

Adding a new brokerage to the platform is a complex task that involves implementing the brokerage API in the LEAN engine, which is the underlying technology of QuantConnect. This requires advanced programming skills and a deep understanding of both the brokerage API and the LEAN engine.

If you want to add a new brokerage to the platform, you can contact QuantConnect for more information (source).

For live trading with Binance or Binance US, you can set the brokerage model and add the crypto asset as follows:

self.SetBrokerageModel(BrokerageName.Binance, AccountType.Cash)
self.AddCrypto('BTCUSDT', Resolution.Minute, Market.Binance)

or for Binance US:

self.SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash)
self.AddCrypto('BTCUSDT', Resolution.Minute, Market.BinanceUS)

Please note that QuantConnect's Binance and Binance US integrations don't support trading with sub-accounts. You must use your main account (source).

Hello everyone,

I’ve been following the conversation about accessing Binance crypto data and thought this might be a good place to share a resource that could be helpful, especially for those using QuantConnect.

We’ve developed an API that provides real-time fair value calculations for cryptocurrencies. It’s designed to integrate smoothly with platforms like QuantConnect, and could be a useful tool for those looking to enhance their data sources.

Here’s a quick example of how you can integrate it into your QuantConnect projects:

import json
from datetime import datetime, timedelta
import pandas as pd
 

class MyCustomDataType(PythonData):
   def __init__(self):
       self.ProjectId = ''
       self.fair_value = 2
       self.fair_value_date = datetime.today().date()
       self.cached_fair_value = {}

   def GetSource(self, config, date, isLiveMode):
       symbol = config.Symbol.Value
       return SubscriptionDataSource(
           f"https://rapidapi.com/somecedric/api/crypto-fair-value-calculator-api/fairvalue?symbol={symbol}×tamp={datetime.utcnow().isoformat()}",
           SubscriptionTransportMedium.Rest
       )

   def Reader(self, config, line, date, isLiveMode):
       if not line.strip():
           return None

       try:
           data = json.loads(line.strip())

           if 'fair_value' not in data:
               return None

           fair_value = float(data['fair_value'])

           df = pd.DataFrame({
               'Time': [date],
               'Open': [fair_value],
               'High': [fair_value],
               'Low': [fair_value],
               'Close': [fair_value],
               'Volume': [0]
           })
           df.set_index('Time', inplace=True)

           custom = MyCustomDataType()
           custom.Symbol = config.Symbol
           custom.Time = date
           custom.EndTime = custom.Time + timedelta(hours=1)  # Assuming hourly data
           custom.Value = fair_value
           custom.DataFrameData = df

           return custom
       except (ValueError, KeyError, json.JSONDecodeError) as e:
           return None


If you’re interested, you can find more details on RapidAPI under Crypto Fair Value Calculator API.

I hope this is helpful to anyone looking to expand their data analysis capabilities. Would love to hear how others are managing their data needs and if there are any tools or methods you find particularly effective!