How to use Python API on Bitfinex?

The Bitfinex Python API enables programmatic trading and portfolio management, requiring setup of the environment, authentication, and careful handling of requests and errors.

Apr 23, 2025 at 12:36 am

Using the Python API on Bitfinex allows traders and developers to interact with the Bitfinex exchange programmatically. This article will guide you through the process of setting up and using the Bitfinex Python API, covering authentication, making API requests, and some common use cases.

Setting Up the Environment

Before you can start using the Bitfinex API, you need to set up your development environment. This involves installing the necessary Python package and setting up your API keys.

• Install the Bitfinex Python client: The first step is to install the Bitfinex Python client. You can do this using pip, Python's package installer. Open your terminal or command prompt and run the following command:

  pip install bitfinex

• Obtain API Keys: To interact with the Bitfinex API, you need to have API keys. Log into your Bitfinex account, navigate to the API section, and generate a new API key. Make sure to keep your API key and secret safe and never share them with anyone.

• Set up Environment Variables: It's a good practice to store your API keys as environment variables. You can set them up in your operating system or in your Python script. Here's how you can do it in a Python script:

  import os
  os.environ['BITFINEX_API_KEY'] = 'your_api_key'
  os.environ['BITFINEX_API_SECRET'] = 'your_api_secret'

Authenticating with the API

Once your environment is set up, you need to authenticate with the Bitfinex API. This involves creating a client object using your API key and secret.

• Create a Client Object: Use the following code to create a client object:

  from bitfinex import ClientV2 as Client
  api_key = os.environ['BITFINEX_API_KEY']
  api_secret = os.environ['BITFINEX_API_SECRET']
  client = Client(api_key, api_secret)

  This client object will be used to make authenticated requests to the Bitfinex API.

Making API Requests

With the client object created, you can now make various API requests to interact with the Bitfinex exchange. Here are some common types of requests you might want to make.

• Fetch Account Balances: To get your account balances, use the following code:

  balances = client.balances()
  for balance in balances:
  print(f"Currency: {balance['currency']}, Amount: {balance['amount']}")

• Place an Order: To place an order, you can use the following code:

  order = client.place_order(
  symbol='tBTCUSD',
  amount='0.01',
  price='50000',
  side='buy',
  type='exchange limit'

  )
print(f"Order ID: {order['id']}")

• Fetch Order History: To retrieve your order history, use the following code:

  orders = client.orders()
  for order in orders:
  print(f"Order ID: {order['id']}, Symbol: {order['symbol']}, Side: {order['side']}")

Handling Responses and Errors

When making API requests, it's important to handle responses and errors properly. The Bitfinex API returns JSON responses, which you can parse and handle in your Python script.

• Parsing Responses: Here's how you can parse and handle a response:

  try:
  response = client.balances()
  for balance in response:
      print(f"Currency: {balance['currency']}, Amount: {balance['amount']}")

  except Exception as e:

  print(f"An error occurred: {e}")

• Error Handling: Make sure to handle errors gracefully. The Bitfinex API might return errors for various reasons, such as invalid parameters or authentication issues. Use try-except blocks to handle these errors:

  try:
  order = client.place_order(
      symbol='tBTCUSD',
      amount='0.01',
      price='50000',
      side='buy',
      type='exchange limit'
  )
  print(f"Order ID: {order['id']}")

  except Exception as e:

  print(f"Failed to place order: {e}")

Common Use Cases

Here are some common use cases for using the Bitfinex Python API.

• Automated Trading: You can use the API to automate your trading strategies. For example, you can implement a simple moving average crossover strategy:

  import time
  

  def moving_average_crossover(client, symbol, short_window, long_window):

  while True:
      try:
          # Fetch the latest candlestick data
          candles = client.candles(symbol=symbol, timeframe='1m', limit=1000)
  
          # Calculate moving averages
          short_ma = sum([candle[2] for candle in candles[-short_window:]]) / short_window
          long_ma = sum([candle[2] for candle in candles[-long_window:]]) / long_window
  
          if short_ma > long_ma:
              # Place a buy order
              client.place_order(
                  symbol=symbol,
                  amount='0.01',
                  price=str(candles[-1][2]),  # Current price
                  side='buy',
                  type='exchange market'
              )
              print(f"Bought {symbol} at {candles[-1][2]}")
          elif short_ma < long_ma:
              # Place a sell order
              client.place_order(
                  symbol=symbol,
                  amount='0.01',
                  price=str(candles[-1][2]),  # Current price
                  side='sell',
                  type='exchange market'
              )
              print(f"Sold {symbol} at {candles[-1][2]}")
  
          time.sleep(60)  # Wait for 1 minute before checking again
      except Exception as e:
          print(f"An error occurred: {e}")
          time.sleep(60)  # Wait for 1 minute before retrying
  

  Example usage

  moving_average_crossover(client, 'tBTCUSD', 50, 200)

• Portfolio Management: You can use the API to manage your portfolio by regularly checking your balances and adjusting your positions accordingly:

  def manage_portfolio(client):
  while True:
      try:
          balances = client.balances()
          for balance in balances:
              if balance['currency'] == 'USD' and float(balance['amount']) > 1000:
                  # If you have more than $1000 in USD, buy more BTC
                  client.place_order(
                      symbol='tBTCUSD',
                      amount=str(float(balance['amount']) / 50000),  # Assuming BTC price is $50,000
                      price='50000',
                      side='buy',
                      type='exchange limit'
                  )
                  print(f"Bought BTC with {balance['amount']} USD")
          time.sleep(3600)  # Wait for 1 hour before checking again
      except Exception as e:
          print(f"An error occurred: {e}")
          time.sleep(60)  # Wait for 1 minute before retrying
  Example usage
  manage_portfolio(client)

FAQs

Q: Can I use the Bitfinex Python API to trade multiple cryptocurrencies at once?

A: Yes, you can use the Bitfinex Python API to trade multiple cryptocurrencies. You can loop through different symbols and place orders for each one as needed. Make sure to handle each request separately and manage your API rate limits.

Q: How do I handle rate limiting when using the Bitfinex API?

A: Bitfinex has rate limits to prevent abuse. You can handle rate limiting by implementing a delay between API requests or by using the API's rate limit headers to dynamically adjust your request frequency. Always check the API documentation for the latest rate limit information.

Q: Is it safe to store my API keys in environment variables?

A: Storing API keys in environment variables is generally considered safer than hardcoding them in your script. However, ensure that your environment variables are properly secured and not accessible to unauthorized users. Consider using a secrets manager for added security.

Q: Can I use the Bitfinex Python API for real-time data streaming?

A: Yes, the Bitfinex API supports real-time data streaming through WebSockets. You can use the bitfinex library to set up a WebSocket connection and receive real-time updates on trades, order books, and more.