Section 1.1: Building the REST Client

At the heart of our API interaction framework is the RESTClient class, which serves as a flexible interface for engaging with RESTful APIs. This class lays the essential groundwork for dispatching HTTP requests and processing responses, forming the basis for more specific client implementations.

class RESTClient:

def __init__(self, endpoint: str):

self._endpoint = endpoint

The RESTClient is initialized with a designated API endpoint, which serves as the base URL for the exchange's API. This endpoint is critical for all future API requests initiated by the client.

Section 1.2: Data Collection Made Easy

Expanding on the RESTClient, the DataClient class is specifically designed to gather market data from exchanges. Inheriting from RESTClient, it adds capabilities for performing GET requests and retrieving data based on defined parameters.

class DataClient(RESTClient):

def __init__(self, endpoint: str, path: str):

super().__init__(endpoint)

self._data_path = path

def get_data(self, params: dict) -> Any:

try:

response = requests.get(self._endpoint + self._data_path, params=params)

data = response.json()

except (requests.exceptions.Timeout, requests.exceptions.ConnectionError, ValueError):

logging.warning("Data request encountered an error")

raise

else:

return data

The get_data function makes a GET request to the specified data path with any necessary parameters, carefully managing exceptions like timeouts and connection issues to ensure the bot remains responsive.

Section 2.1: The Role of the Trade Client

For trading bots that not only gather data but also execute trades, the TradeClient class is essential. This class builds upon DataClient, integrating trade execution functionalities along with data collection and adding important features for managing authentication and trade orders.

class TradeClient(DataClient):

def __init__(self, endpoint: str, data_path: str, trade_path: str, api: str, maker_fees: float, taker_fees: float):

super().__init__(endpoint, data_path)

self._trade_path = trade_path

self._api = api

self.maker_fees_USD_futures = maker_fees

self.taker_fees_USD_futures = taker_fees

def read_config(self):

self._config = configparser.ConfigParser()

self._config.read(os.path.expanduser('~') + '/config.ini')

self._api_key = self._config[self._api]['api_key']

self._api_secret = self._config[self._api]['api_secret']

def _place_order(self, params: dict) -> Response:

headers = {'X-MBX-APIKEY': self._api_key}

params['timestamp'] = int(time.time() * 1000)

signature = hmac.new(self._api_secret.encode('utf-8'), urlencode(params).encode('utf-8'), hashlib.sha256).hexdigest()

params['signature'] = signature

response = requests.post(self._endpoint + self._trade_path, params=params, headers=headers)

data = response.json()

return data

The TradeClient is initialized with specific exchange details, including the API paths for both data and trading, along with associated fees. The read_config method reads API credentials from a configuration file to facilitate authenticated requests. The _place_order method is crucial for constructing a signed order request, guaranteeing secure and authorized transactions.

Section 2.2: Bridging Bots and Exchanges

The creation of classes like RESTClient, DataClient, and TradeClient reflects a methodical approach to connecting trading bots with cryptocurrency exchanges. By simplifying the complexities of API communication into these classes, developers can concentrate on enhancing trading algorithms and strategies. As the cryptocurrency landscape evolves, the need for efficient and dependable trading bots is set to grow, highlighting the increasing importance of these API communication classes.

The first video titled "How to Actually Build a Trading Bot" provides an insightful look into the process of constructing a trading bot from the ground up.

The second video, "How to Code an AI Trading Bot (So You Can Make $$$)," offers valuable techniques for coding a trading bot that can potentially generate profits.