get_aggregated_trades

Instructions

Fetch aggregated trades data from Aster Finance API and return as Markdown table text.

Parameters:
    symbol (str): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive.
    fromId (Optional[int]): Aggregated trade ID to start from. If None, uses time-based query or most recent trades.
    startTime (Optional[int]): Start time in milliseconds since Unix epoch. If None, defaults to API behavior.
    endTime (Optional[int]): End time in milliseconds since Unix epoch. If None, defaults to API behavior.
    limit (Optional[int]): Number of aggregated trades to return (1 to 1000). If None, defaults to 500.

Returns:
    str: Markdown table containing aggTradeId, price, qty, firstTradeId, lastTradeId, time, and isBuyerMaker.

Raises:
    Exception: If the API request fails or data processing encounters an error.

Implementation Reference

• main.py:688-768 (handler)

  The handler function for the 'get_aggregated_trades' tool. It fetches aggregated trade data from the Aster Finance API (/fapi/v1/aggTrades), processes the response using pandas to create a formatted Markdown table with columns aggTradeId, price, qty, firstTradeId, lastTradeId, time, and isBuyerMaker. It handles input parameters for filtering trades and includes error handling for API requests.

  @mcp.tool()
  async def get_aggregated_trades(
      symbol: str,
      fromId: Optional[int] = None,
      startTime: Optional[int] = None,
      endTime: Optional[int] = None,
      limit: Optional[int] = None
  ) -> str:
      """
      Fetch aggregated trades data from Aster Finance API and return as Markdown table text.
      
      Parameters:
          symbol (str): Trading pair symbol (e.g., 'BTCUSDT', 'ETHUSDT'). Case-insensitive.
          fromId (Optional[int]): Aggregated trade ID to start from. If None, uses time-based query or most recent trades.
          startTime (Optional[int]): Start time in milliseconds since Unix epoch. If None, defaults to API behavior.
          endTime (Optional[int]): End time in milliseconds since Unix epoch. If None, defaults to API behavior.
          limit (Optional[int]): Number of aggregated trades to return (1 to 1000). If None, defaults to 500.
      
      Returns:
          str: Markdown table containing aggTradeId, price, qty, firstTradeId, lastTradeId, time, and isBuyerMaker.
      
      Raises:
          Exception: If the API request fails or data processing encounters an error.
      """
      endpoint = "/fapi/v1/aggTrades"
      
      # Construct query parameters
      params = {
          "symbol": symbol.upper(),  # Ensure symbol is uppercase (e.g., BTCUSDT)
      }
      if fromId is not None:
          params["fromId"] = fromId
      if startTime is not None:
          params["startTime"] = startTime
      if endTime is not None:
          params["endTime"] = endTime
      if limit is not None:
          params["limit"] = limit
  
      async with httpx.AsyncClient() as client:
          try:
              # Make GET request to the API
              response = await client.get(f"{BASE_URL}{endpoint}", params=params)
              response.raise_for_status()  # Raise exception for 4xx/5xx errors
              
              # Parse JSON response
              trades_data = response.json()
              
              # Create pandas DataFrame with API response keys
              df = pd.DataFrame(trades_data, columns=["a", "p", "q", "f", "l", "T", "m"])
              
              # Convert time to readable format
              df["T"] = pd.to_datetime(df["T"], unit="ms")
              
              # Select and rename columns for clarity
              df = df[["a", "p", "q", "f", "l", "T", "m"]]
              df = df.rename(columns={
                  "a": "aggTradeId",
                  "p": "price",
                  "q": "qty",
                  "f": "firstTradeId",
                  "l": "lastTradeId",
                  "T": "time",
                  "m": "isBuyerMaker"
              })
              
              # Format numbers
              df["price"] = df["price"].astype(float).round(8)
              df["qty"] = df["qty"].astype(float).round(8)
              
              # Convert DataFrame to Markdown table
              markdown_table = df.to_markdown(index=False)
              
              return markdown_table
          
          except httpx.HTTPStatusError as e:
              # Handle HTTP errors (e.g., 400, 429)
              raise Exception(f"API request failed: {e.response.status_code} - {e.response.text}")
          except Exception as e:
              # Handle other errors (e.g., network issues, pandas errors)
              raise Exception(f"Error processing aggregated trades data: {str(e)}")