paradex_trades
Analyze market transactions to detect large trades, calculate average trade sizes, identify buy/sell imbalances, and monitor execution prices versus order book data for market sentiment and liquidity insights.
Instructions
Analyze actual market transactions to understand market sentiment and liquidity.
Use this tool when you need to:
- Detect large trades that might signal institutional activity
- Calculate average trade size during specific periods
- Identify buy/sell pressure imbalances
- Monitor execution prices vs. order book prices
- Understand market momentum through trade flow
Trade data provides insights into actual market activity versus just orders,
helping you understand how other participants are behaving.
Example use cases:
- Detecting large "whale" transactions that might influence price
- Analyzing trade sizes to gauge market participation
- Identifying periods of aggressive buying or selling
- Understanding trade frequency as an indicator of market interest
- Comparing executed prices to orderbook mid-price for market impact analysis
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| market_id | Yes | Market symbol to get trades for. | |
| start_unix_ms | Yes | Start time in unix milliseconds. | |
| end_unix_ms | Yes | End time in unix milliseconds. |
Implementation Reference
- src/mcp_paradex/tools/market.py:433-477 (handler)The main handler function for the 'paradex_trades' tool. It fetches recent trades for a specified market and time range using the Paradex client, validates them using the Trade model, and returns a structured response including schema information.
@server.tool(name="paradex_trades") async def get_trades( market_id: Annotated[str, Field(description="Market symbol to get trades for.")], start_unix_ms: Annotated[int, Field(description="Start time in unix milliseconds.")], end_unix_ms: Annotated[int, Field(description="End time in unix milliseconds.")], ctx: Context = None, ) -> dict: """ Analyze actual market transactions to understand market sentiment and liquidity. Use this tool when you need to: - Detect large trades that might signal institutional activity - Calculate average trade size during specific periods - Identify buy/sell pressure imbalances - Monitor execution prices vs. order book prices - Understand market momentum through trade flow Trade data provides insights into actual market activity versus just orders, helping you understand how other participants are behaving. Example use cases: - Detecting large "whale" transactions that might influence price - Analyzing trade sizes to gauge market participation - Identifying periods of aggressive buying or selling - Understanding trade frequency as an indicator of market interest - Comparing executed prices to orderbook mid-price for market impact analysis """ try: # Get trades from Paradex client = await get_paradex_client() response = client.fetch_trades( params={"market": market_id, "start_at": start_unix_ms, "end_at": end_unix_ms} ) if "error" in response: raise Exception(response["error"]) trades = trade_adapter.validate_python(response["results"]) results = { "description": Trade.__doc__.strip() if Trade.__doc__ else None, "fields": Trade.model_json_schema(), "results": trades, } return results except Exception as e: await ctx.error(f"Error fetching trades for {market_id}: {e!s}") raise e - src/mcp_paradex/models.py:37-49 (schema)Pydantic model defining the structure of individual trade objects returned in the tool's response. Used for validation and schema generation.
class Trade(BaseModel): """Trade model representing a completed trade on Paradex.""" id: Annotated[str, Field(description="Unique Trade ID per TradeType")] market: Annotated[str, Field(description="Market for which trade was done")] side: Annotated[str, Field(description="Taker side")] size: Annotated[float, Field(description="Trade size")] price: Annotated[float, Field(description="Trade price")] created_at: Annotated[ int, Field(description="Unix Millisecond timestamp at which trade was done") ] trade_type: Annotated[str, Field(description="Trade type, can be FILL or LIQUIDATION")] - src/mcp_paradex/tools/market.py:433-433 (registration)The @server.tool decorator registers the 'paradex_trades' tool with the MCP server.
@server.tool(name="paradex_trades") - TypeAdapter for validating lists of Trade objects returned by the Paradex API.
trade_adapter = TypeAdapter(list[Trade])