calculate_midprice
Calculate the midpoint price for financial market analysis using TA-Lib technical indicators to determine average price levels between high and low values.
Instructions
Calculate Midpoint Price (MIDPRICE).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| kwargs | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/mcp_talib/core/server.py:265-278 (handler)The MCP tool handler for 'calculate_midprice', decorated with @mcp.tool() for automatic registration. Delegates computation to the 'midprice' indicator from the registry.
@mcp.tool() async def calculate_midprice(high: List[float], low: List[float], timeperiod: int = 14) -> Dict[str, Any]: try: indicator = registry.get_indicator("midprice") if not indicator: raise ValueError("MIDPRICE indicator not found") market_data = MarketData(close=[0], high=high, low=low) result = await indicator.calculate(market_data, {"timeperiod": timeperiod}) if result.success: return {"success": True, "values": result.values, "metadata": result.metadata} return {"success": False, "error": result.error_message} except Exception as e: return {"success": False, "error": str(e)} - Core implementation of midprice calculation using TA-Lib's MIDPRICE function. Includes input schema and the calculate method that performs (high + low)/2 averaged over timeperiod.
class MIDPRICEIndicator(BaseIndicator): def __init__(self): super().__init__(name="midprice", description="Midpoint Price over period") @property def input_schema(self) -> Dict[str, Any]: return { "type": "object", "properties": { "high_prices": {"type": "array", "items": {"type": "number"}}, "low_prices": {"type": "array", "items": {"type": "number"}}, "timeperiod": {"type": "integer", "default": 14}, }, "required": ["high_prices", "low_prices"], } async def calculate(self, market_data: MarketData, options: Dict[str, Any] = None) -> IndicatorResult: if options is None: options = {} timeperiod = options.get("timeperiod", 14) high = np.asarray(market_data.high or [], dtype=float) low = np.asarray(market_data.low or [], dtype=float) try: out = ta.MIDPRICE(high, low, timeperiod=timeperiod) return IndicatorResult(indicator_name=self.name, success=True, values={"midprice": out.tolist()}, metadata={"timeperiod": timeperiod, "input_points": len(high), "output_points": len(out)}) except Exception as e: return IndicatorResult(indicator_name=self.name, success=False, values={}, error_message=str(e)) - src/mcp_talib/indicators/__init__.py:37-37 (registration)Registration of the MIDPRICEIndicator class in the central indicator registry, allowing it to be retrieved by name in tool handlers.
registry.register("midprice", MIDPRICEIndicator) - JSON schema defining the input parameters for the midprice indicator, matching the tool's high_prices, low_prices, and timeperiod.
def input_schema(self) -> Dict[str, Any]: return { "type": "object", "properties": { "high_prices": {"type": "array", "items": {"type": "number"}}, "low_prices": {"type": "array", "items": {"type": "number"}}, "timeperiod": {"type": "integer", "default": 14}, }, "required": ["high_prices", "low_prices"], }