calculate_dema
Compute the Double Exponential Moving Average (DEMA) for financial time series analysis to reduce lag in trend identification.
Instructions
Calculate Double Exponential Moving Average (DEMA).
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:166-178 (handler)The handler function for the 'calculate_dema' MCP tool. It retrieves the DEMA indicator from the registry, creates MarketData from input close prices, calls the indicator's calculate method, and returns the result or error.
@mcp.tool() async def calculate_dema(close: List[float], timeperiod: int = 30) -> Dict[str, Any]: try: indicator = registry.get_indicator("dema") if not indicator: raise ValueError("DEMA indicator not found") market_data = MarketData(close=close) 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)} - The DEMAIndicator class providing the core logic for DEMA calculation using TA-Lib's DEMA function. Includes input schema definition and the calculate method.
class DEMAIndicator(BaseIndicator): def __init__(self): super().__init__(name="dema", description="Double Exponential Moving Average (DEMA)") @property def input_schema(self) -> Dict[str, Any]: return { "type": "object", "properties": { "close_prices": {"type": "array", "items": {"type": "number"}}, "timeperiod": {"type": "integer", "default": 30}, }, "required": ["close_prices"], } async def calculate(self, market_data: MarketData, options: Dict[str, Any] = None) -> IndicatorResult: if options is None: options = {} timeperiod = options.get("timeperiod", 30) close = np.asarray(market_data.close, dtype=float) try: out = ta.DEMA(close, timeperiod=timeperiod) return IndicatorResult( indicator_name=self.name, success=True, values={"dema": out.tolist()}, metadata={"timeperiod": timeperiod, "input_points": len(close), "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:30-30 (registration)Registration of the DEMAIndicator in the global IndicatorRegistry under the key 'dema', which is used by the tool handler.
registry.register("dema", DEMAIndicator)