TA-Lib MCP Server

"""MESA Adaptive Moving Average (MAMA) adapter using TA-Lib.""" from typing import Dict, Any import numpy as np import talib as ta from .base import BaseIndicator from ..models.market_data import MarketData from ..models.indicator_result import IndicatorResult class MAMAIndicator(BaseIndicator): def __init__(self): super().__init__(name="mama", description="MESA Adaptive Moving Average (MAMA)") @property def input_schema(self) -> Dict[str, Any]: return { "type": "object", "properties": { "close_prices": {"type": "array", "items": {"type": "number"}}, "fastlimit": {"type": "number", "default": 0.5}, "slowlimit": {"type": "number", "default": 0.05}, }, "required": ["close_prices"], } async def calculate(self, market_data: MarketData, options: Dict[str, Any] = None) -> IndicatorResult: if options is None: options = {} fastlimit = options.get("fastlimit", 0.5) slowlimit = options.get("slowlimit", 0.05) close = np.asarray(market_data.close, dtype=float) try: mama, fama = ta.MAMA(close, fastlimit=fastlimit, slowlimit=slowlimit) return IndicatorResult( indicator_name=self.name, success=True, values={"mama": mama.tolist(), "fama": fama.tolist()}, metadata={"fastlimit": fastlimit, "slowlimit": slowlimit, "input_points": len(close), "output_points": len(mama)}, ) except Exception as e: return IndicatorResult(indicator_name=self.name, success=False, values={}, error_message=str(e))