import requests
import statistics
from datetime import datetime
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
from typing import Type


# ---------- Input Schema ----------
class HistoricalInput(BaseModel):
    symbol: str = Field(default="bitcoin", description="Cryptocurrency ID used by CoinGecko, e.g. 'bitcoin'")
    currency: str = Field(default="usd", description="Fiat currency, e.g., 'usd'")
    days: int = Field(default=30, description="Number of past days to retrieve (e.g., 30, 90, 365)")


# ---------- Tool ----------
class HistoricalDataTool(BaseTool):
    name: str = "get_historical_data"
    description: str = (
        "Fetches historical cryptocurrency market data from CoinGecko and computes "
        "trend, percent change, and volatility. Returns structured JSON."
    )
    args_schema: Type[BaseModel] = HistoricalInput

    def _run(self, symbol: str = "bitcoin", currency: str = "usd", days: int = 30) -> dict:
        url = f"https://api.coingecko.com/api/v3/coins/{symbol}/market_chart"
        params = {"vs_currency": currency, "days": days}

        try:
            response = requests.get(url, params=params, timeout=10)
            response.raise_for_status()
            data = response.json()

            prices = data.get("prices", [])
            if not prices:
                return {"error": f"No historical data found for '{symbol}'."}

            # -------- Extract date + price history --------
            history = [
                {
                    "date": datetime.utcfromtimestamp(p[0] / 1000).strftime("%Y-%m-%d"),
                    "price": float(p[1])
                }
                for p in prices
            ]

            if len(history) < 2:
                return {"error": "Insufficient historical data for volatility or trend analysis."}

            start_price = history[0]["price"]
            end_price = history[-1]["price"]

            # -------- Percent Change --------
            pct_change = ((end_price - start_price) / start_price) * 100

            # -------- Daily Returns + Volatility --------
            daily_returns = [
                (history[i + 1]["price"] - history[i]["price"]) / history[i]["price"]
                for i in range(len(history) - 1)
            ]

            volatility = (
                statistics.stdev(daily_returns) * 100
                if len(daily_returns) > 1
                else 0
            )

            # -------- Trend Classification --------
            if pct_change > 1.5:
                trend = "upward"
            elif pct_change < -1.5:
                trend = "downward"
            else:
                trend = "sideways"

            return {
                "symbol": symbol,
                "currency": currency,
                "days": days,
                "start_price": round(start_price, 2),
                "end_price": round(end_price, 2),
                "pct_change": round(pct_change, 2),
                "volatility_pct": round(volatility, 2),
                "trend": trend,
                "price_history": history,  # optional but useful for advanced analytics
            }

        except Exception as e:
            return {"error": f"HistoricalDataTool failed: {str(e)}"}