app/services/market_data.py

from typing import Tuple
import pandas as pd
import requests
from fastapi import HTTPException

from app.config import get_settings
from app.indicators import add_technical_indicators

settings = get_settings()

ALPHA_VANTAGE_URL = "https://www.alphavantage.co/query"


def fetch_raw_history(ticker: str) -> pd.DataFrame:
    """
    Fetch OHLCV daily data from Alpha Vantage (FREE endpoint).
    Replaces yfinance for HF Spaces compatibility.
    """

    params = {
        "function": "TIME_SERIES_DAILY",
        "symbol": ticker,
        "apikey": settings.alpha_vantage_api_key,
        "outputsize": "compact",
    }

    try:
        response = requests.get(ALPHA_VANTAGE_URL, params=params, timeout=10)
        data = response.json()
    except Exception:
        raise HTTPException(
            status_code=503,
            detail="Market data provider unavailable",
        )

    if "Time Series (Daily)" not in data:
        raise HTTPException(
            status_code=400,
            detail="Invalid ticker or API rate limit exceeded",
        )

    ts = data["Time Series (Daily)"]

    df = pd.DataFrame.from_dict(ts, orient="index").astype(float)
    df.index = pd.to_datetime(df.index)
    df.sort_index(inplace=True)

    df.rename(
        columns={
            "1. open": "Open",
            "2. high": "High",
            "3. low": "Low",
            "4. close": "Close",
            "5. volume": "Volume",
        },
        inplace=True,
    )

    if df.empty or len(df) < settings.history_window + 60:
        raise HTTPException(
            status_code=400,
            detail="Not enough historical data for this ticker.",
        )

    return df[["Open", "High", "Low", "Close", "Volume"]]


def get_enriched_history(ticker: str) -> Tuple[pd.DataFrame, pd.DataFrame]:
    """
    Returns:
    - Raw OHLCV dataframe
    - Technical-indicator-enriched dataframe
    """
    df_raw = fetch_raw_history(ticker)
    df_tech = add_technical_indicators(df_raw)
    return df_raw, df_tech


def last_n_candles(df: pd.DataFrame, n: int) -> list[dict]:
    """
    Used by frontend for recent price chart
    """
    tail = df.tail(n)
    return [
        {
            "date": idx.strftime("%Y-%m-%d"),
            "price": float(row["Close"]),
        }
        for idx, row in tail.iterrows()
    ]