"""
Groww charting API client.

Fetches 1-minute OHLCV candle data for NSE CASH segment tickers.
Handles:
  - Retry with exponential backoff (3 attempts)
  - None / missing values in candle arrays
  - Cumulative volume -> per-candle volume conversion
"""

import time
import logging
import traceback
from datetime import datetime

import numpy as np
import pandas as pd
import requests

logger = logging.getLogger("live_trader")

GROWW_HEADERS = {
    "User-Agent": (
        "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
        "(KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
    ),
    "Accept": "application/json",
}


def fetch_groww_candles(ticker, days=5, max_retries=3):
    """
    Fetch 1-min OHLCV from Groww for the last *days* calendar days.

    Returns a DataFrame [open, high, low, close, volume] with DateTimeIndex,
    or None on complete failure.
    """
    end_ts   = int(time.time() * 1000)
    start_ts = end_ts - (days * 24 * 3600 * 1000)

    url = (
        f"https://groww.in/v1/api/charting_service/v2/chart/exchange/NSE"
        f"/segment/CASH/{ticker}"
        f"?endTimeInMillis={end_ts}"
        f"&intervalInMinutes=1"
        f"&startTimeInMillis={start_ts}"
    )

    for attempt in range(1, max_retries + 1):
        try:
            resp = requests.get(url, headers=GROWW_HEADERS, timeout=15)
            resp.raise_for_status()
            data = resp.json()

            if "candles" not in data or not data["candles"]:
                logger.warning(f"[{ticker}] No candles in API response (attempt {attempt})")
                if attempt < max_retries:
                    time.sleep(2 * attempt)
                    continue
                return None

            rows = []
            for c in data["candles"]:
                # Skip candles with None / missing OHLCV values
                if c[1] is None or c[2] is None or c[3] is None or c[4] is None or c[5] is None:
                    continue
                try:
                    dt = datetime.fromtimestamp(c[0])
                    rows.append({
                        "date":    dt,
                        "open":    float(c[1]),
                        "high":    float(c[2]),
                        "low":     float(c[3]),
                        "close":   float(c[4]),
                        "cum_vol": float(c[5]),
                    })
                except (TypeError, ValueError):
                    continue

            if not rows:
                logger.warning(f"[{ticker}] All candles had None values (attempt {attempt})")
                if attempt < max_retries:
                    time.sleep(2 * attempt)
                    continue
                return None

            df = pd.DataFrame(rows)
            df.set_index("date", inplace=True)
            df.sort_index(inplace=True)

            # Groww volume is cumulative per day -> difference it
            df["date_only"] = df.index.date
            df["volume"]    = df.groupby("date_only")["cum_vol"].diff().fillna(df["cum_vol"])
            df["volume"]    = np.where(df["volume"] < 0, df["cum_vol"], df["volume"])
            df.drop(columns=["cum_vol", "date_only"], inplace=True)

            logger.info(f"[{ticker}] Fetched {len(df)} candles "
                        f"({df.index.min()} -> {df.index.max()})")
            return df

        except requests.exceptions.RequestException as e:
            logger.error(f"[{ticker}] API error attempt {attempt}/{max_retries}: {e}")
            if attempt < max_retries:
                time.sleep(3 * attempt)

        except Exception as e:
            logger.error(f"[{ticker}] Unexpected error attempt {attempt}/{max_retries}: {e}")
            logger.debug(traceback.format_exc())
            if attempt < max_retries:
                time.sleep(3 * attempt)

    return None