"""
P170 Market Data Refresh — yfinance
Updates data/lake/clean/market_data/NSE/ with latest candles.
Run daily before market open or after close.

Usage:
  python refresh_market_data.py              # refresh all symbols
  python refresh_market_data.py --top-n 200  # refresh top 200 only
  python refresh_market_data.py --symbol RELIANCE  # single symbol
"""

import argparse
import time
import sys
from pathlib import Path
from datetime import datetime

import numpy as np
import pandas as pd
import yfinance as yf

CLEAN_DIR = Path("data/lake/clean/market_data/NSE")
LOG_EVERY  = 50

# Known NSE→Yahoo symbol overrides (symbols that differ on Yahoo Finance)
SYMBOL_MAP = {
    "M&M":       "MM.NS",
    "L&T":       "LT.NS",
    "ARE&M":     "ARE&M.NS",
    "MOTHERSON": "MOTHERSON.NS",
}

def nse_to_yf(symbol: str) -> str:
    """Convert NSE symbol to Yahoo Finance ticker."""
    if symbol in SYMBOL_MAP:
        return SYMBOL_MAP[symbol]
    return f"{symbol}.NS"


def get_all_nse_symbols() -> list:
    """Return all symbols currently in the clean data lake."""
    syms = []
    for f in CLEAN_DIR.glob("*_real_market_data_v2.parquet"):
        sym = f.stem.replace("_real_market_data_v2", "")
        syms.append(sym)
    return sorted(syms)


def refresh_symbol(symbol: str, period: str = "1y") -> dict:
    """
    Fetch latest data for one symbol and update the parquet file.
    Returns status dict.
    """
    path = CLEAN_DIR / f"{symbol}_real_market_data_v2.parquet"
    yf_sym = nse_to_yf(symbol)

    try:
        # Load existing data to find last date
        last_date = None
        if path.exists():
            existing = pd.read_parquet(path)
            date_col = "timestamp" if "timestamp" in existing.columns else "date"
            last_date = pd.to_datetime(existing[date_col]).max()

        # Fetch from yfinance
        df = yf.download(
            yf_sym,
            period=period,
            interval="1d",
            progress=False,
            auto_adjust=True,
        )

        if df is None or len(df) == 0:
            return {"symbol": symbol, "status": "no_data", "rows": 0}

        # Normalise columns
        df = df.reset_index()
        df.columns = [c.lower() if isinstance(c, str) else c[0].lower()
                      for c in df.columns]
        df = df.rename(columns={"date": "timestamp", "index": "timestamp"})

        # Keep only OHLCV
        keep = ["timestamp", "open", "high", "low", "close", "volume"]
        df = df[[c for c in keep if c in df.columns]].copy()
        df["timestamp"] = pd.to_datetime(df["timestamp"]).dt.tz_localize(None)
        df = df.dropna(subset=["close"]).sort_values("timestamp").reset_index(drop=True)

        if len(df) == 0:
            return {"symbol": symbol, "status": "empty", "rows": 0}

        # Merge with existing (avoid duplicates) — always enforce clean schema
        if path.exists() and last_date is not None:
            new_rows = df[df["timestamp"] > last_date]
            if len(new_rows) == 0:
                return {"symbol": symbol, "status": "already_fresh",
                        "rows": len(existing), "last": str(last_date.date())}
            # Normalise existing to clean schema before concat
            if date_col == "date":
                existing = existing.rename(columns={"date": "timestamp"})
            existing["timestamp"] = pd.to_datetime(existing["timestamp"])
            keep_cols = ["timestamp","open","high","low","close","volume"]
            existing = existing[[c for c in keep_cols if c in existing.columns]]
            combined = pd.concat([existing, new_rows], ignore_index=True)
            combined = combined.drop_duplicates(subset=["timestamp"]).sort_values("timestamp")
            df = combined

        df.to_parquet(path, index=False)
        last = df["timestamp"].max()
        return {
            "symbol": symbol, "status": "updated",
            "rows": len(df), "last": str(last.date()),
            "yf_sym": yf_sym,
        }

    except Exception as e:
        return {"symbol": symbol, "status": "error", "error": str(e)}


def refresh_all(symbols: list, delay: float = 0.1) -> dict:
    """Refresh all symbols with rate limiting."""
    t0 = time.time()
    results = {"updated": 0, "already_fresh": 0, "no_data": 0, "error": 0}
    errors = []

    print(f"Refreshing {len(symbols)} symbols...")
    for i, sym in enumerate(symbols):
        r = refresh_symbol(sym)
        status = r.get("status", "error")

        if status == "updated":
            results["updated"] += 1
        elif status == "already_fresh":
            results["already_fresh"] += 1
        elif status in ("no_data", "empty"):
            results["no_data"] += 1
        else:
            results["error"] += 1
            errors.append(f"{sym}: {r.get('error','')}")

        if (i + 1) % LOG_EVERY == 0:
            elapsed = time.time() - t0
            rate = (i + 1) / elapsed
            eta = (len(symbols) - i - 1) / rate
            print(f"  [{i+1}/{len(symbols)}] "
                  f"updated={results['updated']} "
                  f"fresh={results['already_fresh']} "
                  f"errors={results['error']} "
                  f"eta={eta:.0f}s")

        time.sleep(delay)

    elapsed = time.time() - t0
    print(f"\nDone in {elapsed:.0f}s")
    print(f"  Updated:      {results['updated']}")
    print(f"  Already fresh:{results['already_fresh']}")
    print(f"  No data:      {results['no_data']}")
    print(f"  Errors:       {results['error']}")
    if errors:
        print(f"\nFirst 10 errors:")
        for e in errors[:10]:
            print(f"  {e}")
    return results


if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--symbol", type=str, default=None)
    ap.add_argument("--top-n",  type=int, default=None)
    ap.add_argument("--period", type=str, default="1y",
                    help="yfinance period: 1y, 2y, 6mo etc")
    ap.add_argument("--delay",  type=float, default=0.15,
                    help="seconds between requests (avoid rate limiting)")
    args = ap.parse_args()

    if args.symbol:
        r = refresh_symbol(args.symbol.upper(), period=args.period)
        print(r)
    else:
        syms = get_all_nse_symbols()
        if args.top_n:
            syms = syms[:args.top_n]
        refresh_all(syms, delay=args.delay)