import sqlite3
from typing import Dict, List, cast
import pandas as pd



def load_sqlite_to_dataframe(db_path, query):
    try:
        conn = sqlite3.connect(db_path)

        df = pd.read_sql_query(query, conn)
        return df
    except sqlite3.Error as excpt:
        print(f"SQLite error: {excpt}")
        raise
    except Exception as excpt:
        print(f"Error: {excpt}")
        raise Exception() from excpt
    finally:
        if "conn" in locals():
            conn.close()


def convert_time_to_UTC(value: str, timezone: str) -> str:

    from zoneinfo import ZoneInfo
    from datetime import datetime

    # Parse it to naive datetime object
    local_dt = datetime.strptime(value, "%Y-%m-%d %H:%M:%S")

    zinfo = ZoneInfo(timezone)
    result: datetime = local_dt.replace(tzinfo=zinfo).astimezone(ZoneInfo("UTC"))

    return result.strftime("%Y-%m-%d %H:%M:%S")


def load_market_data(datafile: str, config: Dict) -> pd.DataFrame:
    from tools.data_loader import load_sqlite_to_dataframe

    instrument_ids = [
        '"' + config["instrument_id_pfx"] + instrument + '"'
        for instrument in config["instruments"]
    ]
    exchange_id = config["exchange_id"]

    query = "select"
    query += " tstamp"
    query += ", tstamp_ns as time_ns"

    query += f", substr(instrument_id, {len(config['instrument_id_pfx']) + 1}) as symbol"
    query += ", open"
    query += ", high"
    query += ", low"
    query += ", close"
    query += ", volume"
    query += ", num_trades"
    query += ", vwap"

    query += f" from {config['db_table_name']}"
    query += f" where exchange_id ='{exchange_id}'"
    query += f" and instrument_id in ({','.join(instrument_ids)})"

    df = load_sqlite_to_dataframe(db_path=datafile, query=query)

    # Trading Hours
    date_str = df["tstamp"][0][0:10]
    trading_hours = config["trading_hours"]

    start_time = convert_time_to_UTC(
        f"{date_str} {trading_hours['begin_session']}", trading_hours["timezone"]
    )
    end_time = convert_time_to_UTC(
        f"{date_str} {trading_hours['end_session']}", trading_hours["timezone"]
    )

    # Perform boolean selection
    df = df[(df["tstamp"] >= start_time) & (df["tstamp"] <= end_time)]
    df["tstamp"] = pd.to_datetime(df["tstamp"])

    return cast(pd.DataFrame, df)


def get_available_instruments_from_db(datafile: str, config: Dict) -> List[str]:
    """
    Auto-detect available instruments from the database by querying distinct instrument_id values.
    Returns instruments without the configured prefix.
    """
    try:
        conn = sqlite3.connect(datafile)

        # Build exclusion list with full instrument_ids
        exclude_instruments = config.get("exclude_instruments", [])
        prefix = config.get("instrument_id_pfx", "")
        exclude_instrument_ids = [f"{prefix}{inst}" for inst in exclude_instruments]
        
        # Query to get distinct instrument_ids
        query = f"""
        SELECT DISTINCT instrument_id 
        FROM {config['db_table_name']} 
        WHERE exchange_id = ?
        """
        
        # Add exclusion clause if there are instruments to exclude
        if exclude_instrument_ids:
            placeholders = ','.join(['?' for _ in exclude_instrument_ids])
            query += f" AND instrument_id NOT IN ({placeholders})"
            cursor = conn.execute(query, (config["exchange_id"],) + tuple(exclude_instrument_ids))
        else:
            cursor = conn.execute(query, (config["exchange_id"],))
        instrument_ids = [row[0] for row in cursor.fetchall()]
        conn.close()

        # Remove the configured prefix to get instrument symbols
        instruments = []
        for instrument_id in instrument_ids:
            if instrument_id.startswith(prefix):
                symbol = instrument_id[len(prefix) :]
                instruments.append(symbol)
            else:
                instruments.append(instrument_id)

        return sorted(instruments)

    except Exception as e:
        print(f"Error auto-detecting instruments from {datafile}: {str(e)}")
        return []


# if __name__ == "__main__":
#     df1 = load_sqlite_to_dataframe(sys.argv[1], table_name="md_1min_bars")

#     print(df1)