import asyncio, json
from typing import Literal
from fastapi import FastAPI
from models import get_session, get_engine, Candle
import pandas as pd, pandas_ta as ta


session = get_session()
app = FastAPI()


def get_ticker_all(
        contract: str,
        target_interval: Literal['1m', '5m', '15m', '30m', '1h', '4h', '1d', '1w']
):
    print(target_interval)
    """
    10초 간격 캔들 데이터를 지정된 봉으로 리샘플링

    Args:
        df: 원본 10초 데이터 (index=ts, columns=open, high, low, close, volume)
        contract: 계약명 (필터링용)
        target_interval: 목표 봉 (예: '1m', '1h', '1d')

    Returns:
        pd.DataFrame: 리샘플링된 OHLCV 데이터
    """
    results = session.query(Candle).filter(Candle.contract.like(f'%{contract}%')).all()

    df = pd.DataFrame([row.__dict__ for row in results])

    # 2. time 컬럼을 datetime으로 변환 (밀리초 -> 초 -> datetime)
    df['ts'] = pd.to_datetime(df['time'], unit='s')  # or 's' if in seconds

    # 3. index로 설정
    df.set_index('ts', inplace=True)

    # 4. resample 주기 설정
    freq_map = {
        '1m': '1Min',
        '5m': '5Min',
        '15m': '15Min',
        '30m': '30Min',
        '1h': '1H',
        '4h': '4H',
        '1d': '1D',
        '1w': '1W'
    }

    if target_interval not in freq_map:
        raise ValueError(f"Unsupported interval: {target_interval}")

    freq = freq_map[target_interval]

    # 4. 리샘플링 (OHLCV)
    ohlc = df['close'].resample(freq).ohlc()  # open, high, low, close
    volume = df['volume'].resample(freq).sum().rename('volume')

    # 5. 병합
    result = pd.concat([ohlc, volume], axis=1).dropna()

    # 6. ✅ index(datetime)를 'time' 컬럼으로 유닉스 밀리초 추가
    result['time'] = (result.index.astype('int64') // 1_000_000_000)  # 나노초 → 밀리초 (int64)
    # 또는 밀리초 단위로 정확히:
    result['time'] = result.index.view('int64') // 1_000_000_000  # pd.Timestamp → 유닉스 ms

    # 7. (옵션) 'time'을 맨 앞으로 이동
    cols = ['time', 'open', 'high', 'low', 'close', 'volume']
    result = result[cols]
    result.tail(1)

    return result

@app.get("/api/candle/{contract}/{time}")
def get_candle(contract: str, time: str):
    results = get_ticker_all(contract, time)

    dict_row = pd.DataFrame(results).to_json(orient='table')
    # payload = json.dumps({"msg": dict_row}, default=str, ensure_ascii=False)
    return json.loads(dict_row)

@app.get("/")
async def test():
    return {"msg":"hello"}