import requests
import os
import pandas as pd
from io import BytesIO
import psycopg2
from psycopg2.extras import execute_values
import logging
from datetime import datetime
import xml.etree.ElementTree as ET
from download_unzip_csv import download_unzip_csv

# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# 配置参数
BASE_URL = "https://data.binance.vision/data/futures/um/monthly/klines/"
# 可以添加多个交易对，例如：["BOBUSDT", "BTCUSDT", "ETHUSDT"]
SYMBOLS = ["BOBUSDT", "BTCUSDT"]  # BOBUSDT数据从2025-11开始可用
INTERVAL = "1d"
START_DATE = "2021-01"
END_DATE = datetime.now().strftime("%Y-%m")

# 代理配置
PROXIES = {
    'http': 'http://localhost:1080',
    'https': 'http://localhost:1080'
}

# PostgreSQL配置
DB_CONFIG = {
    "host": "localhost",
    "database": "your_database",
    "user": "your_username",
    "password": "your_password",
    "port": 5432
}

# K线数据列名
KLINE_COLUMNS = [
    "open_time", "open", "high", "low", "close", "volume",
    "close_time", "quote_asset_volume", "number_of_trades",
    "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
]

def download_kline_data(symbol, interval, year_month):
    """下载指定月份的K线数据"""
    # 拆分年月
    year, month = year_month.split("-")
    filename = f"{symbol}-{interval}-{year}-{month}.zip"
    url = f"{BASE_URL}{symbol}/{interval}/{filename}"
    logger.info(f"Downloading {url}")
    
    try:
        response = requests.get(url, proxies=PROXIES)
        response.raise_for_status()
        logger.info(f"Downloaded {filename} successfully")
        return BytesIO(response.content)
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to download {filename}: {e}")
        return None

def parse_kline_data(zip_data, symbol):
    """解析下载的K线数据"""
    try:
        df = pd.read_csv(zip_data, compression='zip', header=None, names=KLINE_COLUMNS)
        # 跳过标题行
        df = df[df["open_time"] != "open_time"]
        # 添加symbol列
        df["symbol"] = symbol
        # 转换时间戳为datetime
        df["open_time"] = pd.to_datetime(df["open_time"], unit='ms')
        df["close_time"] = pd.to_datetime(df["close_time"], unit='ms')
        # 转换数值列
        numeric_columns = ["open", "high", "low", "close", "volume", 
                          "quote_asset_volume", "number_of_trades",
                          "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"]
        df[numeric_columns] = df[numeric_columns].astype(float)
        df["number_of_trades"] = df["number_of_trades"].astype(int)
        logger.info(f"Parsed {len(df)} rows of K线 data")
        return df
    except Exception as e:
        logger.error(f"Failed to parse K线 data: {e}")
        return None

def create_connection():
    """创建PostgreSQL连接"""
    try:
        conn = psycopg2.connect(**DB_CONFIG)
        logger.info("Connected to PostgreSQL database")
        return conn
    except psycopg2.OperationalError as e:
        logger.error(f"Failed to connect to PostgreSQL: {e}")
        return None

def create_table(conn):
    """创建K线数据表"""
    create_table_query = """
    CREATE TABLE IF NOT EXISTS binance_kline (
        id SERIAL PRIMARY KEY,
        symbol VARCHAR(20) NOT NULL,
        open_time TIMESTAMP NOT NULL,
        open NUMERIC(18, 8) NOT NULL,
        high NUMERIC(18, 8) NOT NULL,
        low NUMERIC(18, 8) NOT NULL,
        close NUMERIC(18, 8) NOT NULL,
        volume NUMERIC(18, 8) NOT NULL,
        close_time TIMESTAMP NOT NULL,
        quote_asset_volume NUMERIC(18, 8) NOT NULL,
        number_of_trades INTEGER NOT NULL,
        taker_buy_base_asset_volume NUMERIC(18, 8) NOT NULL,
        taker_buy_quote_asset_volume NUMERIC(18, 8) NOT NULL,
        ignore NUMERIC(18, 8) NOT NULL,
        UNIQUE(symbol, open_time)
    );
    """
    
    try:
        with conn.cursor() as cur:
            cur.execute(create_table_query)
        conn.commit()
        logger.info("Created binance_kline table")
    except psycopg2.Error as e:
        logger.error(f"Failed to create table: {e}")
        conn.rollback()

def insert_data(conn, df):
    """将K线数据插入到数据库"""
    # 准备插入数据
    insert_query = """
    INSERT INTO binance_kline (
        symbol, open_time, open, high, low, close, volume, 
        close_time, quote_asset_volume, number_of_trades, 
        taker_buy_base_asset_volume, taker_buy_quote_asset_volume, ignore
    ) VALUES %s
    ON CONFLICT (symbol, open_time) DO NOTHING;
    """
    
    # 定义要插入的列顺序
    insert_columns = [
        "symbol", "open_time", "open", "high", "low", "close", "volume",
        "close_time", "quote_asset_volume", "number_of_trades",
        "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
    ]
    
    # 转换DataFrame为元组列表
    data = [tuple(row) for row in df[insert_columns].to_numpy()]
    
    try:
        with conn.cursor() as cur:
            execute_values(cur, insert_query, data)
        conn.commit()
        logger.info(f"Inserted {len(data)} rows into database")
    except psycopg2.Error as e:
        logger.error(f"Failed to insert data: {e}")
        conn.rollback()

def list_s3_files(url, timeout=10):
    """
    从S3存储桶的XML响应中提取所有文件的完整URL
    
    参数:
        url: S3存储桶的列表URL，例如: https://s3-ap-northeast-1.amazonaws.com/data.binance.vision?delimiter=/&prefix=data/futures/um/monthly/klines/BOBUSDT/1d/
        timeout: 请求超时时间，单位秒，默认10秒
    
    返回:
        list: 完整的文件URL列表
    """
    logger.info(f"Listing files from {url}")
    
    try:
        response = requests.get(url, timeout=timeout, proxies=PROXIES)
        response.raise_for_status()
        
        # 解析XML响应
        root = ET.fromstring(response.content)
        
        # S3 XML使用的命名空间
        ns = {'s3': 'http://s3.amazonaws.com/doc/2006-03-01/'}
        
        # 基础下载URL
        base_download_url = "https://data.binance.vision/"
        
        # 提取所有Key元素的文本，构建完整URL
        file_urls = []
        for key_elem in root.findall('.//s3:Key', ns):
            file_path = key_elem.text
            # 只返回zip文件
            if file_path and file_path.endswith('.zip'):
                # 构建完整URL
                full_url = base_download_url + file_path
                file_urls.append(full_url)
        
        logger.info(f"Found {len(file_urls)} files")
        return file_urls
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to list files from {url}: {e}")
        return []
    except ET.ParseError as e:
        logger.error(f"Failed to parse XML response: {e}")
        return []
    except KeyboardInterrupt:
        logger.error(f"Request to {url} was interrupted")
        return []

def download_kline_data_by_url(url):
    """
    通过完整URL下载K线数据
    
    参数:
        url: 完整的K线数据下载URL
    
    返回:
        BytesIO: 下载的数据，或None如果下载失败
    """
    logger.info(f"Downloading {url}")
    
    try:
        response = requests.get(url, proxies=PROXIES)
        response.raise_for_status()
        filename = os.path.basename(url)
        logger.info(f"Downloaded {filename} successfully")
        return BytesIO(response.content)
    except requests.exceptions.RequestException as e:
        logger.error(f"Failed to download {url}: {e}")
        return None

def process_symbol(symbol, interval=INTERVAL):
    """
    处理指定交易对的所有K线数据，包括下载、解析、合并和插入数据库
    
    参数:
        symbol: 交易对，例如: "BTCUSDT"
        interval: 时间间隔，例如: "1d"，默认使用全局INTERVAL
    
    返回:
        pandas.DataFrame: 合并后的K线数据
    """
    logger.info(f"Processing symbol: {symbol}, interval: {interval}")
    
    # 组装S3列表URL
    s3_url = f"https://s3-ap-northeast-1.amazonaws.com/data.binance.vision?delimiter=/&prefix=data/futures/um/monthly/klines/{symbol}/{interval}/"
    
    # 获取所有可下载文件URL
    file_urls = list_s3_files(s3_url)
    
    if not file_urls:
        logger.warning(f"No files found for {symbol}-{interval}")
        return None
    
    # 合并所有DataFrame
    all_dfs = []
    for file_url in file_urls:
        if not file_url.endswith('.zip'):
            continue
        
        try:
            # 调用download_unzip_csv下载并解析数据
            df = download_unzip_csv(file_url, header=None, names=KLINE_COLUMNS)
            
            # 添加symbol列
            df["symbol"] = symbol
            
            # 转换时间戳为datetime（先转换为数值类型以避免FutureWarning）
            df["open_time"] = pd.to_datetime(df["open_time"].astype(float), unit='ms')
            df["close_time"] = pd.to_datetime(df["close_time"].astype(float), unit='ms')
            
            all_dfs.append(df)
            logger.info(f"Processed {os.path.basename(file_url)} with {len(df)} rows")
        except Exception as e:
            logger.error(f"Failed to process {file_url}: {e}")
            continue
    
    if not all_dfs:
        logger.warning(f"No data processed for {symbol}-{interval}")
        return None
    
    # 合并所有DataFrame
    merged_df = pd.concat(all_dfs, ignore_index=True)
    logger.info(f"Merged {len(all_dfs)} files into a single DataFrame with {len(merged_df)} rows")
    
    # 去重
    merged_df = merged_df.drop_duplicates(subset=["symbol", "open_time"])
    logger.info(f"After deduplication, {len(merged_df)} rows remain")
    
    # 插入到PostgreSQL数据库
    conn = create_connection()
    if conn:
        try:
            # 确保表存在
            create_table(conn)
            
            # 插入数据
            insert_data(conn, merged_df)
            logger.info(f"Successfully inserted {len(merged_df)} rows into database for {symbol}")
        finally:
            # 关闭连接
            conn.close()
    
    return merged_df


def main():
    """主函数，处理所有配置的交易对"""
    logger.info("Starting main process for symbols: %s", SYMBOLS)
    
    for symbol in SYMBOLS:
        try:
            process_symbol(symbol, INTERVAL)
        except Exception as e:
            logger.error(f"Failed to process symbol {symbol}: {e}")
    
    logger.info("Script completed successfully")

if __name__ == "__main__":
    # 测试新添加的process_symbol函数
    import sys
    if len(sys.argv) > 1:
        # 从命令行获取交易对
        symbol = sys.argv[1]
        interval = sys.argv[2] if len(sys.argv) > 2 else INTERVAL
        process_symbol(symbol, interval)
    else:
        # 默认运行main函数
        main()