优化表名，将binance_kline改为bn_futures_kline_1d

download_binance_kline.py

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+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
+from dynaconf import Dynaconf
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# 初始化Dynaconf配置
+settings = Dynaconf(
+    envvar_prefix="DYNACONF",
+    settings_files=["settings.toml"],
+    environments=True,
+    load_dotenv=False,
+    default_env="default",
+    env_switcher="BINANCE_ENV"
+)
+
+# 配置参数
+BASE_URL = settings.BASE_URL
+SYMBOLS = settings.SYMBOLS
+INTERVAL = settings.INTERVAL
+
+# 代理配置
+PROXIES = {
+    'http': settings.HTTP_PROXY,
+    'https': settings.HTTPS_PROXY
+} if settings.PROXY_ENABLED else None
+
+# PostgreSQL配置
+DB_CONFIG = {
+    "host": settings.DB_HOST,
+    "database": settings.DB_DATABASE,
+    "user": settings.DB_USER,
+    "password": settings.DB_PASSWORD,
+    "port": settings.DB_PORT
+}
+
+# K线数据列名
+KLINE_COLUMNS = settings.KLINE_COLUMNS
+
+def validate_kline_data(df):
+    """
+    校验K线数据的完整性和合理性
+    
+    参数:
+        df: 包含K线数据的DataFrame
+    
+    返回:
+        bool: 数据是否有效
+        str: 校验结果说明
+    """
+    if df.empty:
+        return False, "数据为空"
+    
+    # 1. 检查必要列是否存在
+    required_columns = ['symbol', 'open_time', 'close_time', 'open', 'high', 'low', 'close', 'volume']
+    missing_columns = [col for col in required_columns if col not in df.columns]
+    if missing_columns:
+        return False, f"缺少必要列: {missing_columns}"
+    
+    # 2. 检查数据类型
+    numeric_columns = ['open', 'high', 'low', 'close', 'volume']
+    for col in numeric_columns:
+        if not pd.api.types.is_numeric_dtype(df[col]):
+            return False, f"列 {col} 不是数值类型"
+    
+    # 3. 检查时间列的连续性
+    df_sorted = df.sort_values('open_time')
+    time_diff = df_sorted['open_time'].diff().dropna()
+    if not time_diff.empty:
+        expected_diff = pd.Timedelta(days=1)  # 默认1天间隔
+        # 允许有少量异常值（如月初月末），但大部分数据应该符合预期间隔
+        if (time_diff != expected_diff).mean() > 0.1:  # 超过10%的数据不符合预期间隔
+            return False, f"时间间隔不符合预期，预期{expected_diff}，实际差异较大"
+    
+    # 4. 检查价格的合理性
+    # 最高价 >= 最低价
+    if (df['high'] < df['low']).any():
+        return False, "存在最高价小于最低价的记录"
+    
+    # 最高价 >= 开盘价和收盘价
+    if ((df['high'] < df['open']) | (df['high'] < df['close'])).any():
+        return False, "存在最高价小于开盘价或收盘价的记录"
+    
+    # 最低价 <= 开盘价和收盘价
+    if ((df['low'] > df['open']) | (df['low'] > df['close'])).any():
+        return False, "存在最低价大于开盘价或收盘价的记录"
+    
+    # 5. 检查交易量的合理性
+    if (df['volume'] < 0).any():
+        return False, "存在交易量为负数的记录"
+    
+    # 6. 检查open_time和close_time的关系
+    if (df['open_time'] >= df['close_time']).any():
+        return False, "存在open_time大于等于close_time的记录"
+    
+    # 7. 检查symbol列是否一致
+    if df['symbol'].nunique() > 1:
+        return False, "同一批次数据包含多个交易对"
+    
+    # 8. 检查是否有重复的open_time
+    if df['open_time'].duplicated().any():
+        return False, "存在重复的open_time记录"
+    
+    # 9. 检查数值字段的范围，确保符合数据库numeric(20,8)的要求
+    # numeric(20,8)意味着：最多20位数字，其中8位小数，因此整数部分最多12位
+    max_value = 10**12 - 1e-8  # 999999999999.99999999
+    min_value = -max_value
+    
+    for col in numeric_columns:
+        if (df[col] > max_value).any() or (df[col] < min_value).any():
+            return False, f"列 {col} 的数值超出数据库字段范围 [-{max_value:.8f}, {max_value:.8f}]"
+    
+    return True, "数据完整性校验通过"
+
+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)
+        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)
+        
+        # 数据完整性校验
+        is_valid, message = validate_kline_data(df)
+        if not is_valid:
+            logger.error(f"K线数据校验失败: {message}")
+            return None
+        
+        logger.info(f"Parsed {len(df)} rows of K线 data, 数据校验通过")
+        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
+    """
+    # 先检查并删除旧表，然后重新创建
+    drop_table_query = """
+    DROP TABLE IF EXISTS bn_futures_kline_1d;
+    """
+    
+    create_table_query = """
+    CREATE TABLE bn_futures_kline_1d (
+        id SERIAL PRIMARY KEY,
+        symbol VARCHAR(20) NOT NULL,
+        open_time TIMESTAMP NOT NULL,
+        open NUMERIC(20, 8) NOT NULL,
+        high NUMERIC(20, 8) NOT NULL,
+        low NUMERIC(20, 8) NOT NULL,
+        close NUMERIC(20, 8) NOT NULL,
+        volume NUMERIC(20, 8) NOT NULL,
+        close_time TIMESTAMP NOT NULL,
+        quote_asset_volume NUMERIC(20, 8) NOT NULL,
+        number_of_trades INTEGER NOT NULL,
+        taker_buy_base_asset_volume NUMERIC(20, 8) NOT NULL,
+        taker_buy_quote_asset_volume NUMERIC(20, 8) NOT NULL,
+        ignore NUMERIC(20, 8) NOT NULL,
+        UNIQUE(symbol, open_time)
+    );
+    """
+    
+    try:
+        with conn.cursor() as cur:
+            cur.execute(drop_table_query)
+            cur.execute(create_table_query)
+        conn.commit()
+        logger.info("Created bn_futures_kline_1d table with updated schema")
+    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，避免重复插入
+    """
+    if df.empty:
+        logger.info("No data to insert")
+        return
+    
+    # 最终数据完整性校验
+    is_valid, message = validate_kline_data(df)
+    if not is_valid:
+        logger.error(f"插入数据库前数据校验失败: {message}")
+        return
+    
+    total_records = len(df)
+    logger.info(f"Preparing to insert {total_records} records")
+    
+    # 定义要插入的列顺序
+    insert_columns = [
+        "symbol", "open_time", "high", "low", "open", "close", "volume",
+        "close_time", "quote_asset_volume", "number_of_trades",
+        "taker_buy_base_asset_volume", "taker_buy_quote_asset_volume", "ignore"
+    ]
+    
+    # 1. 提取所有待插入的(symbol, open_time)组合
+    symbol_time_df = df[['symbol', 'open_time']].drop_duplicates()
+    symbol_time_pairs = symbol_time_df.values.tolist()
+    
+    # 2. 查询数据库中已存在的(symbol, open_time)组合
+    existing_pairs = []
+    batch_size = 500  # 批量检查大小
+    
+    if symbol_time_pairs:
+        # 分批处理，避免SQL语句过长
+        for i in range(0, len(symbol_time_pairs), batch_size):
+            batch_pairs = symbol_time_pairs[i:i+batch_size]
+            
+            # 构建查询条件，使用VALUES列表
+            values_list = ','.join([f"('{symbol}', '{open_time}')" for symbol, open_time in batch_pairs])
+            check_query = f"""
+            SELECT symbol, open_time FROM bn_futures_kline_1d 
+            WHERE (symbol, open_time) IN ({values_list})
+            """
+            
+            try:
+                with conn.cursor() as cur:
+                    cur.execute(check_query)
+                    batch_existing = cur.fetchall()
+                    existing_pairs.extend(batch_existing)
+            except psycopg2.Error as e:
+                logger.error(f"Failed to check existing data in batch {i//batch_size + 1}: {e}")
+                logger.info("Falling back to ON CONFLICT mechanism for this batch")
+                # 对于失败的批次，我们将使用ON CONFLICT机制处理
+                df_filtered = df
+                break
+        else:
+            # 只有当所有批次都成功时才执行过滤
+            # 转换为set以便快速查找
+            existing_set = set((row[0], row[1]) for row in existing_pairs)
+            logger.info(f"Found {len(existing_set)} existing records in database")
+            
+            if existing_set:
+                # 3. 使用更高效的方式过滤DataFrame，避免使用apply
+                # 创建一个包含现有组合的DataFrame
+                existing_df = pd.DataFrame(list(existing_set), columns=['symbol', 'open_time'])
+                
+                # 使用merge进行高效过滤
+                merged_df = df.merge(
+                    existing_df, 
+                    on=['symbol', 'open_time'], 
+                    how='left', 
+                    indicator=True
+                )
+                
+                # 只保留在左表中存在而在右表中不存在的记录
+                df_filtered = merged_df[merged_df['_merge'] == 'left_only'].drop('_merge', axis=1)
+            else:
+                df_filtered = df
+            
+            if df_filtered.empty:
+                logger.info("All records already exist in database, skipping insertion")
+                return
+            
+            logger.info(f"Filtered to {len(df_filtered)} new records to insert (removed {total_records - len(df_filtered)} existing records)")
+    else:
+        df_filtered = df
+    
+    # 4. 准备插入数据
+    insert_query = """
+    INSERT INTO bn_futures_kline_1d (
+        symbol, open_time, high, low, open, 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;
+    """
+    
+    # 转换DataFrame为元组列表
+    data = [tuple(row) for row in df_filtered[insert_columns].to_numpy()]
+    
+    # 5. 执行插入
+    try:
+        with conn.cursor() as cur:
+            execute_values(cur, insert_query, data)
+        conn.commit()
+        logger.info(f"Successfully inserted {len(data)} new 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:
+        # 根据配置决定是否使用代理
+        if PROXIES:
+            response = requests.get(url, timeout=timeout, proxies=PROXIES)
+        else:
+            response = requests.get(url, timeout=timeout)
+        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)
+        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')
+            
+            # 数据完整性校验
+            is_valid, message = validate_kline_data(df)
+            if not is_valid:
+                logger.error(f"文件 {os.path.basename(file_url)} 数据校验失败: {message}")
+                continue
+            
+            all_dfs.append(df)
+            logger.info(f"Processed {os.path.basename(file_url)} with {len(df)} rows, 数据校验通过")
+        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()

download_unzip_csv.py

@@ -0,0 +1,104 @@
+import requests
+import pandas as pd
+from io import BytesIO
+import logging
+
+# 配置日志
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def download_unzip_csv(url, **kwargs):
+    """
+    从URL下载文件，解压并返回CSV内容作为pandas DataFrame
+    
+    参数:
+        url: 文件下载URL
+        **kwargs: 传递给pandas.read_csv的额外参数
+        
+    返回:
+        pandas.DataFrame: 解压后的CSV内容
+    """
+    logger.info(f"Downloading file from {url}")
+    
+    try:
+        # 下载文件
+        response = requests.get(url)
+        response.raise_for_status()  # 检查下载是否成功
+        logger.info(f"Successfully downloaded file from {url}")
+        
+        # 使用BytesIO处理下载的内容
+        zip_data = BytesIO(response.content)
+        
+        # 读取并解压CSV文件
+        logger.info("Reading and decompressing CSV file")
+        df = pd.read_csv(zip_data, compression='zip', **kwargs)
+        
+        # 智能判定首行是否为标题行
+        def is_header_row(row):
+            """智能检测行是否为标题行"""
+            # 检查条件
+            conditions = [
+                # 1. 首行包含常见的时间相关列名
+                any(keyword in str(cell).lower() for keyword in ['time', 'date', 'datetime', 'timestamp'] for cell in row),
+                # 2. 首行包含常见的价格相关列名
+                any(keyword in str(cell).lower() for keyword in ['open', 'high', 'low', 'close', 'volume'] for cell in row),
+                # 3. 首行包含常见的交易相关列名
+                any(keyword in str(cell).lower() for keyword in ['taker', 'quote', 'count', 'ignore'] for cell in row),
+                # 4. 首行全为字符串，而第二行包含数值
+                len(df) > 1 and all(isinstance(str(cell), str) and not str(cell).replace('.', '').isdigit() for cell in row) and \
+                any(str(cell).replace('.', '').isdigit() for cell in df.iloc[1] if pd.notna(cell)),
+                # 5. 首行包含'_'字符（常见于编程命名的列名）
+                any('_' in str(cell) for cell in row)
+            ]
+            return any(conditions)
+        
+        if len(df) > 0 and is_header_row(df.iloc[0]):
+            df = df[1:].reset_index(drop=True)
+            logger.info("Skipped header row")
+        
+        # 转换数值列
+        numeric_columns = ['open', 'high', 'low', 'close', 'volume',
+                          'close_time', 'quote_asset_volume', 'number_of_trades',
+                          'taker_buy_volume', 'taker_buy_quote_volume', 'taker_buy_base_asset_volume',
+                          'taker_buy_quote_asset_volume', 'ignore']
+        
+        # 只转换存在的列
+        for col in numeric_columns:
+            if col in df.columns:
+                try:
+                    if col in ['number_of_trades', 'count']:
+                        df[col] = df[col].astype(int)
+                    else:
+                        df[col] = df[col].astype(float)
+                except ValueError:
+                    logger.warning(f"Could not convert column {col} to numeric type")
+        
+        logger.info(f"Successfully parsed CSV file with {len(df)} rows")
+        return df
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Failed to download file: {e}")
+        raise
+    except Exception as e:
+        logger.error(f"Failed to process file: {e}")
+        raise
+
+if __name__ == "__main__":
+    # 测试函数
+    test_url = "https://data.binance.vision/data/futures/um/monthly/klines/BTCUSDT/1d/BTCUSDT-1d-2024-01.zip"
+    
+    # CSV列名
+    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"
+    ]
+    
+    try:
+        df = download_unzip_csv(test_url, header=None, names=columns)
+        print(f"DataFrame shape: {df.shape}")
+        print("DataFrame head:")
+        print(df.head())
+        print("DataFrame dtypes:")
+        print(df.dtypes)
+    except Exception as e:
+        print(f"Error: {e}")

settings.toml

@@ -0,0 +1,30 @@
+# Binance K线数据下载配置
+
+[default]
+# 基础URL
+BASE_URL = "https://data.binance.vision/data/futures/um/monthly/klines/"
+
+# 默认交易对
+SYMBOLS = ["BOBUSDT", "BTCUSDT"]
+
+# 默认时间间隔
+INTERVAL = "1d"
+
+# 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"
+]
+
+# 代理配置
+PROXY_ENABLED = true
+HTTP_PROXY = "http://localhost:1080"
+HTTPS_PROXY = "http://localhost:1080"
+
+# PostgreSQL数据库配置
+DB_HOST = "localhost"
+DB_PORT = 5432
+DB_DATABASE = "graphql-engine"
+DB_USER = "root"
+DB_PASSWORD = "pgsqlpasswd.."