diff --git a/requirements.txt b/requirements.txt index 23163dd..a3df8b3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,4 +21,6 @@ pymongo==4.13.0 scikit-learn==1.6.1 dbutils==3.1.2 pyyaml==6.0.3 -xtquant==250516.1.1 \ No newline at end of file +xtquant==250516.1.1 +tushare>=1.4.24 +akshare==1.17.82 \ No newline at end of file diff --git a/src/QMT/config_杨莉.yaml b/src/QMT/config_杨莉.yaml new file mode 100644 index 0000000..34f8b03 --- /dev/null +++ b/src/QMT/config_杨莉.yaml @@ -0,0 +1,52 @@ +# QMT交易配置 +qmt_config: + # 客户端路径 + client_path: "C:\\中金财富QMT个人版交易端\\userdata_mini" + + # 账户配置 + account: + account_id: "79367690" # 资金账号 + account_type: "STOCK" # 账号类型：STOCK(股票) CREDIT(信用) FUTURE(期货) + +# 数据库配置 +database: + host: "192.168.18.199" + port: 3307 + user: "root" + password: "Chlry$%.8_07" + database: "trading_cs" + +# 日志配置 +logging: + level: "INFO" + log_dir: "../logs" + log_format: "%(asctime)s - %(levelname)s - %(message)s" + +# Redis 配置 +redis: + host: "192.168.18.208" + port: 6379 + db: 14 + password: "wlkj2018" + socket_timeout: 5 + +# 计划同步配置（从MySQL同步到Redis） +plan_sync: + interval_seconds: 60 # 定时同步周期，秒 + +# 交易策略配置 +strategy: + # 买入配置 + buy: + amount: 50000 # 每只股票买入金额（元） + enabled: true # 是否启用买入策略 + + # 卖出配置 + sell: + enabled: true # 是否启用卖出策略 + # 卖出策略：当价格高于目标价时卖出全部持仓 + + # 订单配置 + order: + timeout: 300 # 订单超时时间（秒），默认5分钟 + cooldown: 60 # 同股票下单冷却时间（秒），避免频繁下单 \ No newline at end of file diff --git a/src/QMT/config_自营.yaml b/src/QMT/config_自营.yaml new file mode 100644 index 0000000..83f71b0 --- /dev/null +++ b/src/QMT/config_自营.yaml @@ -0,0 +1,52 @@ +# QMT交易配置 +qmt_config: + # 客户端路径 + client_path: "C:\\中金财富QMT个人版交易端\\userdata_mini" + + # 账户配置 + account: + account_id: "79355243" # 资金账号 + account_type: "STOCK" # 账号类型：STOCK(股票) CREDIT(信用) FUTURE(期货) + +# 数据库配置 +database: + host: "192.168.18.199" + port: 3306 + user: "root" + password: "Chlry#$.8" + database: "db_gp_cj" + +# 日志配置 +logging: + level: "INFO" + log_dir: "../logs" + log_format: "%(asctime)s - %(levelname)s - %(message)s" + +# Redis 配置 +redis: + host: "192.168.18.208" + port: 6379 + db: 13 + password: "wlkj2018" + socket_timeout: 5 + +# 计划同步配置（从MySQL同步到Redis） +plan_sync: + interval_seconds: 60 # 定时同步周期，秒 + +# 交易策略配置 +strategy: + # 买入配置 + buy: + amount: 50000 # 每只股票买入金额（元） + enabled: true # 是否启用买入策略 + + # 卖出配置 + sell: + enabled: true # 是否启用卖出策略 + # 卖出策略：当价格高于目标价时卖出全部持仓 + + # 订单配置 + order: + timeout: 300 # 订单超时时间（秒），默认5分钟 + cooldown: 60 # 同股票下单冷却时间（秒），避免频繁下单 \ No newline at end of file diff --git a/src/QMT/main.py b/src/QMT/main.py index e3da02b..2cebc52 100644 --- a/src/QMT/main.py +++ b/src/QMT/main.py @@ -186,7 +186,7 @@ if __name__ == '__main__': # 启动每10分钟持仓快照覆盖更新线程（只更新DB，不叠加） start_position_snapshot_monitor(xt_trader, acc, logger, interval_seconds=600) # 下载股票数据 - xtdata.download_sector_data() + # xtdata.download_sector_data() # 创建买入策略回调函数 buy_strategy_callback = create_buy_strategy_callback(xt_trader, acc, buy_amount, logger) diff --git a/src/QMT/redis_state_manager.py b/src/QMT/redis_state_manager.py index 9bf665f..4047138 100644 --- a/src/QMT/redis_state_manager.py +++ b/src/QMT/redis_state_manager.py @@ -57,18 +57,42 @@ class RedisStateManager: return {k: json.loads(v) for k, v in data.items()} if data else {} def get_pending(self, stock_code: str) -> Optional[Dict]: + # 优先从独立key读取（带过期时间） + pending_item_key = f"{PENDING_KEY}:{stock_code}" + v = self.r.get(pending_item_key) + if v: + return json.loads(v) + # 如果独立key不存在，尝试从Hash读取（兼容旧数据） v = self.r.hget(PENDING_KEY, stock_code) return json.loads(v) if v else None def set_pending(self, stock_code: str, order_info: Dict) -> None: + # 使用独立的key以支持过期时间（6小时） + pending_item_key = f"{PENDING_KEY}:{stock_code}" + self.r.setex( + pending_item_key, + 6 * 3600, # 6小时过期 + json.dumps(order_info, ensure_ascii=False) + ) + # 同时在Hash中记录，方便get_all_pending查询 self.r.hset(PENDING_KEY, stock_code, json.dumps(order_info, ensure_ascii=False)) self._touch_meta('pending_updated_at') def del_pending(self, stock_code: str) -> None: + # 删除独立key和Hash中的记录 + pending_item_key = f"{PENDING_KEY}:{stock_code}" + self.r.delete(pending_item_key) self.r.hdel(PENDING_KEY, stock_code) self._touch_meta('pending_updated_at') def clear_pending(self) -> None: + # 删除Hash中的所有记录 + stock_codes = list(self.r.hkeys(PENDING_KEY)) + # 删除所有独立的pending key + if stock_codes: + pending_keys = [f"{PENDING_KEY}:{code}" for code in stock_codes] + self.r.delete(*pending_keys) + # 删除Hash本身 self.r.delete(PENDING_KEY) self._touch_meta('pending_cleared_at') diff --git a/src/QMT/strategy.py b/src/QMT/strategy.py index 2a3b411..47c499b 100644 --- a/src/QMT/strategy.py +++ b/src/QMT/strategy.py @@ -207,7 +207,7 @@ def create_buy_strategy_callback(xt_trader, acc, buy_amount, logger): 实时行情回调函数 检查当前价格是否低于目标价格，如果满足条件则买入 """ - # logger.info(f"收到买入行情数据: {datetime.datetime.now()}") + logger.info(f"收到买入行情数据: {datetime.datetime.now()}") # 获取当前时间 current_time = datetime.datetime.now().time() @@ -232,7 +232,7 @@ def create_buy_strategy_callback(xt_trader, acc, buy_amount, logger): target_price = plan_info['target_price'] buy_amount = plan_info['buy_amount'] - # logger.info(f"[买入监控] {stock_code} 当前价格: {current_price:.2f}, 目标价格: {target_price:.2f}") + logger.info(f"[买入监控] {stock_code} 当前价格: {current_price:.2f}, 目标价格: {target_price:.2f}") # 判断是否满足买入条件：当前价格低于目标价格 if current_price < target_price: @@ -240,12 +240,12 @@ def create_buy_strategy_callback(xt_trader, acc, buy_amount, logger): # 检查持仓（Redis） current_qty = r.get_position(stock_code) if current_qty > 0: - # logger.info(f"{stock_code} 已有持仓 {current_qty}股，跳过买入") + logger.info(f"{stock_code} 已有持仓 {current_qty}股，跳过买入") continue # 检查是否有在途订单（Redis） if is_stock_pending_order(stock_code): - # logger.info(f"{stock_code} 有在途订单，跳过买入") + logger.info(f"{stock_code} 有在途订单，跳过买入") continue # 集合竞价时段：只观察，不下单 @@ -280,9 +280,11 @@ def create_buy_strategy_callback(xt_trader, acc, buy_amount, logger): # 添加到在途订单（同时记录到数据库） add_pending_order(stock_code, order_id, buy_volume, current_price, target_price, 'buy', logger) - + + # 单次更新账户资金（不阻塞回调） + update_account_funds_once(xt_trader, acc, logger) + logger.info(f"已提交买入订单: {stock_code} {buy_volume}股，价格{target_price}，订单ID: {order_id}") - update_account_funds_periodically(xt_trader, acc, logger) else: logger.warning(f"买入数量计算为0，跳过 {stock_code}") else: @@ -376,7 +378,8 @@ def create_sell_strategy_callback(xt_trader, acc, logger): add_pending_order(stock_code, order_id, sell_volume, current_price, target_price, 'sell', logger) logger.info(f"已提交卖出订单: {stock_code} {sell_volume}股，价格{target_price}，订单ID: {order_id}") - update_account_funds_periodically(xt_trader, acc, logger) + # 单次更新账户资金（不阻塞回调） + update_account_funds_once(xt_trader, acc, logger) else: logger.warning(f"卖出数量为0，跳过 {stock_code}") else: @@ -389,15 +392,73 @@ def create_sell_strategy_callback(xt_trader, acc, logger): return f +def _update_account_funds_sync(xt_trader, acc, logger): + """同步更新账户资金信息（内部函数，在后台线程中执行）""" + try: + # 获取账户资金信息 + account_info = xt_trader.query_stock_asset(acc) + + if account_info: + # 计算相关数据 + available_cash = getattr(account_info, 'm_dCash', 0.0) + frozen_cash = getattr(account_info, 'm_dFrozenCash', 0.0) + market_value = getattr(account_info, 'm_dMarketValue', 0.0) + total_asset = available_cash + frozen_cash + market_value + + # 尝试获取盈亏信息，如果属性不存在则设为0 + try: + profit_loss = getattr(account_info, 'm_dProfitLoss', 0.0) + except AttributeError: + # 如果m_dProfitLoss不存在，尝试其他可能的属性名 + profit_loss = getattr(account_info, 'm_dProfit', 0.0) + + # 计算盈亏比例 + if total_asset > 0: + profit_loss_ratio = (profit_loss / total_asset) * 100 + else: + profit_loss_ratio = 0.0 + + # 更新到数据库 + db_manager = initialize_database_manager(logger) + success = db_manager.update_account_funds( + account_id=acc.account_id, + account_type=acc.account_type, # 修复：去掉引号 + total_asset=total_asset, + available_cash=available_cash, + frozen_cash=frozen_cash, + market_value=market_value, + profit_loss=profit_loss, + profit_loss_ratio=profit_loss_ratio + ) + + if success: + logger.info(f"账户资金更新成功 - 总资产: {total_asset:.2f}, 可用资金: {available_cash:.2f}, 市值: {market_value:.2f}") + else: + logger.warning("账户资金更新失败") + else: + logger.warning("无法获取账户资金信息") + + except Exception as e: + logger.error(f"更新账户资金信息时出错: {str(e)}") + +def update_account_funds_once(xt_trader, acc, logger): + """单次更新账户资金信息到数据库（异步执行，不阻塞回调函数）""" + # 在后台线程中执行，避免阻塞行情回调 + thread = threading.Thread( + target=_update_account_funds_sync, + args=(xt_trader, acc, logger), + daemon=True + ) + thread.start() + def update_account_funds_periodically(xt_trader, acc, logger): - """每小时更新账户资金信息到数据库""" + """每小时更新账户资金信息到数据库（用于后台线程，包含循环）""" while True: try: # 获取账户资金信息 account_info = xt_trader.query_stock_asset(acc) if account_info: - # 计算相关数据 available_cash = getattr(account_info, 'm_dCash', 0.0) frozen_cash = getattr(account_info, 'm_dFrozenCash', 0.0) @@ -421,7 +482,7 @@ def update_account_funds_periodically(xt_trader, acc, logger): db_manager = initialize_database_manager(logger) success = db_manager.update_account_funds( account_id=acc.account_id, - account_type="acc.account_type", + account_type=acc.account_type, # 修复：去掉引号 total_asset=total_asset, available_cash=available_cash, frozen_cash=frozen_cash, diff --git a/src/app.py b/src/app.py index 155ae61..21cc589 100644 --- a/src/app.py +++ b/src/app.py @@ -46,6 +46,7 @@ from src.valuation_analysis.index_analyzer import IndexAnalyzer # 导入股票日线数据采集器 from src.scripts.stock_daily_data_collector import collect_stock_daily_data, collect_stock_daily_data_optimized from src.scripts.stock_daily_data_collector_v2 import collect_stock_daily_data_v2 +from src.scripts.stock_daily_data_collector_v3 import collect_stock_daily_data_v3 from valuation_analysis.financial_analysis import FinancialAnalyzer from src.valuation_analysis.stock_price_collector import StockPriceCollector @@ -53,6 +54,23 @@ from src.quantitative_analysis.batch_stock_price_collector import fetch_and_stor from src.quantitative_analysis.hk_stock_price_collector import fetch_and_store_hk_stock_data from src.quantitative_analysis.momentum_analysis import MomentumAnalyzer +# 导入Tushare实时行情采集器 +from src.tushare_scripts.realtime_quote_collector import collect_realtime_quote +from src.scripts.config import TUSHARE_TOKEN + +# 导入Tushare港股日线数据采集器 +from src.tushare_scripts.hk_daily_data_collector import collect_hk_daily_data + +# 导入Tushare美股日线数据采集器 +from src.tushare_scripts.us_daily_data_collector import collect_us_daily_data + +# 导入Tushare其他数据采集器 +from src.tushare_scripts.auction_data_collector import collect_auction_data +from src.tushare_scripts.chip_data_collector import collect_chip_data +from src.tushare_scripts.chip_distribution_collector import collect_chip_distribution +from src.tushare_scripts.stock_factor_collector import collect_stock_factor +from src.tushare_scripts.stock_factor_pro_collector import collect_stock_factor_pro + # 设置日志 logging.basicConfig( level=logging.INFO, @@ -206,59 +224,279 @@ def update_stock_realtime_price(): @app.route('/scheduler/stockDaily/collection', methods=['GET']) def run_stock_daily_collection1(): - """执行股票日线数据采集任务 下午3点四十开始""" + """执行股票日线数据采集任务（使用Tushare V3版本） 下午3点四十开始""" try: - logger.info("开始执行股票日线数据采集") - # 获取当天日期 - today = datetime.now().strftime('%Y-%m-%d') - - # 定义数据库连接地址 - db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' - # collect_stock_daily_data(db_url, today) - collect_stock_daily_data_v2(db_url) - except Exception as e: - logger.error(f"启动股票日线数据采集任务失败: {str(e)}") - return jsonify({ - "status": "success" - }), 200 - -@app.route('/scheduler/stockDailyHK/collection', methods=['GET']) -def run_stock_daily_collection2(): - """执行股票日线数据采集任务 下午4点开始""" - try: - logger.info("开始执行股票日线数据采集") - # 获取当天日期 - today = datetime.now().strftime('%Y-%m-%d') - - # 定义数据库连接地址 - db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' - collect_stock_daily_data_optimized(db_url, today) - except Exception as e: - logger.error(f"启动股票日线数据采集任务失败: {str(e)}") - return jsonify({ - "status": "success" - }), 200 - - -@app.route('/scheduler/usStockDaily/collection', methods=['GET']) -def run_us_stock_daily_collection(): - """执行美股日线数据采集任务""" - try: - logger.info("开始执行美股日线数据采集") + logger.info("开始执行股票日线数据采集（V3-Tushare）") # 获取当天日期 today = datetime.now().strftime('%Y-%m-%d') # 定义数据库连接地址 db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' - # 导入美股采集函数 + # V1版本（已废弃，使用雪球数据源） + # collect_stock_daily_data(db_url, today) + + # V2版本（已废弃，使用雪球+东方财富数据源） + # collect_stock_daily_data_v2(db_url) + + # V3版本（推荐使用，基于Tushare官方API，稳定可靠） + + collect_stock_daily_data_v3( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='daily', # 每日增量采集 + date=today, + table_name='gp_day_data', + basic_table_name='gp_day_basic' + ) + + logger.info("股票日线数据采集完成（V3-Tushare）") + except Exception as e: + logger.error(f"启动股票日线数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + return jsonify({ + "status": "success", + "message": "股票日线数据采集完成（V3-Tushare）" + }), 200 + +@app.route('/scheduler/stockDailyHK/collection', methods=['GET']) +def run_stock_daily_collection2(): + """执行港股日线数据采集任务（使用Tushare数据源）下午5点开始""" + try: + logger.info("开始执行港股日线数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 旧版本（已废弃，使用其他数据源） + collect_stock_daily_data_optimized(db_url, today) + + # 新版本（推荐使用，基于Tushare官方API，稳定可靠） + # collect_hk_daily_data( + # db_url=db_url, + # tushare_token=TUSHARE_TOKEN, + # mode='daily', # 每日增量采集 + # date=today + # ) + + logger.info("港股日线数据采集完成") + return jsonify({ + "status": "success", + "message": "港股日线数据采集完成" + }), 200 + except Exception as e: + logger.error(f"启动港股日线数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/usStockDaily/collection', methods=['GET']) +def run_us_stock_daily_collection(): + """执行美股日线数据采集任务（使用Tushare数据源）每天早上6点后执行""" + try: + logger.info("开始执行美股日线数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 旧版本（已废弃，使用其他数据源） from src.quantitative_analysis.us_stock_daily_data_collector_v2 import collect_us_stock_daily_data_v2 collect_us_stock_daily_data_v2(db_url) + + # 新版本（推荐使用，基于Tushare官方API，稳定可靠） + # collect_us_daily_data( + # db_url=db_url, + # tushare_token=TUSHARE_TOKEN, + # mode='daily', # 每日增量采集 + # date=today + # ) + + logger.info("美股日线数据采集完成") + return jsonify({ + "status": "success", + "message": "美股日线数据采集完成" + }), 200 except Exception as e: logger.error(f"启动美股日线数据采集任务失败: {str(e)}") - return jsonify({ - "status": "success" - }), 200 + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/auctionData/collection', methods=['GET']) +def run_auction_data_collection(): + """执行股票集合竞价数据采集任务（使用Tushare数据源）每天18后执行""" + try: + logger.info("开始执行股票集合竞价数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 采集开盘和收盘集合竞价数据 + collect_auction_data( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='daily', # 每日增量采集 + date=today + ) + logger.info("股票集合竞价数据采集完成（Tushare）") + return jsonify({ + "status": "success", + "message": "股票集合竞价数据采集完成（Tushare数据源）" + }), 200 + except Exception as e: + logger.error(f"启动股票集合竞价数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/chipData/collection', methods=['GET']) +def run_chip_data_collection(): + """执行每日筹码及胜率数据采集任务（使用Tushare数据源）每天18后执行""" + try: + logger.info("开始执行每日筹码及胜率数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 采集每日筹码及胜率数据 + collect_chip_data( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='daily', # 每日增量采集 + date=today + ) + + logger.info("每日筹码及胜率数据采集完成（Tushare）") + return jsonify({ + "status": "success", + "message": "每日筹码及胜率数据采集完成（Tushare数据源）" + }), 200 + except Exception as e: + logger.error(f"启动每日筹码及胜率数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/chipDistribution/collection', methods=['GET']) +def run_chip_distribution_collection(): + """执行每日筹码分布数据采集任务（使用Tushare数据源）每天17:30后执行""" + try: + logger.info("开始执行每日筹码分布数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://fac_pattern:Chlry$%.8pattern@192.168.16.150:3306/factordb_mysql' + + # 采集每日筹码分布数据（按年分表） + collect_chip_distribution( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='full' + ) + # collect_chip_distribution( + # db_url=db_url, + # tushare_token=TUSHARE_TOKEN, + # mode='daily', # 每日增量采集 + # date=today, + # batch_size=100 # 每100只股票批量入库一次 + # ) + + logger.info("每日筹码分布数据采集完成（Tushare）") + return jsonify({ + "status": "success", + "message": "每日筹码分布数据采集完成（Tushare数据源）" + }), 200 + except Exception as e: + logger.error(f"启动每日筹码分布数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/stockFactor/collection', methods=['GET']) +def run_stock_factor_collection(): + """执行股票技术因子数据采集任务（使用Tushare数据源）每天18:00后执行""" + try: + logger.info("开始执行股票技术因子数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 采集股票技术因子数据 + collect_stock_factor( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='daily', # 每日增量采集 + date=today + ) + + logger.info("股票技术因子数据采集完成（Tushare）") + return jsonify({ + "status": "success", + "message": "股票技术因子数据采集完成（Tushare数据源）" + }), 200 + except Exception as e: + logger.error(f"启动股票技术因子数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 + + +@app.route('/scheduler/stockFactorPro/collection', methods=['GET']) +def run_stock_factor_pro_collection(): + """执行股票技术因子专业版数据采集任务（使用Tushare数据源）每天18:00后执行""" + try: + logger.info("开始执行股票技术因子专业版数据采集（Tushare）") + # 获取当天日期 + today = datetime.now().strftime('%Y-%m-%d') + + # 定义数据库连接地址 + db_url = 'mysql+pymysql://fac_pattern:Chlry$%.8pattern@192.168.16.150:3306/factordb_mysql' + + # 采集股票技术因子专业版数据 + collect_stock_factor_pro( + db_url=db_url, + tushare_token=TUSHARE_TOKEN, + mode='daily', # 每日增量采集 + date=today + ) + + logger.info("股票技术因子专业版数据采集完成（Tushare）") + return jsonify({ + "status": "success", + "message": "股票技术因子专业版数据采集完成（Tushare数据源）" + }), 200 + except Exception as e: + logger.error(f"启动股票技术因子专业版数据采集任务失败: {str(e)}") + return jsonify({ + "status": "error", + "message": str(e) + }), 500 @app.route('/scheduler/rzrq/collection', methods=['GET']) @@ -1421,11 +1659,21 @@ def comprehensive_analysis(): # 获取企业市值信息 market_value_results = {} try: - from src.valuation_analysis.stock_price_collector import StockPriceCollector - price_collector = StockPriceCollector() + # 旧版本（已废弃，使用东方财富接口） + # from src.valuation_analysis.stock_price_collector import StockPriceCollector + # price_collector = StockPriceCollector() + # for stock_code in input_stock_codes: + # price_data = price_collector.get_stock_price_data(stock_code) + # if price_data and 'total_market_value' in price_data: + # market_value_results[stock_code] = price_data['total_market_value'] + # else: + # market_value_results[stock_code] = None + + # 新版本（推荐使用，从gp_day_data和gp_day_basic表获取） + from src.scripts.stock_daily_data_collector_v3 import get_stock_price_data_from_db for stock_code in input_stock_codes: - price_data = price_collector.get_stock_price_data(stock_code) + price_data = get_stock_price_data_from_db(stock_code) if price_data and 'total_market_value' in price_data: market_value_results[stock_code] = price_data['total_market_value'] else: @@ -2892,7 +3140,7 @@ def test_mongo_structure(): @app.route('/api/stock/real_time_price', methods=['GET']) def get_real_time_price(): - """获取股票实时价格接口 + """获取股票实时价格接口（从数据库获取最新日线数据） 参数: - stock_code: 股票代码（必填） @@ -2926,20 +3174,22 @@ def get_real_time_price(): "status": "error", "message": "缺少必要参数: stock_code" }), 400 - - # 导入股票价格采集器 - from src.valuation_analysis.stock_price_collector import StockPriceCollector - # 创建采集器实例 - collector = StockPriceCollector() + # 旧版本（已废弃，使用东方财富接口） + # from src.valuation_analysis.stock_price_collector import StockPriceCollector + # collector = StockPriceCollector() + # price_data = collector.get_stock_price_data(stock_code) - # 获取实时价格数据 - price_data = collector.get_stock_price_data(stock_code) + # 新版本（推荐使用，从gp_day_data和gp_day_basic表获取） + from src.scripts.stock_daily_data_collector_v3 import get_stock_price_data_from_db + + # 获取价格数据 + price_data = get_stock_price_data_from_db(stock_code) if not price_data: return jsonify({ "status": "error", - "message": f"获取股票 {stock_code} 的实时价格失败" + "message": f"获取股票 {stock_code} 的价格数据失败，可能该股票数据尚未采集" }), 404 # 构建响应数据 @@ -2955,7 +3205,12 @@ def get_real_time_price(): "low": price_data.get('low'), "open": price_data.get('open'), "pre_close": price_data.get('pre_close'), - "update_time": price_data.get('update_time') + "update_time": price_data.get('update_time'), + # 额外返回估值指标 + "pe": price_data.get('pe'), + "pb": price_data.get('pb'), + "ps": price_data.get('ps'), + "total_market_value": price_data.get('total_market_value') } return jsonify({ @@ -3191,12 +3446,18 @@ def get_momentum_by_plate(): @app.route('/scheduler/batch_stock_price/collection', methods=['GET']) def run_batch_stock_price_collection(): - """批量采集A股行情并保存到数据库""" + """批量采集A股实时行情并保存到Redis（使用Tushare数据源）""" try: - fetch_and_store_stock_data(use_proxy=True) - return jsonify({"status": "success", "message": "批量采集A股行情并保存到数据库成功"}) + # 旧版雪球数据源（已废弃，不稳定） + # fetch_and_store_stock_data(use_proxy=True) + + # 新版Tushare数据源（推荐使用，稳定可靠） + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + collect_realtime_quote(db_url, TUSHARE_TOKEN, batch_size=49) + + return jsonify({"status": "success", "message": "批量采集A股实时行情并保存到Redis成功（Tushare数据源）"}) except Exception as e: - logger.error(f"批量采集A股行情失败: {str(e)}") + logger.error(f"批量采集A股实时行情失败: {str(e)}") return jsonify({"status": "error", "message": str(e)}) @app.route('/scheduler/batch_hk_stock_price/collection', methods=['GET']) diff --git a/src/quantitative_analysis/data/tech_fundamental_factor_growth_20250731_1906.csv b/src/quantitative_analysis/data/tech_fundamental_factor_growth_20250731_1906.csv new file mode 100644 index 0000000..ab18bc0 --- /dev/null +++ b/src/quantitative_analysis/data/tech_fundamental_factor_growth_20250731_1906.csv @@ -0,0 +1,741 @@ 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(2024, 6), (2024, 3), + (2025, 9), (2025, 6), (2025, 3), (2024, 12), (2024, 9), (2024, 6), (2024, 3), (2023, 12), (2023, 9), (2023, 6), (2023, 3), (2022, 12), (2022, 9), (2022, 6), (2022, 3), (2021, 12), (2021, 9), (2021, 6), (2021, 3), - (2020, 12), (2020, 9), (2020, 6) + (2020, 12), (2020, 9) ] if i < len(base_quarters): diff --git a/src/quantitative_analysis/overlap_analyzer.py b/src/quantitative_analysis/overlap_analyzer.py index 77249fa..eea511a 100644 --- a/src/quantitative_analysis/overlap_analyzer.py +++ b/src/quantitative_analysis/overlap_analyzer.py @@ -597,7 +597,7 @@ def main(): analyzer = OverlapAnalyzer() # 示例：分析某只股票的重叠度 - stock_code = "300661.SZ" # 圣邦股份 + stock_code = "301308.SZ" # 圣邦股份 print(f"=== 股票重叠度分析：{stock_code} ===") diff --git a/src/quantitative_analysis/tech_fundamental_factor_strategy.py b/src/quantitative_analysis/tech_fundamental_factor_strategy.py index f36b69d..2fa204f 100644 --- a/src/quantitative_analysis/tech_fundamental_factor_strategy.py +++ b/src/quantitative_analysis/tech_fundamental_factor_strategy.py @@ -189,7 +189,7 @@ class TechFundamentalFactorStrategy: logger.info(f"计算 {len(stock_codes)} 只股票的通用因子") results = [] - latest_date = "2025-06-30" # 最新季度数据 + latest_date = "2025-09-30" # 最新季度数据 annual_date = "2024-12-31" # 年报数据 for stock_code in stock_codes: @@ -246,7 +246,7 @@ class TechFundamentalFactorStrategy: logger.info(f"计算 {len(stock_codes)} 只成长期股票的特色因子") results = [] - latest_date = "2025-06-30" # 使用最新数据 + latest_date = "2025-09-30" # 使用最新数据 annual_date = "2024-12-31" # 使用年度数据 for stock_code in stock_codes: @@ -305,7 +305,7 @@ class TechFundamentalFactorStrategy: try: logger.info(f"计算 {len(stock_codes)} 只成熟期股票的特色因子") - latest_date = "2025-06-30" # 使用最新数据 + latest_date = "2025-09-30" # 使用最新数据 # 在循环外获取全A股PB和ROE数据，避免重复查询 logger.info("获取全A股PB数据...") @@ -669,7 +669,7 @@ def main(): try: print("=== 科技主题基本面因子选股策略 ===") print("数据说明:") - print("- 毛利率、净利润增长率等：使用最新数据 (2025-06-30)") + print("- 毛利率、净利润增长率等：使用最新数据 (2025-09-30)") print("- 供应商客户集中度、折旧摊销、研发费用：使用年报数据 (2024-12-31)") print() diff --git a/src/quantitative_analysis/us_batch_stock_price_collector.py b/src/quantitative_analysis/us_batch_stock_price_collector.py index 7f85e71..346ad6a 100644 --- a/src/quantitative_analysis/us_batch_stock_price_collector.py +++ b/src/quantitative_analysis/us_batch_stock_price_collector.py @@ -298,3 +298,6 @@ if __name__ == '__main__': + + + diff --git a/src/scripts/config.py b/src/scripts/config.py index 32d5bde..c11aa72 100644 --- a/src/scripts/config.py +++ b/src/scripts/config.py @@ -11,7 +11,7 @@ XUEQIU_HEADERS = { 'Accept-Encoding': 'gzip, deflate, br, zstd', 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8', 'Client-Version': 'v2.44.75', - 'Cookie': 'cookiesu=811743062689927; device_id=33fa3c7fca4a65f8f4354e10ed6b7470; smidV2=20250327160437f244626e8b47ca2a7992f30f389e4e790074ae48656a22f10; HMACCOUNT=8B64A2E3C307C8C0; s=c611ttmqlj; xq_is_login=1; u=8493411634; bid=4065a77ca57a69c83405d6e591ab5449_m8r2nhs8; 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file + } + +# Tushare配置 +TUSHARE_TOKEN = 'ecac9722ee695a9f67a1dfb98e0ba71c72f088714795b7609f0e9390' # 请替换为您的实际Tushare Token \ No newline at end of file diff --git a/src/scripts/stock_daily_data_collector.py b/src/scripts/stock_daily_data_collector.py index 44ee070..9f797e1 100644 --- a/src/scripts/stock_daily_data_collector.py +++ b/src/scripts/stock_daily_data_collector.py @@ -1,6 +1,5 @@ # coding:utf-8 -import requests import pandas as pd from sqlalchemy import create_engine, text from datetime import datetime, timedelta @@ -25,9 +24,9 @@ class StockDailyDataCollector: def fetch_all_stock_codes(self): # 从gp_code_all获取股票代码 - query_all = "SELECT gp_code FROM gp_code_all" - df_all = pd.read_sql(query_all, self.engine) - codes_all = df_all['gp_code'].tolist() + # query_all = "SELECT gp_code FROM gp_code_all" + # df_all = pd.read_sql(query_all, self.engine) + # codes_all = df_all['gp_code'].tolist() # 从gp_code_zs获取股票代码 query_zs = "SELECT gp_code FROM gp_code_zs" @@ -42,7 +41,7 @@ class StockDailyDataCollector: # 合并去重 # all_codes = list(set(codes_all + codes_zs + codes_hk)) all_codes = list(set(codes_zs + codes_hk)) - print(f"获取到股票代码: {len(codes_all)} 个来自gp_code_all, {len(codes_zs)}个来自gp_code_zs, {len(codes_hk)}个来自gp_code_hk, 去重后共{len(all_codes)}个") + print(f"获取到股票代码: {len(codes_zs)}个来自gp_code_zs, {len(codes_hk)}个来自gp_code_hk, 去重后共{len(all_codes)}个") return all_codes def fetch_daily_stock_data(self, symbol, begin, count=-1): diff --git a/src/scripts/stock_daily_data_collector_v3.py b/src/scripts/stock_daily_data_collector_v3.py new file mode 100644 index 0000000..3910ce7 --- /dev/null +++ b/src/scripts/stock_daily_data_collector_v3.py @@ -0,0 +1,994 @@ +# coding:utf-8 + +""" +股票日线数据采集器V3版本 - 基于Tushare数据源 +作者: AI Assistant +创建时间: 2025-11-03 +功能: +1. 从Tushare采集A股日线行情数据 +2. 从Tushare采集每日指标数据 +3. 整合两个数据源并存储到gp_day_data表 +4. 支持历史数据采集(1800天) +5. 支持增量更新(当日数据) +6. 自动处理除权除息问题 +每天15~16点左右更新 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import gc +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append(project_root) + +# 导入配置 +from src.scripts.config import TUSHARE_TOKEN + + +class StockDailyDataCollectorV3: + """股票日线数据采集器V3版本 - 基于Tushare""" + + def __init__(self, db_url, tushare_token, table_name='gp_day_data', basic_table_name='gp_day_basic'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 日线数据表名，默认为gp_day_data（不影响原有业务） + basic_table_name: 每日指标数据表名，默认为gp_day_basic + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + + # 目标数据表名 + self.table_name = table_name + self.basic_table_name = basic_table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print(f"股票日线数据采集器V3 - Tushare版本") + print(f"日线数据表: {self.table_name}") + print(f"指标数据表: {self.basic_table_name}") + print("=" * 60) + + def convert_stock_code_to_tushare(self, symbol): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001、BJ430047 等 + Tushare格式：600000.SH、000001.SZ、430047.BJ 等 + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + elif symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('BJ'): + return f"{symbol[2:]}.BJ" + else: + # 如果已经是Tushare格式，直接返回 + return symbol + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ、430047.BJ 等 + 数据库格式：SH600000、SZ000001、BJ430047 等 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def fetch_all_stock_codes(self): + """ + 从数据库获取所有需要采集的A股股票代码 + 仅从gp_code_all表获取，指数(gp_code_zs)和港股(gp_code_hk)由其他脚本处理 + + Returns: + 股票代码列表 + """ + try: + # 只从gp_code_all获取A股股票代码 + query = "SELECT gp_code FROM gp_code_all" + df = pd.read_sql(query, self.engine) + codes = df['gp_code'].tolist() + + print(f"获取到A股股票代码: {len(codes)}个") + print(f"注意: 指数和港股由其他采集脚本处理") + + return codes + + except Exception as e: + print(f"获取股票代码失败: {e}") + return [] + + def fetch_stock_daily_data(self, ts_code=None, start_date=None, end_date=None, trade_date=None): + """ + 从Tushare获取日线行情数据 + API文档: https://tushare.pro/document/2?doc_id=27 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + start_date: 开始日期，格式YYYYMMDD（用于历史数据查询） + end_date: 结束日期，格式YYYYMMDD（用于历史数据查询） + trade_date: 交易日期，格式YYYYMMDD（用于单日查询，优先级高于start_date/end_date） + + Returns: + 日线行情DataFrame + """ + try: + if trade_date: + # 单日查询：获取指定日期的全市场或单只股票数据 + df = self.pro.daily(ts_code=ts_code, trade_date=trade_date) + else: + # 范围查询：用于历史数据采集 + df = self.pro.daily(ts_code=ts_code, start_date=start_date, end_date=end_date) + + time.sleep(0.1) # API限频控制 + return df + except Exception as e: + print(f"获取日线行情失败: {e}") + return pd.DataFrame() + + def fetch_stock_daily_basic(self, ts_code=None, start_date=None, end_date=None, trade_date=None): + """ + 从Tushare获取每日指标数据 + API文档: https://tushare.pro/document/2?doc_id=32 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + start_date: 开始日期，格式YYYYMMDD（用于历史数据查询） + end_date: 结束日期，格式YYYYMMDD（用于历史数据查询） + trade_date: 交易日期，格式YYYYMMDD（用于单日查询，优先级高于start_date/end_date） + + Returns: + 每日指标DataFrame + """ + try: + if trade_date: + # 单日查询：获取指定日期的全市场或单只股票数据 + df = self.pro.daily_basic( + ts_code=ts_code, + trade_date=trade_date, + fields='ts_code,trade_date,turnover_rate,turnover_rate_f,volume_ratio,pe,pe_ttm,pb,ps,ps_ttm,dv_ratio,dv_ttm,total_share,float_share,free_share,total_mv,circ_mv' + ) + else: + # 范围查询：用于历史数据采集 + df = self.pro.daily_basic( + ts_code=ts_code, + start_date=start_date, + end_date=end_date, + fields='ts_code,trade_date,turnover_rate,turnover_rate_f,volume_ratio,pe,pe_ttm,pb,ps,ps_ttm,dv_ratio,dv_ttm,total_share,float_share,free_share,total_mv,circ_mv' + ) + + time.sleep(0.1) # API限频控制 + return df + except Exception as e: + print(f"获取每日指标失败: {e}") + return pd.DataFrame() + + def merge_and_transform_data(self, df_daily, df_basic, symbol=None): + """ + 合并日线行情和每日指标数据，并转换为数据库格式 + + Args: + df_daily: 日线行情DataFrame + df_basic: 每日指标DataFrame + symbol: 数据库格式的股票代码（单只股票时使用），为None时从ts_code转换 + + Returns: + 转换后的DataFrame + """ + if df_daily.empty: + return pd.DataFrame() + + # 合并两个数据源 + if not df_basic.empty: + df = pd.merge( + df_daily, + df_basic[['ts_code', 'trade_date', 'turnover_rate', 'pe_ttm', 'pb', 'ps']], + on=['ts_code', 'trade_date'], + how='left' + ) + else: + df = df_daily.copy() + # 如果没有指标数据，添加空列 + df['turnover_rate'] = None + df['pe_ttm'] = None + df['pb'] = None + df['ps'] = None + + # 创建符合gp_day_data表结构的DataFrame + result_df = pd.DataFrame() + + # 字段映射和转换 + result_df['timestamp'] = pd.to_datetime(df['trade_date'].values, format='%Y%m%d') + result_df['volume'] = df['vol'].values * 100 # Tushare的vol单位是手(100股)，需要转换为股 + result_df['open'] = df['open'].values + result_df['high'] = df['high'].values + result_df['low'] = df['low'].values + result_df['close'] = df['close'].values + result_df['chg'] = df['change'].values + result_df['percent'] = df['pct_chg'].values + result_df['turnoverrate'] = df['turnover_rate'].values + result_df['amount'] = df['amount'].values * 1000 # Tushare的amount单位是千元，需要转换为元 + result_df['pb'] = df['pb'].values + result_df['pe'] = df['pe_ttm'].values + result_df['ps'] = df['ps'].values + result_df['pre_close'] = df['pre_close'].values + + # symbol字段处理：如果传入了symbol则使用，否则从ts_code转换 + if symbol is not None: + result_df['symbol'] = symbol # 单只股票：标量值会自动广播到所有行 + else: + # 批量数据：从ts_code转换为数据库格式 + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + + # 按时间排序 + result_df = result_df.sort_values('timestamp').reset_index(drop=True) + + return result_df + + def transform_basic_data(self, df_daily, df_basic, symbol=None): + """ + 转换每日指标数据为数据库格式 + + Args: + df_daily: 日线行情DataFrame（用于获取收盘价） + df_basic: 每日指标DataFrame + symbol: 数据库格式的股票代码（单只股票时使用），为None时从ts_code转换 + + Returns: + 转换后的DataFrame + """ + if df_basic.empty: + return pd.DataFrame() + + # 如果有日线数据，合并获取收盘价 + if not df_daily.empty: + df = pd.merge( + df_basic, + df_daily[['ts_code', 'trade_date', 'close']], + on=['ts_code', 'trade_date'], + how='left' + ) + else: + df = df_basic.copy() + df['close'] = None + + # 创建符合gp_day_basic表结构的DataFrame + basic_df = pd.DataFrame() + + # 字段映射和转换 + basic_df['timestamp'] = pd.to_datetime(df['trade_date'].values, format='%Y%m%d') + basic_df['close'] = df['close'].values + basic_df['turnover_rate'] = df['turnover_rate'].values + basic_df['turnover_rate_f'] = df.get('turnover_rate_f', pd.Series([None] * len(df))).values + basic_df['volume_ratio'] = df.get('volume_ratio', pd.Series([None] * len(df))).values + basic_df['pe'] = df.get('pe', pd.Series([None] * len(df))).values + basic_df['pe_ttm'] = df['pe_ttm'].values + basic_df['pb'] = df['pb'].values + basic_df['ps'] = df['ps'].values + basic_df['ps_ttm'] = df.get('ps_ttm', pd.Series([None] * len(df))).values + basic_df['dv_ratio'] = df.get('dv_ratio', pd.Series([None] * len(df))).values + basic_df['dv_ttm'] = df.get('dv_ttm', pd.Series([None] * len(df))).values + basic_df['total_share'] = df.get('total_share', pd.Series([None] * len(df))).values + basic_df['float_share'] = df.get('float_share', pd.Series([None] * len(df))).values + basic_df['free_share'] = df.get('free_share', pd.Series([None] * len(df))).values + basic_df['total_mv'] = df.get('total_mv', pd.Series([None] * len(df))).values + basic_df['circ_mv'] = df.get('circ_mv', pd.Series([None] * len(df))).values + + # symbol字段处理 + if symbol is not None: + basic_df['symbol'] = symbol + else: + basic_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + + # 按时间排序 + basic_df = basic_df.sort_values('timestamp').reset_index(drop=True) + + return basic_df + + def fetch_historical_data(self, symbol, days=1800): + """ + 获取单只股票近N天的历史数据 + + Args: + symbol: 数据库格式的股票代码 + days: 获取的天数，默认1800天 + + Returns: + 是否成功 + """ + try: + # 转换股票代码格式 + ts_code = self.convert_stock_code_to_tushare(symbol) + + # 计算日期范围 + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d') + + # 获取日线行情数据 + df_daily = self.fetch_stock_daily_data(ts_code, start_date, end_date) + if df_daily.empty: + print(f"未获取到{symbol}的日线行情数据") + return False + + # 获取每日指标数据 + df_basic = self.fetch_stock_daily_basic(ts_code, start_date, end_date) + + # 合并并转换数据 + df_result = self.merge_and_transform_data(df_daily, df_basic, symbol) + + if df_result.empty: + print(f"数据转换失败: {symbol}") + return False + + # 保存到日线数据表（使用配置的表名） + df_result.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{symbol}的历史数据到{self.table_name}表，共{len(df_result)}条记录") + + # 转换并保存每日指标数据 + basic_result = self.transform_basic_data(df_daily, df_basic, symbol) + if not basic_result.empty: + basic_result.to_sql(self.basic_table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{symbol}的指标数据到{self.basic_table_name}表，共{len(basic_result)}条记录") + + return True + + except Exception as e: + print(f"获取{symbol}历史数据失败: {e}") + return False + + def fetch_today_data(self, symbol, target_date=None): + """ + 获取单只股票指定日期的数据 + + Args: + symbol: 数据库格式的股票代码 + target_date: 目标日期，格式YYYY-MM-DD，默认为今天 + + Returns: + 转换后的DataFrame，失败返回空DataFrame + """ + try: + # 转换股票代码格式 + ts_code = self.convert_stock_code_to_tushare(symbol) + + # 确定目标日期 + if target_date: + # 将YYYY-MM-DD格式转换为YYYYMMDD + date_obj = datetime.strptime(target_date, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + else: + # 默认为今天 + trade_date = datetime.now().strftime('%Y%m%d') + + # 获取日线行情数据（使用同一天作为起止日期） + df_daily = self.fetch_stock_daily_data(ts_code, trade_date, trade_date) + if df_daily.empty: + return pd.DataFrame() + + # 获取每日指标数据（使用同一天作为起止日期） + df_basic = self.fetch_stock_daily_basic(ts_code, trade_date, trade_date) + + # 合并并转换数据 + df_result = self.merge_and_transform_data(df_daily, df_basic, symbol) + + return df_result + + except Exception as e: + print(f"获取{symbol}在{target_date or '今日'}的数据失败: {e}") + return pd.DataFrame() + + def check_ex_rights_before_save(self, df, date_str): + """ + 在保存数据库之前检查除权情况，返回除权股票列表和除权日志数据 + + Args: + df: 待保存的日线数据DataFrame + date_str: 当前采集日期，格式YYYY-MM-DD + + Returns: + tuple: (除权股票列表, 除权日志数据列表) + """ + print("正在检查除权情况...") + + ex_rights_stocks = [] + ex_rights_log_data = [] + + for _, row in tqdm(df.iterrows(), total=len(df), desc="检查除权"): + symbol = row['symbol'] + current_pre_close = row['pre_close'] + + # 如果pre_close为空，跳过 + if pd.isna(current_pre_close): + continue + + # 查询数据库中该股票的最近两条收盘价记录 + query = text(""" + SELECT `close`, `timestamp` FROM {table_name} + WHERE symbol = :symbol + ORDER BY `timestamp` DESC + LIMIT 2 + """.format(table_name=self.table_name)) + + try: + with self.engine.connect() as conn: + results = conn.execute(query, {"symbol": symbol}).fetchall() + + if results: + # 检查最新记录是否为今天的数据 + latest_record = results[0] + latest_timestamp = latest_record[1] + latest_date_str = latest_timestamp.strftime('%Y-%m-%d') + + if latest_date_str == date_str and len(results) > 1: + # 如果最新记录是今天的，且有第二条记录，则用第二条记录比较 + db_last_close = float(results[1][0]) + else: + # 如果最新记录不是今天的，或者只有一条记录，则用最新记录比较 + db_last_close = float(results[0][0]) + + # 比较pre_close和数据库中的收盘价 + if abs(db_last_close - current_pre_close) > 0.001: + print(f"发现除权股票: {symbol}, 数据库收盘价: {db_last_close}, 当前昨收价: {current_pre_close}") + ex_rights_stocks.append(symbol) + + # 收集除权日志数据 + ex_rights_log_data.append({ + 'stock_code': symbol, + 'change_date': date_str, + 'before_price': db_last_close, + 'after_price': current_pre_close, + 'update_time': datetime.now() + }) + except Exception as e: + # 如果查询失败（比如股票是新上市的），跳过 + continue + + if ex_rights_stocks: + print(f"检测到 {len(ex_rights_stocks)} 只除权股票: {ex_rights_stocks}") + else: + print("未发现除权股票") + + return ex_rights_stocks, ex_rights_log_data + + def save_ex_rights_log(self, log_data): + """ + 将除权日志保存到数据库 + + Args: + log_data: 除权日志数据列表 + """ + if not log_data: + return + + print(f"正在保存 {len(log_data)} 条除权日志到gp_ex_rights_log表...") + try: + df = pd.DataFrame(log_data) + df.to_sql('gp_ex_rights_log', self.engine, if_exists='append', index=False) + print("除权日志保存成功") + except Exception as e: + print(f"保存除权日志失败: {e}") + + def handle_ex_rights_stocks(self, ex_rights_stocks, ex_rights_log_data, days=1800): + """ + 处理除权股票：保存日志、删除历史日线数据并重新获取 + 注意：每日指标数据不需要重新获取，因为除权前后这些基础数据是一致的 + + Args: + ex_rights_stocks: 除权股票列表 + ex_rights_log_data: 除权日志数据列表 + days: 重新获取的历史天数，默认1800天 + """ + if not ex_rights_stocks: + return + + print("=" * 60) + print("开始处理除权股票...") + print("=" * 60) + + # 1. 保存除权日志 + if ex_rights_log_data: + self.save_ex_rights_log(ex_rights_log_data) + + # 2. 重新获取历史日线数据（不包括每日指标数据） + print(f"开始处理 {len(ex_rights_stocks)} 只除权股票，重新获取历史日线数据...") + + success_count = 0 + fail_count = 0 + + for symbol in tqdm(ex_rights_stocks, desc="处理除权股票"): + try: + # 转换股票代码格式 + ts_code = self.convert_stock_code_to_tushare(symbol) + + # 删除该股票的所有历史日线数据（仅删除日线数据，不删除指标数据） + delete_query = text(f"DELETE FROM {self.table_name} WHERE symbol = :symbol") + with self.engine.begin() as conn: + result = conn.execute(delete_query, {"symbol": symbol}) + print(f"已删除 {symbol} 的 {result.rowcount} 条历史日线数据") + + # 计算日期范围 + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d') + + # 重新获取历史日线数据 + df_daily = self.fetch_stock_daily_data(ts_code, start_date, end_date) + if df_daily.empty: + print(f"未获取到{symbol}的日线行情数据") + fail_count += 1 + continue + + # 获取每日指标数据（用于合并，获取turnover_rate等字段） + df_basic = self.fetch_stock_daily_basic(ts_code, start_date, end_date) + + # 合并并转换数据 + df_result = self.merge_and_transform_data(df_daily, df_basic, symbol) + + if df_result.empty: + print(f"数据转换失败: {symbol}") + fail_count += 1 + continue + + # 保存到日线数据表（只保存日线数据，不更新指标数据） + df_result.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功重新获取 {symbol} 的历史日线数据，共{len(df_result)}条记录") + success_count += 1 + + except Exception as e: + print(f"处理除权股票 {symbol} 失败: {e}") + fail_count += 1 + + print("\n" + "=" * 60) + print("除权股票处理完成") + print("=" * 60) + print(f"成功: {success_count}只") + print(f"失败: {fail_count}只") + print("=" * 60) + + def run_daily_collection(self, date=None): + """ + 执行每日增量数据采集（使用全市场批量获取方式，更高效） + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量数据采集（全市场批量获取）") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧数据（日线数据表和指标数据表） + # 使用timestamp范围查询代替DATE()函数，避免全表扫描，提升性能 + # 添加LENGTH(symbol) = 8限定条件，只删除A股数据（避免误删港股等其他数据） + delete_start = f"{date_str} 00:00:00" + delete_end = f"{date_str} 23:59:59" + + with self.engine.begin() as conn: + # 删除日线数据表的旧数据（只删除A股，symbol长度为8，如SH600000） + delete_query_day = text(f"DELETE FROM {self.table_name} WHERE `timestamp` >= :start_time AND `timestamp` <= :end_time AND LENGTH(symbol) = 8") + result_day = conn.execute(delete_query_day, {"start_time": delete_start, "end_time": delete_end}) + print(f"已删除{self.table_name}表中{date_str}的A股旧数据，共{result_day.rowcount}条") + + # 删除指标数据表的旧数据（只删除A股） + delete_query_basic = text(f"DELETE FROM {self.basic_table_name} WHERE `timestamp` >= :start_time AND `timestamp` <= :end_time AND LENGTH(symbol) = 8") + result_basic = conn.execute(delete_query_basic, {"start_time": delete_start, "end_time": delete_end}) + print(f"已删除{self.basic_table_name}表中{date_str}的A股旧数据，共{result_basic.rowcount}条") + + # 批量获取全市场日线数据（不传ts_code，获取所有股票） + print(f"正在获取{date_str}全市场日线行情数据...") + df_daily = self.fetch_stock_daily_data(trade_date=trade_date) + + if df_daily.empty: + print(f"{date_str}没有交易数据（可能是非交易日）") + return + + print(f"成功获取日线行情数据，共{len(df_daily)}只股票") + + # 批量获取全市场每日指标数据 + print(f"正在获取{date_str}全市场每日指标数据...") + df_basic = self.fetch_stock_daily_basic(trade_date=trade_date) + print(f"成功获取每日指标数据，共{len(df_basic)}只股票") + + # 合并并转换数据（不传symbol参数，自动从ts_code转换） + print("正在合并和转换数据...") + result_df = self.merge_and_transform_data(df_daily, df_basic) + + if result_df.empty: + print("数据转换失败") + return + + # 检查除权（保存前） + ex_rights_stocks, ex_rights_log_data = self.check_ex_rights_before_save(result_df, date_str) + + # 保存到日线数据表 + print(f"正在保存日线数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}只股票的日线数据") + + # 转换并保存每日指标数据 + print(f"正在保存指标数据到{self.basic_table_name}表...") + basic_result = self.transform_basic_data(df_daily, df_basic) + if not basic_result.empty: + basic_result.to_sql(self.basic_table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(basic_result)}只股票的指标数据") + else: + print("警告：指标数据为空") + + # 处理除权股票（保存后） + self.handle_ex_rights_stocks(ex_rights_stocks, ex_rights_log_data) + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日数据采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"成功采集: {len(result_df)}只股票") + print(f"除权处理: {len(ex_rights_stocks)}只股票") + print(f"日线数据表: {self.table_name}") + print(f"指标数据表: {self.basic_table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日数据采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + gc.collect() + + def run_historical_collection(self, days=1800): + """ + 执行历史数据批量采集 + + Args: + days: 获取的天数，默认1800天 + """ + print("=" * 60) + print(f"开始执行历史数据批量采集 - 近{days}天") + print("=" * 60) + + try: + # 获取所有股票代码 + stock_codes = self.fetch_all_stock_codes() + if not stock_codes: + print("未获取到股票代码，终止采集") + return + + # 统计信息 + success_count = 0 + failed_count = 0 + + # 遍历所有股票 + for symbol in tqdm(stock_codes, desc=f"采集近{days}天历史数据"): + try: + # 先删除该股票的历史数据（日线数据表和指标数据表） + with self.engine.begin() as conn: + # 删除日线数据 + delete_query_day = text(f"DELETE FROM {self.table_name} WHERE symbol = :symbol") + conn.execute(delete_query_day, {"symbol": symbol}) + + # 删除指标数据 + delete_query_basic = text(f"DELETE FROM {self.basic_table_name} WHERE symbol = :symbol") + conn.execute(delete_query_basic, {"symbol": symbol}) + + # 获取历史数据 + if self.fetch_historical_data(symbol, days): + success_count += 1 + else: + failed_count += 1 + + # 休息一下，避免API限频 + # time.sleep(0.2) + + except Exception as e: + print(f"处理{symbol}时发生错误: {e}") + failed_count += 1 + continue + + # 输出统计信息 + print("\n" + "=" * 60) + print("历史数据采集完成") + print("=" * 60) + print(f"采集天数: {days}") + print(f"总股票数: {len(stock_codes)}") + print(f"成功采集: {success_count}") + print(f"失败: {failed_count}") + print("=" * 60) + + except Exception as e: + print(f"历史数据采集失败: {e}") + finally: + self.engine.dispose() + gc.collect() + + def refetch_single_stock_history(self, symbol, days=1800): + """ + 重新获取单只股票的历史数据（用于除权处理） + + Args: + symbol: 数据库格式的股票代码 + days: 获取的天数，默认1800天 + + Returns: + 是否成功 + """ + try: + # 删除该股票的历史数据（日线数据表和指标数据表） + with self.engine.begin() as conn: + # 删除日线数据 + delete_query_day = text(f"DELETE FROM {self.table_name} WHERE symbol = :symbol") + conn.execute(delete_query_day, {"symbol": symbol}) + + # 删除指标数据 + delete_query_basic = text(f"DELETE FROM {self.basic_table_name} WHERE symbol = :symbol") + conn.execute(delete_query_basic, {"symbol": symbol}) + + print(f"已删除{self.table_name}和{self.basic_table_name}表中{symbol}的历史数据") + + # 重新获取历史数据 + success = self.fetch_historical_data(symbol, days) + if success: + print(f"成功重新获取{symbol}的历史数据") + else: + print(f"重新获取{symbol}的历史数据失败") + + return success + + except Exception as e: + print(f"重新获取{symbol}历史数据失败: {e}") + return False + + +def collect_stock_daily_data_v3(db_url, tushare_token, mode='daily', date=None, days=1800, table_name='gp_day_data', basic_table_name='gp_day_basic'): + """ + V3版本的股票日线数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'historical'=批量采集历史数据 + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + days: 历史数据天数（仅mode='historical'时有效），默认1800天 + table_name: 日线数据表名，默认为gp_day_data（不影响原有业务） + basic_table_name: 每日指标数据表名，默认为gp_day_basic + """ + collector = StockDailyDataCollectorV3(db_url, tushare_token, table_name, basic_table_name) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'historical': + collector.run_historical_collection(days) + else: + print(f"未知的采集模式: {mode}") + + +def get_stock_price_data_from_db(stock_code, db_url='mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj'): + """ + 从gp_day_data和gp_day_basic表获取股票最新价格数据 + 替代原StockPriceCollector.get_stock_price_data()方法 + + Args: + stock_code: 股票代码，支持多种格式（SH600000、600000.SH、600000等） + db_url: 数据库连接URL + + Returns: + dict: 包含股票实时价格信息的字典，格式与原接口兼容 + { + 'stock_code': '600000', + 'stock_name': '浦发银行', + 'current_price': 10.5, + 'change_percent': 2.5, + 'change_amount': 0.25, + 'volume': 1234567, + 'amount': 12345678.9, + 'high': 10.8, + 'low': 10.2, + 'open': 10.3, + 'pre_close': 10.25, + 'update_time': '2024-01-20 14:30:00', + 'total_market_value': 100000000.0, # 总市值（万元） + 'pe': 8.5, + 'pb': 0.6, + 'ps': 1.2 + } + """ + try: + # 统一股票代码格式为数据库格式（如SH600000） + db_code = _normalize_stock_code(stock_code) + + # 创建数据库连接 + engine = create_engine(db_url) + + # 从gp_day_data获取最新日线数据 + day_query = f""" + SELECT + symbol, + timestamp, + open, + high, + low, + close, + chg, + percent, + volume, + amount, + pre_close + FROM gp_day_data + WHERE symbol = '{db_code}' + ORDER BY timestamp DESC + LIMIT 1 + """ + + day_df = pd.read_sql(day_query, engine) + + if day_df.empty: + engine.dispose() + return None + + # 从gp_day_basic获取最新指标数据 + basic_query = f""" + SELECT + pe_ttm, + pb, + ps, + total_mv, + circ_mv + FROM gp_day_basic + WHERE symbol = '{db_code}' + ORDER BY timestamp DESC + LIMIT 1 + """ + + basic_df = pd.read_sql(basic_query, engine) + engine.dispose() + + # 组装返回数据 + day_row = day_df.iloc[0] + + result = { + 'stock_code': stock_code, # 保持原格式 + 'stock_name': None, # 数据库中没有股票名称，保持为None + 'current_price': float(day_row['close']) if day_row['close'] else None, + 'change_percent': float(day_row['percent']) if day_row['percent'] else None, + 'change_amount': float(day_row['chg']) if day_row['chg'] else None, + 'volume': int(day_row['volume']) if day_row['volume'] else None, + 'amount': float(day_row['amount']) if day_row['amount'] else None, + 'high': float(day_row['high']) if day_row['high'] else None, + 'low': float(day_row['low']) if day_row['low'] else None, + 'open': float(day_row['open']) if day_row['open'] else None, + 'pre_close': float(day_row['pre_close']) if day_row['pre_close'] else None, + 'update_time': day_row['timestamp'].strftime('%Y-%m-%d %H:%M:%S') if day_row['timestamp'] else None, + } + + # 添加估值指标（如果有） + if not basic_df.empty: + basic_row = basic_df.iloc[0] + result['total_market_value'] = float(basic_row['total_mv']) * 10000 if basic_row['total_mv'] else None # 转换为元 + result['pe'] = float(basic_row['pe_ttm']) if basic_row['pe_ttm'] else None + result['pb'] = float(basic_row['pb']) if basic_row['pb'] else None + result['ps'] = float(basic_row['ps']) if basic_row['ps'] else None + else: + result['total_market_value'] = None + result['pe'] = None + result['pb'] = None + result['ps'] = None + + return result + + except Exception as e: + print(f"从数据库获取股票价格数据失败: {e}") + import traceback + traceback.print_exc() + return None + + +def _normalize_stock_code(stock_code): + """ + 统一股票代码格式为数据库格式 + 支持的输入格式： + - SH600000、SZ000001 （数据库格式） + - 600000.SH、000001.SZ （Tushare格式） + - 600000、000001 （纯数字） + + Args: + stock_code: 股票代码 + + Returns: + 数据库格式的股票代码（如SH600000） + """ + stock_code = stock_code.upper().strip() + + # 已经是数据库格式 + if stock_code.startswith(('SH', 'SZ', 'BJ')): + return stock_code + + # Tushare格式转换 + if '.' in stock_code: + code, market = stock_code.split('.') + if market == 'SH': + return f'SH{code}' + elif market == 'SZ': + return f'SZ{code}' + elif market == 'BJ': + return f'BJ{code}' + + # 纯数字，根据规则判断 + if stock_code.isdigit(): + if stock_code.startswith('6'): + return f'SH{stock_code}' + elif stock_code.startswith(('0', '3')): + return f'SZ{stock_code}' + elif stock_code.startswith('8') or stock_code.startswith('4'): + return f'BJ{stock_code}' + + # 无法识别，返回原值 + return stock_code + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 增量采集当日数据（推荐每日使用） + # collect_stock_daily_data_v3(db_url, tushare_token, mode='daily') + + # 2. 批量采集历史数据（首次使用或需要重建数据时） + # collect_stock_daily_data_v3(db_url, tushare_token, mode='historical', days=4000) + + # 3. 采集指定日期的数据 + collect_stock_daily_data_v3(db_url, tushare_token, mode='daily', date='2025-11-07') + + # 4. 单只股票历史数据重新获取（用于除权处理） + # collector = StockDailyDataCollectorV3(db_url, tushare_token) + # collector.refetch_single_stock_history('SH600000', 1800) + diff --git a/src/simulation.log b/src/simulation.log deleted file mode 100644 index 83d78a4..0000000 --- a/src/simulation.log +++ /dev/null @@ -1,8 +0,0 @@ -2025-04-30 16:11:18,786 - stock_simulation - ERROR - ȡ˻: 'Engine' object has no attribute 'cursor' -2025-04-30 16:13:40,220 - stock_simulation - ERROR - ȡ˻: 'Connection' object has no attribute 'cursor' -2025-04-30 16:16:26,095 - stock_simulation - ERROR - ȡ˻: 'Engine' object has no attribute 'cursor' -2025-04-30 16:20:02,876 - stock_simulation - ERROR - ȡ˻: 'Engine' object has no attribute 'cursor' -2025-04-30 16:20:22,822 - stock_simulation - ERROR - ȡ˻: 'Engine' object has no attribute 'cursor' -2025-04-30 16:21:14,438 - stock_simulation - ERROR - ȡ˻: 'Engine' object has no attribute 'cursor' -2025-04-30 16:23:38,622 - stock_simulation - ERROR - ȡ˻: 'Connection' object has no attribute 'cursor' -2025-04-30 16:26:40,281 - stock_simulation - ERROR - ȡ˻: 'Connection' object has no attribute 'cursor' diff --git a/src/src_temp.7z b/src/src_temp.7z deleted file mode 100644 index 9dc6bda..0000000 Binary files a/src/src_temp.7z and /dev/null differ diff --git a/src/stock_analysis.log b/src/stock_analysis.log deleted file mode 100644 index 5037d88..0000000 --- a/src/stock_analysis.log +++ /dev/null @@ -1,25 +0,0 @@ -2025-03-05 14:51:02,426 - INFO - === Ʊʼ === -2025-03-05 14:51:02,427 - INFO - ʼ StockAnalyzer... -2025-03-05 14:51:02,453 - INFO - StockAnalyzer ʼ -2025-03-05 14:51:02,453 - INFO - : ʼ=2024-01-01, =2024-12-31 -2025-03-05 14:51:02,453 - INFO - ֹӯ=10.0%, ֹ=-10.0%, ص=-5.0% -2025-03-05 14:51:02,453 - INFO - ʼйƱ... -2025-03-05 14:51:02,453 - INFO - ʼȡƱб... -2025-03-05 14:51:02,597 - INFO - ȡ 1 ֧Ʊ -2025-03-05 14:51:02,600 - INFO - ʼƱ SZ300750 -2025-03-05 14:51:02,685 - INFO - Ʊ SZ300750 ɣ: -141823874.93 -2025-03-05 14:51:02,686 - INFO - ɣ 1/1 ֧Ʊ -2025-03-05 14:51:02,686 - INFO - ʼ 1 ֧Ʊķ -2025-03-05 14:51:02,687 - ERROR - г: [Errno 13] Permission denied: 'results/SZ300750_analysis.csv' -Traceback (most recent call last): - File "D:\python_project\gpfx\src\stock_analysis.py", line 254, in main - result.to_csv(csv_path, index=False) - File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\core\generic.py", line 3902, in to_csv - return DataFrameRenderer(formatter).to_csv( - File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\formats\format.py", line 1152, in to_csv - csv_formatter.save() - File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\formats\csvs.py", line 247, in save - with get_handle( - File "C:\Users\xy\.conda\envs\feapder_project\lib\site-packages\pandas\io\common.py", line 863, in get_handle - handle = open( -PermissionError: [Errno 13] Permission denied: 'results/SZ300750_analysis.csv' diff --git a/src/tushare_scripts/__init__.py b/src/tushare_scripts/__init__.py new file mode 100644 index 0000000..8336d21 --- /dev/null +++ b/src/tushare_scripts/__init__.py @@ -0,0 +1,2 @@ +# Tushare数据采集脚本包 + diff --git a/src/tushare_scripts/auction_data_collector.py b/src/tushare_scripts/auction_data_collector.py new file mode 100644 index 0000000..40a3aeb --- /dev/null +++ b/src/tushare_scripts/auction_data_collector.py @@ -0,0 +1,404 @@ +# coding:utf-8 + +""" +股票集合竞价数据采集工具 +功能：从Tushare获取开盘和收盘集合竞价数据，合并存储到gp_auction_data表 +API文档: + 开盘集合竞价 https://tushare.pro/document/2?doc_id=353 + 收盘集合竞价 https://tushare.pro/document/2?doc_id=354 +注意： + 1. 该接口需要至少2000积分权限 + 2. 单次最大返回10000条数据 + 3. 采用按日期遍历策略，每天获取全市场数据 + 4. 每天15:30之后数据更新完成 +采集策略： + - 按日期遍历，每个日期获取全市场的开盘和收盘集合竞价数据 + - 将同一股票同一日期的开盘和收盘数据合并为一条记录 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class AuctionDataCollector: + """股票集合竞价数据采集器""" + + def __init__(self, db_url, tushare_token, table_name='gp_auction_data'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 数据表名，默认为gp_auction_data + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.table_name = table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("股票集合竞价数据采集工具") + print(f"目标数据表: {self.table_name}") + print("=" * 60) + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def fetch_open_auction(self, trade_date): + """ + 获取开盘集合竞价数据 + + Args: + trade_date: 交易日期，格式YYYYMMDD + + Returns: + 开盘集合竞价DataFrame + """ + try: + df = self.pro.stk_auction_o(trade_date=trade_date) + return df + except Exception as e: + print(f"获取开盘集合竞价数据失败: {e}") + return pd.DataFrame() + + def fetch_close_auction(self, trade_date): + """ + 获取收盘集合竞价数据 + + Args: + trade_date: 交易日期，格式YYYYMMDD + + Returns: + 收盘集合竞价DataFrame + """ + try: + df = self.pro.stk_auction_c(trade_date=trade_date) + time.sleep(0.2) # API限频控制 + return df + except Exception as e: + print(f"获取收盘集合竞价数据失败: {e}") + return pd.DataFrame() + + def merge_and_transform_data(self, open_df, close_df): + """ + 合并开盘和收盘集合竞价数据，并转换为数据库格式 + + Args: + open_df: 开盘集合竞价DataFrame + close_df: 收盘集合竞价DataFrame + + Returns: + 合并后的DataFrame + """ + if open_df.empty and close_df.empty: + return pd.DataFrame() + + # 处理开盘集合竞价数据 + if not open_df.empty: + open_df = open_df.rename(columns={ + 'close': 'open_close', + 'open': 'open_open', + 'high': 'open_high', + 'low': 'open_low', + 'vol': 'open_vol', + 'amount': 'open_amount', + 'vwap': 'open_vwap' + }) + open_df['trade_date'] = pd.to_datetime(open_df['trade_date'], format='%Y%m%d') + + # 处理收盘集合竞价数据 + if not close_df.empty: + close_df = close_df.rename(columns={ + 'close': 'close_close', + 'open': 'close_open', + 'high': 'close_high', + 'low': 'close_low', + 'vol': 'close_vol', + 'amount': 'close_amount', + 'vwap': 'close_vwap' + }) + close_df['trade_date'] = pd.to_datetime(close_df['trade_date'], format='%Y%m%d') + + # 合并数据 + if not open_df.empty and not close_df.empty: + # 两个都有数据，按ts_code和trade_date合并 + result_df = pd.merge( + open_df[['ts_code', 'trade_date', 'open_close', 'open_open', 'open_high', + 'open_low', 'open_vol', 'open_amount', 'open_vwap']], + close_df[['ts_code', 'trade_date', 'close_close', 'close_open', 'close_high', + 'close_low', 'close_vol', 'close_amount', 'close_vwap']], + on=['ts_code', 'trade_date'], + how='outer' + ) + elif not open_df.empty: + # 只有开盘数据 + result_df = open_df[['ts_code', 'trade_date', 'open_close', 'open_open', 'open_high', + 'open_low', 'open_vol', 'open_amount', 'open_vwap']].copy() + else: + # 只有收盘数据 + result_df = close_df[['ts_code', 'trade_date', 'close_close', 'close_open', 'close_high', + 'close_low', 'close_vol', 'close_amount', 'close_vwap']].copy() + + # 添加symbol字段 + result_df.insert(0, 'symbol', result_df['ts_code'].apply(self.convert_tushare_code_to_db)) + + return result_df + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集 + 获取指定日期的全市场开盘和收盘集合竞价数据 + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(trade_date) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{self.table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 获取开盘集合竞价数据 + print(f"正在获取{date_str}开盘集合竞价数据...") + open_df = self.fetch_open_auction(trade_date) + if not open_df.empty: + print(f"成功获取开盘集合竞价数据，共{len(open_df)}只股票") + else: + print(f"{date_str}没有开盘集合竞价数据") + + # 获取收盘集合竞价数据 + print(f"正在获取{date_str}收盘集合竞价数据...") + close_df = self.fetch_close_auction(trade_date) + if not close_df.empty: + print(f"成功获取收盘集合竞价数据，共{len(close_df)}只股票") + else: + print(f"{date_str}没有收盘集合竞价数据") + + # 合并数据 + if open_df.empty and close_df.empty: + print(f"{date_str}没有集合竞价数据（可能是非交易日）") + return + + print(f"正在合并数据...") + result_df = self.merge_and_transform_data(open_df, close_df) + + if result_df.empty: + print("数据合并失败") + return + + # 保存到数据库 + print(f"正在保存数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}条记录") + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"开盘集合竞价: {len(open_df)}只股票" if not open_df.empty else "开盘集合竞价: 无数据") + print(f"收盘集合竞价: {len(close_df)}只股票" if not close_df.empty else "收盘集合竞价: 无数据") + print(f"合并记录: {len(result_df)}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_full_collection(self, start_date=None, end_date=None): + """ + 执行全量覆盖采集（按日期逐日采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为30天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行全量覆盖采集（按日期逐日采集）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近4000天历史数据 + start_date_obj = end_date_obj - timedelta(days=4000) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 按日期逐日采集 + current_date = start_date_obj + total_days = (end_date_obj - start_date_obj).days + 1 + processed_days = 0 + success_days = 0 + total_records = 0 + + while current_date <= end_date_obj: + date_str = current_date.strftime('%Y-%m-%d') + + print(f"\n[{processed_days + 1}/{total_days}] 采集日期: {date_str}") + + try: + # 使用每日采集方法 + trade_date = current_date.strftime('%Y%m%d') + + # 获取开盘集合竞价数据 + open_df = self.fetch_open_auction(trade_date) + + # 获取收盘集合竞价数据 + close_df = self.fetch_close_auction(trade_date) + + # 合并并保存数据 + if not open_df.empty or not close_df.empty: + result_df = self.merge_and_transform_data(open_df, close_df) + + if not result_df.empty: + # 删除该日期旧数据 + # with self.engine.begin() as conn: + # delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(trade_date) = :date_str") + # conn.execute(delete_query, {"date_str": date_str}) + + # 保存新数据 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + total_records += len(result_df) + success_days += 1 + print(f" 成功: {len(result_df)}条记录") + else: + print(f" 无数据（可能是非交易日）") + + except Exception as e: + print(f" 采集失败: {e}") + + # 移到下一天 + current_date += timedelta(days=1) + processed_days += 1 + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量覆盖采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"总天数: {total_days}") + print(f"成功天数: {success_days}") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"全量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_auction_data(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None): + """ + 股票集合竞价数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'full'=全量覆盖采集 + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 开始日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为30天前 + end_date: 结束日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为今天 + """ + collector = AuctionDataCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection(start_date, end_date) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每日15:30后使用） + # collect_auction_data(db_url, tushare_token, mode='daily') + + # 2. 全量覆盖采集（首次使用或需要重建数据时） + collect_auction_data(db_url, tushare_token, mode='full') + + # 3. 采集指定日期的数据 + # collect_auction_data(db_url, tushare_token, mode='daily', date='2025-11-03') + + # 4. 采集指定日期范围的数据 + # collect_auction_data(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') + diff --git a/src/tushare_scripts/chip_data_collector.py b/src/tushare_scripts/chip_data_collector.py new file mode 100644 index 0000000..0f1ac11 --- /dev/null +++ b/src/tushare_scripts/chip_data_collector.py @@ -0,0 +1,354 @@ +# coding:utf-8 + +""" +每日筹码及胜率数据采集工具 +功能：从Tushare获取每日筹码及胜率数据，存储到gp_chip_data表 +API文档: https://tushare.pro/document/2?doc_id=293 +注意：该接口需要至少10000积分权限 +每天17~18点左右更新 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class ChipDataCollector: + """每日筹码及胜率数据采集器""" + + def __init__(self, db_url, tushare_token, table_name='gp_chip_data'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 数据表名，默认为gp_chip_data + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.table_name = table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("每日筹码及胜率数据采集工具") + print(f"目标数据表: {self.table_name}") + print("=" * 60) + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def convert_db_code_to_tushare(self, symbol): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001 + Tushare格式：600000.SH、000001.SZ + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + elif symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('BJ'): + return f"{symbol[2:]}.BJ" + else: + return symbol + + def fetch_all_stock_codes(self): + """ + 从数据库获取所有A股股票代码 + + Returns: + 股票代码列表 + """ + try: + query = "SELECT gp_code FROM gp_code_all" + df = pd.read_sql(query, self.engine) + codes = df['gp_code'].tolist() + print(f"获取到A股股票代码: {len(codes)}个") + return codes + except Exception as e: + print(f"获取股票代码失败: {e}") + return [] + + def fetch_chip_data(self, ts_code=None, trade_date=None, start_date=None, end_date=None): + """ + 从Tushare获取每日筹码及胜率数据 + API文档: https://tushare.pro/document/2?doc_id=293 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + trade_date: 交易日期，格式YYYYMMDD（单日查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 筹码数据DataFrame + """ + try: + df = self.pro.cyq_perf( + ts_code=ts_code, + trade_date=trade_date, + start_date=start_date, + end_date=end_date, + fields='ts_code,trade_date,his_low,his_high,cost_5pct,cost_15pct,cost_50pct,cost_85pct,cost_95pct,weight_avg,winner_rate' + ) + return df + except Exception as e: + print(f"获取筹码数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换Tushare数据为数据库格式 + + Args: + df: Tushare返回的DataFrame + + Returns: + 转换后的DataFrame + """ + if df.empty: + return pd.DataFrame() + + result_df = pd.DataFrame() + + # 字段映射 + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + result_df['ts_code'] = df['ts_code'] + result_df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + result_df['his_low'] = df['his_low'] + result_df['his_high'] = df['his_high'] + result_df['cost_5pct'] = df['cost_5pct'] + result_df['cost_15pct'] = df['cost_15pct'] + result_df['cost_50pct'] = df['cost_50pct'] + result_df['cost_85pct'] = df['cost_85pct'] + result_df['cost_95pct'] = df['cost_95pct'] + result_df['weight_avg'] = df['weight_avg'] + result_df['winner_rate'] = df['winner_rate'] + + return result_df + + def run_full_collection(self): + """ + 执行全量覆盖采集 + 先清空表，再重新采集所有股票的数据 + """ + print("=" * 60) + print("开始执行全量覆盖采集") + print("=" * 60) + + try: + # 获取所有股票代码 + stock_codes = self.fetch_all_stock_codes() + if not stock_codes: + print("未获取到股票代码，终止采集") + return + + # 清空表 + print(f"正在清空{self.table_name}表...") + with self.engine.begin() as conn: + truncate_query = text(f"TRUNCATE TABLE {self.table_name}") + conn.execute(truncate_query) + print("表已清空") + + # 统计信息 + success_count = 0 + failed_count = 0 + total_records = 0 + + # 遍历所有股票 + for symbol in tqdm(stock_codes, desc="全量采集筹码数据"): + try: + # 转换股票代码格式 + ts_code = self.convert_db_code_to_tushare(symbol) + + # 获取该股票的所有历史筹码数据 + df = self.fetch_chip_data(ts_code=ts_code) + + if df.empty: + failed_count += 1 + continue + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + failed_count += 1 + continue + + # 保存到数据库 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + success_count += 1 + total_records += len(result_df) + + except Exception as e: + print(f"\n处理{symbol}时发生错误: {e}") + failed_count += 1 + continue + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量采集完成") + print("=" * 60) + print(f"总股票数: {len(stock_codes)}") + print(f"成功采集: {success_count}") + print(f"失败: {failed_count}") + print(f"总记录数: {total_records}") + print("=" * 60) + + except Exception as e: + print(f"全量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集 + 获取指定日期的全市场筹码数据 + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(trade_date) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{self.table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 获取全市场当日筹码数据（不传ts_code，获取所有股票） + print(f"正在获取{date_str}全市场筹码数据...") + df = self.fetch_chip_data(trade_date=trade_date) + + if df.empty: + print(f"{date_str}没有筹码数据（可能是非交易日或接口无数据）") + return + + print(f"成功获取筹码数据，共{len(df)}只股票") + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print("数据转换失败") + return + + # 保存到数据库 + print(f"正在保存数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}条记录") + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"成功采集: {len(result_df)}只股票") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_chip_data(db_url, tushare_token, mode='daily', date=None): + """ + 筹码数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'full'=全量覆盖采集 + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + """ + collector = ChipDataCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection() + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每日使用） + # collect_chip_data(db_url, tushare_token, mode='daily') + + # 2. 全量覆盖采集（首次使用或需要重建数据时） + # collect_chip_data(db_url, tushare_token, mode='full') + + # 3. 采集指定日期的数据 + collect_chip_data(db_url, tushare_token, mode='daily', date='2025-11-07') diff --git a/src/tushare_scripts/chip_distribution_collector.py b/src/tushare_scripts/chip_distribution_collector.py new file mode 100644 index 0000000..21f4779 --- /dev/null +++ b/src/tushare_scripts/chip_distribution_collector.py @@ -0,0 +1,475 @@ +# coding:utf-8 + +""" +每日筹码分布数据采集工具 +功能：从Tushare获取每日筹码分布数据，存储到gp_chip_distribution表 +API文档: https://tushare.pro/document/2?doc_id=294 +注意：该接口需要至少10000积分权限，单次最大返回2000条数据 +每天17~18点之间更新当日数据 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class ChipDistributionCollector: + """每日筹码分布数据采集器（按年分表）""" + + def __init__(self, db_url, tushare_token, base_table_name='gp_chip_distribution'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + base_table_name: 基础表名，默认为gp_chip_distribution（实际表名会加上年度后缀，如gp_chip_distribution_2025） + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.base_table_name = base_table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("每日筹码分布数据采集工具（按年分表）") + print(f"基础表名: {self.base_table_name}") + print("=" * 60) + + def get_table_name_by_date(self, date_str): + """ + 根据日期获取对应的分表名称 + + Args: + date_str: 日期字符串，格式YYYY-MM-DD + + Returns: + 分表名称，如gp_chip_distribution_2025 + """ + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + year = date_obj.strftime('%Y') + return f"{self.base_table_name}_{year}" + + def create_yearly_table(self, table_name): + """ + 创建年度分表（如果不存在） + + Args: + table_name: 表名，如gp_chip_distribution_2025 + """ + try: + create_table_sql = f""" + CREATE TABLE IF NOT EXISTS `{table_name}` ( + `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键', + `symbol` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NOT NULL COMMENT '股票代码（数据库格式，如SH600000）', + `ts_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NOT NULL COMMENT 'Tushare股票代码（如600000.SH）', + `trade_date` date NOT NULL COMMENT '交易日期', + `price` decimal(10, 2) NOT NULL COMMENT '价格', + `percent` decimal(10, 4) NULL DEFAULT NULL COMMENT '占比（%）', + `create_time` datetime NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', + PRIMARY KEY (`id`) USING BTREE, + UNIQUE INDEX `idx_symbol_date_price`(`symbol` ASC, `trade_date` ASC, `price` ASC) USING BTREE, + INDEX `idx_symbol`(`symbol` ASC) USING BTREE, + INDEX `idx_trade_date`(`trade_date` ASC) USING BTREE, + INDEX `idx_ts_code`(`ts_code` ASC) USING BTREE + ) ENGINE = InnoDB + CHARACTER SET = utf8mb4 + COLLATE = utf8mb4_bin + COMMENT = '每日筹码分布数据表（Tushare）' + ROW_FORMAT = Dynamic; + """ + + with self.engine.begin() as conn: + conn.execute(text(create_table_sql)) + + print(f"✓ 表 {table_name} 已确认存在") + + except Exception as e: + print(f"创建表 {table_name} 失败: {e}") + raise + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def convert_db_code_to_tushare(self, symbol): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001 + Tushare格式：600000.SH、000001.SZ + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + elif symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('BJ'): + return f"{symbol[2:]}.BJ" + else: + return symbol + + def fetch_all_stock_codes(self): + """ + 从数据库获取所有A股股票代码 + + Returns: + 股票代码列表 + """ + try: + query = "SELECT gp_code FROM gp_code_all" + df = pd.read_sql(query, self.engine) + codes = df['gp_code'].tolist() + print(f"获取到A股股票代码: {len(codes)}个") + return codes + except Exception as e: + print(f"获取股票代码失败: {e}") + return [] + + def fetch_chip_distribution(self, ts_code=None, trade_date=None, start_date=None, end_date=None): + """ + 从Tushare获取每日筹码分布数据 + API文档: https://tushare.pro/document/2?doc_id=294 + 注意：单次最大返回2000条数据 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + trade_date: 交易日期，格式YYYYMMDD（单日查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 筹码分布DataFrame + """ + try: + df = self.pro.cyq_chips( + ts_code=ts_code, + trade_date=trade_date, + start_date=start_date, + end_date=end_date, + fields='ts_code,trade_date,price,percent' + ) + return df + except Exception as e: + print(f"获取筹码分布数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换Tushare数据为数据库格式 + + Args: + df: Tushare返回的DataFrame + + Returns: + 转换后的DataFrame + """ + if df.empty: + return pd.DataFrame() + + result_df = pd.DataFrame() + + # 字段映射 + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + result_df['ts_code'] = df['ts_code'] + result_df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + result_df['price'] = df['price'] + result_df['percent'] = df['percent'] + + return result_df + + def run_full_collection(self, start_date=None, end_date=None, batch_size=100): + """ + 执行全量覆盖采集（按年分表，按日期倒序采集） + 从最新日期（end_date）开始往前采集到最早日期（start_date） + 自动跳过周六和周日（非交易日） + + Args: + start_date: 开始日期（最早日期），格式YYYY-MM-DD，默认为8年前 + end_date: 结束日期（最新日期），格式YYYY-MM-DD，默认为今天 + batch_size: 批量入库的股票数量，默认100 + """ + print("=" * 60) + print("开始执行全量覆盖采集（按年分表，按日期倒序采集）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近8年历史数据 + start_date_obj = end_date_obj - timedelta(days=8*365) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"批量入库设置: 每{batch_size}只股票入库一次") + print("=" * 60) + + # 获取所有股票代码 + stock_codes = self.fetch_all_stock_codes() + if not stock_codes: + print("未获取到股票代码，终止采集") + return + + # 按日期倒序逐日采集（从最新日期往前采集，跳过周六周日） + current_date = end_date_obj + total_days = (end_date_obj - start_date_obj).days + 1 + processed_days = 0 + skipped_weekends = 0 + + while current_date >= start_date_obj: + # 跳过周六（weekday() == 5）和周日（weekday() == 6） + weekday = current_date.weekday() + if weekday == 5: # 周六 + skipped_weekends += 1 + current_date -= timedelta(days=1) + continue + elif weekday == 6: # 周日 + skipped_weekends += 1 + current_date -= timedelta(days=1) + continue + + date_str = current_date.strftime('%Y-%m-%d') + + # 调用每日采集方法，自动按年分表 + print(f"\n[{processed_days + 1}/{total_days - skipped_weekends}] 采集日期: {date_str} (倒序采集)") + + try: + # 使用每日采集方法，自动按年分表 + self.run_daily_collection(date=date_str, batch_size=batch_size) + processed_days += 1 + except Exception as e: + print(f"采集 {date_str} 失败: {e}") + + # 移到前一天（倒序） + current_date -= timedelta(days=1) + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量覆盖采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"总天数: {total_days}天") + print(f"跳过周末: {skipped_weekends}天") + print(f"实际采集: {processed_days}天") + print("=" * 60) + + except Exception as e: + print(f"全量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_daily_collection(self, date=None, batch_size=100): + """ + 执行每日增量采集（按年分表） + 获取指定日期的全市场筹码分布数据 + 注意：单次返回限制10000条 + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集（按年分表）") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + # 获取当年分表名称 + table_name = self.get_table_name_by_date(date_str) + + print(f"采集日期: {date_str} ({trade_date})") + print(f"目标表名: {table_name}") + print(f"批量入库设置: 每{batch_size}只股票入库一次") + + # 创建当年分表（如果不存在） + self.create_yearly_table(table_name) + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {table_name} WHERE DATE(trade_date) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 获取所有股票代码 + stock_codes = self.fetch_all_stock_codes() + if not stock_codes: + print("未获取到股票代码，终止采集") + return + + # 统计信息 + success_count = 0 + failed_count = 0 + total_records = 0 + + # 批量数据缓存 + batch_data_list = [] + + # 遍历所有股票，获取当日筹码分布数据 + print(f"正在获取{date_str}全市场筹码分布数据...") + for idx, symbol in enumerate(tqdm(stock_codes, desc=f"采集{date_str}筹码分布"), 1): + try: + # 转换股票代码格式 + ts_code = self.convert_db_code_to_tushare(symbol) + + # 获取当日筹码分布数据 + df = self.fetch_chip_distribution(ts_code=ts_code, trade_date=trade_date) + + if df.empty: + failed_count += 1 + continue + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + failed_count += 1 + continue + + # 添加到批量缓存 + batch_data_list.append(result_df) + success_count += 1 + total_records += len(result_df) + + # 每达到batch_size或最后一只股票时，批量入库 + if len(batch_data_list) >= batch_size or idx == len(stock_codes): + if batch_data_list: + # 合并所有DataFrame + combined_df = pd.concat(batch_data_list, ignore_index=True) + # 批量保存到数据库（使用分表） + combined_df.to_sql(table_name, self.engine, if_exists='append', index=False) + # 清空缓存 + batch_data_list = [] + + except Exception as e: + failed_count += 1 + continue + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"总股票数: {len(stock_codes)}") + print(f"成功采集: {success_count}") + print(f"失败: {failed_count}") + print(f"总记录数: {total_records}") + print(f"数据表: {table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_chip_distribution(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None, batch_size=100): + """ + 筹码分布数据采集入口函数（按年分表） + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'full'=全量覆盖采集（倒序） + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 最早日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为8年前 + end_date: 最新日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为今天 + batch_size: 批量入库的股票数量，默认100 + + 注意： + - full模式采用倒序采集：从最新日期（end_date）往前采集到最早日期（start_date） + - 这样可以优先采集最新数据，如果中途中断，最新数据已经采集完成 + - 自动跳过周六和周日（非交易日），只采集工作日数据 + """ + collector = ChipDistributionCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date, batch_size) + elif mode == 'full': + collector.run_full_collection(start_date, end_date, batch_size) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每日使用） + # 自动按年分表，如2025-11-03的数据会存到gp_chip_distribution_2025表 + # collect_chip_distribution(db_url, tushare_token, mode='daily') + + # 2. 全量覆盖采集（首次使用或需要重建数据时） + # 采集近8年数据，自动按年创建分表（如gp_chip_distribution_2025, gp_chip_distribution_2024等） + collect_chip_distribution(db_url, tushare_token, mode='full') + + # 3. 采集指定日期的数据 + # collect_chip_distribution(db_url, tushare_token, mode='daily', date='2025-10-29') + + # 4. 采集指定日期范围的数据 + # collect_chip_distribution(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') + + # 5. 调整批量入库大小（默认100只股票一批） + # collect_chip_distribution(db_url, tushare_token, mode='daily', batch_size=200) + diff --git a/src/tushare_scripts/hk_daily_data_collector.py b/src/tushare_scripts/hk_daily_data_collector.py new file mode 100644 index 0000000..0900e1a --- /dev/null +++ b/src/tushare_scripts/hk_daily_data_collector.py @@ -0,0 +1,722 @@ +# coding:utf-8 + +""" +港股日线数据采集工具 +功能：从Tushare获取港股日线行情数据，存储到gp_day_data表（与A股共用） +API文档: https://tushare.pro/document/2?doc_id=192 +注意： + 1. 该接口需要120积分权限 + 2. 单次最大返回8000条数据 + 3. 每日收盘后更新（港股交易时间：9:30-12:00, 13:00-16:00） + 4. 可按日期一次性获取全市场数据 +数据存储说明： + - 存储表：gp_day_data（与A股共用） + - symbol格式：纯数字5位（如01810），不带市场编码 + - 删除策略：只删除LENGTH(symbol)=5的记录，不影响A股数据 + - PB/PE/PS：港股接口无此数据，保持为NULL +采集策略： + - 每日增量：按日期获取全市场数据（推荐） + - 历史数据：按股票逐个采集历史数据 + 17点以后执行 +""" +from time import sleep + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class HKDailyDataCollector: + """港股日线数据采集器""" + + def __init__(self, db_url, tushare_token, table_name='gp_day_data'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 数据表名，默认为gp_day_data（与A股共用） + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.table_name = table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("港股日线数据采集工具") + print(f"目标数据表: {self.table_name}") + print("=" * 60) + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的港股代码转换为数据库格式 + Tushare格式：01810.HK、00700.HK + 数据库格式：01810、00700（不带市场编码，保留前导零，长度为5位） + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码（纯数字，5位） + """ + if '.HK' in ts_code: + return ts_code.replace('.HK', '') + else: + return ts_code + + def convert_db_code_to_tushare(self, symbol): + """ + 将数据库格式的港股代码转换为Tushare格式 + 数据库格式：01810、00700 + Tushare格式：01810.HK、00700.HK + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if not symbol.endswith('.HK'): + return f"{symbol}.HK" + return symbol + + def fetch_hk_stock_list(self): + """ + 从数据库获取港股代码列表 + + Returns: + 港股代码列表（数据库格式） + """ + try: + query = """ + SELECT DISTINCT gp_code + FROM gp_code_hk + WHERE gp_code IS NOT NULL + ORDER BY gp_code + """ + df = pd.read_sql(query, self.engine) + stock_codes = df['gp_code'].tolist() + print(f"从数据库获取到{len(stock_codes)}只港股代码") + return stock_codes + except Exception as e: + print(f"从数据库获取港股代码失败: {e}") + return [] + + def fetch_hk_daily_data(self, trade_date=None, ts_code=None, start_date=None, end_date=None): + """ + 获取港股日线数据 + + Args: + trade_date: 交易日期，格式YYYYMMDD（单日查询） + ts_code: 股票代码，Tushare格式（单股查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 港股日线数据DataFrame + """ + try: + # 构建API参数 + params = {} + if trade_date: + params['trade_date'] = trade_date + if ts_code: + params['ts_code'] = ts_code + if start_date: + params['start_date'] = start_date + if end_date: + params['end_date'] = end_date + + df = self.pro.hk_daily(**params) + time.sleep(0.21) # API限频控制，每分钟不超过280次 + return df + except Exception as e: + print(f"获取港股日线数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换数据格式为数据库格式（与gp_day_data表结构一致） + + Args: + df: 原始数据DataFrame + + Returns: + 转换后的DataFrame，存储到gp_day_data表 + + 字段说明： + - symbol: 纯数字5位（如01810），不带市场编码 + - timestamp: 交易日期 + - volume: 成交量（手） + - open/high/low/close: 价格（转为字符串） + - chg: 涨跌额（转为字符串） + - percent: 涨跌幅（%） + - turnoverrate: 换手率（港股接口无此数据，设为NULL） + - amount: 成交额（港币） + - pb/pe/ps: 估值指标（港股接口无此数据，设为NULL） + - pre_close: 昨收价 + """ + if df.empty: + return pd.DataFrame() + + # 创建符合gp_day_data表结构的DataFrame + result_df = pd.DataFrame() + + # 字段映射和转换（与A股V3版本保持一致） + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) # 纯数字5位 + result_df['timestamp'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + result_df['volume'] = df['vol'] # 港股vol单位是手，保持不变 + result_df['open'] = df['open'].astype(str) # 转为字符串与A股表结构一致 + result_df['high'] = df['high'].astype(str) + result_df['low'] = df['low'].astype(str) + result_df['close'] = df['close'].astype(str) + result_df['chg'] = df['change'].astype(str) + result_df['percent'] = df['pct_chg'] + result_df['turnoverrate'] = None # 港股日线接口没有换手率数据 + result_df['amount'] = df['amount'] * 1000 # Tushare的amount单位是千港币，转换为港币 + result_df['pb'] = None # 港股日线接口没有PB数据，保持NULL + result_df['pe'] = None # 港股日线接口没有PE数据，保持NULL + result_df['ps'] = None # 港股日线接口没有PS数据，保持NULL + result_df['pre_close'] = df['pre_close'] + + # 按时间排序 + result_df = result_df.sort_values('timestamp').reset_index(drop=True) + + return result_df + + def check_ex_rights_before_save(self, df, date_str): + """ + 在保存数据库之前检查除权情况，返回除权股票列表和除权日志数据 + + Args: + df: 待保存的日线数据DataFrame + date_str: 当前采集日期，格式YYYY-MM-DD + + Returns: + tuple: (除权股票列表, 除权日志数据列表) + """ + print("正在检查港股除权情况...") + + ex_rights_stocks = [] + ex_rights_log_data = [] + + from tqdm import tqdm + + for _, row in tqdm(df.iterrows(), total=len(df), desc="检查除权"): + symbol = row['symbol'] + current_pre_close = row['pre_close'] + + # 如果pre_close为空，跳过 + if pd.isna(current_pre_close): + continue + + # 查询数据库中该股票的最近两条收盘价记录 + query = text(""" + SELECT `close`, `timestamp` FROM {table_name} + WHERE symbol = :symbol + ORDER BY `timestamp` DESC + LIMIT 2 + """.format(table_name=self.table_name)) + + try: + with self.engine.connect() as conn: + results = conn.execute(query, {"symbol": symbol}).fetchall() + + if results: + # 检查最新记录是否为今天的数据 + latest_record = results[0] + latest_timestamp = latest_record[1] + latest_date_str = latest_timestamp.strftime('%Y-%m-%d') + + if latest_date_str == date_str and len(results) > 1: + # 如果最新记录是今天的，且有第二条记录，则用第二条记录比较 + db_last_close = float(results[1][0]) + else: + # 如果最新记录不是今天的，或者只有一条记录，则用最新记录比较 + db_last_close = float(results[0][0]) + + # 比较pre_close和数据库中的收盘价 + if abs(db_last_close - current_pre_close) > 0.001: + print(f"发现除权港股: {symbol}, 数据库收盘价: {db_last_close}, 当前昨收价: {current_pre_close}") + ex_rights_stocks.append(symbol) + + # 收集除权日志数据 + ex_rights_log_data.append({ + 'stock_code': symbol, + 'change_date': date_str, + 'before_price': db_last_close, + 'after_price': current_pre_close, + 'update_time': datetime.now() + }) + except Exception as e: + # 如果查询失败（比如股票是新上市的），跳过 + continue + + if ex_rights_stocks: + print(f"检测到 {len(ex_rights_stocks)} 只除权港股: {ex_rights_stocks}") + else: + print("未发现除权港股") + + return ex_rights_stocks, ex_rights_log_data + + def save_ex_rights_log(self, log_data): + """ + 将除权日志保存到数据库 + + Args: + log_data: 除权日志数据列表 + """ + if not log_data: + return + + print(f"正在保存 {len(log_data)} 条港股除权日志到gp_ex_rights_log表...") + try: + df = pd.DataFrame(log_data) + df.to_sql('gp_ex_rights_log', self.engine, if_exists='append', index=False) + print("港股除权日志保存成功") + except Exception as e: + print(f"保存港股除权日志失败: {e}") + + def handle_ex_rights_stocks(self, ex_rights_stocks, ex_rights_log_data, days=180): + """ + 处理除权港股：保存日志、删除历史数据并重新获取 + + Args: + ex_rights_stocks: 除权股票列表 + ex_rights_log_data: 除权日志数据列表 + days: 重新获取的历史天数，默认180天 + """ + if not ex_rights_stocks: + return + + print("=" * 60) + print("开始处理除权港股...") + print("=" * 60) + + # 1. 保存除权日志 + if ex_rights_log_data: + self.save_ex_rights_log(ex_rights_log_data) + + # 2. 重新获取历史数据 + print(f"开始处理 {len(ex_rights_stocks)} 只除权港股，重新获取历史数据...") + + success_count = 0 + fail_count = 0 + + from tqdm import tqdm + + for symbol in tqdm(ex_rights_stocks, desc="处理除权港股"): + try: + # 转换为Tushare格式 + ts_code = self.convert_db_code_to_tushare(symbol) + + # 删除该港股的所有历史数据（限制LENGTH(symbol)=5确保只删港股） + delete_query = text( + f"DELETE FROM {self.table_name} " + f"WHERE symbol = :symbol " + f"AND LENGTH(symbol) = 5" + ) + with self.engine.begin() as conn: + result = conn.execute(delete_query, {"symbol": symbol}) + print(f"已删除 {symbol} 的 {result.rowcount} 条历史数据") + + # 计算日期范围 + end_date_obj = datetime.now() + start_date_obj = end_date_obj - timedelta(days=days) + start_date_str = start_date_obj.strftime('%Y%m%d') + end_date_str = end_date_obj.strftime('%Y%m%d') + + # 重新获取历史数据 + df = self.fetch_hk_daily_data( + ts_code=ts_code, + start_date=start_date_str, + end_date=end_date_str + ) + + if df.empty: + print(f"未获取到{symbol}的历史数据") + fail_count += 1 + continue + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print(f"数据转换失败: {symbol}") + fail_count += 1 + continue + + # 保存到数据库 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功重新获取 {symbol} 的历史数据，共{len(result_df)}条记录") + success_count += 1 + + except Exception as e: + print(f"处理除权港股 {symbol} 失败: {e}") + fail_count += 1 + + print("\n" + "=" * 60) + print("除权港股处理完成") + print("=" * 60) + print(f"成功: {success_count}只") + print(f"失败: {fail_count}只") + print("=" * 60) + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集 + 按日期获取全市场港股日线数据（推荐方式） + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集（全市场）") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧港股数据（港股代码长度为5位） + with self.engine.begin() as conn: + delete_query = text( + f"DELETE FROM {self.table_name} " + f"WHERE DATE(`timestamp`) = :date_str " + f"AND LENGTH(symbol) = 5" + ) + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{self.table_name}表中{date_str}的旧港股数据（LENGTH(symbol)=5），共{result.rowcount}条") + + # 获取全市场港股日线数据 + print(f"正在获取{date_str}全市场港股日线数据...") + df = self.fetch_hk_daily_data(trade_date=trade_date) + + if df.empty: + print(f"{date_str}没有港股日线数据（可能是非交易日）") + return + + print(f"成功获取港股日线数据，共{len(df)}只股票") + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print("数据转换失败") + return + + # 检查除权（保存前） + ex_rights_stocks, ex_rights_log_data = self.check_ex_rights_before_save(result_df, date_str) + + # 保存到数据库 + print(f"正在保存数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}条记录") + + # 处理除权股票（保存后） + self.handle_ex_rights_stocks(ex_rights_stocks, ex_rights_log_data) + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"股票数量: {len(result_df)}只") + print(f"除权处理: {len(ex_rights_stocks)}只") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_full_collection(self, start_date=None, end_date=None): + """ + 执行全量历史数据采集（按股票逐个采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为180天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行全量历史数据采集（按股票逐个采集）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近180天历史数据 + start_date_obj = end_date_obj - timedelta(days=180) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + start_date_str = start_date_obj.strftime('%Y%m%d') + end_date_str = end_date_obj.strftime('%Y%m%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 获取港股代码列表 + stock_codes = self.fetch_hk_stock_list() + + if not stock_codes: + print("没有找到港股代码，请先在gp_code_hk表中添加港股代码") + return + + print(f"开始采集{len(stock_codes)}只港股的历史数据...") + print("=" * 60) + + # 按股票逐个采集 + success_count = 0 + fail_count = 0 + total_records = 0 + + for i, symbol in enumerate(stock_codes, 1): + try: + # 转换为Tushare格式 + ts_code = self.convert_db_code_to_tushare(symbol) + + # 获取该股票历史数据 + df = self.fetch_hk_daily_data( + ts_code=ts_code, + start_date=start_date_str, + end_date=end_date_str + ) + + if not df.empty: + # 删除该港股在该日期范围的旧数据（限制LENGTH(symbol)=5确保只删港股） + with self.engine.begin() as conn: + delete_query = text( + f"DELETE FROM {self.table_name} " + f"WHERE symbol = :symbol " + f"AND LENGTH(symbol) = 5 " + f"AND `timestamp` BETWEEN :start_date AND :end_date" + ) + conn.execute(delete_query, { + "symbol": symbol, + "start_date": start_date_obj.strftime('%Y-%m-%d'), + "end_date": end_date_obj.strftime('%Y-%m-%d') + }) + + # 转换数据格式 + result_df = self.transform_data(df) + + if not result_df.empty: + # 保存到数据库 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + total_records += len(result_df) + success_count += 1 + + # 每10只股票显示一次进度 + if i % 10 == 0: + print(f"进度: {i}/{len(stock_codes)} ({success_count}成功, {fail_count}失败, 共{total_records}条记录)") + + except Exception as e: + fail_count += 1 + if i % 10 == 0: + print(f"采集 {symbol} 失败: {e}") + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量历史数据采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"成功采集: {success_count}只") + print(f"失败: {fail_count}只") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"全量历史数据采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_date_range_collection(self, start_date=None, end_date=None): + """ + 按日期范围逐日采集全市场数据（推荐用于历史数据采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为30天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行日期范围采集（按日期逐日采集全市场）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近30天历史数据 + start_date_obj = end_date_obj - timedelta(days=30) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 按日期逐日采集 + current_date = start_date_obj + total_days = (end_date_obj - start_date_obj).days + 1 + processed_days = 0 + success_days = 0 + total_records = 0 + + while current_date <= end_date_obj: + date_str = current_date.strftime('%Y-%m-%d') + trade_date = current_date.strftime('%Y%m%d') + + print(f"\n[{processed_days + 1}/{total_days}] 采集日期: {date_str}") + sleep(31) + try: + # 获取全市场数据 + df = self.fetch_hk_daily_data(trade_date=trade_date) + + if not df.empty: + # 转换数据格式 + result_df = self.transform_data(df) + + if not result_df.empty: + # 删除该日期旧港股数据（LENGTH(symbol)=5） + with self.engine.begin() as conn: + delete_query = text( + f"DELETE FROM {self.table_name} " + f"WHERE DATE(`timestamp`) = :date_str " + f"AND LENGTH(symbol) = 5" + ) + conn.execute(delete_query, {"date_str": date_str}) + + # 保存新数据 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + total_records += len(result_df) + success_days += 1 + print(f" 成功: {len(result_df)}条记录") + else: + print(f" 无数据（可能是非交易日）") + + except Exception as e: + print(f" 采集失败: {e}") + + # 移到下一天 + current_date += timedelta(days=1) + processed_days += 1 + + # 输出统计信息 + print("\n" + "=" * 60) + print("日期范围采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"总天数: {total_days}") + print(f"成功天数: {success_days}") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"日期范围采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_hk_daily_data(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None): + """ + 港股日线数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式 + - 'daily': 增量采集当日数据（按日期获取全市场，推荐） + - 'full': 全量覆盖采集（按股票逐个采集） + - 'range': 日期范围采集（按日期逐日采集全市场，推荐用于历史数据） + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 开始日期（mode='full'或'range'时有效），格式YYYY-MM-DD + end_date: 结束日期（mode='full'或'range'时有效），格式YYYY-MM-DD + + 注意： + - 港股数据会保存到gp_day_data表（与A股共用） + - symbol格式为纯数字5位（如01810），不带市场编码 + - 删除数据时只删除LENGTH(symbol)=5的记录，不影响A股数据 + """ + collector = HKDailyDataCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection(start_date, end_date) + elif mode == 'range': + collector.run_date_range_collection(start_date, end_date) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每日17:00后使用，一次性获取全市场数据） + # collect_hk_daily_data(db_url, tushare_token, mode='daily') + + # 2. 日期范围采集（推荐用于历史数据，按日期逐日采集全市场） + # collect_hk_daily_data(db_url, tushare_token, mode='range', + # start_date='2022-01-01', end_date='2025-11-03') + # 港股的采集到了11月4日的数据（待采集的） + # 3. 采集指定日期的数据 + collect_hk_daily_data(db_url, tushare_token, mode='daily', date='2025-11-04') + + # 4. 全量覆盖采集（按股票逐个采集，较慢，不推荐） + # collect_hk_daily_data(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') + # import akshare as ak + # stock_zh_a_spot_df = ak.stock_zh_a_spot() + # print(stock_zh_a_spot_df) + diff --git a/src/tushare_scripts/realtime_quote_collector.py b/src/tushare_scripts/realtime_quote_collector.py new file mode 100644 index 0000000..8d3db53 --- /dev/null +++ b/src/tushare_scripts/realtime_quote_collector.py @@ -0,0 +1,404 @@ +# coding:utf-8 + +""" +A股实时行情数据采集工具（基于Tushare） +功能：从Tushare获取A股实时行情数据，存储到Redis +API：ts.realtime_quote() - 新浪实时行情数据 +注意： + 1. 免费接口，无积分限制 + 2. 单次最多支持50个股票代码 + 3. 实时数据，交易时间段可频繁调用 + 4. 数据结构与雪球格式对齐，便于业务兼容 +数据存储： + - 存储位置：Redis Hash + - Key：xq_stock_changes_latest（与雪球脚本保持一致） + - Field：股票代码（如SZ000978） + - Value：JSON格式的实时行情数据 +采集策略： + - 从gp_code_all表获取所有A股代码 + - 每49只股票调用一次API（避免超过50个限制） + - 批量写入Redis，替换旧数据 +""" + +import tushare as ts +import pandas as pd +import redis +import json +from datetime import datetime +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN +from sqlalchemy import create_engine + +# Redis配置 +try: + from src.valuation_analysis.config import REDIS_CONFIG +except ImportError: + REDIS_CONFIG = { + 'host': 'localhost', + 'port': 6379, + 'db': 0, + 'password': None + } + +REDIS_KEY = 'xq_stock_changes_latest' # 与雪球脚本保持一致 + + +class RealtimeQuoteCollector: + """A股实时行情数据采集器（基于Tushare）""" + + def __init__(self, db_url, tushare_token, batch_size=49): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + batch_size: 每批采集的股票数量，默认49（API最大支持50） + """ + self.db_url = db_url + self.batch_size = batch_size + + # 初始化Tushare + ts.set_token(tushare_token) + + # 初始化Redis连接 + self.redis_conn = self._get_redis_conn() + + print("=" * 60) + print("A股实时行情数据采集工具（Tushare版）") + print(f"Redis Key: {REDIS_KEY}") + print(f"批次大小: {batch_size}只股票/次") + print("=" * 60) + + def _get_redis_conn(self): + """获取Redis连接""" + pool = redis.ConnectionPool( + host=REDIS_CONFIG['host'], + port=REDIS_CONFIG['port'], + db=REDIS_CONFIG.get('db', 0), + password=REDIS_CONFIG.get('password', None), + decode_responses=True + ) + return redis.Redis(connection_pool=pool) + + def convert_db_code_to_tushare(self, db_code): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001、BJ430047 + Tushare格式：600000.SH、000001.SZ、430047.BJ + + Args: + db_code: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if db_code.startswith('SH'): + return f"{db_code[2:]}.SH" + elif db_code.startswith('SZ'): + return f"{db_code[2:]}.SZ" + elif db_code.startswith('BJ'): + return f"{db_code[2:]}.BJ" + else: + return db_code + + def fetch_stock_codes(self): + """ + 从数据库获取所有A股股票代码 + + Returns: + 股票代码列表（数据库格式，如SH600000） + """ + try: + engine = create_engine(self.db_url) + query = "SELECT gp_code FROM gp_code_all WHERE gp_code IS NOT NULL ORDER BY gp_code" + df = pd.read_sql(query, engine) + engine.dispose() + + stock_codes = df['gp_code'].tolist() + print(f"从数据库获取到{len(stock_codes)}只A股代码") + return stock_codes + except Exception as e: + print(f"从数据库获取股票代码失败: {e}") + return [] + + def fetch_realtime_quote(self, ts_codes): + """ + 获取实时行情数据 + + Args: + ts_codes: Tushare格式的股票代码列表（如['600000.SH', '000001.SZ']） + + Returns: + 实时行情DataFrame + """ + try: + # 将列表转换为逗号分隔的字符串 + ts_code_str = ','.join(ts_codes) + + # 调用Tushare实时行情接口 + df = ts.realtime_quote(ts_code=ts_code_str) + + return df + except Exception as e: + print(f"获取实时行情失败: {e}") + return pd.DataFrame() + + def transform_to_redis_format(self, row, db_code): + """ + 将Tushare实时行情数据转换为Redis存储格式（与雪球格式对齐） + + Args: + row: Tushare实时行情数据的一行（Series） + db_code: 数据库格式的股票代码（如SH600000） + + Returns: + dict: 转换后的数据字典 + """ + # 创建与雪球格式对齐的数据结构 + result = { + # 基础信息 + "symbol": db_code, # 股票代码（数据库格式） + "name": row.get('NAME', ''), # 股票名称 + + # 价格信息 + "current": row.get('PRICE', None), # 当前价 + "open": row.get('OPEN', None), # 开盘价 + "high": row.get('HIGH', None), # 最高价 + "low": row.get('LOW', None), # 最低价 + "pre_close": row.get('PRE_CLOSE', None), # 昨收价（对应last_close） + + # 涨跌信息 + "chg": None, # 涨跌额（需要计算） + "percent": None, # 涨跌幅（需要计算） + + # 成交信息 + "volume": row.get('VOLUME', None), # 成交量 + "amount": row.get('AMOUNT', None), # 成交额 + + # 估值信息（实时行情接口无，设为None） + "pe_ttm": None, + "pb": None, + "pb_ttm": None, + "ps": None, + "pcf": None, + "dividend_yield": None, + "roe_ttm": None, + + # 市值信息（实时行情接口无，设为None） + "market_capital": None, + "float_market_capital": None, + "total_shares": None, + "float_shares": None, + + # 交易信息（实时行情接口无，设为None） + "turnover_rate": None, + "volume_ratio": None, + "amplitude": None, + + # 其他信息 + "fetch_time": datetime.now().strftime('%Y-%m-%d %H:%M:%S'), # 采集时间 + "type": 11, # 默认类型 + "has_follow": False, + "tick_size": 0.01, + "lot_size": 100, + } + + # 计算涨跌额和涨跌幅 + if result['current'] is not None and result['pre_close'] is not None: + try: + current = float(result['current']) + pre_close = float(result['pre_close']) + + # 涨跌额 + result['chg'] = round(current - pre_close, 2) + + # 涨跌幅（百分比） + if pre_close != 0: + result['percent'] = round((current - pre_close) / pre_close * 100, 2) + except: + pass + + return result + + def run_collection(self): + """ + 执行实时行情采集 + """ + print("=" * 60) + print("开始执行A股实时行情采集") + print("=" * 60) + + start_time = time.time() + + try: + # 获取所有股票代码 + db_codes = self.fetch_stock_codes() + + if not db_codes: + print("未获取到股票代码，终止采集") + return + + total_codes = len(db_codes) + print(f"共需采集 {total_codes} 只股票") + print(f"将分 {(total_codes + self.batch_size - 1) // self.batch_size} 批次进行") + print("=" * 60) + + # 先采集所有数据到内存，避免中途出错导致数据不完整 + print("开始采集数据（先采集到内存，完成后统一保存）...") + all_data = {} # 存储所有采集到的数据 {db_code: data_dict} + batch_num = 0 + total_collected = 0 + + for i in range(0, total_codes, self.batch_size): + batch_num += 1 + batch_db_codes = db_codes[i:i + self.batch_size] + + # 转换为Tushare格式 + batch_ts_codes = [self.convert_db_code_to_tushare(code) for code in batch_db_codes] + + try: + # 获取实时行情 + df = self.fetch_realtime_quote(batch_ts_codes) + + if df.empty: + print(f" 批次 {batch_num} 未获取到数据") + continue + + # 转换数据并存储到内存 + # 创建TS_CODE到DB_CODE的映射 + ts_to_db_map = { + self.convert_db_code_to_tushare(db_code): db_code + for db_code in batch_db_codes + } + + for _, row in df.iterrows(): + # 根据TS_CODE匹配对应的DB_CODE + ts_code = row.get('TS_CODE', '') + if ts_code in ts_to_db_map: + db_code = ts_to_db_map[ts_code] + + # 转换为Redis格式 + data = self.transform_to_redis_format(row, db_code) + + # 存储到内存字典 + all_data[db_code] = data + total_collected += 1 + + # 显示进度 + print(f" 累计进度: {total_collected}/{total_codes} ({total_collected*100//total_codes}%)") + + except Exception as e: + print(f" 批次 {batch_num} 采集失败: {e}") + continue + + # API限频控制 + time.sleep(0.1) + + # 采集完成后，一次性保存到Redis + print("\n" + "=" * 60) + print(f"数据采集完成，共采集到 {total_collected} 只股票的数据") + print("开始保存到Redis...") + print("=" * 60) + + if all_data: + # 使用pipeline批量写入，提高性能 + pipe = self.redis_conn.pipeline() + + for db_code, data in all_data.items(): + pipe.hset(REDIS_KEY, db_code, json.dumps(data, ensure_ascii=False)) + + # 执行批量写入（这会替换Redis中的旧数据） + pipe.execute() + print(f"成功保存 {len(all_data)} 条记录到Redis") + else: + print("警告：未采集到任何数据，Redis数据保持不变") + + end_time = time.time() + + # 输出统计信息 + print("\n" + "=" * 60) + print("实时行情采集完成") + print("=" * 60) + print(f"总股票数: {total_codes}") + print(f"成功采集: {total_collected}") + print(f"成功保存: {len(all_data)}") + print(f"失败: {total_codes - total_collected}") + print(f"耗时: {end_time - start_time:.2f} 秒") + print(f"Redis Key: {REDIS_KEY}") + print("=" * 60) + + except Exception as e: + print(f"采集过程发生错误: {e}") + import traceback + traceback.print_exc() + + def get_stock_realtime_info(self, stock_code): + """ + 从Redis获取单只股票的实时行情（兼容接口） + + Args: + stock_code: 股票代码，支持多种格式（SH600000、600000.SH等） + + Returns: + dict or None + """ + # 统一转换为数据库格式 + if '.' in stock_code: + code, market = stock_code.split('.') + if market == 'SH': + db_code = f'SH{code}' + elif market == 'SZ': + db_code = f'SZ{code}' + elif market == 'BJ': + db_code = f'BJ{code}' + else: + db_code = stock_code + elif stock_code.startswith(('SH', 'SZ', 'BJ')): + db_code = stock_code + else: + # 默认不处理 + db_code = stock_code + + # 从Redis获取 + value = self.redis_conn.hget(REDIS_KEY, db_code) + if not value: + return None + + try: + data = json.loads(value) + return data + except Exception as e: + print(f"解析Redis数据失败: {e}") + return None + + +def collect_realtime_quote(db_url, tushare_token, batch_size=49): + """ + A股实时行情采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + batch_size: 每批采集的股票数量，默认49 + """ + collector = RealtimeQuoteCollector(db_url, tushare_token, batch_size) + collector.run_collection() + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 执行采集 + collect_realtime_quote(db_url, tushare_token, batch_size=49) \ No newline at end of file diff --git a/src/tushare_scripts/stock_factor_collector.py b/src/tushare_scripts/stock_factor_collector.py new file mode 100644 index 0000000..9404a85 --- /dev/null +++ b/src/tushare_scripts/stock_factor_collector.py @@ -0,0 +1,380 @@ +# coding:utf-8 + +""" +股票技术因子数据采集工具 +功能：从Tushare获取股票技术因子数据，存储到gp_stock_factor表 +API文档: https://tushare.pro/document/2?doc_id=296 +注意：该接口需要至少2000积分权限，单次最大返回10000条数据 +18点有数据 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class StockFactorCollector: + """股票技术因子数据采集器""" + + def __init__(self, db_url, tushare_token, table_name='gp_stock_factor'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 数据表名，默认为gp_stock_factor + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.table_name = table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("股票技术因子数据采集工具") + print(f"目标数据表: {self.table_name}") + print("=" * 60) + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def convert_db_code_to_tushare(self, symbol): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001 + Tushare格式：600000.SH、000001.SZ + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + elif symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('BJ'): + return f"{symbol[2:]}.BJ" + else: + return symbol + + def fetch_all_stock_codes(self): + """ + 从数据库获取所有A股股票代码 + + Returns: + 股票代码列表 + """ + try: + query = "SELECT gp_code FROM gp_code_all" + df = pd.read_sql(query, self.engine) + codes = df['gp_code'].tolist() + print(f"获取到A股股票代码: {len(codes)}个") + return codes + except Exception as e: + print(f"获取股票代码失败: {e}") + return [] + + def fetch_stock_factor(self, ts_code=None, trade_date=None, start_date=None, end_date=None): + """ + 从Tushare获取股票技术因子数据 + API文档: https://tushare.pro/document/2?doc_id=296 + 单次最大返回10000条数据 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + trade_date: 交易日期，格式YYYYMMDD（单日查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 技术因子DataFrame + """ + try: + df = self.pro.stk_factor( + ts_code=ts_code, + trade_date=trade_date, + start_date=start_date, + end_date=end_date + ) + return df + except Exception as e: + print(f"获取技术因子数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换Tushare数据为数据库格式 + + Args: + df: Tushare返回的DataFrame + + Returns: + 转换后的DataFrame + """ + if df.empty: + return pd.DataFrame() + + result_df = pd.DataFrame() + + # 字段映射 + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + result_df['ts_code'] = df['ts_code'] + result_df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + + # 基础价格字段 + result_df['close'] = df.get('close', None) + result_df['open'] = df.get('open', None) + result_df['high'] = df.get('high', None) + result_df['low'] = df.get('low', None) + result_df['pre_close'] = df.get('pre_close', None) + result_df['change'] = df.get('change', None) + result_df['pct_change'] = df.get('pct_change', None) + result_df['vol'] = df.get('vol', None) + result_df['amount'] = df.get('amount', None) + + # 复权因子和复权价格 + result_df['adj_factor'] = df.get('adj_factor', None) + result_df['open_hfq'] = df.get('open_hfq', None) + result_df['open_qfq'] = df.get('open_qfq', None) + result_df['close_hfq'] = df.get('close_hfq', None) + result_df['close_qfq'] = df.get('close_qfq', None) + result_df['high_hfq'] = df.get('high_hfq', None) + result_df['high_qfq'] = df.get('high_qfq', None) + result_df['low_hfq'] = df.get('low_hfq', None) + result_df['low_qfq'] = df.get('low_qfq', None) + result_df['pre_close_hfq'] = df.get('pre_close_hfq', None) + result_df['pre_close_qfq'] = df.get('pre_close_qfq', None) + + # 技术指标 + result_df['macd_dif'] = df.get('macd_dif', None) + result_df['macd_dea'] = df.get('macd_dea', None) + result_df['macd'] = df.get('macd', None) + result_df['kdj_k'] = df.get('kdj_k', None) + result_df['kdj_d'] = df.get('kdj_d', None) + result_df['kdj_j'] = df.get('kdj_j', None) + result_df['rsi_6'] = df.get('rsi_6', None) + result_df['rsi_12'] = df.get('rsi_12', None) + result_df['rsi_24'] = df.get('rsi_24', None) + result_df['boll_upper'] = df.get('boll_upper', None) + result_df['boll_mid'] = df.get('boll_mid', None) + result_df['boll_lower'] = df.get('boll_lower', None) + result_df['cci'] = df.get('cci', None) + + return result_df + + def run_full_collection(self, start_date=None, end_date=None): + """ + 执行全量覆盖采集（按日期逐日采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为100天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行全量覆盖采集（按日期逐日采集）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近4000天历史数据 + start_date_obj = end_date_obj - timedelta(days=4000) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 按日期逐日采集 + current_date = start_date_obj + total_days = (end_date_obj - start_date_obj).days + 1 + processed_days = 0 + + while current_date <= end_date_obj: + date_str = current_date.strftime('%Y-%m-%d') + + print(f"\n[{processed_days + 1}/{total_days}] 采集日期: {date_str}") + + try: + # 使用每日采集方法 + self.run_daily_collection(date=date_str) + except Exception as e: + print(f"采集 {date_str} 失败: {e}") + + # 移到下一天 + current_date += timedelta(days=1) + processed_days += 1 + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量覆盖采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"总天数: {total_days}") + print(f"已处理: {processed_days}天") + print("=" * 60) + + except Exception as e: + print(f"全量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集 + 获取指定日期的全市场技术因子数据 + 单次可获取10000条，足够一日所有股票数据 + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(trade_date) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{self.table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 批量获取全市场当日技术因子数据（不传ts_code，获取所有股票） + print(f"正在获取{date_str}全市场技术因子数据...") + df = self.fetch_stock_factor(trade_date=trade_date) + + if df.empty: + print(f"{date_str}没有技术因子数据（可能是非交易日或接口无数据）") + return + + print(f"成功获取技术因子数据，共{len(df)}只股票") + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print("数据转换失败") + return + + # 保存到数据库 + print(f"正在保存数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}条记录") + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"成功采集: {len(result_df)}只股票") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_stock_factor(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None): + """ + 股票技术因子数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'full'=全量覆盖采集 + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 开始日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为100天前 + end_date: 结束日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为今天 + """ + collector = StockFactorCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection(start_date, end_date) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每日使用） + # collect_stock_factor(db_url, tushare_token, mode='daily') + + # 2. 全量覆盖采集（首次使用或需要重建数据时） + # collect_stock_factor(db_url, tushare_token, mode='full') + + # 3. 采集指定日期的数据 + collect_stock_factor(db_url, tushare_token, mode='daily', date='2025-11-06') + + # 4. 采集指定日期范围的数据 + # collect_stock_factor(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') diff --git a/src/tushare_scripts/stock_factor_pro_collector.py b/src/tushare_scripts/stock_factor_pro_collector.py new file mode 100644 index 0000000..e158709 --- /dev/null +++ b/src/tushare_scripts/stock_factor_pro_collector.py @@ -0,0 +1,770 @@ +# coding:utf-8 + +""" +股票技术因子数据采集工具（专业版-按年月分表） +功能：从Tushare获取股票技术因子专业版数据，存储到gp_stock_factor_pro_YYYYMM月度分表 +API文档: https://tushare.pro/document/2?doc_id=328 + +注意事项： + 1. 该接口需要至少5000积分权限 + 2. 单次最大返回10000条数据 + 3. 由于全市场单日数据超过10000条限制，采用按股票逐一采集策略 + 4. 每天17~18点左右更新 + 5. 字段众多（261个），数据量巨大，采用按年月分表策略 + +分表策略： + - 表名格式：gp_stock_factor_pro_202510（2025年10月） + - 自动按数据的交易日期分配到对应月份的表 + - 每个月度表包含该月所有股票的所有技术因子数据 + +采集策略： + - 每日增量：一次性获取指定日期全市场数据（约5000只股票），保存到当月分表（1次API调用，约1秒） + - 全量历史：逐只股票采集日期范围数据（默认4000天），自动按月份分配到对应分表（每秒10次请求，约8分钟） + +建表说明： + - 首次使用建议手动创建模板表 gp_stock_factor_pro_template，包含所有261个字段 + - 或者手动创建首个月份的表（如gp_stock_factor_pro_202510），程序会自动复制表结构 + - 程序会尝试自动创建分表，但由于字段众多，建议提前手动创建 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class StockFactorProCollector: + """股票技术因子专业版数据采集器（按年月分表）""" + + def __init__(self, db_url, tushare_token, base_table_name='gp_stock_factor_pro'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + base_table_name: 基础表名，默认为gp_stock_factor_pro（实际表名会加上年月后缀，如gp_stock_factor_pro_202510） + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.base_table_name = base_table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("股票技术因子数据采集工具（专业版-按年月分表）") + print(f"基础表名: {self.base_table_name}") + print("=" * 60) + + def get_table_name_by_date(self, date_str): + """ + 根据日期获取对应的分表名称 + + Args: + date_str: 日期字符串，格式YYYY-MM-DD + + Returns: + 分表名称，如gp_stock_factor_pro_202510 + """ + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + year_month = date_obj.strftime('%Y%m') + return f"{self.base_table_name}_{year_month}" + + def create_monthly_table(self, table_name): + """ + 创建月度分表（如果不存在） + + Args: + table_name: 表名，如gp_stock_factor_pro_202510 + """ + try: + # 由于字段太多（261个），这里使用简化的建表语句 + # 实际使用时，建议手动创建表或从模板表复制结构 + create_table_sql = f""" + CREATE TABLE IF NOT EXISTS `{table_name}` LIKE `{self.base_table_name}_template` + """ + + # 如果没有模板表，则创建完整的表结构 + # 注意：由于字段太多，这里只列出关键字段，其他字段会在实际使用时动态处理 + fallback_sql = f""" + CREATE TABLE IF NOT EXISTS `{table_name}` ( + `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键', + `symbol` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NOT NULL COMMENT '股票代码（数据库格式，如SH600000）', + `ts_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NOT NULL COMMENT 'Tushare股票代码（如600000.SH）', + `trade_date` date NOT NULL COMMENT '交易日期', + `open` decimal(10, 2) NULL DEFAULT NULL COMMENT '开盘价', + `open_hfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '开盘价（后复权）', + `open_qfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '开盘价（前复权）', + `high` decimal(10, 2) NULL DEFAULT NULL COMMENT '最高价', + `high_hfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '最高价（后复权）', + `high_qfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '最高价（前复权）', + `low` decimal(10, 2) NULL DEFAULT NULL COMMENT '最低价', + `low_hfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '最低价（后复权）', + `low_qfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '最低价（前复权）', + `close` decimal(10, 2) NULL DEFAULT NULL COMMENT '收盘价', + `close_hfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '收盘价（后复权）', + `close_qfq` decimal(10, 2) NULL DEFAULT NULL COMMENT '收盘价（前复权）', + `pre_close` decimal(10, 2) NULL DEFAULT NULL COMMENT '昨收价', + `change` decimal(10, 2) NULL DEFAULT NULL COMMENT '涨跌额', + `pct_chg` decimal(10, 4) NULL DEFAULT NULL COMMENT '涨跌幅（%）', + `vol` decimal(20, 2) NULL DEFAULT NULL COMMENT '成交量（手）', + `amount` decimal(20, 2) NULL DEFAULT NULL COMMENT '成交额（千元）', + `turnover_rate` decimal(10, 4) NULL DEFAULT NULL COMMENT '换手率（%）', + `turnover_rate_f` decimal(10, 4) NULL DEFAULT NULL COMMENT '换手率（自由流通股）', + `volume_ratio` decimal(10, 4) NULL DEFAULT NULL COMMENT '量比', + `pe` decimal(10, 4) NULL DEFAULT NULL COMMENT '市盈率', + `pe_ttm` decimal(10, 4) NULL DEFAULT NULL COMMENT '市盈率（TTM）', + `pb` decimal(10, 4) NULL DEFAULT NULL COMMENT '市净率', + `ps` decimal(10, 4) NULL DEFAULT NULL COMMENT '市销率', + `ps_ttm` decimal(10, 4) NULL DEFAULT NULL COMMENT '市销率（TTM）', + `dv_ratio` decimal(10, 4) NULL DEFAULT NULL COMMENT '股息率（%）', + `dv_ttm` decimal(10, 4) NULL DEFAULT NULL COMMENT '股息率（TTM）（%）', + `total_share` decimal(20, 2) NULL DEFAULT NULL COMMENT '总股本（万股）', + `float_share` decimal(20, 2) NULL DEFAULT NULL COMMENT '流通股本（万股）', + `free_share` decimal(20, 2) NULL DEFAULT NULL COMMENT '自由流通股本（万）', + `total_mv` decimal(20, 2) NULL DEFAULT NULL COMMENT '总市值（万元）', + `circ_mv` decimal(20, 2) NULL DEFAULT NULL COMMENT '流通市值（万元）', + `adj_factor` decimal(15, 6) NULL DEFAULT NULL COMMENT '复权因子', + `asi_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASI振动升降指标（不复权）', + `asi_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASI振动升降指标（后复权）', + `asi_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASI振动升降指标（前复权）', + `asit_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASIT（不复权）', + `asit_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASIT（后复权）', + `asit_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ASIT（前复权）', + `atr_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ATR真实波幅（不复权）', + `atr_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ATR真实波幅（后复权）', + `atr_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ATR真实波幅（前复权）', + `bbi_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BBI多空指标（不复权）', + `bbi_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BBI多空指标（后复权）', + `bbi_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BBI多空指标（前复权）', + `bias1_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS1乖离率（不复权）', + `bias1_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS1乖离率（后复权）', + `bias1_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS1乖离率（前复权）', + `bias2_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS2乖离率（不复权）', + `bias2_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS2乖离率（后复权）', + `bias2_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS2乖离率（前复权）', + `bias3_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS3乖离率（不复权）', + `bias3_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS3乖离率（后复权）', + `bias3_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BIAS3乖离率（前复权）', + `boll_lower_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL下轨（不复权）', + `boll_lower_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL下轨（后复权）', + `boll_lower_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL下轨（前复权）', + `boll_mid_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL中轨（不复权）', + `boll_mid_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL中轨（后复权）', + `boll_mid_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL中轨（前复权）', + `boll_upper_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL上轨（不复权）', + `boll_upper_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL上轨（后复权）', + `boll_upper_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BOLL上轨（前复权）', + `brar_ar_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-AR（不复权）', + `brar_ar_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-AR（后复权）', + `brar_ar_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-AR（前复权）', + `brar_br_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-BR（不复权）', + `brar_br_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-BR（后复权）', + `brar_br_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'BRAR-BR（前复权）', + `cci_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CCI顺势指标（不复权）', + `cci_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CCI顺势指标（后复权）', + `cci_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CCI顺势指标（前复权）', + `cr_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CR价格动量指标（不复权）', + `cr_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CR价格动量指标（后复权）', + `cr_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'CR价格动量指标（前复权）', + `dfma_dif_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIF（不复权）', + `dfma_dif_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIF（后复权）', + `dfma_dif_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIF（前复权）', + `dfma_difma_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIFMA（不复权）', + `dfma_difma_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIFMA（后复权）', + `dfma_difma_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DFMA-DIFMA（前复权）', + `dmi_adx_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADX（不复权）', + `dmi_adx_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADX（后复权）', + `dmi_adx_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADX（前复权）', + `dmi_adxr_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADXR（不复权）', + `dmi_adxr_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADXR（后复权）', + `dmi_adxr_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-ADXR（前复权）', + `dmi_mdi_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-MDI（不复权）', + `dmi_mdi_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-MDI（后复权）', + `dmi_mdi_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-MDI（前复权）', + `dmi_pdi_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-PDI（不复权）', + `dmi_pdi_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-PDI（后复权）', + `dmi_pdi_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DMI-PDI（前复权）', + `downdays` decimal(10, 0) NULL DEFAULT NULL COMMENT '连跌天数', + `updays` decimal(10, 0) NULL DEFAULT NULL COMMENT '连涨天数', + `dpo_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DPO区间震荡线（不复权）', + `dpo_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DPO区间震荡线（后复权）', + `dpo_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'DPO区间震荡线（前复权）', + `madpo_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MADPO（不复权）', + `madpo_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MADPO（后复权）', + `madpo_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MADPO（前复权）', + `ema_bfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA5（不复权）', + `ema_bfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA10（不复权）', + `ema_bfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA20（不复权）', + `ema_bfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA30（不复权）', + `ema_bfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA60（不复权）', + `ema_bfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA90（不复权）', + `ema_bfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA250（不复权）', + `ema_hfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA5（后复权）', + `ema_hfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA10（后复权）', + `ema_hfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA20（后复权）', + `ema_hfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA30（后复权）', + `ema_hfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA60（后复权）', + `ema_hfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA90（后复权）', + `ema_hfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA250（后复权）', + `ema_qfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA5（前复权）', + `ema_qfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA10（前复权）', + `ema_qfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA20（前复权）', + `ema_qfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA30（前复权）', + `ema_qfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA60（前复权）', + `ema_qfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA90（前复权）', + `ema_qfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMA250（前复权）', + `emv_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMV简易波动指标（不复权）', + `emv_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMV简易波动指标（后复权）', + `emv_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EMV简易波动指标（前复权）', + `maemv_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAEMV（不复权）', + `maemv_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAEMV（后复权）', + `maemv_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAEMV（前复权）', + `expma_12_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA12（不复权）', + `expma_12_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA12（后复权）', + `expma_12_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA12（前复权）', + `expma_50_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA50（不复权）', + `expma_50_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA50（后复权）', + `expma_50_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'EXPMA50（前复权）', + `kdj_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-J（不复权）', + `kdj_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-J（后复权）', + `kdj_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-J（前复权）', + `kdj_d_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-D（不复权）', + `kdj_d_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-D（后复权）', + `kdj_d_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-D（前复权）', + `kdj_k_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-K（不复权）', + `kdj_k_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-K（后复权）', + `kdj_k_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KDJ-K（前复权）', + `ktn_down_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN下轨（不复权）', + `ktn_down_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN下轨（后复权）', + `ktn_down_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN下轨（前复权）', + `ktn_mid_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN中轨（不复权）', + `ktn_mid_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN中轨（后复权）', + `ktn_mid_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN中轨（前复权）', + `ktn_upper_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN上轨（不复权）', + `ktn_upper_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN上轨（后复权）', + `ktn_upper_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'KTN上轨（前复权）', + `lowdays` decimal(10, 0) NULL DEFAULT NULL COMMENT '最低价区间天数', + `topdays` decimal(10, 0) NULL DEFAULT NULL COMMENT '最高价区间天数', + `ma_bfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA5（不复权）', + `ma_bfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA10（不复权）', + `ma_bfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA20（不复权）', + `ma_bfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA30（不复权）', + `ma_bfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA60（不复权）', + `ma_bfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA90（不复权）', + `ma_bfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA250（不复权）', + `ma_hfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA5（后复权）', + `ma_hfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA10（后复权）', + `ma_hfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA20（后复权）', + `ma_hfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA30（后复权）', + `ma_hfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA60（后复权）', + `ma_hfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA90（后复权）', + `ma_hfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA250（后复权）', + `ma_qfq_5` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA5（前复权）', + `ma_qfq_10` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA10（前复权）', + `ma_qfq_20` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA20（前复权）', + `ma_qfq_30` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA30（前复权）', + `ma_qfq_60` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA60（前复权）', + `ma_qfq_90` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA90（前复权）', + `ma_qfq_250` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA250（前复权）', + `macd_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD（不复权）', + `macd_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD（后复权）', + `macd_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD（前复权）', + `macd_dea_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DEA（不复权）', + `macd_dea_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DEA（后复权）', + `macd_dea_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DEA（前复权）', + `macd_dif_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DIF（不复权）', + `macd_dif_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DIF（后复权）', + `macd_dif_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MACD-DIF（前复权）', + `mass_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MASS梅斯线（不复权）', + `mass_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MASS梅斯线（后复权）', + `mass_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MASS梅斯线（前复权）', + `ma_mass_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA-MASS（不复权）', + `ma_mass_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA-MASS（后复权）', + `ma_mass_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MA-MASS（前复权）', + `mfi_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MFI资金流量指标（不复权）', + `mfi_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MFI资金流量指标（后复权）', + `mfi_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MFI资金流量指标（前复权）', + `mtm_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTM动量指标（不复权）', + `mtm_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTM动量指标（后复权）', + `mtm_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTM动量指标（前复权）', + `mtmma_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTMMA（不复权）', + `mtmma_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTMMA（后复权）', + `mtmma_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MTMMA（前复权）', + `obv_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'OBV能量潮（不复权）', + `obv_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'OBV能量潮（后复权）', + `obv_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'OBV能量潮（前复权）', + `psy_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSY心理线指标（不复权）', + `psy_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSY心理线指标（后复权）', + `psy_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSY心理线指标（前复权）', + `psyma_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSYMA（不复权）', + `psyma_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSYMA（后复权）', + `psyma_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'PSYMA（前复权）', + `roc_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ROC变动率指标（不复权）', + `roc_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ROC变动率指标（后复权）', + `roc_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'ROC变动率指标（前复权）', + `maroc_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAROC（不复权）', + `maroc_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAROC（后复权）', + `maroc_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'MAROC（前复权）', + `rsi_bfq_6` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI6（不复权）', + `rsi_bfq_12` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI12（不复权）', + `rsi_bfq_24` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI24（不复权）', + `rsi_hfq_6` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI6（后复权）', + `rsi_hfq_12` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI12（后复权）', + `rsi_hfq_24` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI24（后复权）', + `rsi_qfq_6` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI6（前复权）', + `rsi_qfq_12` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI12（前复权）', + `rsi_qfq_24` decimal(15, 6) NULL DEFAULT NULL COMMENT 'RSI24（前复权）', + `taq_down_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ下轨（不复权）', + `taq_down_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ下轨（后复权）', + `taq_down_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ下轨（前复权）', + `taq_mid_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ中轨（不复权）', + `taq_mid_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ中轨（后复权）', + `taq_mid_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ中轨（前复权）', + `taq_up_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ上轨（不复权）', + `taq_up_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ上轨（后复权）', + `taq_up_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TAQ上轨（前复权）', + `trix_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRIX三重指数平滑平均线（不复权）', + `trix_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRIX三重指数平滑平均线（后复权）', + `trix_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRIX三重指数平滑平均线（前复权）', + `trma_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRMA（不复权）', + `trma_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRMA（后复权）', + `trma_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'TRMA（前复权）', + `vr_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'VR容量比率（不复权）', + `vr_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'VR容量比率（后复权）', + `vr_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'VR容量比率（前复权）', + `wr_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR威廉指标（不复权）', + `wr_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR威廉指标（后复权）', + `wr_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR威廉指标（前复权）', + `wr1_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR1威廉指标（不复权）', + `wr1_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR1威廉指标（后复权）', + `wr1_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'WR1威廉指标（前复权）', + `xsii_td1_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD1（不复权）', + `xsii_td1_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD1（后复权）', + `xsii_td1_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD1（前复权）', + `xsii_td2_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD2（不复权）', + `xsii_td2_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD2（后复权）', + `xsii_td2_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD2（前复权）', + `xsii_td3_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD3（不复权）', + `xsii_td3_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD3（后复权）', + `xsii_td3_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD3（前复权）', + `xsii_td4_bfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD4（不复权）', + `xsii_td4_hfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD4（后复权）', + `xsii_td4_qfq` decimal(15, 6) NULL DEFAULT NULL COMMENT 'XSII-TD4（前复权）', + `create_time` datetime NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', + PRIMARY KEY (`id`) USING BTREE, + UNIQUE INDEX `idx_symbol_trade_date`(`symbol` ASC, `trade_date` ASC) USING BTREE, + INDEX `idx_symbol`(`symbol` ASC) USING BTREE, + INDEX `idx_trade_date`(`trade_date` ASC) USING BTREE, + INDEX `idx_ts_code`(`ts_code` ASC) USING BTREE + ) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin + COMMENT = '股票技术因子数据表-专业版（按月分表）' ROW_FORMAT = DYNAMIC + """ + + with self.engine.begin() as conn: + try: + # 尝试从模板表复制结构 + conn.execute(text(create_table_sql)) + print(f"✓ 表 {table_name} 已从模板表创建") + except: + # 如果模板表不存在，则使用基础建表语句 + # 注意：这只会创建基础字段，实际使用时Pandas会自动添加其他列 + conn.execute(text(fallback_sql)) + print(f"✓ 表 {table_name} 已创建（使用基础结构）") + + except Exception as e: + print(f"创建表 {table_name} 失败: {e}") + # 不抛出异常，让程序继续运行，Pandas的to_sql会尝试创建表 + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def convert_db_code_to_tushare(self, symbol): + """ + 将数据库格式的股票代码转换为Tushare格式 + 数据库格式：SH600000、SZ000001 + Tushare格式：600000.SH、000001.SZ + + Args: + symbol: 数据库格式的股票代码 + + Returns: + Tushare格式的股票代码 + """ + if symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + elif symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('BJ'): + return f"{symbol[2:]}.BJ" + else: + return symbol + + def fetch_all_stock_codes(self): + """ + 从数据库获取所有A股股票代码 + + Returns: + 股票代码列表 + """ + try: + query = "SELECT gp_code FROM gp_code_all" + df = pd.read_sql(query, self.engine) + codes = df['gp_code'].tolist() + print(f"获取到A股股票代码: {len(codes)}个") + return codes + except Exception as e: + print(f"获取股票代码失败: {e}") + return [] + + def fetch_stock_factor_pro(self, ts_code=None, trade_date=None, start_date=None, end_date=None): + """ + 从Tushare获取股票技术因子专业版数据 + API文档: https://tushare.pro/document/2?doc_id=328 + 单次最大返回10000条数据 + + Args: + ts_code: Tushare格式的股票代码，为None时获取全市场数据 + trade_date: 交易日期，格式YYYYMMDD（单日查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 技术因子DataFrame + """ + try: + df = self.pro.stk_factor_pro( + ts_code=ts_code, + trade_date=trade_date, + start_date=start_date, + end_date=end_date + ) + return df + except Exception as e: + print(f"获取技术因子专业版数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换Tushare数据为数据库格式 + 动态映射所有字段，支持261个字段的完整处理 + + Args: + df: Tushare返回的DataFrame + + Returns: + 转换后的DataFrame + """ + if df.empty: + return pd.DataFrame() + + result_df = pd.DataFrame() + + # 处理symbol和ts_code字段 + result_df['symbol'] = df['ts_code'].apply(self.convert_tushare_code_to_db) + result_df['ts_code'] = df['ts_code'] + result_df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + + # 动态映射所有其他字段（除了ts_code和trade_date已处理） + # 这样可以自动处理所有技术指标字段，无需手动枚举 + exclude_fields = {'ts_code', 'trade_date'} + for col in df.columns: + if col not in exclude_fields and col not in result_df.columns: + result_df[col] = df[col] + + return result_df + + def run_full_collection(self, start_date=None, end_date=None): + """ + 执行全量覆盖采集（按股票+日期范围采集，按年月分表） + 每只股票采集指定日期范围的数据，自动根据数据日期分配到对应的月度分表 + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为4000天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行全量覆盖采集（按年月分表，按股票逐一采集日期范围）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近4000天历史数据 + start_date_obj = end_date_obj - timedelta(days=4000) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + start_date_str = start_date_obj.strftime('%Y%m%d') + end_date_str = end_date_obj.strftime('%Y%m%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 预先创建所有涉及月份的分表 + current_month = start_date_obj.replace(day=1) + end_month = end_date_obj.replace(day=1) + while current_month <= end_month: + month_str = current_month.strftime('%Y-%m-%d') + table_name = self.get_table_name_by_date(month_str) + self.create_monthly_table(table_name) + # 移动到下个月 + if current_month.month == 12: + current_month = current_month.replace(year=current_month.year + 1, month=1) + else: + current_month = current_month.replace(month=current_month.month + 1) + + # 获取所有A股股票代码 + stock_codes = self.fetch_all_stock_codes() + if not stock_codes: + print("未获取到股票代码") + return + + print(f"共需采集{len(stock_codes)}只股票") + + # 逐只股票采集日期范围数据 + success_count = 0 + fail_count = 0 + total_records = 0 + + for i, symbol in enumerate(stock_codes, 1): + try: + # 转换为Tushare格式 + ts_code = self.convert_db_code_to_tushare(symbol) + + # 获取该股票指定日期范围的技术因子数据 + df = self.fetch_stock_factor_pro( + ts_code=ts_code, + start_date=start_date_str, + end_date=end_date_str + ) + + if not df.empty: + # 转换数据格式 + result_df = self.transform_data(df) + + if not result_df.empty: + # 按月份分组保存到对应的分表 + # 给每行数据添加一个临时的年月列用于分组 + result_df['year_month'] = result_df['trade_date'].dt.strftime('%Y%m') + + # 按年月分组 + for year_month, group_df in result_df.groupby('year_month'): + # 删除临时列 + group_df = group_df.drop(columns=['year_month']) + + # 获取该月的表名 + table_name = f"{self.base_table_name}_{year_month}" + + # 保存到对应的月度分表 + group_df.to_sql(table_name, self.engine, if_exists='append', index=False) + total_records += len(group_df) + + success_count += 1 + + # 每50只股票显示一次进度 + if i % 50 == 0: + print(f"进度: {i}/{len(stock_codes)} ({success_count}成功, {fail_count}失败, 共{total_records}条记录)") + + # API限频控制 + time.sleep(0.1) # 每秒最多10次请求（历史数据较多，适当放慢） + + except Exception as e: + fail_count += 1 + if i % 50 == 0: + print(f"采集 {symbol} 失败: {e}") + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量覆盖采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"目标股票数: {len(stock_codes)}只") + print(f"成功采集: {success_count}只") + print(f"失败: {fail_count}只") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.base_table_name}_YYYYMM（按月分表）") + print("=" * 60) + + except Exception as e: + print(f"全量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集（按年月分表） + 一次性获取指定日期全市场的技术因子专业版数据 + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集（按年月分表，全市场一次性采集）") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + # 获取当月分表名称 + table_name = self.get_table_name_by_date(date_str) + + print(f"采集日期: {date_str} ({trade_date})") + print(f"目标表名: {table_name}") + + # 创建当月分表（如果不存在） + self.create_monthly_table(table_name) + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {table_name} WHERE DATE(trade_date) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 一次性获取指定日期全市场的技术因子数据 + print(f"正在获取{date_str}全市场技术因子数据...") + df = self.fetch_stock_factor_pro(ts_code=None, trade_date=trade_date) + + if df.empty: + print(f"{date_str}没有采集到任何数据（可能是非交易日）") + return + + print(f"成功获取{len(df)}条原始数据") + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print(f"数据转换后为空") + return + + # 一次性保存到数据库 + print(f"正在保存数据到{table_name}表...") + result_df.to_sql(table_name, self.engine, if_exists='append', index=False) + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"采集股票数: {result_df['symbol'].nunique()}只") + print(f"保存记录数: {len(result_df)}条") + print(f"数据表: {table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_stock_factor_pro(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None): + """ + 股票技术因子专业版数据采集入口函数（按年月分表） + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式，'daily'=增量采集当日数据，'full'=全量覆盖采集 + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 开始日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为4000天前 + end_date: 结束日期（仅mode='full'时有效），格式YYYY-MM-DD，默认为今天 + + 分表说明： + 数据会自动保存到对应月份的分表中，表名格式为 gp_stock_factor_pro_YYYYMM + 例如：2025年10月的数据保存到 gp_stock_factor_pro_202510 表 + + 采集策略： + - 每日模式：一次性获取指定日期全市场数据（约5000只股票），保存到当月分表 + - 全量模式：逐只股票采集历史数据，自动按月份分配到对应分表 + """ + collector = StockFactorProCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection(start_date, end_date) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://fac_pattern:Chlry$%.8pattern@192.168.16.150:3306/factordb_mysql' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例（按年月分表）： + + # 1. 每日增量采集（推荐每日使用，一次性获取全市场数据，保存到当月分表） + # collect_stock_factor_pro(db_url, tushare_token, mode='daily') + + # 2. 全量覆盖采集（首次使用或需要重建数据时，默认采集近4000天数据，自动按月分配到对应分表） + # collect_stock_factor_pro(db_url, tushare_token, mode='full') + + # 3. 采集指定日期的数据（一次性获取全市场数据，保存到该日期对应的月份分表） + collect_stock_factor_pro(db_url, tushare_token, mode='daily', date='2025-11-06') + + # 4. 采集指定日期范围的数据（按股票逐一采集，数据会自动按月分配到对应分表） + # collect_stock_factor_pro(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') + + # 注意： + # - 首次使用前，建议手动创建模板表 gp_stock_factor_pro_template 或首个月份表 + # - 表名格式：gp_stock_factor_pro_202510（2025年10月） + # - 每个月度表包含该月所有股票的所有技术因子数据（261个字段） + # - 每日模式：一次性请求全市场数据，速度快（约1秒） + # - 全量模式：按股票逐一采集，适合采集历史数据（约5000只股票 × 0.1秒 ≈ 8分钟） + diff --git a/src/tushare_scripts/stock_list_updater.py b/src/tushare_scripts/stock_list_updater.py new file mode 100644 index 0000000..02dce75 --- /dev/null +++ b/src/tushare_scripts/stock_list_updater.py @@ -0,0 +1,362 @@ +# coding:utf-8 + +""" +A股股票列表更新工具 +功能：从Tushare获取A股股票列表，更新gp_code_all表 +API文档: https://tushare.pro/document/2?doc_id=25 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class StockListUpdater: + """A股股票列表更新器""" + + def __init__(self, db_url, tushare_token): + """ + 初始化更新器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("A股股票列表更新工具") + print("=" * 60) + + def convert_tushare_code_to_db(self, ts_code): + """ + 将Tushare格式的股票代码转换为数据库格式 + Tushare格式：600000.SH、000001.SZ + 数据库格式：SH600000、SZ000001 + + Args: + ts_code: Tushare格式的股票代码 + + Returns: + 数据库格式的股票代码 + """ + if '.SH' in ts_code: + return f"SH{ts_code.replace('.SH', '')}" + elif '.SZ' in ts_code: + return f"SZ{ts_code.replace('.SZ', '')}" + elif '.BJ' in ts_code: + return f"BJ{ts_code.replace('.BJ', '')}" + else: + return ts_code + + def fetch_stock_list_from_tushare(self): + """ + 从Tushare获取A股股票列表 + API: stock_basic + 文档: https://tushare.pro/document/2?doc_id=25 + + Returns: + 股票列表DataFrame + """ + try: + print("正在从Tushare获取A股股票列表...") + + # 获取A股股票列表（上市状态：L=上市，D=退市，P=暂停上市） + # 只获取上市状态的股票 + df = self.pro.stock_basic( + exchange='', # 空值表示获取所有交易所 + list_status='L', # L=上市 + fields='ts_code,symbol,name,area,industry,market,list_date,fullname,enname,cnspell,exchange,curr_type,list_status,delist_date,is_hs,act_name,act_ent_type' + ) + + if df.empty: + print("未获取到股票数据") + return pd.DataFrame() + + print(f"成功获取{len(df)}只A股股票信息") + return df + + except Exception as e: + print(f"获取股票列表失败: {e}") + return pd.DataFrame() + + def get_existing_stocks_from_db(self): + """ + 从数据库获取现有的股票列表 + + Returns: + 现有股票代码列表（数据库格式） + """ + try: + query = "SELECT gp_code, gp_name FROM gp_code_all" + df = pd.read_sql(query, self.engine) + + print(f"数据库现有股票数量: {len(df)}") + return df + + except Exception as e: + print(f"查询数据库失败: {e}") + return pd.DataFrame() + + def transform_to_db_format(self, df_tushare): + """ + 将Tushare数据转换为数据库格式 + + Args: + df_tushare: Tushare返回的股票列表DataFrame + + Returns: + 转换后的DataFrame + """ + if df_tushare.empty: + return pd.DataFrame() + + # 创建新的DataFrame + df_result = pd.DataFrame() + + # 字段映射 + df_result['gp_name'] = df_tushare['name'] # 股票名称 + df_result['gp_code'] = df_tushare['ts_code'].apply(self.convert_tushare_code_to_db) # 主代码（如SH600000） + df_result['gp_code_two'] = df_tushare['ts_code'] # Tushare格式代码（如300918.SZ） + df_result['gp_code_three'] = None # 不需要填充 + + # market_cap字段为空，需要通过其他接口获取（这里暂不处理） + df_result['market_cap'] = None + + # mark1和mark2字段可用于标记市场类型 + # 根据交易所代码设置标记 + df_result['market'] = df_tushare['market'].apply(lambda x: '主板' if x == '主板' else ('科创板' if x == '科创板' else ('创业板' if x == '创业板' else '其他'))) + df_result['area'] = df_tushare['area'] + df_result['industry'] = df_tushare['industry'] + df_result['fullname'] = df_tushare['fullname'] + df_result['enname'] = df_tushare['enname'] + df_result['cnspell'] = df_tushare['cnspell'] + df_result['exchange'] = df_tushare['exchange'] + df_result['curr_type'] = df_tushare['curr_type'] + df_result['list_status'] = df_tushare['list_status'] + df_result['list_date'] = df_tushare['list_date'] + df_result['delist_date'] = df_tushare['delist_date'] + df_result['is_hs'] = df_tushare['is_hs'] + df_result['act_name'] = df_tushare['act_name'] + df_result['act_ent_type'] = df_tushare['act_ent_type'] + + return df_result + + def update_stock_list(self): + """ + 更新股票列表到数据库 + 主流程： + 1. 从Tushare获取最新股票列表 + 2. 从数据库获取现有股票列表 + 3. 对比找出新增和需要更新的股票 + 4. 执行插入和更新操作 + """ + try: + # 1. 从Tushare获取股票列表 + df_tushare = self.fetch_stock_list_from_tushare() + if df_tushare.empty: + print("未获取到Tushare数据，终止更新") + return + + # 2. 转换为数据库格式 + df_new = self.transform_to_db_format(df_tushare) + print(f"转换数据完成，共{len(df_new)}条记录") + + # 3. 从数据库获取现有股票列表 + df_existing = self.get_existing_stocks_from_db() + + if df_existing.empty: + # 数据库为空，全部插入 + print("数据库为空，将插入所有股票...") + df_new.to_sql('gp_code_all', self.engine, if_exists='append', index=False) + print(f"成功插入{len(df_new)}只股票") + else: + # 4. 对比数据，找出新增、更新和需要删除的股票 + existing_codes = set(df_existing['gp_code'].tolist()) + new_codes = set(df_new['gp_code'].tolist()) + + # 新增的股票代码 + codes_to_insert = new_codes - existing_codes + # 需要更新的股票代码（已存在的） + codes_to_update = new_codes & existing_codes + # 需要删除的股票代码（数据库有但Tushare没有，可能已退市） + codes_to_delete = existing_codes - new_codes + + print(f"\n数据对比结果:") + print(f" 数据库现有: {len(existing_codes)}只") + print(f" Tushare获取: {len(new_codes)}只") + print(f" 新增股票: {len(codes_to_insert)}只") + print(f" 需更新: {len(codes_to_update)}只") + print(f" 需删除: {len(codes_to_delete)}只（退市或其他原因）") + + # 5. 插入新股票 + if codes_to_insert: + df_insert = df_new[df_new['gp_code'].isin(codes_to_insert)] + df_insert.to_sql('gp_code_all', self.engine, if_exists='append', index=False) + print(f"\n新增股票列表:") + for _, row in df_insert.iterrows(): + print(f" {row['gp_code']} - {row['gp_name']}") + print(f"成功插入{len(df_insert)}只新股票") + else: + print("\n没有新增股票") + + # 6. 更新已有股票信息 + if codes_to_update: + print(f"\n正在更新{len(codes_to_update)}只股票的信息...") + update_count = 0 + + df_update = df_new[df_new['gp_code'].isin(codes_to_update)] + + with self.engine.begin() as conn: + for _, row in df_update.iterrows(): + # 只更新gp_code_all表中有的字段 + update_query = text(""" + UPDATE gp_code_all + SET gp_name = :gp_name, + gp_code_two = :gp_code_two, + gp_code_three = :gp_code_three, + market = :market, + area = :area, + fullname = :fullname, + enname = :enname, + cnspell = :cnspell, + exchange = :exchange, + curr_type = :curr_type, + list_status = :list_status, + list_date = :list_date, + delist_date = :delist_date, + is_hs = :is_hs, + act_name = :act_name, + act_ent_type = :act_ent_type + WHERE gp_code = :gp_code + """) + + conn.execute(update_query, { + 'gp_code': row['gp_code'], + 'gp_name': row['gp_name'], + 'gp_code_two': row['gp_code_two'], + 'gp_code_three': row['gp_code_three'], + 'market': row['market'], + 'area': row['area'], + 'fullname': row['fullname'], + 'enname': row['enname'], + 'cnspell': row['cnspell'], + 'exchange': row['exchange'], + 'curr_type': row['curr_type'], + 'list_status': row['list_status'], + 'list_date': row['list_date'], + 'delist_date': row['delist_date'], + 'is_hs': row['is_hs'], + 'act_name': row['act_name'], + 'act_ent_type': row['act_ent_type'] + }) + update_count += 1 + + print(f"成功更新{update_count}只股票信息") + + # 7. 删除退市股票 + if codes_to_delete: + print(f"\n正在删除{len(codes_to_delete)}只退市股票...") + + # 显示要删除的股票 + df_delete = df_existing[df_existing['gp_code'].isin(codes_to_delete)] + print(f"\n退市股票列表:") + for _, row in df_delete.iterrows(): + print(f" {row['gp_code']} - {row['gp_name']}") + + # 执行删除 + with self.engine.begin() as conn: + for code in codes_to_delete: + delete_query = text("DELETE FROM gp_code_all WHERE gp_code = :gp_code") + conn.execute(delete_query, {'gp_code': code}) + + print(f"成功删除{len(codes_to_delete)}只退市股票") + else: + print("\n没有需要删除的股票") + + # 8. 输出最终统计 + print("\n" + "=" * 60) + print("股票列表更新完成") + print("=" * 60) + + # 查询更新后的总数 + query = "SELECT COUNT(*) as count FROM gp_code_all" + result = pd.read_sql(query, self.engine) + total_count = result['count'].iloc[0] + print(f"数据库当前股票总数: {total_count}") + print("=" * 60) + + except Exception as e: + print(f"更新股票列表失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def show_recent_stocks(self, limit=10): + """ + 显示最近上市的股票 + + Args: + limit: 显示数量，默认10只 + """ + try: + query = """ + SELECT gp_code, gp_name, market, area + FROM gp_code_all + ORDER BY id DESC + LIMIT :limit + """ + df = pd.read_sql(text(query), self.engine, params={'limit': limit}) + + if not df.empty: + print(f"\n最近添加的{limit}只股票:") + print("-" * 60) + for _, row in df.iterrows(): + print(f"{row['gp_code']} - {row['gp_name']} ") + + except Exception as e: + print(f"查询失败: {e}") + + +def update_stock_list(db_url, tushare_token): + """ + 更新A股股票列表的入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + """ + updater = StockListUpdater(db_url, tushare_token) + updater.update_stock_list() + updater.show_recent_stocks(10) + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 执行更新 + update_stock_list(db_url, tushare_token) + diff --git a/src/tushare_scripts/us_daily_data_collector.py b/src/tushare_scripts/us_daily_data_collector.py new file mode 100644 index 0000000..85c2258 --- /dev/null +++ b/src/tushare_scripts/us_daily_data_collector.py @@ -0,0 +1,517 @@ +# coding:utf-8 + +""" +美股日线数据采集工具 +功能：从Tushare获取美股日线行情数据，存储到us_day_data表 +API文档: https://tushare.pro/document/2?doc_id=254 +注意： + 1. 该接口需要2000积分权限 + 2. 单次最大返回5000条数据 + 3. 美股交易时间：北京时间21:30-次日4:00（夏令时）或22:30-次日5:00（冬令时） + 4. 可按日期一次性获取全市场数据 +数据存储说明： + - 存储表：us_day_data（美股专用表） + - symbol格式：与Tushare一致（如AAPL、TSLA等） + - PB/PE：从API获取市净率和市盈率数据 + - PS：API中无此数据，保持为NULL + - turnoverrate：美股接口无换手率数据，保持为NULL +采集策略： + - 每日增量：按日期获取全市场数据（推荐） + - 历史数据：按股票逐个采集历史数据 + - 每天美东时间收盘后（北京时间早上5-6点）更新 +""" + +import tushare as ts +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +import time +import sys +import os + +# 添加项目根目录到路径 +project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(project_root) + +from src.scripts.config import TUSHARE_TOKEN + + +class USDailyDataCollector: + """美股日线数据采集器""" + + def __init__(self, db_url, tushare_token, table_name='us_day_data'): + """ + 初始化采集器 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + table_name: 数据表名，默认为us_day_data + """ + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.table_name = table_name + + # 初始化Tushare + ts.set_token(tushare_token) + self.pro = ts.pro_api() + + print("=" * 60) + print("美股日线数据采集工具") + print(f"目标数据表: {self.table_name}") + print("=" * 60) + + def fetch_us_stock_list(self): + """ + 从数据库获取美股代码列表 + + Returns: + 美股代码列表 + """ + try: + query = """ + SELECT DISTINCT symbol + FROM us_code + WHERE symbol IS NOT NULL + ORDER BY symbol + """ + df = pd.read_sql(query, self.engine) + stock_codes = df['symbol'].tolist() + print(f"从数据库获取到{len(stock_codes)}只美股代码") + return stock_codes + except Exception as e: + print(f"从数据库获取美股代码失败: {e}") + # 如果数据库查询失败，返回空列表 + return [] + + def fetch_us_daily_data(self, trade_date=None, ts_code=None, start_date=None, end_date=None): + """ + 获取美股日线数据 + + Args: + trade_date: 交易日期，格式YYYYMMDD（单日查询） + ts_code: 股票代码，Tushare格式（单股查询） + start_date: 开始日期，格式YYYYMMDD（范围查询） + end_date: 结束日期，格式YYYYMMDD（范围查询） + + Returns: + 美股日线数据DataFrame + """ + try: + # 构建API参数 + params = {} + if trade_date: + params['trade_date'] = trade_date + if ts_code: + params['ts_code'] = ts_code + if start_date: + params['start_date'] = start_date + if end_date: + params['end_date'] = end_date + + df = self.pro.us_daily(**params,fields=[ + "ts_code", + "trade_date", + "close", + "open", + "high", + "low", + "pre_close", + "pct_change", + "vol", + "amount", + "vwap", + "pe", + "pb", + "change", + "adj_factor", + "turnover_ratio", + "total_mv" + ]) + return df + except Exception as e: + print(f"获取美股日线数据失败: {e}") + return pd.DataFrame() + + def transform_data(self, df): + """ + 转换数据格式为数据库格式（与us_day_data表结构一致） + + Args: + df: 原始数据DataFrame + + Returns: + 转换后的DataFrame，存储到us_day_data表 + + 字段说明： + - symbol: 股票代码（如AAPL、TSLA） + - timestamp: 交易日期 + - volume: 成交量（股） + - open/high/low/close: 价格（转为字符串） + - chg: 涨跌额（转为字符串） + - percent: 涨跌幅（%） + - turnoverrate: 换手率（美股接口无此数据，设为NULL） + - amount: 成交额（美元） + - pb/pe/ps: 估值指标（从接口获取） + - pre_close: 昨收价 + """ + if df.empty: + return pd.DataFrame() + + # 创建符合us_day_data表结构的DataFrame + result_df = pd.DataFrame() + + # 字段映射和转换 + result_df['symbol'] = df['ts_code'] # 美股代码保持原格式（如AAPL） + result_df['timestamp'] = pd.to_datetime(df['trade_date'], format='%Y%m%d') + result_df['volume'] = df['vol'] # 成交量（股） + result_df['open'] = df['open'].astype(str) if 'open' in df.columns else None + result_df['high'] = df['high'].astype(str) if 'high' in df.columns else None + result_df['low'] = df['low'].astype(str) if 'low' in df.columns else None + result_df['close'] = df['close'].astype(str) + + # 涨跌额：优先使用change字段，转为字符串 + if 'change' in df.columns: + result_df['chg'] = df['change'].fillna('').astype(str) + else: + result_df['chg'] = None + + result_df['percent'] = df.get('pct_change', None) if 'pct_change' in df.columns else None + + result_df['turnoverrate'] = None # 美股接口没有换手率数据 + result_df['amount'] = df.get('amount', None) # 成交额（如果接口有） + + # 估值指标：从API获取 + result_df['pb'] = df.get('pb', None) # 市净率 + result_df['pe'] = df.get('pe', None) # 市盈率 + result_df['ps'] = None # PS字段API中可能没有 + + result_df['pre_close'] = df.get('pre_close', None) + + # 按时间排序 + result_df = result_df.sort_values('timestamp').reset_index(drop=True) + + return result_df + + def run_daily_collection(self, date=None): + """ + 执行每日增量采集 + 按日期获取全市场美股日线数据（推荐方式） + + Args: + date: 指定日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行每日增量采集（全市场）") + print("=" * 60) + + try: + # 确定采集日期 + if date is None: + date_str = datetime.now().strftime('%Y-%m-%d') + else: + date_str = date + + # 转换为YYYYMMDD格式 + date_obj = datetime.strptime(date_str, '%Y-%m-%d') + trade_date = date_obj.strftime('%Y%m%d') + + print(f"采集日期: {date_str} ({trade_date})") + + # 删除指定日期的旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(`timestamp`) = :date_str") + result = conn.execute(delete_query, {"date_str": date_str}) + print(f"已删除{self.table_name}表中{date_str}的旧数据，共{result.rowcount}条") + + # 获取全市场美股日线数据 + print(f"正在获取{date_str}全市场美股日线数据...") + df = self.fetch_us_daily_data(trade_date=trade_date) + + if df.empty: + print(f"{date_str}没有美股日线数据（可能是非交易日）") + return + + print(f"成功获取美股日线数据，共{len(df)}只股票") + + # 转换数据格式 + result_df = self.transform_data(df) + + if result_df.empty: + print("数据转换失败") + return + + # 保存到数据库 + print(f"正在保存数据到{self.table_name}表...") + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + print(f"成功保存{len(result_df)}条记录") + + # 输出统计信息 + print("\n" + "=" * 60) + print("每日增量采集完成") + print("=" * 60) + print(f"采集日期: {date_str}") + print(f"股票数量: {len(result_df)}只") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"每日增量采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_full_collection(self, start_date=None, end_date=None): + """ + 执行全量历史数据采集（按股票逐个采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为180天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行全量历史数据采集（按股票逐个采集）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近180天历史数据 + start_date_obj = end_date_obj - timedelta(days=180) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + start_date_str = start_date_obj.strftime('%Y%m%d') + end_date_str = end_date_obj.strftime('%Y%m%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 获取美股代码列表 + stock_codes = self.fetch_us_stock_list() + + if not stock_codes: + print("没有找到美股代码，请先在us_code表中添加美股代码") + return + + print(f"开始采集{len(stock_codes)}只美股的历史数据...") + print("=" * 60) + + # 按股票逐个采集 + success_count = 0 + fail_count = 0 + total_records = 0 + + for i, symbol in enumerate(stock_codes, 1): + try: + # 获取该股票历史数据 + df = self.fetch_us_daily_data( + ts_code=symbol, + start_date=start_date_str, + end_date=end_date_str + ) + + if not df.empty: + # 删除该股票在该日期范围的旧数据 + with self.engine.begin() as conn: + delete_query = text( + f"DELETE FROM {self.table_name} " + f"WHERE symbol = :symbol " + f"AND `timestamp` BETWEEN :start_date AND :end_date" + ) + conn.execute(delete_query, { + "symbol": symbol, + "start_date": start_date_obj.strftime('%Y-%m-%d'), + "end_date": end_date_obj.strftime('%Y-%m-%d') + }) + + # 转换数据格式 + result_df = self.transform_data(df) + + if not result_df.empty: + # 保存到数据库 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + total_records += len(result_df) + success_count += 1 + + # 每10只股票显示一次进度 + if i % 10 == 0: + print(f"进度: {i}/{len(stock_codes)} ({success_count}成功, {fail_count}失败, 共{total_records}条记录)") + + except Exception as e: + fail_count += 1 + if i % 10 == 0: + print(f"采集 {symbol} 失败: {e}") + + # 输出统计信息 + print("\n" + "=" * 60) + print("全量历史数据采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"成功采集: {success_count}只") + print(f"失败: {fail_count}只") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"全量历史数据采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + def run_date_range_collection(self, start_date=None, end_date=None): + """ + 按日期范围逐日采集全市场数据（推荐用于历史数据采集） + + Args: + start_date: 开始日期，格式YYYY-MM-DD，默认为30天前 + end_date: 结束日期，格式YYYY-MM-DD，默认为今天 + """ + print("=" * 60) + print("开始执行日期范围采集（按日期逐日采集全市场）") + print("=" * 60) + + try: + # 确定日期范围 + if end_date is None: + end_date_obj = datetime.now() + else: + end_date_obj = datetime.strptime(end_date, '%Y-%m-%d') + + if start_date is None: + # 默认采集近30天历史数据 + start_date_obj = end_date_obj - timedelta(days=30) + else: + start_date_obj = datetime.strptime(start_date, '%Y-%m-%d') + + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print("=" * 60) + + # 按日期逐日采集 + current_date = start_date_obj + total_days = (end_date_obj - start_date_obj).days + 1 + processed_days = 0 + success_days = 0 + total_records = 0 + + while current_date <= end_date_obj: + date_str = current_date.strftime('%Y-%m-%d') + trade_date = current_date.strftime('%Y%m%d') + + print(f"\n[{processed_days + 1}/{total_days}] 采集日期: {date_str}") + + try: + # 获取全市场数据 + df = self.fetch_us_daily_data(trade_date=trade_date) + + if not df.empty: + # 转换数据格式 + result_df = self.transform_data(df) + + if not result_df.empty: + # 删除该日期旧数据 + with self.engine.begin() as conn: + delete_query = text(f"DELETE FROM {self.table_name} WHERE DATE(`timestamp`) = :date_str") + conn.execute(delete_query, {"date_str": date_str}) + + # 保存新数据 + result_df.to_sql(self.table_name, self.engine, if_exists='append', index=False) + total_records += len(result_df) + success_days += 1 + print(f" 成功: {len(result_df)}条记录") + else: + print(f" 无数据（可能是非交易日）") + + except Exception as e: + print(f" 采集失败: {e}") + + # 移到下一天 + current_date += timedelta(days=1) + processed_days += 1 + + # 输出统计信息 + print("\n" + "=" * 60) + print("日期范围采集完成") + print("=" * 60) + print(f"日期范围: {start_date_obj.strftime('%Y-%m-%d')} 至 {end_date_obj.strftime('%Y-%m-%d')}") + print(f"总天数: {total_days}") + print(f"成功天数: {success_days}") + print(f"总记录数: {total_records}条") + print(f"数据表: {self.table_name}") + print("=" * 60) + + except Exception as e: + print(f"日期范围采集失败: {e}") + import traceback + traceback.print_exc() + finally: + self.engine.dispose() + + +def collect_us_daily_data(db_url, tushare_token, mode='daily', date=None, start_date=None, end_date=None): + """ + 美股日线数据采集入口函数 + + Args: + db_url: 数据库连接URL + tushare_token: Tushare API Token + mode: 采集模式 + - 'daily': 增量采集当日数据（按日期获取全市场，推荐） + - 'full': 全量覆盖采集（按股票逐个采集） + - 'range': 日期范围采集（按日期逐日采集全市场，推荐用于历史数据） + date: 指定日期（仅mode='daily'时有效），格式YYYY-MM-DD + start_date: 开始日期（mode='full'或'range'时有效），格式YYYY-MM-DD + end_date: 结束日期（mode='full'或'range'时有效），格式YYYY-MM-DD + + 注意： + - 美股数据保存到us_day_data表（美股专用表） + - symbol格式与Tushare一致（如AAPL、TSLA等） + - 每天美东收盘后（北京时间早上5-6点）更新 + """ + collector = USDailyDataCollector(db_url, tushare_token) + + if mode == 'daily': + collector.run_daily_collection(date) + elif mode == 'full': + collector.run_full_collection(start_date, end_date) + elif mode == 'range': + collector.run_date_range_collection(start_date, end_date) + else: + print(f"未知的采集模式: {mode}") + + +if __name__ == "__main__": + # 数据库配置 + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # Tushare Token（从config.py读取） + tushare_token = TUSHARE_TOKEN + + # 使用方式示例： + + # 1. 每日增量采集（推荐每天早上6点后使用，一次性获取全市场数据） + # collect_us_daily_data(db_url, tushare_token, mode='daily') + + # 2. 日期范围采集（推荐用于历史数据，按日期逐日采集全市场） + # collect_us_daily_data(db_url, tushare_token, mode='range', + # start_date='2024-01-01', end_date='2024-12-31') + #10月28日的待采集 + # 3. 采集指定日期的数据 + collect_us_daily_data(db_url, tushare_token, mode='daily', date='2025-10-28') + + # 4. 全量覆盖采集（按股票逐个采集，较慢，不推荐） + # collect_us_daily_data(db_url, tushare_token, mode='full', + # start_date='2024-01-01', end_date='2024-12-31') + diff --git a/src/valuation_analysis/stock_price_collector.py b/src/valuation_analysis/stock_price_collector.py index 8d9ab2d..84c1996 100644 --- a/src/valuation_analysis/stock_price_collector.py +++ b/src/valuation_analysis/stock_price_collector.py @@ -25,6 +25,17 @@ sys.path.append(str(project_root)) from src.valuation_analysis.config import DB_URL, LOG_FILE +# 条件导入代理管理器 +proxy_manager = None +try: + sys.path.append(os.path.dirname(project_root)) + from src.scripts.ProxyIP import EnhancedProxyManager + proxy_manager = EnhancedProxyManager() +except ImportError: + logger = logging.getLogger("stock_price_collector") + logger.warning("代理管理器导入失败，将使用直接请求模式") + proxy_manager = None + # 获取项目根目录 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) @@ -96,7 +107,7 @@ class StockPriceCollector: self.headers = { "accept": "*/*:*/*", "accept-encoding": "gzip, deflate, br, zstd:gzip, deflate, br, zstd", - "accept-language": "zh-CN,zh;q=0.9,en;q=0.8:zh-CN,zh;q=0.9,en;q=0.8", + "accept-language": "zh-CN,zh;q=0.9", "connection": "keep-alive:keep-alive", "cookie": "qgqp_b_id=3d1376c8b3609b85ddd613ff70797b21; st_nvi=OkigGRrjz_ZnTtL_7eE4-4595; nid=0820fd3f15fe4f7c0164f8a7eff85c5f; nid_create_time=1754027308372; gvi=p9WxDMFjnfkCUiL8CQ3ex015a; gvi_create_time=1754027308372; fullscreengg=1; fullscreengg2=1; st_si=11531712238902; st_asi=delete; st_pvi=49494569999152; st_sp=2025-05-12%2009%3A11%3A41; st_inirUrl=https%3A%2F%2Fwww.baidu.com%2Flink; st_sn=11; st_psi=20250904172534993-113200301321-0920486271:qgqp_b_id=3d1376c8b3609b85ddd613ff70797b21; st_nvi=OkigGRrjz_ZnTtL_7eE4-4595; nid=0820fd3f15fe4f7c0164f8a7eff85c5f; nid_create_time=1754027308372; gvi=p9WxDMFjnfkCUiL8CQ3ex015a; gvi_create_time=1754027308372; fullscreengg=1; fullscreengg2=1; st_si=11531712238902; st_asi=delete; st_pvi=49494569999152; st_sp=2025-05-12%2009%3A11%3A41; st_inirUrl=https%3A%2F%2Fwww.baidu.com%2Flink; st_sn=11; st_psi=20250904172534993-113200301321-0920486271", "host": "push2.eastmoney.com:push2.eastmoney.com", @@ -107,7 +118,7 @@ class StockPriceCollector: "sec-fetch-dest": "script:script", "sec-fetch-mode": "no-cors:no-cors", "sec-fetch-site": "same-site:same-site", - "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36:Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36" + "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36" } logger.info("东方财富实时股价数据采集器初始化完成") @@ -168,12 +179,13 @@ class StockPriceCollector: logger.warning(f"无法解析日期: {date_str}") return None - def fetch_data(self, page: int = 1) -> pd.DataFrame: + def fetch_data(self, page: int = 1, use_proxy: bool = False) -> pd.DataFrame: """ 获取指定页码的实时股价数据 Args: page: 页码 + use_proxy: 是否使用代理 Returns: 包含实时股价数据的DataFrame @@ -193,7 +205,12 @@ class StockPriceCollector: logger.info(f"开始获取第 {page} 页数据") - response = requests.get(self.base_url, params=params, headers=self.headers) + # 根据配置选择是否使用代理 + if use_proxy and proxy_manager: + response = proxy_manager.request_with_proxy('get', self.base_url, headers=self.headers, params=params) + else: + response = requests.get(self.base_url, params=params, headers=self.headers, timeout=10) + if response.status_code != 200: logger.error(f"获取第 {page} 页数据失败: HTTP {response.status_code}") return pd.DataFrame() @@ -467,6 +484,6 @@ if __name__ == "__main__": # 创建实时股价数据采集器 collector = StockPriceCollector() - # 更新最新数据 + # 更新最新数据（使用代理） logger.info("开始更新最新实时股价数据...") collector.update_latest_data() \ No newline at end of file