From aa4be0e64c9ee7a5bd89c64a131009baefa97285 Mon Sep 17 00:00:00 2001 From: liao Date: Thu, 3 Jul 2025 15:57:04 +0800 Subject: [PATCH] commit; --- data.sql | 517 ++ requirements.txt | 3 +- src/app.py | 32 +- .../average_distance_factor.py | 310 + .../batch_stock_price_collector.py | 19 +- .../company_lifecycle_factor.py | 317 + .../data/前五供应商占比.csv | 5412 +++++++++++++++++ .../data/前五客户供应商的原始数据.xlsx | Bin 0 -> 1298649 bytes .../data/前五客户占比.csv | 5412 +++++++++++++++++ .../financial_data_collector.py | 344 +- .../financial_indicator_analyzer.py | 1328 ++++ .../hk_stock_price_collector.py | 197 + .../supplier_customer_ratio_updater.py | 306 + .../tech_fundamental_factor_strategy.py | 729 +++ src/scripts/ProxyIP.py | 342 ++ src/scripts/config.py | 2 +- src/scripts/stock_daily_data_collector.py | 42 +- src/scripts/stock_daily_data_collector_v2.py | 328 + tools/stock_code_formatter.py | 244 + tools/trigger_batch_collection.py | 35 + 20 files changed, 15844 insertions(+), 75 deletions(-) create mode 100644 data.sql create mode 100644 src/quantitative_analysis/average_distance_factor.py create mode 100644 src/quantitative_analysis/company_lifecycle_factor.py create mode 100644 src/quantitative_analysis/data/前五供应商占比.csv create mode 100644 src/quantitative_analysis/data/前五客户供应商的原始数据.xlsx create mode 100644 src/quantitative_analysis/data/前五客户占比.csv create mode 100644 src/quantitative_analysis/financial_indicator_analyzer.py create mode 100644 src/quantitative_analysis/hk_stock_price_collector.py create mode 100644 src/quantitative_analysis/supplier_customer_ratio_updater.py create mode 100644 src/quantitative_analysis/tech_fundamental_factor_strategy.py create mode 100644 src/scripts/ProxyIP.py create mode 100644 src/scripts/stock_daily_data_collector_v2.py create mode 100644 tools/stock_code_formatter.py create mode 100644 tools/trigger_batch_collection.py diff --git a/data.sql b/data.sql new file mode 100644 index 0000000..3deaf09 --- /dev/null +++ b/data.sql @@ -0,0 +1,517 @@ +/* + Navicat Premium Dump SQL + + Source Server : 192.168.18.199(gpfx) + Source Server Type : MySQL + Source Server Version : 90200 (9.2.0) + Source Host : 192.168.18.199:3306 + Source Schema : db_gp_cj + + Target Server Type : MySQL + Target Server Version : 90200 (9.2.0) + File Encoding : 65001 + + Date: 26/06/2025 11:41:15 +*/ + +SET NAMES utf8mb4; +SET FOREIGN_KEY_CHECKS = 0; + +-- ---------------------------- +-- Table structure for eastmoney_rzrq_data +-- ---------------------------- +DROP TABLE IF EXISTS `eastmoney_rzrq_data`; +CREATE TABLE `eastmoney_rzrq_data` ( + `trade_date` date NOT NULL, + `index_value` decimal(10, 4) NULL DEFAULT NULL COMMENT '指数', + `change_percent` decimal(10, 4) NULL DEFAULT NULL COMMENT '涨跌幅', + `float_market_value` decimal(20, 2) NULL DEFAULT NULL COMMENT '流通市值', + `change_percent_3d` decimal(10, 4) NULL DEFAULT NULL COMMENT '3日涨跌幅', + `change_percent_5d` decimal(10, 4) NULL DEFAULT NULL COMMENT '5日涨跌幅', + `change_percent_10d` decimal(10, 4) NULL DEFAULT NULL COMMENT '10日涨跌幅', + `financing_balance` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资余额', + `financing_balance_ratio` decimal(10, 4) NULL DEFAULT NULL COMMENT '融资余额占比', + `financing_buy_amount` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资买入额', + `financing_buy_amount_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融资买入额', + `financing_buy_amount_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融资买入额', + `financing_buy_amount_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融资买入额', + `financing_repay_amount` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资偿还额', + `financing_repay_amount_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融资偿还额', + `financing_repay_amount_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融资偿还额', + `financing_repay_amount_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融资偿还额', + `financing_net_amount` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资净额', + `financing_net_amount_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融资净额', + `financing_net_amount_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融资净额', + `financing_net_amount_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融资净额', + `securities_balance` decimal(20, 2) NULL DEFAULT NULL COMMENT '融券余额', + `securities_volume` decimal(20, 2) NULL DEFAULT NULL COMMENT '融券余量', + `securities_repay_volume` decimal(20, 2) NULL DEFAULT NULL COMMENT '融券偿还量', + `securities_repay_volume_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融券偿还量', + `securities_repay_volume_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融券偿还量', + `securities_repay_volume_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融券偿还量', + `securities_sell_volume` decimal(20, 2) NULL DEFAULT NULL COMMENT '融券卖出量', + `securities_sell_volume_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融券卖出量', + `securities_sell_volume_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融券卖出量', + `securities_sell_volume_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融券卖出量', + `securities_net_volume` decimal(20, 2) NULL DEFAULT NULL COMMENT '融券净量', + `securities_net_volume_3d` decimal(20, 2) NULL DEFAULT NULL COMMENT '3日融券净量', + `securities_net_volume_5d` decimal(20, 2) NULL DEFAULT NULL COMMENT '5日融券净量', + `securities_net_volume_10d` decimal(20, 2) NULL DEFAULT NULL COMMENT '10日融券净量', + `total_rzrq_balance` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资融券余额', + `total_rzrq_balance_cz` decimal(20, 2) NULL DEFAULT NULL COMMENT '融资融券余额差值', + `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP, + `updated_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, + PRIMARY KEY (`trade_date`) USING BTREE +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '东方财富融资融券数据表' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for fear_greed_index +-- ---------------------------- +DROP TABLE IF EXISTS `fear_greed_index`; +CREATE TABLE `fear_greed_index` ( + `id` int NOT NULL AUTO_INCREMENT, + `index_value` decimal(5, 2) NOT NULL COMMENT '恐贪指数值(0-100)', + `trading_date` date NOT NULL COMMENT '交易日期', + `update_time` datetime NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', + PRIMARY KEY (`id`) USING BTREE, + UNIQUE INDEX `uk_trading_date`(`trading_date` ASC) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 1003 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '市场恐贪指数数据' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for follow_stock +-- ---------------------------- +DROP TABLE IF EXISTS `follow_stock`; +CREATE TABLE `follow_stock` ( + `id` bigint NOT NULL AUTO_INCREMENT, + `stock_code` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码', + `stock_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票名称', + `add_time` datetime NULL DEFAULT NULL COMMENT '添加时间', + `status` tinyint NULL DEFAULT 1 COMMENT '状态', + PRIMARY KEY (`id`) USING BTREE, + UNIQUE INDEX `uk_stock_code`(`stock_code` ASC) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 22 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '关注的股票' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for fund_cang +-- ---------------------------- +DROP TABLE IF EXISTS `fund_cang`; +CREATE TABLE `fund_cang` ( + `id` int NOT NULL AUTO_INCREMENT, + `fund_id` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `fund_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `company_money` decimal(20, 2) NULL DEFAULT NULL, + `company_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `company_id` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `fund_data` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 1874595 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for fundamental_analysis +-- ---------------------------- +DROP TABLE IF EXISTS `fundamental_analysis`; +CREATE TABLE `fundamental_analysis` ( + `id` int NOT NULL AUTO_INCREMENT, + `stock_code` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码', + `stock_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票名称', + `dimension` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '分析维度', + `ai_response` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT 'AI分析结果', + `reasoning_process` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL COMMENT '推理过程', + `references` json NULL COMMENT '参考资料', + `update_time` datetime NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', + `extra_info` json NULL COMMENT '扩展信息', + PRIMARY KEY (`id`) USING BTREE, + INDEX `idx_stock_dimension`(`stock_code` ASC, `dimension` ASC) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 12104 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '基本面分析结果表' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_category_industry +-- ---------------------------- +DROP TABLE IF EXISTS `gp_category_industry`; +CREATE TABLE `gp_category_industry` ( + `id` int NOT NULL AUTO_INCREMENT COMMENT 'id', + `category_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '分类名称', + `belong_industry` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '所属行业', + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 52 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_code_all +-- ---------------------------- +DROP TABLE IF EXISTS `gp_code_all`; +CREATE TABLE `gp_code_all` ( + `id` int NOT NULL AUTO_INCREMENT, + `gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_two` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_three` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `market_cap` decimal(20, 2) NULL DEFAULT NULL, + `mark1` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `mark2` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 6686 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin COMMENT = '所有个股代码-爬取指数用' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_code_hk +-- ---------------------------- +DROP TABLE IF EXISTS `gp_code_hk`; +CREATE TABLE `gp_code_hk` ( + `id` int NOT NULL AUTO_INCREMENT, + `gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_two` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_three` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `market_cap` decimal(20, 2) NULL DEFAULT NULL, + `mark1` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `mark2` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 2956 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin COMMENT = '所有指数代码-爬取数据使用' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_code_zs +-- ---------------------------- +DROP TABLE IF EXISTS `gp_code_zs`; +CREATE TABLE `gp_code_zs` ( + `id` int NOT NULL AUTO_INCREMENT, + `gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_two` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `gp_code_three` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `market_cap` decimal(20, 2) NULL DEFAULT NULL, + `mark1` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `mark2` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 6686 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin COMMENT = '所有指数代码-爬取数据使用' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_data +-- ---------------------------- +DROP TABLE IF EXISTS `gp_data`; +CREATE TABLE `gp_data` ( + `id` int NOT NULL AUTO_INCREMENT, + `pg_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `xiangsidu` decimal(10, 2) NULL DEFAULT NULL COMMENT '控制相似度为多少的时候进行回测 和下面的数字是关联的', + `ii` int NULL DEFAULT NULL COMMENT '相似度的条数', + `raye_ga_hc` decimal(10, 2) NULL DEFAULT NULL COMMENT '回测涨跌幅大于多少的数据', + `huice_function_num` int NULL DEFAULT NULL COMMENT '回测判断胜率方法 0是代表相差1%或者同涨同跌都算胜 1是代表相差1%算胜 2是代表同涨同跌算胜', + `yes_yuce` int NULL DEFAULT NULL, + `no_yuce` int NULL DEFAULT NULL, + `yes_yuce_twoday` int NULL DEFAULT NULL, + `no_yuce_twoday` int NULL DEFAULT NULL, + `yes_yuce_fiveday` int NULL DEFAULT NULL, + `no_yuce_fiveday` int NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 42690 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_day_data +-- ---------------------------- +DROP TABLE IF EXISTS `gp_day_data`; +CREATE TABLE `gp_day_data` ( + `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键', + `symbol` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '个股代码', + `timestamp` timestamp NULL DEFAULT NULL COMMENT '时间戳', + `volume` bigint NULL DEFAULT NULL COMMENT '数量', + `open` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '开始价', + `high` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最高价', + `low` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最低价', + `close` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '结束价', + `chg` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '变化数值', + `percent` decimal(10, 2) NULL DEFAULT NULL COMMENT '变化百分比', + `turnoverrate` decimal(10, 2) NULL DEFAULT NULL COMMENT '换手率', + `amount` bigint NULL DEFAULT NULL COMMENT '成交金额', + `pb` decimal(10, 2) NULL DEFAULT NULL COMMENT '当前PB', + `pe` decimal(10, 2) NULL DEFAULT NULL COMMENT '当前PE', + `ps` decimal(10, 2) NULL DEFAULT NULL COMMENT '当前PS', + `create_time` datetime NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, + PRIMARY KEY (`id`) USING BTREE, + INDEX `idx_symbol`(`symbol` ASC) USING BTREE, + INDEX `idx_timestamp`(`timestamp` ASC) USING BTREE, + INDEX `idx_symbol_time`(`symbol` ASC, `timestamp` ASC) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 28356293 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_ex_rights_log +-- ---------------------------- +DROP TABLE IF EXISTS `gp_ex_rights_log`; +CREATE TABLE `gp_ex_rights_log` ( + `id` int NOT NULL AUTO_INCREMENT, + `stock_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '股票代码', + `change_date` date NULL DEFAULT NULL COMMENT '除权变动日期', + `before_price` decimal(10, 3) NULL DEFAULT NULL COMMENT '变动前收盘价（数据库中的价格）', + `after_price` decimal(10, 3) NULL DEFAULT NULL COMMENT '变动后收盘价（API获取的价格）', + `update_time` datetime NULL DEFAULT NULL COMMENT '脚本执行的更新时间', + `optimization_flag` int NULL DEFAULT NULL COMMENT '波段优化标志，1为已经完成优化', + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 285 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin COMMENT = '股票除权日志表' ROW_FORMAT = DYNAMIC; + +-- ---------------------------- +-- Table structure for gp_gnbk +-- ---------------------------- +DROP TABLE IF EXISTS `gp_gnbk`; +CREATE TABLE `gp_gnbk` ( + `id` bigint NULL DEFAULT NULL, + `bk_code` bigint NULL DEFAULT NULL, + `bk_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_code` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_gnbk_all +-- ---------------------------- +DROP TABLE IF EXISTS `gp_gnbk_all`; +CREATE TABLE `gp_gnbk_all` ( + `id` bigint NULL DEFAULT NULL, + `bk_code` bigint NULL DEFAULT NULL, + `bk_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_code` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_gnbk_gn +-- ---------------------------- +DROP TABLE IF EXISTS `gp_gnbk_gn`; +CREATE TABLE `gp_gnbk_gn` ( + `id` bigint NULL DEFAULT NULL, + `bk_code` bigint NULL DEFAULT NULL, + `bk_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_code` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_hybk +-- ---------------------------- +DROP TABLE IF EXISTS `gp_hybk`; +CREATE TABLE `gp_hybk` ( + `id` bigint NULL DEFAULT NULL, + `bk_code` bigint NULL DEFAULT NULL, + `bk_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_code` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `gp_name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `Unnamed: 5` double NULL DEFAULT NULL, + `Unnamed: 6` double NULL DEFAULT NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_main_business +-- ---------------------------- +DROP TABLE IF EXISTS `gp_main_business`; +CREATE TABLE `gp_main_business` ( + `stock_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码', + `stock_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票简称', + `report_date` varchar(8) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '报告期(YYYYMMDD)', + `product_rank` varchar(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '项目排名(1-5)', + `product_name` varchar(100) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '项目名称', + `revenue` decimal(20, 2) NULL DEFAULT NULL COMMENT '项目收入', + `cost` decimal(20, 2) NULL DEFAULT NULL COMMENT '项目成本', + `profit` decimal(20, 2) NULL DEFAULT NULL COMMENT '项目毛利', + `profit_margin` decimal(10, 4) NULL DEFAULT NULL COMMENT '项目毛利率', + PRIMARY KEY (`stock_code`, `report_date`, `product_rank`) USING BTREE +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_min_data +-- ---------------------------- +DROP TABLE IF EXISTS `gp_min_data`; +CREATE TABLE `gp_min_data` ( + `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键', + `symbol` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '个股代码', + `timestamp` timestamp NULL DEFAULT NULL COMMENT '时间戳', + `volume` bigint NULL DEFAULT NULL COMMENT '数量', + `open` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '开始价', + `high` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最高价', + `low` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '最低价', + `close` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '结束价', + `chg` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '变化数值', + `percent` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '变化百分比', + `turnoverrate` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL, + `amount` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_bin NULL DEFAULT NULL COMMENT '成交金额', + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 61116588 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_bin ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_product_category +-- ---------------------------- +DROP TABLE IF EXISTS `gp_product_category`; +CREATE TABLE `gp_product_category` ( + `id` int NOT NULL AUTO_INCREMENT, + `category_name` varchar(100) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL, + `product_name` varchar(200) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL, + `stock_code` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL, + `stock_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL, + `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP, + PRIMARY KEY (`id`) USING BTREE, + INDEX `idx_stock_code`(`stock_code` ASC) USING BTREE, + INDEX `idx_product_name`(`product_name` ASC) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 35869 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_zygc +-- ---------------------------- +DROP TABLE IF EXISTS `gp_zygc`; +CREATE TABLE `gp_zygc` ( + `id` int NOT NULL AUTO_INCREMENT, + `gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票名称', + `gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票代码', + `zygc_xmmc` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '主营构成(按行业)-项目名称', + `zygc_xmsr` decimal(20, 0) NULL DEFAULT NULL COMMENT '主营构成(按行业)-项目收入', + `zygc_xmmlr` decimal(10, 2) NULL DEFAULT NULL COMMENT '主营构成(按行业)-项目毛利率', + `belong_time` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '所属财报期', + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 12774 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '个股的主营构成' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for gp_zyyw +-- ---------------------------- +DROP TABLE IF EXISTS `gp_zyyw`; +CREATE TABLE `gp_zyyw` ( + `id` int NOT NULL AUTO_INCREMENT, + `gp_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票名称', + `gp_code` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票代码', + `zyyw_name` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '主营业务名称', + `zyyw_zb` decimal(10, 2) NULL DEFAULT NULL COMMENT '主营业务占比', + `belong_time` varchar(20) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '所属财报期', + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 25280 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '上市公司主营业务占比' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for hk_hot_stocks +-- ---------------------------- +DROP TABLE IF EXISTS `hk_hot_stocks`; +CREATE TABLE `hk_hot_stocks` ( + `id` int NOT NULL AUTO_INCREMENT, + `symbol` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL, + `name` varchar(100) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL, + `value` int NULL DEFAULT NULL, + `increment` int NULL DEFAULT NULL, + `rank_change` int NULL DEFAULT NULL, + `percent` float NULL DEFAULT NULL, + `current` float NULL DEFAULT NULL, + `chg` float NULL DEFAULT NULL, + `exchange` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL, + `stock_type` int NULL DEFAULT NULL, + `add_time` datetime NULL DEFAULT NULL, + `status` varchar(1) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL, + PRIMARY KEY (`id`) USING BTREE +) ENGINE = InnoDB AUTO_INCREMENT = 53981 CHARACTER SET = sjis COLLATE = sjis_japanese_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for limitup_analysis_stock_changes +-- ---------------------------- +DROP TABLE IF EXISTS `limitup_analysis_stock_changes`; +CREATE TABLE `limitup_analysis_stock_changes` ( + `symbol` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `net_profit_cagr` double NULL DEFAULT NULL, + `north_net_inflow` double NULL DEFAULT NULL, + `ps` double NULL DEFAULT NULL, + `type` bigint NULL DEFAULT NULL, + `percent` double NULL DEFAULT NULL, + `has_follow` tinyint(1) NULL DEFAULT NULL, + `tick_size` double NULL DEFAULT NULL, + `pb_ttm` double NULL DEFAULT NULL, + `float_shares` bigint NULL DEFAULT NULL, + `current` double NULL DEFAULT NULL, + `amplitude` double NULL DEFAULT NULL, + `pcf` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `current_year_percent` double NULL DEFAULT NULL, + `float_market_capital` double NULL DEFAULT NULL, + `north_net_inflow_time` double NULL DEFAULT NULL, + `market_capital` double NULL DEFAULT NULL, + `dividend_yield` double NULL DEFAULT NULL, + `lot_size` bigint NULL DEFAULT NULL, + `roe_ttm` double NULL DEFAULT NULL, + `total_percent` double NULL DEFAULT NULL, + `percent5m` double NULL DEFAULT NULL, + `income_cagr` double NULL DEFAULT NULL, + `amount` double NULL DEFAULT NULL, + `chg` double NULL DEFAULT NULL, + `issue_date_ts` bigint NULL DEFAULT NULL, + `eps` double NULL DEFAULT NULL, + `main_net_inflows` double NULL DEFAULT NULL, + `volume` bigint NULL DEFAULT NULL, + `volume_ratio` double NULL DEFAULT NULL, + `pb` double NULL DEFAULT NULL, + `followers` bigint NULL DEFAULT NULL, + `turnover_rate` double NULL DEFAULT NULL, + `mapping_quote_current` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `first_percent` double NULL DEFAULT NULL, + `name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `pe_ttm` double NULL DEFAULT NULL, + `dual_counter_mapping_symbol` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `total_shares` bigint NULL DEFAULT NULL, + `limitup_days` bigint NULL DEFAULT NULL, + `id` bigint NULL DEFAULT NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for rzrq_data +-- ---------------------------- +DROP TABLE IF EXISTS `rzrq_data`; +CREATE TABLE `rzrq_data` ( + `trade_date` date NOT NULL COMMENT '交易日期', + `sh_financing_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '上海融资余额（亿元）', + `sz_financing_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '深圳融资余额（亿元）', + `bj_financing_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '北京融资余额（亿元）', + `total_financing_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '融资余额合计（亿元）', + `sh_financing_buy` decimal(12, 2) NULL DEFAULT NULL COMMENT '上海融资买入额（亿元）', + `sz_financing_buy` decimal(12, 2) NULL DEFAULT NULL COMMENT '深圳融资买入额（亿元）', + `bj_financing_buy` decimal(12, 2) NULL DEFAULT NULL COMMENT '北京融资买入额（亿元）', + `total_financing_buy` decimal(12, 2) NULL DEFAULT NULL COMMENT '融资买入额合计（亿元）', + `sh_securities_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '上海融券余量余额（亿元）', + `sz_securities_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '深圳融券余量余额（亿元）', + `bj_securities_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '北京融券余量余额（亿元）', + `total_securities_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '融券余量余额合计（亿元）', + `sh_rzrq_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '上海融资融券余额（亿元）', + `sz_rzrq_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '深圳融资融券余额（亿元）', + `bj_rzrq_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '北京融资融券余额（亿元）', + `total_rzrq_balance` decimal(12, 2) NULL DEFAULT NULL COMMENT '融资融券余额合计（亿元）', + `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP, + `updated_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, + PRIMARY KEY (`trade_date`) USING BTREE +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '融资融券数据表' ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for stock_price_changes +-- ---------------------------- +DROP TABLE IF EXISTS `stock_price_changes`; +CREATE TABLE `stock_price_changes` ( + `symbol` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `name` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `current` double NULL DEFAULT NULL, + `percent` double NULL DEFAULT NULL, + `time_mark` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL, + `add_time` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci ROW_FORMAT = Dynamic; + +-- ---------------------------- +-- Table structure for stock_price_data +-- ---------------------------- +DROP TABLE IF EXISTS `stock_price_data`; +CREATE TABLE `stock_price_data` ( + `stock_code` varchar(10) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '股票代码', + `stock_name` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '股票名称', + `latest_price` decimal(10, 2) NULL DEFAULT NULL COMMENT '最新价', + `change_percent` decimal(10, 2) NULL DEFAULT NULL COMMENT '涨跌幅', + `change_amount` decimal(10, 2) NULL DEFAULT NULL COMMENT '涨跌额', + `volume` bigint NULL DEFAULT NULL COMMENT '成交量(手)', + `amount` decimal(20, 2) NULL DEFAULT NULL COMMENT '成交额', + `amplitude` decimal(10, 2) NULL DEFAULT NULL COMMENT '振幅', + `turnover_rate` decimal(10, 2) NULL DEFAULT NULL COMMENT '换手率', + `pe_ratio` decimal(10, 2) NULL DEFAULT NULL COMMENT '市盈率', + `high_price` decimal(10, 2) NULL DEFAULT NULL COMMENT '最高价', + `low_price` decimal(10, 2) NULL DEFAULT NULL COMMENT '最低价', + `open_price` decimal(10, 2) NULL DEFAULT NULL COMMENT '开盘价', + `pre_close` decimal(10, 2) NULL DEFAULT NULL COMMENT '昨收价', + `total_market_value` decimal(20, 2) NULL DEFAULT NULL COMMENT '总市值', + `float_market_value` decimal(20, 2) NULL DEFAULT NULL COMMENT '流通市值', + `pb_ratio` decimal(10, 2) NULL DEFAULT NULL COMMENT '市净率', + `list_date` date NULL DEFAULT NULL COMMENT '上市日期', + `update_time` timestamp NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT '更新时间', + `created_at` timestamp NULL DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间', + PRIMARY KEY (`stock_code`) USING BTREE +) ENGINE = InnoDB CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '实时股价数据表' ROW_FORMAT = Dynamic; + +SET FOREIGN_KEY_CHECKS = 1; diff --git a/requirements.txt b/requirements.txt index 5216a58..8feab48 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,4 +17,5 @@ google-genai redis==5.2.1 pandas==2.2.3 apscheduler==3.11.0 -pymongo==4.13.0 \ No newline at end of file +pymongo==4.13.0 +scikit-learn==1.6.1 diff --git a/src/app.py b/src/app.py index 895f387..a8bfac9 100644 --- a/src/app.py +++ b/src/app.py @@ -42,10 +42,12 @@ 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 +from src.scripts.stock_daily_data_collector_v2 import collect_stock_daily_data_v2 from valuation_analysis.financial_analysis import FinancialAnalyzer from src.valuation_analysis.stock_price_collector import StockPriceCollector -from src.quantitative_analysis.batch_stock_price_collector import fetch_and_store_stock_data, get_stock_realtime_info_from_redis +from src.quantitative_analysis.batch_stock_price_collector import fetch_and_store_stock_data +from src.quantitative_analysis.hk_stock_price_collector import fetch_and_store_hk_stock_data from src.quantitative_analysis.momentum_analysis import MomentumAnalyzer # 设置日志 @@ -207,6 +209,24 @@ def run_stock_daily_collection1(): # 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(db_url, today) @@ -3002,6 +3022,16 @@ def run_batch_stock_price_collection(): logger.error(f"批量采集A股行情失败: {str(e)}") return jsonify({"status": "error", "message": str(e)}) +@app.route('/scheduler/batch_hk_stock_price/collection', methods=['GET']) +def run_batch_hk_stock_price_collection(): + """批量采集A股行情并保存到数据库""" + try: + fetch_and_store_hk_stock_data() + return jsonify({"status": "success", "message": "批量采集A股行情并保存到数据库成功"}) + except Exception as e: + logger.error(f"批量采集A股行情失败: {str(e)}") + return jsonify({"status": "error", "message": str(e)}) + if __name__ == '__main__': # 启动Web服务器 diff --git a/src/quantitative_analysis/average_distance_factor.py b/src/quantitative_analysis/average_distance_factor.py new file mode 100644 index 0000000..3394523 --- /dev/null +++ b/src/quantitative_analysis/average_distance_factor.py @@ -0,0 +1,310 @@ +# coding:utf-8 +#计算股价平均距离因子-行业个股列表来计算 + +import pandas as pd +import numpy as np +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from sklearn.preprocessing import StandardScaler +from sklearn.metrics.pairwise import euclidean_distances +import warnings +warnings.filterwarnings('ignore') + +class AverageDistanceFactor: + """平均距离因子计算器""" + + def __init__(self, db_url): + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + + def get_industry_stocks(self, industry_name=None, concept_name=None): + """获取指定行业或概念的股票列表""" + if industry_name: + query = text(""" + SELECT DISTINCT gp_code as symbol + FROM gp_hybk + WHERE bk_name = :name + """) + params = {"name": industry_name} + elif concept_name: + query = text(""" + SELECT DISTINCT gp_code as symbol + FROM gp_gnbk + WHERE bk_name = :name + """) + params = {"name": concept_name} + else: + raise ValueError("必须提供 industry_name 或 concept_name 之一") + + try: + with self.engine.connect() as conn: + result = conn.execute(query, params).fetchall() + + if result: + symbols = [row[0] for row in result] + print(f"获取到 {len(symbols)} 只股票") + return symbols + else: + print(f"未找到{'行业' if industry_name else '概念'} {industry_name or concept_name} 的股票") + return [] + except Exception as e: + print(f"获取股票列表失败: {e}") + return [] + + def get_stock_data(self, symbols, days=20): + """获取股票的历史数据""" + if not symbols: + return pd.DataFrame() + + # 计算开始日期 + end_date = datetime.now() + start_date = end_date - timedelta(days=days * 2) # 多取一些数据以防节假日 + + # 构建SQL查询 + symbols_str = "', '".join(symbols) + query = f""" + SELECT symbol, timestamp, volume, open, high, low, close, + chg, percent, turnoverrate, amount, pb, pe, ps + FROM gp_day_data + WHERE symbol IN ('{symbols_str}') + AND timestamp >= '{start_date.strftime('%Y-%m-%d')}' + ORDER BY symbol, timestamp DESC + """ + + try: + df = pd.read_sql(query, self.engine) + print(f"获取到 {len(df)} 条历史数据") + return df + except Exception as e: + print(f"获取历史数据失败: {e}") + return pd.DataFrame() + + def calculate_technical_indicators(self, df, days=20): + """计算技术指标""" + result_data = [] + + for symbol in df['symbol'].unique(): + stock_data = df[df['symbol'] == symbol].copy() + stock_data = stock_data.sort_values('timestamp') + + # 只取最近N天的数据 + stock_data = stock_data.tail(days) + + if len(stock_data) < days: + continue # 数据不足，跳过 + + # 转换数据类型 + for col in ['open', 'high', 'low', 'close', 'chg']: + stock_data[col] = pd.to_numeric(stock_data[col], errors='coerce') + + # 计算各种技术指标 + indicators = self._compute_indicators(stock_data) + indicators['symbol'] = symbol + result_data.append(indicators) + + return pd.DataFrame(result_data) + + def _compute_indicators(self, data): + """计算具体的技术指标""" + indicators = {} + + # 1. 收益率指标 + data['returns'] = data['close'].pct_change() + indicators['return_5d'] = data['returns'].tail(5).sum() # 5日累计收益率 + indicators['return_10d'] = data['returns'].tail(10).sum() # 10日累计收益率 + indicators['return_20d'] = data['returns'].tail(20).sum() # 20日累计收益率 + + # 2. 波动率指标 + indicators['volatility_5d'] = data['returns'].tail(5).std() # 5日波动率 + indicators['volatility_10d'] = data['returns'].tail(10).std() # 10日波动率 + indicators['volatility_20d'] = data['returns'].tail(20).std() # 20日波动率 + + # 3. 价格相对位置 + indicators['price_position_5d'] = (data['close'].iloc[-1] - data['low'].tail(5).min()) / (data['high'].tail(5).max() - data['low'].tail(5).min()) + indicators['price_position_10d'] = (data['close'].iloc[-1] - data['low'].tail(10).min()) / (data['high'].tail(10).max() - data['low'].tail(10).min()) + indicators['price_position_20d'] = (data['close'].iloc[-1] - data['low'].tail(20).min()) / (data['high'].tail(20).max() - data['low'].tail(20).min()) + + # 4. 移动平均偏离度 + ma_5 = data['close'].tail(5).mean() + ma_10 = data['close'].tail(10).mean() + ma_20 = data['close'].tail(20).mean() + current_price = data['close'].iloc[-1] + + indicators['ma_deviation_5d'] = (current_price - ma_5) / ma_5 + indicators['ma_deviation_10d'] = (current_price - ma_10) / ma_10 + indicators['ma_deviation_20d'] = (current_price - ma_20) / ma_20 + + # 5. 成交量相关指标 + indicators['volume_ratio_5d'] = data['volume'].tail(5).mean() / data['volume'].mean() + indicators['volume_ratio_10d'] = data['volume'].tail(10).mean() / data['volume'].mean() + indicators['turnover_avg_5d'] = data['turnoverrate'].tail(5).mean() + indicators['turnover_avg_10d'] = data['turnoverrate'].tail(10).mean() + + # 6. 价格振幅指标 + data['amplitude'] = (data['high'] - data['low']) / data['close'] + indicators['amplitude_avg_5d'] = data['amplitude'].tail(5).mean() + indicators['amplitude_avg_10d'] = data['amplitude'].tail(10).mean() + + # 7. 趋势强度（连续涨跌） + indicators['consecutive_up'] = self._count_consecutive(data['percent'] > 0) + indicators['consecutive_down'] = self._count_consecutive(data['percent'] < 0) + + # 8. 估值动量（如果有PE、PB、PS数据） + if 'pe' in data.columns and not data['pe'].isna().all(): + pe_change = data['pe'].pct_change().tail(5).mean() + indicators['pe_momentum'] = pe_change if not np.isnan(pe_change) else 0 + else: + indicators['pe_momentum'] = 0 + + if 'pb' in data.columns and not data['pb'].isna().all(): + pb_change = data['pb'].pct_change().tail(5).mean() + indicators['pb_momentum'] = pb_change if not np.isnan(pb_change) else 0 + else: + indicators['pb_momentum'] = 0 + + # 处理NaN值 + for key, value in indicators.items(): + if np.isnan(value) or np.isinf(value): + indicators[key] = 0 + + return indicators + + def _count_consecutive(self, condition_series): + """计算连续满足条件的天数""" + if len(condition_series) == 0: + return 0 + + count = 0 + for value in reversed(condition_series.tolist()): + if value: + count += 1 + else: + break + return count + + def calculate_distance_factor(self, indicators_df): + """计算平均距离因子""" + if len(indicators_df) < 2: + print("股票数量不足，无法计算距离因子") + return pd.DataFrame() + + # 准备特征矩阵 + feature_columns = [col for col in indicators_df.columns if col != 'symbol'] + X = indicators_df[feature_columns].values + + # 标准化 + scaler = StandardScaler() + X_scaled = scaler.fit_transform(X) + + # 计算距离矩阵 + distances = euclidean_distances(X_scaled) + + # 计算每只股票的平均距离 + n_stocks = len(indicators_df) + avg_distances = [] + + for i in range(n_stocks): + # 排除自己与自己的距离（对角线元素） + other_distances = np.concatenate([distances[i, :i], distances[i, i+1:]]) + avg_distance = np.mean(other_distances) + avg_distances.append(avg_distance) + + # 创建结果DataFrame + result_df = pd.DataFrame({ + 'symbol': indicators_df['symbol'], + 'avg_distance_factor': avg_distances + }) + + # 按距离因子降序排列 + result_df = result_df.sort_values('avg_distance_factor', ascending=False) + result_df['rank'] = range(1, len(result_df) + 1) + + return result_df + + def analyze_industry(self, industry_name=None, concept_name=None, days=20): + """分析指定行业或概念的平均距离因子""" + print(f"开始分析{'行业' if industry_name else '概念'}: {industry_name or concept_name}") + + # 1. 获取股票列表 + symbols = self.get_industry_stocks(industry_name, concept_name) + if not symbols: + return pd.DataFrame() + + # 2. 获取历史数据 + stock_data = self.get_stock_data(symbols, days) + if stock_data.empty: + return pd.DataFrame() + + # 3. 计算技术指标 + print("计算技术指标...") + indicators_df = self.calculate_technical_indicators(stock_data, days) + if indicators_df.empty: + return pd.DataFrame() + + print(f"成功计算了 {len(indicators_df)} 只股票的技术指标") + + # 4. 计算平均距离因子 + print("计算平均距离因子...") + distance_df = self.calculate_distance_factor(indicators_df) + + if not distance_df.empty: + print(f"计算完成，共 {len(distance_df)} 只股票") + print(distance_df.to_string(index=False)) + + return distance_df + + def get_available_industries(self): + """获取可用的行业列表""" + query = "SELECT DISTINCT bk_name FROM gp_hybk ORDER BY bk_name" + try: + df = pd.read_sql(query, self.engine) + return df['bk_name'].tolist() + except Exception as e: + print(f"获取行业列表失败: {e}") + return [] + + def get_available_concepts(self): + """获取可用的概念列表""" + query = "SELECT DISTINCT bk_name FROM gp_gnbk ORDER BY bk_name" + try: + df = pd.read_sql(query, self.engine) + return df['bk_name'].tolist() + except Exception as e: + print(f"获取概念列表失败: {e}") + return [] + + def __del__(self): + if hasattr(self, 'engine'): + self.engine.dispose() + +def main(): + """主函数示例""" + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + + # 创建分析器 + analyzer = AverageDistanceFactor(db_url) + + # 示例1: 分析特定行业 + result = analyzer.analyze_industry(industry_name="证券") + + # 示例2: 分析特定概念 + # result = analyzer.analyze_industry(concept_name="3D打印") + + # 示例3: 查看可用的行业列表 + # industries = analyzer.get_available_industries() + # print("可用行业列表（前10个）:") + # for industry in industries[:10]: + # print(f" - {industry}") + + # 示例4: 查看可用的概念列表 + # concepts = analyzer.get_available_concepts() + # print("\n可用概念列表（前10个）:") + # for concept in concepts[:10]: + # print(f" - {concept}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/quantitative_analysis/batch_stock_price_collector.py b/src/quantitative_analysis/batch_stock_price_collector.py index df270c1..2089c34 100644 --- a/src/quantitative_analysis/batch_stock_price_collector.py +++ b/src/quantitative_analysis/batch_stock_price_collector.py @@ -10,6 +10,9 @@ import json project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(project_root) +# 导入代理管理器 +from src.scripts.ProxyIP import EnhancedProxyManager + # 读取雪球headers和Redis配置 try: from src.scripts.config import XUEQIU_HEADERS @@ -28,6 +31,9 @@ except ImportError: REDIS_KEY = 'xq_stock_changes_latest' # 存放行情的主键 +# 创建全局代理管理器实例 +proxy_manager = EnhancedProxyManager() + def get_redis_conn(): """获取Redis连接""" @@ -62,8 +68,9 @@ def fetch_and_store_stock_data(page_size=90): 'type': stock_type } - # 初次请求以获取总页数 - response = requests.get(base_url, headers=headers, params=params) + # 初次请求以获取总页数，使用代理 + response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params) + # response = requests.get(base_url, headers=headers, params=params) if response.status_code != 200: print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}") continue @@ -74,10 +81,12 @@ def fetch_and_store_stock_data(page_size=90): for page in range(1, total_pages + 1): params['page'] = page - response = requests.get(base_url, headers=headers, params=params) + # response = requests.get(base_url, headers=headers, params=params) + response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params) if response.status_code == 200: data = response.json() all_data.extend(data['data']['list']) + print(f"成功采集第 {page}/{total_pages} 页数据") else: print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}") # 转换为 DataFrame @@ -99,8 +108,12 @@ def fetch_and_store_stock_data(page_size=90): pipe.hset(REDIS_KEY, symbol, json.dumps(value, ensure_ascii=False)) pipe.execute() print(f"成功将数据写入Redis哈希 {REDIS_KEY}，共{len(df)}条记录。") + + # 返回DataFrame供其他脚本使用 + return df else: print("未获取到任何数据。") + return pd.DataFrame() def format_stock_code(stock_code): diff --git a/src/quantitative_analysis/company_lifecycle_factor.py b/src/quantitative_analysis/company_lifecycle_factor.py new file mode 100644 index 0000000..5cc228b --- /dev/null +++ b/src/quantitative_analysis/company_lifecycle_factor.py @@ -0,0 +1,317 @@ +# coding:utf-8 +# 判断企业生命周期 +import pandas as pd +import pymongo +import logging +from typing import Dict, List, Optional +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 +try: + from valuation_analysis.config import MONGO_CONFIG2 +except ImportError: + import importlib.util + config_path = os.path.join(project_root, 'valuation_analysis', 'config.py') + spec = importlib.util.spec_from_file_location("config", config_path) + config_module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(config_module) + MONGO_CONFIG2 = config_module.MONGO_CONFIG2 + +# 导入股票代码格式化工具 +try: + from tools.stock_code_formatter import StockCodeFormatter +except ImportError: + import importlib.util + formatter_path = os.path.join(os.path.dirname(project_root), 'tools', 'stock_code_formatter.py') + spec = importlib.util.spec_from_file_location("stock_code_formatter", formatter_path) + formatter_module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(formatter_module) + StockCodeFormatter = formatter_module.StockCodeFormatter + +# 配置日志 +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +class CompanyLifecycleFactor: + """企业生命周期阶段因子计算器""" + + def __init__(self): + """初始化""" + self.mongo_client = None + self.db = None + self.collection = None + self.connect_mongodb() + + # 初始化股票代码格式化工具 + self.stock_formatter = StockCodeFormatter() + + # 定义企业生命周期阶段 + self.lifecycle_stages = { + 1: "引入期", + 2: "成长期", + 3: "成熟期", + 4: "震荡期", + 5: "衰退期" + } + + # 现金流组合模式映射到生命周期阶段 + self.cashflow_pattern_mapping = { + ('负', '负', '正'): 1, # 引入期 + ('正', '负', '正'): 2, # 成长期 + ('正', '负', '负'): 3, # 成熟期 + ('负', '正', '正'): 4, # 震荡期 + ('正', '正', '负'): 5, # 衰退期 + ('负', '正', '负'): 4, # 震荡期（变种） + ('负', '负', '负'): 4, # 震荡期（困难期） + ('正', '正', '正'): 2, # 成长期（变种，现金充足） + } + + def connect_mongodb(self): + """连接MongoDB数据库""" + try: + self.mongo_client = pymongo.MongoClient( + host=MONGO_CONFIG2['host'], + port=MONGO_CONFIG2['port'], + username=MONGO_CONFIG2['username'], + password=MONGO_CONFIG2['password'] + ) + self.db = self.mongo_client[MONGO_CONFIG2['db']] + self.collection = self.db['eastmoney_financial_data_v2'] + + # 测试连接 + self.mongo_client.admin.command('ping') + logger.info("MongoDB连接成功") + + except Exception as e: + logger.error(f"MongoDB连接失败: {str(e)}") + raise + + + + + def get_annual_financial_data(self, stock_code: str, year: int) -> Optional[Dict]: + """ + 获取指定股票指定年份的年报数据 + + Args: + stock_code: 股票代码，支持多种格式 (300661.SZ, 300661, SZ300661) + year: 年份，如2024 + + Returns: + Dict: 年报财务数据，如果没有找到则返回None + """ + try: + # 标准化股票代码格式 + normalized_code = self.stock_formatter.to_dot_format(stock_code) + # 构建年报日期（12-31结尾） + report_date = f"{year}-12-31" + + # 查询指定股票指定年份的年报数据 + query = { + "stock_code": normalized_code, + "report_date": report_date + } + + annual_data = self.collection.find_one(query) + + if annual_data: + logger.info(f"找到年报数据: {stock_code} (标准化后: {normalized_code}) - {report_date}") + return annual_data + else: + logger.warning(f"未找到年报数据: {stock_code} (标准化后: {normalized_code}) - {report_date}") + return None + + except Exception as e: + logger.error(f"获取年报数据失败: {stock_code} - {year} - {str(e)}") + return None + + def extract_cashflow_values(self, financial_data: Dict) -> tuple: + """ + 从财务数据中提取现金流量表的三个关键指标 + + Returns: + Tuple: (经营现金流净额, 投资现金流净额, 筹资现金流净额) + """ + try: + cash_flow_statement = financial_data.get('cash_flow_statement', {}) + + # 提取三个现金流指标 + operating_cashflow = cash_flow_statement.get('NETCASH_OPERATE') + investing_cashflow = cash_flow_statement.get('NETCASH_INVEST') + financing_cashflow = cash_flow_statement.get('NETCASH_FINANCE') + + # 转换为浮点数 + def safe_float_convert(value): + if value is None or value == '': + return None + try: + return float(value) + except (ValueError, TypeError): + return None + + operating_cashflow = safe_float_convert(operating_cashflow) + investing_cashflow = safe_float_convert(investing_cashflow) + financing_cashflow = safe_float_convert(financing_cashflow) + + return operating_cashflow, investing_cashflow, financing_cashflow + + except Exception as e: + logger.error(f"提取现金流数据失败: {str(e)}") + return None, None, None + + def classify_cashflow_pattern(self, operating_cf: float, investing_cf: float, financing_cf: float) -> tuple: + """将现金流数值分类为正负""" + def classify_value(value): + if value is None: + return "未知" + return "正" if value >= 0 else "负" + + operating_pattern = classify_value(operating_cf) + investing_pattern = classify_value(investing_cf) + financing_pattern = classify_value(financing_cf) + + return operating_pattern, investing_pattern, financing_pattern + + def determine_lifecycle_stage(self, cashflow_pattern: tuple) -> int: + """ + 根据现金流模式确定企业生命周期阶段 + + Returns: + int: 阶段ID (1-5)，0表示未知 + """ + stage_id = self.cashflow_pattern_mapping.get(cashflow_pattern, 0) + return stage_id + + def calculate_lifecycle_factor(self, stock_code: str, year: int) -> Dict: + """ + 计算指定股票指定年份的企业生命周期因子 + + Args: + stock_code: 股票代码，支持多种格式 (300661.SZ, 300661, SZ300661) + year: 年份 + + Returns: + Dict: 生命周期因子结果 + """ + try: + # 获取年报数据 + financial_data = self.get_annual_financial_data(stock_code, year) + if not financial_data: + return { + 'stock_code': stock_code, + 'year': year, + 'stage_id': 0, + 'stage_name': '数据缺失' + } + + # 提取现金流数据 + operating_cf, investing_cf, financing_cf = self.extract_cashflow_values(financial_data) + + if None in [operating_cf, investing_cf, financing_cf]: + return { + 'stock_code': stock_code, + 'year': year, + 'stage_id': 0, + 'stage_name': '数据不完整' + } + + # 分类现金流模式 + cashflow_pattern = self.classify_cashflow_pattern(operating_cf, investing_cf, financing_cf) + + # 确定生命周期阶段 + stage_id = self.determine_lifecycle_stage(cashflow_pattern) + stage_name = self.lifecycle_stages.get(stage_id, '未知阶段') + + return { + 'stock_code': stock_code, + 'year': year, + 'stage_id': stage_id, + 'stage_name': stage_name + } + + except Exception as e: + logger.error(f"计算生命周期因子失败: {stock_code} - {year} - {str(e)}") + return { + 'stock_code': stock_code, + 'year': year, + 'stage_id': 0, + 'stage_name': '计算失败' + } + + def batch_calculate_lifecycle_factors(self, stock_codes: List[str], year: int) -> pd.DataFrame: + """ + 批量计算多只股票指定年份的企业生命周期因子 + + Args: + stock_codes: 股票代码列表 + year: 年份 + + Returns: + pd.DataFrame: 包含所有股票生命周期因子的DataFrame + """ + results = [] + total_stocks = len(stock_codes) + + logger.info(f"开始批量计算 {total_stocks} 只股票 {year} 年的企业生命周期因子") + + for i, stock_code in enumerate(stock_codes, 1): + # 显示进度 + if i % 100 == 0 or i == total_stocks: + progress = (i / total_stocks) * 100 + logger.info(f"进度: [{i}/{total_stocks}] ({progress:.1f}%)") + + result = self.calculate_lifecycle_factor(stock_code, year) + results.append(result) + + # 转换为DataFrame + df = pd.DataFrame(results) + + # 统计各阶段分布 + stage_distribution = df['stage_name'].value_counts() + logger.info(f"{year}年企业生命周期阶段分布:") + for stage, count in stage_distribution.items(): + percentage = (count / len(df)) * 100 + logger.info(f" {stage}: {count} 只 ({percentage:.1f}%)") + + return df + + def __del__(self): + """关闭数据库连接""" + if hasattr(self, 'mongo_client') and self.mongo_client: + self.mongo_client.close() + +def main(): + """主函数示例""" + try: + # 创建生命周期因子计算器 + lifecycle_calculator = CompanyLifecycleFactor() + + # 示例1: 计算单只股票2024年的生命周期阶段 + print("=== 单只股票分析示例 ===") + result = lifecycle_calculator.calculate_lifecycle_factor('600519.SH', 2024) + print(f"股票: {result['stock_code']}") + print(f"年份: {result['year']}") + print(f"生命周期阶段: {result['stage_name']}") + + # 示例2: 批量分析 + print("\n=== 批量分析示例 ===") + test_stocks = ['300879.SZ', '301123.SZ', '300884.SZ', '300918.SZ', '600908.SH'] + df_results = lifecycle_calculator.batch_calculate_lifecycle_factors(test_stocks, 2024) + + print("\n2024年生命周期阶段结果:") + print(df_results[['stock_code', 'stage_name']].to_string(index=False)) + + # 保存结果 + # df_results.to_csv(f"company_lifecycle_{2024}.csv", index=False, encoding='utf-8-sig') + # print(f"\n结果已保存到: company_lifecycle_{2024}.csv") + + except Exception as e: + logger.error(f"程序执行失败: {str(e)}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/quantitative_analysis/data/前五供应商占比.csv b/src/quantitative_analysis/data/前五供应商占比.csv new file mode 100644 index 0000000..7e2c8fe --- /dev/null +++ b/src/quantitative_analysis/data/前五供应商占比.csv @@ -0,0 +1,5412 @@ +stock_code,20200331,20200630,20200930,20201231,20210331,20210630,20210930,20211231,20220331,20220630,20220930,20221231,20230331,20230630,20230930,20231231,20240331,20240630,20240930,20241231,20250331 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b/src/quantitative_analysis/financial_data_collector.py @@ -127,7 +127,7 @@ class FinancialDataCollectorV2: List[str]: 股票代码列表 """ try: - query = "SELECT DISTINCT gp_code_two FROM gp_code_all WHERE gp_code_two IS NOT NULL AND gp_code_two != ''" + query = "SELECT DISTINCT gp_code_two FROM gp_code_all_copy1 WHERE gp_code_two IS NOT NULL AND gp_code_two != ''" with self.mysql_engine.connect() as conn: df = pd.read_sql(text(query), conn) @@ -274,46 +274,164 @@ class FinancialDataCollectorV2: return [] def fetch_balance_sheet(self, stock_code: str, periods: int = 21) -> List[Dict]: - """获取资产负债表数据""" + """获取资产负债表数据 - 支持API自动切换 (G→B→S→I)""" date_filter = self.build_date_filter(stock_code, periods) - url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GBALANCE&sty=F10_FINANCE_GBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=012481899342117453' + + # 先尝试G系列API + g_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GBALANCE&sty=F10_FINANCE_GBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=012481899342117453' headers = {"Content-Type": "application/json"} try: - response = requests.get(url, headers=headers, timeout=30) + response = requests.get(g_url, headers=headers, timeout=30) response.raise_for_status() data = response.json() - if 'result' in data and 'data' in data['result']: - logger.info(f"成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期") - return data['result']['data'] + # 检查G系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"G系列API返回空数据，尝试B系列API - {stock_code}") + + # 切换到B系列API + b_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_BBALANCE&sty=F10_FINANCE_BBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=08691713756184818' + + response = requests.get(b_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + # 检查B系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"B系列API也返回空数据，尝试S系列API - {stock_code}") + + # 切换到S系列API (证券公司) + s_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_SBALANCE&sty=F10_FINANCE_SBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=08691713756184818' + + response = requests.get(s_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + # 检查S系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"S系列API也返回空数据，尝试I系列API - {stock_code}") + + # 切换到I系列API + i_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_IBALANCE&sty=F10_FINANCE_IBALANCE&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=08691713756184818' + + response = requests.get(i_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"I系列API成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"I系列API也无法获取资产负债表数据 - {stock_code}") + return [] + else: + # S系列API成功 + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"S系列API成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"S系列API资产负债表数据格式异常 - {stock_code}") + return [] + else: + # B系列API成功 + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"B系列API成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"B系列API资产负债表数据格式异常 - {stock_code}") + return [] else: - logger.warning("资产负债表数据格式异常") - return [] + # G系列API成功 + if 'result' in data and 'data' in data['result']: + logger.info(f"G系列API成功获取资产负债表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning("G系列API资产负债表数据格式异常") + return [] except Exception as e: logger.error(f"获取资产负债表失败: {str(e)}") return [] def fetch_cash_flow_statement(self, stock_code: str, periods: int = 21) -> List[Dict]: - """获取现金流量表数据""" + """获取现金流量表数据 - 支持API自动切换 (G→B→S→I)""" date_filter = self.build_date_filter(stock_code, periods) - url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GCASHFLOW&sty=APP_F10_GCASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=04664977872701077' + + # 先尝试G系列API + g_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_GCASHFLOW&sty=APP_F10_GCASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=04664977872701077' headers = {"Content-Type": "application/json"} try: - response = requests.get(url, headers=headers, timeout=30) + response = requests.get(g_url, headers=headers, timeout=30) response.raise_for_status() data = response.json() - if 'result' in data and 'data' in data['result']: - logger.info(f"成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期") - return data['result']['data'] + # 检查G系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"G系列API返回空数据，尝试B系列API - {stock_code}") + + # 切换到B系列API + b_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_BCASHFLOW&sty=APP_F10_BCASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=03391901368547232' + + response = requests.get(b_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + # 检查B系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"B系列API也返回空数据，尝试S系列API - {stock_code}") + + # 切换到S系列API (证券公司) + s_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_SCASHFLOW&sty=APP_F10_SCASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=03391901368547232' + + response = requests.get(s_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + # 检查S系列API是否返回有效数据 + if data.get('code') == 9201 or data.get('success') == False or not data.get('result') or not data.get('result', {}).get('data'): + logger.info(f"S系列API也返回空数据，尝试I系列API - {stock_code}") + + # 切换到I系列API + i_url = f'https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FINANCE_ICASHFLOW&sty=APP_F10_ICASHFLOW&filter={date_filter}&p=1&ps={periods}&sr=-1&st=REPORT_DATE&source=HSF10&client=PC&v=03391901368547232' + + response = requests.get(i_url, headers=headers, timeout=30) + response.raise_for_status() + data = response.json() + + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"I系列API成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"I系列API也无法获取现金流量表数据 - {stock_code}") + return [] + else: + # S系列API成功 + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"S系列API成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"S系列API现金流量表数据格式异常 - {stock_code}") + return [] + else: + # B系列API成功 + if 'result' in data and data['result'] and 'data' in data['result']: + logger.info(f"B系列API成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning(f"B系列API现金流量表数据格式异常 - {stock_code}") + return [] else: - logger.warning("现金流量表数据格式异常") - return [] + # G系列API成功 + if 'result' in data and 'data' in data['result']: + logger.info(f"G系列API成功获取现金流量表数据，共 {len(data['result']['data'])} 个报告期") + return data['result']['data'] + else: + logger.warning("G系列API现金流量表数据格式异常") + return [] except Exception as e: logger.error(f"获取现金流量表失败: {str(e)}") @@ -421,9 +539,124 @@ class FinancialDataCollectorV2: logger.error(f"保存数据到MongoDB失败: {str(e)}") return False + def check_missing_data(self, stock_code: str) -> List[str]: + """ + 检查MongoDB中哪些报告期的资产负债表或现金流量表数据为空 + + Args: + stock_code: 股票代码 + + Returns: + List[str]: 需要更新的报告期列表 + """ + try: + # 查询该股票的所有记录 + records = list(self.collection.find({'stock_code': stock_code})) + + missing_periods = [] + + for record in records: + balance_empty = not record.get('balance_sheet') or record.get('balance_sheet') == {} + cash_empty = not record.get('cash_flow_statement') or record.get('cash_flow_statement') == {} + + # 如果资产负债表或现金流量表为空，则需要更新 + if balance_empty or cash_empty: + missing_periods.append(record.get('report_date')) + logger.debug(f"发现需要更新的数据: {stock_code} - {record.get('report_date')} (资产负债表空: {balance_empty}, 现金流量表空: {cash_empty})") + + if missing_periods: + logger.info(f"股票 {stock_code} 有 {len(missing_periods)} 个报告期需要更新数据") + else: + logger.info(f"股票 {stock_code} 的数据完整，无需更新") + + return missing_periods + + except Exception as e: + logger.error(f"检查缺失数据失败: {str(e)}") + return [] + + def update_missing_financial_data(self, stock_code: str, missing_periods: List[str]) -> bool: + """ + 更新缺失的财务数据（只更新资产负债表和现金流量表） + + Args: + stock_code: 股票代码 + missing_periods: 需要更新的报告期列表 + + Returns: + bool: 是否更新成功 + """ + try: + if not missing_periods: + return True + + logger.info(f"开始更新股票 {stock_code} 缺失的财务数据") + + # 获取资产负债表和现金流量表数据 + balance_data = self.fetch_balance_sheet(stock_code, periods=21) + time.sleep(1) + + cash_data = self.fetch_cash_flow_statement(stock_code, periods=21) + time.sleep(1) + + # 创建按报告日期索引的字典 + balance_dict = {item['REPORT_DATE'][:10]: item for item in balance_data if item.get('REPORT_DATE')} + cash_dict = {item['REPORT_DATE'][:10]: item for item in cash_data if item.get('REPORT_DATE')} + + updated_count = 0 + + for report_date in missing_periods: + try: + # 查找当前记录 + current_record = self.collection.find_one({ + 'stock_code': stock_code, + 'report_date': report_date + }) + + if not current_record: + logger.warning(f"未找到记录: {stock_code} - {report_date}") + continue + + # 准备更新的字段 + update_fields = {} + + # 检查是否需要更新资产负债表 + balance_empty = not current_record.get('balance_sheet') or current_record.get('balance_sheet') == {} + if balance_empty and report_date in balance_dict: + update_fields['balance_sheet'] = balance_dict[report_date] + logger.debug(f"更新资产负债表: {stock_code} - {report_date}") + + # 检查是否需要更新现金流量表 + cash_empty = not current_record.get('cash_flow_statement') or current_record.get('cash_flow_statement') == {} + if cash_empty and report_date in cash_dict: + update_fields['cash_flow_statement'] = cash_dict[report_date] + logger.debug(f"更新现金流量表: {stock_code} - {report_date}") + + # 如果有字段需要更新 + if update_fields: + update_fields['collect_time'] = datetime.datetime.now() # 更新采集时间 + + self.collection.update_one( + {'stock_code': stock_code, 'report_date': report_date}, + {'$set': update_fields} + ) + updated_count += 1 + logger.info(f"成功更新: {stock_code} - {report_date}") + + except Exception as e: + logger.error(f"更新记录失败: {stock_code} - {report_date} - {str(e)}") + continue + + logger.info(f"股票 {stock_code} 更新完成，共更新 {updated_count} 个报告期") + return True + + except Exception as e: + logger.error(f"更新缺失财务数据失败: {str(e)}") + return False + def collect_financial_data(self, stock_code: str, periods: int = 21) -> bool: """ - 采集单只股票的财务数据 + 采集单只股票的财务数据 - 增量更新模式 Args: stock_code: 股票代码，如'300750.SZ' @@ -433,37 +666,20 @@ class FinancialDataCollectorV2: bool: 是否采集成功 """ try: - logger.info(f"开始采集股票 {stock_code} 的财务数据（{periods}个报告期）") + logger.info(f"开始检查股票 {stock_code} 的财务数据") - # 获取三张财务报表数据 - profit_data = self.fetch_profit_statement(stock_code, periods) - time.sleep(1) # 避免请求过于频繁 + # 检查哪些报告期的数据缺失 + missing_periods = self.check_missing_data(stock_code) - balance_data = self.fetch_balance_sheet(stock_code, periods) - time.sleep(1) + if not missing_periods: + logger.info(f"股票 {stock_code} 数据完整，跳过") + return True - cash_data = self.fetch_cash_flow_statement(stock_code, periods) - time.sleep(1) - - # 检查至少有一张表有数据 - if not any([profit_data, balance_data, cash_data]): - logger.error(f"股票 {stock_code} 没有获取到任何财务数据") - return False - - # 处理财务数据 - financial_data_list = self.process_financial_data( - stock_code, profit_data, balance_data, cash_data - ) - - if not financial_data_list: - logger.error(f"股票 {stock_code} 的财务数据处理失败") - return False - - # 保存到MongoDB - success = self.save_to_mongodb(financial_data_list) + # 更新缺失的数据 + success = self.update_missing_financial_data(stock_code, missing_periods) if success: - logger.info(f"股票 {stock_code} 的财务数据采集完成") + logger.info(f"股票 {stock_code} 的财务数据更新完成") return success @@ -473,19 +689,19 @@ class FinancialDataCollectorV2: def batch_collect_financial_data(self, stock_codes: List[str], periods: int = 21) -> Dict: """ - 批量采集多只股票的财务数据 + 批量更新多只股票的缺失财务数据 Args: stock_codes: 股票代码列表 periods: 获取多少个报告期，默认21个季度 Returns: - Dict: 采集结果统计 + Dict: 更新结果统计 """ - results = {'success': 0, 'failed': 0, 'failed_stocks': []} + results = {'success': 0, 'failed': 0, 'failed_stocks': [], 'skipped': 0} total_stocks = len(stock_codes) - logger.info(f"开始批量采集 {total_stocks} 只股票的财务数据") + logger.info(f"开始批量检查和更新 {total_stocks} 只股票的财务数据") for index, stock_code in enumerate(stock_codes, 1): try: @@ -496,11 +712,11 @@ class FinancialDataCollectorV2: success = self.collect_financial_data(stock_code, periods) if success: results['success'] += 1 - logger.info(f"SUCCESS [{index}/{total_stocks}] {stock_code} 采集成功") + logger.info(f"SUCCESS [{index}/{total_stocks}] {stock_code} 处理成功") else: results['failed'] += 1 results['failed_stocks'].append(stock_code) - logger.warning(f"FAILED [{index}/{total_stocks}] {stock_code} 采集失败") + logger.warning(f"FAILED [{index}/{total_stocks}] {stock_code} 处理失败") # 每只股票之间暂停一下，避免请求过于频繁 time.sleep(2) @@ -519,7 +735,7 @@ class FinancialDataCollectorV2: continue success_rate = (results['success'] / total_stocks) * 100 - logger.info(f"批量采集完成: 成功{results['success']}只，失败{results['failed']}只，成功率: {success_rate:.2f}%") + logger.info(f"批量更新完成: 成功{results['success']}只，失败{results['failed']}只，成功率: {success_rate:.2f}%") if results['failed_stocks']: logger.info(f"失败的股票数量: {len(results['failed_stocks'])}") @@ -541,7 +757,7 @@ class FinancialDataCollectorV2: def main(): - """主函数 - 批量采集所有股票的财务数据""" + """主函数 - 批量更新所有股票的缺失财务数据""" collector = FinancialDataCollectorV2() try: @@ -555,30 +771,32 @@ def main(): logger.info(f"从数据库获取到 {len(stock_codes)} 只股票") - # 可以选择采集所有股票或者部分股票进行测试 + # 可以选择处理所有股票或者部分股票进行测试 # 如果要测试，可以取前几只股票 - # 测试模式：只采集前10只股票 - TEST_MODE = False # 设置为False将采集所有股票 + # 测试模式：只处理前10只股票 + TEST_MODE = False # 设置为False将处理所有股票 if TEST_MODE: test_count = min(10, len(stock_codes)) # 最多取10只股票测试 stock_codes = stock_codes[:test_count] - logger.info(f"TEST MODE: 仅采集前 {test_count} 只股票") + logger.info(f"TEST MODE: 仅处理前 {test_count} 只股票") else: - logger.info(f"PRODUCTION MODE: 将采集全部 {len(stock_codes)} 只股票") + logger.info(f"PRODUCTION MODE: 将处理全部 {len(stock_codes)} 只股票") - logger.info(f"开始批量采集 {len(stock_codes)} 只股票的财务数据") + logger.info(f"开始批量检查和更新 {len(stock_codes)} 只股票的财务数据") + logger.info("注意: 本次运行为增量更新模式，只会更新缺失的资产负债表和现金流量表数据") - # 批量采集 + # 批量更新 results = collector.batch_collect_financial_data(stock_codes, periods=21) # 输出最终结果 print(f"\n{'='*50}") - print(f"批量采集完成统计") + print(f"批量更新完成统计") print(f"{'='*50}") - print(f"SUCCESS 成功采集: {results['success']} 只股票") - print(f"FAILED 采集失败: {results['failed']} 只股票") + print(f"SUCCESS 成功处理: {results['success']} 只股票") + print(f"FAILED 处理失败: {results['failed']} 只股票") print(f"SUCCESS RATE 成功率: {(results['success'] / len(stock_codes) * 100):.2f}%") + print(f"\n说明: 成功处理包括数据完整(无需更新)和成功更新缺失数据的股票") if results['failed_stocks']: print(f"\n失败的股票列表:") @@ -593,7 +811,7 @@ def main(): logger.info("用户中断程序执行") print("\n警告: 程序被用户中断") except Exception as e: - logger.error(f"采集过程中出现错误: {str(e)}") + logger.error(f"更新过程中出现错误: {str(e)}") print(f"\n错误: 程序执行出错: {str(e)}") finally: collector.close_connection() diff --git a/src/quantitative_analysis/financial_indicator_analyzer.py b/src/quantitative_analysis/financial_indicator_analyzer.py new file mode 100644 index 0000000..d53c5f9 --- /dev/null +++ b/src/quantitative_analysis/financial_indicator_analyzer.py @@ -0,0 +1,1328 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +财务指标分析器 +用于分析MongoDB中eastmoney_financial_data_v2集合的财务数据 +只返回具体的数值信息，不进行评估 +""" + +import sys +import pymongo +import datetime +import logging +from typing import Dict, List, Optional, Union +from pathlib import Path +from sqlalchemy import create_engine, text +import numpy as np + +# 添加项目根路径到Python路径 +project_root = Path(__file__).parent.parent.parent +sys.path.append(str(project_root)) + +# 导入配置 +from src.valuation_analysis.config import MONGO_CONFIG2, DB_URL + +# 导入股票代码格式转换工具 +from tools.stock_code_formatter import StockCodeFormatter + +# 设置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + + +class FinancialIndicatorAnalyzer: + """财务指标分析器""" + + def __init__(self): + """初始化""" + # MongoDB连接 + self.mongo_client = None + self.db = None + self.collection_name = 'eastmoney_financial_data_v2' + self.collection = None + + # MySQL连接 + self.mysql_engine = None + + # 股票代码格式转换器 + self.code_formatter = StockCodeFormatter() + + self.connect_mongodb() + self.connect_mysql() + + def connect_mongodb(self): + """连接MongoDB数据库""" + try: + self.mongo_client = pymongo.MongoClient( + host=MONGO_CONFIG2['host'], + port=MONGO_CONFIG2['port'], + username=MONGO_CONFIG2['username'], + password=MONGO_CONFIG2['password'] + ) + self.db = self.mongo_client[MONGO_CONFIG2['db']] + self.collection = self.db[self.collection_name] + + # 测试连接 + self.mongo_client.admin.command('ping') + logger.info(f"MongoDB连接成功，使用集合: {self.collection_name}") + + except Exception as e: + logger.error(f"MongoDB连接失败: {str(e)}") + raise + + def connect_mysql(self): + """连接MySQL数据库""" + try: + self.mysql_engine = create_engine( + DB_URL, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + + # 测试连接 + with self.mysql_engine.connect() as conn: + conn.execute(text("SELECT 1")) + + logger.info("MySQL数据库连接成功") + + except Exception as e: + logger.error(f"MySQL数据库连接失败: {str(e)}") + raise + + def normalize_stock_code(self, stock_code: str) -> str: + """ + 标准化股票代码格式，转换为数据库中使用的格式 (如 SZ300661) + + Args: + stock_code: 输入的股票代码，支持多种格式: + - 300661.SZ -> SZ300661 + - 300661 -> SZ300661 + - SZ300661 -> SZ300661 (已是标准格式) + + Returns: + str: 标准化后的股票代码 + """ + stock_code = stock_code.strip().upper() + + if '.' in stock_code: # 处理 300661.SZ 格式 + parts = stock_code.split('.') + if len(parts) == 2: + stock_code = f"{parts[1]}{parts[0]}" + elif stock_code.isdigit(): # 处理 300661 格式 + if stock_code.startswith(('60', '68')): + stock_code = f"SH{stock_code}" + elif stock_code.startswith(('00', '30', '20')): + stock_code = f"SZ{stock_code}" + elif stock_code.startswith(('8', '43', '87')): + stock_code = f"BJ{stock_code}" + # 如果已经是 SZ300661 格式，则不需要处理 + + return stock_code + + def get_latest_pe_pb_data(self, stock_code: str) -> Optional[Dict]: + """ + 从gp_day_data表获取股票最新的PE和PB数据 + + Args: + stock_code: 股票代码，支持多种格式 (300661.SZ, 300661, SZ300661) + + Returns: + Dict: 包含pe和pb的字典，如果没有找到则返回None + """ + try: + # 标准化股票代码格式 + normalized_code = self.code_formatter.to_prefix_format(stock_code) + + query = text(""" + SELECT pe, pb, `timestamp` + FROM gp_day_data + WHERE symbol = :stock_code + ORDER BY `timestamp` DESC + LIMIT 1 + """) + + with self.mysql_engine.connect() as conn: + result = conn.execute(query, {"stock_code": normalized_code}).fetchone() + + if result: + return { + 'pe': float(result[0]) if result[0] is not None else None, + 'pb': float(result[1]) if result[1] is not None else None, + 'timestamp': result[2] + } + else: + logger.warning(f"未找到股票 {stock_code} (标准化后: {normalized_code}) 的PE/PB数据") + return None + + except Exception as e: + logger.error(f"获取股票 {stock_code} PE/PB数据失败: {str(e)}") + return None + + def get_financial_data(self, stock_code: str, report_date: Optional[str] = None) -> Optional[Dict]: + """ + 获取指定股票的财务数据 + + Args: + stock_code: 股票代码 + report_date: 报告日期，格式 'YYYY-MM-DD'，如果为None则获取最新数据 + + Returns: + Dict: 财务数据，如果没有找到则返回None + """ + try: + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + if report_date: + # 查询指定日期的数据 + query = { + 'stock_code': formatted_stock_code, + 'report_date': report_date + } + data = self.collection.find_one(query) + else: + # 查询最新数据 + query = {'stock_code': formatted_stock_code} + data = self.collection.find_one( + query, + sort=[('report_date', -1)] + ) + + return data + + except Exception as e: + logger.error(f"获取财务数据失败 {stock_code} - {report_date}: {str(e)}") + return None + + def safe_divide(self, numerator: Union[float, int, None], denominator: Union[float, int, None]) -> Optional[float]: + """ + 安全除法，处理None值和零除错误 + + Args: + numerator: 分子 + denominator: 分母 + + Returns: + float: 计算结果，如果无法计算则返回None + """ + try: + if numerator is None or denominator is None or denominator == 0: + return None + return float(numerator) / float(denominator) + except (TypeError, ValueError, ZeroDivisionError): + return None + + # ==================== 盈利能力指标 ==================== + + def analyze_roe(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析净资产收益率(ROE) + 公式: ROE = 归母净利润 / 归母股东权益 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: ROE值(%)，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取归母净利润 + profit = data.get('profit_statement', {}).get('PARENT_NETPROFIT') + # 获取归母股东权益 + equity = data.get('balance_sheet', {}).get('TOTAL_PARENT_EQUITY') + + roe = self.safe_divide(profit, equity) + if roe: + roe = roe * 100 # 转换为百分比 + + return roe + + except Exception as e: + logger.error(f"分析ROE失败 {stock_code}: {str(e)}") + return None + + def analyze_gross_profit_margin(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析毛利率 + 公式: 毛利率 = (营业收入 - 营业成本) / 营业收入 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 毛利率(%)，如果无法计算则返回None + """ + try: + fm_stock_code = self.code_formatter.to_dot_format(stock_code) + data = self.get_financial_data(fm_stock_code, report_date) + if not data: + return None + + # 获取营业收入和营业成本 + revenue = data.get('profit_statement', {}).get('OPERATE_INCOME') + cost = data.get('profit_statement', {}).get('OPERATE_COST') + + if revenue and cost: + gross_profit = revenue - cost + margin = self.safe_divide(gross_profit, revenue) + if margin: + margin = margin * 100 # 转换为百分比 + else: + margin = None + + return margin + + except Exception as e: + logger.error(f"分析毛利率失败 {stock_code}: {str(e)}") + return None + + def analyze_net_profit_margin(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析净利率 + 公式: 净利率 = 归母净利润 / 营业收入 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 净利率(%)，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取归母净利润和营业收入 + profit = data.get('profit_statement', {}).get('PARENT_NETPROFIT') + revenue = data.get('profit_statement', {}).get('OPERATE_INCOME') + + margin = self.safe_divide(profit, revenue) + if margin: + margin = margin * 100 # 转换为百分比 + + return margin + + except Exception as e: + logger.error(f"分析净利率失败 {stock_code}: {str(e)}") + return None + + # ==================== 成长能力指标 ==================== + + def analyze_revenue_growth(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析营业收入增长率（使用YOY数据） + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 增长率(%)，如果无法获取则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取营业收入同比增长率 + growth_rate = data.get('profit_statement', {}).get('OPERATE_INCOME_YOY') + + return growth_rate + + except Exception as e: + logger.error(f"分析营业收入增长率失败 {stock_code}: {str(e)}") + return None + + def analyze_profit_growth(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析净利润增长率（使用YOY数据） + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 增长率(%)，如果无法获取则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取归母净利润同比增长率 + growth_rate = data.get('profit_statement', {}).get('PARENT_NETPROFIT_YOY') + + return growth_rate + + except Exception as e: + logger.error(f"分析净利润增长率失败 {stock_code}: {str(e)}") + return None + + def analyze_total_assets_growth(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析总资产增长率（使用YOY数据） + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 增长率(%)，如果无法获取则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取总资产同比增长率 + growth_rate = data.get('balance_sheet', {}).get('TOTAL_ASSETS_YOY') + + return growth_rate + + except Exception as e: + logger.error(f"分析总资产增长率失败 {stock_code}: {str(e)}") + return None + + def analyze_growth_capability(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析成长能力指标 + 公式: 成长能力 = 最新净利润增长率 / 过去8个季度净利润同比变化的标准差 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 成长能力指标，如果无法计算则返回None + """ + try: + # 将股票代码转换为点分格式 (如 430139.BJ) + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + if not formatted_stock_code: + logger.error(f"无法格式化股票代码: {stock_code}") + return None + + # 生成需要查询的季度列表（往前推13个季度） + # 原因：需要8个季度计算增长率，每个增长率需要去年同期数据(+4)， + # 加上1个季度缓冲：8+4+1=13个季度 + quarters = self._generate_quarters(report_date or "2025-03-31", 13) + + # 获取多个季度的财务数据 + quarterly_data = [] + for quarter in quarters: + data = self.get_financial_data(formatted_stock_code, quarter) + if data: + profit = data.get('profit_statement', {}).get('PARENT_NETPROFIT') + quarterly_data.append({ + 'quarter': quarter, + 'profit': profit + }) + + if len(quarterly_data) < 9: + logger.warning(f"股票 {stock_code} (格式化后: {formatted_stock_code}) 原始季度数据不足({len(quarterly_data)})，无法计算成长能力") + return None + + # 处理季度数据的累积性，计算单季度净利润 + processed_data = self._process_quarterly_profits(quarterly_data) + + if len(processed_data) < 9: + logger.warning(f"股票 {stock_code} (格式化后: {formatted_stock_code}) 处理后季度数据不足({len(processed_data)})") + return None + + # 计算每个季度的同比增长率 + growth_rates = self._calculate_yoy_growth_rates(processed_data) + + if len(growth_rates) < 8: + logger.warning(f"股票 {stock_code} (格式化后: {formatted_stock_code}) 同比增长率数据不足") + return None + + # 取最新8个季度的增长率计算标准差 + recent_8_quarters = growth_rates[-8:] + + # 最新的净利润增长率 + latest_growth_rate = recent_8_quarters[-1] + + # 计算标准差 + std_dev = np.std(recent_8_quarters) + + if std_dev == 0: + return None # 避免除零错误 + + # 成长能力 = 净利润增长率 / 标准差 + growth_capability = latest_growth_rate / std_dev + + return growth_capability + + except Exception as e: + logger.error(f"分析成长能力失败 {stock_code}: {str(e)}") + return None + + def _generate_quarters(self, start_date: str, num_quarters: int) -> List[str]: + """ + 生成季度列表 + + Args: + start_date: 起始日期 (YYYY-MM-DD) + num_quarters: 需要的季度数量 + + Returns: + List[str]: 季度列表，格式为 YYYY-MM-DD + """ + from datetime import datetime, timedelta + import calendar + + quarters = [] + year, month, day = map(int, start_date.split('-')) + + # 确定起始季度 + if month <= 3: + quarter = 1 + elif month <= 6: + quarter = 2 + elif month <= 9: + quarter = 3 + else: + quarter = 4 + + current_year = year + current_quarter = quarter + + for _ in range(num_quarters): + # 根据季度生成日期 + if current_quarter == 1: + quarter_date = f"{current_year}-03-31" + elif current_quarter == 2: + quarter_date = f"{current_year}-06-30" + elif current_quarter == 3: + quarter_date = f"{current_year}-09-30" + else: # quarter == 4 + quarter_date = f"{current_year}-12-31" + + quarters.append(quarter_date) + + # 移动到上一个季度 + current_quarter -= 1 + if current_quarter == 0: + current_quarter = 4 + current_year -= 1 + + return quarters + + def _process_quarterly_profits(self, quarterly_data: List[Dict]) -> List[Dict]: + """ + 处理季度数据的累积性，计算单季度净利润 + + Args: + quarterly_data: 包含季度和累积利润的数据列表 + + Returns: + List[Dict]: 处理后的单季度利润数据 + """ + # 按季度排序（从旧到新） + quarterly_data.sort(key=lambda x: x['quarter']) + + processed_data = [] + + # 创建一个字典来快速查找季度数据 + quarter_dict = {data['quarter']: data['profit'] for data in quarterly_data if data['profit'] is not None} + + for data in quarterly_data: + quarter = data['quarter'] + cumulative_profit = data['profit'] + + if cumulative_profit is None: + continue + + # 判断季度类型 + month = quarter.split('-')[1] + + if month == '03': # 一季报，直接使用 + single_quarter_profit = cumulative_profit + else: + # 需要减去前面季度的累积值 + if month == '06': # 半年报 = 半年报 - 一季报 + prev_quarter = quarter.replace('-06-30', '-03-31') + elif month == '09': # 三季报 = 三季报 - 半年报 + prev_quarter = quarter.replace('-09-30', '-06-30') + else: # month == '12', 年报 = 年报 - 三季报 + prev_quarter = quarter.replace('-12-31', '-09-30') + + # 查找前一个季度的累积利润 + prev_cumulative_profit = quarter_dict.get(prev_quarter) + + if prev_cumulative_profit is not None: + single_quarter_profit = cumulative_profit - prev_cumulative_profit + else: + # 如果缺少前一季度数据，我们仍然可以尝试使用这个数据 + # 但需要标记它可能不准确 + logger.warning(f"缺少前一季度数据 {prev_quarter}，直接使用累积值 {quarter}") + single_quarter_profit = cumulative_profit + + processed_data.append({ + 'quarter': quarter, + 'single_quarter_profit': single_quarter_profit + }) + + return processed_data + + def _calculate_yoy_growth_rates(self, processed_data: List[Dict]) -> List[float]: + """ + 计算同比增长率 + + Args: + processed_data: 处理后的单季度利润数据 + + Returns: + List[float]: 同比增长率列表 + """ + growth_rates = [] + + # 按季度排序 + processed_data.sort(key=lambda x: x['quarter']) + + for i in range(4, len(processed_data)): # 从第5个季度开始计算同比 + current_profit = processed_data[i]['single_quarter_profit'] + prev_year_profit = processed_data[i-4]['single_quarter_profit'] # 去年同期 + + if prev_year_profit is not None and prev_year_profit != 0 and current_profit is not None: + growth_rate = (current_profit - prev_year_profit) / abs(prev_year_profit) * 100 + growth_rates.append(growth_rate) + + return growth_rates + + # ==================== 营运效率指标 ==================== + + def analyze_admin_expense_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析管理费用/营业收入占比 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 管理费用率(%)，如果无法计算则返回None + """ + try: + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + data = self.get_financial_data(formatted_stock_code, report_date) + if not data: + return None + + # 获取管理费用和营业收入 + admin_expense = data.get('profit_statement', {}).get('MANAGE_EXPENSE') + revenue = data.get('profit_statement', {}).get('OPERATE_INCOME') + + ratio = self.safe_divide(admin_expense, revenue) + if ratio: + ratio = ratio * 100 # 转换为百分比 + + return ratio + + except Exception as e: + logger.error(f"分析管理费用率失败 {stock_code}: {str(e)}") + return None + + def analyze_rd_expense_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析研发费用/营业收入占比 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 研发费用率(%)，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取研发费用和营业收入 + rd_expense = data.get('profit_statement', {}).get('RESEARCH_EXPENSE') + revenue = data.get('profit_statement', {}).get('OPERATE_INCOME') + + ratio = self.safe_divide(rd_expense, revenue) + if ratio: + ratio = ratio * 100 # 转换为百分比 + + return ratio + + except Exception as e: + logger.error(f"分析研发费用率失败 {stock_code}: {str(e)}") + return None + + def analyze_sales_expense_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析销售费用/营业收入占比 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 销售费用率(%)，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取销售费用和营业收入 + sales_expense = data.get('profit_statement', {}).get('SALE_EXPENSE') + revenue = data.get('profit_statement', {}).get('OPERATE_INCOME') + + ratio = self.safe_divide(sales_expense, revenue) + if ratio: + ratio = ratio * 100 # 转换为百分比 + + return ratio + + except Exception as e: + logger.error(f"分析销售费用率失败 {stock_code}: {str(e)}") + return None + + # ==================== 规模扩张指标 ==================== + + def analyze_asset_liability_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析资产负债率 + 公式: 资产负债率 = 总负债 / 总资产 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 资产负债率(%)，如果无法计算则返回None + """ + try: + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + data = self.get_financial_data(formatted_stock_code, report_date) + if not data: + return None + + # 获取总负债和总资产 + total_liabilities = data.get('balance_sheet', {}).get('TOTAL_LIABILITIES') + total_assets = data.get('balance_sheet', {}).get('TOTAL_ASSETS') + + ratio = self.safe_divide(total_liabilities, total_assets) + if ratio: + ratio = ratio * 100 # 转换为百分比 + + return ratio + + except Exception as e: + logger.error(f"分析资产负债率失败 {stock_code}: {str(e)}") + return None + + def analyze_current_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析流动比率 + 公式: 流动比率 = 流动资产 / 流动负债 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 流动比率，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取流动资产和流动负债 + current_assets = data.get('balance_sheet', {}).get('TOTAL_CURRENT_ASSETS') + current_liabilities = data.get('balance_sheet', {}).get('TOTAL_CURRENT_LIAB') + + ratio = self.safe_divide(current_assets, current_liabilities) + + return ratio + + except Exception as e: + logger.error(f"分析流动比率失败 {stock_code}: {str(e)}") + return None + + def analyze_quick_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析速动比率 + 公式: 速动比率 = (流动资产 - 存货) / 流动负债 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 速动比率，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取流动资产、存货和流动负债 + current_assets = data.get('balance_sheet', {}).get('TOTAL_CURRENT_ASSETS') + inventory = data.get('balance_sheet', {}).get('INVENTORY', 0) + current_liabilities = data.get('balance_sheet', {}).get('TOTAL_CURRENT_LIAB') + + if current_assets and current_liabilities: + quick_assets = current_assets - (inventory or 0) + ratio = self.safe_divide(quick_assets, current_liabilities) + else: + ratio = None + + return ratio + + except Exception as e: + logger.error(f"分析速动比率失败 {stock_code}: {str(e)}") + return None + + # ==================== 研发投入指标 ==================== + + def analyze_rd_investment_growth(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析研发费用增长率（使用YOY数据） + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 增长率(%)，如果无法获取则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取研发费用同比增长率 + growth_rate = data.get('profit_statement', {}).get('RESEARCH_EXPENSE_YOY') + + return growth_rate + + except Exception as e: + logger.error(f"分析研发费用增长率失败 {stock_code}: {str(e)}") + return None + + def analyze_rd_intensity(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析研发强度（研发费用/总资产） + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 研发强度(%)，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取研发费用和总资产 + rd_expense = data.get('profit_statement', {}).get('RESEARCH_EXPENSE') + total_assets = data.get('balance_sheet', {}).get('TOTAL_ASSETS') + + ratio = self.safe_divide(rd_expense, total_assets) + if ratio: + ratio = ratio * 100 # 转换为百分比 + + return ratio + + except Exception as e: + logger.error(f"分析研发强度失败 {stock_code}: {str(e)}") + return None + + # ==================== 供应商客户占比指标 ==================== + + def analyze_supplier_concentration(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析前五供应商占比 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 供应商集中度(%)，如果无法获取则返回None + """ + try: + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + data = self.get_financial_data(formatted_stock_code, report_date) + if not data: + return None + + # 获取前五供应商占比 + supplier_ratio = data.get('top_five_suppliers_ratio') + + if supplier_ratio: + supplier_ratio = supplier_ratio * 100 # 转换为百分比 + + return supplier_ratio + + except Exception as e: + logger.error(f"分析供应商集中度失败 {stock_code}: {str(e)}") + return None + + def analyze_customer_concentration(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析前五客户占比 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 客户集中度(%)，如果无法获取则返回None + """ + try: + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + data = self.get_financial_data(formatted_stock_code, report_date) + if not data: + return None + + # 获取前五客户占比 + customer_ratio = data.get('top_five_customers_ratio') + + if customer_ratio: + customer_ratio = customer_ratio * 100 # 转换为百分比 + + return customer_ratio + + except Exception as e: + logger.error(f"分析客户集中度失败 {stock_code}: {str(e)}") + return None + + # ==================== 现金流指标 ==================== + + def analyze_operating_cash_flow_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析经营现金流量比率 + 公式: 经营现金流量比率 = 经营活动现金流量净额 / 流动负债 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 经营现金流量比率，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取经营活动现金流量净额和流动负债 + operating_cash_flow = data.get('cash_flow_statement', {}).get('NETCASH_OPERATE') + current_liabilities = data.get('balance_sheet', {}).get('TOTAL_CURRENT_LIAB') + + ratio = self.safe_divide(operating_cash_flow, current_liabilities) + + return ratio + + except Exception as e: + logger.error(f"分析经营现金流量比率失败 {stock_code}: {str(e)}") + return None + + def analyze_cash_flow_coverage_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析现金流量覆盖比率 + 公式: 现金流量覆盖比率 = 经营活动现金流量净额 / 归母净利润 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: 现金流量覆盖比率，如果无法计算则返回None + """ + try: + data = self.get_financial_data(stock_code, report_date) + if not data: + return None + + # 获取经营活动现金流量净额和归母净利润 + operating_cash_flow = data.get('cash_flow_statement', {}).get('NETCASH_OPERATE') + net_profit = data.get('profit_statement', {}).get('PARENT_NETPROFIT') + + ratio = self.safe_divide(operating_cash_flow, net_profit) + + return ratio + + except Exception as e: + logger.error(f"分析现金流量覆盖比率失败 {stock_code}: {str(e)}") + return None + + # ==================== 估值指标 ==================== + + def analyze_pe_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析PE比率，从gp_day_data表获取最新PE数据 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新（注意：当前只支持获取最新PE数据） + + Returns: + float: PE比率，如果无法获取则返回None + """ + try: + pe_pb_data = self.get_latest_pe_pb_data(stock_code) + if pe_pb_data and pe_pb_data.get('pe') is not None: + return pe_pb_data['pe'] + else: + logger.warning(f"未找到股票 {stock_code} 的PE数据") + return None + + except Exception as e: + logger.error(f"分析PE比率失败 {stock_code}: {str(e)}") + return None + + def analyze_pb_ratio(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析PB比率，从gp_day_data表获取最新PB数据 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新（注意：当前只支持获取最新PB数据） + + Returns: + float: PB比率，如果无法获取则返回None + """ + try: + pe_pb_data = self.get_latest_pe_pb_data(stock_code) + if pe_pb_data and pe_pb_data.get('pb') is not None: + return pe_pb_data['pb'] + else: + logger.warning(f"未找到股票 {stock_code} 的PB数据") + return None + + except Exception as e: + logger.error(f"分析PB比率失败 {stock_code}: {str(e)}") + return None + + def get_all_stocks_pb_data(self) -> Dict[str, float]: + """ + 获取全A股最新的PB数据 + + Returns: + Dict[str, float]: {股票代码: PB值} 的字典 + """ + try: + query = text(""" + SELECT symbol, pb + FROM gp_day_data + WHERE `timestamp` = ( + SELECT MAX(`timestamp`) FROM gp_day_data + ) + AND pb IS NOT NULL + AND pb > 0 + """) + + pb_data = {} + with self.mysql_engine.connect() as conn: + result = conn.execute(query) + for row in result: + symbol = row[0] + pb = float(row[1]) + # 转换为点分格式 + formatted_code = self.code_formatter.to_dot_format(symbol) + if formatted_code: + pb_data[formatted_code] = pb + + logger.info(f"获取到 {len(pb_data)} 只股票的PB数据") + return pb_data + + except Exception as e: + logger.error(f"获取全A股PB数据失败: {str(e)}") + return {} + + def get_all_stocks_roe_data(self, report_date: Optional[str] = None) -> Dict[str, float]: + """ + 获取全A股的ROE数据（使用MongoDB聚合查询优化性能） + + Args: + report_date: 报告日期，默认最新 + + Returns: + Dict[str, float]: {股票代码: ROE值} 的字典 + """ + try: + if report_date: + # 查询指定日期的数据 + match_stage = {"report_date": report_date} + else: + # 查询每只股票的最新数据 + match_stage = {} + + pipeline = [ + {"$match": match_stage}, + { + "$sort": { + "stock_code": 1, + "report_date": -1 + } + } + ] + + # 如果没有指定日期，则获取每只股票的最新数据 + if not report_date: + pipeline.extend([ + { + "$group": { + "_id": "$stock_code", + "latest_data": {"$first": "$$ROOT"} + } + }, + { + "$replaceRoot": {"newRoot": "$latest_data"} + } + ]) + + # 添加ROE计算 + pipeline.extend([ + { + "$addFields": { + "parent_netprofit": { + "$toDouble": "$profit_statement.PARENT_NETPROFIT" + }, + "total_parent_equity": { + "$toDouble": "$balance_sheet.TOTAL_PARENT_EQUITY" + } + } + }, + { + "$match": { + "parent_netprofit": {"$ne": None, "$ne": 0}, + "total_parent_equity": {"$ne": None, "$ne": 0} + } + }, + { + "$addFields": { + "roe": { + "$multiply": [ + {"$divide": ["$parent_netprofit", "$total_parent_equity"]}, + 100 + ] + } + } + }, + { + "$project": { + "stock_code": 1, + "roe": 1 + } + } + ]) + + roe_data = {} + cursor = self.collection.aggregate(pipeline) + + for doc in cursor: + stock_code = doc.get('stock_code') + roe = doc.get('roe') + if stock_code and roe is not None: + roe_data[stock_code] = float(roe) + + logger.info(f"获取到 {len(roe_data)} 只股票的ROE数据") + return roe_data + + except Exception as e: + logger.error(f"获取全A股ROE数据失败: {str(e)}") + return {} + + def calculate_pb_roe_rank_factor(self, stock_code: str, all_pb_data: Dict[str, float], all_roe_data: Dict[str, float]) -> Optional[float]: + """ + 计算单只股票的PB-ROE排名因子 + 公式: Rank(PB_MRQ) - Rank(ROE_TTM) + + Args: + stock_code: 股票代码 + all_pb_data: 全A股PB数据字典 + all_roe_data: 全A股ROE数据字典 + + Returns: + float: PB-ROE排名因子，如果无法计算则返回None + """ + try: + # 标准化股票代码 + formatted_stock_code = self.code_formatter.to_dot_format(stock_code) + if not formatted_stock_code: + return None + + # 计算PB排名（升序，越小越好） + pb_values = list(all_pb_data.values()) + pb_values.sort() + target_pb = all_pb_data[formatted_stock_code] + pb_rank = pb_values.index(target_pb) + 1 + + # 计算ROE排名（降序，越大越好） + roe_values = list(all_roe_data.values()) + roe_values.sort(reverse=True) + target_roe = all_roe_data[formatted_stock_code] + roe_rank = roe_values.index(target_roe) + 1 + + # 计算因子：Rank(PB) - Rank(ROE) + pb_roe_factor = pb_rank - roe_rank + + return float(pb_roe_factor) + + except Exception as e: + logger.error(f"计算股票 {stock_code} PB-ROE排名因子失败: {str(e)}") + return None + + def analyze_pb_roe_rank_factor(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析PB-ROE排名因子（保持向后兼容） + 公式: Rank(PB_MRQ) - Rank(ROE_TTM) + 排名越小越好，因子值越低说明PB估值较低、ROE较高 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: PB-ROE排名因子，如果无法计算则返回None + """ + try: + # 获取全A股数据 + all_pb_data = self.get_all_stocks_pb_data() + all_roe_data = self.get_all_stocks_roe_data(report_date) + + if not all_pb_data or not all_roe_data: + return None + + return self.calculate_pb_roe_rank_factor(stock_code, all_pb_data, all_roe_data) + + except Exception as e: + logger.error(f"分析PB-ROE排名因子失败 {stock_code}: {str(e)}") + return None + + def analyze_pb_roe(self, stock_code: str, report_date: Optional[str] = None) -> Optional[float]: + """ + 分析PB-ROE指标（原有方法，保持向后兼容） + 公式: PB/ROE，用于评估股票的价值投资潜力 + + Args: + stock_code: 股票代码 + report_date: 报告日期，默认最新 + + Returns: + float: PB-ROE指标，如果无法计算则返回None + """ + try: + # 获取PB值 + pb = self.analyze_pb_ratio(stock_code, report_date) + # 获取ROE值 + roe = self.analyze_roe(stock_code, report_date) + + if pb is not None and roe is not None and roe != 0: + # ROE以百分比形式返回，需要转换为小数 + roe_decimal = roe / 100 + pb_roe = pb / roe_decimal + return pb_roe + else: + logger.warning(f"无法计算股票 {stock_code} 的PB-ROE指标: PB={pb}, ROE={roe}") + return None + + except Exception as e: + logger.error(f"分析PB-ROE指标失败 {stock_code}: {str(e)}") + return None + + def close_connection(self): + """关闭数据库连接""" + try: + if self.mongo_client: + self.mongo_client.close() + logger.info("MongoDB连接已关闭") + except Exception as e: + logger.error(f"关闭MongoDB连接失败: {str(e)}") + + try: + if self.mysql_engine: + self.mysql_engine.dispose() + logger.info("MySQL连接已关闭") + except Exception as e: + logger.error(f"关闭MySQL连接失败: {str(e)}") + + +def main(): + """主函数 - 示例用法""" + analyzer = None + try: + # 创建分析器实例 + analyzer = FinancialIndicatorAnalyzer() + + # 示例：分析某只股票的财务指标 + stock_code = "300661.SZ" # 圣邦股份 + + print(f"=== 财务指标分析：{stock_code} ===") + + # 盈利能力指标 + roe = analyzer.analyze_roe(stock_code) + print(f"ROE: {roe:.2f}%" if roe else "ROE: 无数据") + + gross_margin = analyzer.analyze_gross_profit_margin(stock_code) + print(f"毛利率: {gross_margin:.2f}%" if gross_margin else "毛利率: 无数据") + + net_margin = analyzer.analyze_net_profit_margin(stock_code) + print(f"净利率: {net_margin:.2f}%" if net_margin else "净利率: 无数据") + + # 成长能力指标 + growth_capability = analyzer.analyze_growth_capability(stock_code) + print(f"成长能力: {growth_capability:.2f}" if growth_capability else "成长能力: 无数据") + + # 营运效率指标 + admin_ratio = analyzer.analyze_admin_expense_ratio(stock_code) + print(f"管理费用率: {admin_ratio:.2f}%" if admin_ratio else "管理费用率: 无数据") + + rd_ratio = analyzer.analyze_rd_expense_ratio(stock_code) + print(f"研发费用率: {rd_ratio:.2f}%" if rd_ratio else "研发费用率: 无数据") + + # 规模扩张指标 + asset_liability = analyzer.analyze_asset_liability_ratio(stock_code) + print(f"资产负债率: {asset_liability:.2f}%" if asset_liability else "资产负债率: 无数据") + + current_ratio = analyzer.analyze_current_ratio(stock_code) + print(f"流动比率: {current_ratio:.2f}" if current_ratio else "流动比率: 无数据") + + # 供应商客户占比指标 + supplier_conc = analyzer.analyze_supplier_concentration(stock_code,'2024-12-31') + print(f"供应商集中度: {supplier_conc:.2f}%" if supplier_conc else "供应商集中度: 无数据") + + customer_conc = analyzer.analyze_customer_concentration(stock_code, '2024-12-31') + print(f"客户集中度: {customer_conc:.2f}%" if customer_conc else "客户集中度: 无数据") + + # 估值指标 + pe_ratio = analyzer.analyze_pe_ratio(stock_code) + print(f"PE比率: {pe_ratio:.2f}" if pe_ratio else "PE比率: 无数据") + + pb_ratio = analyzer.analyze_pb_ratio(stock_code) + print(f"PB比率: {pb_ratio:.2f}" if pb_ratio else "PB比率: 无数据") + + pb_roe = analyzer.analyze_pb_roe(stock_code) + print(f"PB-ROE指标: {pb_roe:.2f}" if pb_roe else "PB-ROE指标: 无数据") + + except Exception as e: + logger.error(f"程序执行失败: {str(e)}") + finally: + if analyzer: + analyzer.close_connection() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/quantitative_analysis/hk_stock_price_collector.py b/src/quantitative_analysis/hk_stock_price_collector.py new file mode 100644 index 0000000..c1cf63f --- /dev/null +++ b/src/quantitative_analysis/hk_stock_price_collector.py @@ -0,0 +1,197 @@ +import requests +import pandas as pd +from datetime import datetime +import sys +import os +import redis +import json + +# 添加项目根目录到路径，便于导入scripts.config +project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.append(project_root) + +# 导入代理管理器 +from src.scripts.ProxyIP import EnhancedProxyManager + +# 读取雪球headers和Redis配置 +try: + from src.scripts.config import XUEQIU_HEADERS + from src.valuation_analysis.config import REDIS_CONFIG +except ImportError: + XUEQIU_HEADERS = { + 'User-Agent': 'Mozilla/5.0', + 'Cookie': '', # 需要填写雪球cookie + } + REDIS_CONFIG = { + 'host': 'localhost', + 'port': 6379, + 'db': 0, + 'password': None + } + +REDIS_KEY = 'xq_hk_stock_changes_latest' # 存放港股行情的主键 + +# 创建全局代理管理器实例 +proxy_manager = EnhancedProxyManager() + + +def get_redis_conn(): + """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 fetch_and_store_hk_stock_data(page_size=90): + """ + 批量采集雪球港股所有股票的最新行情数据，并保存到Redis。 + :param page_size: 每页采集数量 + """ + base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json' + headers = XUEQIU_HEADERS + + all_data = [] + + # 使用港股API参数 + params = { + 'page': 1, + 'size': page_size, + 'order': 'desc', + 'order_by': 'dividend_yield', + 'market': 'HK', # 港股市场 + 'type': 'hk' # 港股类型 + } + + # 初次请求以获取总页数，使用代理 + try: + response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params) + if response.status_code != 200: + print(f"请求港股数据失败，状态码：{response.status_code}") + return + except Exception as e: + print(f"请求港股数据时发生异常：{e}") + return + + data = response.json() + total_count = data['data']['count'] + total_pages = (total_count // page_size) + 1 + + print(f"开始采集港股数据，共 {total_pages} 页，{total_count} 条记录") + + # 循环获取所有页面的数据 + for page in range(1, total_pages + 1): + params['page'] = page + try: + response = proxy_manager.request_with_proxy('get', base_url, headers=headers, params=params) + if response.status_code == 200: + data = response.json() + all_data.extend(data['data']['list']) + print(f"成功采集港股第 {page}/{total_pages} 页数据") + else: + print(f"请求港股数据第 {page} 页失败，状态码：{response.status_code}") + except Exception as e: + print(f"请求港股数据第 {page} 页时发生异常：{e}") + continue + + # 转换为 DataFrame + df = pd.DataFrame(all_data) + + if not df.empty: + df['fetch_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S') + # 存入Redis，使用hash结构，key为symbol，value为json字符串 + r = get_redis_conn() + pipe = r.pipeline() + # 先清空旧数据 + r.delete(REDIS_KEY) + for _, row in df.iterrows(): + symbol = row.get('symbol') + if not symbol: + continue + # 只保留必要字段，也可直接存row.to_dict() + value = row.to_dict() + pipe.hset(REDIS_KEY, symbol, json.dumps(value, ensure_ascii=False)) + pipe.execute() + print(f"成功将港股数据写入Redis哈希 {REDIS_KEY}，共{len(df)}条记录。") + else: + print("未获取到任何港股数据。") + + +def format_hk_stock_code(stock_code): + """ + 统一港股代码格式，支持0700.HK、HK0700等 + 返回雪球格式（如0700.HK）和Redis存储格式 + """ + stock_code = stock_code.upper() + if '.HK' in stock_code: + return stock_code, stock_code + elif stock_code.startswith('HK'): + code = stock_code[2:] + return f'{code}.HK', f'{code}.HK' + else: + # 假设是纯数字，添加.HK后缀 + return f'{stock_code}.HK', f'{stock_code}.HK' + + +def get_hk_stock_realtime_info_from_redis(stock_code): + """ + 根据港股代码从Redis查询实时行情，并封装为指定结构。 + :param stock_code: 支持0700.HK、HK0700等格式 + :return: dict or None + """ + _, redis_code = format_hk_stock_code(stock_code) + r = get_redis_conn() + value = r.hget(REDIS_KEY, redis_code) + if not value: + return None + try: + data = json.loads(value) + except Exception: + return None + + # 封装为指定结构 + result = { + "code": None, + "crawlDate": None, + "marketValue": None, + "maxPrice": None, + "minPrice": None, + "nowPrice": None, + "pbRate": None, + "rangeRiseAndFall": None, + "shortName": None, + "todayStartPrice": None, + "ttm": None, + "turnoverRate": None, + "yesterdayEndPrice": None + } + + # 赋值映射 + result["code"] = data.get("symbol") + result["crawlDate"] = data.get("fetch_time") + result["marketValue"] = data.get("market_capital") + result["maxPrice"] = data.get("high") if "high" in data else data.get("high52w") + result["minPrice"] = data.get("low") if "low" in data else data.get("low52w") + result["nowPrice"] = data.get("current") + result["pbRate"] = data.get("pb") + result["rangeRiseAndFall"] = data.get("percent") + result["shortName"] = data.get("name") + result["todayStartPrice"] = data.get("open") + result["ttm"] = data.get("pe_ttm") + result["turnoverRate"] = data.get("turnover_rate") + result["yesterdayEndPrice"] = data.get("last_close") if "last_close" in data else data.get("pre_close") + + # 兼容部分字段缺失 + if result["maxPrice"] is None and "high" in data: + result["maxPrice"] = data["high"] + if result["minPrice"] is None and "low" in data: + result["minPrice"] = data["low"] + return result + + +if __name__ == '__main__': + fetch_and_store_hk_stock_data() \ No newline at end of file diff --git a/src/quantitative_analysis/supplier_customer_ratio_updater.py b/src/quantitative_analysis/supplier_customer_ratio_updater.py new file mode 100644 index 0000000..880c754 --- /dev/null +++ b/src/quantitative_analysis/supplier_customer_ratio_updater.py @@ -0,0 +1,306 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +供应商客户占比数据更新器 +用于将前五供应商占比和前五客户占比数据更新到MongoDB的eastmoney_financial_data_v2集合中 +这个脚本要在财务脚本之后执行 +""" + +import os +import sys +import pandas as pd +import pymongo +import datetime +import logging +from typing import Dict, List, Optional +from pathlib import Path + +# 添加项目根路径到Python路径 +project_root = Path(__file__).parent.parent.parent +sys.path.append(str(project_root)) + +# 导入配置 +from src.valuation_analysis.config import MONGO_CONFIG2 + +# 设置日志 +logging.basicConfig( + level=logging.ERROR, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + + +class SupplierCustomerRatioUpdater: + """供应商客户占比数据更新器""" + + def __init__(self): + """初始化""" + self.mongo_client = None + self.db = None + self.collection_name = 'eastmoney_financial_data_v2' + self.collection = None + + # CSV文件路径 - 使用绝对路径 + current_dir = Path(__file__).parent + self.supplier_file = current_dir / 'data' / '前五供应商占比.csv' + self.customer_file = current_dir / 'data' / '前五客户占比.csv' + + # 季度日期映射 - 根据CSV文件中的列名 + self.quarter_mapping = { + '20200331': '2020-03-31', + '20200630': '2020-06-30', + '20200930': '2020-09-30', + '20201231': '2020-12-31', + '20210331': '2021-03-31', + '20210630': '2021-06-30', + '20210930': '2021-09-30', + '20211231': '2021-12-31', + '20220331': '2022-03-31', + '20220630': '2022-06-30', + '20220930': '2022-09-30', + '20221231': '2022-12-31', + '20230331': '2023-03-31', + '20230630': '2023-06-30', + '20230930': '2023-09-30', + '20231231': '2023-12-31', + '20240331': '2024-03-31', + '20240630': '2024-06-30', + '20240930': '2024-09-30', + '20241231': '2024-12-31', + '20250331': '2025-03-31' + } + + self.connect_mongodb() + + def connect_mongodb(self): + """连接MongoDB数据库""" + try: + self.mongo_client = pymongo.MongoClient( + host=MONGO_CONFIG2['host'], + port=MONGO_CONFIG2['port'], + username=MONGO_CONFIG2['username'], + password=MONGO_CONFIG2['password'] + ) + self.db = self.mongo_client[MONGO_CONFIG2['db']] + self.collection = self.db[self.collection_name] + + # 测试连接 + self.mongo_client.admin.command('ping') + logger.info(f"MongoDB连接成功，使用集合: {self.collection_name}") + + except Exception as e: + logger.error(f"MongoDB连接失败: {str(e)}") + raise + + def load_csv_data(self, file_path: str) -> pd.DataFrame: + """加载CSV文件数据""" + try: + if not os.path.exists(file_path): + logger.error(f"文件不存在: {file_path}") + return pd.DataFrame() + + df = pd.read_csv(file_path, encoding='utf-8') + logger.info(f"成功加载文件: {file_path}, 数据行数: {len(df)}") + return df + + except Exception as e: + logger.error(f"加载CSV文件失败 {file_path}: {str(e)}") + return pd.DataFrame() + + def parse_ratio_data(self, df: pd.DataFrame, data_type: str) -> Dict[str, Dict[str, float]]: + """ + 解析占比数据 + + Args: + df: CSV数据DataFrame + data_type: 数据类型，'supplier' 或 'customer' + + Returns: + Dict: {stock_code: {report_date: ratio_value}} + """ + ratio_data = {} + + try: + for _, row in df.iterrows(): + stock_code = row['stock_code'] + if pd.isna(stock_code): + continue + + ratio_data[stock_code] = {} + + # 遍历所有季度列 + for quarter_col, report_date in self.quarter_mapping.items(): + if quarter_col in row: + ratio_value = row[quarter_col] + + # 处理数据：如果值为0或nan，跳过；否则除以100转换为小数 + if pd.notna(ratio_value) and ratio_value > 0: + # 将百分比数值转换为小数（如56.22 -> 0.5622） + ratio_data[stock_code][report_date] = float(ratio_value) / 100.0 + else: + # 0值或空值不添加到字典中 + continue + + logger.info(f"解析{data_type}数据完成，共处理 {len(ratio_data)} 只股票") + return ratio_data + + except Exception as e: + logger.error(f"解析{data_type}数据失败: {str(e)}") + return {} + + def update_mongodb_record(self, stock_code: str, report_date: str, + supplier_ratio: Optional[float], customer_ratio: Optional[float]) -> bool: + """ + 更新MongoDB中的单条记录 + + Args: + stock_code: 股票代码 + report_date: 报告日期 + supplier_ratio: 前五供应商占比 + customer_ratio: 前五客户占比 + + Returns: + bool: 是否更新成功 + """ + try: + # 查找记录 + filter_condition = { + 'stock_code': stock_code, + 'report_date': report_date + } + + existing_record = self.collection.find_one(filter_condition) + + if not existing_record: + # 记录不存在，跳过 + logger.debug(f"记录不存在，跳过: {stock_code} - {report_date}") + return False + + # 准备更新字段 + update_fields = {} + + if supplier_ratio is not None: + update_fields['top_five_suppliers_ratio'] = supplier_ratio + + if customer_ratio is not None: + update_fields['top_five_customers_ratio'] = customer_ratio + + if not update_fields: + return False + + # 添加更新时间 + update_fields['ratio_update_time'] = datetime.datetime.now() + + # 执行更新 + result = self.collection.update_one( + filter_condition, + {'$set': update_fields} + ) + + if result.modified_count > 0: + logger.debug(f"更新成功: {stock_code} - {report_date}") + return True + else: + logger.debug(f"无需更新: {stock_code} - {report_date}") + return False + + except Exception as e: + logger.error(f"更新记录失败 {stock_code} - {report_date}: {str(e)}") + return False + + def batch_update_ratios(self): + """批量更新所有占比数据""" + try: + # 加载CSV数据 + logger.info("开始加载CSV文件...") + supplier_df = self.load_csv_data(self.supplier_file) + customer_df = self.load_csv_data(self.customer_file) + + if supplier_df.empty and customer_df.empty: + logger.error("没有可用的CSV数据") + return False + + # 解析数据 + logger.info("解析数据中...") + supplier_data = self.parse_ratio_data(supplier_df, 'supplier') if not supplier_df.empty else {} + customer_data = self.parse_ratio_data(customer_df, 'customer') if not customer_df.empty else {} + + # 获取所有涉及的股票代码 + all_stock_codes = set() + all_stock_codes.update(supplier_data.keys()) + all_stock_codes.update(customer_data.keys()) + + logger.info(f"开始更新数据，共涉及 {len(all_stock_codes)} 只股票") + + # 统计 + total_count = 0 + updated_count = 0 + + # 逐个股票更新 + for stock_code in all_stock_codes: + supplier_ratios = supplier_data.get(stock_code, {}) + customer_ratios = customer_data.get(stock_code, {}) + + # 获取所有日期 + all_dates = set() + all_dates.update(supplier_ratios.keys()) + all_dates.update(customer_ratios.keys()) + + # 逐个日期更新 + for report_date in all_dates: + supplier_ratio = supplier_ratios.get(report_date) + customer_ratio = customer_ratios.get(report_date) + + total_count += 1 + + if self.update_mongodb_record(stock_code, report_date, supplier_ratio, customer_ratio): + updated_count += 1 + + # 每处理100条记录输出一次进度 + if total_count % 100 == 0: + logger.info(f"已处理 {total_count} 条记录，更新 {updated_count} 条") + + logger.info(f"数据更新完成！总计处理 {total_count} 条记录，成功更新 {updated_count} 条") + return True + + except Exception as e: + logger.error(f"批量更新失败: {str(e)}") + return False + + def close_connection(self): + """关闭数据库连接""" + try: + if self.mongo_client: + self.mongo_client.close() + logger.info("MongoDB连接已关闭") + except Exception as e: + logger.error(f"关闭MongoDB连接失败: {str(e)}") + + +def main(): + """主函数""" + logger.info("=== 供应商客户占比数据更新器启动 ===") + + updater = None + try: + # 创建更新器实例 + updater = SupplierCustomerRatioUpdater() + + # 执行批量更新 + success = updater.batch_update_ratios() + + if success: + logger.info("=== 数据更新成功完成 ===") + else: + logger.error("=== 数据更新失败 ===") + + except Exception as e: + logger.error(f"程序执行失败: {str(e)}") + finally: + if updater: + updater.close_connection() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/quantitative_analysis/tech_fundamental_factor_strategy.py b/src/quantitative_analysis/tech_fundamental_factor_strategy.py new file mode 100644 index 0000000..3e454bb --- /dev/null +++ b/src/quantitative_analysis/tech_fundamental_factor_strategy.py @@ -0,0 +1,729 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +科技主题基本面因子选股策略 +整合企业生命周期、财务指标和平均距离因子分析 +""" + +import sys +import pymongo +import pandas as pd +import numpy as np +import logging +from typing import Dict, List, Optional, Tuple +from pathlib import Path +from sqlalchemy import create_engine, text +from datetime import datetime +import math + +# 添加项目根路径到Python路径 +project_root = Path(__file__).parent.parent.parent +sys.path.append(str(project_root)) + +# 导入依赖的工具类 +from src.quantitative_analysis.company_lifecycle_factor import CompanyLifecycleFactor +from src.quantitative_analysis.financial_indicator_analyzer import FinancialIndicatorAnalyzer +from src.quantitative_analysis.average_distance_factor import AverageDistanceFactor +from src.valuation_analysis.config import MONGO_CONFIG2, DB_URL + +# 设置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +logger = logging.getLogger(__name__) + + +class TechFundamentalFactorStrategy: + """科技主题基本面因子选股策略""" + + def __init__(self): + """初始化策略""" + self.lifecycle_calculator = CompanyLifecycleFactor() + self.financial_analyzer = FinancialIndicatorAnalyzer() + self.distance_calculator = AverageDistanceFactor(DB_URL) + + # MySQL连接 + self.mysql_engine = create_engine( + DB_URL, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + + # 科技概念板块列表 + self.tech_concepts = [ + "5G概念", "物联网", "云计算", "边缘计算", "信息安全", "国产软件", + "大数据", "数据中心", "芯片", "MCU芯片", "汽车芯片", "存储芯片", + "人工智能", "AIGC概念", "ChatGPT概念", "CPO概念", "华为鸿蒙", + "华为海思", "华为算力", "量子科技", "区块链", "数字货币", "工业互联", + "操作系统", "光刻机", "第三代半导体", "元宇宙概念", "云游戏", "信创", + "东数西算", "PCB概念", "先进封装", "EDA概念", "Web3概念", "数据确权", + "数据要素", "数字水印", "工业软件", "6G概念", "时空大数据", "算力租赁", + "光通信", "英伟达概念", "星闪概念", "液冷服务器", "多模态AI", "Sora概念", + "AI手机PC", "铜缆高速连接", "车联网", "财税数字化", "智谱AI", "AI智能体", + "DeepSeek概念", "AI医疗概念" + ] + # self.tech_concepts = [ + # "5G概念" + # ] + + logger.info("科技主题基本面因子选股策略初始化完成") + + def get_tech_stocks(self) -> pd.DataFrame: + """ + 获取科技概念板块的股票列表 + + Returns: + pd.DataFrame: 包含股票代码和名称的DataFrame + """ + try: + # 构建查询条件 + concepts_str = "', '".join(self.tech_concepts) + query = text(f""" + SELECT DISTINCT gp_code as stock_code, gp_name as stock_name, bk_name as concept_name + FROM gp_gnbk + WHERE bk_name IN ('{concepts_str}') + ORDER BY gp_code + """) + + with self.mysql_engine.connect() as conn: + df = pd.read_sql(query, conn) + + logger.info(f"获取到 {len(df)} 只科技概念股票") + return df + + except Exception as e: + logger.error(f"获取科技概念股票失败: {str(e)}") + return pd.DataFrame() + + def filter_by_lifecycle(self, stock_codes: List[str], year: int = 2024) -> Dict[str, List[str]]: + """ + 根据企业生命周期筛选股票 + + Args: + stock_codes: 股票代码列表 + year: 分析年份 + + Returns: + Dict: 包含成长期和成熟期股票的字典 + """ + try: + logger.info(f"开始分析 {len(stock_codes)} 只股票的企业生命周期") + + # 批量计算生命周期 + lifecycle_df = self.lifecycle_calculator.batch_calculate_lifecycle_factors(stock_codes, year) + + # 筛选目标阶段的股票 + # 引入期(1)和成长期(2)合并为成长期，成熟期(3)保持不变 + growth_stage_stocks = lifecycle_df[ + lifecycle_df['stage_id'].isin([1, 2]) + ]['stock_code'].tolist() + + mature_stage_stocks = lifecycle_df[ + lifecycle_df['stage_id'] == 3 + ]['stock_code'].tolist() + + result = { + 'growth': growth_stage_stocks, + 'mature': mature_stage_stocks + } + + logger.info(f"成长期股票: {len(growth_stage_stocks)} 只") + logger.info(f"成熟期股票: {len(mature_stage_stocks)} 只") + + return result + + except Exception as e: + logger.error(f"生命周期筛选失败: {str(e)}") + return {'growth': [], 'mature': []} + + def calculate_distance_factors(self, growth_stocks: List[str], mature_stocks: List[str]) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + 分别计算成长期和成熟期股票的平均距离因子 + + Args: + growth_stocks: 成长期股票列表 + mature_stocks: 成熟期股票列表 + + Returns: + Tuple: (成长期距离因子DataFrame, 成熟期距离因子DataFrame) + """ + try: + growth_distance_df = pd.DataFrame() + mature_distance_df = pd.DataFrame() + + # 计算成长期股票距离因子 + if growth_stocks: + logger.info(f"计算 {len(growth_stocks)} 只成长期股票的距离因子") + growth_data = self.distance_calculator.get_stock_data(growth_stocks) + if not growth_data.empty: + growth_indicators = self.distance_calculator.calculate_technical_indicators(growth_data) + growth_distance_df = self.distance_calculator.calculate_distance_factor(growth_indicators) + + # 计算成熟期股票距离因子 + if mature_stocks: + logger.info(f"计算 {len(mature_stocks)} 只成熟期股票的距离因子") + mature_data = self.distance_calculator.get_stock_data(mature_stocks) + if not mature_data.empty: + mature_indicators = self.distance_calculator.calculate_technical_indicators(mature_data) + mature_distance_df = self.distance_calculator.calculate_distance_factor(mature_indicators) + + return growth_distance_df, mature_distance_df + + except Exception as e: + logger.error(f"计算距离因子失败: {str(e)}") + return pd.DataFrame(), pd.DataFrame() + + def calculate_common_factors(self, stock_codes: List[str]) -> pd.DataFrame: + """ + 计算通用因子 + + Args: + stock_codes: 股票代码列表 + + Returns: + pd.DataFrame: 包含通用因子的DataFrame + """ + try: + logger.info(f"计算 {len(stock_codes)} 只股票的通用因子") + + results = [] + latest_date = "2025-03-31" # 最新季度数据 + annual_date = "2024-12-31" # 年报数据 + + for stock_code in stock_codes: + try: + + factor_data = {'stock_code': stock_code} + + # 1. 毛利率（使用最新数据） + gross_margin = self.financial_analyzer.analyze_gross_profit_margin(stock_code, latest_date) + factor_data['gross_profit_margin'] = gross_margin + + # 2. 成长能力指标 + growth_capability = self.financial_analyzer.analyze_growth_capability(stock_code) + if growth_capability is not None: + # 成长能力越高越好，使用sigmoid函数映射到0-1 + growth_score = 1 / (1 + math.exp(-growth_capability)) + else: + growth_score = 0.5 # 默认中性评分 + factor_data['growth_score'] = growth_score + + # 3. 前五大供应商占比（使用年报数据） + supplier_conc = self.financial_analyzer.analyze_supplier_concentration(stock_code, annual_date) + factor_data['supplier_concentration'] = supplier_conc + + # 4. 前五大客户占比（使用年报数据） + customer_conc = self.financial_analyzer.analyze_customer_concentration(stock_code, annual_date) + factor_data['customer_concentration'] = customer_conc + + results.append(factor_data) + + except Exception as e: + logger.warning(f"计算股票 {stock_code} 通用因子失败: {str(e)}") + continue + + df = pd.DataFrame(results) + logger.info(f"成功计算 {len(df)} 只股票的通用因子") + return df + + except Exception as e: + logger.error(f"计算通用因子失败: {str(e)}") + return pd.DataFrame() + + def calculate_growth_specific_factors(self, stock_codes: List[str]) -> pd.DataFrame: + """ + 计算成长期特色因子 + + Args: + stock_codes: 成长期股票代码列表 + + Returns: + pd.DataFrame: 包含成长期特色因子的DataFrame + """ + try: + logger.info(f"计算 {len(stock_codes)} 只成长期股票的特色因子") + + results = [] + latest_date = "2025-03-31" # 使用最新数据 + annual_date = "2024-12-31" # 使用年度数据 + + for stock_code in stock_codes: + try: + + factor_data = {'stock_code': stock_code} + + # 1. 管理费用率（使用最新数据） + admin_ratio = self.financial_analyzer.analyze_admin_expense_ratio(stock_code, latest_date) + factor_data['admin_expense_ratio'] = admin_ratio + + # 2. 研发费用折旧摊销占比（使用年度数据） + # financial_data = self.financial_analyzer.get_financial_data(stock_code, latest_date) + financial_data = self.financial_analyzer.get_financial_data(stock_code, annual_date) + if financial_data: + intangible_amortize = financial_data.get('cash_flow_statement', {}).get('IA_AMORTIZE', 0) + rd_expense = financial_data.get('profit_statement', {}).get('RESEARCH_EXPENSE', 0) + + if rd_expense and rd_expense != 0: + rd_amortize_ratio = intangible_amortize / rd_expense if intangible_amortize else 0 + else: + rd_amortize_ratio = None # 使用None而不是0，避免这些股票获得最高分 + + factor_data['rd_amortize_ratio'] = rd_amortize_ratio + else: + factor_data['rd_amortize_ratio'] = None + + # 3. 资产负债率（使用最新数据） + asset_liability_ratio = self.financial_analyzer.analyze_asset_liability_ratio(stock_code, latest_date) + factor_data['asset_liability_ratio'] = asset_liability_ratio + + results.append(factor_data) + + except Exception as e: + logger.warning(f"计算股票 {stock_code} 成长期特色因子失败: {str(e)}") + continue + + df = pd.DataFrame(results) + logger.info(f"成功计算 {len(df)} 只成长期股票的特色因子") + return df + + except Exception as e: + logger.error(f"计算成长期特色因子失败: {str(e)}") + return pd.DataFrame() + + def calculate_mature_specific_factors(self, stock_codes: List[str]) -> pd.DataFrame: + """ + 计算成熟期特色因子 + + Args: + stock_codes: 成熟期股票代码列表 + + Returns: + pd.DataFrame: 包含成熟期特色因子的DataFrame + """ + try: + logger.info(f"计算 {len(stock_codes)} 只成熟期股票的特色因子") + + latest_date = "2025-03-31" # 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...") + all_pb_data = self.financial_analyzer.get_all_stocks_pb_data() + + logger.info("获取全A股ROE数据...") + all_roe_data = self.financial_analyzer.get_all_stocks_roe_data(latest_date) + + results = [] + + for stock_code in stock_codes: + try: + factor_data = {'stock_code': stock_code} + + # 1. 应收账款周转率（使用最新数据） + formatted_stock_code = self.financial_analyzer.code_formatter.to_dot_format(stock_code) + financial_data = self.financial_analyzer.get_financial_data(formatted_stock_code, latest_date) + if financial_data: + revenue = financial_data.get('profit_statement', {}).get('OPERATE_INCOME', 0) + accounts_rece = financial_data.get('balance_sheet', {}).get('ACCOUNTS_RECE', 0) + + if accounts_rece and accounts_rece != 0: + turnover_ratio = revenue / accounts_rece if revenue else 0 + else: + turnover_ratio = None # 使用None而不是0 + + factor_data['accounts_receivable_turnover'] = turnover_ratio + else: + factor_data['accounts_receivable_turnover'] = None + + # 2. 研发强度（使用最新数据） + rd_intensity = self.financial_analyzer.analyze_rd_expense_ratio(stock_code, latest_date) + factor_data['rd_intensity'] = rd_intensity + + # 3. PB-ROE排名因子：使用预获取的全A股数据 + if all_pb_data and all_roe_data: + pb_roe_rank_factor = self.financial_analyzer.calculate_pb_roe_rank_factor( + stock_code, all_pb_data, all_roe_data + ) + factor_data['pb_roe_rank_factor'] = pb_roe_rank_factor + else: + factor_data['pb_roe_rank_factor'] = None + + results.append(factor_data) + + except Exception as e: + logger.warning(f"计算股票 {stock_code} 成熟期特色因子失败: {str(e)}") + continue + + df = pd.DataFrame(results) + logger.info(f"成功计算 {len(df)} 只成熟期股票的特色因子") + return df + + except Exception as e: + logger.error(f"计算成熟期特色因子失败: {str(e)}") + return pd.DataFrame() + + def run_strategy(self, year: int = 2024) -> Dict[str, pd.DataFrame]: + """ + 运行完整的选股策略 + + Args: + year: 分析年份 + + Returns: + Dict: 包含成长期和成熟期股票分析结果的字典 + """ + try: + logger.info("开始运行科技主题基本面因子选股策略") + + # 1. 获取科技概念股票 + tech_stocks_df = self.get_tech_stocks() + if tech_stocks_df.empty: + logger.error("未获取到科技概念股票") + return {} + + stock_codes = tech_stocks_df['stock_code'].unique().tolist() + logger.info(f"共获取到 {len(stock_codes)} 只科技概念股票") + + # 2. 按企业生命周期筛选 + lifecycle_result = self.filter_by_lifecycle(stock_codes, year) + growth_stocks = lifecycle_result['growth'] + mature_stocks = lifecycle_result['mature'] + + if not growth_stocks and not mature_stocks: + logger.warning("未找到符合条件的成长期或成熟期股票") + return {} + + # 3. 计算平均距离因子 + growth_distance_df, mature_distance_df = self.calculate_distance_factors(growth_stocks, mature_stocks) + + # 4. 计算通用因子 + all_qualified_stocks = growth_stocks + mature_stocks + common_factors_df = self.calculate_common_factors(all_qualified_stocks) + + # 5. 计算特色因子 + growth_specific_df = self.calculate_growth_specific_factors(growth_stocks) if growth_stocks else pd.DataFrame() + mature_specific_df = self.calculate_mature_specific_factors(mature_stocks) if mature_stocks else pd.DataFrame() + + # 6. 合并结果并计算分数 + result = {} + + # 处理成长期股票 + if not growth_specific_df.empty: + # 成长期结果合并 + growth_result = growth_specific_df.copy() + + # 合并距离因子 + if not growth_distance_df.empty: + growth_result = growth_result.merge( + growth_distance_df[['symbol', 'avg_distance_factor']], + left_on='stock_code', right_on='symbol', how='left' + ).drop('symbol', axis=1) + + # 合并通用因子 + if not common_factors_df.empty: + growth_result = growth_result.merge( + common_factors_df, on='stock_code', how='left' + ) + + # 计算因子分数 + growth_result = self.calculate_factor_scores(growth_result, 'growth') + + # 计算总分并排序 + growth_result = self.calculate_total_score(growth_result, 'growth') + + result['growth'] = growth_result + logger.info(f"成长期结果: {len(growth_result)} 只股票") + + # 处理成熟期股票 + if not mature_specific_df.empty: + # 成熟期结果合并 + mature_result = mature_specific_df.copy() + + # 合并距离因子 + if not mature_distance_df.empty: + mature_result = mature_result.merge( + mature_distance_df[['symbol', 'avg_distance_factor']], + left_on='stock_code', right_on='symbol', how='left' + ).drop('symbol', axis=1) + + # 合并通用因子 + if not common_factors_df.empty: + mature_result = mature_result.merge( + common_factors_df, on='stock_code', how='left' + ) + + # 计算因子分数 + mature_result = self.calculate_factor_scores(mature_result, 'mature') + + # 计算总分并排序 + mature_result = self.calculate_total_score(mature_result, 'mature') + + result['mature'] = mature_result + logger.info(f"成熟期结果: {len(mature_result)} 只股票") + + logger.info("科技主题基本面因子选股策略运行完成") + return result + + except Exception as e: + logger.error(f"策略运行失败: {str(e)}") + return {} + + def calculate_factor_scores(self, df: pd.DataFrame, stage: str) -> pd.DataFrame: + """ + 计算单因子打分（0-100分位数） + + Args: + df: 包含因子数据的DataFrame + stage: 阶段类型 ('growth' 或 'mature') + + Returns: + pd.DataFrame: 包含因子分数的DataFrame + """ + try: + if df.empty: + return df + + df_scored = df.copy() + + # 定义因子方向（正向为True，负向为False） + factor_directions = { + # 通用因子 + 'gross_profit_margin': True, # 毛利率_环比增量 - 正向 + 'growth_score': True, # 成长能力 - 正向 + 'supplier_concentration': False, # 前5大供应商金额占比合计 - 负向 + 'customer_concentration': False, # 前5大客户收入金额占比合计 - 负向 + 'avg_distance_factor': False, # 平均距离因子 - 负向 + + # 成长期特色因子 + 'admin_expense_ratio': False, # 管理费用/营业总收入_环比增量 - 负向 + 'rd_amortize_ratio': False, # 研发费用折旧摊销占比_环比增量 - 负向 + 'asset_liability_ratio': True, # 资产负债率 - 正向 + + # 成熟期特色因子 + 'accounts_receivable_turnover': True, # 应收账款周转率 - 正向 + 'rd_intensity': True, # 研发费用直接投入占比_环比增量 - 正向 + 'pb_roe_rank_factor': False # PB-ROE排名因子 - 负向（越小越好） + } + + # 为每个因子计算分位数分数 + for column in df.columns: + if column == 'stock_code': + continue + + # 只对有效值进行排名计算 + values = df_scored[column].dropna() + if len(values) <= 1: + # 如果只有一个值或没有值，所有股票都得50分或0分 + if len(values) == 1: + df_scored[f'{column}_score'] = df_scored[column].apply(lambda x: 50 if pd.notna(x) else 0) + else: + df_scored[f'{column}_score'] = 0 + continue + + # 根据因子方向确定排序方式 + is_positive = factor_directions.get(column, True) + + # 计算排名分数 + if is_positive: + # 正向因子：值越大分数越高 + ranked_values = values.rank(pct=True) * 100 + else: + # 负向因子：值越小分数越高 + ranked_values = (1 - values.rank(pct=True)) * 100 + + # 初始化分数列 + df_scored[f'{column}_score'] = 0.0 + + # 将分数赋值给对应的行 + for idx in ranked_values.index: + df_scored.loc[idx, f'{column}_score'] = ranked_values[idx] + + logger.info(f"完成 {stage} 阶段 {len(df_scored)} 只股票的因子打分") + return df_scored + + except Exception as e: + logger.error(f"计算因子分数失败: {str(e)}") + import traceback + traceback.print_exc() + return df + + def calculate_total_score(self, df: pd.DataFrame, stage: str) -> pd.DataFrame: + """ + 计算总分 + 使用公式：总分 = 1/8 * Mean(Si) + Mean(Si)/Std(Si) + + Args: + df: 包含因子分数的DataFrame + stage: 阶段类型 ('growth' 或 'mature') + + Returns: + pd.DataFrame: 包含总分的DataFrame + """ + try: + if df.empty: + return df + + df_result = df.copy() + + # 定义因子权重（注意：这里是factor_score而不是factor） + if stage == 'growth': + factor_weights = { + # 通用因子 + 'gross_profit_margin_score': 1/8, + 'growth_score_score': 1/8, # 注意这里是growth_score_score + 'supplier_concentration_score': 1/8, + 'customer_concentration_score': 1/8, + 'avg_distance_factor_score': 1/8, + + # 成长期特色因子 + 'admin_expense_ratio_score': 1/8, + 'rd_amortize_ratio_score': 1/8, + 'asset_liability_ratio_score': 1/8 + } + else: # mature + factor_weights = { + # 通用因子 + 'gross_profit_margin_score': 1/8, + 'growth_score_score': 1/8, # 注意这里是growth_score_score + 'supplier_concentration_score': 1/8, + 'customer_concentration_score': 1/8, + 'avg_distance_factor_score': 1/8, + + # 成熟期特色因子 + 'accounts_receivable_turnover_score': 1/8, + 'rd_intensity_score': 1/8, + 'pb_roe_rank_factor_score': 1/8 + } + + # 计算每只股票的总分 + total_scores = [] + + for index, row in df_result.iterrows(): + # 获取该股票的所有因子分数 + factor_scores = [] + valid_weights = [] + + for factor, weight in factor_weights.items(): + if factor in row and pd.notna(row[factor]) and row[factor] > 0: + factor_scores.append(row[factor]) + valid_weights.append(weight) + + if len(factor_scores) == 0: + total_scores.append(0) + continue + + factor_scores = np.array(factor_scores) + valid_weights = np.array(valid_weights) + + # 重新标准化权重 + valid_weights = valid_weights / valid_weights.sum() + + # 计算加权平均分数 + mean_score = np.average(factor_scores, weights=valid_weights) + + # 计算调整项 Mean(Si)/Std(Si) + if len(factor_scores) > 1 and np.std(factor_scores) > 0: + adjustment = np.mean(factor_scores) / np.std(factor_scores) + else: + adjustment = 0 + + # 计算总分：1/8 * Mean(Si) + Mean(Si)/Std(Si) + total_score = (1/8) * mean_score + adjustment + total_scores.append(total_score) + + df_result['total_score'] = total_scores + + # 按总分降序排列 + df_result = df_result.sort_values('total_score', ascending=False).reset_index(drop=True) + df_result['rank'] = range(1, len(df_result) + 1) + + logger.info(f"完成 {stage} 阶段 {len(df_result)} 只股票的总分计算") + return df_result + + except Exception as e: + logger.error(f"计算总分失败: {str(e)}") + import traceback + traceback.print_exc() + return df + + def close_connections(self): + """关闭所有数据库连接""" + try: + if hasattr(self, 'lifecycle_calculator'): + del self.lifecycle_calculator + if hasattr(self, 'financial_analyzer'): + self.financial_analyzer.close_connection() + if hasattr(self, 'distance_calculator'): + del self.distance_calculator + if hasattr(self, 'mysql_engine'): + self.mysql_engine.dispose() + logger.info("数据库连接已关闭") + except Exception as e: + logger.error(f"关闭连接失败: {str(e)}") + + +def main(): + """主函数 - 科技主题基本面因子选股策略""" + strategy = None + 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 (2025-03-31)") + 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() + + # 创建策略实例 + strategy = TechFundamentalFactorStrategy() + logger.info("策略实例创建成功") + + # 运行策略 + results = strategy.run_strategy(year=2024) + + # 输出结果 + if not results: + print("未获得分析结果") + return + + for stage, df in results.items(): + print(f"\n=== {stage.upper()} 阶段股票分析结果 ===") + print(f"股票数量: {len(df)}") + + if not df.empty: + # 调试：显示所有列名 + print(f"数据列: {list(df.columns)}") + # 显示前5只股票的关键指标 + print("\n前5只股票:") + display_columns = [ + 'stock_code', 'gross_profit_margin', 'growth_score', + 'supplier_concentration', 'customer_concentration', + 'total_score', 'rank' + ] + available_columns = [col for col in display_columns if col in df.columns] + print(df[available_columns].head(5).to_string(index=False)) + + # 保存完整结果 + output_file = f"tech_fundamental_factor_{stage}_{datetime.now().strftime('%Y%m%d_%H%M')}.csv" + df.to_csv(output_file, index=False, encoding='utf-8-sig') + print(f"\n完整结果已保存到: {output_file}") + + # 显示统计信息 + print(f"\n统计信息:") + print(f" 平均总分: {df['total_score'].mean():.2f}") + print(f" 最高总分: {df['total_score'].max():.2f}") + print(f" 最低总分: {df['total_score'].min():.2f}") + + print(f"\n=== 策略运行完成 ===") + + except Exception as e: + logger.error(f"程序执行失败: {str(e)}") + import traceback + traceback.print_exc() + finally: + if strategy: + strategy.close_connections() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/scripts/ProxyIP.py b/src/scripts/ProxyIP.py new file mode 100644 index 0000000..e045b9d --- /dev/null +++ b/src/scripts/ProxyIP.py @@ -0,0 +1,342 @@ +import requests +import redis +import random +import time +import threading +import json +from typing import Dict, List, Optional, Union +from datetime import datetime, timedelta +from concurrent.futures import ThreadPoolExecutor, as_completed + + +class EnhancedProxyManager: + def __init__( + self, + proxy_api_url: str = 'https://dps.kdlapi.com/api/getdps?secret_id=o4itop21b4byqg47eevx&signature=3d3fuvm6raah1xyjecl6bby1mj6gtx0c&num=3&format=json', + valid_check_url: str = 'https://dps.kdlapi.com/api/checkdpsvalid?secret_id=o4itop21b4byqg47eevx&signature=3d3fuvm6raah1xyjecl6bby1mj6gtx0c', + redis_host: str = '192.168.18.123', + redis_port: int = 6379, + redis_db: int = 7, + redis_password: str = 'wlkj2018', + redis_key: str = 'proxy_pool', + update_interval: int = 3600, + max_retries: int = 3, + proxy_timeout: int = 10, + auto_refresh: bool = False, + max_pool_size: int = 50, + enable_api_validation: bool = True # 新增：是否启用API验证开关 + ): + """ + 增强版代理管理器 - 支持多IP池和手动代理管理 + + :param proxy_api_url: 获取代理的API地址 + :param redis_host: Redis主机地址 + :param redis_port: Redis端口 + :param redis_db: Redis数据库 + :param redis_password: Redis密码 + :param redis_key: Redis中存储代理的键前缀 + :param update_interval: 代理更新间隔(秒) + :param max_retries: 最大重试次数 + :param proxy_timeout: 代理连接超时时间(秒) + :param auto_refresh: 是否自动从API获取代理 + :param max_pool_size: 代理池最大容量 + """ + self.proxy_api_url = proxy_api_url + self.redis_key_prefix = redis_key + self.update_interval = update_interval + self.max_retries = max_retries + self.proxy_timeout = proxy_timeout + self.auto_refresh = auto_refresh + self.max_pool_size = max_pool_size + self.valid_check_url = valid_check_url + self.enable_api_validation = enable_api_validation + + # Redis连接 + self.redis_conn = redis.StrictRedis( + host=redis_host, + port=redis_port, + db=redis_db, + password=redis_password, + decode_responses=True + ) + + # 线程安全控制 + self.lock = threading.Lock() + self.condition = threading.Condition() + + # 启动维护线程 + if self.auto_refresh: + self._start_maintenance_thread() + + def _start_maintenance_thread(self): + """启动后台维护线程""" + + def maintenance_loop(): + while True: + with self.condition: + self._refresh_api_proxies() + self.condition.notify_all() + time.sleep(self.update_interval) + + thread = threading.Thread(target=maintenance_loop, daemon=True) + thread.start() + + def _get_redis_key(self, proxy_type: str) -> str: + """获取Redis键名""" + return f"{self.redis_key_prefix}:{proxy_type}" + + def _check_proxy_valid(self, proxy_list: List[str]) -> Dict[str, bool]: + """通过API检查代理是否有效""" + if not self.enable_api_validation or not proxy_list: + return {} + + try: + # 拼接代理参数：proxy=ip1:port1,ip2:port2 + proxy_param = '&proxy=' + ','.join(proxy_list) + response = requests.get(self.valid_check_url + proxy_param, timeout=10) + + if response.status_code == 200: + data = response.json() + if data.get('code') == 0: + return data.get('data', {}) + except Exception as e: + print(f"API验证代理有效性失败: {e}") + return {} + + def _refresh_api_proxies(self) -> bool: + """从API获取最新代理并存入Redis""" + api_key = self._get_redis_key('api') + + # 前置检查：如果已有足够数量且未过期的代理，则不需要刷新 + existing_proxies = self.redis_conn.hgetall(api_key) + + # 检查代理数量是否足够（大于等于4）且未过期 + if len(existing_proxies) >= 4: + # 检查代理是否过期（假设代理有效期为24小时） + current_time = datetime.now() + need_refresh = False + + for proxy_json in existing_proxies.values(): + proxy = json.loads(proxy_json) + last_checked = datetime.strptime(proxy['last_checked'], "%Y-%m-%d %H:%M:%S") + if (current_time - last_checked) > timedelta(hours=1): + need_refresh = True + break + + if not need_refresh: + print("当前有足够数量且未过期的代理，无需刷新") + return False + + try: + response = requests.get(self.proxy_api_url, timeout=self.proxy_timeout) + if response.status_code == 200: + data = response.json() + if data.get('code') == 0 and data.get('data'): + # 清空旧API代理 + api_key = self._get_redis_key('api') + # self.redis_conn.delete(api_key) + + # 添加新代理 + for proxy_data in data['data']['proxy_list']: + proxy = { + 'http': f"http://{proxy_data}", + 'https': f"http://{proxy_data}", + 'server': proxy_data, + 'source': 'api', + 'last_checked': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + 'status': 'active' + } + self.redis_conn.hset( + api_key, + proxy_data, + json.dumps(proxy) + ) + return True + except Exception as e: + print(f"更新API代理失败: {e}") + return False + + def add_manual_proxies(self, proxies: Union[List[str], str]) -> int: + """ + 手动添加代理到池中 + + :param proxies: 代理列表(格式: ["ip:port", ...] 或 "ip:port") + :return: 成功添加的代理数量 + """ + if isinstance(proxies, str): + proxies = [proxies] + + added = 0 + manual_key = self._get_redis_key('manual') + + # 验证并添加代理 + def _check_and_add(proxy): + nonlocal added + proxy_dict = { + 'http': f"http://{proxy}", + 'https': f"http://{proxy}", + 'server': proxy, + 'source': 'manual', + 'last_checked': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + 'status': 'active' + } + + if self._is_proxy_working(proxy_dict): + with self.lock: + # 检查是否已存在 + if not self.redis_conn.hexists(manual_key, proxy): + self.redis_conn.hset(manual_key, proxy, json.dumps(proxy_dict)) + added += 1 + + # 使用线程池并行验证 + with ThreadPoolExecutor(max_workers=10) as executor: + executor.map(_check_and_add, proxies) + + print(f"成功添加 {added} 个手动代理") + return added + + def _is_proxy_working(self, proxy: Dict) -> bool: + """检查代理是否可用""" + test_urls = [ + "http://httpbin.org/ip", + "http://www.google.com/gen_204" + ] + + proxies = { + 'http': proxy['http'], + 'https': proxy['https'] + } + + for url in test_urls: + try: + response = requests.get( + url, + proxies=proxies, + timeout=self.proxy_timeout, + allow_redirects=False + ) + if 200 <= response.status_code < 500: + return True + except: + continue + return False + + def get_random_proxy(self) -> Optional[Dict]: + """ + 随机获取一个可用代理 + + :return: 代理字典或None(如果无可用代理) + """ + # 优先从API代理获取 + # api_key = self._get_redis_key('api') + manual_key = self._get_redis_key('manual') + + # 获取所有活跃代理 + proxies = [] + + # 先检查API代理 + # api_proxies = self.redis_conn.hgetall(api_key) + # for proxy_json in api_proxies.values(): + # proxy = json.loads(proxy_json) + # if proxy.get('status') == 'active': + # proxies.append(proxy) + + # 如果API代理不可用或auto_refresh关闭，检查手动代理 + if not proxies or not self.auto_refresh: + manual_proxies = self.redis_conn.hgetall(manual_key) + for proxy_json in manual_proxies.values(): + proxy = json.loads(proxy_json) + if proxy.get('status') == 'active': + proxies.append(proxy) + + if not proxies: + if self.auto_refresh: + print("代理池为空，尝试从API获取...") + self._refresh_api_proxies() + return self.get_random_proxy() + else: + print("代理池为空且自动刷新已关闭") + return None + + # 随机选择一个代理 + selected = random.choice(proxies) + selected['_redis_key'] = self._get_redis_key(selected['source']) + return selected + + def mark_proxy_failed(self, proxy: Dict): + """标记代理为失败并从池中移除""" + if '_redis_key' not in proxy: + return + + # 如果是API代理且启用验证，先检查是否真的失效 + if proxy.get('source') == 'api' and self.enable_api_validation: + valid_status = self._check_proxy_valid([proxy['server']]) + is_valid = valid_status.get(proxy['server'], False) + + if is_valid: + print(f"代理 {proxy['server']} API验证仍有效，暂不移除") + return + + with self.lock: + self.redis_conn.hdel(proxy['_redis_key'], proxy['server']) + print(f"代理 {proxy['server']} 已被移除") + + def request_with_proxy( + self, + method: str, + url: str, + retry_count: int = 0, + **kwargs + ) -> requests.Response: + """ + 使用代理发送请求 + + :param method: HTTP方法 + :param url: 请求URL + :param retry_count: 内部重试计数 + :param kwargs: 其他requests参数 + :return: Response对象 + """ + if retry_count >= self.max_retries: + raise requests.exceptions.RequestException(f"达到最大重试次数 {self.max_retries}") + + proxy = self.get_random_proxy() + if not proxy: + raise requests.exceptions.RequestException("无可用代理") + + try: + response = requests.request( + method, + url, + proxies={ + 'http': proxy['http'], + 'https': proxy['https'] + }, + timeout=self.proxy_timeout, + **kwargs + ) + + if response.status_code >= 400: + raise requests.exceptions.HTTPError( + f"HTTP错误: {response.status_code}", + response=response + ) + + return response + + except requests.exceptions.RequestException as e: + print(f"代理 {proxy['server']} 请求失败: {e}") + self.mark_proxy_failed(proxy) + return self.request_with_proxy(method, url, retry_count + 1, **kwargs) + + def get_pool_status(self) -> Dict: + """获取代理池状态""" + api_key = self._get_redis_key('api') + manual_key = self._get_redis_key('manual') + + return { + 'api_proxies': self.redis_conn.hlen(api_key), + 'manual_proxies': self.redis_conn.hlen(manual_key), + 'auto_refresh': self.auto_refresh, + 'last_update': datetime.now().strftime("%Y-%m-%d %H:%M:%S") + } \ No newline at end of file diff --git a/src/scripts/config.py b/src/scripts/config.py index 72bf64b..6eec6da 100644 --- a/src/scripts/config.py +++ b/src/scripts/config.py @@ -11,7 +11,7 @@ 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b/src/scripts/stock_daily_data_collector.py @@ -6,6 +6,7 @@ from sqlalchemy import create_engine, text from datetime import datetime, timedelta from tqdm import tqdm from src.scripts.config import XUEQIU_HEADERS +from src.scripts.ProxyIP import EnhancedProxyManager import gc class StockDailyDataCollector: @@ -19,6 +20,8 @@ class StockDailyDataCollector: pool_recycle=3600 ) self.headers = XUEQIU_HEADERS + # 初始化代理管理器 + self.proxy_manager = EnhancedProxyManager() def fetch_all_stock_codes(self): # 从gp_code_all获取股票代码 @@ -37,15 +40,18 @@ class StockDailyDataCollector: codes_hk = df_hk['gp_code'].tolist() # 合并去重 - all_codes = list(set(codes_all + 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") + all_codes = list(set(codes_hk)) + # all_codes = list(set(codes_all + 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") return all_codes def fetch_daily_stock_data(self, symbol, begin, count=-1): """获取日线数据，count=-1表示最新一天，-2表示最近两天，-1800表示最近1800天""" url = f"https://stock.xueqiu.com/v5/stock/chart/kline.json?symbol={symbol}&begin={begin}&period=day&type=before&count={count}&indicator=kline,pe,pb,ps,pcf,market_capital,agt,ggt,balance" try: - response = requests.get(url, headers=self.headers, timeout=20) + # 使用代理管理器发送请求 + # response = requests.get(url, headers=self.headers, timeout=20) + response = self.proxy_manager.request_with_proxy('get', url, headers=self.headers) return response.json() except Exception as e: print(f"Request error for {symbol}: {e}") @@ -85,9 +91,9 @@ class StockDailyDataCollector: start_date = datetime.strptime(date, '%Y-%m-%d') date_str = date - delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str") - with self.engine.begin() as conn: - conn.execute(delete_query, {"date_str": f"{date_str}%"}) + # delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str") + # with self.engine.begin() as conn: + # conn.execute(delete_query, {"date_str": f"{date_str}%"}) stock_codes = self.fetch_all_stock_codes() begin = int(start_date.replace(hour=0, minute=0, second=0, microsecond=0).timestamp() * 1000) @@ -253,6 +259,30 @@ class StockDailyDataCollector: except Exception as e: print(f"!!! Error saving ex-rights log: {e}") + def fetch_single_stock_history(self, symbol, days=1800): + """ + 获取单只股票的历史数据并保存到数据库 + :param symbol: 股票代码 + :param days: 获取的天数，默认1800天 + :return: 是否成功 + """ + print(f"开始获取 {symbol} 最近 {days} 天的历史数据...") + begin = int(datetime.now().timestamp() * 1000) + data = self.fetch_daily_stock_data(symbol, begin, count=-days) + + if data.get('error_code') == 0: + df = self.transform_data(data, symbol) + if df is not None and not df.empty: + df.to_sql('gp_day_data', self.engine, if_exists='append', index=False) + print(f"成功保存 {symbol} 的历史数据，共 {len(df)} 条记录") + return True + else: + print(f"未能转换 {symbol} 的数据") + return False + else: + print(f"获取 {symbol} 数据失败: {data.get('error_description')}") + return False + def collect_stock_daily_data(db_url, date=None): collector = StockDailyDataCollector(db_url) collector.fetch_data_for_date(date) diff --git a/src/scripts/stock_daily_data_collector_v2.py b/src/scripts/stock_daily_data_collector_v2.py new file mode 100644 index 0000000..79f426b --- /dev/null +++ b/src/scripts/stock_daily_data_collector_v2.py @@ -0,0 +1,328 @@ +# coding:utf-8 + +import requests +import pandas as pd +from sqlalchemy import create_engine, text +from datetime import datetime, timedelta +from tqdm import tqdm +import sys +import os +import gc + +# 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) + +from src.scripts.config import XUEQIU_HEADERS +from src.scripts.ProxyIP import EnhancedProxyManager +from src.quantitative_analysis.batch_stock_price_collector import fetch_and_store_stock_data +from src.valuation_analysis.stock_price_collector import StockPriceCollector +from src.scripts.stock_daily_data_collector import StockDailyDataCollector + + +class StockDailyDataCollectorV2: + """股票日线数据采集器V2版本 - 整合雪球和东方财富数据""" + + def __init__(self, db_url): + self.engine = create_engine( + db_url, + pool_size=5, + max_overflow=10, + pool_recycle=3600 + ) + self.headers = XUEQIU_HEADERS + self.proxy_manager = EnhancedProxyManager() + # 创建东方财富数据采集器 + self.eastmoney_collector = StockPriceCollector(db_url) + # 创建原版采集器用于单只股票历史数据获取 + self.original_collector = StockDailyDataCollector(db_url) + + def convert_symbol_format(self, symbol): + """ + 将雪球格式的股票代码转换为东方财富格式 + 雪球格式：SZ300177 -> 东方财富格式：300177.SZ + """ + if symbol.startswith('SZ'): + return f"{symbol[2:]}.SZ" + elif symbol.startswith('SH'): + return f"{symbol[2:]}.SH" + else: + return symbol + + def convert_eastmoney_to_xueqiu_format(self, stock_code): + """ + 将东方财富格式的股票代码转换为雪球格式 + 东方财富格式：300177.SZ -> 雪球格式：SZ300177 + """ + if '.SZ' in stock_code: + return f"SZ{stock_code.replace('.SZ', '')}" + elif '.SH' in stock_code: + return f"SH{stock_code.replace('.SH', '')}" + else: + return stock_code + + def fetch_eastmoney_data(self): + """获取东方财富的实时数据""" + print("正在获取东方财富数据...") + df = self.eastmoney_collector.fetch_all_data() + if not df.empty: + # 转换股票代码格式为雪球格式 + df['symbol'] = df['stock_code'].apply(self.convert_eastmoney_to_xueqiu_format) + print(f"成功获取东方财富数据，共 {len(df)} 条记录") + return df + + def merge_data(self, xueqiu_df, eastmoney_df): + """合并雪球和东方财富数据""" + print("正在合并雪球和东方财富数据...") + + # 基于symbol进行合并 + merged_df = pd.merge( + xueqiu_df, + eastmoney_df[['symbol', 'high_price', 'low_price', 'open_price', 'pre_close', 'list_date']], + on='symbol', + how='left' + ) + + print(f"数据合并完成，共 {len(merged_df)} 条记录") + return merged_df + + def transform_to_gp_day_data(self, merged_df): + """将合并后的数据转换为gp_day_data表结构""" + print("正在转换数据格式...") + + # 创建符合gp_day_data表结构的DataFrame + gp_day_df = pd.DataFrame() + + # 映射字段 + gp_day_df['symbol'] = merged_df['symbol'] + gp_day_df['timestamp'] = pd.to_datetime(merged_df['fetch_time']) + gp_day_df['volume'] = merged_df['volume'] + gp_day_df['open'] = merged_df['open_price'] + gp_day_df['high'] = merged_df['high_price'] + gp_day_df['low'] = merged_df['low_price'] + gp_day_df['close'] = merged_df['current'] + gp_day_df['chg'] = merged_df['chg'] + gp_day_df['percent'] = merged_df['percent'] + gp_day_df['turnoverrate'] = merged_df['turnover_rate'] + gp_day_df['amount'] = merged_df['amount'] + gp_day_df['pb'] = merged_df['pb'] + gp_day_df['pe'] = merged_df['pe_ttm'] + gp_day_df['ps'] = merged_df['ps'] + + # 添加pre_close字段（用于除权检查） + gp_day_df['pre_close'] = merged_df['pre_close'] + + print(f"数据转换完成，共 {len(gp_day_df)} 条记录") + return gp_day_df + + def save_to_database(self, df): + """保存数据到数据库""" + if df.empty: + print("没有数据需要保存") + return + + print(f"正在保存数据到数据库，共 {len(df)} 条记录...") + + # 删除今日数据 + today_str = datetime.now().strftime('%Y-%m-%d') + delete_query = text("DELETE FROM gp_day_data WHERE `timestamp` LIKE :date_str") + + try: + with self.engine.begin() as conn: + conn.execute(delete_query, {"date_str": f"{today_str}%"}) + print(f"已删除今日 {today_str} 的旧数据") + except Exception as e: + print(f"删除今日数据失败: {e}") + + # 分批保存数据 + batch_size = 1000 + for i in range(0, len(df), batch_size): + batch = df.iloc[i:i+batch_size] + try: + batch.to_sql('gp_day_data', self.engine, if_exists='append', index=False) + print(f"已保存第 {i//batch_size + 1} 批数据") + except Exception as e: + print(f"保存第 {i//batch_size + 1} 批数据失败: {e}") + + print("数据保存完成") + + def check_ex_rights_before_save(self, df): + """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""" + print("步骤5.1: 检查除权情况（保存前）...") + + ex_rights_stocks = [] + ex_rights_log_data = [] + today_str = datetime.now().strftime('%Y-%m-%d') + + 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 gp_day_data + WHERE symbol = :symbol + ORDER BY `timestamp` DESC + LIMIT 2 + """) + + 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 == today_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]) + # print(f"股票 {symbol}: 检测到今日已有数据，使用昨日收盘价 {db_last_close} 进行比较") + 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({ + 'symbol': symbol, + 'date': today_str, + 'db_price': db_last_close, + 'api_price': current_pre_close, + 'log_time': datetime.now() + }) + + except Exception as e: + print(f"查询 {symbol} 历史数据失败: {e}") + 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: list): + """将除权日志保存到数据库""" + 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) + # 确保列名与数据库字段匹配 + df = df.rename(columns={ + 'symbol': 'stock_code', + 'date': 'change_date', + 'db_price': 'before_price', + 'api_price': 'after_price', + 'log_time': 'update_time' + }) + 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): + """处理除权股票：保存日志、删除历史数据并重新获取1800天数据""" + if not ex_rights_stocks: + return + + print("步骤6: 处理除权股票...") + + # 6.1 保存除权日志 + if ex_rights_log_data: + self.save_ex_rights_log(ex_rights_log_data) + + # 6.2 重新获取历史数据 + print(f"开始处理 {len(ex_rights_stocks)} 只除权股票，重新获取历史数据...") + + for symbol in tqdm(ex_rights_stocks, desc="处理除权股票"): + try: + # 删除该股票的所有历史数据 + delete_query = text("DELETE FROM gp_day_data WHERE symbol = :symbol") + with self.engine.begin() as conn: + conn.execute(delete_query, {"symbol": symbol}) + print(f"已删除 {symbol} 的历史数据") + + # 重新获取1800天的历史数据 + success = self.original_collector.fetch_single_stock_history(symbol, 1800) + 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") + + except Exception as e: + print(f"处理除权股票 {symbol} 失败: {e}") + + def run_daily_collection(self): + """执行每日数据采集""" + print("=" * 60) + print("股票日线数据采集器V2 - 开始运行") + print("=" * 60) + + try: + # 1. 获取雪球数据 + print("步骤1: 获取雪球数据...") + xueqiu_df = fetch_and_store_stock_data() + if xueqiu_df.empty: + print("雪球数据获取失败，终止运行") + return + + # 2. 获取东方财富数据 + print("步骤2: 获取东方财富数据...") + eastmoney_df = self.fetch_eastmoney_data() + if eastmoney_df.empty: + print("东方财富数据获取失败，终止运行") + return + + # 3. 合并数据 + print("步骤3: 合并数据...") + merged_df = self.merge_data(xueqiu_df, eastmoney_df) + + # 4. 转换数据格式 + print("步骤4: 转换数据格式...") + gp_day_df = self.transform_to_gp_day_data(merged_df) + + # 5. 检查除权（保存前） + ex_rights_stocks, ex_rights_log_data = self.check_ex_rights_before_save(gp_day_df) + + # 5.2. 保存到数据库 + print("步骤5.2: 保存到数据库...") + self.save_to_database(gp_day_df) + + # 6. 处理除权股票（保存后） + self.handle_ex_rights_stocks(ex_rights_stocks, ex_rights_log_data) + + print("=" * 60) + print("股票日线数据采集完成") + print("=" * 60) + + except Exception as e: + print(f"采集过程中发生错误: {e}") + finally: + # 清理资源 + self.engine.dispose() + gc.collect() + + +def collect_stock_daily_data_v2(db_url): + """V2版本的股票日线数据采集入口函数""" + collector = StockDailyDataCollectorV2(db_url) + collector.run_daily_collection() + + +if __name__ == "__main__": + db_url = 'mysql+pymysql://root:Chlry#$.8@192.168.18.199:3306/db_gp_cj' + collect_stock_daily_data_v2(db_url) \ No newline at end of file diff --git a/tools/stock_code_formatter.py b/tools/stock_code_formatter.py new file mode 100644 index 0000000..62752a0 --- /dev/null +++ b/tools/stock_code_formatter.py @@ -0,0 +1,244 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +股票代码格式转换工具类 + +提供多种股票代码格式之间的转换功能 +""" + +import re +from typing import Optional + + +class StockCodeFormatter: + """股票代码格式转换器""" + + def __init__(self): + """初始化""" + pass + + def _parse_stock_code(self, stock_code: str) -> tuple: + """ + 解析股票代码，提取代码和市场信息 + + Args: + stock_code: 输入的股票代码 + + Returns: + tuple: (代码数字, 市场标识) 如 ('688008', 'SH') + """ + if not stock_code: + return None, None + + stock_code = stock_code.strip().upper() + + # 处理 688008.SH 格式 + if '.' in stock_code: + parts = stock_code.split('.') + if len(parts) == 2: + code, market = parts[0], parts[1] + return code, market + + # 处理 SH688008 格式 + elif stock_code.startswith(('SZ', 'SH', 'BJ')): + market = stock_code[:2] + code = stock_code[2:] + return code, market + + # 处理纯数字 688008 格式 + elif stock_code.isdigit(): + # 根据数字前缀自动判断市场 + if stock_code.startswith(('60', '68')): + return stock_code, 'SH' + elif stock_code.startswith(('00', '30', '20')): + return stock_code, 'SZ' + elif stock_code.startswith(('8', '43', '87')): + return stock_code, 'BJ' + else: + return stock_code, None + + return None, None + + def to_dot_format(self, stock_code: str) -> Optional[str]: + """ + 转换为点分格式 (如: 688008.SH) + + Args: + stock_code: 输入的股票代码，支持多种格式 + + Returns: + str: 点分格式的股票代码，如果无法转换则返回None + + Examples: + >>> formatter = StockCodeFormatter() + >>> formatter.to_dot_format('SH688008') + '688008.SH' + >>> formatter.to_dot_format('688008') + '688008.SH' + >>> formatter.to_dot_format('688008.SH') + '688008.SH' + """ + code, market = self._parse_stock_code(stock_code) + + if code and market: + return f"{code}.{market}" + + return None + + def to_prefix_format(self, stock_code: str) -> Optional[str]: + """ + 转换为前缀格式 (如: SH688008) + + Args: + stock_code: 输入的股票代码，支持多种格式 + + Returns: + str: 前缀格式的股票代码，如果无法转换则返回None + + Examples: + >>> formatter = StockCodeFormatter() + >>> formatter.to_prefix_format('688008.SH') + 'SH688008' + >>> formatter.to_prefix_format('688008') + 'SH688008' + >>> formatter.to_prefix_format('SH688008') + 'SH688008' + """ + code, market = self._parse_stock_code(stock_code) + + if code and market: + return f"{market}{code}" + + return None + + def to_number_only(self, stock_code: str) -> Optional[str]: + """ + 转换为纯数字格式 (如: 688008) + + Args: + stock_code: 输入的股票代码，支持多种格式 + + Returns: + str: 纯数字格式的股票代码，如果无法转换则返回None + + Examples: + >>> formatter = StockCodeFormatter() + >>> formatter.to_number_only('688008.SH') + '688008' + >>> formatter.to_number_only('SH688008') + '688008' + >>> formatter.to_number_only('688008') + '688008' + """ + code, market = self._parse_stock_code(stock_code) + + if code: + return code + + return None + + def get_market(self, stock_code: str) -> Optional[str]: + """ + 获取股票代码对应的市场标识 + + Args: + stock_code: 输入的股票代码，支持多种格式 + + Returns: + str: 市场标识 ('SH', 'SZ', 'BJ')，如果无法识别则返回None + """ + code, market = self._parse_stock_code(stock_code) + return market + + def is_valid_stock_code(self, stock_code: str) -> bool: + """ + 验证股票代码是否有效 + + Args: + stock_code: 输入的股票代码 + + Returns: + bool: 是否为有效的股票代码 + """ + code, market = self._parse_stock_code(stock_code) + return code is not None and market is not None + + def batch_convert(self, stock_codes: list, target_format: str = 'dot') -> dict: + """ + 批量转换股票代码格式 + + Args: + stock_codes: 股票代码列表 + target_format: 目标格式 ('dot', 'prefix', 'number') + + Returns: + dict: {原始代码: 转换后代码} 的映射字典 + """ + result = {} + + for stock_code in stock_codes: + if target_format == 'dot': + converted = self.to_dot_format(stock_code) + elif target_format == 'prefix': + converted = self.to_prefix_format(stock_code) + elif target_format == 'number': + converted = self.to_number_only(stock_code) + else: + converted = None + + result[stock_code] = converted + + return result + + +def main(): + """示例用法""" + print("=== 股票代码格式转换工具示例 ===") + + # 创建格式转换器 + formatter = StockCodeFormatter() + + # 测试用例 + test_codes = [ + '688008.SH', # 沪市科创板 + 'SH688008', # 前缀格式 + '688008', # 纯数字 + '300661.SZ', # 深市创业板 + 'SZ300661', # 前缀格式 + '300661', # 纯数字 + '000858.SZ', # 深市主板 + '600519.SH', # 沪市主板 + '430123.BJ', # 北交所 + 'BJ430123', # 北交所前缀格式 + ] + + print("\n原始代码 -> 点分格式 -> 前缀格式 -> 纯数字格式 -> 市场") + print("-" * 70) + + for code in test_codes: + dot_format = formatter.to_dot_format(code) + prefix_format = formatter.to_prefix_format(code) + number_format = formatter.to_number_only(code) + market = formatter.get_market(code) + is_valid = formatter.is_valid_stock_code(code) + + status = "✓" if is_valid else "✗" + print(f"{code:12} -> {dot_format or 'None':12} -> {prefix_format or 'None':12} -> {number_format or 'None':8} -> {market or 'None':4} {status}") + + # 批量转换示例 + print(f"\n=== 批量转换示例 ===") + batch_codes = ['SH688008', '300661', 'BJ430123'] + + dot_results = formatter.batch_convert(batch_codes, 'dot') + print("转换为点分格式:") + for original, converted in dot_results.items(): + print(f" {original} -> {converted}") + + prefix_results = formatter.batch_convert(batch_codes, 'prefix') + print("转换为前缀格式:") + for original, converted in prefix_results.items(): + print(f" {original} -> {converted}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/trigger_batch_collection.py b/tools/trigger_batch_collection.py new file mode 100644 index 0000000..55beabd --- /dev/null +++ b/tools/trigger_batch_collection.py @@ -0,0 +1,35 @@ +import requests +import json + +def trigger_batch_stock_price_collection(): + """ + 一个简单的工具函数，用于触发批量采集A股行情的调度器接口，并打印响应。 + """ + # 目标URL + url = "http://192.168.16.214:5089/scheduler/batch_stock_price/collection" + + print(f"正在向以下地址发送GET请求:\n{url}\n") + + try: + # 发送GET请求，可以设置一个超时时间（例如10秒） + response = requests.get(url, timeout=30) + + # 检查响应状态码 + print(f"请求完成，HTTP状态码: {response.status_code}\n") + + # 尝试将响应体解析为JSON格式并打印 + try: + response_json = response.json() + print("服务器响应内容 (JSON格式):") + # 使用json.dumps美化输出 + print(json.dumps(response_json, indent=2, ensure_ascii=False)) + except json.JSONDecodeError: + print("服务器响应内容 (非JSON格式):") + print(response.text) + + except requests.exceptions.RequestException as e: + # 处理请求过程中可能出现的异常（如网络问题、超时等） + print(f"请求失败，发生异常: {e}") + +if __name__ == '__main__': + trigger_batch_stock_price_collection() \ No newline at end of file

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